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TheLartians/PyPropagate | notebooks/Examples/Zoneplate.ipynb | 1 | 1225720 | null | gpl-3.0 |
meli-lewis/pycaribbean2016 | pycaribbean.ipynb | 1 | 161244 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to data cleaning with Jupyter and pandas\n",
"## Meli Lewis, data engineer at [Simple](http://simple.com/)\n",
"### [melidata.com](http://melidata.com)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from IPython.display import display, Image, YouTubeVideo\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The case for open source data tools"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- [Reproducibility and Transparency](http://www.nature.com/news/interactive-notebooks-sharing-the-code-1.16261)\n",
"\n",
"- Cost -- compare capabilities between software you already use and open source [here](https://en.wikipedia.org/wiki/Comparison_of_statistical_packages)\n",
"\n",
"- Allows a diversity of platforms on a given team"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The case for notebooks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- They're amenable to sketching, and they're amenable to reproducibility.\n",
"\n",
"- You can retrace your own steps and also make a narrative for someone else to follow.\n",
"\n",
"- Built-in documentation streamlines your workflow; magic methods anticipate it."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Jupyter Notebook: some cool tips for beginners"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. [Tab completion](https://en.wikipedia.org/wiki/Command-line_completion)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"scrolled": true
},
"source": [
"pd.re\n",
"from math import "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. built-in documentation\n",
" * shift-tab brings up brief function documentation"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"?pd.read_csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Markdown cells, which can display other markup languages"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# [Markdown](https://daringfireball.net/projects/markdown/) and HTML\n",
"<img src='http://i.imgur.com/WypQf94.gif' align='left'></span>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"\n",
"# LaTeX\n",
"<span style=\"font-size: 24px\"> $\\bar{x} = \\frac{\\sum_{i=1}^{n}w_i\\cdot x_i}{\\sum_{i=1}^{n}w_i}$</span>\n",
"\n",
"\n",
"# Syntax highlighting for other languages\n",
"```R\n",
"x <- c(0:10, 50)\n",
"xm <- mean(x)\n",
"c(xm, mean(x, trim = 0.10))\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The final cell type is \"Raw NBConvert\""
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"# [Markdown](https://daringfireball.net/projects/markdown/) and HTML\n",
"<img src='http://i.imgur.com/WypQf94.gif' align='left'></span>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"\n",
"# LaTeX\n",
"<span style=\"font-size: 24px\"> $\\bar{x} = \\frac{\\sum_{i=1}^{n}w_i\\cdot x_i}{\\sum_{i=1}^{n}w_i}$</span>\n",
"\n",
"\n",
"# Syntax highlighting for other languages\n",
"```R\n",
"x <- c(0:10, 50)\n",
"xm <- mean(x)\n",
"c(xm, mean(x, trim = 0.10))\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. [magic methods](http://ipython.org/ipython-doc/dev/interactive/tutorial.html#magics-explained)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%quickref"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello world\n"
]
}
],
"source": [
"%%python2\n",
"\n",
"print \"hello world\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from IPython.display import display, Image, YouTubeVideo\n",
"\n",
"%matplotlib inline\n",
"?pd.read_csv\n",
"%quickref\n",
"%%python2\n",
"\n",
"print \"hello world\"\n",
"%history\n"
]
}
],
"source": [
"%history"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"application/json": {
"cell": {
"!": "OSMagics",
"HTML": "Other",
"SVG": "Other",
"bash": "Other",
"capture": "ExecutionMagics",
"debug": "ExecutionMagics",
"file": "Other",
"html": "DisplayMagics",
"javascript": "DisplayMagics",
"latex": "DisplayMagics",
"perl": "Other",
"prun": "ExecutionMagics",
"pypy": "Other",
"python": "Other",
"python2": "Other",
"python3": "Other",
"ruby": "Other",
"script": "ScriptMagics",
"sh": "Other",
"svg": "DisplayMagics",
"sx": "OSMagics",
"system": "OSMagics",
"time": "ExecutionMagics",
"timeit": "ExecutionMagics",
"writefile": "OSMagics"
},
"line": {
"alias": "OSMagics",
"alias_magic": "BasicMagics",
"autocall": "AutoMagics",
"automagic": "AutoMagics",
"autosave": "KernelMagics",
"bookmark": "OSMagics",
"cat": "Other",
"cd": "OSMagics",
"clear": "KernelMagics",
"colors": "BasicMagics",
"config": "ConfigMagics",
"connect_info": "KernelMagics",
"cp": "Other",
"debug": "ExecutionMagics",
"dhist": "OSMagics",
"dirs": "OSMagics",
"doctest_mode": "BasicMagics",
"ed": "Other",
"edit": "KernelMagics",
"env": "OSMagics",
"gui": "BasicMagics",
"hist": "Other",
"history": "HistoryMagics",
"install_default_config": "DeprecatedMagics",
"install_ext": "ExtensionMagics",
"install_profiles": "DeprecatedMagics",
"killbgscripts": "ScriptMagics",
"ldir": "Other",
"less": "KernelMagics",
"lf": "Other",
"lk": "Other",
"ll": "Other",
"load": "CodeMagics",
"load_ext": "ExtensionMagics",
"loadpy": "CodeMagics",
"logoff": "LoggingMagics",
"logon": "LoggingMagics",
"logstart": "LoggingMagics",
"logstate": "LoggingMagics",
"logstop": "LoggingMagics",
"ls": "Other",
"lsmagic": "BasicMagics",
"lx": "Other",
"macro": "ExecutionMagics",
"magic": "BasicMagics",
"man": "KernelMagics",
"matplotlib": "PylabMagics",
"mkdir": "Other",
"more": "KernelMagics",
"mv": "Other",
"notebook": "BasicMagics",
"page": "BasicMagics",
"pastebin": "CodeMagics",
"pdb": "ExecutionMagics",
"pdef": "NamespaceMagics",
"pdoc": "NamespaceMagics",
"pfile": "NamespaceMagics",
"pinfo": "NamespaceMagics",
"pinfo2": "NamespaceMagics",
"popd": "OSMagics",
"pprint": "BasicMagics",
"precision": "BasicMagics",
"profile": "BasicMagics",
"prun": "ExecutionMagics",
"psearch": "NamespaceMagics",
"psource": "NamespaceMagics",
"pushd": "OSMagics",
"pwd": "OSMagics",
"pycat": "OSMagics",
"pylab": "PylabMagics",
"qtconsole": "KernelMagics",
"quickref": "BasicMagics",
"recall": "HistoryMagics",
"rehashx": "OSMagics",
"reload_ext": "ExtensionMagics",
"rep": "Other",
"rerun": "HistoryMagics",
"reset": "NamespaceMagics",
"reset_selective": "NamespaceMagics",
"rm": "Other",
"rmdir": "Other",
"run": "ExecutionMagics",
"save": "CodeMagics",
"sc": "OSMagics",
"set_env": "OSMagics",
"store": "StoreMagics",
"sx": "OSMagics",
"system": "OSMagics",
"tb": "ExecutionMagics",
"time": "ExecutionMagics",
"timeit": "ExecutionMagics",
"unalias": "OSMagics",
"unload_ext": "ExtensionMagics",
"who": "NamespaceMagics",
"who_ls": "NamespaceMagics",
"whos": "NamespaceMagics",
"xdel": "NamespaceMagics",
"xmode": "BasicMagics"
}
},
"text/plain": [
"Available line magics:\n",
"%alias %alias_magic %autocall %automagic %autosave %bookmark %cat %cd %clear %colors %config %connect_info %cp %debug %dhist %dirs %doctest_mode %ed %edit %env %gui %hist %history %install_default_config %install_ext %install_profiles %killbgscripts %ldir %less %lf %lk %ll %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %lx %macro %magic %man %matplotlib %mkdir %more %mv %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %popd %pprint %precision %profile %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %rm %rmdir %run %save %sc %set_env %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n",
"\n",
"Available cell magics:\n",
"%%! %%HTML %%SVG %%bash %%capture %%debug %%file %%html %%javascript %%latex %%perl %%prun %%pypy %%python %%python2 %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n",
"\n",
"Automagic is ON, % prefix IS NOT needed for line magics."
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# list the available line magics\n",
"%lsmagic"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. [multimedia](https://ipython.org/ipython-doc/dev/api/generated/IPython.display.html)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
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UugNRE09TA50jQJMQuMOea4lTsypo5DI1vOvkZUMs2F0rQHvxDSxDvRbtW1mw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\ntGVgCdBbUEfxdinR1z6l+cL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" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.YouTubeVideo at 0x104023fd0>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"YouTubeVideo(\"L4Hbv4ugUWk\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## 6. Sharing! Notebooks are viewable on [NBViewer](https://nbviewer.jupyter.org/) and [on github](https://github.com/meli-lewis/osb2015/blob/master/OSB2015_intro_data.ipynb), and also exportable as PDF or HTML."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Shell commands!"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[34mIRS_data\u001b[m\u001b[m \u001b[34mj\u001b[m\u001b[m pycaribbean.ipynb \u001b[34mtourism_data\u001b[m\u001b[m\r\n"
]
}
],
"source": [
"!ls"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Keyboard shortcuts! Your implementation may vary.\n",
"\n",
"Use [what's here](http://johnlaudun.org/20131228-ipython-notebook-keyboard-shortcuts/) or [roll your own](http://jupyter-notebook.readthedocs.org/en/latest/examples/Notebook/Custom%20Keyboard%20Shortcuts.html)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to exploratory data analysis with Pandas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## First: United States income\n",
"Source: [IRS.gov](http://www.irs.gov/uac/SOI-Tax-Stats-Individual-Income-Tax-Statistics-ZIP-Code-Data-%28SOI%29)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Input"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pd.read_csv?"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# read in a CSV\n",
"# specify that zipcode should be treated as a string rather than an int!\n",
"AGI = pd.read_csv('IRS_data/12zpallagi.csv',dtype={'zipcode': str})"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 166904 entries, 0 to 166903\n",
"Data columns (total 77 columns):\n",
"STATEFIPS 166904 non-null int64\n",
"STATE 166904 non-null object\n",
"zipcode 166904 non-null object\n",
"AGI_STUB 166904 non-null int64\n",
"N1 166904 non-null float64\n",
"MARS1 166904 non-null float64\n",
"MARS2 166904 non-null float64\n",
"MARS4 166904 non-null float64\n",
"PREP 166904 non-null float64\n",
"N2 166904 non-null float64\n",
"NUMDEP 166904 non-null float64\n",
"A00100 166904 non-null float64\n",
"N00200 166904 non-null float64\n",
"A00200 166904 non-null float64\n",
"N00300 166904 non-null float64\n",
"A00300 166904 non-null float64\n",
"N00600 166904 non-null float64\n",
"A00600 166904 non-null float64\n",
"N00650 166904 non-null float64\n",
"A00650 166904 non-null float64\n",
"N00900 166904 non-null float64\n",
"A00900 166904 non-null float64\n",
"SCHF 166904 non-null float64\n",
"N01000 166904 non-null float64\n",
"A01000 166904 non-null float64\n",
"N01400 166904 non-null float64\n",
"A01400 166904 non-null float64\n",
"N01700 166904 non-null float64\n",
"A01700 166904 non-null float64\n",
"N02300 166904 non-null float64\n",
"A02300 166904 non-null float64\n",
"N02500 166904 non-null float64\n",
"A02500 166904 non-null float64\n",
"N03300 166904 non-null float64\n",
"A03300 166904 non-null float64\n",
"N00101 166904 non-null float64\n",
"A00101 166904 non-null float64\n",
"N04470 166904 non-null float64\n",
"A04470 166904 non-null float64\n",
"N18425 166904 non-null float64\n",
"A18425 166904 non-null float64\n",
"N18450 166904 non-null float64\n",
"A18450 166904 non-null float64\n",
"N18500 166904 non-null float64\n",
"A18500 166904 non-null float64\n",
"N18300 166904 non-null float64\n",
"A18300 166904 non-null float64\n",
"N19300 166904 non-null float64\n",
"A19300 166904 non-null float64\n",
"N19700 166904 non-null float64\n",
"A19700 166904 non-null float64\n",
"N04800 166904 non-null float64\n",
"A04800 166904 non-null float64\n",
"N07100 166904 non-null float64\n",
"A07100 166904 non-null float64\n",
"N07220 166904 non-null float64\n",
"A07220 166904 non-null float64\n",
"N07180 166904 non-null float64\n",
"A07180 166904 non-null float64\n",
"N07260 166904 non-null float64\n",
"A07260 166904 non-null float64\n",
"N59660 166904 non-null float64\n",
"A59660 166904 non-null float64\n",
"N59720 166904 non-null float64\n",
"A59720 166904 non-null float64\n",
"N11070 166904 non-null float64\n",
"A11070 166904 non-null float64\n",
"N09600 166904 non-null float64\n",
"A09600 166904 non-null float64\n",
"N06500 166904 non-null float64\n",
"A06500 166904 non-null float64\n",
"N10300 166904 non-null float64\n",
"A10300 166904 non-null float64\n",
"N11901 166904 non-null float64\n",
"A11901 166904 non-null float64\n",
"N11902 166904 non-null float64\n",
"A11902 166904 non-null float64\n",
"dtypes: float64(73), int64(2), object(2)\n",
"memory usage: 99.3+ MB\n"
]
}
],
"source": [
"AGI.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transformation"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# you can select columns by label or position!\n",
"AGI_column_subset = AGI[['STATE','AGI_STUB','zipcode','N1','A00100']]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 166904 entries, 0 to 166903\n",
"Data columns (total 5 columns):\n",
"STATE 166904 non-null object\n",
"AGI_STUB 166904 non-null int64\n",
"zipcode 166904 non-null object\n",
"N1 166904 non-null float64\n",
"A00100 166904 non-null float64\n",
"dtypes: float64(2), int64(1), object(2)\n",
"memory usage: 7.6+ MB\n"
]
}
],
"source": [
"# get information about type for a given field, and how many values you can expect for each\n",
"AGI_column_subset.info()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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>AGI_STUB</th>\n",
" <th>N1</th>\n",
" <th>A00100</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>166904.000000</td>\n",
" <td>166904.000000</td>\n",
" <td>1.669040e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>3.499940</td>\n",
" <td>1703.184106</td>\n",
" <td>1.093321e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.707905</td>\n",
" <td>36669.348742</td>\n",
" <td>2.346716e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.000000</td>\n",
" <td>60.000000</td>\n",
" <td>3.869000e+03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3.000000</td>\n",
" <td>220.000000</td>\n",
" <td>1.412900e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>5.000000</td>\n",
" <td>910.000000</td>\n",
" <td>5.508425e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>6.000000</td>\n",
" <td>6404780.000000</td>\n",
" <td>4.601720e+08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AGI_STUB N1 A00100\n",
"count 166904.000000 166904.000000 1.669040e+05\n",
"mean 3.499940 1703.184106 1.093321e+05\n",
"std 1.707905 36669.348742 2.346716e+06\n",
"min 1.000000 0.000000 0.000000e+00\n",
"25% 2.000000 60.000000 3.869000e+03\n",
"50% 3.000000 220.000000 1.412900e+04\n",
"75% 5.000000 910.000000 5.508425e+04\n",
"max 6.000000 6404780.000000 4.601720e+08"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"AGI_column_subset.describe()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"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>STATE</th>\n",
" <th>AGI_STUB</th>\n",
" <th>zipcode</th>\n",
" <th>N1</th>\n",
" <th>A00100</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>AL</td>\n",
" <td>1</td>\n",
" <td>35004</td>\n",
" <td>1600</td>\n",
" <td>20639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>AL</td>\n",
" <td>2</td>\n",
" <td>35004</td>\n",
" <td>1310</td>\n",
" <td>48501</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>AL</td>\n",
" <td>3</td>\n",
" <td>35004</td>\n",
" <td>900</td>\n",
" <td>55790</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>AL</td>\n",
" <td>4</td>\n",
" <td>35004</td>\n",
" <td>590</td>\n",
" <td>50978</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>AL</td>\n",
" <td>5</td>\n",
" <td>35004</td>\n",
" <td>480</td>\n",
" <td>59932</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>AL</td>\n",
" <td>6</td>\n",
" <td>35004</td>\n",
" <td>50</td>\n",
" <td>21723</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" STATE AGI_STUB zipcode N1 A00100\n",
"6 AL 1 35004 1600 20639\n",
"7 AL 2 35004 1310 48501\n",
"8 AL 3 35004 900 55790\n",
"9 AL 4 35004 590 50978\n",
"10 AL 5 35004 480 59932\n",
"11 AL 6 35004 50 21723"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# note this is inclusive!\n",
"AGI_column_subset.ix[6:11]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"AGI_column_subset = AGI_column_subset.rename(columns={'N1':'population','A00100':'amount'})\n",
"# AGI_column_subset.rename(columns={'N1':'population','A00100':'amount'},inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"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>STATE</th>\n",
" <th>AGI_STUB</th>\n",
" <th>zipcode</th>\n",
" <th>population</th>\n",
" <th>amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AL</td>\n",
" <td>1</td>\n",
" <td>00000</td>\n",
" <td>889920</td>\n",
" <td>11517112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>AL</td>\n",
" <td>2</td>\n",
" <td>00000</td>\n",
" <td>491150</td>\n",
" <td>17617800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AL</td>\n",
" <td>3</td>\n",
" <td>00000</td>\n",
" <td>254280</td>\n",
" <td>15644666</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>AL</td>\n",
" <td>4</td>\n",
" <td>00000</td>\n",
" <td>160160</td>\n",
" <td>13885434</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>AL</td>\n",
" <td>5</td>\n",
" <td>00000</td>\n",
" <td>183320</td>\n",
" <td>24641055</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" STATE AGI_STUB zipcode population amount\n",
"0 AL 1 00000 889920 11517112\n",
"1 AL 2 00000 491150 17617800\n",
"2 AL 3 00000 254280 15644666\n",
"3 AL 4 00000 160160 13885434\n",
"4 AL 5 00000 183320 24641055"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# easy way to look at a subset of data without having to specify rows\n",
"AGI_column_subset.head()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# group by zipcode and sum other values, resetting index\n",
"AGI_grouped = AGI_column_subset.groupby('zipcode').sum().reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"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>zipcode</th>\n",
" <th>AGI_STUB</th>\n",
" <th>population</th>\n",
" <th>amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>00000</td>\n",
" <td>1071</td>\n",
" <td>142098490</td>\n",
" <td>9123982917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>01001</td>\n",
" <td>21</td>\n",
" <td>8780</td>\n",
" <td>458716</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>01002</td>\n",
" <td>21</td>\n",
" <td>9460</td>\n",
" <td>732849</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>01005</td>\n",
" <td>21</td>\n",
" <td>2230</td>\n",
" <td>122744</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>01007</td>\n",
" <td>21</td>\n",
" <td>7320</td>\n",
" <td>467891</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" zipcode AGI_STUB population amount\n",
"0 00000 1071 142098490 9123982917\n",
"1 01001 21 8780 458716\n",
"2 01002 21 9460 732849\n",
"3 01005 21 2230 122744\n",
"4 01007 21 7320 467891"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"AGI_grouped.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 00000?"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"<img src=\"http://i0.kym-cdn.com/photos/images/original/000/383/596/dec.gif\" align=\"left\">"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"10254.987012987012"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"AGI_grouped['population'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#this can also be done using the na_values parameter upon being read in\n",
"# null_zips = (AGI_grouped['zipcode'] == '00000')\n",
"null_zips = AGI_grouped['zipcode'].isin(['00000', '99999'])\n",
"AGI_grouped.loc[null_zips, 'zipcode'] = np.nan"
]
},
{
"cell_type": "code",
"execution_count": 22,
"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>zipcode</th>\n",
" <th>AGI_STUB</th>\n",
" <th>population</th>\n",
" <th>amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>1071</td>\n",
" <td>142098490</td>\n",
" <td>9123982917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>01001</td>\n",
" <td>21</td>\n",
" <td>8780</td>\n",
" <td>458716</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>01002</td>\n",
" <td>21</td>\n",
" <td>9460</td>\n",
" <td>732849</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>01005</td>\n",
" <td>21</td>\n",
" <td>2230</td>\n",
" <td>122744</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>01007</td>\n",
" <td>21</td>\n",
" <td>7320</td>\n",
" <td>467891</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" zipcode AGI_STUB population amount\n",
"0 NaN 1071 142098490 9123982917\n",
"1 01001 21 8780 458716\n",
"2 01002 21 9460 732849\n",
"3 01005 21 2230 122744\n",
"4 01007 21 7320 467891"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"AGI_grouped.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"http://45.media.tumblr.com/tumblr_m9hbpdSJIX1qhy6c9o1_400.gif\" align=\"left\">"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"AGI_notnull = AGI_grouped.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"5024.9671693484379"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"AGI_notnull['population'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"AGI_grouped.dropna(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# make a new column with the real amount, not in thousands\n",
"AGI_grouped['actual_amount'] = AGI_grouped['amount'] * 1000"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Keep in mind you have options, and use magic methods to test implementation inline!"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10 loops, best of 3: 15 ms per loop\n"
]
}
],
"source": [
"%timeit applied = AGI_grouped['amount'].apply(lambda x: x * 1000)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10000 loops, best of 3: 66.9 µs per loop\n"
]
}
],
"source": [
"#being vectorized operations, this is happening at the C level and thereby much faster\n",
"%timeit vectorized = AGI_grouped['amount'] * 1000"
]
},
{
"cell_type": "code",
"execution_count": 29,
"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>zipcode</th>\n",
" <th>AGI_STUB</th>\n",
" <th>population</th>\n",
" <th>amount</th>\n",
" <th>actual_amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>27714</th>\n",
" <td>99801</td>\n",
" <td>21</td>\n",
" <td>12370</td>\n",
" <td>784594</td>\n",
" <td>784594000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27715</th>\n",
" <td>99824</td>\n",
" <td>21</td>\n",
" <td>1130</td>\n",
" <td>82023</td>\n",
" <td>82023000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27716</th>\n",
" <td>99827</td>\n",
" <td>21</td>\n",
" <td>1360</td>\n",
" <td>62933</td>\n",
" <td>62933000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27717</th>\n",
" <td>99835</td>\n",
" <td>21</td>\n",
" <td>4650</td>\n",
" <td>272187</td>\n",
" <td>272187000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27718</th>\n",
" <td>99901</td>\n",
" <td>21</td>\n",
" <td>6330</td>\n",
" <td>389809</td>\n",
" <td>389809000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" zipcode AGI_STUB population amount actual_amount\n",
"27714 99801 21 12370 784594 784594000\n",
"27715 99824 21 1130 82023 82023000\n",
"27716 99827 21 1360 62933 62933000\n",
"27717 99835 21 4650 272187 272187000\n",
"27718 99901 21 6330 389809 389809000"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"AGI_grouped.tail()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# make a mean, using standard math operations!\n",
"AGI_grouped['weighted_mean_AGI'] = AGI_grouped['actual_amount']/AGI_grouped['population']"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#use anonymous functions to change every value in a column!\n",
"#because this is an apply, much slower\n",
"AGI_grouped['weighted_mean_AGI']= AGI_grouped['weighted_mean_AGI'].apply(lambda x: round(x, 0))"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 27718 entries, 1 to 27718\n",
"Data columns (total 6 columns):\n",
"zipcode 27718 non-null object\n",
"AGI_STUB 27718 non-null int64\n",
"population 27718 non-null float64\n",
"amount 27718 non-null float64\n",
"actual_amount 27718 non-null float64\n",
"weighted_mean_AGI 27718 non-null float64\n",
"dtypes: float64(4), int64(1), object(1)\n",
"memory usage: 1.5+ MB\n"
]
}
],
"source": [
"AGI_grouped.info()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# drop columns you won't need\n",
"AGI_grouped.drop(['AGI_STUB','amount','actual_amount'],axis=1,inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"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>zipcode</th>\n",
" <th>population</th>\n",
" <th>weighted_mean_AGI</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>01001</td>\n",
" <td>8780</td>\n",
" <td>52246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>01002</td>\n",
" <td>9460</td>\n",
" <td>77468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>01005</td>\n",
" <td>2230</td>\n",
" <td>55042</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>01007</td>\n",
" <td>7320</td>\n",
" <td>63920</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>01008</td>\n",
" <td>640</td>\n",
" <td>60136</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" zipcode population weighted_mean_AGI\n",
"1 01001 8780 52246\n",
"2 01002 9460 77468\n",
"3 01005 2230 55042\n",
"4 01007 7320 63920\n",
"5 01008 640 60136"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"AGI_grouped.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Merging! Better than in traffic!\n",
"\"Group by\" knows aggregating strings is nonsensical, and so drops those.\n",
"\n",
"But let's add state information to the mix again!"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# also look into pandas.Series.unique\n",
"AGI_subset_geo = AGI[['zipcode','STATE']].drop_duplicates()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"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>zipcode</th>\n",
" <th>STATE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>00000</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>35004</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>35005</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>35006</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>35007</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>35010</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>35014</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>35016</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>35019</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>35020</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>35022</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>35023</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>35031</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>35033</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>35034</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>35035</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td>35040</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>35042</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>35043</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td>35044</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td>35045</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126</th>\n",
" <td>35046</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132</th>\n",
" <td>35049</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>138</th>\n",
" <td>35051</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>144</th>\n",
" <td>35053</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>150</th>\n",
" <td>35054</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>156</th>\n",
" <td>35055</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>162</th>\n",
" <td>35057</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>168</th>\n",
" <td>35058</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>174</th>\n",
" <td>35061</td>\n",
" <td>AL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166724</th>\n",
" <td>82922</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166730</th>\n",
" <td>82923</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166736</th>\n",
" <td>82925</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166742</th>\n",
" <td>82930</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166748</th>\n",
" <td>82932</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166754</th>\n",
" <td>82933</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166760</th>\n",
" <td>82935</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166766</th>\n",
" <td>82937</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166772</th>\n",
" <td>82941</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166778</th>\n",
" <td>83001</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166784</th>\n",
" <td>83011</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166790</th>\n",
" <td>83012</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166796</th>\n",
" <td>83013</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166802</th>\n",
" <td>83014</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166808</th>\n",
" <td>83101</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166814</th>\n",
" <td>83110</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166820</th>\n",
" <td>83111</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166826</th>\n",
" <td>83112</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166832</th>\n",
" <td>83113</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166838</th>\n",
" <td>83114</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166844</th>\n",
" <td>83115</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166850</th>\n",
" <td>83118</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166856</th>\n",
" <td>83120</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166862</th>\n",
" <td>83122</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166868</th>\n",
" <td>83123</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166874</th>\n",
" <td>83126</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166880</th>\n",
" <td>83127</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166886</th>\n",
" <td>83128</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166892</th>\n",
" <td>83414</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166898</th>\n",
" <td>99999</td>\n",
" <td>WY</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>27820 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" zipcode STATE\n",
"0 00000 AL\n",
"6 35004 AL\n",
"12 35005 AL\n",
"18 35006 AL\n",
"24 35007 AL\n",
"30 35010 AL\n",
"36 35014 AL\n",
"42 35016 AL\n",
"48 35019 AL\n",
"54 35020 AL\n",
"60 35022 AL\n",
"66 35023 AL\n",
"72 35031 AL\n",
"78 35033 AL\n",
"84 35034 AL\n",
"90 35035 AL\n",
"96 35040 AL\n",
"102 35042 AL\n",
"108 35043 AL\n",
"114 35044 AL\n",
"120 35045 AL\n",
"126 35046 AL\n",
"132 35049 AL\n",
"138 35051 AL\n",
"144 35053 AL\n",
"150 35054 AL\n",
"156 35055 AL\n",
"162 35057 AL\n",
"168 35058 AL\n",
"174 35061 AL\n",
"... ... ...\n",
"166724 82922 WY\n",
"166730 82923 WY\n",
"166736 82925 WY\n",
"166742 82930 WY\n",
"166748 82932 WY\n",
"166754 82933 WY\n",
"166760 82935 WY\n",
"166766 82937 WY\n",
"166772 82941 WY\n",
"166778 83001 WY\n",
"166784 83011 WY\n",
"166790 83012 WY\n",
"166796 83013 WY\n",
"166802 83014 WY\n",
"166808 83101 WY\n",
"166814 83110 WY\n",
"166820 83111 WY\n",
"166826 83112 WY\n",
"166832 83113 WY\n",
"166838 83114 WY\n",
"166844 83115 WY\n",
"166850 83118 WY\n",
"166856 83120 WY\n",
"166862 83122 WY\n",
"166868 83123 WY\n",
"166874 83126 WY\n",
"166880 83127 WY\n",
"166886 83128 WY\n",
"166892 83414 WY\n",
"166898 99999 WY\n",
"\n",
"[27820 rows x 2 columns]"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"AGI_subset_geo"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#merge rather than join if you want to use a common column other than the index\n",
"AGI_final = pd.merge(AGI_grouped, AGI_subset_geo, how='left', on='zipcode')"
]
},
{
"cell_type": "code",
"execution_count": 38,
"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>zipcode</th>\n",
" <th>population</th>\n",
" <th>weighted_mean_AGI</th>\n",
" <th>STATE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>27713</th>\n",
" <td>99801</td>\n",
" <td>12370</td>\n",
" <td>63427</td>\n",
" <td>AK</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27714</th>\n",
" <td>99824</td>\n",
" <td>1130</td>\n",
" <td>72587</td>\n",
" <td>AK</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27715</th>\n",
" <td>99827</td>\n",
" <td>1360</td>\n",
" <td>46274</td>\n",
" <td>AK</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27716</th>\n",
" <td>99835</td>\n",
" <td>4650</td>\n",
" <td>58535</td>\n",
" <td>AK</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27717</th>\n",
" <td>99901</td>\n",
" <td>6330</td>\n",
" <td>61581</td>\n",
" <td>AK</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" zipcode population weighted_mean_AGI STATE\n",
"27713 99801 12370 63427 AK\n",
"27714 99824 1130 72587 AK\n",
"27715 99827 1360 46274 AK\n",
"27716 99835 4650 58535 AK\n",
"27717 99901 6330 61581 AK"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"AGI_final.tail()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"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>zipcode</th>\n",
" <th>population</th>\n",
" <th>weighted_mean_AGI</th>\n",
" <th>STATE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>9031</th>\n",
" <td>33109</td>\n",
" <td>250</td>\n",
" <td>2694776</td>\n",
" <td>FL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26104</th>\n",
" <td>94027</td>\n",
" <td>3220</td>\n",
" <td>1464534</td>\n",
" <td>CA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4926</th>\n",
" <td>19035</td>\n",
" <td>2040</td>\n",
" <td>1052019</td>\n",
" <td>PA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2194</th>\n",
" <td>10005</td>\n",
" <td>5580</td>\n",
" <td>983554</td>\n",
" <td>NY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9168</th>\n",
" <td>33480</td>\n",
" <td>5580</td>\n",
" <td>966673</td>\n",
" <td>FL</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" zipcode population weighted_mean_AGI STATE\n",
"9031 33109 250 2694776 FL\n",
"26104 94027 3220 1464534 CA\n",
"4926 19035 2040 1052019 PA\n",
"2194 10005 5580 983554 NY\n",
"9168 33480 5580 966673 FL"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# this gives you the greatest weighted_mean_AGI first\n",
"AGI_final.sort_values(by='weighted_mean_AGI',ascending=False).head()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"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>population</th>\n",
" <th>weighted_mean_AGI</th>\n",
" </tr>\n",
" <tr>\n",
" <th>STATE</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>DC</th>\n",
" <td>14558.181818</td>\n",
" <td>103480.590909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CT</th>\n",
" <td>6529.118774</td>\n",
" <td>96062.421456</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NJ</th>\n",
" <td>7737.192661</td>\n",
" <td>89242.225688</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MA</th>\n",
" <td>6602.619543</td>\n",
" <td>87019.825364</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CA</th>\n",
" <td>10900.944032</td>\n",
" <td>75537.775455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MD</th>\n",
" <td>6939.034653</td>\n",
" <td>73676.836634</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NV</th>\n",
" <td>9425.968992</td>\n",
" <td>71209.976744</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NY</th>\n",
" <td>5914.349515</td>\n",
" <td>68860.152104</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ND</th>\n",
" <td>1174.930070</td>\n",
" <td>68097.318182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FL</th>\n",
" <td>9633.849509</td>\n",
" <td>65399.196292</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RI</th>\n",
" <td>7275.217391</td>\n",
" <td>65351.202899</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NH</th>\n",
" <td>2872.173913</td>\n",
" <td>64188.408696</td>\n",
" </tr>\n",
" <tr>\n",
" <th>WY</th>\n",
" <td>2525.794393</td>\n",
" <td>64128.813084</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DE</th>\n",
" <td>7718.000000</td>\n",
" <td>63196.363636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CO</th>\n",
" <td>5891.367089</td>\n",
" <td>62926.582278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>WA</th>\n",
" <td>6312.862903</td>\n",
" <td>61806.048387</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TX</th>\n",
" <td>6823.705302</td>\n",
" <td>60807.772503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IL</th>\n",
" <td>4855.851671</td>\n",
" <td>60416.809291</td>\n",
" </tr>\n",
" <tr>\n",
" <th>VA</th>\n",
" <td>4682.682620</td>\n",
" <td>59560.884131</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AK</th>\n",
" <td>5259.245283</td>\n",
" <td>58695.433962</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MN</th>\n",
" <td>3252.553729</td>\n",
" <td>58641.551201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>UT</th>\n",
" <td>6037.540984</td>\n",
" <td>57716.355191</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AZ</th>\n",
" <td>8895.644599</td>\n",
" <td>56730.749129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NE</th>\n",
" <td>1756.219008</td>\n",
" <td>56224.725207</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PA</th>\n",
" <td>4363.623719</td>\n",
" <td>55447.786969</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HI</th>\n",
" <td>10498.965517</td>\n",
" <td>55156.913793</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IA</th>\n",
" <td>1689.345455</td>\n",
" <td>55083.106667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SD</th>\n",
" <td>1376.089965</td>\n",
" <td>53786.121107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KS</th>\n",
" <td>2144.527363</td>\n",
" <td>53320.759536</td>\n",
" </tr>\n",
" <tr>\n",
" <th>WI</th>\n",
" <td>3825.617978</td>\n",
" <td>52697.198034</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OK</th>\n",
" <td>2836.599265</td>\n",
" <td>52071.439338</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LA</th>\n",
" <td>4272.217295</td>\n",
" <td>51511.485588</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OR</th>\n",
" <td>4855.170455</td>\n",
" <td>51207.985795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OH</th>\n",
" <td>5419.438315</td>\n",
" <td>50741.277834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MI</th>\n",
" <td>5076.033708</td>\n",
" <td>50450.595506</td>\n",
" </tr>\n",
" <tr>\n",
" <th>VT</th>\n",
" <td>1288.813559</td>\n",
" <td>50049.114407</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IN</th>\n",
" <td>4405.780089</td>\n",
" <td>49091.734027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MT</th>\n",
" <td>1979.911111</td>\n",
" <td>49056.662222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GA</th>\n",
" <td>6297.087087</td>\n",
" <td>48479.487988</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NC</th>\n",
" <td>5710.524862</td>\n",
" <td>48392.198895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ID</th>\n",
" <td>3087.428571</td>\n",
" <td>47607.995238</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ME</th>\n",
" <td>1649.646739</td>\n",
" <td>47555.201087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TN</th>\n",
" <td>4769.761905</td>\n",
" <td>46735.782313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SC</th>\n",
" <td>5373.512064</td>\n",
" <td>45564.546917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MO</th>\n",
" <td>2998.018018</td>\n",
" <td>45385.798423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AL</th>\n",
" <td>3456.539130</td>\n",
" <td>45051.260870</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NM</th>\n",
" <td>3826.333333</td>\n",
" <td>43959.495238</td>\n",
" </tr>\n",
" <tr>\n",
" <th>WV</th>\n",
" <td>1422.684825</td>\n",
" <td>43259.171206</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KY</th>\n",
" <td>2771.065449</td>\n",
" <td>42960.374429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AR</th>\n",
" <td>2376.510204</td>\n",
" <td>42197.508163</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MS</th>\n",
" <td>3254.347826</td>\n",
" <td>40630.513587</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" population weighted_mean_AGI\n",
"STATE \n",
"DC 14558.181818 103480.590909\n",
"CT 6529.118774 96062.421456\n",
"NJ 7737.192661 89242.225688\n",
"MA 6602.619543 87019.825364\n",
"CA 10900.944032 75537.775455\n",
"MD 6939.034653 73676.836634\n",
"NV 9425.968992 71209.976744\n",
"NY 5914.349515 68860.152104\n",
"ND 1174.930070 68097.318182\n",
"FL 9633.849509 65399.196292\n",
"RI 7275.217391 65351.202899\n",
"NH 2872.173913 64188.408696\n",
"WY 2525.794393 64128.813084\n",
"DE 7718.000000 63196.363636\n",
"CO 5891.367089 62926.582278\n",
"WA 6312.862903 61806.048387\n",
"TX 6823.705302 60807.772503\n",
"IL 4855.851671 60416.809291\n",
"VA 4682.682620 59560.884131\n",
"AK 5259.245283 58695.433962\n",
"MN 3252.553729 58641.551201\n",
"UT 6037.540984 57716.355191\n",
"AZ 8895.644599 56730.749129\n",
"NE 1756.219008 56224.725207\n",
"PA 4363.623719 55447.786969\n",
"HI 10498.965517 55156.913793\n",
"IA 1689.345455 55083.106667\n",
"SD 1376.089965 53786.121107\n",
"KS 2144.527363 53320.759536\n",
"WI 3825.617978 52697.198034\n",
"OK 2836.599265 52071.439338\n",
"LA 4272.217295 51511.485588\n",
"OR 4855.170455 51207.985795\n",
"OH 5419.438315 50741.277834\n",
"MI 5076.033708 50450.595506\n",
"VT 1288.813559 50049.114407\n",
"IN 4405.780089 49091.734027\n",
"MT 1979.911111 49056.662222\n",
"GA 6297.087087 48479.487988\n",
"NC 5710.524862 48392.198895\n",
"ID 3087.428571 47607.995238\n",
"ME 1649.646739 47555.201087\n",
"TN 4769.761905 46735.782313\n",
"SC 5373.512064 45564.546917\n",
"MO 2998.018018 45385.798423\n",
"AL 3456.539130 45051.260870\n",
"NM 3826.333333 43959.495238\n",
"WV 1422.684825 43259.171206\n",
"KY 2771.065449 42960.374429\n",
"AR 2376.510204 42197.508163\n",
"MS 3254.347826 40630.513587"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# chain methods!\n",
"AGI_final.groupby('STATE').mean().sort_values(by='weighted_mean_AGI',ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# not sure if this is PEP8-compliant\n",
"top_5_states = AGI_final.groupby('STATE').mean().sort_values(\n",
" by='weighted_mean_AGI',ascending=False).reset_index().head()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10984d5c0>"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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lQF61MW/UVlD0VkteXh55eXm1dRVptC699FJeeukl1q9fz/jx42ndujWzZs1i6dKl/PjH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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10985fc50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"top_5_states.plot(kind='bar',x='STATE')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# pandas and world tourism data"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# `cat` is an easy way to examine file contents in place\n",
"\n",
"# !cat tourism_data/581a4d76-9f6d-4786-b22c-73d59627d1e2_v2.csv"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# read in a CSV\n",
"df = pd.read_csv('tourism_data/581a4d76-9f6d-4786-b22c-73d59627d1e2_v2.csv',skiprows=4)\n",
"# df = pd.read_csv('tourism_data/581a4d76-9f6d-4786-b22c-73d59627d1e2_v2.csv')"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 248 entries, 0 to 247\n",
"Data columns (total 61 columns):\n",
"Country Name 248 non-null object\n",
"Country Code 248 non-null object\n",
"Indicator Name 248 non-null object\n",
"Indicator Code 248 non-null object\n",
"1960 0 non-null float64\n",
"1961 0 non-null float64\n",
"1962 0 non-null float64\n",
"1963 0 non-null float64\n",
"1964 0 non-null float64\n",
"1965 0 non-null float64\n",
"1966 0 non-null float64\n",
"1967 0 non-null float64\n",
"1968 0 non-null float64\n",
"1969 0 non-null float64\n",
"1970 0 non-null float64\n",
"1971 0 non-null float64\n",
"1972 0 non-null float64\n",
"1973 0 non-null float64\n",
"1974 0 non-null float64\n",
"1975 0 non-null float64\n",
"1976 0 non-null float64\n",
"1977 0 non-null float64\n",
"1978 0 non-null float64\n",
"1979 0 non-null float64\n",
"1980 0 non-null float64\n",
"1981 0 non-null float64\n",
"1982 0 non-null float64\n",
"1983 0 non-null float64\n",
"1984 0 non-null float64\n",
"1985 0 non-null float64\n",
"1986 0 non-null float64\n",
"1987 0 non-null float64\n",
"1988 0 non-null float64\n",
"1989 0 non-null float64\n",
"1990 0 non-null float64\n",
"1991 0 non-null float64\n",
"1992 0 non-null float64\n",
"1993 0 non-null float64\n",
"1994 0 non-null float64\n",
"1995 204 non-null float64\n",
"1996 206 non-null float64\n",
"1997 207 non-null float64\n",
"1998 210 non-null float64\n",
"1999 210 non-null float64\n",
"2000 215 non-null float64\n",
"2001 216 non-null float64\n",
"2002 215 non-null float64\n",
"2003 220 non-null float64\n",
"2004 222 non-null float64\n",
"2005 225 non-null float64\n",
"2006 227 non-null float64\n",
"2007 230 non-null float64\n",
"2008 230 non-null float64\n",
"2009 228 non-null float64\n",
"2010 229 non-null float64\n",
"2011 225 non-null float64\n",
"2012 217 non-null float64\n",
"2013 205 non-null float64\n",
"2014 0 non-null float64\n",
"2015 0 non-null float64\n",
"Unnamed: 60 0 non-null float64\n",
"dtypes: float64(57), object(4)\n",
"memory usage: 120.1+ KB\n"
]
}
],
"source": [
"# get information about type for a given field, and how many values you can expect for each\n",
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_subset = df.dropna(axis=1,how='all')"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 248 entries, 0 to 247\n",
"Data columns (total 23 columns):\n",
"Country Name 248 non-null object\n",
"Country Code 248 non-null object\n",
"Indicator Name 248 non-null object\n",
"Indicator Code 248 non-null object\n",
"1995 204 non-null float64\n",
"1996 206 non-null float64\n",
"1997 207 non-null float64\n",
"1998 210 non-null float64\n",
"1999 210 non-null float64\n",
"2000 215 non-null float64\n",
"2001 216 non-null float64\n",
"2002 215 non-null float64\n",
"2003 220 non-null float64\n",
"2004 222 non-null float64\n",
"2005 225 non-null float64\n",
"2006 227 non-null float64\n",
"2007 230 non-null float64\n",
"2008 230 non-null float64\n",
"2009 228 non-null float64\n",
"2010 229 non-null float64\n",
"2011 225 non-null float64\n",
"2012 217 non-null float64\n",
"2013 205 non-null float64\n",
"dtypes: float64(19), object(4)\n",
"memory usage: 46.5+ KB\n"
]
}
],
"source": [
"df_subset.info()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# df_subset.drop(['Indicator Name','Indicator Code'], axis=1, inplace=True)\n",
"df_subset = df_subset.drop(['Indicator Name','Indicator Code'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country Name</th>\n",
" <th>Country Code</th>\n",
" <th>1995</th>\n",
" <th>1996</th>\n",
" <th>1997</th>\n",
" <th>1998</th>\n",
" <th>1999</th>\n",
" <th>2000</th>\n",
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" <th>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Aruba</td>\n",
" <td>ABW</td>\n",
" <td>619000</td>\n",
" <td>641000</td>\n",
" <td>650000</td>\n",
" <td>647000</td>\n",
" <td>683000</td>\n",
" <td>721000</td>\n",
" <td>691000</td>\n",
" <td>643000</td>\n",
" <td>...</td>\n",
" <td>728000</td>\n",
" <td>733000</td>\n",
" <td>694000</td>\n",
" <td>772000</td>\n",
" <td>827000</td>\n",
" <td>813000</td>\n",
" <td>824000</td>\n",
" <td>869000</td>\n",
" <td>904000</td>\n",
" <td>979000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Andorra</td>\n",
" <td>AND</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2347000</td>\n",
" <td>2949000</td>\n",
" <td>3516000</td>\n",
" <td>3387000</td>\n",
" <td>...</td>\n",
" <td>2791000</td>\n",
" <td>2418000</td>\n",
" <td>2227000</td>\n",
" <td>2189000</td>\n",
" <td>2059000</td>\n",
" <td>1830000</td>\n",
" <td>1808000</td>\n",
" <td>2242000</td>\n",
" <td>2238000</td>\n",
" <td>2335000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Afghanistan</td>\n",
" <td>AFG</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Angola</td>\n",
" <td>AGO</td>\n",
" <td>9000</td>\n",
" <td>21000</td>\n",
" <td>45000</td>\n",
" <td>52000</td>\n",
" <td>45000</td>\n",
" <td>51000</td>\n",
" <td>67000</td>\n",
" <td>91000</td>\n",
" <td>...</td>\n",
" <td>194000</td>\n",
" <td>210000</td>\n",
" <td>121000</td>\n",
" <td>195000</td>\n",
" <td>294000</td>\n",
" <td>366000</td>\n",
" <td>425000</td>\n",
" <td>481000</td>\n",
" <td>528000</td>\n",
" <td>650000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Albania</td>\n",
" <td>ALB</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1062000</td>\n",
" <td>1247000</td>\n",
" <td>1711000</td>\n",
" <td>2191000</td>\n",
" <td>2469000</td>\n",
" <td>3156000</td>\n",
" <td>2857000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" Country Name Country Code 1995 1996 1997 1998 1999 2000 \\\n",
"0 Aruba ABW 619000 641000 650000 647000 683000 721000 \n",
"1 Andorra AND NaN NaN NaN NaN 2347000 2949000 \n",
"2 Afghanistan AFG NaN NaN NaN NaN NaN NaN \n",
"3 Angola AGO 9000 21000 45000 52000 45000 51000 \n",
"4 Albania ALB NaN NaN NaN NaN NaN NaN \n",
"\n",
" 2001 2002 ... 2004 2005 2006 2007 2008 \\\n",
"0 691000 643000 ... 728000 733000 694000 772000 827000 \n",
"1 3516000 3387000 ... 2791000 2418000 2227000 2189000 2059000 \n",
"2 NaN NaN ... NaN NaN NaN NaN NaN \n",
"3 67000 91000 ... 194000 210000 121000 195000 294000 \n",
"4 NaN NaN ... NaN NaN NaN 1062000 1247000 \n",
"\n",
" 2009 2010 2011 2012 2013 \n",
"0 813000 824000 869000 904000 979000 \n",
"1 1830000 1808000 2242000 2238000 2335000 \n",
"2 NaN NaN NaN NaN NaN \n",
"3 366000 425000 481000 528000 650000 \n",
"4 1711000 2191000 2469000 3156000 2857000 \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_subset.head()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df_melted = pd.melt(df_subset, id_vars=['Country Name', 'Country Code'])"
]
},
{
"cell_type": "code",
"execution_count": 51,
"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>Country Name</th>\n",
" <th>Country Code</th>\n",
" <th>variable</th>\n",
" <th>value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Aruba</td>\n",
" <td>ABW</td>\n",
" <td>1995</td>\n",
" <td>6.190000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Andorra</td>\n",
" <td>AND</td>\n",
" <td>1995</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Afghanistan</td>\n",
" <td>AFG</td>\n",
" <td>1995</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Angola</td>\n",
" <td>AGO</td>\n",
" <td>1995</td>\n",
" <td>9.000000e+03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Albania</td>\n",
" <td>ALB</td>\n",
" <td>1995</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Arab World</td>\n",
" <td>ARB</td>\n",
" <td>1995</td>\n",
" <td>2.566348e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>United Arab Emirates</td>\n",
" <td>ARE</td>\n",
" <td>1995</td>\n",
" <td>2.315000e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Argentina</td>\n",
" <td>ARG</td>\n",
" <td>1995</td>\n",
" <td>2.289000e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Armenia</td>\n",
" <td>ARM</td>\n",
" <td>1995</td>\n",
" <td>1.200000e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>American Samoa</td>\n",
" <td>ASM</td>\n",
" <td>1995</td>\n",
" <td>3.400000e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Antigua and Barbuda</td>\n",
" <td>ATG</td>\n",
" <td>1995</td>\n",
" <td>2.200000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Australia</td>\n",
" <td>AUS</td>\n",
" <td>1995</td>\n",
" <td>3.726000e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Austria</td>\n",
" <td>AUT</td>\n",
" <td>1995</td>\n",
" <td>1.717300e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Azerbaijan</td>\n",
" <td>AZE</td>\n",
" <td>1995</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Burundi</td>\n",
" <td>BDI</td>\n",
" <td>1995</td>\n",
" <td>3.400000e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Belgium</td>\n",
" <td>BEL</td>\n",
" <td>1995</td>\n",
" <td>5.560000e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Benin</td>\n",
" <td>BEN</td>\n",
" <td>1995</td>\n",
" <td>1.380000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Burkina Faso</td>\n",
" <td>BFA</td>\n",
" <td>1995</td>\n",
" <td>1.240000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Bangladesh</td>\n",
" <td>BGD</td>\n",
" <td>1995</td>\n",
" <td>1.560000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Bulgaria</td>\n",
" <td>BGR</td>\n",
" <td>1995</td>\n",
" <td>3.466000e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Bahrain</td>\n",
" <td>BHR</td>\n",
" <td>1995</td>\n",
" <td>2.311000e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Bahamas, The</td>\n",
" <td>BHS</td>\n",
" <td>1995</td>\n",
" <td>1.598000e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Bosnia and Herzegovina</td>\n",
" <td>BIH</td>\n",
" <td>1995</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Belarus</td>\n",
" <td>BLR</td>\n",
" <td>1995</td>\n",
" <td>1.610000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Belize</td>\n",
" <td>BLZ</td>\n",
" <td>1995</td>\n",
" <td>1.310000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Bermuda</td>\n",
" <td>BMU</td>\n",
" <td>1995</td>\n",
" <td>3.870000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Bolivia</td>\n",
" <td>BOL</td>\n",
" <td>1995</td>\n",
" <td>2.840000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Brazil</td>\n",
" <td>BRA</td>\n",
" <td>1995</td>\n",
" <td>1.991000e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Barbados</td>\n",
" <td>BRB</td>\n",
" <td>1995</td>\n",
" <td>4.420000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Brunei Darussalam</td>\n",
" <td>BRN</td>\n",
" <td>1995</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4682</th>\n",
" <td>Togo</td>\n",
" <td>TGO</td>\n",
" <td>2013</td>\n",
" <td>3.270000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4683</th>\n",
" <td>Thailand</td>\n",
" <td>THA</td>\n",
" <td>2013</td>\n",
" <td>2.654700e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4684</th>\n",
" <td>Tajikistan</td>\n",
" <td>TJK</td>\n",
" <td>2013</td>\n",
" <td>2.080000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4685</th>\n",
" <td>Turkmenistan</td>\n",
" <td>TKM</td>\n",
" <td>2013</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4686</th>\n",
" <td>Timor-Leste</td>\n",
" <td>TLS</td>\n",
" <td>2013</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4687</th>\n",
" <td>Tonga</td>\n",
" <td>TON</td>\n",
" <td>2013</td>\n",
" <td>4.500000e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4688</th>\n",
" <td>Trinidad and Tobago</td>\n",
" <td>TTO</td>\n",
" <td>2013</td>\n",
" <td>4.340000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4689</th>\n",
" <td>Tunisia</td>\n",
" <td>TUN</td>\n",
" <td>2013</td>\n",
" <td>6.269000e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4690</th>\n",
" <td>Turkey</td>\n",
" <td>TUR</td>\n",
" <td>2013</td>\n",
" <td>3.779500e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4691</th>\n",
" <td>Tuvalu</td>\n",
" <td>TUV</td>\n",
" <td>2013</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4692</th>\n",
" <td>Tanzania</td>\n",
" <td>TZA</td>\n",
" <td>2013</td>\n",
" <td>1.063000e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4693</th>\n",
" <td>Uganda</td>\n",
" <td>UGA</td>\n",
" <td>2013</td>\n",
" <td>1.206000e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4694</th>\n",
" <td>Ukraine</td>\n",
" <td>UKR</td>\n",
" <td>2013</td>\n",
" <td>2.467100e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4695</th>\n",
" <td>Upper middle income</td>\n",
" <td>UMC</td>\n",
" <td>2013</td>\n",
" <td>2.648748e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4696</th>\n",
" <td>Uruguay</td>\n",
" <td>URY</td>\n",
" <td>2013</td>\n",
" <td>2.683000e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4697</th>\n",
" <td>United States</td>\n",
" <td>USA</td>\n",
" <td>2013</td>\n",
" <td>6.976800e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4698</th>\n",
" <td>Uzbekistan</td>\n",
" <td>UZB</td>\n",
" <td>2013</td>\n",
" <td>1.969000e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4699</th>\n",
" <td>St. Vincent and the Grenadines</td>\n",
" <td>VCT</td>\n",
" <td>2013</td>\n",
" <td>7.200000e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4700</th>\n",
" <td>Venezuela, RB</td>\n",
" <td>VEN</td>\n",
" <td>2013</td>\n",
" <td>9.860000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4701</th>\n",
" <td>Virgin Islands (U.S.)</td>\n",
" <td>VIR</td>\n",
" <td>2013</td>\n",
" <td>5.700000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4702</th>\n",
" <td>Vietnam</td>\n",
" <td>VNM</td>\n",
" <td>2013</td>\n",
" <td>7.572000e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4703</th>\n",
" <td>Vanuatu</td>\n",
" <td>VUT</td>\n",
" <td>2013</td>\n",
" <td>1.100000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4704</th>\n",
" <td>West Bank and Gaza</td>\n",
" <td>PSE</td>\n",
" <td>2013</td>\n",
" <td>5.450000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4705</th>\n",
" <td>World</td>\n",
" <td>WLD</td>\n",
" <td>2013</td>\n",
" <td>1.123200e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4706</th>\n",
" <td>Samoa</td>\n",
" <td>WSM</td>\n",
" <td>2013</td>\n",
" <td>1.160000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4707</th>\n",
" <td>Yemen, Rep.</td>\n",
" <td>YEM</td>\n",
" <td>2013</td>\n",
" <td>9.900000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4708</th>\n",
" <td>South Africa</td>\n",
" <td>ZAF</td>\n",
" <td>2013</td>\n",
" <td>9.537000e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4709</th>\n",
" <td>Congo, Dem. Rep.</td>\n",
" <td>COD</td>\n",
" <td>2013</td>\n",
" <td>1.910000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4710</th>\n",
" <td>Zambia</td>\n",
" <td>ZMB</td>\n",
" <td>2013</td>\n",
" <td>9.150000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4711</th>\n",
" <td>Zimbabwe</td>\n",
" <td>ZWE</td>\n",
" <td>2013</td>\n",
" <td>1.833000e+06</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4712 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" Country Name Country Code variable value\n",
"0 Aruba ABW 1995 6.190000e+05\n",
"1 Andorra AND 1995 NaN\n",
"2 Afghanistan AFG 1995 NaN\n",
"3 Angola AGO 1995 9.000000e+03\n",
"4 Albania ALB 1995 NaN\n",
"5 Arab World ARB 1995 2.566348e+07\n",
"6 United Arab Emirates ARE 1995 2.315000e+06\n",
"7 Argentina ARG 1995 2.289000e+06\n",
"8 Armenia ARM 1995 1.200000e+04\n",
"9 American Samoa ASM 1995 3.400000e+04\n",
"10 Antigua and Barbuda ATG 1995 2.200000e+05\n",
"11 Australia AUS 1995 3.726000e+06\n",
"12 Austria AUT 1995 1.717300e+07\n",
"13 Azerbaijan AZE 1995 NaN\n",
"14 Burundi BDI 1995 3.400000e+04\n",
"15 Belgium BEL 1995 5.560000e+06\n",
"16 Benin BEN 1995 1.380000e+05\n",
"17 Burkina Faso BFA 1995 1.240000e+05\n",
"18 Bangladesh BGD 1995 1.560000e+05\n",
"19 Bulgaria BGR 1995 3.466000e+06\n",
"20 Bahrain BHR 1995 2.311000e+06\n",
"21 Bahamas, The BHS 1995 1.598000e+06\n",
"22 Bosnia and Herzegovina BIH 1995 NaN\n",
"23 Belarus BLR 1995 1.610000e+05\n",
"24 Belize BLZ 1995 1.310000e+05\n",
"25 Bermuda BMU 1995 3.870000e+05\n",
"26 Bolivia BOL 1995 2.840000e+05\n",
"27 Brazil BRA 1995 1.991000e+06\n",
"28 Barbados BRB 1995 4.420000e+05\n",
"29 Brunei Darussalam BRN 1995 NaN\n",
"... ... ... ... ...\n",
"4682 Togo TGO 2013 3.270000e+05\n",
"4683 Thailand THA 2013 2.654700e+07\n",
"4684 Tajikistan TJK 2013 2.080000e+05\n",
"4685 Turkmenistan TKM 2013 NaN\n",
"4686 Timor-Leste TLS 2013 NaN\n",
"4687 Tonga TON 2013 4.500000e+04\n",
"4688 Trinidad and Tobago TTO 2013 4.340000e+05\n",
"4689 Tunisia TUN 2013 6.269000e+06\n",
"4690 Turkey TUR 2013 3.779500e+07\n",
"4691 Tuvalu TUV 2013 NaN\n",
"4692 Tanzania TZA 2013 1.063000e+06\n",
"4693 Uganda UGA 2013 1.206000e+06\n",
"4694 Ukraine UKR 2013 2.467100e+07\n",
"4695 Upper middle income UMC 2013 2.648748e+08\n",
"4696 Uruguay URY 2013 2.683000e+06\n",
"4697 United States USA 2013 6.976800e+07\n",
"4698 Uzbekistan UZB 2013 1.969000e+06\n",
"4699 St. Vincent and the Grenadines VCT 2013 7.200000e+04\n",
"4700 Venezuela, RB VEN 2013 9.860000e+05\n",
"4701 Virgin Islands (U.S.) VIR 2013 5.700000e+05\n",
"4702 Vietnam VNM 2013 7.572000e+06\n",
"4703 Vanuatu VUT 2013 1.100000e+05\n",
"4704 West Bank and Gaza PSE 2013 5.450000e+05\n",
"4705 World WLD 2013 1.123200e+09\n",
"4706 Samoa WSM 2013 1.160000e+05\n",
"4707 Yemen, Rep. YEM 2013 9.900000e+05\n",
"4708 South Africa ZAF 2013 9.537000e+06\n",
"4709 Congo, Dem. Rep. COD 2013 1.910000e+05\n",
"4710 Zambia ZMB 2013 9.150000e+05\n",
"4711 Zimbabwe ZWE 2013 1.833000e+06\n",
"\n",
"[4712 rows x 4 columns]"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_melted"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_melted.rename(columns={'variable':'Year','value':'Tourists'},inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"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>Country Name</th>\n",
" <th>Country Code</th>\n",
" <th>Year</th>\n",
" <th>Tourists</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>Dominican Republic</td>\n",
" <td>DOM</td>\n",
" <td>1995</td>\n",
" <td>1776000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>304</th>\n",
" <td>Dominican Republic</td>\n",
" <td>DOM</td>\n",
" <td>1996</td>\n",
" <td>1926000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>552</th>\n",
" <td>Dominican Republic</td>\n",
" <td>DOM</td>\n",
" <td>1997</td>\n",
" <td>2211000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>800</th>\n",
" <td>Dominican Republic</td>\n",
" <td>DOM</td>\n",
" <td>1998</td>\n",
" <td>2309000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1048</th>\n",
" <td>Dominican Republic</td>\n",
" <td>DOM</td>\n",
" <td>1999</td>\n",
" <td>2649000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1296</th>\n",
" <td>Dominican Republic</td>\n",
" <td>DOM</td>\n",
" <td>2000</td>\n",
" <td>2978000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1544</th>\n",
" <td>Dominican Republic</td>\n",
" <td>DOM</td>\n",
" <td>2001</td>\n",
" <td>2882000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1792</th>\n",
" <td>Dominican Republic</td>\n",
" <td>DOM</td>\n",
" <td>2002</td>\n",
" <td>2811000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2040</th>\n",
" <td>Dominican Republic</td>\n",
" <td>DOM</td>\n",
" <td>2003</td>\n",
" <td>3282000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2288</th>\n",
" <td>Dominican Republic</td>\n",
" <td>DOM</td>\n",
" <td>2004</td>\n",
" <td>3450000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2536</th>\n",
" <td>Dominican Republic</td>\n",
" <td>DOM</td>\n",
" <td>2005</td>\n",
" <td>3691000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2784</th>\n",
" <td>Dominican Republic</td>\n",
" <td>DOM</td>\n",
" <td>2006</td>\n",
" <td>3965000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3032</th>\n",
" <td>Dominican Republic</td>\n",
" <td>DOM</td>\n",
" <td>2007</td>\n",
" <td>3980000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3280</th>\n",
" <td>Dominican Republic</td>\n",
" <td>DOM</td>\n",
" <td>2008</td>\n",
" <td>3980000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3528</th>\n",
" <td>Dominican Republic</td>\n",
" <td>DOM</td>\n",
" <td>2009</td>\n",
" <td>3992000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3776</th>\n",
" <td>Dominican Republic</td>\n",
" <td>DOM</td>\n",
" <td>2010</td>\n",
" <td>4125000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4024</th>\n",
" <td>Dominican Republic</td>\n",
" <td>DOM</td>\n",
" <td>2011</td>\n",
" <td>4306000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4272</th>\n",
" <td>Dominican Republic</td>\n",
" <td>DOM</td>\n",
" <td>2012</td>\n",
" <td>4563000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4520</th>\n",
" <td>Dominican Republic</td>\n",
" <td>DOM</td>\n",
" <td>2013</td>\n",
" <td>4690000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Country Name Country Code Year Tourists\n",
"56 Dominican Republic DOM 1995 1776000\n",
"304 Dominican Republic DOM 1996 1926000\n",
"552 Dominican Republic DOM 1997 2211000\n",
"800 Dominican Republic DOM 1998 2309000\n",
"1048 Dominican Republic DOM 1999 2649000\n",
"1296 Dominican Republic DOM 2000 2978000\n",
"1544 Dominican Republic DOM 2001 2882000\n",
"1792 Dominican Republic DOM 2002 2811000\n",
"2040 Dominican Republic DOM 2003 3282000\n",
"2288 Dominican Republic DOM 2004 3450000\n",
"2536 Dominican Republic DOM 2005 3691000\n",
"2784 Dominican Republic DOM 2006 3965000\n",
"3032 Dominican Republic DOM 2007 3980000\n",
"3280 Dominican Republic DOM 2008 3980000\n",
"3528 Dominican Republic DOM 2009 3992000\n",
"3776 Dominican Republic DOM 2010 4125000\n",
"4024 Dominican Republic DOM 2011 4306000\n",
"4272 Dominican Republic DOM 2012 4563000\n",
"4520 Dominican Republic DOM 2013 4690000"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_melted[df_melted['Country Code']== 'DOM']"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x109b3efd0>"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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3WWPhRFMLTjSFUVEBfftmPYYHHtj8C7whWbhwc9KZPh3++U847jg44gi4/fZs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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109b49cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_melted[df_melted['Country Code']== 'DOM'].plot(x='Year')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| unlicense |
keiikegami/voting | Translate_Julia_.ipynb | 1 | 33728 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# やること\n",
"\n",
"MatlabのコードをJuliaに翻訳する。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 準備"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"using Optim\n",
"using StatsFuns\n",
"using DataFrames\n",
"\n",
"# inverse beta distribution function を行列に対応するように拡張\n",
"import StatsFuns.betainvcdf\n",
"betainvcdf(alpha::Number, beta::Number, x::Array) = reshape([betainvcdf(alpha, beta, i) for i in reshape(x, 1, size(x, 1)*size(x, 2)) ], size(x, 1), size(x, 2))\n",
"\n",
"# maxをArray{String, 1}に対応するように拡張\n",
"# しかしArray型で入っているのでAnyに対応させる\n",
"import Base.max\n",
"max(number::Real, comparison::Any) = [max(number, parse(Int, i)) for i in comparison] \n",
"\n",
"# normpdfを配列に拡張\n",
"import StatsFuns.normpdf\n",
"normpdf(array::Array{Float64, 2}) = reshape([normpdf(i) for i in reshape(array, 1, size(array, 1)*size(array, 2))], size(array, 1), size(array, 2))\n",
"\n",
"# parameterの初期値を作成\n",
"# inivalueは260こ\n",
"# learning_paramsは13こ\n",
"iii = 1 #kokokaeruiii;\n",
"ini = DataFrame(randn(260, 100))\n",
"learn = DataFrame(randn(13, 100))\n",
"writetable(*(\"inivalue_\", \"$iii\", \".txt\"), ini)\n",
"writetable(*(\"learning_params\", \"$iii\", \".txt\"), learn)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## new_loglike2の翻訳"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"function new_loglike(param::Array{Float64,1}, DATA::Array{Real,2}, Cand::Array{Int64, 2})\n",
" \n",
" # これなんだかわからないのでコメントアウト\n",
" #if i_bayes == 1\n",
" #param = param\n",
" #end\n",
" \n",
" FAlph = Array(Float64, 2, 1)\n",
" # setting parameters\n",
" param[2] = -1\n",
" C0 = 0\n",
" # Raceの変更に伴い、Cxも4つの要素にする必要がある。\n",
" q = 4\n",
" Cx = param[1:q]\n",
" Cz = -abs(param[q+1:q+3])\n",
" FAlph[1,1] = abs(param[q+4])\n",
" FAlph[2,1] = abs(param[q+5])\n",
" param[q+4:q+5] = FAlph[1:2,1]\n",
" Sig_xsi = max(0.5, abs(param[q+6]))\n",
" param[q+6] = Sig_xsi\n",
" DeltaO = 0.6891\n",
" DeltaMO = 0.5366\n",
" # Raceをいじった関係でこのvkを4*4 = 16この要素にしなくちゃいけない\n",
" vk = param[12:27]\n",
" composite = param[75:149]\n",
" Tij = abs(param[150:260])\n",
" \n",
" # new parameters\n",
" # rho_eta = abs(param[261])\n",
" rho_eta = 1\n",
" rho_chi = param[262:265]\n",
" mu_chi = param[266:269]\n",
" chi = param[270:273]\n",
" \n",
" \n",
" # making log likelihood\n",
" # simulated values\n",
" N_T_ij = [0;1;3;6]\n",
" # 上で拡張した関数を使用\n",
" Alpha = betainvcdf(FAlph[1,1],FAlph[2,1], SimAlp)\n",
" Xsi = Sig_xsi*SimXsi\n",
"\n",
" # calculating utilities\n",
" # Base Utility for Sincere, [# of categories X # of candidates]\n",
" # VSinは48×4\n",
" VSin = X[:,2:end]*[vk[1:4] vk[5:8] vk[9:12] vk[13:16]]-C0-X[:, 2:end]*Cx*ones(1,4)\n",
" # Base Utility for Strategic, [# of categories X # of candidates]\n",
" # VStrも48×4\n",
" VStr = X[:, 2:end]*[vk[1:4] vk[5:8] vk[9:12] vk[13:16]]\n",
"\n",
" #Eligible voters accounting for Open and Modified Open\n",
" DELTA = DeltaO*Open+DeltaMO*MOpen\n",
" RTOT = RDemHat.*(1+DELTA)-VOther\n",
"\n",
" # store signals in advance\n",
" signals = randn(4,T) * sqrt(1/rho_eta)\n",
" \n",
" for i in 1:4\n",
" signals[i, :] = signals[i, :] + chi[i, 1]\n",
" end\n",
"\n",
" loglik_s = zeros(size(Cand,1),1)\n",
"\n",
" for S in 1:size(Cand,1)\n",
" if Cand[S, 15] - Cand[S, 14] < 21 || S == 30 || S == 34 #Excluding Utah, Wisconsin, and small\n",
" else\n",
" \n",
" N_candS = sum(Cand[S,1:4] .!= 0)\n",
" M = Cand[S, 15] - Cand[S, 14] + 1 # Number of municipalities in State S\n",
" \n",
" if Cand[S, 11] == 0\n",
" T_s = zeros(N_T_ij[N_candS,1],1)\n",
" COMPOSITE = zeros(N_candS,1)\n",
" else\n",
" T_s = Tij[Cand[S, 11]:Cand[S, 12],1]\n",
" COMPOSITE = composite[Cand[S, 16]:Cand[S, 17],1]\n",
" end\n",
"\n",
" # VSTR_s = []\n",
" Dropped_s = find(Cand[S,1:4] .== 0) # index of candidates withdrawn.\n",
" Rem = Cand[S,1:4] .!= 0\n",
" Candidate_s = find(Cand[S,1:4] .== 1) # index of cadidate\n",
" Senate_s = DATA[Cand[S, 14], 27]*Cz[1,1] # Cost from Senate elections\n",
" Governer_s = DATA[Cand[S, 14], 29]*Cz[3,1] # Cost from GOvernor elections\n",
" dFX_s = dFX[Cand[S, 14]:Cand[S, 15], :]\n",
" date = Cand[S, 13] # election date (1 ~ 14)\n",
"\n",
" # というか列の削除をする必要があるのか\n",
" temp = Cand[S,1:4]\n",
" PatternCand_d = Cand[S, 24:27]\n",
" # temp[:, find(Cand[S,24:27] .== 0)] = []\n",
" # PatternCand_d[:, find(Cand[S,24:27] .== 0)] = []\n",
" temp = temp[Cand[S,24:27] .!= 0.0]\n",
" PatternCand_d = PatternCand_d[Cand[S,24:27] .!= 0.0]\n",
"\n",
" Alpha_s = reshape(Alpha[Cand[S, 14]:Cand[S, 15], :, :], length(Cand[S, 14]:Cand[S, 15]), 100)\n",
" Xsi_s = Xsi[Cand[S, 14]:Cand[S, 15], :, :]\n",
"\n",
" # そもそも削除する必要があるか\n",
" VSin_s = VSin\n",
" # VSin_s[:, Dropped_s] = []\n",
" VSin_s = VSin_s[:, Rem]\n",
" VStr_s = VStr\n",
" # println(size(VStr_s, 2))\n",
" # VStr_s[:, Dropped_s] = []\n",
" VStr_s = VStr_s[:, Rem]\n",
"\n",
" # make XiOmg_s whose size is [1, num of candidates in the state]\n",
" # take necessary parameters\n",
" rho_chi_s = rho_chi[Candidate_s, 1]\n",
" mu_chi_s = mu_chi[Candidate_s, 1]\n",
" chi_s = chi[Candidate_s, 1]\n",
" # calculating XiOmg_s\n",
" # signals_s = signals[:, date]\n",
" \n",
" # cumsum は1次元配列にしか使えないので注意ダメだったらsqueeze\n",
" cum_signals = cumsum(signals, 2)\n",
" upper = rho_chi_s + mu_chi_s + rho_eta * cum_signals[Candidate_s, date]\n",
" under = rho_chi_s + date * rho_eta\n",
" XiOmg_s = upper ./ under\n",
"\n",
" VSin_s = VSin_s + ones(size(VSin_s,1), 1)*XiOmg_s' -Senate_s-Governer_s\n",
" \n",
" # col9は0~4、4は？\n",
"\n",
" if Cand[S, 9] == 1 && Cand[S, 10] == 1\n",
" VSTR_s = ones(48, 4)\n",
" Composite = ones(size(X,1),1)*COMPOSITE'\n",
" VSTR_s[:,1] = T_s[1,1]*(VStr_s[:,1]-VStr_s[:,2])+T_s[2,1]*(VStr_s[:,1]-VStr_s[:,3])\n",
" +T_s[3,1]*(VStr_s[:,1]-VStr_s[:,4])+Composite[:,1]\n",
" VSTR_s[:,2] = T_s[1,1]*(VStr_s[:,2]-VStr_s[:,1])+T_s[4,1]*(VStr_s[:,2]-VStr_s[:,3])\n",
" +T_s[5,1]*(VStr_s[:,2]-VStr_s[:,4])+Composite[:,2]\n",
" VSTR_s[:,3] = T_s[2,1]*(VStr_s[:,3]-VStr_s[:,1])+T_s[4,1]*(VStr_s[:,3]-VStr_s[:,2])\n",
" +T_s[6,1]*(VStr_s[:,3]-VStr_s[:,4])+Composite[:,3]\n",
" VSTR_s[:,4] = T_s[3,1]*(VStr_s[:,4]-VStr_s[:,1])+T_s[5,1]*(VStr_s[:,4]-VStr_s[:,2])\n",
" +T_s[6,1]*(VStr_s[:,4]-VStr_s[:,3])+Composite[:,4]\n",
"\n",
" elseif (Cand[S, 9] == 2 || Cand[S, 9] == 3 || Cand[S, 9] == 4) && Cand[S, 10] == 1\n",
" VSTR_s = ones(48, 3)\n",
" Composite = ones(size(X,1),1)*COMPOSITE'\n",
" VSTR_s[:,1] = T_s[1,1]*(VStr_s[:,1]-VStr_s[:,2])+T_s[2,1]*(VStr_s[:,1]-VStr_s[:,3])\n",
" +Composite[:,1]\n",
" VSTR_s[:,2] = T_s[1,1]*(VStr_s[:,2]-VStr_s[:,1])+T_s[3,1]*(VStr_s[:,2]-VStr_s[:,3])\n",
" +Composite[:,2]\n",
" VSTR_s[:,3] = T_s[2,1]*(VStr_s[:,3]-VStr_s[:,1])+T_s[3,1]*(VStr_s[:,3]-VStr_s[:,2])\n",
" +Composite[:,3]\n",
"\n",
"\n",
" elseif Cand[S, 10] == 0 # after super tuesday\n",
" VSTR_s=VSin_s+C0+X(:,2:end)*Cx(1:4)*ones(1,N_candS) ?\n",
" # VSTR_s = VSin_s + 2*(Senate_s+Governer_s)\n",
" end\n",
"\n",
" # Utiltiy of Strategic with no house elections\n",
" VSTR_s = VSTR_s - C0 - X[:,2:end]*Cx*ones(1,N_candS) - Senate_s - Governer_s\n",
"\n",
" # eligible voters\n",
" RTot_s = RTOT[Cand[S, 14]:Cand[S, 15], :]\n",
" RTot_s = max(RTot_s, sum(Votes[Cand[S, 14]:Cand[S, 15], :], 2))\n",
" Votes_s = Votes[Cand[S, 14]:Cand[S, 15], :]./(RTot_s*ones(1,4)) #vote share data\n",
" # 行削除\n",
" # Votes_s[:, Dropped_s] = []\n",
" Votes_s = Votes_s[:, Rem]\n",
" loglik_m = zeros(M,1)\n",
" \n",
" VSTR_ss = Array(Float64, N_sim,N_candS)\n",
" VSIN_ss = Array(Float64, N_sim,N_candS)\n",
"\n",
" # ここ以降が遅い\n",
" # どっちも遅いが、beforeの方が平均的に遅い\n",
" if Cand[S, 10] == 1\n",
" \n",
" A1 = Array(Float64, N_dFX, N_candS)\n",
" A2 = Array(Float64, N_dFX, N_candS)\n",
" B1 = Array(Float64, 1, N_candS)\n",
" B2 = Array(Float64, 1, N_candS)\n",
" cont1 = Array(Float64, N_dFX)\n",
" cont2 = Array(Float64, N_dFX)\n",
" #eVSIN_ss = Array(Float64, N_dFX, N_candS)\n",
" #eVSTR_ss = Array(Float64, N_dFX, N_candS)\n",
" \n",
" for m in 1:M\n",
" VSin_s = VSin_s - Cz[2,1]*DATA[Cand[S, 14] + m - 1, 28]\n",
" VSTR_s = VSTR_s - Cz[2,1]*DATA[Cand[S, 14] + m - 1, 28]\n",
" \n",
" # for文に書き換え\n",
" for sim in 1:N_sim\n",
" \n",
" # nakami = max(min(VSin_s + ones(N_dFX,1)*reshape(Xsi_s[m,1:N_candS, sim], 1, N_candS), 200), -200)\n",
" # nakami2 = max(min(VSTR_s + ones(N_dFX,1)*reshape(Xsi_s[m,1:N_candS,sim], 1, N_candS), 200), -200)\n",
" for i in 1:N_candS\n",
" for j in 1:N_dFX\n",
" A1[j, i] = VSin_s[j, i] + Xsi_s[m, i, sim]\n",
" A2[j, i] = VSTR_s[j, i] + Xsi_s[m, i, sim]\n",
" end\n",
" end\n",
" nakami = max(min(A1, 200.0), -200.0)\n",
" nakami2 = max(min(A2, 200.0), -200.0)\n",
" \n",
" \n",
" eVSIN_ss = exp(nakami)./ (1+sum(exp(nakami),2)*ones(1,N_candS))\n",
" eVSTR_ss = exp(nakami2)./ (1+sum(exp(nakami2),2)*ones(1,N_candS))\n",
" #naka = sum(exp(nakami), 2)\n",
" #naka2 = sum(exp(nakami2), 2)\n",
" #for i in 1:N_candS\n",
" # for j in 1:N_dFX\n",
" # eVSIN_ss[j, i] = exp(nakami[j,i])/(1+ naka[j])\n",
" # eVSTR_ss[j, i] = exp(nakami2[j,i])/(1+ naka2[j])\n",
" #end\n",
" #end\n",
" \n",
" \n",
" # VSTR_ss[sim, :] = reshape(dFX_s[m, :],1, 48)*eVSTR_ss\n",
" # VSIN_ss[sim, :] = reshape(dFX_s[m, :], 1, 48)*eVSIN_ss\n",
" for i in 1:N_candS\n",
" for j in 1:N_dFX\n",
" cont1[j] = dFX_s[m, j]*eVSTR_ss[j, i]\n",
" cont2[j] = dFX_s[m, j]*eVSIN_ss[j, i]\n",
" end\n",
" VSTR_ss[sim, i] = sum(cont1)\n",
" VSIN_ss[sim, i] = sum(cont2)\n",
" end\n",
" \n",
" end\n",
"\n",
" Alp_ss = Alpha_s[m, :]\n",
" VSHARE = VSTR_ss.*(Alp_ss*ones(1,N_candS)) + VSIN_ss.*(1-Alp_ss*ones(1,N_candS))\n",
" # pdfはStatsFunsのnorrmpdfを使用\n",
" loglik_m[m,1] = log(sum(prod(normpdf((ones(N_sim,1)*reshape(Votes_s[m,:], 1, N_candS) - VSHARE)/bandwidth),2),1)/N_sim)[1]\n",
" end\n",
" \n",
" loglik_s[S,1] = sum(loglik_m)\n",
"\n",
" elseif Cand[S, 10] == 0\n",
" \n",
" A1 = Array(Float64, N_dFX, N_candS)\n",
" B2 = Array(Float64, 1, N_candS)\n",
" cont2 = Array(Float64, N_dFX)\n",
" #eVSIN_ss = Array(Float64, N_dFX, N_candS)\n",
" \n",
" for m in 1:M\n",
" VSin_s = VSin_s-Cz[2,1]*DATA[Cand[S, 14]+m-1, 28]\n",
" AST = 1./(1+exp(C0+X[:,2:end]*Cx*ones(1,N_candS) + Cz[2,1]*DATA[Cand[S, 14]+m-1, 28]+Senate_s+Governer_s))\n",
" # AST: Turnout of strategic voters after super tuesday\n",
" for sim in 1:N_sim\n",
" \n",
" # nakami = max(min(VSin_s + ones(N_dFX,1)*reshape(Xsi_s[m,1:N_candS, sim], 1, N_candS), 200), -200)\n",
" for i in 1:N_candS\n",
" for j in 1:N_dFX\n",
" A1[j, i] = VSin_s[j, i] + Xsi_s[m, i, sim]\n",
" end\n",
" end\n",
" nakami = max(min(A1, 200.0), -200.0)\n",
" \n",
" \n",
" eVSIN_ss = exp(nakami)./ (1 + sum(exp(nakami),2)*ones(1,N_candS))\n",
" # naka = sum(exp(nakami), 2)\n",
" # for i in 1:N_candS\n",
" # for j in 1:N_dFX\n",
" # eVSIN_ss[j, i] = exp(nakami[j,i])/(1+ naka[j])\n",
" #end\n",
" #end\n",
" \n",
" \n",
" # VSIN_ss[sim, :] = reshape(dFX_s[m, :], 1, 48) * eVSIN_ss\n",
" for i in 1:N_candS\n",
" for j in 1:N_dFX\n",
" cont2[j] = dFX_s[m, j]*eVSIN_ss[j, i]\n",
" end\n",
" VSIN_ss[sim, i] = sum(cont2)\n",
" end\n",
" \n",
" end\n",
" \n",
" VSHARE = VSIN_ss\n",
" # pdfはStatsFunsのnorrmpdfを使用\n",
" loglik_m[m,1] = log(sum(prod(normpdf((ones(N_sim,1)*reshape(Votes_s[m,:], 1, N_candS) - VSHARE)/bandwidth),2),1)/N_sim)[1]\n",
" end\n",
" loglik_s[S,1] = sum(loglik_m)\n",
" end\n",
" end\n",
" \n",
" # これなんだろ\n",
" if S == 10\n",
" S = S\n",
" end\n",
" end\n",
"\n",
" loglik = sum(loglik_s)\n",
" return loglik = -loglik\n",
"\n",
"end"
]
}
],
"source": [
";cat loglike.jl"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"include(\"loglike.jl\");"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## New_bayes2の翻訳 Candの作成"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# define T as num of period\n",
"T = 14\n",
"bandwidth = 0.05\n",
"N_sim = 100\n",
"n_eval = 0\n",
"I = 4\n",
"N_cand = 4\n",
"# load iii.txt -ASCII;\n",
"# load jjj.txt -ASCII;\n",
"\n",
"# dataのロード\n",
"data = readtable(\"data.csv\", header = false)\n",
"data = convert(Array, data)\n",
"\n",
"# initial valueのロード\n",
"iii = 1\n",
"jjj = 1\n",
"kkk = 1\n",
"\n",
"inivalue = readtable(*(\"inivalue_\", \"$iii\", \".txt\"))\n",
"inivalue = inivalue[:, jjj]\n",
"\n",
"learning_params = readtable(*(\"learning_params\", \"$iii\", \".txt\"))\n",
"learning_params = learning_params[:, jjj]\n",
"\n",
"parameter = vcat(1000000000, 0, inivalue, learning_params)\n",
"writetable(*(\"parameter_\", \"$iii\", \"_\", \"$jjj\", \"_\", \"$kkk\", \".txt\"), DataFrame(X = parameter))\n",
"\n",
"\n",
"# ここから本題\n",
"D = data[:,45] .> 100 # Remove municipality with small populaion <100\n",
"# 行削除はどうするのか、そもそもいるのか\n",
"# data[D, :] = [];\n",
"data = data[D, :]\n",
"\n",
"# Construct X : Regressor for individual characteristics\n",
"X = [1 1 1 1 1];\n",
"dFX = ones(size(data, 1))\n",
"Race = [1 0 0; 0 1 0; 0 0 1];\n",
"Educ = 0.1*[16;14;12;9];\n",
"Incm = log([20000;35000;72500;120000]);\n",
"\n",
"# (white,black,otherasian+indian+other)\n",
"dFXRace = [data[:,121] data[:,124] data[:,122] + data[:,123] + data[:,125]];\n",
"\n",
"dFXEduc = [data[:,126:128] (1- sum(data[:,126:128], 2))]; # (overba,underba,hs,other)\n",
"dFXIncm = [sum(data[:,129:132], 2) sum(data[:,133:136], 2) sum(data[:,137:140], 2) sum(data[:,141:144], 2)];\n",
"\n",
"# dFXIncm=data(:,129:144); % (income 16 category:\n",
"# -10K,15K,20K,25K,30K,35K,40,45,50,60,75,100,125,150,200,200+)\n",
"# ここは精査する必要がありそう。\n",
"for r in 1:3\n",
" for e in 1:4\n",
" for i in 1:4\n",
" X = [X; reshape([Race[r,1:3] ; Educ[e] ; Incm[i]], 1, 5)];\n",
" dFX = [dFX dFXRace[:,r].*dFXEduc[:,e].*dFXIncm[:,i]];\n",
" end\n",
" end\n",
"end\n",
"\n",
"del = trues(size(X, 1))\n",
"del[1] = false\n",
"X = X[del, :]\n",
"del2 = trues(size(dFX, 2))\n",
"del2[1] = false\n",
"dFX = dFX[:, del2]\n",
"\n",
"N_dFX = size(dFX,2);\n",
"N_muni = size(dFX,1); # (number of municipalities)\n",
"# data3,4,5,6はまさかのstring型\n",
"# (votes: clark, dean, edwards, kerry)municipalityごとに、いない奴は0になるようにしてる\n",
"Votes = [max(0,data[:,3]) max(0,data[:,4]) max(0,data[:,5]) max(0,data[:,6])]; \n",
"VTotal = data[:,117]; # (total number of votes)\n",
"RDemHat = data[:,148]; # (registred number of democrats predicted)\n",
"PopTot = data[:,45]; # (total population)\n",
"Open = data[:,118]; # open\n",
"MOpen = data[:,119]; # modified open\n",
"VOther = VTotal - sum(Votes,2); #Votes of Penna & Sharpton etc.\n",
"RegDem = RDemHat./PopTot; # (fraction of registered democrats in population)\n",
"\n",
"# make Cand\n",
"# 何で18列目から21列めまでを開けてる？\n",
"Cand = zeros(35,27); \n",
"\n",
" # Candidates whose name is on ballot (column 1;4)\n",
" # Column 5 corresponds to ichiban maeno parameter\n",
" # noichi (for T_ij), Col6 to ichiban saigono parameter.\n",
" # Same for Columns 7 and 8 (for E[qi|omega]).\n",
" # Column 9: Who's in the race.\n",
" # Column 10: 1 if Before (and including) super tuesday\n",
" # Column 11: ？\n",
" # Column 12: ？\n",
" # Column 13: Date t (1,2,...14). 6 correspond to\n",
" # super Tues.\n",
" # Column 14: Municipality Number Start\n",
" # Column 15: Municipality Number End;\n",
" # 下のE[qi|omega_piv]-E[qi|omega_piv]って0では？\n",
" # Column 16: Atamadashi for\n",
" # E[qi|omega_piv]-E[qi|omega_piv]\n",
" # Column 17: Owaridashi for\n",
" # E[qi|omega_piv]-E[qi|omega_piv]\n",
" # the remains is explained below\n",
"\n",
"j = 0\n",
"N_T_ij = [0;1;3;6]; # The number of T_ij that we need when the number of candidates are 1,2,3,4.\n",
"for i in 1:size(data, 1)\n",
" if j == data[i,149] - 1\n",
" Cand[j+1,1:4] = squeeze(ones(1,4), 1)+min(data[i,3:6], squeeze(zeros(1,4), 1));\n",
" if j == 0\n",
" Cand[j+1,5] = 0;\n",
" Cand[j+1,6] = 0;\n",
" Cand[j+1,11] = 0;\n",
" else\n",
" Cand[j+1,5] = Cand[j,6] + (sum(Cand[j+1,1:4])>1);\n",
" Cand[j+1,6] = Cand[j+1,5] + N_T_ij[convert(Int64, sum(Cand[j+1,1:4])),1] - 1+(sum(Cand[j+1,1:4])==1);\n",
" end\n",
"\n",
" # 1 if before or on super Tues.\n",
"\n",
" Cand[j+1,10] = (data[i,120] - 92<=0);\n",
" Cand[j+1,7] = sum(sum(Cand[1:j+1,1:4]))-sum(Cand[j+1,1:4])+1;\n",
" Cand[j+1,8] = sum(sum(Cand[1:j+1,1:4]));\n",
"\n",
" Cand[j+1,17] = sum(sum(Cand[1:j+1,1:4].*(Cand[1:j+1,10]*ones(1,4))));\n",
" Cand[j+1,16] = Cand[j+1,17]-sum(Cand[j+1,1:4]*Cand[j+1,10])+1;\n",
" Cand[j+1,16:17] = Cand[j+1,16:17]*Cand[j+1,10];\n",
"\n",
" # Baai-wake: [Clark,Dean,Edawards]\n",
" # Type 0: [000] Type 1:[111] Type2:[101] Type3: [011] Type4: [010]\n",
" if Cand[j+1,1:3] == [0,0,0]\n",
" Cand[j+1,9] = 0;\n",
" elseif Cand[j+1,1:3] == [1, 1, 1]\n",
" Cand[j+1,9] = 1;\n",
" elseif Cand[j+1,1:3] == [1, 0, 1]\n",
" Cand[j+1,9] = 2;\n",
" elseif Cand[j+1,1:3] == [0, 1, 1]\n",
" Cand[j+1,9] = 3;\n",
" else\n",
" Cand[j+1,9] = 4;\n",
" end\n",
"\n",
" #Atamadashi for params\n",
" if j > 0\n",
" a = sum((Cand[1:j,6] - Cand[1:j,5]+1).*Cand[1:j,10],1)+Cand[j+1,10]*(sum(Cand[j+1,1:4]) > 1)\n",
" Cand[j+1,11] = a[1];\n",
" b = sum((Cand[1:j+1,6] - Cand[1:j+1,5]+1).*Cand[1:j+1,10],1)\n",
" Cand[j+1,12] = b[1];\n",
" end\n",
" \n",
" Cand[j+1,13] = data[i,120];\n",
" Cand[j+1,14] = i;\n",
" j = j+1;\n",
" \n",
" end\n",
"\n",
"\n",
" Cand[:,11] = Cand[:,10].*Cand[:,11]; # Atamadashi ignoring post-super\n",
" Cand[:,12] = Cand[:,10].*Cand[:,12]; # Tues states\n",
" Cand[1:end-1,15] = Cand[2:end,14]-1;\n",
" Cand[end,15] = i;\n",
"\n",
"end\n",
"\n",
"iij = unique(Cand[:,13]);\n",
"for i in 1:size(unique(Cand[:,13]),1)\n",
" for j in 1:size(Cand[:,13],1)\n",
" if Cand[j,13] == iij[i]\n",
" Cand[j,13] = i;\n",
" end\n",
" end\n",
"end\n",
"\n",
"w = convert(Int64, maximum(Cand[:,13]))\n",
"NNCan = ones(w, 1)\n",
"A_Exi = ones(w, 2)\n",
"PatternCandall = ones(w, 4)\n",
"NNCan[1,1] = 4;\n",
"A_Exi[1,:] = [1 4];\n",
"PatternCandall[1,:] = [1 1 1 1];\n",
"\n",
"for S in 2:w\n",
" # of candidate on date S\n",
" Temp = Cand;\n",
" # 行削除はどうする\n",
" # Temp[(Temp[:,13] .!= S), :]=[];\n",
" Temp = Temp[Temp[:,13] .== S, :]\n",
" NNCan[S,1] = sum(sum(Temp[:,1:4],1) .> 0);\n",
" PatternCandall[S,1:4] = (sum(Temp[:,1:4]) > 0);\n",
" # Atamadashi for ExiOmega\n",
" A_Exi[S,:] = [A_Exi[S-1,2]+1 (A_Exi[S-1,2] + NNCan[S,1])];\n",
"end\n",
"\n",
"for S = 1:size(Cand,1)\n",
" SS = convert(Int64, Cand[S,13]);\n",
" Cand[S,22:23] = A_Exi[SS,:];\n",
" Cand[S,24:27] = PatternCandall[SS,:];\n",
"end\n",
"\n",
"\n",
"# parameter description\n",
"# Xi|Omg (118x1) : For each state, we have at most 4 values (total 118)\n",
"# Xi|OmgPiv (56x1) : For each state, we have at most 3 values (total 56)\n",
"# Tij (105x1) : For each state, we have at most 6 values (total 105)\n",
"# Set to zero if after Super Tuesday.\n",
"# Cx (sizeXk x1) : Cost function parameter. Number of Xk\n",
"# Cz (3 x 1) : Cost function parameter related to other elections\n",
"# vk (sizeXk x4) : Preference paramether. Number of Xk x Number of Cand\n",
"# FAlph (2x1) : Distribution of alpha (beta dist), 2x1\n",
"# Sig_xsi (1x1) : variance of Xsi\n",
"# DeltaO (1x1) : Increase of eligible voters for open election\n",
"# DeltaMO (1X1) : Increase of eligible voters for modified opene elec.\n",
"\n",
"# load inivalue.txt inivalue -ASCII\n",
"# load indicator.txt indicator -ASCII;\n",
"# load inival.txt inival -ASCII\n",
"\n",
"DATA = data;\n",
"srand(10);\n",
"\n",
"# add XiOmg\n",
"# load the simulated XiOmg data\n",
"# reshape the data to [T, N_cand, N_sim]\n",
"SimXsi = randn(N_muni, N_cand, N_sim);\n",
"SimAlp = rand(N_muni, N_sim);\n",
"\n",
"Cand = convert(Array{Int64, 2}, Cand)\n",
"Cand = sortrows(Cand, by = x->(x[13]))"
]
}
],
"source": [
";cat bayes.jl"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"include(\"bayes.jl\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 尤度関数が動くことを確認\n",
"\n",
"かなり遅いので最適化以前に尤度の計算をスムーズに行えるようにするべき？\n",
"\n",
"例えば、\n",
"\n",
"- 行列で操作しているところをfor文に変える（あまり効かないっぽい）\n",
"- 変数ごとの型を最適化しておく\n",
"- Candを事前にやる（やった）\n",
"\n",
"などが考えられる。\n",
"\n",
"部分ごとに時間計測した結果最後のループで1秒ずつぐらいかかってることがわかった。（それ以外の部分は大して負担ではない）\n",
"\n",
"以下実行した改良\n",
"\n",
"- Candの処理（Int64化と並び替え）をfunctionの外で行い直接アクセスできるようにした。→2秒ほど改善\n",
"- とりあえずreshapeの回数を減らした。→配列の事前設置がややこしいのでやめ\n",
"- for文に書き換える→さらに2,3秒改善(一つfor文を使うと遅くなる処理があったのでそれを除いてfor文化した。また、メインで時間がかかっているループ内のみ書き換えている）"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 38.814163 seconds (262.30 M allocations: 10.311 GB, 5.36% gc time)\n"
]
},
{
"data": {
"text/plain": [
"349408.00362013566"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# このセルは残す\n",
"# 行列処理のtime(Candを外に出した。)\n",
"@time new_loglike(parameter, DATA, Cand)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 36.975457 seconds (492.10 M allocations: 13.481 GB, 9.00% gc time)\n"
]
},
{
"data": {
"text/plain": [
"322118.23684017756"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# このセルは残す\n",
"# for文のtime\n",
"@time new_loglike(parameter, DATA, Cand)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 34.392904 seconds (490.25 M allocations: 13.414 GB, 9.30% gc time)\n"
]
},
{
"data": {
"text/plain": [
"110960.00849019532"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# このセルは残す\n",
"# for文、rho_etaを固定、VSTRの計算を訂正\n",
"@time new_loglike(parameter, DATA, Cand)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## New_Bayes2の翻訳 最適化パート\n",
"\n",
"あまりに時間がかかったので途中で中断しました。"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "LoadError",
"evalue": "LoadError: InterruptException:\nwhile loading In[11], in expression starting on line 1",
"output_type": "error",
"traceback": [
"LoadError: InterruptException:\nwhile loading In[11], in expression starting on line 1",
"",
" in _generic_matmatmul!(::Array{Real,2}, ::Char, ::Char, ::Array{Real,2}, ::Array{Float64,2}) at ./linalg/matmul.jl:533",
" in generic_matmatmul!(::Array{Real,2}, ::Char, ::Char, ::Array{Real,2}, ::Array{Float64,2}) at ./linalg/matmul.jl:447",
" in *(::Array{Real,2}, ::Array{Float64,2}) at ./linalg/matmul.jl:129",
" in new_loglike(::Array{Float64,1}, ::Array{Real,2}) at /Users/susu/Desktop/Hong Kong/RA/voting_git/loglike.jl:195"
]
}
],
"source": [
"result = optimize(new_loglike(parameter, DATA), parameter, BFGS())\n",
"theta = Optim.minimizer(result)\n",
"likelihood = Optim.minimum(result)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.5.0",
"language": "julia",
"name": "julia-0.5"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.5.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
rmoleary/Track-world-records | WorldRecords.ipynb | 2 | 1081878 | null | gpl-3.0 |
boffi/boffi.github.io | nb/print+display.ipynb | 1 | 1041 | {
"metadata": {
"name": "print+display"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.core.display import display\n",
"print 1\n",
"display(4**1)\n",
"print 2\n",
"display(4**2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1\n"
]
},
{
"output_type": "display_data",
"text": [
"4"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2\n"
]
},
{
"output_type": "display_data",
"text": [
"16"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/image_classification/labs/3_tf_hub_transfer_learning.ipynb | 2 | 21218 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TensorFlow Transfer Learning\n",
"\n",
"This notebook shows how to use pre-trained models from [TensorFlowHub](https://www.tensorflow.org/hub). Sometimes, there is not enough data, computational resources, or time to train a model from scratch to solve a particular problem. We'll use a pre-trained model to classify flowers with better accuracy than a new model for use in a mobile application.\n",
"\n",
"## Learning Objectives\n",
"1. Know how to apply image augmentation\n",
"2. Know how to download and use a TensorFlow Hub module as a layer in Keras."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import pathlib\n",
"from PIL import Image\n",
"\n",
"import IPython.display as display\n",
"import matplotlib.pylab as plt\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"from tensorflow.keras import Sequential\n",
"from tensorflow.keras.layers import (\n",
" Conv2D, Dense, Dropout, Flatten, MaxPooling2D, Softmax)\n",
"import tensorflow_hub as hub"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exploring the data\n",
"\n",
"As usual, let's take a look at the data before we start building our model. We'll be using a creative-commons licensed flower photo dataset of 3670 images falling into 5 categories: 'daisy', 'roses', 'dandelion', 'sunflowers', and 'tulips'.\n",
"\n",
"The below [tf.keras.utils.get_file](https://www.tensorflow.org/api_docs/python/tf/keras/utils/get_file) command downloads a dataset to the local Keras cache. To see the files through a terminal, copy the output of the cell below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_dir = tf.keras.utils.get_file(\n",
" 'flower_photos',\n",
" 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',\n",
" untar=True)\n",
"\n",
"# Print data path\n",
"print(\"cd\", data_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use python's built in [pathlib](https://docs.python.org/3/library/pathlib.html) tool to get a sense of this unstructured data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_dir = pathlib.Path(data_dir)\n",
"\n",
"image_count = len(list(data_dir.glob('*/*.jpg')))\n",
"print(\"There are\", image_count, \"images.\")\n",
"\n",
"CLASS_NAMES = np.array(\n",
" [item.name for item in data_dir.glob('*') if item.name != \"LICENSE.txt\"])\n",
"print(\"These are the available classes:\", CLASS_NAMES)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's display the images so we can see what our model will be trying to learn."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"roses = list(data_dir.glob('roses/*'))\n",
"\n",
"for image_path in roses[:3]:\n",
" display.display(Image.open(str(image_path)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Building the dataset\n",
"\n",
"Keras has some convenient methods to read in image data. For instance [tf.keras.preprocessing.image.ImageDataGenerator](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator) is great for small local datasets. A tutorial on how to use it can be found [here](https://www.tensorflow.org/tutorials/load_data/images), but what if we have so many images, it doesn't fit on a local machine? We can use [tf.data.datasets](https://www.tensorflow.org/api_docs/python/tf/data/Dataset) to build a generator based on files in a Google Cloud Storage Bucket.\n",
"\n",
"We have already prepared these images to be stored on the cloud in `gs://cloud-ml-data/img/flower_photos/`. The images are randomly split into a training set with 90% data and an iterable with 10% data listed in CSV files:\n",
"\n",
"Training set: [train_set.csv](https://storage.cloud.google.com/cloud-ml-data/img/flower_photos/train_set.csv) \n",
"Evaluation set: [eval_set.csv](https://storage.cloud.google.com/cloud-ml-data/img/flower_photos/eval_set.csv) \n",
"\n",
"Explore the format and contents of the train.csv by running:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!gsutil cat gs://cloud-ml-data/img/flower_photos/train_set.csv | head -5 > /tmp/input.csv\n",
"!cat /tmp/input.csv"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!gsutil cat gs://cloud-ml-data/img/flower_photos/train_set.csv | sed 's/,/ /g' | awk '{print $2}' | sort | uniq > /tmp/labels.txt\n",
"!cat /tmp/labels.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's figure out how to read one of these images from the cloud. TensorFlow's [tf.io.read_file](https://www.tensorflow.org/api_docs/python/tf/io/read_file) can help us read the file contents, but the result will be a [Base64 image string](https://en.wikipedia.org/wiki/Base64). Hmm... not very readable for humans or Tensorflow.\n",
"\n",
"Thankfully, TensorFlow's [tf.image.decode_jpeg](https://www.tensorflow.org/api_docs/python/tf/io/decode_jpeg) function can decode this string into an integer array, and [tf.image.convert_image_dtype](https://www.tensorflow.org/api_docs/python/tf/image/convert_image_dtype) can cast it into a 0 - 1 range float. Finally, we'll use [tf.image.resize](https://www.tensorflow.org/api_docs/python/tf/image/resize) to force image dimensions to be consistent for our neural network.\n",
"\n",
"We'll wrap these into a function as we'll be calling these repeatedly. While we're at it, let's also define our constants for our neural network."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"IMG_HEIGHT = 224\n",
"IMG_WIDTH = 224\n",
"IMG_CHANNELS = 3\n",
"\n",
"BATCH_SIZE = 32\n",
"# 10 is a magic number tuned for local training of this dataset.\n",
"SHUFFLE_BUFFER = 10 * BATCH_SIZE\n",
"AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
"\n",
"VALIDATION_IMAGES = 370\n",
"VALIDATION_STEPS = VALIDATION_IMAGES // BATCH_SIZE"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def decode_img(img, reshape_dims):\n",
" # Convert the compressed string to a 3D uint8 tensor.\n",
" img = tf.image.decode_jpeg(img, channels=IMG_CHANNELS)\n",
" # Use `convert_image_dtype` to convert to floats in the [0,1] range.\n",
" img = tf.image.convert_image_dtype(img, tf.float32)\n",
" # Resize the image to the desired size.\n",
" return tf.image.resize(img, reshape_dims)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Is it working? Let's see!\n",
"\n",
"**TODO 1.a:** Run the `decode_img` function and plot it to see a happy looking daisy."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img = tf.io.read_file(\n",
" \"gs://cloud-ml-data/img/flower_photos/daisy/754296579_30a9ae018c_n.jpg\")\n",
"\n",
"# Uncomment to see the image string.\n",
"#print(img)\n",
"# TODO: decode image and plot it"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One flower down, 3669 more of them to go. Rather than load all the photos in directly, we'll use the file paths given to us in the csv and load the images when we batch. [tf.io.decode_csv](https://www.tensorflow.org/api_docs/python/tf/io/decode_csv) reads in csv rows (or each line in a csv file), while [tf.math.equal](https://www.tensorflow.org/api_docs/python/tf/math/equal) will help us format our label such that it's a boolean array with a truth value corresponding to the class in `CLASS_NAMES`, much like the labels for the MNIST Lab."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def decode_csv(csv_row):\n",
" record_defaults = [\"path\", \"flower\"]\n",
" filename, label_string = tf.io.decode_csv(csv_row, record_defaults)\n",
" image_bytes = tf.io.read_file(filename=filename)\n",
" label = tf.math.equal(CLASS_NAMES, label_string)\n",
" return image_bytes, label"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we'll transform the images to give our network more variety to train on. There are a number of [image manipulation functions](https://www.tensorflow.org/api_docs/python/tf/image). We'll cover just a few:\n",
"\n",
"* [tf.image.random_crop](https://www.tensorflow.org/api_docs/python/tf/image/random_crop) - Randomly deletes the top/bottom rows and left/right columns down to the dimensions specified.\n",
"* [tf.image.random_flip_left_right](https://www.tensorflow.org/api_docs/python/tf/image/random_flip_left_right) - Randomly flips the image horizontally\n",
"* [tf.image.random_brightness](https://www.tensorflow.org/api_docs/python/tf/image/random_brightness) - Randomly adjusts how dark or light the image is.\n",
"* [tf.image.random_contrast](https://www.tensorflow.org/api_docs/python/tf/image/random_contrast) - Randomly adjusts image contrast.\n",
"\n",
"**TODO 1.b:** Augment the image using the random functions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"MAX_DELTA = 63.0 / 255.0 # Change brightness by at most 17.7%\n",
"CONTRAST_LOWER = 0.2\n",
"CONTRAST_UPPER = 1.8\n",
"\n",
"\n",
"def read_and_preprocess(image_bytes, label, random_augment=False):\n",
" if random_augment:\n",
" img = decode_img(image_bytes, [IMG_HEIGHT + 10, IMG_WIDTH + 10])\n",
" # TODO: augment the image.\n",
" else:\n",
" img = decode_img(image_bytes, [IMG_WIDTH, IMG_HEIGHT])\n",
" return img, label\n",
"\n",
"\n",
"def read_and_preprocess_with_augment(image_bytes, label):\n",
" return read_and_preprocess(image_bytes, label, random_augment=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we'll make a function to craft our full dataset using [tf.data.dataset](https://www.tensorflow.org/api_docs/python/tf/data/Dataset). The [tf.data.TextLineDataset](https://www.tensorflow.org/api_docs/python/tf/data/TextLineDataset) will read in each line in our train/eval csv files to our `decode_csv` function.\n",
"\n",
"[.cache](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#cache) is key here. It will store the dataset in memory"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def load_dataset(csv_of_filenames, batch_size, training=True):\n",
" dataset = tf.data.TextLineDataset(filenames=csv_of_filenames) \\\n",
" .map(decode_csv).cache()\n",
"\n",
" if training:\n",
" dataset = dataset \\\n",
" .map(read_and_preprocess_with_augment) \\\n",
" .shuffle(SHUFFLE_BUFFER) \\\n",
" .repeat(count=None) # Indefinately.\n",
" else:\n",
" dataset = dataset \\\n",
" .map(read_and_preprocess) \\\n",
" .repeat(count=1) # Each photo used once.\n",
"\n",
" # Prefetch prepares the next set of batches while current batch is in use.\n",
" return dataset.batch(batch_size=batch_size).prefetch(buffer_size=AUTOTUNE)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll test it out with our training set. A batch size of one will allow us to easily look at each augmented image."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train_path = \"gs://cloud-ml-data/img/flower_photos/train_set.csv\"\n",
"train_data = load_dataset(train_path, 1)\n",
"itr = iter(train_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**TODO 1.c:** Run the below cell repeatedly to see the results of different batches. The images have been un-normalized for human eyes. Can you tell what type of flowers they are? Is it fair for the AI to learn on?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"image_batch, label_batch = next(itr)\n",
"img = image_batch[0]\n",
"plt.imshow(img)\n",
"print(label_batch[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note:** It may take a 4-5 minutes to see result of different batches. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## MobileNetV2\n",
"\n",
"These flower photos are much larger than handwritting recognition images in MNIST. They are about 10 times as many pixels per axis **and** there are three color channels, making the information here over 200 times larger!\n",
"\n",
"How do our current techniques stand up? Copy your best model architecture over from the <a href=\"2_mnist_models.ipynb\">MNIST models lab</a> and see how well it does after training for 5 epochs of 50 steps.\n",
"\n",
"**TODO 2.a** Copy over the most accurate model from 2_mnist_models.ipynb or build a new CNN Keras model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"eval_path = \"gs://cloud-ml-data/img/flower_photos/eval_set.csv\"\n",
"nclasses = len(CLASS_NAMES)\n",
"hidden_layer_1_neurons = 400\n",
"hidden_layer_2_neurons = 100\n",
"dropout_rate = 0.25\n",
"num_filters_1 = 64\n",
"kernel_size_1 = 3\n",
"pooling_size_1 = 2\n",
"num_filters_2 = 32\n",
"kernel_size_2 = 3\n",
"pooling_size_2 = 2\n",
"\n",
"layers = [\n",
" # TODO: Add your image model.\n",
"]\n",
"\n",
"old_model = Sequential(layers)\n",
"old_model.compile(\n",
" optimizer='adam',\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
"train_ds = load_dataset(train_path, BATCH_SIZE)\n",
"eval_ds = load_dataset(eval_path, BATCH_SIZE, training=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"old_model.fit_generator(\n",
" train_ds,\n",
" epochs=5,\n",
" steps_per_epoch=5,\n",
" validation_data=eval_ds,\n",
" validation_steps=VALIDATION_STEPS\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If your model is like mine, it learns a little bit, slightly better then random, but *ugh*, it's too slow! With a batch size of 32, 5 epochs of 5 steps is only getting through about a quarter of our images. Not to mention, this is a much larger problem then MNIST, so wouldn't we need a larger model? But how big do we need to make it?\n",
"\n",
"Enter Transfer Learning. Why not take advantage of someone else's hard work? We can take the layers of a model that's been trained on a similar problem to ours and splice it into our own model.\n",
"\n",
"[Tensorflow Hub](https://tfhub.dev/s?module-type=image-augmentation,image-classification,image-others,image-style-transfer,image-rnn-agent) is a database of models, many of which can be used for Transfer Learning. We'll use a model called [MobileNet](https://tfhub.dev/google/imagenet/mobilenet_v2_035_224/feature_vector/4) which is an architecture optimized for image classification on mobile devices, which can be done with [TensorFlow Lite](https://github.com/tensorflow/hub/blob/master/examples/colab/tf2_image_retraining.ipynb). Let's compare how a model trained on [ImageNet](http://www.image-net.org/) data compares to one built from scratch.\n",
"\n",
"The `tensorflow_hub` python package has a function to include a Hub model as a [layer in Keras](https://www.tensorflow.org/hub/api_docs/python/hub/KerasLayer). We'll set the weights of this model as un-trainable. Even though this is a compressed version of full scale image classification models, it still has over four hundred thousand paramaters! Training all these would not only add to our computation, but it is also prone to over-fitting. We'll add some L2 regularization and Dropout to prevent that from happening to our trainable weights.\n",
"\n",
"**TODO 2.b**: Add a Hub Keras Layer at the top of the model using the handle provided."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"module_selection = \"mobilenet_v2_100_224\"\n",
"module_handle = \"https://tfhub.dev/google/imagenet/{}/feature_vector/4\" \\\n",
" .format(module_selection)\n",
"\n",
"transfer_model = tf.keras.Sequential([\n",
" # TODO\n",
" tf.keras.layers.Dropout(rate=0.2),\n",
" tf.keras.layers.Dense(\n",
" nclasses,\n",
" activation='softmax',\n",
" kernel_regularizer=tf.keras.regularizers.l2(0.0001))\n",
"])\n",
"transfer_model.build((None,)+(IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))\n",
"transfer_model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Even though we're only adding one more `Dense` layer in order to get the probabilities for each of the 5 flower types, we end up with over six thousand parameters to train ourselves. Wow!\n",
"\n",
"Moment of truth. Let's compile this new model and see how it compares to our MNIST architecture."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"transfer_model.compile(\n",
" optimizer='adam',\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
"train_ds = load_dataset(train_path, BATCH_SIZE)\n",
"eval_ds = load_dataset(eval_path, BATCH_SIZE, training=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"transfer_model.fit(\n",
" train_ds,\n",
" epochs=5,\n",
" steps_per_epoch=5,\n",
" validation_data=eval_ds,\n",
" validation_steps=VALIDATION_STEPS\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alright, looking better!\n",
"\n",
"Still, there's clear room to improve. Data bottlenecks are especially prevalent with image data due to the size of the image files. There's much to consider such as the computation of augmenting images and the bandwidth to transfer images between machines.\n",
"\n",
"Think life is too short, and there has to be a better way? In the next lab, we'll blast away these problems by developing a cloud strategy to train with TPUs!\n",
"\n",
"## Bonus Exercise\n",
"\n",
"Keras has a [local way](https://keras.io/models/sequential/) to do distributed training, but we'll be using a different technique in the next lab. Want to give the local way a try? Check out this excellent [blog post](https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly) to get started. Or want to go full-blown Keras? It also has a number of [pre-trained models](https://keras.io/applications/) ready to use."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright 2019 Google Inc.\n",
"Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
"http://www.apache.org/licenses/LICENSE-2.0\n",
"Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
]
}
],
"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"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| apache-2.0 |
mattwaite/JOUR491-Data-Visualization | Assignments/16_LiveFireExercise/LiveFireExercise.ipynb | 1 | 34624 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Live fire: Census estimates release day\n",
"\n",
"Every year, the US Census Bureau releases new estimates of the population of every metropolitan area, county, city and town in the US. They are estimates because they only do the headcount census every 10 years. Between then, they use data and modeling to estimate what the population is. Every 10 years, they recalibrate their models based on how close they came to getting it right, given the headcount census. \n",
"\n",
"Today, we're going to simulate being in a newsroom on the day these new data are released. We're going to look at how a local news organization handled it, and we're going to show how a little bit of R and ggplot knowhow can make this better, easier and pushbutton quick next year. \n",
"\n",
"First, let's talk about how [a local newspaper covered it](http://journalstar.com/business/local/census-nebraska-s-big-counties-growing-rest-of-state-not/article_4317e30c-2a4b-5184-a888-ccebd4a22a04.html). What did they choose to focus on? What numerical measures did they use? Were they the right ones? Were they useful? Did they use any visuals? What could they have done differently?\n",
"\n",
"Now let's take our own crack at this. You are now on deadline. You have until the end of class to create a visual story out of this data, looking at the state of Nebraska. You will need to:\n",
"\n",
"* Create some tables of data to show trends.\n",
"* Create at least two visualizations of the data.\n",
"\n",
"Some suggestions: Fastest growing? Fastest shrinking? Gainers to losers? One-year change vs since 2010? Every county in a lattice chart? Urban vs rural? Counties that have lost population every year this decade? Gained?\n",
"\n",
"Pair up, plan what you are going to do, and get started. To help you, here's some boilerplate code to get you going. **NOTE THE `read.csv` BITS. IT'S PULLING THE DATA STRAIGHT FROM THE URL.**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"Attaching package: ‘dplyr’\n",
"\n",
"The following objects are masked from ‘package:stats’:\n",
"\n",
" filter, lag\n",
"\n",
"The following objects are masked from ‘package:base’:\n",
"\n",
" intersect, setdiff, setequal, union\n",
"\n"
]
}
],
"source": [
"library(dplyr)\n",
"library(ggplot2)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"counties <- read.csv(url(\"https://www2.census.gov/programs-surveys/popest/datasets/2010-2017/counties/totals/co-est2017-alldata.csv\"))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>SUMLEV</th><th scope=col>REGION</th><th scope=col>DIVISION</th><th scope=col>STATE</th><th scope=col>COUNTY</th><th scope=col>STNAME</th><th scope=col>CTYNAME</th><th scope=col>CENSUS2010POP</th><th scope=col>ESTIMATESBASE2010</th><th scope=col>POPESTIMATE2010</th><th scope=col>⋯</th><th scope=col>RDOMESTICMIG2015</th><th scope=col>RDOMESTICMIG2016</th><th scope=col>RDOMESTICMIG2017</th><th scope=col>RNETMIG2011</th><th scope=col>RNETMIG2012</th><th scope=col>RNETMIG2013</th><th scope=col>RNETMIG2014</th><th scope=col>RNETMIG2015</th><th scope=col>RNETMIG2016</th><th scope=col>RNETMIG2017</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>40 </td><td>3 </td><td>6 </td><td>1 </td><td>0 </td><td>Alabama </td><td>Alabama </td><td>4779736 </td><td>4780135 </td><td>4785579 </td><td>⋯ </td><td> -0.3172050 </td><td> -0.404473 </td><td> 0.7888823 </td><td> 0.4507405 </td><td> 0.9393925 </td><td> 1.3642955 </td><td> 0.6942708 </td><td> 0.6785751 </td><td> 0.5589306 </td><td> 1.708218 </td></tr>\n",
"\t<tr><td>50 </td><td>3 </td><td>6 </td><td>1 </td><td>1 </td><td>Alabama </td><td>Autauga County</td><td> 54571 </td><td> 54571 </td><td> 54750 </td><td>⋯ </td><td> -1.9507393 </td><td> 4.831269 </td><td> 1.0471015 </td><td> 5.9118318 </td><td>-6.1021012 </td><td>-4.0502819 </td><td> 2.0993255 </td><td> -1.6590399 </td><td> 5.1037088 </td><td> 1.317904 </td></tr>\n",
"\t<tr><td>50 </td><td>3 </td><td>6 </td><td>1 </td><td>3 </td><td>Alabama </td><td>Baldwin County</td><td> 182265 </td><td> 182265 </td><td> 183110 </td><td>⋯ </td><td> 17.0478719 </td><td> 20.493601 </td><td> 22.3831750 </td><td>16.2859400 </td><td>17.1967858 </td><td>22.6152855 </td><td>20.3809040 </td><td> 17.9037487 </td><td> 21.3172439 </td><td> 23.163873 </td></tr>\n",
"\t<tr><td>50 </td><td>3 </td><td>6 </td><td>1 </td><td>5 </td><td>Alabama </td><td>Barbour County</td><td> 27457 </td><td> 27457 </td><td> 27332 </td><td>⋯ </td><td>-16.2224360 </td><td>-18.755525 </td><td>-19.0423948 </td><td> 0.2560211 </td><td>-6.8224333 </td><td>-8.0189202 </td><td>-5.5497616 </td><td>-16.4110690 </td><td>-18.9476921 </td><td>-19.159940 </td></tr>\n",
"\t<tr><td>50 </td><td>3 </td><td>6 </td><td>1 </td><td>7 </td><td>Alabama </td><td>Bibb County </td><td> 22915 </td><td> 22919 </td><td> 22872 </td><td>⋯ </td><td> 0.9313878 </td><td> -1.416117 </td><td> -0.8829827 </td><td>-5.0419800 </td><td>-4.0966456 </td><td>-5.8900379 </td><td> 1.2434497 </td><td> 1.8184237 </td><td> -0.5310439 </td><td> 0.000000 </td></tr>\n",
"\t<tr><td>50 </td><td>3 </td><td>6 </td><td>1 </td><td>9 </td><td>Alabama </td><td>Blount County </td><td> 57322 </td><td> 57324 </td><td> 57381 </td><td>⋯ </td><td> -1.5633685 </td><td> -1.736835 </td><td> 6.2124162 </td><td> 0.2435990 </td><td>-1.3546723 </td><td>-0.4860352 </td><td>-1.7713100 </td><td> -0.5384936 </td><td> -0.6599972 </td><td> 7.285313 </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|llllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
" SUMLEV & REGION & DIVISION & STATE & COUNTY & STNAME & CTYNAME & CENSUS2010POP & ESTIMATESBASE2010 & POPESTIMATE2010 & ⋯ & RDOMESTICMIG2015 & RDOMESTICMIG2016 & RDOMESTICMIG2017 & RNETMIG2011 & RNETMIG2012 & RNETMIG2013 & RNETMIG2014 & RNETMIG2015 & RNETMIG2016 & RNETMIG2017\\\\\n",
"\\hline\n",
"\t 40 & 3 & 6 & 1 & 0 & Alabama & Alabama & 4779736 & 4780135 & 4785579 & ⋯ & -0.3172050 & -0.404473 & 0.7888823 & 0.4507405 & 0.9393925 & 1.3642955 & 0.6942708 & 0.6785751 & 0.5589306 & 1.708218 \\\\\n",
"\t 50 & 3 & 6 & 1 & 1 & Alabama & Autauga County & 54571 & 54571 & 54750 & ⋯ & -1.9507393 & 4.831269 & 1.0471015 & 5.9118318 & -6.1021012 & -4.0502819 & 2.0993255 & -1.6590399 & 5.1037088 & 1.317904 \\\\\n",
"\t 50 & 3 & 6 & 1 & 3 & Alabama & Baldwin County & 182265 & 182265 & 183110 & ⋯ & 17.0478719 & 20.493601 & 22.3831750 & 16.2859400 & 17.1967858 & 22.6152855 & 20.3809040 & 17.9037487 & 21.3172439 & 23.163873 \\\\\n",
"\t 50 & 3 & 6 & 1 & 5 & Alabama & Barbour County & 27457 & 27457 & 27332 & ⋯ & -16.2224360 & -18.755525 & -19.0423948 & 0.2560211 & -6.8224333 & -8.0189202 & -5.5497616 & -16.4110690 & -18.9476921 & -19.159940 \\\\\n",
"\t 50 & 3 & 6 & 1 & 7 & Alabama & Bibb County & 22915 & 22919 & 22872 & ⋯ & 0.9313878 & -1.416117 & -0.8829827 & -5.0419800 & -4.0966456 & -5.8900379 & 1.2434497 & 1.8184237 & -0.5310439 & 0.000000 \\\\\n",
"\t 50 & 3 & 6 & 1 & 9 & Alabama & Blount County & 57322 & 57324 & 57381 & ⋯ & -1.5633685 & -1.736835 & 6.2124162 & 0.2435990 & -1.3546723 & -0.4860352 & -1.7713100 & -0.5384936 & -0.6599972 & 7.285313 \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"SUMLEV | REGION | DIVISION | STATE | COUNTY | STNAME | CTYNAME | CENSUS2010POP | ESTIMATESBASE2010 | POPESTIMATE2010 | ⋯ | RDOMESTICMIG2015 | RDOMESTICMIG2016 | RDOMESTICMIG2017 | RNETMIG2011 | RNETMIG2012 | RNETMIG2013 | RNETMIG2014 | RNETMIG2015 | RNETMIG2016 | RNETMIG2017 | \n",
"|---|---|---|---|---|---|\n",
"| 40 | 3 | 6 | 1 | 0 | Alabama | Alabama | 4779736 | 4780135 | 4785579 | ⋯ | -0.3172050 | -0.404473 | 0.7888823 | 0.4507405 | 0.9393925 | 1.3642955 | 0.6942708 | 0.6785751 | 0.5589306 | 1.708218 | \n",
"| 50 | 3 | 6 | 1 | 1 | Alabama | Autauga County | 54571 | 54571 | 54750 | ⋯ | -1.9507393 | 4.831269 | 1.0471015 | 5.9118318 | -6.1021012 | -4.0502819 | 2.0993255 | -1.6590399 | 5.1037088 | 1.317904 | \n",
"| 50 | 3 | 6 | 1 | 3 | Alabama | Baldwin County | 182265 | 182265 | 183110 | ⋯ | 17.0478719 | 20.493601 | 22.3831750 | 16.2859400 | 17.1967858 | 22.6152855 | 20.3809040 | 17.9037487 | 21.3172439 | 23.163873 | \n",
"| 50 | 3 | 6 | 1 | 5 | Alabama | Barbour County | 27457 | 27457 | 27332 | ⋯ | -16.2224360 | -18.755525 | -19.0423948 | 0.2560211 | -6.8224333 | -8.0189202 | -5.5497616 | -16.4110690 | -18.9476921 | -19.159940 | \n",
"| 50 | 3 | 6 | 1 | 7 | Alabama | Bibb County | 22915 | 22919 | 22872 | ⋯ | 0.9313878 | -1.416117 | -0.8829827 | -5.0419800 | -4.0966456 | -5.8900379 | 1.2434497 | 1.8184237 | -0.5310439 | 0.000000 | \n",
"| 50 | 3 | 6 | 1 | 9 | Alabama | Blount County | 57322 | 57324 | 57381 | ⋯ | -1.5633685 | -1.736835 | 6.2124162 | 0.2435990 | -1.3546723 | -0.4860352 | -1.7713100 | -0.5384936 | -0.6599972 | 7.285313 | \n",
"\n",
"\n"
],
"text/plain": [
" SUMLEV REGION DIVISION STATE COUNTY STNAME CTYNAME CENSUS2010POP\n",
"1 40 3 6 1 0 Alabama Alabama 4779736 \n",
"2 50 3 6 1 1 Alabama Autauga County 54571 \n",
"3 50 3 6 1 3 Alabama Baldwin County 182265 \n",
"4 50 3 6 1 5 Alabama Barbour County 27457 \n",
"5 50 3 6 1 7 Alabama Bibb County 22915 \n",
"6 50 3 6 1 9 Alabama Blount County 57322 \n",
" ESTIMATESBASE2010 POPESTIMATE2010 ⋯ RDOMESTICMIG2015 RDOMESTICMIG2016\n",
"1 4780135 4785579 ⋯ -0.3172050 -0.404473 \n",
"2 54571 54750 ⋯ -1.9507393 4.831269 \n",
"3 182265 183110 ⋯ 17.0478719 20.493601 \n",
"4 27457 27332 ⋯ -16.2224360 -18.755525 \n",
"5 22919 22872 ⋯ 0.9313878 -1.416117 \n",
"6 57324 57381 ⋯ -1.5633685 -1.736835 \n",
" RDOMESTICMIG2017 RNETMIG2011 RNETMIG2012 RNETMIG2013 RNETMIG2014 RNETMIG2015\n",
"1 0.7888823 0.4507405 0.9393925 1.3642955 0.6942708 0.6785751\n",
"2 1.0471015 5.9118318 -6.1021012 -4.0502819 2.0993255 -1.6590399\n",
"3 22.3831750 16.2859400 17.1967858 22.6152855 20.3809040 17.9037487\n",
"4 -19.0423948 0.2560211 -6.8224333 -8.0189202 -5.5497616 -16.4110690\n",
"5 -0.8829827 -5.0419800 -4.0966456 -5.8900379 1.2434497 1.8184237\n",
"6 6.2124162 0.2435990 -1.3546723 -0.4860352 -1.7713100 -0.5384936\n",
" RNETMIG2016 RNETMIG2017\n",
"1 0.5589306 1.708218 \n",
"2 5.1037088 1.317904 \n",
"3 21.3172439 23.163873 \n",
"4 -18.9476921 -19.159940 \n",
"5 -0.5310439 0.000000 \n",
"6 -0.6599972 7.285313 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"head(counties)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>'SUMLEV'</li>\n",
"\t<li>'REGION'</li>\n",
"\t<li>'DIVISION'</li>\n",
"\t<li>'STATE'</li>\n",
"\t<li>'COUNTY'</li>\n",
"\t<li>'STNAME'</li>\n",
"\t<li>'CTYNAME'</li>\n",
"\t<li>'CENSUS2010POP'</li>\n",
"\t<li>'ESTIMATESBASE2010'</li>\n",
"\t<li>'POPESTIMATE2010'</li>\n",
"\t<li>'POPESTIMATE2011'</li>\n",
"\t<li>'POPESTIMATE2012'</li>\n",
"\t<li>'POPESTIMATE2013'</li>\n",
"\t<li>'POPESTIMATE2014'</li>\n",
"\t<li>'POPESTIMATE2015'</li>\n",
"\t<li>'POPESTIMATE2016'</li>\n",
"\t<li>'POPESTIMATE2017'</li>\n",
"\t<li>'NPOPCHG_2010'</li>\n",
"\t<li>'NPOPCHG_2011'</li>\n",
"\t<li>'NPOPCHG_2012'</li>\n",
"\t<li>'NPOPCHG_2013'</li>\n",
"\t<li>'NPOPCHG_2014'</li>\n",
"\t<li>'NPOPCHG_2015'</li>\n",
"\t<li>'NPOPCHG_2016'</li>\n",
"\t<li>'NPOPCHG_2017'</li>\n",
"\t<li>'BIRTHS2010'</li>\n",
"\t<li>'BIRTHS2011'</li>\n",
"\t<li>'BIRTHS2012'</li>\n",
"\t<li>'BIRTHS2013'</li>\n",
"\t<li>'BIRTHS2014'</li>\n",
"\t<li>'BIRTHS2015'</li>\n",
"\t<li>'BIRTHS2016'</li>\n",
"\t<li>'BIRTHS2017'</li>\n",
"\t<li>'DEATHS2010'</li>\n",
"\t<li>'DEATHS2011'</li>\n",
"\t<li>'DEATHS2012'</li>\n",
"\t<li>'DEATHS2013'</li>\n",
"\t<li>'DEATHS2014'</li>\n",
"\t<li>'DEATHS2015'</li>\n",
"\t<li>'DEATHS2016'</li>\n",
"\t<li>'DEATHS2017'</li>\n",
"\t<li>'NATURALINC2010'</li>\n",
"\t<li>'NATURALINC2011'</li>\n",
"\t<li>'NATURALINC2012'</li>\n",
"\t<li>'NATURALINC2013'</li>\n",
"\t<li>'NATURALINC2014'</li>\n",
"\t<li>'NATURALINC2015'</li>\n",
"\t<li>'NATURALINC2016'</li>\n",
"\t<li>'NATURALINC2017'</li>\n",
"\t<li>'INTERNATIONALMIG2010'</li>\n",
"\t<li>'INTERNATIONALMIG2011'</li>\n",
"\t<li>'INTERNATIONALMIG2012'</li>\n",
"\t<li>'INTERNATIONALMIG2013'</li>\n",
"\t<li>'INTERNATIONALMIG2014'</li>\n",
"\t<li>'INTERNATIONALMIG2015'</li>\n",
"\t<li>'INTERNATIONALMIG2016'</li>\n",
"\t<li>'INTERNATIONALMIG2017'</li>\n",
"\t<li>'DOMESTICMIG2010'</li>\n",
"\t<li>'DOMESTICMIG2011'</li>\n",
"\t<li>'DOMESTICMIG2012'</li>\n",
"\t<li>'DOMESTICMIG2013'</li>\n",
"\t<li>'DOMESTICMIG2014'</li>\n",
"\t<li>'DOMESTICMIG2015'</li>\n",
"\t<li>'DOMESTICMIG2016'</li>\n",
"\t<li>'DOMESTICMIG2017'</li>\n",
"\t<li>'NETMIG2010'</li>\n",
"\t<li>'NETMIG2011'</li>\n",
"\t<li>'NETMIG2012'</li>\n",
"\t<li>'NETMIG2013'</li>\n",
"\t<li>'NETMIG2014'</li>\n",
"\t<li>'NETMIG2015'</li>\n",
"\t<li>'NETMIG2016'</li>\n",
"\t<li>'NETMIG2017'</li>\n",
"\t<li>'RESIDUAL2010'</li>\n",
"\t<li>'RESIDUAL2011'</li>\n",
"\t<li>'RESIDUAL2012'</li>\n",
"\t<li>'RESIDUAL2013'</li>\n",
"\t<li>'RESIDUAL2014'</li>\n",
"\t<li>'RESIDUAL2015'</li>\n",
"\t<li>'RESIDUAL2016'</li>\n",
"\t<li>'RESIDUAL2017'</li>\n",
"\t<li>'GQESTIMATESBASE2010'</li>\n",
"\t<li>'GQESTIMATES2010'</li>\n",
"\t<li>'GQESTIMATES2011'</li>\n",
"\t<li>'GQESTIMATES2012'</li>\n",
"\t<li>'GQESTIMATES2013'</li>\n",
"\t<li>'GQESTIMATES2014'</li>\n",
"\t<li>'GQESTIMATES2015'</li>\n",
"\t<li>'GQESTIMATES2016'</li>\n",
"\t<li>'GQESTIMATES2017'</li>\n",
"\t<li>'RBIRTH2011'</li>\n",
"\t<li>'RBIRTH2012'</li>\n",
"\t<li>'RBIRTH2013'</li>\n",
"\t<li>'RBIRTH2014'</li>\n",
"\t<li>'RBIRTH2015'</li>\n",
"\t<li>'RBIRTH2016'</li>\n",
"\t<li>'RBIRTH2017'</li>\n",
"\t<li>'RDEATH2011'</li>\n",
"\t<li>'RDEATH2012'</li>\n",
"\t<li>'RDEATH2013'</li>\n",
"\t<li>'RDEATH2014'</li>\n",
"\t<li>'RDEATH2015'</li>\n",
"\t<li>'RDEATH2016'</li>\n",
"\t<li>'RDEATH2017'</li>\n",
"\t<li>'RNATURALINC2011'</li>\n",
"\t<li>'RNATURALINC2012'</li>\n",
"\t<li>'RNATURALINC2013'</li>\n",
"\t<li>'RNATURALINC2014'</li>\n",
"\t<li>'RNATURALINC2015'</li>\n",
"\t<li>'RNATURALINC2016'</li>\n",
"\t<li>'RNATURALINC2017'</li>\n",
"\t<li>'RINTERNATIONALMIG2011'</li>\n",
"\t<li>'RINTERNATIONALMIG2012'</li>\n",
"\t<li>'RINTERNATIONALMIG2013'</li>\n",
"\t<li>'RINTERNATIONALMIG2014'</li>\n",
"\t<li>'RINTERNATIONALMIG2015'</li>\n",
"\t<li>'RINTERNATIONALMIG2016'</li>\n",
"\t<li>'RINTERNATIONALMIG2017'</li>\n",
"\t<li>'RDOMESTICMIG2011'</li>\n",
"\t<li>'RDOMESTICMIG2012'</li>\n",
"\t<li>'RDOMESTICMIG2013'</li>\n",
"\t<li>'RDOMESTICMIG2014'</li>\n",
"\t<li>'RDOMESTICMIG2015'</li>\n",
"\t<li>'RDOMESTICMIG2016'</li>\n",
"\t<li>'RDOMESTICMIG2017'</li>\n",
"\t<li>'RNETMIG2011'</li>\n",
"\t<li>'RNETMIG2012'</li>\n",
"\t<li>'RNETMIG2013'</li>\n",
"\t<li>'RNETMIG2014'</li>\n",
"\t<li>'RNETMIG2015'</li>\n",
"\t<li>'RNETMIG2016'</li>\n",
"\t<li>'RNETMIG2017'</li>\n",
"</ol>\n"
],
"text/latex": [
"\\begin{enumerate*}\n",
"\\item 'SUMLEV'\n",
"\\item 'REGION'\n",
"\\item 'DIVISION'\n",
"\\item 'STATE'\n",
"\\item 'COUNTY'\n",
"\\item 'STNAME'\n",
"\\item 'CTYNAME'\n",
"\\item 'CENSUS2010POP'\n",
"\\item 'ESTIMATESBASE2010'\n",
"\\item 'POPESTIMATE2010'\n",
"\\item 'POPESTIMATE2011'\n",
"\\item 'POPESTIMATE2012'\n",
"\\item 'POPESTIMATE2013'\n",
"\\item 'POPESTIMATE2014'\n",
"\\item 'POPESTIMATE2015'\n",
"\\item 'POPESTIMATE2016'\n",
"\\item 'POPESTIMATE2017'\n",
"\\item 'NPOPCHG\\_2010'\n",
"\\item 'NPOPCHG\\_2011'\n",
"\\item 'NPOPCHG\\_2012'\n",
"\\item 'NPOPCHG\\_2013'\n",
"\\item 'NPOPCHG\\_2014'\n",
"\\item 'NPOPCHG\\_2015'\n",
"\\item 'NPOPCHG\\_2016'\n",
"\\item 'NPOPCHG\\_2017'\n",
"\\item 'BIRTHS2010'\n",
"\\item 'BIRTHS2011'\n",
"\\item 'BIRTHS2012'\n",
"\\item 'BIRTHS2013'\n",
"\\item 'BIRTHS2014'\n",
"\\item 'BIRTHS2015'\n",
"\\item 'BIRTHS2016'\n",
"\\item 'BIRTHS2017'\n",
"\\item 'DEATHS2010'\n",
"\\item 'DEATHS2011'\n",
"\\item 'DEATHS2012'\n",
"\\item 'DEATHS2013'\n",
"\\item 'DEATHS2014'\n",
"\\item 'DEATHS2015'\n",
"\\item 'DEATHS2016'\n",
"\\item 'DEATHS2017'\n",
"\\item 'NATURALINC2010'\n",
"\\item 'NATURALINC2011'\n",
"\\item 'NATURALINC2012'\n",
"\\item 'NATURALINC2013'\n",
"\\item 'NATURALINC2014'\n",
"\\item 'NATURALINC2015'\n",
"\\item 'NATURALINC2016'\n",
"\\item 'NATURALINC2017'\n",
"\\item 'INTERNATIONALMIG2010'\n",
"\\item 'INTERNATIONALMIG2011'\n",
"\\item 'INTERNATIONALMIG2012'\n",
"\\item 'INTERNATIONALMIG2013'\n",
"\\item 'INTERNATIONALMIG2014'\n",
"\\item 'INTERNATIONALMIG2015'\n",
"\\item 'INTERNATIONALMIG2016'\n",
"\\item 'INTERNATIONALMIG2017'\n",
"\\item 'DOMESTICMIG2010'\n",
"\\item 'DOMESTICMIG2011'\n",
"\\item 'DOMESTICMIG2012'\n",
"\\item 'DOMESTICMIG2013'\n",
"\\item 'DOMESTICMIG2014'\n",
"\\item 'DOMESTICMIG2015'\n",
"\\item 'DOMESTICMIG2016'\n",
"\\item 'DOMESTICMIG2017'\n",
"\\item 'NETMIG2010'\n",
"\\item 'NETMIG2011'\n",
"\\item 'NETMIG2012'\n",
"\\item 'NETMIG2013'\n",
"\\item 'NETMIG2014'\n",
"\\item 'NETMIG2015'\n",
"\\item 'NETMIG2016'\n",
"\\item 'NETMIG2017'\n",
"\\item 'RESIDUAL2010'\n",
"\\item 'RESIDUAL2011'\n",
"\\item 'RESIDUAL2012'\n",
"\\item 'RESIDUAL2013'\n",
"\\item 'RESIDUAL2014'\n",
"\\item 'RESIDUAL2015'\n",
"\\item 'RESIDUAL2016'\n",
"\\item 'RESIDUAL2017'\n",
"\\item 'GQESTIMATESBASE2010'\n",
"\\item 'GQESTIMATES2010'\n",
"\\item 'GQESTIMATES2011'\n",
"\\item 'GQESTIMATES2012'\n",
"\\item 'GQESTIMATES2013'\n",
"\\item 'GQESTIMATES2014'\n",
"\\item 'GQESTIMATES2015'\n",
"\\item 'GQESTIMATES2016'\n",
"\\item 'GQESTIMATES2017'\n",
"\\item 'RBIRTH2011'\n",
"\\item 'RBIRTH2012'\n",
"\\item 'RBIRTH2013'\n",
"\\item 'RBIRTH2014'\n",
"\\item 'RBIRTH2015'\n",
"\\item 'RBIRTH2016'\n",
"\\item 'RBIRTH2017'\n",
"\\item 'RDEATH2011'\n",
"\\item 'RDEATH2012'\n",
"\\item 'RDEATH2013'\n",
"\\item 'RDEATH2014'\n",
"\\item 'RDEATH2015'\n",
"\\item 'RDEATH2016'\n",
"\\item 'RDEATH2017'\n",
"\\item 'RNATURALINC2011'\n",
"\\item 'RNATURALINC2012'\n",
"\\item 'RNATURALINC2013'\n",
"\\item 'RNATURALINC2014'\n",
"\\item 'RNATURALINC2015'\n",
"\\item 'RNATURALINC2016'\n",
"\\item 'RNATURALINC2017'\n",
"\\item 'RINTERNATIONALMIG2011'\n",
"\\item 'RINTERNATIONALMIG2012'\n",
"\\item 'RINTERNATIONALMIG2013'\n",
"\\item 'RINTERNATIONALMIG2014'\n",
"\\item 'RINTERNATIONALMIG2015'\n",
"\\item 'RINTERNATIONALMIG2016'\n",
"\\item 'RINTERNATIONALMIG2017'\n",
"\\item 'RDOMESTICMIG2011'\n",
"\\item 'RDOMESTICMIG2012'\n",
"\\item 'RDOMESTICMIG2013'\n",
"\\item 'RDOMESTICMIG2014'\n",
"\\item 'RDOMESTICMIG2015'\n",
"\\item 'RDOMESTICMIG2016'\n",
"\\item 'RDOMESTICMIG2017'\n",
"\\item 'RNETMIG2011'\n",
"\\item 'RNETMIG2012'\n",
"\\item 'RNETMIG2013'\n",
"\\item 'RNETMIG2014'\n",
"\\item 'RNETMIG2015'\n",
"\\item 'RNETMIG2016'\n",
"\\item 'RNETMIG2017'\n",
"\\end{enumerate*}\n"
],
"text/markdown": [
"1. 'SUMLEV'\n",
"2. 'REGION'\n",
"3. 'DIVISION'\n",
"4. 'STATE'\n",
"5. 'COUNTY'\n",
"6. 'STNAME'\n",
"7. 'CTYNAME'\n",
"8. 'CENSUS2010POP'\n",
"9. 'ESTIMATESBASE2010'\n",
"10. 'POPESTIMATE2010'\n",
"11. 'POPESTIMATE2011'\n",
"12. 'POPESTIMATE2012'\n",
"13. 'POPESTIMATE2013'\n",
"14. 'POPESTIMATE2014'\n",
"15. 'POPESTIMATE2015'\n",
"16. 'POPESTIMATE2016'\n",
"17. 'POPESTIMATE2017'\n",
"18. 'NPOPCHG_2010'\n",
"19. 'NPOPCHG_2011'\n",
"20. 'NPOPCHG_2012'\n",
"21. 'NPOPCHG_2013'\n",
"22. 'NPOPCHG_2014'\n",
"23. 'NPOPCHG_2015'\n",
"24. 'NPOPCHG_2016'\n",
"25. 'NPOPCHG_2017'\n",
"26. 'BIRTHS2010'\n",
"27. 'BIRTHS2011'\n",
"28. 'BIRTHS2012'\n",
"29. 'BIRTHS2013'\n",
"30. 'BIRTHS2014'\n",
"31. 'BIRTHS2015'\n",
"32. 'BIRTHS2016'\n",
"33. 'BIRTHS2017'\n",
"34. 'DEATHS2010'\n",
"35. 'DEATHS2011'\n",
"36. 'DEATHS2012'\n",
"37. 'DEATHS2013'\n",
"38. 'DEATHS2014'\n",
"39. 'DEATHS2015'\n",
"40. 'DEATHS2016'\n",
"41. 'DEATHS2017'\n",
"42. 'NATURALINC2010'\n",
"43. 'NATURALINC2011'\n",
"44. 'NATURALINC2012'\n",
"45. 'NATURALINC2013'\n",
"46. 'NATURALINC2014'\n",
"47. 'NATURALINC2015'\n",
"48. 'NATURALINC2016'\n",
"49. 'NATURALINC2017'\n",
"50. 'INTERNATIONALMIG2010'\n",
"51. 'INTERNATIONALMIG2011'\n",
"52. 'INTERNATIONALMIG2012'\n",
"53. 'INTERNATIONALMIG2013'\n",
"54. 'INTERNATIONALMIG2014'\n",
"55. 'INTERNATIONALMIG2015'\n",
"56. 'INTERNATIONALMIG2016'\n",
"57. 'INTERNATIONALMIG2017'\n",
"58. 'DOMESTICMIG2010'\n",
"59. 'DOMESTICMIG2011'\n",
"60. 'DOMESTICMIG2012'\n",
"61. 'DOMESTICMIG2013'\n",
"62. 'DOMESTICMIG2014'\n",
"63. 'DOMESTICMIG2015'\n",
"64. 'DOMESTICMIG2016'\n",
"65. 'DOMESTICMIG2017'\n",
"66. 'NETMIG2010'\n",
"67. 'NETMIG2011'\n",
"68. 'NETMIG2012'\n",
"69. 'NETMIG2013'\n",
"70. 'NETMIG2014'\n",
"71. 'NETMIG2015'\n",
"72. 'NETMIG2016'\n",
"73. 'NETMIG2017'\n",
"74. 'RESIDUAL2010'\n",
"75. 'RESIDUAL2011'\n",
"76. 'RESIDUAL2012'\n",
"77. 'RESIDUAL2013'\n",
"78. 'RESIDUAL2014'\n",
"79. 'RESIDUAL2015'\n",
"80. 'RESIDUAL2016'\n",
"81. 'RESIDUAL2017'\n",
"82. 'GQESTIMATESBASE2010'\n",
"83. 'GQESTIMATES2010'\n",
"84. 'GQESTIMATES2011'\n",
"85. 'GQESTIMATES2012'\n",
"86. 'GQESTIMATES2013'\n",
"87. 'GQESTIMATES2014'\n",
"88. 'GQESTIMATES2015'\n",
"89. 'GQESTIMATES2016'\n",
"90. 'GQESTIMATES2017'\n",
"91. 'RBIRTH2011'\n",
"92. 'RBIRTH2012'\n",
"93. 'RBIRTH2013'\n",
"94. 'RBIRTH2014'\n",
"95. 'RBIRTH2015'\n",
"96. 'RBIRTH2016'\n",
"97. 'RBIRTH2017'\n",
"98. 'RDEATH2011'\n",
"99. 'RDEATH2012'\n",
"100. 'RDEATH2013'\n",
"101. 'RDEATH2014'\n",
"102. 'RDEATH2015'\n",
"103. 'RDEATH2016'\n",
"104. 'RDEATH2017'\n",
"105. 'RNATURALINC2011'\n",
"106. 'RNATURALINC2012'\n",
"107. 'RNATURALINC2013'\n",
"108. 'RNATURALINC2014'\n",
"109. 'RNATURALINC2015'\n",
"110. 'RNATURALINC2016'\n",
"111. 'RNATURALINC2017'\n",
"112. 'RINTERNATIONALMIG2011'\n",
"113. 'RINTERNATIONALMIG2012'\n",
"114. 'RINTERNATIONALMIG2013'\n",
"115. 'RINTERNATIONALMIG2014'\n",
"116. 'RINTERNATIONALMIG2015'\n",
"117. 'RINTERNATIONALMIG2016'\n",
"118. 'RINTERNATIONALMIG2017'\n",
"119. 'RDOMESTICMIG2011'\n",
"120. 'RDOMESTICMIG2012'\n",
"121. 'RDOMESTICMIG2013'\n",
"122. 'RDOMESTICMIG2014'\n",
"123. 'RDOMESTICMIG2015'\n",
"124. 'RDOMESTICMIG2016'\n",
"125. 'RDOMESTICMIG2017'\n",
"126. 'RNETMIG2011'\n",
"127. 'RNETMIG2012'\n",
"128. 'RNETMIG2013'\n",
"129. 'RNETMIG2014'\n",
"130. 'RNETMIG2015'\n",
"131. 'RNETMIG2016'\n",
"132. 'RNETMIG2017'\n",
"\n",
"\n"
],
"text/plain": [
" [1] \"SUMLEV\" \"REGION\" \"DIVISION\" \n",
" [4] \"STATE\" \"COUNTY\" \"STNAME\" \n",
" [7] \"CTYNAME\" \"CENSUS2010POP\" \"ESTIMATESBASE2010\" \n",
" [10] \"POPESTIMATE2010\" \"POPESTIMATE2011\" \"POPESTIMATE2012\" \n",
" [13] \"POPESTIMATE2013\" \"POPESTIMATE2014\" \"POPESTIMATE2015\" \n",
" [16] \"POPESTIMATE2016\" \"POPESTIMATE2017\" \"NPOPCHG_2010\" \n",
" [19] \"NPOPCHG_2011\" \"NPOPCHG_2012\" \"NPOPCHG_2013\" \n",
" [22] \"NPOPCHG_2014\" \"NPOPCHG_2015\" \"NPOPCHG_2016\" \n",
" [25] \"NPOPCHG_2017\" \"BIRTHS2010\" \"BIRTHS2011\" \n",
" [28] \"BIRTHS2012\" \"BIRTHS2013\" \"BIRTHS2014\" \n",
" [31] \"BIRTHS2015\" \"BIRTHS2016\" \"BIRTHS2017\" \n",
" [34] \"DEATHS2010\" \"DEATHS2011\" \"DEATHS2012\" \n",
" [37] \"DEATHS2013\" \"DEATHS2014\" \"DEATHS2015\" \n",
" [40] \"DEATHS2016\" \"DEATHS2017\" \"NATURALINC2010\" \n",
" [43] \"NATURALINC2011\" \"NATURALINC2012\" \"NATURALINC2013\" \n",
" [46] \"NATURALINC2014\" \"NATURALINC2015\" \"NATURALINC2016\" \n",
" [49] \"NATURALINC2017\" \"INTERNATIONALMIG2010\" \"INTERNATIONALMIG2011\" \n",
" [52] \"INTERNATIONALMIG2012\" \"INTERNATIONALMIG2013\" \"INTERNATIONALMIG2014\" \n",
" [55] \"INTERNATIONALMIG2015\" \"INTERNATIONALMIG2016\" \"INTERNATIONALMIG2017\" \n",
" [58] \"DOMESTICMIG2010\" \"DOMESTICMIG2011\" \"DOMESTICMIG2012\" \n",
" [61] \"DOMESTICMIG2013\" \"DOMESTICMIG2014\" \"DOMESTICMIG2015\" \n",
" [64] \"DOMESTICMIG2016\" \"DOMESTICMIG2017\" \"NETMIG2010\" \n",
" [67] \"NETMIG2011\" \"NETMIG2012\" \"NETMIG2013\" \n",
" [70] \"NETMIG2014\" \"NETMIG2015\" \"NETMIG2016\" \n",
" [73] \"NETMIG2017\" \"RESIDUAL2010\" \"RESIDUAL2011\" \n",
" [76] \"RESIDUAL2012\" \"RESIDUAL2013\" \"RESIDUAL2014\" \n",
" [79] \"RESIDUAL2015\" \"RESIDUAL2016\" \"RESIDUAL2017\" \n",
" [82] \"GQESTIMATESBASE2010\" \"GQESTIMATES2010\" \"GQESTIMATES2011\" \n",
" [85] \"GQESTIMATES2012\" \"GQESTIMATES2013\" \"GQESTIMATES2014\" \n",
" [88] \"GQESTIMATES2015\" \"GQESTIMATES2016\" \"GQESTIMATES2017\" \n",
" [91] \"RBIRTH2011\" \"RBIRTH2012\" \"RBIRTH2013\" \n",
" [94] \"RBIRTH2014\" \"RBIRTH2015\" \"RBIRTH2016\" \n",
" [97] \"RBIRTH2017\" \"RDEATH2011\" \"RDEATH2012\" \n",
"[100] \"RDEATH2013\" \"RDEATH2014\" \"RDEATH2015\" \n",
"[103] \"RDEATH2016\" \"RDEATH2017\" \"RNATURALINC2011\" \n",
"[106] \"RNATURALINC2012\" \"RNATURALINC2013\" \"RNATURALINC2014\" \n",
"[109] \"RNATURALINC2015\" \"RNATURALINC2016\" \"RNATURALINC2017\" \n",
"[112] \"RINTERNATIONALMIG2011\" \"RINTERNATIONALMIG2012\" \"RINTERNATIONALMIG2013\"\n",
"[115] \"RINTERNATIONALMIG2014\" \"RINTERNATIONALMIG2015\" \"RINTERNATIONALMIG2016\"\n",
"[118] \"RINTERNATIONALMIG2017\" \"RDOMESTICMIG2011\" \"RDOMESTICMIG2012\" \n",
"[121] \"RDOMESTICMIG2013\" \"RDOMESTICMIG2014\" \"RDOMESTICMIG2015\" \n",
"[124] \"RDOMESTICMIG2016\" \"RDOMESTICMIG2017\" \"RNETMIG2011\" \n",
"[127] \"RNETMIG2012\" \"RNETMIG2013\" \"RNETMIG2014\" \n",
"[130] \"RNETMIG2015\" \"RNETMIG2016\" \"RNETMIG2017\" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"colnames(counties)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's some code to filter out just Nebraska counties, remove the statewide total number and calculate percent change into a field called change. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"nebraska <- counties %>% \n",
"filter(STNAME == \"Nebraska\") %>% \n",
"filter(SUMLEV == 50) %>% \n",
"mutate(change = ((POPESTIMATE2017-POPESTIMATE2016)/POPESTIMATE2016)*100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Homework:\n",
"\n",
"Read Tufte 2,3 and 5 and be prepared for a disussion of lying with charts. Also, prepare a pitch for your next visual story, which is due Thursday of Dead Week. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"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": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
ARM-software/lisa | ipynb/deprecated/examples/trace_analysis/TraceAnalysis_TasksLatencies.ipynb | 2 | 310094 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"# Trace Analysis Examples\n",
"\n",
"## Tasks Latencies\n",
"\n",
"This notebook shows the features provided for task latency profiling. It will be necessary to collect the following events:\n",
" \n",
"Details on idle states profiling ar given in **Latency DataFrames and Latency Plots ** below."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"marked": false,
"read_only": false
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2017-02-17 19:51:33,920 INFO : root : Using LISA logging configuration:\n",
"2017-02-17 19:51:33,922 INFO : root : /data/Code/lisa/logging.conf\n"
]
}
],
"source": [
"import logging\n",
"from conf import LisaLogging\n",
"LisaLogging.setup()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"marked": false,
"read_only": false
}
},
"outputs": [],
"source": [
"# Generate plots inline\n",
"%matplotlib inline\n",
"\n",
"import json\n",
"import os\n",
"\n",
"# Support to access the remote target\n",
"import devlib\n",
"from env import TestEnv\n",
"\n",
"# Support for workload generation\n",
"from wlgen import RTA, Ramp\n",
"\n",
"# Support for trace analysis\n",
"from trace import Trace\n",
"\n",
"# Support for plotting\n",
"import numpy\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import trappy"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Target Configuration\n",
"The target configuration is used to describe and configure your test environment.\n",
"You can find more details in **examples/utils/testenv_example.ipynb**."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"marked": false,
"read_only": false
}
},
"outputs": [],
"source": [
"# Setup target configuration\n",
"my_conf = {\n",
"\n",
" # Target platform and board\n",
" \"platform\" : 'linux',\n",
" \"board\" : 'juno',\n",
" \"host\" : '192.168.0.1',\n",
" \"password\" : 'juno',\n",
"\n",
" # Folder where all the results will be collected\n",
" \"results_dir\" : \"TraceAnalysis_TaskLatencies\",\n",
"\n",
" # Define devlib modules to load\n",
" \"modules\" : ['cpufreq'],\n",
" \"exclude_modules\" : [ 'hwmon' ],\n",
"\n",
" # FTrace events to collect for all the tests configuration which have\n",
" # the \"ftrace\" flag enabled\n",
" \"ftrace\" : {\n",
" \"events\" : [\n",
" \"sched_switch\",\n",
" \"sched_wakeup\",\n",
" \"sched_load_avg_cpu\",\n",
" \"sched_load_avg_task\",\n",
" ],\n",
" \n",
" \"buffsize\" : 100 * 1024,\n",
" },\n",
"\n",
" # Tools required by the experiments\n",
" \"tools\" : [ 'trace-cmd', 'rt-app' ],\n",
" \n",
" # Comment this line to calibrate RTApp in your own platform\n",
" # \"rtapp-calib\" : {\"0\": 360, \"1\": 142, \"2\": 138, \"3\": 352, \"4\": 352, \"5\": 353},\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2017-02-17 19:51:34,465 INFO : TestEnv : Using base path: /data/Code/lisa\n",
"2017-02-17 19:51:34,466 INFO : TestEnv : Loading custom (inline) target configuration\n",
"2017-02-17 19:51:34,467 INFO : TestEnv : Devlib modules to load: ['bl', 'cpufreq']\n",
"2017-02-17 19:51:34,468 INFO : TestEnv : Connecting linux target:\n",
"2017-02-17 19:51:34,469 INFO : TestEnv : username : root\n",
"2017-02-17 19:51:34,470 INFO : TestEnv : host : 192.168.0.1\n",
"2017-02-17 19:51:34,471 INFO : TestEnv : password : juno\n",
"2017-02-17 19:51:34,472 INFO : TestEnv : Connection settings:\n",
"2017-02-17 19:51:34,473 INFO : TestEnv : {'username': 'root', 'host': '192.168.0.1', 'password': 'juno'}\n",
"2017-02-17 19:51:38,957 INFO : TestEnv : Initializing target workdir:\n",
"2017-02-17 19:51:38,959 INFO : TestEnv : /root/devlib-target\n",
"2017-02-17 19:51:41,908 INFO : TestEnv : Topology:\n",
"2017-02-17 19:51:41,910 INFO : TestEnv : [[0, 3, 4, 5], [1, 2]]\n",
"2017-02-17 19:51:43,175 INFO : TestEnv : Loading default EM:\n",
"2017-02-17 19:51:43,177 INFO : TestEnv : /data/Code/lisa/libs/utils/platforms/juno.json\n",
"2017-02-17 19:51:44,416 WARNING : LinuxTarget : Event [sched_load_avg_cpu] not available for tracing\n",
"2017-02-17 19:51:44,419 WARNING : LinuxTarget : Event [sched_load_avg_task] not available for tracing\n",
"2017-02-17 19:51:44,420 INFO : TestEnv : Enabled tracepoints:\n",
"2017-02-17 19:51:44,422 INFO : TestEnv : sched_switch\n",
"2017-02-17 19:51:44,423 INFO : TestEnv : sched_wakeup\n",
"2017-02-17 19:51:44,425 INFO : TestEnv : sched_load_avg_cpu\n",
"2017-02-17 19:51:44,426 INFO : TestEnv : sched_load_avg_task\n",
"2017-02-17 19:51:44,427 WARNING : TestEnv : Using configuration provided RTApp calibration\n",
"2017-02-17 19:51:44,429 INFO : TestEnv : Using RT-App calibration values:\n",
"2017-02-17 19:51:44,430 INFO : TestEnv : {\"0\": 360, \"1\": 142, \"2\": 138, \"3\": 352, \"4\": 352, \"5\": 353}\n",
"2017-02-17 19:51:44,432 INFO : EnergyMeter : HWMON module not enabled\n",
"2017-02-17 19:51:44,434 WARNING : EnergyMeter : Energy sampling disabled by configuration\n",
"2017-02-17 19:51:44,435 INFO : TestEnv : Set results folder to:\n",
"2017-02-17 19:51:44,436 INFO : TestEnv : /data/Code/lisa/results/TraceAnalysis_TaskLatencies\n",
"2017-02-17 19:51:44,438 INFO : TestEnv : Experiment results available also in:\n",
"2017-02-17 19:51:44,439 INFO : TestEnv : /data/Code/lisa/results_latest\n"
]
}
],
"source": [
"# Initialize a test environment using:\n",
"te = TestEnv(my_conf, wipe=False, force_new=True)\n",
"target = te.target"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Workload Configuration and Execution\n",
"\n",
"Detailed information on RTApp can be found in **examples/wlgen/rtapp_example.ipynb**."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [],
"source": [
"def experiment(te):\n",
"\n",
" # Create and RTApp RAMP task\n",
" rtapp = RTA(te.target, 'ramp', calibration=te.calibration())\n",
" rtapp.conf(kind='profile',\n",
" params={\n",
" 'ramp' : Ramp(\n",
" start_pct = 60,\n",
" end_pct = 20,\n",
" delta_pct = 5,\n",
" time_s = 0.5).get()\n",
" })\n",
"\n",
" # FTrace the execution of this workload\n",
" te.ftrace.start()\n",
" rtapp.run(out_dir=te.res_dir)\n",
" te.ftrace.stop()\n",
"\n",
" # Collect and keep track of the trace\n",
" trace_file = os.path.join(te.res_dir, 'trace.dat')\n",
" te.ftrace.get_trace(trace_file)\n",
" \n",
" # Collect and keep track of the Kernel Functions performance data\n",
" stats_file = os.path.join(te.res_dir, 'trace.stats')\n",
" te.ftrace.get_stats(stats_file)\n",
"\n",
" # Dump platform descriptor\n",
" te.platform_dump(te.res_dir)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2017-02-17 19:51:44,484 INFO : Workload : Setup new workload ramp\n",
"2017-02-17 19:51:44,798 INFO : Workload : Workload duration defined by longest task\n",
"2017-02-17 19:51:44,800 INFO : Workload : Default policy: SCHED_OTHER\n",
"2017-02-17 19:51:44,801 INFO : Workload : ------------------------\n",
"2017-02-17 19:51:44,803 INFO : Workload : task [ramp], sched: using default policy\n",
"2017-02-17 19:51:44,804 INFO : Workload : | calibration CPU: 1\n",
"2017-02-17 19:51:44,806 INFO : Workload : | loops count: 1\n",
"2017-02-17 19:51:44,808 INFO : Workload : + phase_000001: duration 0.500000 [s] (5 loops)\n",
"2017-02-17 19:51:44,809 INFO : Workload : | period 100000 [us], duty_cycle 60 %\n",
"2017-02-17 19:51:44,811 INFO : Workload : | run_time 60000 [us], sleep_time 40000 [us]\n",
"2017-02-17 19:51:44,812 INFO : Workload : + phase_000002: duration 0.500000 [s] (5 loops)\n",
"2017-02-17 19:51:44,813 INFO : Workload : | period 100000 [us], duty_cycle 55 %\n",
"2017-02-17 19:51:44,815 INFO : Workload : | run_time 55000 [us], sleep_time 45000 [us]\n",
"2017-02-17 19:51:44,816 INFO : Workload : + phase_000003: duration 0.500000 [s] (5 loops)\n",
"2017-02-17 19:51:44,817 INFO : Workload : | period 100000 [us], duty_cycle 50 %\n",
"2017-02-17 19:51:44,818 INFO : Workload : | run_time 50000 [us], sleep_time 50000 [us]\n",
"2017-02-17 19:51:44,820 INFO : Workload : + phase_000004: duration 0.500000 [s] (5 loops)\n",
"2017-02-17 19:51:44,821 INFO : Workload : | period 100000 [us], duty_cycle 45 %\n",
"2017-02-17 19:51:44,822 INFO : Workload : | run_time 45000 [us], sleep_time 55000 [us]\n",
"2017-02-17 19:51:44,823 INFO : Workload : + phase_000005: duration 0.500000 [s] (5 loops)\n",
"2017-02-17 19:51:44,824 INFO : Workload : | period 100000 [us], duty_cycle 40 %\n",
"2017-02-17 19:51:44,826 INFO : Workload : | run_time 40000 [us], sleep_time 60000 [us]\n",
"2017-02-17 19:51:44,827 INFO : Workload : + phase_000006: duration 0.500000 [s] (5 loops)\n",
"2017-02-17 19:51:44,828 INFO : Workload : | period 100000 [us], duty_cycle 35 %\n",
"2017-02-17 19:51:44,829 INFO : Workload : | run_time 35000 [us], sleep_time 65000 [us]\n",
"2017-02-17 19:51:44,830 INFO : Workload : + phase_000007: duration 0.500000 [s] (5 loops)\n",
"2017-02-17 19:51:44,831 INFO : Workload : | period 100000 [us], duty_cycle 30 %\n",
"2017-02-17 19:51:44,832 INFO : Workload : | run_time 30000 [us], sleep_time 70000 [us]\n",
"2017-02-17 19:51:44,833 INFO : Workload : + phase_000008: duration 0.500000 [s] (5 loops)\n",
"2017-02-17 19:51:44,834 INFO : Workload : | period 100000 [us], duty_cycle 25 %\n",
"2017-02-17 19:51:44,836 INFO : Workload : | run_time 25000 [us], sleep_time 75000 [us]\n",
"2017-02-17 19:51:44,837 INFO : Workload : + phase_000009: duration 0.500000 [s] (5 loops)\n",
"2017-02-17 19:51:44,838 INFO : Workload : | period 100000 [us], duty_cycle 20 %\n",
"2017-02-17 19:51:44,839 INFO : Workload : | run_time 20000 [us], sleep_time 80000 [us]\n",
"2017-02-17 19:51:50,397 INFO : Workload : Workload execution START:\n",
"2017-02-17 19:51:50,399 INFO : Workload : /root/devlib-target/bin/rt-app /root/devlib-target/ramp_00.json 2>&1\n"
]
}
],
"source": [
"experiment(te)"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Parse Trace and Profiling Data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"marked": false,
"read_only": false
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2017-02-17 19:51:59,998 INFO : root : Content of the output folder /data/Code/lisa/results/TraceAnalysis_TaskLatencies\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[01;34m/data/Code/lisa/results/TraceAnalysis_TaskLatencies\u001b[00m\r\n",
"├── output.log\r\n",
"├── platform.json\r\n",
"├── ramp_00.json\r\n",
"├── rt-app-ramp-0.log\r\n",
"├── \u001b[01;35mtask_activations_5019_5019__ramp,_rt-app.png\u001b[00m\r\n",
"├── \u001b[01;35mtask_activations_5083_5083__ramp,_rt-app.png\u001b[00m\r\n",
"├── \u001b[01;35mtask_latencies_5019_5019__ramp,_rt-app.png\u001b[00m\r\n",
"├── \u001b[01;35mtask_latencies_5083_5083__ramp,_rt-app.png\u001b[00m\r\n",
"├── \u001b[01;35mtask_runtimes_5019_5019__ramp,_rt-app.png\u001b[00m\r\n",
"├── \u001b[01;35mtask_runtimes_5083_5083__ramp,_rt-app.png\u001b[00m\r\n",
"├── trace.dat\r\n",
"├── trace.raw.txt\r\n",
"└── trace.txt\r\n",
"\r\n",
"0 directories, 13 files\r\n"
]
}
],
"source": [
"# Base folder where tests folder are located\n",
"res_dir = te.res_dir\n",
"logging.info('Content of the output folder %s', res_dir)\n",
"!tree {res_dir}"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"marked": false,
"read_only": false
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2017-02-17 19:52:00,120 INFO : root : LITTLE cluster max capacity: 447\n"
]
}
],
"source": [
"with open(os.path.join(res_dir, 'platform.json'), 'r') as fh:\n",
" platform = json.load(fh)\n",
"logging.info('LITTLE cluster max capacity: %d',\n",
" platform['nrg_model']['little']['cpu']['cap_max'])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"marked": false,
"read_only": false
}
},
"outputs": [],
"source": [
"trace_file = os.path.join(res_dir, 'trace.dat')\n",
"trace = Trace(trace_file, my_conf['ftrace']['events'], platform)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Trace visualization"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"marked": false,
"read_only": false
}
},
"outputs": [
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7.4989560000249185], [8.0107010000501759, 8.0107280000229366], [8.1210070000379346, 8.121065000013914], [8.1214390000095591, 8.1214850000105798], [8.1216080000158399, 8.1216290000011213], [8.121822000015527, 8.121859000006225], [8.1220880000037141, 8.1220970000140369], [8.1222710000001825, 8.1223120000213385]], \"2\": [[0.96781000000191852, 0.96783100004540756], [1.0112800000351854, 1.011346000013873], [1.6119040000485256, 1.6119780000299215], [1.6121170000405982, 1.6121700000367127], [1.6122409999952652, 1.6122520000208169], [1.6123179999995045, 1.6123280000174418], [1.6123930000467226, 1.6124040000140667], [1.6124670000281185, 1.6124770000460558], [1.6125400000018999, 1.6125500000198372], [1.6126550000044517, 1.6126660000300035], [1.6127260000212118, 1.6127370000467636], [1.6129240000154823, 1.6129360000486486], [1.6130840000114404, 1.6131920000188984], [1.7696520000463352, 1.7697210000478663], [1.7697970000444911, 1.7698490000329912], [1.7699200000497513, 1.7699310000170954], [1.769996999995783, 1.7700080000213347], [1.7700710000353865, 1.7700820000027306], [1.7701460000243969, 1.7701560000423342], [1.7702189999981783, 1.7702310000313446], [1.7703259999980219, 1.7703380000311881], [1.7704080000403337, 1.7704180000000633], [1.7704820000217296, 1.7704920000396669], [1.7705580000183545, 1.7705680000362918], [1.7706330000073649, 1.7706430000253022], [1.7707070000469685, 1.7707180000143126], [1.7707810000283644, 1.7707910000463016], [1.7708550000097603, 1.7708650000276975], [1.7709290000493638, 1.7709400000167079], [1.7710030000307597, 1.771013000048697], [1.7710770000121556, 1.7710870000300929], [1.7711510000517592, 1.7711600000038743], [1.7712240000255406, 1.7712340000434779], [1.7712980000069365, 1.7713090000324883], [1.7713740000035614, 1.7713840000214987], [1.7714490000507794, 1.7714580000028946], [1.7715220000245608, 1.7715330000501126], [1.7715980000211857, 1.7716070000315085], [1.7716709999949671, 1.7716810000129044], [1.7717460000421852, 1.7717560000019148], [1.7718210000311956, 1.7718300000415184], [1.7718950000125915, 1.7719050000305288], [1.771969000052195, 1.7719800000195391], [1.7720440000412054, 1.7720530000515282], [1.7721180000226013, 1.7721280000405386], [1.7721930000116117, 1.772203000029549], [1.7722680000006221, 1.7722770000109449], [1.7723420000402257, 1.7723530000075698], [1.7724190000444651, 1.7724290000041947], [1.7724940000334755, 1.7725030000437982], [1.7725710000377148, 1.7725809999974445], [1.7726470000343397, 1.7726580000016838], [1.773161000048276, 1.7731690000509843], [6.667105000000447, 6.6671700000297278], [6.710136000008788, 6.7102080000331625], [7.3156290000188164, 7.3157080000382848], [7.3158200000179932, 7.315879000001587], [7.315954000048805, 7.3160220000427216], [7.3162900000461377, 7.3163400000194088], [7.316493000020273, 7.3165420000441372], [7.3167350000003353, 7.3168000000296161], [7.316891000024043, 7.3169160000397824], [7.3169900000211783, 7.3170140000293031], [7.31708599999547, 7.3171100000035949], [7.3172970000305213, 7.3173220000462607], [7.3174770000041462, 7.3175020000198856], [7.4718320000101812, 7.471839000005275], [7.4723130000056699, 7.4724170000408776], [7.4731960000353865, 7.4732150000054389]], \"3\": [[0.96442700002808124, 0.96461000002454966]], \"5\": [[7.4727850000490434, 7.4729600000428036]]}}});\n",
" }); /* TRAPPY_PUBLISH_REMOVE_LINE */\n",
" </script>\n",
" </div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"trappy.plotter.plot_trace(trace.ftrace)"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"# Latency Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Latency DataFrames"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" DataFrame of task's wakeup/suspend events\n",
"\n",
" The returned DataFrame has these columns\n",
" - Time: the time an event related to this task happened\n",
" - target_cpu: the CPU where the task has been scheduled\n",
" reported only for wakeup events\n",
" - curr_state: the current task state:\n",
" A letter which corresponds to the standard events reported by the\n",
" prev_state field of a sched_switch event.\n",
" Only exception is 'A', which is used to represent active tasks,\n",
" i.e. tasks RUNNING on a CPU\n",
" - next_state: the next status for the task\n",
" - t_start: the time when the current status started, it matches Time\n",
" - t_delta: the interval of time after witch the task will switch to the\n",
" next_state\n",
"\n",
" :param task: the task to report wakeup latencies for\n",
" :type task: int or str\n",
" \n"
]
}
],
"source": [
"print trace.data_frame.latency_df.__doc__"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>target_cpu</th>\n",
" <th>__cpu</th>\n",
" <th>curr_state</th>\n",
" <th>next_state</th>\n",
" <th>t_start</th>\n",
" <th>t_delta</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Time</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1.778588</th>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>A</td>\n",
" <td>R</td>\n",
" <td>1.778588</td>\n",
" <td>0.000433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1.779021</th>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>R</td>\n",
" <td>A</td>\n",
" <td>1.779021</td>\n",
" <td>0.000135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1.779156</th>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>A</td>\n",
" <td>R</td>\n",
" <td>1.779156</td>\n",
" <td>0.715133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.494289</th>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>R</td>\n",
" <td>A</td>\n",
" <td>2.494289</td>\n",
" <td>0.000040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.494329</th>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>A</td>\n",
" <td>R</td>\n",
" <td>2.494329</td>\n",
" <td>0.001559</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" target_cpu __cpu curr_state next_state t_start t_delta\n",
"Time \n",
"1.778588 NaN 1.0 A R 1.778588 0.000433\n",
"1.779021 NaN 1.0 R A 1.779021 0.000135\n",
"1.779156 NaN 1.0 A R 1.779156 0.715133\n",
"2.494289 NaN 1.0 R A 2.494289 0.000040\n",
"2.494329 NaN 1.0 A R 2.494329 0.001559"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Report full set of task status informations available from the trace\n",
"trace.data_frame.latency_df('ramp').head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"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>__comm</th>\n",
" <th>__cpu</th>\n",
" <th>__pid</th>\n",
" <th>next_comm</th>\n",
" <th>next_pid</th>\n",
" <th>next_prio</th>\n",
" <th>prev_comm</th>\n",
" <th>prev_pid</th>\n",
" <th>prev_prio</th>\n",
" <th>prev_state</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Time</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>0.000013</th>\n",
" <td>trace-cmd</td>\n",
" <td>1</td>\n",
" <td>5137</td>\n",
" <td>sudo</td>\n",
" <td>5136</td>\n",
" <td>120</td>\n",
" <td>trace-cmd</td>\n",
" <td>5137</td>\n",
" <td>120</td>\n",
" <td>64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.001866</th>\n",
" <td>sudo</td>\n",
" <td>1</td>\n",
" <td>5136</td>\n",
" <td>swapper/1</td>\n",
" <td>0</td>\n",
" <td>120</td>\n",
" <td>sudo</td>\n",
" <td>5136</td>\n",
" <td>120</td>\n",
" <td>64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.001998</th>\n",
" <td><idle></td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>sh</td>\n",
" <td>5096</td>\n",
" <td>120</td>\n",
" <td>swapper/0</td>\n",
" <td>0</td>\n",
" <td>120</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.002457</th>\n",
" <td>sh</td>\n",
" <td>0</td>\n",
" <td>5096</td>\n",
" <td>kworker/u12:1</td>\n",
" <td>4563</td>\n",
" <td>120</td>\n",
" <td>sh</td>\n",
" <td>5096</td>\n",
" <td>120</td>\n",
" <td>4096</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0.002526</th>\n",
" <td>kworker/u12:1</td>\n",
" <td>0</td>\n",
" <td>4563</td>\n",
" <td>sshd</td>\n",
" <td>4428</td>\n",
" <td>120</td>\n",
" <td>kworker/u12:1</td>\n",
" <td>4563</td>\n",
" <td>120</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" __comm __cpu __pid next_comm next_pid next_prio \\\n",
"Time \n",
"0.000013 trace-cmd 1 5137 sudo 5136 120 \n",
"0.001866 sudo 1 5136 swapper/1 0 120 \n",
"0.001998 <idle> 0 0 sh 5096 120 \n",
"0.002457 sh 0 5096 kworker/u12:1 4563 120 \n",
"0.002526 kworker/u12:1 0 4563 sshd 4428 120 \n",
"\n",
" prev_comm prev_pid prev_prio prev_state \n",
"Time \n",
"0.000013 trace-cmd 5137 120 64 \n",
"0.001866 sudo 5136 120 64 \n",
"0.001998 swapper/0 0 120 0 \n",
"0.002457 sh 5096 120 4096 \n",
"0.002526 kworker/u12:1 4563 120 1 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Report information on sched_switch events\n",
"df = trace.data_frame.trace_event('sched_switch')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" DataFrame of task's wakeup latencies\n",
"\n",
" The returned DataFrame has these columns:\n",
" - Time: the time the task wakeups\n",
" - wakeup_latency: the time the task waited before getting a CPU\n",
"\n",
" :param task: the task to report wakeup latencies for\n",
" :type task: int or str\n",
" \n"
]
}
],
"source": [
"print trace.data_frame.latency_wakeup_df.__doc__"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"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>wakeup_latency</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Time</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2.578911</th>\n",
" <td>0.000244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.678908</th>\n",
" <td>0.000022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.778907</th>\n",
" <td>0.000020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.878907</th>\n",
" <td>0.000021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.978903</th>\n",
" <td>0.000021</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" wakeup_latency\n",
"Time \n",
"2.578911 0.000244\n",
"2.678908 0.000022\n",
"2.778907 0.000020\n",
"2.878907 0.000021\n",
"2.978903 0.000021"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Report WAKEUP events and their duration\n",
"trace.data_frame.latency_wakeup_df('ramp').head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" DataFrame of task's preemption latencies\n",
"\n",
" The returned DataFrame has these columns:\n",
" - Time: the time the has been preempted\n",
" - preemption_latency: the time the task waited before getting again a CPU\n",
"\n",
" :param task: the task to report wakeup latencies for\n",
" :type task: int or str\n",
" \n"
]
}
],
"source": [
"print trace.data_frame.latency_preemption_df.__doc__"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"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>preempt_latency</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Time</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1.779021</th>\n",
" <td>0.000135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.494289</th>\n",
" <td>0.000040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.495888</th>\n",
" <td>0.000014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.496282</th>\n",
" <td>0.000009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.506273</th>\n",
" <td>0.000014</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" preempt_latency\n",
"Time \n",
"1.779021 0.000135\n",
"2.494289 0.000040\n",
"2.495888 0.000014\n",
"2.496282 0.000009\n",
"2.506273 0.000014"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Report PREEMPTION events and their duration\n",
"trace.data_frame.latency_preemption_df('ramp').head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Latency Plots"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Generate a set of plots to report the WAKEUP and PREEMPT latencies the\n",
" specified task has been subject to. A WAKEUP latencies is the time from\n",
" when a task becomes RUNNABLE till the first time it gets a CPU.\n",
" A PREEMPT latencies is the time from when a RUNNABLE task is suspended\n",
" because of the CPU is assigned to another task till when the task\n",
" enters the CPU again.\n",
"\n",
" :param task: the task to report latencies for\n",
" :type task: int or list(str)\n",
"\n",
" :param kind: the kind of latencies to report (WAKEUP and/or PREEMPT\")\n",
" :type kind: str\n",
"\n",
" :param tag: a string to add to the plot title\n",
" :type tag: str\n",
"\n",
" :param threshold_ms: the minimum acceptable [ms] value to report\n",
" graphically in the generated plots\n",
" :type threshold_ms: int or float\n",
" \n"
]
}
],
"source": [
"print trace.analysis.latency.plotLatency.__doc__"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2017-02-17 19:52:01,228 INFO : Analysis : Found: 38 WAKEUP latencies\n",
"2017-02-17 19:52:01,265 INFO : Analysis : Found: 14 PREEMPT latencies\n",
"2017-02-17 19:52:01,267 INFO : Analysis : Total: 52 latency events\n",
"2017-02-17 19:52:01,269 INFO : Analysis : 100.0 % samples below 1 [ms] threshold\n",
"2017-02-17 19:52:01,399 WARNING : Analysis : Event [sched_overutilized] not found, plot DISABLED!\n"
]
},
{
"data": {
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v4SEAAAAA0Et4CAAAAAD0Eh4CAAAAAL2EhwAAAABAL+EhAAAAANBLeAgAAAAA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\nF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"text/plain": [
"<matplotlib.figure.Figure at 0x7f0b10f87ad0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot latency events for a specified task\n",
"latency_stats_df = trace.analysis.latency.plotLatency('ramp')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>50%</th>\n",
" <th>95%</th>\n",
" <th>99%</th>\n",
" <th>max</th>\n",
" <th>100.0%</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>latency</th>\n",
" <td>52.0</td>\n",
" <td>0.000027</td>\n",
" <td>0.000035</td>\n",
" <td>0.000009</td>\n",
" <td>0.00002</td>\n",
" <td>0.000036</td>\n",
" <td>0.000188</td>\n",
" <td>0.000244</td>\n",
" <td>0.001</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 50% 95% 99% \\\n",
"latency 52.0 0.000027 0.000035 0.000009 0.00002 0.000036 0.000188 \n",
"\n",
" max 100.0% \n",
"latency 0.000244 0.001 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Plot statistics on task latencies\n",
"latency_stats_df.T"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Draw a plot that shows intervals of time when the execution of a\n",
" RUNNABLE task has been delayed. The plot reports:\n",
" WAKEUP lantecies as RED colored bands\n",
" PREEMPTION lantecies as BLUE colored bands\n",
"\n",
" The optional axes parameter allows to plot the signal on an existing\n",
" graph.\n",
"\n",
" :param task: the task to report latencies for\n",
" :type task: str\n",
"\n",
" :param axes: axes on which to plot the signal\n",
" :type axes: :mod:`matplotlib.axes.Axes`\n",
" \n"
]
}
],
"source": [
"print trace.analysis.latency.plotLatencyBands.__doc__"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f0ad5e955d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot latency events for a specified task\n",
"trace.analysis.latency.plotLatencyBands('ramp')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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SJEmSJEnSgCYnJxkbGwMYy8x5+9bAnomSJEmSJEmSajGZKEmSJEmSJKkWk4mS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\nSGpW53PE/8vM1dUXBksoSYo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"text/plain": [
"<matplotlib.figure.Figure at 0x7f0b20272e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Zoom into a spefific time frame\n",
"trace.setXTimeRange(4.28,4.29)\n",
"trace.analysis.latency.plotLatencyBands('ramp')"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"# Activations Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Activations DataFrames"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" DataFrame of task's wakeup intrvals\n",
"\n",
" The returned DataFrame has these columns:\n",
" - Time: the wakeup time for the task\n",
" - activation_interval: the time since the previous wakeup events\n",
"\n",
" :param task: the task to report runtimes for\n",
" :type task: int or str\n",
" \n"
]
}
],
"source": [
"print trace.data_frame.activations_df.__doc__"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false,
"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>activation_interval</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Time</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2.578911</th>\n",
" <td>0.099997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.678908</th>\n",
" <td>0.099999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.778907</th>\n",
" <td>0.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.878907</th>\n",
" <td>0.099996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.978903</th>\n",
" <td>0.100001</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" activation_interval\n",
"Time \n",
"2.578911 0.099997\n",
"2.678908 0.099999\n",
"2.778907 0.100000\n",
"2.878907 0.099996\n",
"2.978903 0.100001"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Report the sequence of activations intervals:\n",
"# Time: wakeup time\n",
"# activation_internal: time interval wrt previous wakeup\n",
"trace.data_frame.activations_df('ramp').head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Activations Plots"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Plots \"activation intervals\" for the specified task\n",
"\n",
" An \"activation interval\" is time incurring between two consecutive\n",
" wakeups of a task. A set of plots is generated to report:\n",
" - Activations interval at wakeup time: every time a task wakeups a\n",
" point is plotted to represent the time interval since the previous\n",
" wakeup.\n",
" - Activations interval cumulative function: reports the cumulative\n",
" function of the activation intervals.\n",
" - Activations interval histogram: reports a 64 bins histogram of\n",
" the activation iternals.\n",
"\n",
" All plots are parameterized based on the value of threshold_ms, which\n",
" can be used to filter activations intervals bigger than 2 times this\n",
" value.\n",
" Such a threshold is useful to filter out from the plots outliers thus\n",
" focusing the analysis in the most critical periodicity under analysis.\n",
" The number and percentage of discarded samples is reported in output.\n",
" A default threshold of 16 [ms] is used, which is useful for example\n",
" to analyze a 60Hz rendering pipelines.\n",
"\n",
" A PNG of the generated plots is generated and saved in the same folder\n",
" where the trace is.\n",
"\n",
" :param task: the task to report latencies for\n",
" :type task: int or list(str)\n",
"\n",
" :param tag: a string to add to the plot title\n",
" :type tag: str\n",
"\n",
" :param threshold_ms: the minimum acceptable [ms] value to report\n",
" graphically in the generated plots\n",
" :type threshold_ms: int or float\n",
"\n",
" :returns: a DataFrame with statistics on ploted activation intervals\n",
" \n"
]
}
],
"source": [
"print trace.analysis.latency.plotActivations.__doc__"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2017-02-17 19:52:03,201 INFO : Analysis : Found: 38 activations for [5144: ramp, rt-app]\n",
"2017-02-17 19:52:03,203 WARNING : Analysis : Discarding 1 activation intervals (above 2 x threshold_ms, 2.6% of the overall activations)\n",
"2017-02-17 19:52:03,205 INFO : Analysis : 100.0 % samples below 120 [ms] threshold\n",
"2017-02-17 19:52:03,258 WARNING : Analysis : Event [sched_overutilized] not found, plot DISABLED!\n"
]
},
{
"data": {
"image/png": 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PWm38O8q8HBjzEkcyP7GIY2vzPJDsjjIvn8+8xJHMTyzz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\nafcKgHEUAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0b118ef590>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot activation internvals for a specified task\n",
"activations_df = trace.analysis.latency.plotActivations('ramp', threshold_ms=120)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false,
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>50%</th>\n",
" <th>95%</th>\n",
" <th>99%</th>\n",
" <th>max</th>\n",
" <th>100.0%</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>activation_interval</th>\n",
" <td>37.0</td>\n",
" <td>0.100002</td>\n",
" <td>0.000012</td>\n",
" <td>0.099966</td>\n",
" <td>0.1</td>\n",
" <td>0.100028</td>\n",
" <td>0.100039</td>\n",
" <td>0.10004</td>\n",
" <td>0.12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 50% 95% \\\n",
"activation_interval 37.0 0.100002 0.000012 0.099966 0.1 0.100028 \n",
"\n",
" 99% max 100.0% \n",
"activation_interval 0.100039 0.10004 0.12 "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Plot statistics on task activation intervals\n",
"activations_df.T"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"# Runtimes Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Runtimes DataFrames"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" DataFrame of task's runtime each time the task blocks\n",
"\n",
" The returned DataFrame has these columns:\n",
" - Time: the time the task completed an activation (i.e. sleep or exit)\n",
" - running_time: the time the task spent RUNNING since its last wakeup\n",
"\n",
" :param task: the task to report runtimes for\n",
" :type task: int or str\n",
" \n"
]
}
],
"source": [
"print trace.data_frame.runtimes_df.__doc__"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false,
"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>running_time</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Time</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2.506287</th>\n",
" <td>0.790959</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.579155</th>\n",
" <td>0.059508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.678930</th>\n",
" <td>0.059534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.778927</th>\n",
" <td>0.054048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.898286</th>\n",
" <td>0.054230</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" running_time\n",
"Time \n",
"2.506287 0.790959\n",
"2.579155 0.059508\n",
"2.678930 0.059534\n",
"2.778927 0.054048\n",
"2.898286 0.054230"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Report the sequence of running times:\n",
"# Time: task block time (i.e. sleep or exit)\n",
"# running_time: cumulative ruinning times since last wakeup event\n",
"trace.data_frame.runtimes_df('ramp').head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"frozen": false,
"read_only": false
}
},
"source": [
"## Runtimes Plots"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Plots \"running times\" for the specified task\n",
"\n",
" A \"running time\" is the sum of all the time intervals a task executed\n",
" in between a wakeup and the next sleep (or exit).\n",
" A set of plots is generated to report:\n",
" - Running times at block time: every time a task blocks a\n",
" point is plotted to represent the cumulative time the task has be\n",
" running since its last wakeup\n",
" - Running time cumulative function: reports the cumulative\n",
" function of the running times.\n",
" - Running times histogram: reports a 64 bins histogram of\n",
" the running times.\n",
"\n",
" All plots are parameterized based on the value of threshold_ms, which\n",
" can be used to filter running times bigger than 2 times this value.\n",
" Such a threshold is useful to filter out from the plots outliers thus\n",
" focusing the analysis in the most critical periodicity under analysis.\n",
" The number and percentage of discarded samples is reported in output.\n",
" A default threshold of 16 [ms] is used, which is useful for example to\n",
" analyze a 60Hz rendering pipelines.\n",
"\n",
" A PNG of the generated plots is generated and saved in the same folder\n",
" where the trace is.\n",
"\n",
" :param task: the task to report latencies for\n",
" :type task: int or list(str)\n",
"\n",
" :param tag: a string to add to the plot title\n",
" :type tag: str\n",
"\n",
" :param threshold_ms: the minimum acceptable [ms] value to report\n",
" graphically in the generated plots\n",
" :type threshold_ms: int or float\n",
"\n",
" :returns: a DataFrame with statistics on ploted running times\n",
" \n"
]
}
],
"source": [
"print trace.analysis.latency.plotRuntimes.__doc__"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false,
"run_control": {
"frozen": false,
"read_only": false
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2017-02-17 19:52:04,119 INFO : Analysis : Found: 39 activations for [5144: ramp, rt-app]\n",
"2017-02-17 19:52:04,121 WARNING : Analysis : Discarding 1 running times (above 2 x threshold_ms, 2.6% of the overall activations)\n",
"2017-02-17 19:52:04,123 INFO : Analysis : 100.0 % samples below 120 [ms] threshold\n",
"2017-02-17 19:52:04,172 WARNING : Analysis : Event [sched_overutilized] not found, plot DISABLED!\n"
]
},
{
"data": {
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rLb9WOfXSfb0e8lEPeYD6KC/q5VyYdXY9ap0BMzMzMzPr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NUPA0RJytlDtxzN//7U77J4xQ7e/oWrWbyiq+iSJKmevBk4bYL2q4GfAwaIkiRJ05TbQ1Qi4oMR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\nJEmSJEmSNCkDREmSJEmSJEmT+j8ikTTvVpkXhgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0b10df61d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot activation internvals for a specified task\n",
"runtimes_df = trace.analysis.latency.plotRuntimes('ramp', threshold_ms=120)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false,
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>50%</th>\n",
" <th>95%</th>\n",
" <th>99%</th>\n",
" <th>max</th>\n",
" <th>100.0%</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>running_time</th>\n",
" <td>38.0</td>\n",
" <td>0.036271</td>\n",
" <td>0.012981</td>\n",
" <td>0.000277</td>\n",
" <td>0.0326</td>\n",
" <td>0.055088</td>\n",
" <td>0.059524</td>\n",
" <td>0.059534</td>\n",
" <td>0.12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 50% 95% 99% \\\n",
"running_time 38.0 0.036271 0.012981 0.000277 0.0326 0.055088 0.059524 \n",
"\n",
" max 100.0% \n",
"running_time 0.059534 0.12 "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Plot statistics on task running times\n",
"runtimes_df.T"
]
}
],
"metadata": {
"_draft": {
"nbviewer_url": "https://gist.github.com/ec38b4edb2da1ef21e2aa9b1d6c64f65"
},
"gist": {
"data": {
"description": "TraceAnalysis_TasksLatencies.ipynb",
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},
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| apache-2.0 |
Ledoux/ShareYourSystem | Pythonlogy/ShareYourSystem/Standards/Modelers/Inserter/Presentation.ipynb | 1 | 18039 | {
"nbformat": 3,
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"cells": [
{
"source": "\n<!--\nFrozenIsBool False\n-->\n\n#Inserter\n\n##Doc\n----\n\n\n> \n> Inserter instances can insert a RowedVariablesList into a table\n> checking maybe before if this line is new in the table or not\n> depending on identifying items.\n> \n> \n\n----\n\n<small>\nView the Inserter notebook on [NbViewer](http://nbviewer.ipython.org/url/shareyoursystem.ouvaton.org/Inserter.ipynb)\n</small>\n\n",
"cell_type": "markdown",
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{
"source": "\n<!--\nFrozenIsBool False\n-->\n\n##Code\n\n----\n\n<ClassDocStr>\n\n----\n\n```python\n# -*- coding: utf-8 -*-\n\"\"\"\n\n<DefineSource>\n@Date : Fri Nov 14 13:20:38 2014 \\n\n@Author : Erwan Ledoux \\n\\n\n</DefineSource>\n\n\nInserter instances can insert a RowedVariablesList into a table\nchecking maybe before if this line is new in the table or not\ndepending on identifying items.\n\n\"\"\"\n\n#<DefineAugmentation>\nimport ShareYourSystem as SYS\nimport collections\nBaseModuleStr=\"ShareYourSystem.Standards.Modelers.Rower\"\nDecorationModuleStr=\"ShareYourSystem.Standards.Classors.Classer\"\nSYS.setSubModule(globals())\n#</DefineAugmentation>\n\n#<ImportSpecificModules>\n#</ImportSpecificModules>\n\n#<DefineClass>\n@DecorationClass()\nclass InserterClass(\n\t\t\t\t\tBaseClass,\n\t\t\t\t):\n\t\n\t#Definition\n\tRepresentingKeyStrsList=[\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t'InsertedNotRowGetStrsList',\n\t\t\t\t\t\t\t\t\t'InsertedNotRowColumnStrsList',\n\t\t\t\t\t\t\t\t\t'InsertedNotRowPickOrderedDict',\n\t\t\t\t\t\t\t\t\t'InsertedIndexInt'\n\t\t\t\t\t\t\t\t]\n\n\tdef default_init(self,\n\t\t\t\t\t_InsertedNotRowGetStrsList=None,\n\t\t\t\t\t_InsertedNotRowColumnStrsList=None,\n\t\t\t\t\t_InsertedNotRowPickOrderedDict=None,\n\t\t\t\t\t_InsertedIndexInt=-1,\t\t\n\t\t\t\t\t**_KwargVariablesDict\n\t\t\t\t\t):\n\n\t\t#Call the parent init method\n\t\tBaseClass.__init__(self,**_KwargVariablesDict)\n\t\t\t\n\tdef setRowingKeyStrsList(self,_SettingValueVariable):\n\t\t\n\t\t#Hook\n\t\tBaseClass.setRowingKeyStrsList(self,_SettingValueVariable)\n\n\t\t#Bind \n\t\tself.InsertedNotRowGetStrsList=list(set(SYS.unzip(\n\t\t\tself.ModelingDescriptionTuplesList,[0]\n\t\t))-set(self.RowingKeyStrsList))\n\n\t\t#set\n\t\tself.InsertedNotRowColumnStrsList=map(\n\t\t\tlambda __NotRowGetStr:\n\t\t\tself.RowedGetStrToColumnStrOrderedDict[__NotRowGetStr],\n\t\t\tself.InsertedNotRowGetStrsList\n\t\t)\n\tRowingKeyStrsList=property(\n\t\t\t\t\t\t\t\t\tBaseClass.RowingKeyStrsList.fget,\n\t\t\t\t\t\t\t\t\tsetRowingKeyStrsList,\n\t\t\t\t\t\t\t\t\tBaseClass.RowingKeyStrsList.fdel,\n\t\t\t\t\t\t\t\t\tBaseClass.RowingKeyStrsList.__doc__\n\t\t\t\t\t\t\t\t)\n\n\t\n\tdef do_insert(self):\n\t\t\"\"\" \"\"\"\n\n\t\t#debug\n\t\t'''\n\t\tself.debug('row maybe before...')\n\t\t'''\n\n\t\t#<NotHook>\n\t\t#row first\n\t\tself.row()\n\t\t#</NotHook>\n\n\t\t#debug\n\t\t'''\n\t\tself.debug(\n\t\t\t\t\t('self.',self,['RowedIsBool'])\n\t\t\t\t)\n\t\tself.NodePointDeriveNoder.debug([\n\t\t\t\t('NOTE : ...ParentSpeaking...')\n\t\t\t])\n\t\t'''\n\n\t\t\"\"\"\n\t\tmap(\n\t\t\t\tlambda __InitVariable:\n\t\t\t\tsetattr(\n\t\t\t\t\tself,\n\t\t\t\t\t__KeyStr,\n\t\t\t\t\tSYS.getInitiatedVariableWithKeyStr(__KeyStr)\t\n\t\t\t\t) if __InitVariable==None else None,\n\t\t\t\tmap(\n\t\t\t\t\t\tlambda __KeyStr:\n\t\t\t\t\t\t(\n\t\t\t\t\t\t\t__KeyStr,\n\t\t\t\t\t\t\tgetattr(self,__KeyStr)\n\t\t\t\t\t\t),\n\t\t\t\t\t\t[\n\t\t\t\t\t\t\t'InsertedNotRowPickOrderedDict',\n\t\t\t\t\t\t\t'InsertedNotRowGetStrsList',\n\t\t\t\t\t\t\t'InsertedNotRowGetStrsList',\n\t\t\t\t\t\t\t'InsertedNotRowPickOrderedDict'\n\t\t\t\t\t\t]\n\t\t\t\t\t)\n\t\t\t)\n\t\t\"\"\"\n\n\t\t#debug\n\t\t'''\n\t\tself.debug(('self.',self,['InsertedNotRowPickOrderedDict']))\n\t\t'''\n\t\t\n\t\t#Append and row if it is new\n\t\tif self.RowedIsBool==False:\n\n\t\t\t#Check\n\t\t\tif self.TabledTable!=None:\n\n\t\t\t\t#debug\n\t\t\t\t'''\n\t\t\t\tself.debug('This is a new row')\n\t\t\t\t'''\n\n\t\t\t\t#Get the row\n\t\t\t\tRow=None\n\t\t\t\tRow=self.TabledTable.row\n\n\t\t\t\t#debug\n\t\t\t\t'''\n\t\t\t\tself.debug(('self.',self,['InsertedNotRowPickOrderedDict']))\n\t\t\t\t'''\n\t\t\t\t\n\t\t\t\t#Pick and update\t\t\t\t\n\t\t\t\tself.InsertedNotRowPickOrderedDict.update(\n\t\t\t\tzip(\n\t\t\t\t\tself.InsertedNotRowColumnStrsList,\n\t\t\t\t\tself.NodePointDeriveNoder.pick(\n\t\t\t\t\t\tself.InsertedNotRowGetStrsList\n\t\t\t\t\t\t)\n\t\t\t\t\t)\n\t\t\t\t)\n\n\t\t\t\t#debug\n\t\t\t\t'''\n\t\t\t\tself.debug(('self.',self,[\n\t\t\t\t\t\t\t\t\t\t\t'RowedPickOrderedDict',\n\t\t\t\t\t\t\t\t\t\t\t'InsertedNotRowPickOrderedDict'\n\t\t\t\t\t\t\t\t\t\t]))\n\t\t\t\t'''\n\n\t\t\t\t#Definition the InsertedItemTuplesList\n\t\t\t\tInsertedItemTuplesList=[\n\t\t\t\t\t\t\t\t\t\t('RowInt',self.RowedIndexInt)\n\t\t\t\t\t\t\t\t\t]+self.RowedPickOrderedDict.items(\n\t\t\t\t\t\t\t\t\t)+self.InsertedNotRowPickOrderedDict.items()\n\t\t\t\t\t\n\t\t\t\t#import tables\n\t\t\t\t#print(tables.tableextension.Row)\n\n\t\t\t\t#debug\n\t\t\t\t'''\n\t\t\t\tself.debug(\n\t\t\t\t\t\t\t[\n\t\t\t\t\t\t\t\t'This is a new row',\n\t\t\t\t\t\t\t\t'Colnames are : '+str(self.TabledTable.colnames),\n\t\t\t\t\t\t\t\t'InsertedItemTuplesList is '+str(InsertedItemTuplesList),\n\t\t\t\t\t\t\t\t'self.TabledTable is '+str(dir(self.TabledTable)),\n\t\t\t\t\t\t\t\t'self.ModeledDescriptionClass is '+(str(self.ModeledDescriptionClass.columns) if hasattr(self.ModeledDescriptionClass,'columns') else \"\"),\n\t\t\t\t\t\t\t\t'Row is '+str(dir(Row)),\n\t\t\t\t\t\t\t\t'Row.table is '+str(Row.table),\n\t\t\t\t\t\t\t\t'TabularedTablesOrderedDict is '+str(self.TabularedTablesOrderedDict)\n\t\t\t\t\t\t\t]\n\t\t\t\t\t\t)\n\t\t\t\t'''\n\n\t\t\t\t#set\n\t\t\t\tmap(\n\t\t\t\t\t\tlambda __InsertingTuple:\n\t\t\t\t\t\tRow.__setitem__(*__InsertingTuple),\n\t\t\t\t\t\tInsertedItemTuplesList\n\t\t\t\t\t)\n\n\t\t\t\t#debug\n\t\t\t\t'''\n\t\t\t\tself.debug('The Row setting was good, so append insert')\n\t\t\t\t'''\n\n\t\t\t\t#Append and Insert\n\t\t\t\tRow.append()\n\t\t\t\tself.TabledTable.insert()\n\t\t\t\t\n\t\telse:\n\n\t\t\t#debug\n\t\t\t'''\n\t\t\tself.debug(\n\t\t\t\t\t\t[\n\t\t\t\t\t\t\t'This is maybe not an IdentifyingInserter',\n\t\t\t\t\t\t\t'Or it is already rowed',\n\t\t\t\t\t\t\t'self.InsertedIsBoolsList is '+str(self.InsertedIsBoolsList)\n\t\t\t\t\t\t]\n\t\t\t\t\t)\n\t\t\t'''\n\t\t\tpass\n\t\t\t\n\t\t#<NotHook>\n\t\t#Return self\n\t\t#return self\n\t\t#</NotHook>\n\n#</DefineClass>\n\n```\n\n<small>\nView the Inserter sources on <a href=\"https://github.com/Ledoux/ShareYourSystem/tree/master/Pythonlogy/ShareYourSystem/Databasers/Inserter\" target=\"_blank\">Github</a>\n</small>\n\n",
"cell_type": "markdown",
"prompt_number": 1,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
}
},
{
"source": "\n<!---\nFrozenIsBool True\n-->\n\n##Example\n\nLet's create an empty class, which will automatically receive\nspecial attributes from the decorating ClassorClass,\nspecially the NameStr, that should be the ClassStr\nwithout the TypeStr in the end.",
"cell_type": "markdown",
"prompt_number": 2,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
}
},
{
"cell_type": "code",
"prompt_number": 3,
"language": "python",
"input": [
"\n",
"#ImportModules\n",
"import tables\n",
"import ShareYourSystem as SYS\n",
"from ShareYourSystem.Standards.Noders import Structurer\n",
"from ShareYourSystem.Standards.Modelers import Inserter\n",
"\n",
"#Definition of a Structurer instance with a noded datar\n",
"MyStructurer=Structurer.StructurerClass().collect(\n",
" \"Datome\",\n",
" \"Things\",\n",
" Inserter.InserterClass().update(\n",
" [\n",
" (\n",
" 'Attr_ModelingDescriptionTuplesList',\n",
" [\n",
" #GetStr #ColumnStr #Col\n",
" ('MyInt','MyInt',tables.Int64Col()),\n",
" ('MyStr','MyStr',tables.StringCol(10)),\n",
" ('MyIntsList','MyIntsList',(tables.Int64Col(shape=3)))\n",
" ]\n",
" ),\n",
" ('Attr_RowingKeyStrsList',['MyInt'])\n",
" ]\n",
" )\n",
")\n",
"\n",
"#Definition a structure with a db\n",
"MyStructurer.update(\n",
" [\n",
" ('MyInt',1),\n",
" ('MyStr',\"bonjour\"),\n",
" ('MyIntsList',[2,4,6])\n",
" ]\n",
").command(\n",
" _UpdateList=[('insert',{'LiargVariablesList':[]})],\n",
" **{'GatheringVariablesList':['<Datome>ThingsInserter']} \n",
").update(\n",
" [\n",
" ('MyInt',0),\n",
" ('MyStr',\"hello\"),\n",
" ('MyIntsList',[0,0,0])\n",
" ]\n",
").command(\n",
" _UpdateList=[('insert',{'LiargVariablesList':[]})], \n",
")\n",
"\n",
"#Definition the AttestedStr\n",
"SYS._attest(\n",
" [\n",
" 'MyStructurer is '+SYS._str(\n",
" MyStructurer,\n",
" **{\n",
" 'RepresentingBaseKeyStrsListBool':False,\n",
" 'RepresentingAlineaIsBool':False\n",
" }\n",
" ),\n",
" 'hdf5 file is : '+MyStructurer.hdfview().hdfclose().HdformatedConsoleStr\n",
" ]\n",
") \n",
"\n",
"#Print\n",
"\n",
"\n"
],
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"\n",
"*****Start of the Attest *****\n",
"\n",
"MyStructurer is < (StructurerClass), 4565259152>\n",
" /{ \n",
" / '<New><Instance>DatomeCollectionOrderedDict' : \n",
" / /{ \n",
" / / 'ThingsInserter' : < (InserterClass), 4565260880>\n",
" / / /{ \n",
" / / / '<New><Instance>IdInt' : 4565260880\n",
" / / / '<New><Instance>NewtorkAttentionStr' : \n",
" / / / '<New><Instance>NewtorkCatchStr' : \n",
" / / / '<New><Instance>NewtorkCollectionStr' : \n",
" / / / '<New><Instance>NodeCollectionStr' : Datome\n",
" / / / '<New><Instance>NodeIndexInt' : 0\n",
" / / / '<New><Instance>NodeKeyStr' : ThingsInserter\n",
" / / / '<New><Instance>NodePointDeriveNoder' : {...}< (StructurerClass), 4565259152>\n",
" / / / '<New><Instance>NodePointOrderedDict' : {...}< (OrderedDict), 4565728520>\n",
" / / / '<New><Instance>_ModelingDescriptionTuplesList' : \n",
" / / / /[\n",
" / / / / 0 : \n",
" / / / / /(\n",
" / / / / / 0 : MyInt\n",
" / / / / / 1 : MyInt\n",
" / / / / / 2 : Int64Col(shape=(), dflt=0, pos=None)\n",
" / / / / /)\n",
" / / / / 1 : \n",
" / / / / /(\n",
" / / / / / 0 : MyStr\n",
" / / / / / 1 : MyStr\n",
" / / / / / 2 : StringCol(itemsize=10, shape=(), dflt='', pos=None)\n",
" / / / / /)\n",
" / / / / 2 : \n",
" / / / / /(\n",
" / / / / / 0 : MyIntsList\n",
" / / / / / 1 : MyIntsList\n",
" / / / / / 2 : Int64Col(shape=(3,), dflt=0, pos=None)\n",
" / / / / /)\n",
" / / / /]\n",
" / / / '<New><Instance>_RowingKeyStrsList' : ['MyInt']\n",
" / / / '<Spe><Class>InsertedIndexInt' : -1\n",
" / / / '<Spe><Instance>InsertedNotRowColumnStrsList' : ['MyStr', 'MyIntsList']\n",
" / / / '<Spe><Instance>InsertedNotRowGetStrsList' : ['MyStr', 'MyIntsList']\n",
" / / / '<Spe><Instance>InsertedNotRowPickOrderedDict' : \n",
" / / / /{ \n",
" / / / /}\n",
" / / /}\n",
" / /}\n",
" / '<New><Instance>IdInt' : 4565259152\n",
" / '<New><Instance>MyInt' : 0\n",
" / '<New><Instance>MyIntsList' : [0, 0, 0]\n",
" / '<New><Instance>MyStr' : hello\n",
" / '<New><Instance>NewtorkAttentionStr' : \n",
" / '<New><Instance>NewtorkCatchStr' : \n",
" / '<New><Instance>NewtorkCollectionStr' : \n",
" / '<New><Instance>NodeCollectionStr' : Globals\n",
" / '<New><Instance>NodeIndexInt' : -1\n",
" / '<New><Instance>NodeKeyStr' : TopStructurer\n",
" / '<New><Instance>NodePointDeriveNoder' : None\n",
" / '<New><Instance>NodePointOrderedDict' : None\n",
" / '<Spe><Class>StructuringBeforeUpdateList' : None\n",
" / '<Spe><Class>StructuringNodeCollectionStrsList' : []\n",
" /}\n",
"\n",
"------\n",
"\n",
"hdf5 file is : / Group\n",
"/TopStructurer Group\n",
"/TopStructurer/FirstChildStructurer Group\n",
"/TopStructurer/FirstChildStructurer/GrandChildStructurer Group\n",
"/TopStructurer/SecondChildStructurer Group\n",
"/TopStructurer/SecondChildStructurer/OtherGrandChildStructurer Group\n",
"/xx0xxThingsFindoerTable Dataset {3/Inf}\n",
" Data:\n",
" (0) {RowInt=0, MyInt=1, MyIntsList=[0,0,1], MyStr=\"bonjour\"},\n",
" (1) {RowInt=1, MyInt=0, MyIntsList=[0,0,1], MyStr=\"guten tag\"},\n",
" (2) {RowInt=2, MyInt=1, MyIntsList=[0,0,0], MyStr=\"bonjour\"}\n",
"/xx0xxThingsInserterTable Dataset {2/Inf}\n",
" Data:\n",
" (0) {RowInt=0, MyInt=1, MyIntsList=[2,4,6], MyStr=\"bonjour\"},\n",
" (1) {RowInt=1, MyInt=0, MyIntsList=[0,0,0], MyStr=\"hello\"}\n",
"/xx0xxThingsRecovererTable Dataset {3/Inf}\n",
" Data:\n",
" (0) {RowInt=0, MyInt=1, MyIntsList=[0,0,1], MyStr=\"bonjour\"},\n",
" (1) {RowInt=1, MyInt=0, MyIntsList=[0,0,1], MyStr=\"guten tag\"},\n",
" (2) {RowInt=2, MyInt=1, MyIntsList=[0,0,0], MyStr=\"bonjour\"}\n",
"/xx0xxThingsRetrieverTable Dataset {2/Inf}\n",
" Data:\n",
" (0) {RowInt=0, MyInt=1, MyIntsList=[2,4,6], MyStr=\"bonjour\"},\n",
" (1) {RowInt=1, MyInt=0, MyIntsList=[0,0,0], MyStr=\"guten tag\"}\n",
"/xx0xxThingsRowerTable Dataset {0/Inf}\n",
" Data:\n",
"\n",
"/xx0xxThingsTablerTable Dataset {0/Inf}\n",
" Data:\n",
"\n",
"/xx0xx__UnitsInt_3__ThingsMergerTable Dataset {2/Inf}\n",
" Data:\n",
" (0) {RowInt=0, MyInt=0, MyIntsList=[0,0,1], MyStr=\"hello\"},\n",
" (1) {RowInt=1, MyInt=1, MyIntsList=[0,0,1], MyStr=\"bonjour\"}\n",
"/xx0xx__UnitsInt_3__ThingsShaperTable Dataset {2/Inf}\n",
" Data:\n",
" (0) {RowInt=0, MyInt=0, MyIntsList=[0,0,1], MyStr=\"hello\"},\n",
" (1) {RowInt=1, MyInt=1, MyIntsList=[0,0,1], MyStr=\"bonjour\"}\n",
"/xx1xx__UnitsInt_2__ThingsMergerTable Dataset {1/Inf}\n",
" Data:\n",
" (0) {RowInt=0, MyInt=0, MyIntsList=[0,0], MyStr=\"\"}\n",
"/xx1xx__UnitsInt_2__ThingsShaperTable Dataset {1/Inf}\n",
" Data:\n",
" (0) {RowInt=0, MyInt=0, MyIntsList=[0,0], MyStr=\"\"}\n",
"\n",
"\n",
"*****End of the Attest *****\n",
"\n",
"\n"
]
}
],
"collapsed": false,
"metadata": {
"slideshow": {
"slide_type": "-"
}
}
}
]
}
],
"metadata": {
"name": "",
"signature": ""
},
"nbformat_minor": 0
} | mit |
decisionstats/pythonfordatascience | python+with+postgres.ipynb | 1 | 11825 | {
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import psycopg2\n",
"import pandas as pd\n",
"import sqlalchemy as sa\n",
"import time\n",
"import seaborn as sns\n",
"import re"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: psycopg2 in c:\\users\\dell\\anaconda3\\lib\\site-packages\n"
]
}
],
"source": [
"! pip install psycopg2"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"parameters = { \n",
" 'username': 'postgres', \n",
" 'password': 'root',\n",
" 'server': 'localhost',\n",
" 'database': 'ajay'\n",
" }\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"connection= 'postgresql://{username}:{password}@{server}:5432/{database}'.format(**parameters)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"postgresql://postgres:root@localhost:5432/ajay\n"
]
}
],
"source": [
"print (connection)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"engine = sa.create_engine(connection, encoding=\"utf-8\")\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"insp = sa.inspect(engine)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['information_schema', 'public']\n"
]
}
],
"source": [
"db_list = insp.get_schema_names()\n",
"print(db_list)\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['sales', 'iris']"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"engine.table_names()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data3= pd.read_sql_query('select * from \"sales\" limit 10',con=engine)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 10 entries, 0 to 9\n",
"Data columns (total 4 columns):\n",
"customer_id 10 non-null int64\n",
"sales 10 non-null int64\n",
"date 10 non-null object\n",
"product_id 10 non-null int64\n",
"dtypes: int64(3), object(1)\n",
"memory usage: 400.0+ bytes\n"
]
}
],
"source": [
"data3.info()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"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>customer_id</th>\n",
" <th>sales</th>\n",
" <th>date</th>\n",
" <th>product_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10001</td>\n",
" <td>5230</td>\n",
" <td>2017-02-07</td>\n",
" <td>524</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10002</td>\n",
" <td>2781</td>\n",
" <td>2017-05-12</td>\n",
" <td>469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10003</td>\n",
" <td>2083</td>\n",
" <td>2016-12-18</td>\n",
" <td>917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>10004</td>\n",
" <td>214</td>\n",
" <td>2015-01-19</td>\n",
" <td>354</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>10005</td>\n",
" <td>9407</td>\n",
" <td>2016-09-26</td>\n",
" <td>292</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>10006</td>\n",
" <td>4705</td>\n",
" <td>2015-10-17</td>\n",
" <td>380</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>10007</td>\n",
" <td>4729</td>\n",
" <td>2016-01-02</td>\n",
" <td>469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>10008</td>\n",
" <td>7715</td>\n",
" <td>2015-09-12</td>\n",
" <td>480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>10009</td>\n",
" <td>9898</td>\n",
" <td>2015-04-05</td>\n",
" <td>611</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10010</td>\n",
" <td>5797</td>\n",
" <td>2015-08-13</td>\n",
" <td>959</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" customer_id sales date product_id\n",
"0 10001 5230 2017-02-07 524\n",
"1 10002 2781 2017-05-12 469\n",
"2 10003 2083 2016-12-18 917\n",
"3 10004 214 2015-01-19 354\n",
"4 10005 9407 2016-09-26 292\n",
"5 10006 4705 2015-10-17 380\n",
"6 10007 4729 2016-01-02 469\n",
"7 10008 7715 2015-09-12 480\n",
"8 10009 9898 2015-04-05 611\n",
"9 10010 5797 2015-08-13 959"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data3"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data5= pd.read_sql_query('select * from \"iris\" limit 10',con=engine)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"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>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" <th>species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5.4</td>\n",
" <td>3.9</td>\n",
" <td>1.7</td>\n",
" <td>0.4</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>4.6</td>\n",
" <td>3.4</td>\n",
" <td>1.4</td>\n",
" <td>0.3</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>5.0</td>\n",
" <td>3.4</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>4.4</td>\n",
" <td>2.9</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4.9</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.1</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal_length sepal_width petal_length petal_width species\n",
"0 5.1 3.5 1.4 0.2 setosa\n",
"1 4.9 3.0 1.4 0.2 setosa\n",
"2 4.7 3.2 1.3 0.2 setosa\n",
"3 4.6 3.1 1.5 0.2 setosa\n",
"4 5.0 3.6 1.4 0.2 setosa\n",
"5 5.4 3.9 1.7 0.4 setosa\n",
"6 4.6 3.4 1.4 0.3 setosa\n",
"7 5.0 3.4 1.5 0.2 setosa\n",
"8 4.4 2.9 1.4 0.2 setosa\n",
"9 4.9 3.1 1.5 0.1 setosa"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data5"
]
},
{
"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": 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
}
| apache-2.0 |
GutenkunstLab/SloppyCell | SloppyCell/AST generic visit.ipynb | 1 | 3596 | {
"cells": [
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"from ast import *"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"x = \"(4)/3/7\""
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"def strip_parse(x):\n",
" return parse(x)\n",
"nums = []\n",
"denoms = []\n",
"class PowForDoubleStar(NodeVisitor):\n",
" def visit_BinOp(self, node):\n",
" node.left = self.visit(node.left)\n",
" node.right = self.visit(node.right)\n",
" print(node.op)\n",
" if not (isinstance(node.op, Mult) or isinstance(node.op, Div)):\n",
" nums.append(node.value)\n",
" \n",
"# if isinstance(node.left, Div) or isinstance(node.left, Mult):\n",
"# print(\"entered here\")\n",
"# else:\n",
"# nums.append(node.left)\n",
"# if isinstance(node.right, Div) or isinstance(node.right, Mult):\n",
"# if isinstance(node, Mult):\n",
"# _collect_num_denom(node.right, nums, denoms)\n",
"# elif isinstance(node, Div):\n",
"# _collect_num_denom(node.right, denoms, nums)\n",
"# else:\n",
"# if isinstance(node, Mult):\n",
"# nums.append(node.right)\n",
"# elif isinstance(node, Div):\n",
"# denoms.append(node.right)\n",
" self.generic_visit(node)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Module(body=[Expr(value=BinOp(left=BinOp(left=Num(n=4), op=Div(), right=Num(n=3)), op=Div(), right=Num(n=7)))])\n",
"<_ast.Div object at 0x7efdb6582190>\n",
"<_ast.Div object at 0x7efdb6582190>\n",
"[]\n",
"[]\n"
]
}
],
"source": [
"tree = strip_parse(x)\n",
"print(dump(tree))\n",
"PowForDoubleStar().visit(tree)\n",
"print(nums)\n",
"print(denoms)\n",
"if not isinstance(ast, BinOp):\n",
" nums.append(ast.value)\n",
" return\n",
" if (isinstance(ast.op, Div) or isinstance(ast.op, Mult)):\n",
" if isinstance(ast.left, BinOp):\n",
" _collect_num_denom(ast.left, nums, denoms)\n",
" else:\n",
" nums.append(ast.left.value)\n",
" \n",
" if isinstance(ast.right, BinOp):\n",
" if isinstance(ast.op, Mult):\n",
" _collect_num_denom(ast.right, nums, denoms)\n",
" elif isinstance(ast.op, Div):\n",
" _collect_num_denom(ast.right, denoms, nums)\n",
" else:\n",
" if isinstance(ast.op, Mult):\n",
" nums.append(ast.right.value)\n",
" elif isinstance(ast.op, Div):\n",
" denoms.append(ast.right.value)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.18"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-3-clause |
ryo8128/study_python | j01_ipython.ipynb | 1 | 9074 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# マジックコマンドとショートカットキーを身につける"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"shift+enterで実行し下のセルに移動"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"esc+bで下にセルを挿入。esc+aで上にセルを挿入。enterで編集モード。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"esc+mでMarkdownセルに変更"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"マジックコマンド一覧を確認"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/json": {
"cell": {
"!": "OSMagics",
"HTML": "Other",
"SVG": "Other",
"bash": "Other",
"capture": "ExecutionMagics",
"debug": "ExecutionMagics",
"file": "Other",
"html": "DisplayMagics",
"javascript": "DisplayMagics",
"js": "DisplayMagics",
"latex": "DisplayMagics",
"perl": "Other",
"prun": "ExecutionMagics",
"pypy": "Other",
"python": "Other",
"python2": "Other",
"python3": "Other",
"ruby": "Other",
"script": "ScriptMagics",
"sh": "Other",
"svg": "DisplayMagics",
"sx": "OSMagics",
"system": "OSMagics",
"time": "ExecutionMagics",
"timeit": "ExecutionMagics",
"writefile": "OSMagics"
},
"line": {
"alias": "OSMagics",
"alias_magic": "BasicMagics",
"autocall": "AutoMagics",
"automagic": "AutoMagics",
"autosave": "KernelMagics",
"bookmark": "OSMagics",
"cat": "Other",
"cd": "OSMagics",
"clear": "KernelMagics",
"colors": "BasicMagics",
"config": "ConfigMagics",
"connect_info": "KernelMagics",
"cp": "Other",
"debug": "ExecutionMagics",
"dhist": "OSMagics",
"dirs": "OSMagics",
"doctest_mode": "BasicMagics",
"ed": "Other",
"edit": "KernelMagics",
"env": "OSMagics",
"gui": "BasicMagics",
"hist": "Other",
"history": "HistoryMagics",
"killbgscripts": "ScriptMagics",
"ldir": "Other",
"less": "KernelMagics",
"lf": "Other",
"lk": "Other",
"ll": "Other",
"load": "CodeMagics",
"load_ext": "ExtensionMagics",
"loadpy": "CodeMagics",
"logoff": "LoggingMagics",
"logon": "LoggingMagics",
"logstart": "LoggingMagics",
"logstate": "LoggingMagics",
"logstop": "LoggingMagics",
"ls": "Other",
"lsmagic": "BasicMagics",
"lx": "Other",
"macro": "ExecutionMagics",
"magic": "BasicMagics",
"man": "KernelMagics",
"matplotlib": "PylabMagics",
"mkdir": "Other",
"more": "KernelMagics",
"mv": "Other",
"notebook": "BasicMagics",
"page": "BasicMagics",
"pastebin": "CodeMagics",
"pdb": "ExecutionMagics",
"pdef": "NamespaceMagics",
"pdoc": "NamespaceMagics",
"pfile": "NamespaceMagics",
"pinfo": "NamespaceMagics",
"pinfo2": "NamespaceMagics",
"popd": "OSMagics",
"pprint": "BasicMagics",
"precision": "BasicMagics",
"profile": "BasicMagics",
"prun": "ExecutionMagics",
"psearch": "NamespaceMagics",
"psource": "NamespaceMagics",
"pushd": "OSMagics",
"pwd": "OSMagics",
"pycat": "OSMagics",
"pylab": "PylabMagics",
"qtconsole": "KernelMagics",
"quickref": "BasicMagics",
"recall": "HistoryMagics",
"rehashx": "OSMagics",
"reload_ext": "ExtensionMagics",
"rep": "Other",
"rerun": "HistoryMagics",
"reset": "NamespaceMagics",
"reset_selective": "NamespaceMagics",
"rm": "Other",
"rmdir": "Other",
"run": "ExecutionMagics",
"save": "CodeMagics",
"sc": "OSMagics",
"set_env": "OSMagics",
"store": "StoreMagics",
"sx": "OSMagics",
"system": "OSMagics",
"tb": "ExecutionMagics",
"time": "ExecutionMagics",
"timeit": "ExecutionMagics",
"unalias": "OSMagics",
"unload_ext": "ExtensionMagics",
"who": "NamespaceMagics",
"who_ls": "NamespaceMagics",
"whos": "NamespaceMagics",
"xdel": "NamespaceMagics",
"xmode": "BasicMagics"
}
},
"text/plain": [
"Available line magics:\n",
"%alias %alias_magic %autocall %automagic %autosave %bookmark %cat %cd %clear %colors %config %connect_info %cp %debug %dhist %dirs %doctest_mode %ed %edit %env %gui %hist %history %killbgscripts %ldir %less %lf %lk %ll %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %lx %macro %magic %man %matplotlib %mkdir %more %mv %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %popd %pprint %precision %profile %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %rm %rmdir %run %save %sc %set_env %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n",
"\n",
"Available cell magics:\n",
"%%! %%HTML %%SVG %%bash %%capture %%debug %%file %%html %%javascript %%js %%latex %%perl %%prun %%pypy %%python %%python2 %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n",
"\n",
"Automagic is ON, % prefix IS NOT needed for line magics."
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%lsmagic"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"現在のディレクトリを確認"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'/Users/ry8128/workspace/study_python'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%pwd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"コマンドの説明を確認するには?をつける"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%pwd?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/javascript": [
"IPython.toolbar.add_button_group([\n",
"{\n",
" 'label':'renumber all code cells',\n",
" 'icon':'icon-list-ol',\n",
" 'callback':function() {\n",
" var cells = IPython.notbook.get_cells();\n",
" cells = cells.filter(function(c)\n",
" {\n",
" return c instanceof IPython.CodeCell;\n",
" })\n",
" for (var i = 0; i < cells.length; i++) {\n",
" cells[i].set_input_prompt(i+1);\n",
" }\n",
" }\n",
"}]);"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%javascript\n",
"IPython.toolbar.add_button_group([\n",
"{\n",
" 'label':'renumber all code cells',\n",
" 'icon':'icon-list-ol',\n",
" 'callback':function() {\n",
" var cells = IPython.notbook.get_cells();\n",
" cells = cells.filter(function(c)\n",
" {\n",
" return c instanceof IPython.CodeCell;\n",
" })\n",
" for (var i = 0; i < cells.length; i++) {\n",
" cells[i].set_input_prompt(i+1);\n",
" }\n",
" }\n",
"}]);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
danlewis85/pycno | Pycnophylactic Smoothing.ipynb | 1 | 129385 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Table of Contents\n",
"<p><div class=\"lev2 toc-item\"><a href=\"#Smooth-Pycnophylactic-Interpolation-using-GeoPandas-and-Rasterio\" data-toc-modified-id=\"Smooth-Pycnophylactic-Interpolation-using-GeoPandas-and-Rasterio\"><span class=\"toc-item-num\">0.1 </span>Smooth Pycnophylactic Interpolation using GeoPandas and Rasterio</a></div><div class=\"lev1 toc-item\"><a href=\"#Usage\" data-toc-modified-id=\"Usage-1\"><span class=\"toc-item-num\">1 </span>Usage</a></div><div class=\"lev1 toc-item\"><a href=\"#Reference\" data-toc-modified-id=\"Reference-3\"><span class=\"toc-item-num\">2 </span>Reference</a></div><div class=\"lev1 toc-item\"><a href=\"#Acknowledgement\" data-toc-modified-id=\"Acknowledgement-3\"><span class=\"toc-item-num\">3 </span>Acknowledgement</a></div><div class=\"lev1 toc-item\"><a href=\"#Example\" data-toc-modified-id=\"Example-3\"><span class=\"toc-item-num\">4 </span>Example</a></div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Smooth Pycnophylactic Interpolation using GeoPandas and Rasterio"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Usage\n",
"\n",
"The basic function pycno() accepts a GeoPandas GeoDataFrame, the column name of the field that you wish to smooth and the desired cell size of the output raster.\n",
"\n",
"The algorithm then iterative smooths the chosen data values, adjusting for non-negativity and preserving volume. The parameter r allows the adjustment of the realxation parameter, but in practice the default is fine. The converge parameter sets the stopping point, which is calculated as the initial maximum value * 10 raise to the negative of the converge parameter. Generally, 3 is a good default, but it may need adjustment for some applications, generally the larger the converge parameter, the more iterations are required and the longer the function takes to complete and return an output.\n",
"\n",
"The handle_nulls parameter is based on Tobler's discussion of boundary conditions. The basic implementation (handle_nulls = False) uses the standard numpy convolve function and sets nodata values to 0. Tobler calls this the Dirichlet condition, acknowledging that values other than 0 are possible dependent upon context, although not implemented here. The other option, Tobler calls the Neumann condition, in which the density gradient at edges is set to zero. This is not implemented here, instead the astropy convolve function is used in preference, which has much the same effect. The astropy convolve function replaces null values using the kernel as an interpolation function (handle_nulls = True), this is the default option.\n",
"\n",
"The function outputs a 2D numpy array, geotransformation information, and the coordinate reference system.\n",
"\n",
"As the main output is a numpy array, the matplotlib.pyplot function imshow() can be used to get a quick look at the output.\n",
"\n",
"The save_pycno() helper function takes the outputs from pycno() and saves the array as a Raster using Rasterio, which can then be used in a GIS etc.\n",
"\n",
"The extract_values() function allows for the estimation of value sums from the smooth pycnophylactic interpolation surface to a set of polygons.\n",
"\n",
"NB The field used for smoothing must be non-negative, it is also assumed that an equal-area projection is being used. The GeoDataFrame should be structured so that 1 row is 1 record. Multipolygons are fine, but if they are split into singleparts you will likely get an incorrect output.\n",
"\n",
"## Reference\n",
"\n",
"Tobler W. 1979. Smooth Pycnophylactic Interpolation for Geographical Regions. Journal of the American Statistical Association. 74: 367: 519-530.\n",
"\n",
"## Acknowledgement\n",
"\n",
"The code here is (for the most part) adapted from Chris Brunsdon's (https://github.com/chrisbrunsdon) R package 'pycno' - https://github.com/cran/pycno\n",
"\n",
"## Example"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Some required libraries\n",
"import geopandas as gpd\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# import the pycnophylactic smoothing function\n",
"from pycno.pycno import pycno, save_pycno, extract_values\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Get some data for London\n",
"# Read in London Boroughs geojson and project to British National Grid.\n",
"ldn_boro = gpd.read_file('data/LDN_Boro.geojson').to_crs(epsg=27700)\n",
"\n",
"# Get some population data for London Boroughs\n",
"ldn_pop = pd.read_excel('data/Pandas_Lon_Pop.xlsx')\n",
"\n",
"# Merge the population data with the geospatial data\n",
"ldn_boro = ldn_boro.merge(ldn_pop, how='left', left_on='GSS_CODE', right_on='New Code')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"pycno\\pycno.py:138: RuntimeWarning: invalid value encountered in less\n",
" value_array[value_array<0] = 0.0\n",
"pycno\\pycno.py:146: RuntimeWarning: invalid value encountered in absolute\n",
" if nanmax(absolute(old - value_array)) < stopper:\n"
]
}
],
"source": [
"# Create the smooth pycnophylactic surface\n",
"res, trans, crs = pycno(ldn_boro,2015,50,converge=4,verbose=False)\n",
"\n",
"# NB runtime warnings derive from np.nan not being handled by the < and abs operators. Nothing to worry about here.\n",
"\n",
"# Save the output as a raster\n",
"save_pycno(res,trans,crs,'london_pycno.tif')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-0.5, 1166.5, 900.5, -0.5)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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M+O3dDk006uKvqP+q9rypaQw35NKwCBkT6OwS0gKt16CbHwLqVeiOx2FWrgFUEW24yC6P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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x152c8cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, (ax1,ax2) = plt.subplots(1,2,figsize=(16,8))\n",
"\n",
"# ax1\n",
"ldn_boro['popdense2015'] = ldn_boro[2015]/(ldn_boro['geometry'].area * 1000000)\n",
"ldn_boro.plot(column = 'popdense2015',cmap = 'Reds', scheme = 'Quantiles', k=5,ax=ax1)\n",
"ax1.axis('equal')\n",
"\n",
"# ax2\n",
"ax2.imshow(res, cmap = 'Reds')\n",
"ax2.axis('equal')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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>GSS_CODE</th>\n",
" <th>HECTARES</th>\n",
" <th>NAME</th>\n",
" <th>NONLD_AREA</th>\n",
" <th>ONS_INNER</th>\n",
" <th>SUB_2006</th>\n",
" <th>SUB_2009</th>\n",
" <th>geometry</th>\n",
" <th>New Code</th>\n",
" <th>Area name</th>\n",
" <th>...</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>2014</th>\n",
" <th>2015</th>\n",
" <th>popdense2015</th>\n",
" <th>Estimate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>E09000021</td>\n",
" <td>3726.117</td>\n",
" <td>Kingston upon Thames</td>\n",
" <td>0.000</td>\n",
" <td>F</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>POLYGON ((516401.6008347637 160201.7994140856,...</td>\n",
" <td>E09000021</td>\n",
" <td>Kingston upon Thames</td>\n",
" <td>...</td>\n",
" <td>156000.0</td>\n",
" <td>157300</td>\n",
" <td>158600</td>\n",
" <td>160400</td>\n",
" <td>163900</td>\n",
" <td>166800</td>\n",
" <td>170000</td>\n",
" <td>173525</td>\n",
" <td>4.656992e-09</td>\n",
" <td>173525.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>E09000008</td>\n",
" <td>8649.441</td>\n",
" <td>Croydon</td>\n",
" <td>0.000</td>\n",
" <td>F</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>POLYGON ((535009.2008389848 159504.6994170326,...</td>\n",
" <td>E09000008</td>\n",
" <td>Croydon</td>\n",
" <td>...</td>\n",
" <td>349300.0</td>\n",
" <td>352800</td>\n",
" <td>358000</td>\n",
" <td>364800</td>\n",
" <td>368900</td>\n",
" <td>372800</td>\n",
" <td>376000</td>\n",
" <td>379031</td>\n",
" <td>4.382143e-09</td>\n",
" <td>379031.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>E09000006</td>\n",
" <td>15013.487</td>\n",
" <td>Bromley</td>\n",
" <td>0.000</td>\n",
" <td>F</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>POLYGON ((540373.6008397819 157530.3994170626,...</td>\n",
" <td>E09000006</td>\n",
" <td>Bromley</td>\n",
" <td>...</td>\n",
" <td>305000.0</td>\n",
" <td>306900</td>\n",
" <td>308600</td>\n",
" <td>310600</td>\n",
" <td>314000</td>\n",
" <td>317900</td>\n",
" <td>321300</td>\n",
" <td>324857</td>\n",
" <td>2.163768e-09</td>\n",
" <td>324857.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>E09000018</td>\n",
" <td>5658.541</td>\n",
" <td>Hounslow</td>\n",
" <td>60.755</td>\n",
" <td>F</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>POLYGON ((521975.8008395391 178099.9994231847,...</td>\n",
" <td>E09000018</td>\n",
" <td>Hounslow</td>\n",
" <td>...</td>\n",
" <td>237900.0</td>\n",
" <td>243400</td>\n",
" <td>249200</td>\n",
" <td>254900</td>\n",
" <td>259100</td>\n",
" <td>262400</td>\n",
" <td>265600</td>\n",
" <td>268770</td>\n",
" <td>4.805795e-09</td>\n",
" <td>268770.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>E09000009</td>\n",
" <td>5554.428</td>\n",
" <td>Ealing</td>\n",
" <td>0.000</td>\n",
" <td>F</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>POLYGON ((510253.5008375183 182881.5994233852,...</td>\n",
" <td>E09000009</td>\n",
" <td>Ealing</td>\n",
" <td>...</td>\n",
" <td>324000.0</td>\n",
" <td>330000</td>\n",
" <td>334100</td>\n",
" <td>339300</td>\n",
" <td>340700</td>\n",
" <td>342500</td>\n",
" <td>342100</td>\n",
" <td>343059</td>\n",
" <td>6.176308e-09</td>\n",
" <td>343059.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 68 columns</p>\n",
"</div>"
],
"text/plain": [
" GSS_CODE HECTARES NAME NONLD_AREA ONS_INNER SUB_2006 \\\n",
"0 E09000021 3726.117 Kingston upon Thames 0.000 F \n",
"1 E09000008 8649.441 Croydon 0.000 F \n",
"2 E09000006 15013.487 Bromley 0.000 F \n",
"3 E09000018 5658.541 Hounslow 60.755 F \n",
"4 E09000009 5554.428 Ealing 0.000 F \n",
"\n",
" SUB_2009 geometry New Code \\\n",
"0 POLYGON ((516401.6008347637 160201.7994140856,... E09000021 \n",
"1 POLYGON ((535009.2008389848 159504.6994170326,... E09000008 \n",
"2 POLYGON ((540373.6008397819 157530.3994170626,... E09000006 \n",
"3 POLYGON ((521975.8008395391 178099.9994231847,... E09000018 \n",
"4 POLYGON ((510253.5008375183 182881.5994233852,... E09000009 \n",
"\n",
" Area name ... 2008 2009 2010 2011 2012 \\\n",
"0 Kingston upon Thames ... 156000.0 157300 158600 160400 163900 \n",
"1 Croydon ... 349300.0 352800 358000 364800 368900 \n",
"2 Bromley ... 305000.0 306900 308600 310600 314000 \n",
"3 Hounslow ... 237900.0 243400 249200 254900 259100 \n",
"4 Ealing ... 324000.0 330000 334100 339300 340700 \n",
"\n",
" 2013 2014 2015 popdense2015 Estimate \n",
"0 166800 170000 173525 4.656992e-09 173525.0 \n",
"1 372800 376000 379031 4.382143e-09 379031.0 \n",
"2 317900 321300 324857 2.163768e-09 324857.0 \n",
"3 262400 265600 268770 4.805795e-09 268770.0 \n",
"4 342500 342100 343059 6.176308e-09 343059.0 \n",
"\n",
"[5 rows x 68 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check that volumes have been preserved by extracting the estimates back to the original\n",
"ldn_pop_est = extract_values(res,ldn_boro,trans)\n",
"ldn_pop_est.head()"
]
}
],
"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 |
5agado/data-science-learning | graphics/Dynamical Systems.ipynb | 1 | 89938 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"toc": true
},
"source": [
"<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
"<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Intro\" data-toc-modified-id=\"Intro-1\"><span class=\"toc-item-num\">1 </span>Intro</a></span></li><li><span><a href=\"#Logistig-Map\" data-toc-modified-id=\"Logistig-Map-2\"><span class=\"toc-item-num\">2 </span>Logistig Map</a></span></li><li><span><a href=\"#Ordinary-Differential-Equations-(ODEs)-[TOFIX]\" data-toc-modified-id=\"Ordinary-Differential-Equations-(ODEs)-[TOFIX]-3\"><span class=\"toc-item-num\">3 </span>Ordinary Differential Equations (ODEs) [TOFIX]</a></span></li><li><span><a href=\"#Partial-Differential-Equations-(PDEs)-[TOFIX]\" data-toc-modified-id=\"Partial-Differential-Equations-(PDEs)-[TOFIX]-4\"><span class=\"toc-item-num\">4 </span>Partial Differential Equations (PDEs) [TOFIX]</a></span></li></ul></div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Intro\n",
"This notebook explores introductory concepts and examples of **dynamical systems** and simulation of mathematical models for data generation.\n",
"\n",
"Resources:\n",
"* Python Interactive Computing and Visualization Cookbook - Second Edition"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Basic libraries import\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import animation\n",
"from pathlib import Path\n",
"from datetime import datetime\n",
"import cv2\n",
"from tqdm import tqdm\n",
"\n",
"# Plotting\n",
"%matplotlib inline\n",
"\n",
"sns.set_context(\"paper\")\n",
"sns.set_style(\"darkgrid\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Logistig Map\n",
"An example of chaotic system, arising from a simple nonlinear equation. Generally used to model the evolution of a population"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def logistic(r: float, x):\n",
" \"\"\"\n",
" Logistic function\n",
" :param r: logistic coefficient\n",
" :param x: input\n",
" \"\"\"\n",
" return r * x * (1 - x)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plost sample graph of the logistic function\n",
"r = 2\n",
"x = np.linspace(0, 1)\n",
"ax = sns.lineplot(x, logistic(r, x))\n",
"ax.set(xlabel='x', ylabel='logistic(x)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def plot_logistic_map(r, x0, n):\n",
" \"\"\"\n",
" Plot iteration over logistic map\n",
" :param r: logistic coefficient\n",
" :param x: initial input value\n",
" :param n: number of iterations\n",
" \"\"\"\n",
" # plot logistic function over fixed linespace\n",
" x = np.linspace(0, 1)\n",
" ax = sns.lineplot(x, logistic(r, x))\n",
" \n",
" # iteratively apply logistic from initial value\n",
" # and plot directions\n",
" # (x, x) -> (x, y)\n",
" # (x, y) -> (y, y)\n",
" x = x0\n",
" for i in range(n):\n",
" y = logistic(r, x)\n",
" # Plot the two lines.\n",
" ax.plot([x, x], [x, y], 'k', lw=1)\n",
" ax.plot([x, y], [y, y], 'k', lw=1)\n",
" # Plot the positions with increasing\n",
" # opacity.\n",
" ax.plot([x], [y], 'ok', ms=10, alpha=(i + 1) / n)\n",
" x = y\n",
" \n",
" ax.set_title(f\"r={r:.1f}, x_0={x0:.1f}\")"
]
},
{
"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": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_logistic_map(3.5, .1, 20)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def plot_bifurcation_diagram(x0, min_r, max_r, nb_r_vals, nb_iter, nb_last_iter):\n",
" \"\"\"\n",
" Plot bifurcation diagram by simulating logistic map runs for different coefficient values.\n",
" For each plot results for the last nb_last_iter results\n",
" :param x0: initial input value\n",
" :param min_r: min value for logistic coefficient\n",
" :param max_r: max value for logistic coefficient\n",
" :param nb_r_vals: number of values on which to run the simulation\n",
" :param nb_iter: number of iterations for each logistic run\n",
" :param nb_last_iter: number of last iterations to plot\n",
" \"\"\"\n",
" # setup plot\n",
" fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n",
" ax.set_xlim(min_r, max_r)\n",
" ax.set_title(\"Bifurcation diagram\")\n",
" \n",
" # range of logistic coefficient values over which we run the simulation\n",
" r = np.linspace(min_r, max_r, nb_r_vals)\n",
" # initial condition (for all simulations)\n",
" x = x0 * np.ones(nb_r_vals)\n",
" \n",
" # run simulation\n",
" for i in range(nb_iter):\n",
" x = logistic(r, x)\n",
" # plot values if last iterations\n",
" if i >= (nb_iter - nb_last_iter):\n",
" ax.plot(r, x, ',k', alpha=.25)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_bifurcation_diagram(1e-5, 1.5, 4, 10000, 1000, 100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Ordinary Differential Equations (ODEs) [TOFIX]\n",
"Function that depends on the derivative of a single independent variable (rate of change of a quantity depends on its value)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"import scipy.integrate as spi"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# model params\n",
"m = 1. # particle's mass\n",
"k = 1. # drag coefficient\n",
"g = 9.81 # gravity accelleration"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"p0 = (0, 0) # initial position\n",
"v0 = (4, 10) # initial speed vector"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# encode everything in single vector to use scipy solver\n",
"v0 = np.zeros(4)\n",
"v0[2] = 4.\n",
"v0[3] = 10."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def derive_velocity(v, t0, k):\n",
" u, udot = v[:2], v[2:]\n",
" # we compute the second derivative of p\n",
" udotdot = -k / m * udot\n",
" udotdot[1] -= g\n",
" \n",
" return np.r_[udot, udotdot]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"def plot_system_simulation():\n",
" fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n",
" \n",
" # simulate system on 30 linearly spaced times between t=0 and t=3.\n",
" t = np.linspace(0., 3., 30)\n",
" \n",
" # We simulate the system for different values of k.\n",
" for k in np.linspace(0., 1., 5):\n",
" # We simulate the system and evaluate $v$ on the\n",
" # given times.\n",
" v = spi.odeint(derive_velocity, v0, t, args=(k,))\n",
" # We plot the particle's trajectory.\n",
" ax.plot(v[:, 0], v[:, 1], 'o-', mew=1, ms=8, mec='w', label=f'k={k:.1f}')\n",
" ax.legend()\n",
" ax.set_xlim(0, 12)\n",
" ax.set_ylim(0, 6)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_system_simulation()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Partial Differential Equations (PDEs) [TOFIX]\n",
"Dynamical systems involving both time and space. Hard to solve analytically, rely on numerical simulations."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "data-science",
"language": "python",
"name": "data-science"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
},
"notify_time": "30",
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": true,
"toc_position": {},
"toc_section_display": "block",
"toc_window_display": true
},
"toc-autonumbering": true,
"toc-showcode": false,
"toc-showmarkdowntxt": true,
"toc-showtags": false
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
jpn--/larch | book/example/legacy/302_itin_nl.ipynb | 1 | 5397 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 302: Itinerary Choice using Simple Nested Logit"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import larch\n",
"larch.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This example is an itinerary choice model built using the example\n",
"itinerary choice dataset included with Larch. See example 300 for details.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from larch.data_warehouse import example_file\n",
"itin = pd.read_csv(example_file(\"arc\"), index_col=['id_case','id_alt'])\n",
"d = larch.DataFrames(itin, ch='choice', crack=True, autoscale_weights=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will be building a nested logit model, but in order to do so we need to rationalize the alternative\n",
"numbers. As given, our raw itinerary choice data has a lot of alternatives, but they are not\n",
"ordered or numbered in a regular way; each elemental alternative has\n",
"an arbitrary code number assigned to it, and the code numbers for one case\n",
"are not comparable to another case. We need to renumber the alternatives in\n",
"a manner that is more suited for our application, such that based on the code\n",
"number we can programatically extract a the relevant features of the alternative\n",
"that we will want to use in building our nested logit model. In this example\n",
"we want to test a model which has nests based on level of service.\n",
"To renumber, first we will define the relevant categories and values, and establish a numbering\n",
"system using a special object:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"d1 = d.new_systematic_alternatives(\n",
" groupby='nb_cnxs',\n",
" name='alternative_code',\n",
" padding_levels=4,\n",
" groupby_prefixes=['Cnx'],\n",
" overwrite=False,\n",
" complete_features_list={'nb_cnxs':[0,1,2]},\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we compare the new data with the old data, we'll see that we have created a few more alternative."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"d.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"d1.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's make our model. The utility function we will use is the same as the one we used for\n",
"the MNL version of the model.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m = larch.Model(dataservice=d1)\n",
"\n",
"v = [\n",
" \"timeperiod==2\",\n",
" \"timeperiod==3\",\n",
" \"timeperiod==4\",\n",
" \"timeperiod==5\",\n",
" \"timeperiod==6\",\n",
" \"timeperiod==7\",\n",
" \"timeperiod==8\",\n",
" \"timeperiod==9\",\n",
" \"carrier==2\",\n",
" \"carrier==3\",\n",
" \"carrier==4\",\n",
" \"carrier==5\",\n",
" \"equipment==2\",\n",
" \"fare_hy\",\n",
" \"fare_ly\", \n",
" \"elapsed_time\", \n",
" \"nb_cnxs\", \n",
"]\n",
"from larch.roles import PX\n",
"m.utility_ca = sum(PX(i) for i in v)\n",
"\n",
"m.choice_ca_var = 'choice'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we just end our model specification here, we will have a plain MNL model. To change to\n",
"a nested logit model, all we need to do is add the nests. We can do this easily, using the \n",
"special `magic_nesting` method, that uses the structure of the data that we defined above."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m.magic_nesting()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m.load_data()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m.maximize_loglike()"
]
}
],
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| gpl-3.0 |
ryo8128/study_python | a05_data_select.ipynb | 1 | 396932 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# データ抽出と置換"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"通常のデータフレームの条件抽出とwhere,mask,loc,ilocを使う方法について説明します。"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import pandas_datareader.data as web#株価など時系列データをwebから取得するパッケージ\n",
"from datetime import datetime"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ticker = ['AAPL','IBM','MSFT','GOOG']\n",
"start = '2016-01-01'#datetime(2016,1,1)\n",
"end = '2016-12-31'#datetime(2016,12,31)\n",
"df = web.DataReader(ticker,'google',start,end)['Close',:,:]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>AMZN</th>\n",
" <th>FB</th>\n",
" <th>INTU</th>\n",
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" <tr>\n",
" <th>Date</th>\n",
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" <tbody>\n",
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" <th>2012-01-03</th>\n",
" <td>179.03</td>\n",
" <td>NaN</td>\n",
" <td>52.46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-04</th>\n",
" <td>177.51</td>\n",
" <td>NaN</td>\n",
" <td>52.30</td>\n",
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" <tr>\n",
" <th>2012-01-05</th>\n",
" <td>177.61</td>\n",
" <td>NaN</td>\n",
" <td>52.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-06</th>\n",
" <td>182.61</td>\n",
" <td>NaN</td>\n",
" <td>53.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-09</th>\n",
" <td>178.56</td>\n",
" <td>NaN</td>\n",
" <td>53.18</td>\n",
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" <th>2012-01-10</th>\n",
" <td>179.34</td>\n",
" <td>NaN</td>\n",
" <td>54.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-11</th>\n",
" <td>178.90</td>\n",
" <td>NaN</td>\n",
" <td>55.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-12</th>\n",
" <td>175.93</td>\n",
" <td>NaN</td>\n",
" <td>55.28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-13</th>\n",
" <td>178.42</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-17</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>55.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-18</th>\n",
" <td>189.44</td>\n",
" <td>NaN</td>\n",
" <td>56.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-19</th>\n",
" <td>194.45</td>\n",
" <td>NaN</td>\n",
" <td>57.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-20</th>\n",
" <td>190.93</td>\n",
" <td>NaN</td>\n",
" <td>57.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-23</th>\n",
" <td>186.09</td>\n",
" <td>NaN</td>\n",
" <td>57.41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-24</th>\n",
" <td>187.00</td>\n",
" <td>NaN</td>\n",
" <td>57.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-25</th>\n",
" <td>187.80</td>\n",
" <td>NaN</td>\n",
" <td>57.52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-26</th>\n",
" <td>193.32</td>\n",
" <td>NaN</td>\n",
" <td>57.49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-27</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>57.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-30</th>\n",
" <td>192.15</td>\n",
" <td>NaN</td>\n",
" <td>56.56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-31</th>\n",
" <td>194.44</td>\n",
" <td>NaN</td>\n",
" <td>56.44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-02-01</th>\n",
" <td>179.46</td>\n",
" <td>NaN</td>\n",
" <td>57.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-02-02</th>\n",
" <td>181.72</td>\n",
" <td>NaN</td>\n",
" <td>57.86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-02-03</th>\n",
" <td>187.68</td>\n",
" <td>NaN</td>\n",
" <td>58.47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-02-06</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>57.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-02-07</th>\n",
" <td>184.19</td>\n",
" <td>NaN</td>\n",
" <td>57.62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-02-08</th>\n",
" <td>185.48</td>\n",
" <td>NaN</td>\n",
" <td>57.56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-02-09</th>\n",
" <td>184.98</td>\n",
" <td>NaN</td>\n",
" <td>57.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-02-10</th>\n",
" <td>185.54</td>\n",
" <td>NaN</td>\n",
" <td>56.68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-02-13</th>\n",
" <td>191.59</td>\n",
" <td>NaN</td>\n",
" <td>56.59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-02-14</th>\n",
" <td>191.30</td>\n",
" <td>NaN</td>\n",
" <td>56.77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-11-18</th>\n",
" <td>366.18</td>\n",
" <td>45.83</td>\n",
" <td>73.41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-11-19</th>\n",
" <td>364.94</td>\n",
" <td>46.36</td>\n",
" <td>73.36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-11-20</th>\n",
" <td>362.57</td>\n",
" <td>46.43</td>\n",
" <td>73.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-11-21</th>\n",
" <td>368.92</td>\n",
" <td>46.70</td>\n",
" <td>73.16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-11-22</th>\n",
" <td>372.31</td>\n",
" <td>46.23</td>\n",
" <td>72.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-11-25</th>\n",
" <td>376.64</td>\n",
" <td>44.82</td>\n",
" <td>72.73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-11-26</th>\n",
" <td>381.37</td>\n",
" <td>45.89</td>\n",
" <td>72.84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-11-27</th>\n",
" <td>386.71</td>\n",
" <td>46.49</td>\n",
" <td>73.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-11-29</th>\n",
" <td>393.62</td>\n",
" <td>47.01</td>\n",
" <td>74.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-02</th>\n",
" <td>392.30</td>\n",
" <td>47.06</td>\n",
" <td>74.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-03</th>\n",
" <td>384.66</td>\n",
" <td>46.73</td>\n",
" <td>74.36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-04</th>\n",
" <td>385.96</td>\n",
" <td>48.62</td>\n",
" <td>74.34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-05</th>\n",
" <td>384.49</td>\n",
" <td>48.34</td>\n",
" <td>74.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-06</th>\n",
" <td>386.95</td>\n",
" <td>47.94</td>\n",
" <td>74.91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-09</th>\n",
" <td>384.89</td>\n",
" <td>48.84</td>\n",
" <td>74.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-10</th>\n",
" <td>387.78</td>\n",
" <td>50.24</td>\n",
" <td>75.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-11</th>\n",
" <td>382.19</td>\n",
" <td>49.38</td>\n",
" <td>74.53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-12</th>\n",
" <td>381.25</td>\n",
" <td>51.83</td>\n",
" <td>74.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-13</th>\n",
" <td>384.24</td>\n",
" <td>53.32</td>\n",
" <td>75.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-16</th>\n",
" <td>388.97</td>\n",
" <td>53.81</td>\n",
" <td>74.86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-17</th>\n",
" <td>387.65</td>\n",
" <td>54.86</td>\n",
" <td>74.69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-18</th>\n",
" <td>395.96</td>\n",
" <td>55.57</td>\n",
" <td>75.81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-19</th>\n",
" <td>395.19</td>\n",
" <td>55.05</td>\n",
" <td>75.69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-20</th>\n",
" <td>402.20</td>\n",
" <td>55.12</td>\n",
" <td>75.28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-23</th>\n",
" <td>402.92</td>\n",
" <td>57.77</td>\n",
" <td>76.32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-24</th>\n",
" <td>399.20</td>\n",
" <td>NaN</td>\n",
" <td>76.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-26</th>\n",
" <td>404.39</td>\n",
" <td>57.73</td>\n",
" <td>76.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-27</th>\n",
" <td>398.08</td>\n",
" <td>55.44</td>\n",
" <td>76.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-30</th>\n",
" <td>393.37</td>\n",
" <td>53.71</td>\n",
" <td>76.57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-12-31</th>\n",
" <td>398.79</td>\n",
" <td>54.65</td>\n",
" <td>76.32</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>502 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" AMZN FB INTU\n",
"Date \n",
"2012-01-03 179.03 NaN 52.46\n",
"2012-01-04 177.51 NaN 52.30\n",
"2012-01-05 177.61 NaN 52.54\n",
"2012-01-06 182.61 NaN 53.12\n",
"2012-01-09 178.56 NaN 53.18\n",
"2012-01-10 179.34 NaN 54.75\n",
"2012-01-11 178.90 NaN 55.18\n",
"2012-01-12 175.93 NaN 55.28\n",
"2012-01-13 178.42 NaN NaN\n",
"2012-01-17 NaN NaN 55.25\n",
"2012-01-18 189.44 NaN 56.45\n",
"2012-01-19 194.45 NaN 57.10\n",
"2012-01-20 190.93 NaN 57.09\n",
"2012-01-23 186.09 NaN 57.41\n",
"2012-01-24 187.00 NaN 57.33\n",
"2012-01-25 187.80 NaN 57.52\n",
"2012-01-26 193.32 NaN 57.49\n",
"2012-01-27 NaN NaN 57.35\n",
"2012-01-30 192.15 NaN 56.56\n",
"2012-01-31 194.44 NaN 56.44\n",
"2012-02-01 179.46 NaN 57.88\n",
"2012-02-02 181.72 NaN 57.86\n",
"2012-02-03 187.68 NaN 58.47\n",
"2012-02-06 NaN NaN 57.90\n",
"2012-02-07 184.19 NaN 57.62\n",
"2012-02-08 185.48 NaN 57.56\n",
"2012-02-09 184.98 NaN 57.60\n",
"2012-02-10 185.54 NaN 56.68\n",
"2012-02-13 191.59 NaN 56.59\n",
"2012-02-14 191.30 NaN 56.77\n",
"... ... ... ...\n",
"2013-11-18 366.18 45.83 73.41\n",
"2013-11-19 364.94 46.36 73.36\n",
"2013-11-20 362.57 46.43 73.14\n",
"2013-11-21 368.92 46.70 73.16\n",
"2013-11-22 372.31 46.23 72.03\n",
"2013-11-25 376.64 44.82 72.73\n",
"2013-11-26 381.37 45.89 72.84\n",
"2013-11-27 386.71 46.49 73.54\n",
"2013-11-29 393.62 47.01 74.23\n",
"2013-12-02 392.30 47.06 74.23\n",
"2013-12-03 384.66 46.73 74.36\n",
"2013-12-04 385.96 48.62 74.34\n",
"2013-12-05 384.49 48.34 74.01\n",
"2013-12-06 386.95 47.94 74.91\n",
"2013-12-09 384.89 48.84 74.40\n",
"2013-12-10 387.78 50.24 75.00\n",
"2013-12-11 382.19 49.38 74.53\n",
"2013-12-12 381.25 51.83 74.23\n",
"2013-12-13 384.24 53.32 75.02\n",
"2013-12-16 388.97 53.81 74.86\n",
"2013-12-17 387.65 54.86 74.69\n",
"2013-12-18 395.96 55.57 75.81\n",
"2013-12-19 395.19 55.05 75.69\n",
"2013-12-20 402.20 55.12 75.28\n",
"2013-12-23 402.92 57.77 76.32\n",
"2013-12-24 399.20 NaN 76.42\n",
"2013-12-26 404.39 57.73 76.35\n",
"2013-12-27 398.08 55.44 76.09\n",
"2013-12-30 393.37 53.71 76.57\n",
"2013-12-31 398.79 54.65 76.32\n",
"\n",
"[502 rows x 3 columns]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 抽出するか取り除くか"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['AAPL', 'GOOG', 'IBM', 'MSFT']"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(df.columns)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"取り除く方法"
]
},
{
"cell_type": "code",
"execution_count": 65,
"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>IBM</th>\n",
" <th>MSFT</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th>2016-01-04</th>\n",
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" <th>2016-01-06</th>\n",
" <td>135.169998</td>\n",
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" <td>132.860001</td>\n",
" <td>52.169998</td>\n",
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" <tr>\n",
" <th>2016-01-08</th>\n",
" <td>131.630005</td>\n",
" <td>52.330002</td>\n",
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"text/plain": [
" IBM MSFT\n",
"Date \n",
"2016-01-04 135.949997 54.799999\n",
"2016-01-05 135.850006 55.049999\n",
"2016-01-06 135.169998 54.049999\n",
"2016-01-07 132.860001 52.169998\n",
"2016-01-08 131.630005 52.330002"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_drop = df.drop(['AAPL', 'GOOG'],axis=1)#axis=0なら行を指定して取り除く。axis=1なら列を指定して取り除く。\n",
"df_drop.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"抽出する方法(データフレームで説明した方法)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <th>IBM</th>\n",
" <th>MSFT</th>\n",
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" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
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" <tr>\n",
" <th>2016-01-04</th>\n",
" <td>135.949997</td>\n",
" <td>54.799999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-05</th>\n",
" <td>135.850006</td>\n",
" <td>55.049999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-06</th>\n",
" <td>135.169998</td>\n",
" <td>54.049999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-07</th>\n",
" <td>132.860001</td>\n",
" <td>52.169998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-08</th>\n",
" <td>131.630005</td>\n",
" <td>52.330002</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" IBM MSFT\n",
"Date \n",
"2016-01-04 135.949997 54.799999\n",
"2016-01-05 135.850006 55.049999\n",
"2016-01-06 135.169998 54.049999\n",
"2016-01-07 132.860001 52.169998\n",
"2016-01-08 131.630005 52.330002"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_select = df[['IBM', 'MSFT']]\n",
"df_select.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 条件抽出"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AAPL</th>\n",
" <th>GOOG</th>\n",
" <th>IBM</th>\n",
" <th>MSFT</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-01-04</th>\n",
" <td>105.349998</td>\n",
" <td>741.840027</td>\n",
" <td>135.949997</td>\n",
" <td>54.799999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-05</th>\n",
" <td>102.709999</td>\n",
" <td>742.580017</td>\n",
" <td>135.850006</td>\n",
" <td>55.049999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-06</th>\n",
" <td>100.699997</td>\n",
" <td>743.619995</td>\n",
" <td>135.169998</td>\n",
" <td>54.049999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-22</th>\n",
" <td>101.419998</td>\n",
" <td>725.250000</td>\n",
" <td>122.500000</td>\n",
" <td>52.290001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-01</th>\n",
" <td>100.529999</td>\n",
" <td>718.809998</td>\n",
" <td>134.369995</td>\n",
" <td>52.580002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-02</th>\n",
" <td>100.750000</td>\n",
" <td>718.849976</td>\n",
" <td>136.300003</td>\n",
" <td>52.950001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-03</th>\n",
" <td>101.500000</td>\n",
" <td>712.419983</td>\n",
" <td>137.800003</td>\n",
" <td>52.349998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-04</th>\n",
" <td>103.010002</td>\n",
" <td>710.890015</td>\n",
" <td>137.800003</td>\n",
" <td>52.029999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-07</th>\n",
" <td>101.870003</td>\n",
" <td>695.159973</td>\n",
" <td>140.149994</td>\n",
" <td>51.029999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-08</th>\n",
" <td>101.029999</td>\n",
" <td>693.969971</td>\n",
" <td>139.070007</td>\n",
" <td>51.650002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-09</th>\n",
" <td>101.120003</td>\n",
" <td>705.239990</td>\n",
" <td>140.410004</td>\n",
" <td>52.840000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-10</th>\n",
" <td>101.169998</td>\n",
" <td>712.820007</td>\n",
" <td>140.190002</td>\n",
" <td>52.049999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-11</th>\n",
" <td>102.260002</td>\n",
" <td>726.820007</td>\n",
" <td>142.360001</td>\n",
" <td>53.070000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-14</th>\n",
" <td>102.519997</td>\n",
" <td>730.489990</td>\n",
" <td>142.779999</td>\n",
" <td>53.169998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-15</th>\n",
" <td>104.580002</td>\n",
" <td>728.330017</td>\n",
" <td>142.960007</td>\n",
" <td>53.590000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-16</th>\n",
" <td>105.970001</td>\n",
" <td>736.090027</td>\n",
" <td>144.789993</td>\n",
" <td>54.349998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-17</th>\n",
" <td>105.800003</td>\n",
" <td>737.780029</td>\n",
" <td>147.039993</td>\n",
" <td>54.660000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-18</th>\n",
" <td>105.919998</td>\n",
" <td>737.599976</td>\n",
" <td>147.089996</td>\n",
" <td>53.490002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-21</th>\n",
" <td>105.910004</td>\n",
" <td>742.090027</td>\n",
" <td>148.630005</td>\n",
" <td>53.860001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-22</th>\n",
" <td>106.720001</td>\n",
" <td>740.750000</td>\n",
" <td>148.100006</td>\n",
" <td>54.070000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-23</th>\n",
" <td>106.129997</td>\n",
" <td>738.059998</td>\n",
" <td>145.399994</td>\n",
" <td>53.970001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-24</th>\n",
" <td>105.669998</td>\n",
" <td>735.299988</td>\n",
" <td>147.949997</td>\n",
" <td>54.209999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-28</th>\n",
" <td>105.190002</td>\n",
" <td>733.530029</td>\n",
" <td>148.399994</td>\n",
" <td>53.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-29</th>\n",
" <td>107.680000</td>\n",
" <td>744.770020</td>\n",
" <td>149.330002</td>\n",
" <td>54.709999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-30</th>\n",
" <td>109.559998</td>\n",
" <td>750.530029</td>\n",
" <td>148.410004</td>\n",
" <td>55.049999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-31</th>\n",
" <td>108.989998</td>\n",
" <td>744.950012</td>\n",
" <td>151.449997</td>\n",
" <td>55.230000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-04-01</th>\n",
" <td>109.989998</td>\n",
" <td>749.909973</td>\n",
" <td>152.520004</td>\n",
" <td>55.570000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-04-04</th>\n",
" <td>111.120003</td>\n",
" <td>745.289978</td>\n",
" <td>152.070007</td>\n",
" <td>55.430000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-04-05</th>\n",
" <td>109.809998</td>\n",
" <td>737.799988</td>\n",
" <td>150.000000</td>\n",
" <td>54.560001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-04-06</th>\n",
" <td>110.959999</td>\n",
" <td>745.690002</td>\n",
" <td>150.020004</td>\n",
" <td>55.119999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-17</th>\n",
" <td>109.949997</td>\n",
" <td>771.229980</td>\n",
" <td>159.800003</td>\n",
" <td>60.639999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-18</th>\n",
" <td>110.059998</td>\n",
" <td>760.539978</td>\n",
" <td>160.389999</td>\n",
" <td>60.349998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-21</th>\n",
" <td>111.730003</td>\n",
" <td>769.200012</td>\n",
" <td>162.770004</td>\n",
" <td>60.860001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-22</th>\n",
" <td>111.800003</td>\n",
" <td>768.270020</td>\n",
" <td>162.669998</td>\n",
" <td>61.119999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-23</th>\n",
" <td>111.230003</td>\n",
" <td>760.989990</td>\n",
" <td>161.979996</td>\n",
" <td>60.400002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-25</th>\n",
" <td>111.790001</td>\n",
" <td>761.679993</td>\n",
" <td>163.139999</td>\n",
" <td>60.529999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-28</th>\n",
" <td>111.570000</td>\n",
" <td>768.239990</td>\n",
" <td>164.520004</td>\n",
" <td>60.610001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-29</th>\n",
" <td>111.459999</td>\n",
" <td>770.840027</td>\n",
" <td>163.529999</td>\n",
" <td>61.090000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-30</th>\n",
" <td>110.519997</td>\n",
" <td>758.039978</td>\n",
" <td>162.220001</td>\n",
" <td>60.259998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-01</th>\n",
" <td>109.489998</td>\n",
" <td>747.919983</td>\n",
" <td>159.820007</td>\n",
" <td>59.200001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-02</th>\n",
" <td>109.900002</td>\n",
" <td>750.500000</td>\n",
" <td>160.020004</td>\n",
" <td>59.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-05</th>\n",
" <td>109.110001</td>\n",
" <td>762.520020</td>\n",
" <td>159.839996</td>\n",
" <td>60.220001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-06</th>\n",
" <td>109.949997</td>\n",
" <td>759.109985</td>\n",
" <td>160.350006</td>\n",
" <td>59.950001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-07</th>\n",
" <td>111.029999</td>\n",
" <td>771.190002</td>\n",
" <td>164.789993</td>\n",
" <td>61.369999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-08</th>\n",
" <td>112.120003</td>\n",
" <td>776.419983</td>\n",
" <td>165.360001</td>\n",
" <td>61.009998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-09</th>\n",
" <td>113.949997</td>\n",
" <td>789.289978</td>\n",
" <td>166.520004</td>\n",
" <td>61.970001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-12</th>\n",
" <td>113.300003</td>\n",
" <td>789.270020</td>\n",
" <td>165.500000</td>\n",
" <td>62.169998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-13</th>\n",
" <td>115.190002</td>\n",
" <td>796.099976</td>\n",
" <td>168.289993</td>\n",
" <td>62.980000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-14</th>\n",
" <td>115.190002</td>\n",
" <td>797.070007</td>\n",
" <td>168.509995</td>\n",
" <td>62.680000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-15</th>\n",
" <td>115.820000</td>\n",
" <td>797.849976</td>\n",
" <td>168.020004</td>\n",
" <td>62.580002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-16</th>\n",
" <td>115.970001</td>\n",
" <td>790.799988</td>\n",
" <td>166.729996</td>\n",
" <td>62.299999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-19</th>\n",
" <td>116.639999</td>\n",
" <td>794.200012</td>\n",
" <td>166.679993</td>\n",
" <td>63.619999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-20</th>\n",
" <td>116.949997</td>\n",
" <td>796.419983</td>\n",
" <td>167.600006</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-21</th>\n",
" <td>117.059998</td>\n",
" <td>794.559998</td>\n",
" <td>167.330002</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-22</th>\n",
" <td>116.290001</td>\n",
" <td>791.260010</td>\n",
" <td>167.059998</td>\n",
" <td>63.549999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-23</th>\n",
" <td>116.519997</td>\n",
" <td>789.909973</td>\n",
" <td>166.710007</td>\n",
" <td>63.240002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-27</th>\n",
" <td>117.260002</td>\n",
" <td>791.549988</td>\n",
" <td>167.139999</td>\n",
" <td>63.279999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-28</th>\n",
" <td>116.760002</td>\n",
" <td>785.049988</td>\n",
" <td>166.190002</td>\n",
" <td>62.990002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-29</th>\n",
" <td>116.730003</td>\n",
" <td>782.789978</td>\n",
" <td>166.600006</td>\n",
" <td>62.900002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-30</th>\n",
" <td>115.820000</td>\n",
" <td>771.820007</td>\n",
" <td>165.990005</td>\n",
" <td>62.139999</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>156 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" AAPL GOOG IBM MSFT\n",
"Date \n",
"2016-01-04 105.349998 741.840027 135.949997 54.799999\n",
"2016-01-05 102.709999 742.580017 135.850006 55.049999\n",
"2016-01-06 100.699997 743.619995 135.169998 54.049999\n",
"2016-01-22 101.419998 725.250000 122.500000 52.290001\n",
"2016-03-01 100.529999 718.809998 134.369995 52.580002\n",
"2016-03-02 100.750000 718.849976 136.300003 52.950001\n",
"2016-03-03 101.500000 712.419983 137.800003 52.349998\n",
"2016-03-04 103.010002 710.890015 137.800003 52.029999\n",
"2016-03-07 101.870003 695.159973 140.149994 51.029999\n",
"2016-03-08 101.029999 693.969971 139.070007 51.650002\n",
"2016-03-09 101.120003 705.239990 140.410004 52.840000\n",
"2016-03-10 101.169998 712.820007 140.190002 52.049999\n",
"2016-03-11 102.260002 726.820007 142.360001 53.070000\n",
"2016-03-14 102.519997 730.489990 142.779999 53.169998\n",
"2016-03-15 104.580002 728.330017 142.960007 53.590000\n",
"2016-03-16 105.970001 736.090027 144.789993 54.349998\n",
"2016-03-17 105.800003 737.780029 147.039993 54.660000\n",
"2016-03-18 105.919998 737.599976 147.089996 53.490002\n",
"2016-03-21 105.910004 742.090027 148.630005 53.860001\n",
"2016-03-22 106.720001 740.750000 148.100006 54.070000\n",
"2016-03-23 106.129997 738.059998 145.399994 53.970001\n",
"2016-03-24 105.669998 735.299988 147.949997 54.209999\n",
"2016-03-28 105.190002 733.530029 148.399994 53.540001\n",
"2016-03-29 107.680000 744.770020 149.330002 54.709999\n",
"2016-03-30 109.559998 750.530029 148.410004 55.049999\n",
"2016-03-31 108.989998 744.950012 151.449997 55.230000\n",
"2016-04-01 109.989998 749.909973 152.520004 55.570000\n",
"2016-04-04 111.120003 745.289978 152.070007 55.430000\n",
"2016-04-05 109.809998 737.799988 150.000000 54.560001\n",
"2016-04-06 110.959999 745.690002 150.020004 55.119999\n",
"... ... ... ... ...\n",
"2016-11-17 109.949997 771.229980 159.800003 60.639999\n",
"2016-11-18 110.059998 760.539978 160.389999 60.349998\n",
"2016-11-21 111.730003 769.200012 162.770004 60.860001\n",
"2016-11-22 111.800003 768.270020 162.669998 61.119999\n",
"2016-11-23 111.230003 760.989990 161.979996 60.400002\n",
"2016-11-25 111.790001 761.679993 163.139999 60.529999\n",
"2016-11-28 111.570000 768.239990 164.520004 60.610001\n",
"2016-11-29 111.459999 770.840027 163.529999 61.090000\n",
"2016-11-30 110.519997 758.039978 162.220001 60.259998\n",
"2016-12-01 109.489998 747.919983 159.820007 59.200001\n",
"2016-12-02 109.900002 750.500000 160.020004 59.250000\n",
"2016-12-05 109.110001 762.520020 159.839996 60.220001\n",
"2016-12-06 109.949997 759.109985 160.350006 59.950001\n",
"2016-12-07 111.029999 771.190002 164.789993 61.369999\n",
"2016-12-08 112.120003 776.419983 165.360001 61.009998\n",
"2016-12-09 113.949997 789.289978 166.520004 61.970001\n",
"2016-12-12 113.300003 789.270020 165.500000 62.169998\n",
"2016-12-13 115.190002 796.099976 168.289993 62.980000\n",
"2016-12-14 115.190002 797.070007 168.509995 62.680000\n",
"2016-12-15 115.820000 797.849976 168.020004 62.580002\n",
"2016-12-16 115.970001 790.799988 166.729996 62.299999\n",
"2016-12-19 116.639999 794.200012 166.679993 63.619999\n",
"2016-12-20 116.949997 796.419983 167.600006 63.540001\n",
"2016-12-21 117.059998 794.559998 167.330002 63.540001\n",
"2016-12-22 116.290001 791.260010 167.059998 63.549999\n",
"2016-12-23 116.519997 789.909973 166.710007 63.240002\n",
"2016-12-27 117.260002 791.549988 167.139999 63.279999\n",
"2016-12-28 116.760002 785.049988 166.190002 62.990002\n",
"2016-12-29 116.730003 782.789978 166.600006 62.900002\n",
"2016-12-30 115.820000 771.820007 165.990005 62.139999\n",
"\n",
"[156 rows x 4 columns]"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['AAPL'] > 100]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 真偽値のSeries"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"どのような仕組みか"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Date\n",
"2016-01-04 True\n",
"2016-01-05 True\n",
"2016-01-06 True\n",
"2016-01-07 False\n",
"2016-01-08 False\n",
"2016-01-11 False\n",
"2016-01-12 False\n",
"2016-01-13 False\n",
"2016-01-14 False\n",
"2016-01-15 False\n",
"2016-01-19 False\n",
"2016-01-20 False\n",
"2016-01-21 False\n",
"2016-01-22 True\n",
"2016-01-25 False\n",
"2016-01-26 False\n",
"2016-01-27 False\n",
"2016-01-28 False\n",
"2016-01-29 False\n",
"2016-02-01 False\n",
"2016-02-02 False\n",
"2016-02-03 False\n",
"2016-02-04 False\n",
"2016-02-05 False\n",
"2016-02-08 False\n",
"2016-02-09 False\n",
"2016-02-10 False\n",
"2016-02-11 False\n",
"2016-02-12 False\n",
"2016-02-16 False\n",
" ... \n",
"2016-11-17 True\n",
"2016-11-18 True\n",
"2016-11-21 True\n",
"2016-11-22 True\n",
"2016-11-23 True\n",
"2016-11-25 True\n",
"2016-11-28 True\n",
"2016-11-29 True\n",
"2016-11-30 True\n",
"2016-12-01 True\n",
"2016-12-02 True\n",
"2016-12-05 True\n",
"2016-12-06 True\n",
"2016-12-07 True\n",
"2016-12-08 True\n",
"2016-12-09 True\n",
"2016-12-12 True\n",
"2016-12-13 True\n",
"2016-12-14 True\n",
"2016-12-15 True\n",
"2016-12-16 True\n",
"2016-12-19 True\n",
"2016-12-20 True\n",
"2016-12-21 True\n",
"2016-12-22 True\n",
"2016-12-23 True\n",
"2016-12-27 True\n",
"2016-12-28 True\n",
"2016-12-29 True\n",
"2016-12-30 True\n",
"Name: AAPL, dtype: bool"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['AAPL'] > 100"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 真偽値DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<div>\n",
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" <thead>\n",
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" <th>AAPL</th>\n",
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" <tr>\n",
" <th>Date</th>\n",
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" <th>2016-01-08</th>\n",
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" <th>2016-01-11</th>\n",
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" <th>2016-01-12</th>\n",
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" <td>False</td>\n",
" <td>False</td>\n",
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" <th>2016-01-13</th>\n",
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" <th>2016-01-14</th>\n",
" <td>False</td>\n",
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" <td>False</td>\n",
" <td>False</td>\n",
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" <tr>\n",
" <th>2016-01-15</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
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" <tr>\n",
" <th>2016-01-19</th>\n",
" <td>False</td>\n",
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" <td>False</td>\n",
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" <tr>\n",
" <th>2016-01-20</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
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" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-21</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
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" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-22</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
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" <th>2016-01-25</th>\n",
" <td>False</td>\n",
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" <th>2016-01-26</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-27</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-28</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-29</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-01</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-02</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-03</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-04</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-05</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
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" <th>2016-02-08</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-09</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-10</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
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" <tr>\n",
" <th>2016-02-11</th>\n",
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" <th>2016-02-16</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
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" <tr>\n",
" <th>2016-11-17</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-18</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-21</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-22</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
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" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-23</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-25</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-28</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-29</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-30</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-01</th>\n",
" <td>False</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2016-12-02</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-05</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-06</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-07</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-08</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-09</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-12</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-13</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-14</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-15</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-16</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-19</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-20</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-21</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-22</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-23</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-27</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-28</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-29</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-30</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>252 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" AAPL GOOG IBM MSFT\n",
"Date \n",
"2016-01-04 False True False False\n",
"2016-01-05 False True False False\n",
"2016-01-06 False True False False\n",
"2016-01-07 False True False False\n",
"2016-01-08 False True False False\n",
"2016-01-11 False True False False\n",
"2016-01-12 False True False False\n",
"2016-01-13 False True False False\n",
"2016-01-14 False True False False\n",
"2016-01-15 False True False False\n",
"2016-01-19 False True False False\n",
"2016-01-20 False True False False\n",
"2016-01-21 False True False False\n",
"2016-01-22 False True False False\n",
"2016-01-25 False True False False\n",
"2016-01-26 False True False False\n",
"2016-01-27 False True False False\n",
"2016-01-28 False True False False\n",
"2016-01-29 False True False False\n",
"2016-02-01 False True False False\n",
"2016-02-02 False True False False\n",
"2016-02-03 False True False False\n",
"2016-02-04 False True False False\n",
"2016-02-05 False True False False\n",
"2016-02-08 False True False False\n",
"2016-02-09 False True False False\n",
"2016-02-10 False True False False\n",
"2016-02-11 False True False False\n",
"2016-02-12 False True False False\n",
"2016-02-16 False True False False\n",
"... ... ... ... ...\n",
"2016-11-17 False True True False\n",
"2016-11-18 False True True False\n",
"2016-11-21 False True True False\n",
"2016-11-22 False True True False\n",
"2016-11-23 False True True False\n",
"2016-11-25 False True True False\n",
"2016-11-28 False True True False\n",
"2016-11-29 False True True False\n",
"2016-11-30 False True True False\n",
"2016-12-01 False True True False\n",
"2016-12-02 False True True False\n",
"2016-12-05 False True True False\n",
"2016-12-06 False True True False\n",
"2016-12-07 False True True False\n",
"2016-12-08 False True True False\n",
"2016-12-09 False True True False\n",
"2016-12-12 False True True False\n",
"2016-12-13 False True True False\n",
"2016-12-14 False True True False\n",
"2016-12-15 False True True False\n",
"2016-12-16 False True True False\n",
"2016-12-19 False True True False\n",
"2016-12-20 False True True False\n",
"2016-12-21 False True True False\n",
"2016-12-22 False True True False\n",
"2016-12-23 False True True False\n",
"2016-12-27 False True True False\n",
"2016-12-28 False True True False\n",
"2016-12-29 False True True False\n",
"2016-12-30 False True True False\n",
"\n",
"[252 rows x 4 columns]"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df > 150"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <td>714.469971</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-11</th>\n",
" <td>NaN</td>\n",
" <td>716.030029</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-12</th>\n",
" <td>NaN</td>\n",
" <td>726.070007</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-13</th>\n",
" <td>NaN</td>\n",
" <td>700.559998</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-14</th>\n",
" <td>NaN</td>\n",
" <td>714.719971</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-15</th>\n",
" <td>NaN</td>\n",
" <td>694.450012</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-19</th>\n",
" <td>NaN</td>\n",
" <td>701.789978</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-20</th>\n",
" <td>NaN</td>\n",
" <td>698.450012</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-21</th>\n",
" <td>NaN</td>\n",
" <td>706.590027</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-22</th>\n",
" <td>NaN</td>\n",
" <td>725.250000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-25</th>\n",
" <td>NaN</td>\n",
" <td>711.669983</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-26</th>\n",
" <td>NaN</td>\n",
" <td>713.039978</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-27</th>\n",
" <td>NaN</td>\n",
" <td>699.989990</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-28</th>\n",
" <td>NaN</td>\n",
" <td>730.960022</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-29</th>\n",
" <td>NaN</td>\n",
" <td>742.950012</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-01</th>\n",
" <td>NaN</td>\n",
" <td>752.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-02</th>\n",
" <td>NaN</td>\n",
" <td>764.650024</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-03</th>\n",
" <td>NaN</td>\n",
" <td>726.950012</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-04</th>\n",
" <td>NaN</td>\n",
" <td>708.010010</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-05</th>\n",
" <td>NaN</td>\n",
" <td>683.570007</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-08</th>\n",
" <td>NaN</td>\n",
" <td>682.739990</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-09</th>\n",
" <td>NaN</td>\n",
" <td>678.109985</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-10</th>\n",
" <td>NaN</td>\n",
" <td>684.119995</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-11</th>\n",
" <td>NaN</td>\n",
" <td>683.109985</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-12</th>\n",
" <td>NaN</td>\n",
" <td>682.400024</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-16</th>\n",
" <td>NaN</td>\n",
" <td>691.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-17</th>\n",
" <td>NaN</td>\n",
" <td>771.229980</td>\n",
" <td>159.800003</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-18</th>\n",
" <td>NaN</td>\n",
" <td>760.539978</td>\n",
" <td>160.389999</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-21</th>\n",
" <td>NaN</td>\n",
" <td>769.200012</td>\n",
" <td>162.770004</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-22</th>\n",
" <td>NaN</td>\n",
" <td>768.270020</td>\n",
" <td>162.669998</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-23</th>\n",
" <td>NaN</td>\n",
" <td>760.989990</td>\n",
" <td>161.979996</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-25</th>\n",
" <td>NaN</td>\n",
" <td>761.679993</td>\n",
" <td>163.139999</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-28</th>\n",
" <td>NaN</td>\n",
" <td>768.239990</td>\n",
" <td>164.520004</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-29</th>\n",
" <td>NaN</td>\n",
" <td>770.840027</td>\n",
" <td>163.529999</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-30</th>\n",
" <td>NaN</td>\n",
" <td>758.039978</td>\n",
" <td>162.220001</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-01</th>\n",
" <td>NaN</td>\n",
" <td>747.919983</td>\n",
" <td>159.820007</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-02</th>\n",
" <td>NaN</td>\n",
" <td>750.500000</td>\n",
" <td>160.020004</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-05</th>\n",
" <td>NaN</td>\n",
" <td>762.520020</td>\n",
" <td>159.839996</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-06</th>\n",
" <td>NaN</td>\n",
" <td>759.109985</td>\n",
" <td>160.350006</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-07</th>\n",
" <td>NaN</td>\n",
" <td>771.190002</td>\n",
" <td>164.789993</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-08</th>\n",
" <td>NaN</td>\n",
" <td>776.419983</td>\n",
" <td>165.360001</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-09</th>\n",
" <td>NaN</td>\n",
" <td>789.289978</td>\n",
" <td>166.520004</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-12</th>\n",
" <td>NaN</td>\n",
" <td>789.270020</td>\n",
" <td>165.500000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-13</th>\n",
" <td>NaN</td>\n",
" <td>796.099976</td>\n",
" <td>168.289993</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-14</th>\n",
" <td>NaN</td>\n",
" <td>797.070007</td>\n",
" <td>168.509995</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-15</th>\n",
" <td>NaN</td>\n",
" <td>797.849976</td>\n",
" <td>168.020004</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-16</th>\n",
" <td>NaN</td>\n",
" <td>790.799988</td>\n",
" <td>166.729996</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-19</th>\n",
" <td>NaN</td>\n",
" <td>794.200012</td>\n",
" <td>166.679993</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-20</th>\n",
" <td>NaN</td>\n",
" <td>796.419983</td>\n",
" <td>167.600006</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-21</th>\n",
" <td>NaN</td>\n",
" <td>794.559998</td>\n",
" <td>167.330002</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-22</th>\n",
" <td>NaN</td>\n",
" <td>791.260010</td>\n",
" <td>167.059998</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-23</th>\n",
" <td>NaN</td>\n",
" <td>789.909973</td>\n",
" <td>166.710007</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-27</th>\n",
" <td>NaN</td>\n",
" <td>791.549988</td>\n",
" <td>167.139999</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-28</th>\n",
" <td>NaN</td>\n",
" <td>785.049988</td>\n",
" <td>166.190002</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-29</th>\n",
" <td>NaN</td>\n",
" <td>782.789978</td>\n",
" <td>166.600006</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-30</th>\n",
" <td>NaN</td>\n",
" <td>771.820007</td>\n",
" <td>165.990005</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>252 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" AAPL GOOG IBM MSFT\n",
"Date \n",
"2016-01-04 NaN 741.840027 NaN NaN\n",
"2016-01-05 NaN 742.580017 NaN NaN\n",
"2016-01-06 NaN 743.619995 NaN NaN\n",
"2016-01-07 NaN 726.390015 NaN NaN\n",
"2016-01-08 NaN 714.469971 NaN NaN\n",
"2016-01-11 NaN 716.030029 NaN NaN\n",
"2016-01-12 NaN 726.070007 NaN NaN\n",
"2016-01-13 NaN 700.559998 NaN NaN\n",
"2016-01-14 NaN 714.719971 NaN NaN\n",
"2016-01-15 NaN 694.450012 NaN NaN\n",
"2016-01-19 NaN 701.789978 NaN NaN\n",
"2016-01-20 NaN 698.450012 NaN NaN\n",
"2016-01-21 NaN 706.590027 NaN NaN\n",
"2016-01-22 NaN 725.250000 NaN NaN\n",
"2016-01-25 NaN 711.669983 NaN NaN\n",
"2016-01-26 NaN 713.039978 NaN NaN\n",
"2016-01-27 NaN 699.989990 NaN NaN\n",
"2016-01-28 NaN 730.960022 NaN NaN\n",
"2016-01-29 NaN 742.950012 NaN NaN\n",
"2016-02-01 NaN 752.000000 NaN NaN\n",
"2016-02-02 NaN 764.650024 NaN NaN\n",
"2016-02-03 NaN 726.950012 NaN NaN\n",
"2016-02-04 NaN 708.010010 NaN NaN\n",
"2016-02-05 NaN 683.570007 NaN NaN\n",
"2016-02-08 NaN 682.739990 NaN NaN\n",
"2016-02-09 NaN 678.109985 NaN NaN\n",
"2016-02-10 NaN 684.119995 NaN NaN\n",
"2016-02-11 NaN 683.109985 NaN NaN\n",
"2016-02-12 NaN 682.400024 NaN NaN\n",
"2016-02-16 NaN 691.000000 NaN NaN\n",
"... ... ... ... ...\n",
"2016-11-17 NaN 771.229980 159.800003 NaN\n",
"2016-11-18 NaN 760.539978 160.389999 NaN\n",
"2016-11-21 NaN 769.200012 162.770004 NaN\n",
"2016-11-22 NaN 768.270020 162.669998 NaN\n",
"2016-11-23 NaN 760.989990 161.979996 NaN\n",
"2016-11-25 NaN 761.679993 163.139999 NaN\n",
"2016-11-28 NaN 768.239990 164.520004 NaN\n",
"2016-11-29 NaN 770.840027 163.529999 NaN\n",
"2016-11-30 NaN 758.039978 162.220001 NaN\n",
"2016-12-01 NaN 747.919983 159.820007 NaN\n",
"2016-12-02 NaN 750.500000 160.020004 NaN\n",
"2016-12-05 NaN 762.520020 159.839996 NaN\n",
"2016-12-06 NaN 759.109985 160.350006 NaN\n",
"2016-12-07 NaN 771.190002 164.789993 NaN\n",
"2016-12-08 NaN 776.419983 165.360001 NaN\n",
"2016-12-09 NaN 789.289978 166.520004 NaN\n",
"2016-12-12 NaN 789.270020 165.500000 NaN\n",
"2016-12-13 NaN 796.099976 168.289993 NaN\n",
"2016-12-14 NaN 797.070007 168.509995 NaN\n",
"2016-12-15 NaN 797.849976 168.020004 NaN\n",
"2016-12-16 NaN 790.799988 166.729996 NaN\n",
"2016-12-19 NaN 794.200012 166.679993 NaN\n",
"2016-12-20 NaN 796.419983 167.600006 NaN\n",
"2016-12-21 NaN 794.559998 167.330002 NaN\n",
"2016-12-22 NaN 791.260010 167.059998 NaN\n",
"2016-12-23 NaN 789.909973 166.710007 NaN\n",
"2016-12-27 NaN 791.549988 167.139999 NaN\n",
"2016-12-28 NaN 785.049988 166.190002 NaN\n",
"2016-12-29 NaN 782.789978 166.600006 NaN\n",
"2016-12-30 NaN 771.820007 165.990005 NaN\n",
"\n",
"[252 rows x 4 columns]"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df > 150]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 抽出に論理式も使える"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "cannot compare a dtyped [float64] array with a scalar of type [bool]",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/anaconda/lib/python3.6/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mna_op\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m 876\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 877\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 878\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/anaconda/lib/python3.6/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0mxor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbool_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'xor'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'^'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m rand_=bool_method(lambda x, y: operator.and_(y, x),\n\u001b[0m\u001b[1;32m 126\u001b[0m names('rand_'), op('&')),\n",
"\u001b[0;31mTypeError\u001b[0m: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/anaconda/lib/python3.6/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mna_op\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m 894\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscalar_binop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 896\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/lib.pyx\u001b[0m in \u001b[0;36mpandas.lib.scalar_binop (pandas/lib.c:16177)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Buffer dtype mismatch, expected 'Python object' but got 'double'",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-71-a8801ee86291>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'AAPL'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m110\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'GOOG'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m750\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/anaconda/lib/python3.6/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 927\u001b[0m is_integer_dtype(np.asarray(other)) else fill_bool)\n\u001b[1;32m 928\u001b[0m return filler(self._constructor(\n\u001b[0;32m--> 929\u001b[0;31m \u001b[0mna_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\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 930\u001b[0m index=self.index)).__finalize__(self)\n\u001b[1;32m 931\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/anaconda/lib/python3.6/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mna_op\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m 897\u001b[0m raise TypeError(\"cannot compare a dtyped [{0}] array with \"\n\u001b[1;32m 898\u001b[0m \"a scalar of type [{1}]\".format(\n\u001b[0;32m--> 899\u001b[0;31m x.dtype, type(y).__name__))\n\u001b[0m\u001b[1;32m 900\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 901\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: cannot compare a dtyped [float64] array with a scalar of type [bool]"
]
}
],
"source": [
"df['AAPL']>110 & df['GOOG']>750"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"論理演算&が先に行われるのでエラーになる。<br>\n",
"正しい書き方"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Date\n",
"2016-01-04 False\n",
"2016-01-05 False\n",
"2016-01-06 False\n",
"2016-01-07 False\n",
"2016-01-08 False\n",
"2016-01-11 False\n",
"2016-01-12 False\n",
"2016-01-13 False\n",
"2016-01-14 False\n",
"2016-01-15 False\n",
"2016-01-19 False\n",
"2016-01-20 False\n",
"2016-01-21 False\n",
"2016-01-22 False\n",
"2016-01-25 False\n",
"2016-01-26 False\n",
"2016-01-27 False\n",
"2016-01-28 False\n",
"2016-01-29 False\n",
"2016-02-01 False\n",
"2016-02-02 False\n",
"2016-02-03 False\n",
"2016-02-04 False\n",
"2016-02-05 False\n",
"2016-02-08 False\n",
"2016-02-09 False\n",
"2016-02-10 False\n",
"2016-02-11 False\n",
"2016-02-12 False\n",
"2016-02-16 False\n",
" ... \n",
"2016-11-17 False\n",
"2016-11-18 True\n",
"2016-11-21 True\n",
"2016-11-22 True\n",
"2016-11-23 True\n",
"2016-11-25 True\n",
"2016-11-28 True\n",
"2016-11-29 True\n",
"2016-11-30 True\n",
"2016-12-01 False\n",
"2016-12-02 False\n",
"2016-12-05 False\n",
"2016-12-06 False\n",
"2016-12-07 True\n",
"2016-12-08 True\n",
"2016-12-09 True\n",
"2016-12-12 True\n",
"2016-12-13 True\n",
"2016-12-14 True\n",
"2016-12-15 True\n",
"2016-12-16 True\n",
"2016-12-19 True\n",
"2016-12-20 True\n",
"2016-12-21 True\n",
"2016-12-22 True\n",
"2016-12-23 True\n",
"2016-12-27 True\n",
"2016-12-28 True\n",
"2016-12-29 True\n",
"2016-12-30 True\n",
"dtype: bool"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(df['AAPL']>110) & (df['GOOG']>700)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
" AAPL GOOG IBM MSFT\n",
"Date \n",
"2016-04-13 112.040001 751.719971 151.229996 55.349998\n",
"2016-04-14 112.099998 753.200012 151.160004 55.360001\n",
"2016-09-14 111.769997 762.489990 154.050003 56.259998\n",
"2016-09-15 115.570000 771.760010 155.660004 57.189999\n",
"2016-09-16 114.919998 768.880005 153.839996 57.250000\n",
"2016-09-19 113.580002 765.700012 154.869995 56.930000\n",
"2016-09-20 113.570000 771.409973 154.449997 56.810001\n",
"2016-09-21 113.550003 776.219971 155.529999 57.759998\n",
"2016-09-22 114.620003 787.210022 156.110001 57.820000\n",
"2016-09-23 112.709999 786.900024 154.979996 57.430000\n",
"2016-09-26 112.879997 774.210022 153.979996 56.900002\n",
"2016-09-27 113.089996 783.010010 156.770004 57.950001\n",
"2016-09-28 113.949997 781.559998 158.289993 58.029999\n",
"2016-09-29 112.180000 775.010010 158.110001 57.400002\n",
"2016-09-30 113.050003 777.289978 158.850006 57.599998\n",
"2016-10-03 112.519997 772.559998 157.610001 57.419998\n",
"2016-10-04 113.000000 776.429993 156.460007 57.240002\n",
"2016-10-05 113.050003 776.469971 157.080002 57.639999\n",
"2016-10-06 113.889999 776.859985 156.880005 57.740002\n",
"2016-10-07 114.059998 775.080017 155.669998 57.799999\n",
"2016-10-10 116.050003 785.940002 157.020004 58.040001\n",
"2016-10-11 116.300003 783.070007 154.789993 57.189999\n",
"2016-10-12 117.339996 786.140015 154.289993 57.110001\n",
"2016-10-13 116.980003 778.190002 153.720001 56.919998\n",
"2016-10-14 117.629997 778.530029 154.449997 57.419998\n",
"2016-10-17 117.550003 779.960022 154.770004 57.220001\n",
"2016-10-18 117.470001 795.260010 150.720001 57.660000\n",
"2016-10-19 117.120003 801.500000 151.259995 57.529999\n",
"2016-10-20 117.059998 796.969971 151.520004 57.250000\n",
"2016-10-21 116.599998 799.369995 149.630005 59.660000\n",
"... ... ... ... ...\n",
"2016-11-01 111.489998 783.609985 152.789993 59.799999\n",
"2016-11-02 111.589996 768.700012 151.949997 59.430000\n",
"2016-11-07 110.410004 782.520020 155.720001 60.419998\n",
"2016-11-08 111.059998 790.510010 155.169998 60.470001\n",
"2016-11-09 110.879997 785.309998 154.809998 60.169998\n",
"2016-11-18 110.059998 760.539978 160.389999 60.349998\n",
"2016-11-21 111.730003 769.200012 162.770004 60.860001\n",
"2016-11-22 111.800003 768.270020 162.669998 61.119999\n",
"2016-11-23 111.230003 760.989990 161.979996 60.400002\n",
"2016-11-25 111.790001 761.679993 163.139999 60.529999\n",
"2016-11-28 111.570000 768.239990 164.520004 60.610001\n",
"2016-11-29 111.459999 770.840027 163.529999 61.090000\n",
"2016-11-30 110.519997 758.039978 162.220001 60.259998\n",
"2016-12-07 111.029999 771.190002 164.789993 61.369999\n",
"2016-12-08 112.120003 776.419983 165.360001 61.009998\n",
"2016-12-09 113.949997 789.289978 166.520004 61.970001\n",
"2016-12-12 113.300003 789.270020 165.500000 62.169998\n",
"2016-12-13 115.190002 796.099976 168.289993 62.980000\n",
"2016-12-14 115.190002 797.070007 168.509995 62.680000\n",
"2016-12-15 115.820000 797.849976 168.020004 62.580002\n",
"2016-12-16 115.970001 790.799988 166.729996 62.299999\n",
"2016-12-19 116.639999 794.200012 166.679993 63.619999\n",
"2016-12-20 116.949997 796.419983 167.600006 63.540001\n",
"2016-12-21 117.059998 794.559998 167.330002 63.540001\n",
"2016-12-22 116.290001 791.260010 167.059998 63.549999\n",
"2016-12-23 116.519997 789.909973 166.710007 63.240002\n",
"2016-12-27 117.260002 791.549988 167.139999 63.279999\n",
"2016-12-28 116.760002 785.049988 166.190002 62.990002\n",
"2016-12-29 116.730003 782.789978 166.600006 62.900002\n",
"2016-12-30 115.820000 771.820007 165.990005 62.139999\n",
"\n",
"[66 rows x 4 columns]"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df['AAPL']>110) & (df['GOOG']>750)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"|による論理和抽出"
]
},
{
"cell_type": "code",
"execution_count": 74,
"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>AAPL</th>\n",
" <th>GOOG</th>\n",
" <th>IBM</th>\n",
" <th>MSFT</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-08-05</th>\n",
" <td>107.480003</td>\n",
" <td>782.219971</td>\n",
" <td>163.500000</td>\n",
" <td>57.959999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-08-08</th>\n",
" <td>108.370003</td>\n",
" <td>781.760010</td>\n",
" <td>162.039993</td>\n",
" <td>58.060001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-08-09</th>\n",
" <td>108.809998</td>\n",
" <td>784.260010</td>\n",
" <td>161.770004</td>\n",
" <td>58.200001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-08-10</th>\n",
" <td>108.000000</td>\n",
" <td>784.679993</td>\n",
" <td>162.080002</td>\n",
" <td>58.020000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-08-11</th>\n",
" <td>107.930000</td>\n",
" <td>784.849976</td>\n",
" <td>163.529999</td>\n",
" <td>58.299999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-08-12</th>\n",
" <td>108.180000</td>\n",
" <td>783.219971</td>\n",
" <td>161.949997</td>\n",
" <td>57.939999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-08-15</th>\n",
" <td>109.480003</td>\n",
" <td>782.440002</td>\n",
" <td>161.880005</td>\n",
" <td>58.119999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-09-06</th>\n",
" <td>107.699997</td>\n",
" <td>780.080017</td>\n",
" <td>160.350006</td>\n",
" <td>57.610001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-09-07</th>\n",
" <td>108.360001</td>\n",
" <td>780.349976</td>\n",
" <td>161.639999</td>\n",
" <td>57.660000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-09-15</th>\n",
" <td>115.570000</td>\n",
" <td>771.760010</td>\n",
" <td>155.660004</td>\n",
" <td>57.189999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-09-22</th>\n",
" <td>114.620003</td>\n",
" <td>787.210022</td>\n",
" <td>156.110001</td>\n",
" <td>57.820000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-09-23</th>\n",
" <td>112.709999</td>\n",
" <td>786.900024</td>\n",
" <td>154.979996</td>\n",
" <td>57.430000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-09-27</th>\n",
" <td>113.089996</td>\n",
" <td>783.010010</td>\n",
" <td>156.770004</td>\n",
" <td>57.950001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-09-28</th>\n",
" <td>113.949997</td>\n",
" <td>781.559998</td>\n",
" <td>158.289993</td>\n",
" <td>58.029999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-10-10</th>\n",
" <td>116.050003</td>\n",
" <td>785.940002</td>\n",
" <td>157.020004</td>\n",
" <td>58.040001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-10-11</th>\n",
" <td>116.300003</td>\n",
" <td>783.070007</td>\n",
" <td>154.789993</td>\n",
" <td>57.189999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-10-12</th>\n",
" <td>117.339996</td>\n",
" <td>786.140015</td>\n",
" <td>154.289993</td>\n",
" <td>57.110001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-10-13</th>\n",
" <td>116.980003</td>\n",
" <td>778.190002</td>\n",
" <td>153.720001</td>\n",
" <td>56.919998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-10-14</th>\n",
" <td>117.629997</td>\n",
" <td>778.530029</td>\n",
" <td>154.449997</td>\n",
" <td>57.419998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-10-17</th>\n",
" <td>117.550003</td>\n",
" <td>779.960022</td>\n",
" <td>154.770004</td>\n",
" <td>57.220001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-10-18</th>\n",
" <td>117.470001</td>\n",
" <td>795.260010</td>\n",
" <td>150.720001</td>\n",
" <td>57.660000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-10-19</th>\n",
" <td>117.120003</td>\n",
" <td>801.500000</td>\n",
" <td>151.259995</td>\n",
" <td>57.529999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-10-20</th>\n",
" <td>117.059998</td>\n",
" <td>796.969971</td>\n",
" <td>151.520004</td>\n",
" <td>57.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-10-21</th>\n",
" <td>116.599998</td>\n",
" <td>799.369995</td>\n",
" <td>149.630005</td>\n",
" <td>59.660000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-10-24</th>\n",
" <td>117.650002</td>\n",
" <td>813.109985</td>\n",
" <td>150.570007</td>\n",
" <td>61.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-10-25</th>\n",
" <td>118.250000</td>\n",
" <td>807.669983</td>\n",
" <td>150.880005</td>\n",
" <td>60.990002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-10-26</th>\n",
" <td>115.589996</td>\n",
" <td>799.070007</td>\n",
" <td>151.809998</td>\n",
" <td>60.630001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-10-27</th>\n",
" <td>114.480003</td>\n",
" <td>795.349976</td>\n",
" <td>153.350006</td>\n",
" <td>60.099998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-10-28</th>\n",
" <td>113.720001</td>\n",
" <td>795.369995</td>\n",
" <td>152.610001</td>\n",
" <td>59.869999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-10-31</th>\n",
" <td>113.540001</td>\n",
" <td>784.539978</td>\n",
" <td>153.690002</td>\n",
" <td>59.919998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-01</th>\n",
" <td>111.489998</td>\n",
" <td>783.609985</td>\n",
" <td>152.789993</td>\n",
" <td>59.799999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-07</th>\n",
" <td>110.410004</td>\n",
" <td>782.520020</td>\n",
" <td>155.720001</td>\n",
" <td>60.419998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-08</th>\n",
" <td>111.059998</td>\n",
" <td>790.510010</td>\n",
" <td>155.169998</td>\n",
" <td>60.470001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-09</th>\n",
" <td>110.879997</td>\n",
" <td>785.309998</td>\n",
" <td>154.809998</td>\n",
" <td>60.169998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-09</th>\n",
" <td>113.949997</td>\n",
" <td>789.289978</td>\n",
" <td>166.520004</td>\n",
" <td>61.970001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-12</th>\n",
" <td>113.300003</td>\n",
" <td>789.270020</td>\n",
" <td>165.500000</td>\n",
" <td>62.169998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-13</th>\n",
" <td>115.190002</td>\n",
" <td>796.099976</td>\n",
" <td>168.289993</td>\n",
" <td>62.980000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-14</th>\n",
" <td>115.190002</td>\n",
" <td>797.070007</td>\n",
" <td>168.509995</td>\n",
" <td>62.680000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-15</th>\n",
" <td>115.820000</td>\n",
" <td>797.849976</td>\n",
" <td>168.020004</td>\n",
" <td>62.580002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-16</th>\n",
" <td>115.970001</td>\n",
" <td>790.799988</td>\n",
" <td>166.729996</td>\n",
" <td>62.299999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-19</th>\n",
" <td>116.639999</td>\n",
" <td>794.200012</td>\n",
" <td>166.679993</td>\n",
" <td>63.619999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-20</th>\n",
" <td>116.949997</td>\n",
" <td>796.419983</td>\n",
" <td>167.600006</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-21</th>\n",
" <td>117.059998</td>\n",
" <td>794.559998</td>\n",
" <td>167.330002</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-22</th>\n",
" <td>116.290001</td>\n",
" <td>791.260010</td>\n",
" <td>167.059998</td>\n",
" <td>63.549999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-23</th>\n",
" <td>116.519997</td>\n",
" <td>789.909973</td>\n",
" <td>166.710007</td>\n",
" <td>63.240002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-27</th>\n",
" <td>117.260002</td>\n",
" <td>791.549988</td>\n",
" <td>167.139999</td>\n",
" <td>63.279999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-28</th>\n",
" <td>116.760002</td>\n",
" <td>785.049988</td>\n",
" <td>166.190002</td>\n",
" <td>62.990002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-29</th>\n",
" <td>116.730003</td>\n",
" <td>782.789978</td>\n",
" <td>166.600006</td>\n",
" <td>62.900002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-30</th>\n",
" <td>115.820000</td>\n",
" <td>771.820007</td>\n",
" <td>165.990005</td>\n",
" <td>62.139999</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AAPL GOOG IBM MSFT\n",
"Date \n",
"2016-08-05 107.480003 782.219971 163.500000 57.959999\n",
"2016-08-08 108.370003 781.760010 162.039993 58.060001\n",
"2016-08-09 108.809998 784.260010 161.770004 58.200001\n",
"2016-08-10 108.000000 784.679993 162.080002 58.020000\n",
"2016-08-11 107.930000 784.849976 163.529999 58.299999\n",
"2016-08-12 108.180000 783.219971 161.949997 57.939999\n",
"2016-08-15 109.480003 782.440002 161.880005 58.119999\n",
"2016-09-06 107.699997 780.080017 160.350006 57.610001\n",
"2016-09-07 108.360001 780.349976 161.639999 57.660000\n",
"2016-09-15 115.570000 771.760010 155.660004 57.189999\n",
"2016-09-22 114.620003 787.210022 156.110001 57.820000\n",
"2016-09-23 112.709999 786.900024 154.979996 57.430000\n",
"2016-09-27 113.089996 783.010010 156.770004 57.950001\n",
"2016-09-28 113.949997 781.559998 158.289993 58.029999\n",
"2016-10-10 116.050003 785.940002 157.020004 58.040001\n",
"2016-10-11 116.300003 783.070007 154.789993 57.189999\n",
"2016-10-12 117.339996 786.140015 154.289993 57.110001\n",
"2016-10-13 116.980003 778.190002 153.720001 56.919998\n",
"2016-10-14 117.629997 778.530029 154.449997 57.419998\n",
"2016-10-17 117.550003 779.960022 154.770004 57.220001\n",
"2016-10-18 117.470001 795.260010 150.720001 57.660000\n",
"2016-10-19 117.120003 801.500000 151.259995 57.529999\n",
"2016-10-20 117.059998 796.969971 151.520004 57.250000\n",
"2016-10-21 116.599998 799.369995 149.630005 59.660000\n",
"2016-10-24 117.650002 813.109985 150.570007 61.000000\n",
"2016-10-25 118.250000 807.669983 150.880005 60.990002\n",
"2016-10-26 115.589996 799.070007 151.809998 60.630001\n",
"2016-10-27 114.480003 795.349976 153.350006 60.099998\n",
"2016-10-28 113.720001 795.369995 152.610001 59.869999\n",
"2016-10-31 113.540001 784.539978 153.690002 59.919998\n",
"2016-11-01 111.489998 783.609985 152.789993 59.799999\n",
"2016-11-07 110.410004 782.520020 155.720001 60.419998\n",
"2016-11-08 111.059998 790.510010 155.169998 60.470001\n",
"2016-11-09 110.879997 785.309998 154.809998 60.169998\n",
"2016-12-09 113.949997 789.289978 166.520004 61.970001\n",
"2016-12-12 113.300003 789.270020 165.500000 62.169998\n",
"2016-12-13 115.190002 796.099976 168.289993 62.980000\n",
"2016-12-14 115.190002 797.070007 168.509995 62.680000\n",
"2016-12-15 115.820000 797.849976 168.020004 62.580002\n",
"2016-12-16 115.970001 790.799988 166.729996 62.299999\n",
"2016-12-19 116.639999 794.200012 166.679993 63.619999\n",
"2016-12-20 116.949997 796.419983 167.600006 63.540001\n",
"2016-12-21 117.059998 794.559998 167.330002 63.540001\n",
"2016-12-22 116.290001 791.260010 167.059998 63.549999\n",
"2016-12-23 116.519997 789.909973 166.710007 63.240002\n",
"2016-12-27 117.260002 791.549988 167.139999 63.279999\n",
"2016-12-28 116.760002 785.049988 166.190002 62.990002\n",
"2016-12-29 116.730003 782.789978 166.600006 62.900002\n",
"2016-12-30 115.820000 771.820007 165.990005 62.139999"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df['AAPL']>115) | (df['GOOG']>780)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"andやorではなく&や|を使う。andと&の違いはまた調べてきます。"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ValueError",
"evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-75-c22ae1aa80a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'AAPL'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m110\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'GOOG'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m750\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/anaconda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 915\u001b[0m raise ValueError(\"The truth value of a {0} is ambiguous. \"\n\u001b[1;32m 916\u001b[0m \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 917\u001b[0;31m .format(self.__class__.__name__))\n\u001b[0m\u001b[1;32m 918\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 919\u001b[0m \u001b[0m__bool__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
]
}
],
"source": [
"(df['AAPL']>110) and (df['GOOG']>750)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"アップルとグーグルの株価が一定以上のときのIBMとマイクロソフトの株価を抽出する。（実務では年齢とBMIが一定以上の血圧とコレステロール値を抽出するなど）"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <th>2016-12-30</th>\n",
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],
"text/plain": [
" IBM MSFT\n",
"Date \n",
"2016-04-13 151.229996 55.349998\n",
"2016-04-14 151.160004 55.360001\n",
"2016-09-14 154.050003 56.259998\n",
"2016-09-15 155.660004 57.189999\n",
"2016-09-16 153.839996 57.250000\n",
"2016-09-19 154.869995 56.930000\n",
"2016-09-20 154.449997 56.810001\n",
"2016-09-21 155.529999 57.759998\n",
"2016-09-22 156.110001 57.820000\n",
"2016-09-23 154.979996 57.430000\n",
"2016-09-26 153.979996 56.900002\n",
"2016-09-27 156.770004 57.950001\n",
"2016-09-28 158.289993 58.029999\n",
"2016-09-29 158.110001 57.400002\n",
"2016-09-30 158.850006 57.599998\n",
"2016-10-03 157.610001 57.419998\n",
"2016-10-04 156.460007 57.240002\n",
"2016-10-05 157.080002 57.639999\n",
"2016-10-06 156.880005 57.740002\n",
"2016-10-07 155.669998 57.799999\n",
"2016-10-10 157.020004 58.040001\n",
"2016-10-11 154.789993 57.189999\n",
"2016-10-12 154.289993 57.110001\n",
"2016-10-13 153.720001 56.919998\n",
"2016-10-14 154.449997 57.419998\n",
"2016-10-17 154.770004 57.220001\n",
"2016-10-18 150.720001 57.660000\n",
"2016-10-19 151.259995 57.529999\n",
"2016-10-20 151.520004 57.250000\n",
"2016-10-21 149.630005 59.660000\n",
"... ... ...\n",
"2016-11-01 152.789993 59.799999\n",
"2016-11-02 151.949997 59.430000\n",
"2016-11-07 155.720001 60.419998\n",
"2016-11-08 155.169998 60.470001\n",
"2016-11-09 154.809998 60.169998\n",
"2016-11-18 160.389999 60.349998\n",
"2016-11-21 162.770004 60.860001\n",
"2016-11-22 162.669998 61.119999\n",
"2016-11-23 161.979996 60.400002\n",
"2016-11-25 163.139999 60.529999\n",
"2016-11-28 164.520004 60.610001\n",
"2016-11-29 163.529999 61.090000\n",
"2016-11-30 162.220001 60.259998\n",
"2016-12-07 164.789993 61.369999\n",
"2016-12-08 165.360001 61.009998\n",
"2016-12-09 166.520004 61.970001\n",
"2016-12-12 165.500000 62.169998\n",
"2016-12-13 168.289993 62.980000\n",
"2016-12-14 168.509995 62.680000\n",
"2016-12-15 168.020004 62.580002\n",
"2016-12-16 166.729996 62.299999\n",
"2016-12-19 166.679993 63.619999\n",
"2016-12-20 167.600006 63.540001\n",
"2016-12-21 167.330002 63.540001\n",
"2016-12-22 167.059998 63.549999\n",
"2016-12-23 166.710007 63.240002\n",
"2016-12-27 167.139999 63.279999\n",
"2016-12-28 166.190002 62.990002\n",
"2016-12-29 166.600006 62.900002\n",
"2016-12-30 165.990005 62.139999\n",
"\n",
"[66 rows x 2 columns]"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df['AAPL']>110) & (df['GOOG']>750)][['IBM','MSFT']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"条件に合う部分を置換する。"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df['IBM'] = 100"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <td>50.990002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-19</th>\n",
" <td>96.660004</td>\n",
" <td>701.789978</td>\n",
" <td>100</td>\n",
" <td>50.560001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-20</th>\n",
" <td>96.790001</td>\n",
" <td>698.450012</td>\n",
" <td>100</td>\n",
" <td>50.790001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-21</th>\n",
" <td>96.300003</td>\n",
" <td>706.590027</td>\n",
" <td>100</td>\n",
" <td>50.480000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-22</th>\n",
" <td>101.419998</td>\n",
" <td>725.250000</td>\n",
" <td>100</td>\n",
" <td>52.290001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-25</th>\n",
" <td>99.440002</td>\n",
" <td>711.669983</td>\n",
" <td>100</td>\n",
" <td>51.790001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-26</th>\n",
" <td>99.989998</td>\n",
" <td>713.039978</td>\n",
" <td>100</td>\n",
" <td>52.169998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-27</th>\n",
" <td>93.419998</td>\n",
" <td>699.989990</td>\n",
" <td>100</td>\n",
" <td>51.220001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-28</th>\n",
" <td>94.089996</td>\n",
" <td>730.960022</td>\n",
" <td>100</td>\n",
" <td>52.060001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-29</th>\n",
" <td>97.339996</td>\n",
" <td>742.950012</td>\n",
" <td>100</td>\n",
" <td>55.090000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-01</th>\n",
" <td>96.430000</td>\n",
" <td>752.000000</td>\n",
" <td>100</td>\n",
" <td>54.709999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-02</th>\n",
" <td>94.480003</td>\n",
" <td>764.650024</td>\n",
" <td>100</td>\n",
" <td>53.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-03</th>\n",
" <td>96.349998</td>\n",
" <td>726.950012</td>\n",
" <td>100</td>\n",
" <td>52.160000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-04</th>\n",
" <td>96.599998</td>\n",
" <td>708.010010</td>\n",
" <td>100</td>\n",
" <td>52.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-05</th>\n",
" <td>94.019997</td>\n",
" <td>683.570007</td>\n",
" <td>100</td>\n",
" <td>50.160000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-08</th>\n",
" <td>95.010002</td>\n",
" <td>682.739990</td>\n",
" <td>100</td>\n",
" <td>49.410000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-09</th>\n",
" <td>94.989998</td>\n",
" <td>678.109985</td>\n",
" <td>100</td>\n",
" <td>49.279999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-10</th>\n",
" <td>94.269997</td>\n",
" <td>684.119995</td>\n",
" <td>100</td>\n",
" <td>49.709999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-11</th>\n",
" <td>93.699997</td>\n",
" <td>683.109985</td>\n",
" <td>100</td>\n",
" <td>49.689999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-12</th>\n",
" <td>93.989998</td>\n",
" <td>682.400024</td>\n",
" <td>100</td>\n",
" <td>50.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-16</th>\n",
" <td>96.639999</td>\n",
" <td>691.000000</td>\n",
" <td>100</td>\n",
" <td>51.090000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-17</th>\n",
" <td>109.949997</td>\n",
" <td>771.229980</td>\n",
" <td>100</td>\n",
" <td>60.639999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-18</th>\n",
" <td>110.059998</td>\n",
" <td>760.539978</td>\n",
" <td>100</td>\n",
" <td>60.349998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-21</th>\n",
" <td>111.730003</td>\n",
" <td>769.200012</td>\n",
" <td>100</td>\n",
" <td>60.860001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-22</th>\n",
" <td>111.800003</td>\n",
" <td>768.270020</td>\n",
" <td>100</td>\n",
" <td>61.119999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-23</th>\n",
" <td>111.230003</td>\n",
" <td>760.989990</td>\n",
" <td>100</td>\n",
" <td>60.400002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-25</th>\n",
" <td>111.790001</td>\n",
" <td>761.679993</td>\n",
" <td>100</td>\n",
" <td>60.529999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-28</th>\n",
" <td>111.570000</td>\n",
" <td>768.239990</td>\n",
" <td>100</td>\n",
" <td>60.610001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-29</th>\n",
" <td>111.459999</td>\n",
" <td>770.840027</td>\n",
" <td>100</td>\n",
" <td>61.090000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-30</th>\n",
" <td>110.519997</td>\n",
" <td>758.039978</td>\n",
" <td>100</td>\n",
" <td>60.259998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-01</th>\n",
" <td>109.489998</td>\n",
" <td>747.919983</td>\n",
" <td>100</td>\n",
" <td>59.200001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-02</th>\n",
" <td>109.900002</td>\n",
" <td>750.500000</td>\n",
" <td>100</td>\n",
" <td>59.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-05</th>\n",
" <td>109.110001</td>\n",
" <td>762.520020</td>\n",
" <td>100</td>\n",
" <td>60.220001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-06</th>\n",
" <td>109.949997</td>\n",
" <td>759.109985</td>\n",
" <td>100</td>\n",
" <td>59.950001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-07</th>\n",
" <td>111.029999</td>\n",
" <td>771.190002</td>\n",
" <td>100</td>\n",
" <td>61.369999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-08</th>\n",
" <td>112.120003</td>\n",
" <td>776.419983</td>\n",
" <td>100</td>\n",
" <td>61.009998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-09</th>\n",
" <td>113.949997</td>\n",
" <td>789.289978</td>\n",
" <td>100</td>\n",
" <td>61.970001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-12</th>\n",
" <td>113.300003</td>\n",
" <td>789.270020</td>\n",
" <td>100</td>\n",
" <td>62.169998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-13</th>\n",
" <td>115.190002</td>\n",
" <td>796.099976</td>\n",
" <td>100</td>\n",
" <td>62.980000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-14</th>\n",
" <td>115.190002</td>\n",
" <td>797.070007</td>\n",
" <td>100</td>\n",
" <td>62.680000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-15</th>\n",
" <td>115.820000</td>\n",
" <td>797.849976</td>\n",
" <td>100</td>\n",
" <td>62.580002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-16</th>\n",
" <td>115.970001</td>\n",
" <td>790.799988</td>\n",
" <td>100</td>\n",
" <td>62.299999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-19</th>\n",
" <td>116.639999</td>\n",
" <td>794.200012</td>\n",
" <td>100</td>\n",
" <td>63.619999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-20</th>\n",
" <td>116.949997</td>\n",
" <td>796.419983</td>\n",
" <td>100</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-21</th>\n",
" <td>117.059998</td>\n",
" <td>794.559998</td>\n",
" <td>100</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-22</th>\n",
" <td>116.290001</td>\n",
" <td>791.260010</td>\n",
" <td>100</td>\n",
" <td>63.549999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-23</th>\n",
" <td>116.519997</td>\n",
" <td>789.909973</td>\n",
" <td>100</td>\n",
" <td>63.240002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-27</th>\n",
" <td>117.260002</td>\n",
" <td>791.549988</td>\n",
" <td>100</td>\n",
" <td>63.279999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-28</th>\n",
" <td>116.760002</td>\n",
" <td>785.049988</td>\n",
" <td>100</td>\n",
" <td>62.990002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-29</th>\n",
" <td>116.730003</td>\n",
" <td>782.789978</td>\n",
" <td>100</td>\n",
" <td>62.900002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-30</th>\n",
" <td>115.820000</td>\n",
" <td>771.820007</td>\n",
" <td>100</td>\n",
" <td>62.139999</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>252 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" AAPL GOOG IBM MSFT\n",
"Date \n",
"2016-01-04 105.349998 741.840027 100 54.799999\n",
"2016-01-05 102.709999 742.580017 100 55.049999\n",
"2016-01-06 100.699997 743.619995 100 54.049999\n",
"2016-01-07 96.449997 726.390015 100 52.169998\n",
"2016-01-08 96.959999 714.469971 100 52.330002\n",
"2016-01-11 98.529999 716.030029 100 52.299999\n",
"2016-01-12 99.959999 726.070007 100 52.779999\n",
"2016-01-13 97.389999 700.559998 100 51.639999\n",
"2016-01-14 99.519997 714.719971 100 53.110001\n",
"2016-01-15 97.129997 694.450012 100 50.990002\n",
"2016-01-19 96.660004 701.789978 100 50.560001\n",
"2016-01-20 96.790001 698.450012 100 50.790001\n",
"2016-01-21 96.300003 706.590027 100 50.480000\n",
"2016-01-22 101.419998 725.250000 100 52.290001\n",
"2016-01-25 99.440002 711.669983 100 51.790001\n",
"2016-01-26 99.989998 713.039978 100 52.169998\n",
"2016-01-27 93.419998 699.989990 100 51.220001\n",
"2016-01-28 94.089996 730.960022 100 52.060001\n",
"2016-01-29 97.339996 742.950012 100 55.090000\n",
"2016-02-01 96.430000 752.000000 100 54.709999\n",
"2016-02-02 94.480003 764.650024 100 53.000000\n",
"2016-02-03 96.349998 726.950012 100 52.160000\n",
"2016-02-04 96.599998 708.010010 100 52.000000\n",
"2016-02-05 94.019997 683.570007 100 50.160000\n",
"2016-02-08 95.010002 682.739990 100 49.410000\n",
"2016-02-09 94.989998 678.109985 100 49.279999\n",
"2016-02-10 94.269997 684.119995 100 49.709999\n",
"2016-02-11 93.699997 683.109985 100 49.689999\n",
"2016-02-12 93.989998 682.400024 100 50.500000\n",
"2016-02-16 96.639999 691.000000 100 51.090000\n",
"... ... ... ... ...\n",
"2016-11-17 109.949997 771.229980 100 60.639999\n",
"2016-11-18 110.059998 760.539978 100 60.349998\n",
"2016-11-21 111.730003 769.200012 100 60.860001\n",
"2016-11-22 111.800003 768.270020 100 61.119999\n",
"2016-11-23 111.230003 760.989990 100 60.400002\n",
"2016-11-25 111.790001 761.679993 100 60.529999\n",
"2016-11-28 111.570000 768.239990 100 60.610001\n",
"2016-11-29 111.459999 770.840027 100 61.090000\n",
"2016-11-30 110.519997 758.039978 100 60.259998\n",
"2016-12-01 109.489998 747.919983 100 59.200001\n",
"2016-12-02 109.900002 750.500000 100 59.250000\n",
"2016-12-05 109.110001 762.520020 100 60.220001\n",
"2016-12-06 109.949997 759.109985 100 59.950001\n",
"2016-12-07 111.029999 771.190002 100 61.369999\n",
"2016-12-08 112.120003 776.419983 100 61.009998\n",
"2016-12-09 113.949997 789.289978 100 61.970001\n",
"2016-12-12 113.300003 789.270020 100 62.169998\n",
"2016-12-13 115.190002 796.099976 100 62.980000\n",
"2016-12-14 115.190002 797.070007 100 62.680000\n",
"2016-12-15 115.820000 797.849976 100 62.580002\n",
"2016-12-16 115.970001 790.799988 100 62.299999\n",
"2016-12-19 116.639999 794.200012 100 63.619999\n",
"2016-12-20 116.949997 796.419983 100 63.540001\n",
"2016-12-21 117.059998 794.559998 100 63.540001\n",
"2016-12-22 116.290001 791.260010 100 63.549999\n",
"2016-12-23 116.519997 789.909973 100 63.240002\n",
"2016-12-27 117.260002 791.549988 100 63.279999\n",
"2016-12-28 116.760002 785.049988 100 62.990002\n",
"2016-12-29 116.730003 782.789978 100 62.900002\n",
"2016-12-30 115.820000 771.820007 100 62.139999\n",
"\n",
"[252 rows x 4 columns]"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"条件でも置換可能。"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df[(df['AAPL']>110) & (df['GOOG']>750)] = 200"
]
},
{
"cell_type": "code",
"execution_count": 80,
"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>AAPL</th>\n",
" <th>GOOG</th>\n",
" <th>IBM</th>\n",
" <th>MSFT</th>\n",
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" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
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" <th>2016-01-04</th>\n",
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" <th>2016-01-06</th>\n",
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" <th>2016-01-07</th>\n",
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" <th>2016-01-08</th>\n",
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" <td>52.330002</td>\n",
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" <th>2016-01-11</th>\n",
" <td>98.529999</td>\n",
" <td>716.030029</td>\n",
" <td>100</td>\n",
" <td>52.299999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-12</th>\n",
" <td>99.959999</td>\n",
" <td>726.070007</td>\n",
" <td>100</td>\n",
" <td>52.779999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-13</th>\n",
" <td>97.389999</td>\n",
" <td>700.559998</td>\n",
" <td>100</td>\n",
" <td>51.639999</td>\n",
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" <tr>\n",
" <th>2016-01-14</th>\n",
" <td>99.519997</td>\n",
" <td>714.719971</td>\n",
" <td>100</td>\n",
" <td>53.110001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-15</th>\n",
" <td>97.129997</td>\n",
" <td>694.450012</td>\n",
" <td>100</td>\n",
" <td>50.990002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-19</th>\n",
" <td>96.660004</td>\n",
" <td>701.789978</td>\n",
" <td>100</td>\n",
" <td>50.560001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-20</th>\n",
" <td>96.790001</td>\n",
" <td>698.450012</td>\n",
" <td>100</td>\n",
" <td>50.790001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-21</th>\n",
" <td>96.300003</td>\n",
" <td>706.590027</td>\n",
" <td>100</td>\n",
" <td>50.480000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-22</th>\n",
" <td>101.419998</td>\n",
" <td>725.250000</td>\n",
" <td>100</td>\n",
" <td>52.290001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-25</th>\n",
" <td>99.440002</td>\n",
" <td>711.669983</td>\n",
" <td>100</td>\n",
" <td>51.790001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-26</th>\n",
" <td>99.989998</td>\n",
" <td>713.039978</td>\n",
" <td>100</td>\n",
" <td>52.169998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-27</th>\n",
" <td>93.419998</td>\n",
" <td>699.989990</td>\n",
" <td>100</td>\n",
" <td>51.220001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-28</th>\n",
" <td>94.089996</td>\n",
" <td>730.960022</td>\n",
" <td>100</td>\n",
" <td>52.060001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-29</th>\n",
" <td>97.339996</td>\n",
" <td>742.950012</td>\n",
" <td>100</td>\n",
" <td>55.090000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-01</th>\n",
" <td>96.430000</td>\n",
" <td>752.000000</td>\n",
" <td>100</td>\n",
" <td>54.709999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-02</th>\n",
" <td>94.480003</td>\n",
" <td>764.650024</td>\n",
" <td>100</td>\n",
" <td>53.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-03</th>\n",
" <td>96.349998</td>\n",
" <td>726.950012</td>\n",
" <td>100</td>\n",
" <td>52.160000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-04</th>\n",
" <td>96.599998</td>\n",
" <td>708.010010</td>\n",
" <td>100</td>\n",
" <td>52.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-05</th>\n",
" <td>94.019997</td>\n",
" <td>683.570007</td>\n",
" <td>100</td>\n",
" <td>50.160000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-08</th>\n",
" <td>95.010002</td>\n",
" <td>682.739990</td>\n",
" <td>100</td>\n",
" <td>49.410000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-09</th>\n",
" <td>94.989998</td>\n",
" <td>678.109985</td>\n",
" <td>100</td>\n",
" <td>49.279999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-10</th>\n",
" <td>94.269997</td>\n",
" <td>684.119995</td>\n",
" <td>100</td>\n",
" <td>49.709999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-11</th>\n",
" <td>93.699997</td>\n",
" <td>683.109985</td>\n",
" <td>100</td>\n",
" <td>49.689999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-12</th>\n",
" <td>93.989998</td>\n",
" <td>682.400024</td>\n",
" <td>100</td>\n",
" <td>50.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-16</th>\n",
" <td>96.639999</td>\n",
" <td>691.000000</td>\n",
" <td>100</td>\n",
" <td>51.090000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-17</th>\n",
" <td>109.949997</td>\n",
" <td>771.229980</td>\n",
" <td>100</td>\n",
" <td>60.639999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-18</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-21</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-22</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-23</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-25</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-28</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-29</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-30</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-01</th>\n",
" <td>109.489998</td>\n",
" <td>747.919983</td>\n",
" <td>100</td>\n",
" <td>59.200001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-02</th>\n",
" <td>109.900002</td>\n",
" <td>750.500000</td>\n",
" <td>100</td>\n",
" <td>59.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-05</th>\n",
" <td>109.110001</td>\n",
" <td>762.520020</td>\n",
" <td>100</td>\n",
" <td>60.220001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-06</th>\n",
" <td>109.949997</td>\n",
" <td>759.109985</td>\n",
" <td>100</td>\n",
" <td>59.950001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-07</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-08</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-09</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-12</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-13</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-14</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-15</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-16</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-19</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-20</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-21</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-22</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-23</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-27</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-28</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-29</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-30</th>\n",
" <td>200.000000</td>\n",
" <td>200.000000</td>\n",
" <td>200</td>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>252 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" AAPL GOOG IBM MSFT\n",
"Date \n",
"2016-01-04 105.349998 741.840027 100 54.799999\n",
"2016-01-05 102.709999 742.580017 100 55.049999\n",
"2016-01-06 100.699997 743.619995 100 54.049999\n",
"2016-01-07 96.449997 726.390015 100 52.169998\n",
"2016-01-08 96.959999 714.469971 100 52.330002\n",
"2016-01-11 98.529999 716.030029 100 52.299999\n",
"2016-01-12 99.959999 726.070007 100 52.779999\n",
"2016-01-13 97.389999 700.559998 100 51.639999\n",
"2016-01-14 99.519997 714.719971 100 53.110001\n",
"2016-01-15 97.129997 694.450012 100 50.990002\n",
"2016-01-19 96.660004 701.789978 100 50.560001\n",
"2016-01-20 96.790001 698.450012 100 50.790001\n",
"2016-01-21 96.300003 706.590027 100 50.480000\n",
"2016-01-22 101.419998 725.250000 100 52.290001\n",
"2016-01-25 99.440002 711.669983 100 51.790001\n",
"2016-01-26 99.989998 713.039978 100 52.169998\n",
"2016-01-27 93.419998 699.989990 100 51.220001\n",
"2016-01-28 94.089996 730.960022 100 52.060001\n",
"2016-01-29 97.339996 742.950012 100 55.090000\n",
"2016-02-01 96.430000 752.000000 100 54.709999\n",
"2016-02-02 94.480003 764.650024 100 53.000000\n",
"2016-02-03 96.349998 726.950012 100 52.160000\n",
"2016-02-04 96.599998 708.010010 100 52.000000\n",
"2016-02-05 94.019997 683.570007 100 50.160000\n",
"2016-02-08 95.010002 682.739990 100 49.410000\n",
"2016-02-09 94.989998 678.109985 100 49.279999\n",
"2016-02-10 94.269997 684.119995 100 49.709999\n",
"2016-02-11 93.699997 683.109985 100 49.689999\n",
"2016-02-12 93.989998 682.400024 100 50.500000\n",
"2016-02-16 96.639999 691.000000 100 51.090000\n",
"... ... ... ... ...\n",
"2016-11-17 109.949997 771.229980 100 60.639999\n",
"2016-11-18 200.000000 200.000000 200 200.000000\n",
"2016-11-21 200.000000 200.000000 200 200.000000\n",
"2016-11-22 200.000000 200.000000 200 200.000000\n",
"2016-11-23 200.000000 200.000000 200 200.000000\n",
"2016-11-25 200.000000 200.000000 200 200.000000\n",
"2016-11-28 200.000000 200.000000 200 200.000000\n",
"2016-11-29 200.000000 200.000000 200 200.000000\n",
"2016-11-30 200.000000 200.000000 200 200.000000\n",
"2016-12-01 109.489998 747.919983 100 59.200001\n",
"2016-12-02 109.900002 750.500000 100 59.250000\n",
"2016-12-05 109.110001 762.520020 100 60.220001\n",
"2016-12-06 109.949997 759.109985 100 59.950001\n",
"2016-12-07 200.000000 200.000000 200 200.000000\n",
"2016-12-08 200.000000 200.000000 200 200.000000\n",
"2016-12-09 200.000000 200.000000 200 200.000000\n",
"2016-12-12 200.000000 200.000000 200 200.000000\n",
"2016-12-13 200.000000 200.000000 200 200.000000\n",
"2016-12-14 200.000000 200.000000 200 200.000000\n",
"2016-12-15 200.000000 200.000000 200 200.000000\n",
"2016-12-16 200.000000 200.000000 200 200.000000\n",
"2016-12-19 200.000000 200.000000 200 200.000000\n",
"2016-12-20 200.000000 200.000000 200 200.000000\n",
"2016-12-21 200.000000 200.000000 200 200.000000\n",
"2016-12-22 200.000000 200.000000 200 200.000000\n",
"2016-12-23 200.000000 200.000000 200 200.000000\n",
"2016-12-27 200.000000 200.000000 200 200.000000\n",
"2016-12-28 200.000000 200.000000 200 200.000000\n",
"2016-12-29 200.000000 200.000000 200 200.000000\n",
"2016-12-30 200.000000 200.000000 200 200.000000\n",
"\n",
"[252 rows x 4 columns]"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"いろいろ置換したのでコピーを使って元に戻す。"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = df2.copy()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"条件抽出した上で、特定の列を置換したい。"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" if __name__ == '__main__':\n"
]
}
],
"source": [
"df[(df['AAPL']>110) & (df['GOOG']>750)]['MSFT']=0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## whereとmaskによる抽出や置換"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"whereを使うと良い。条件抽出し、抽出されなかったものをNaNで置換する。"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AAPL</th>\n",
" <th>GOOG</th>\n",
" <th>IBM</th>\n",
" <th>MSFT</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-01-04</th>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2016-01-06</th>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>54.049999</td>\n",
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" <th>2016-01-07</th>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>52.169998</td>\n",
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" <th>2016-01-08</th>\n",
" <td>96.959999</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>52.330002</td>\n",
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" <tr>\n",
" <th>2016-01-11</th>\n",
" <td>98.529999</td>\n",
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" <tr>\n",
" <th>2016-01-12</th>\n",
" <td>99.959999</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>52.779999</td>\n",
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" <th>2016-01-13</th>\n",
" <td>97.389999</td>\n",
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" <th>2016-01-14</th>\n",
" <td>99.519997</td>\n",
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" <td>NaN</td>\n",
" <td>53.110001</td>\n",
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" <tr>\n",
" <th>2016-01-15</th>\n",
" <td>97.129997</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>50.990002</td>\n",
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" <tr>\n",
" <th>2016-01-19</th>\n",
" <td>96.660004</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>50.560001</td>\n",
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" <tr>\n",
" <th>2016-01-20</th>\n",
" <td>96.790001</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>50.790001</td>\n",
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" <tr>\n",
" <th>2016-01-21</th>\n",
" <td>96.300003</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>50.480000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-22</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>52.290001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-25</th>\n",
" <td>99.440002</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>51.790001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-26</th>\n",
" <td>99.989998</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>52.169998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-27</th>\n",
" <td>93.419998</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>51.220001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-28</th>\n",
" <td>94.089996</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>52.060001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-29</th>\n",
" <td>97.339996</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>55.090000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-01</th>\n",
" <td>96.430000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>54.709999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-02</th>\n",
" <td>94.480003</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>53.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-03</th>\n",
" <td>96.349998</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>52.160000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-04</th>\n",
" <td>96.599998</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>52.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-05</th>\n",
" <td>94.019997</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>50.160000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-08</th>\n",
" <td>95.010002</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>49.410000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-09</th>\n",
" <td>94.989998</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>49.279999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-10</th>\n",
" <td>94.269997</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>49.709999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-11</th>\n",
" <td>93.699997</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>49.689999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-12</th>\n",
" <td>93.989998</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>50.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-16</th>\n",
" <td>96.639999</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>51.090000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-17</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>60.639999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-18</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>60.349998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-21</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>60.860001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-22</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>61.119999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-23</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>60.400002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-25</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>60.529999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-28</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>60.610001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-29</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>61.090000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-30</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>60.259998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-01</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>59.200001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-02</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>59.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-05</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>60.220001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-06</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>59.950001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-07</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>61.369999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-08</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>61.009998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-09</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>61.970001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-12</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>62.169998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-13</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>62.980000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-14</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>62.680000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-15</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>62.580002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-16</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>62.299999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-19</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>63.619999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-20</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-21</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-22</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>63.549999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-23</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>63.240002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-27</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>63.279999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-28</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>62.990002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-29</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>62.900002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-30</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>62.139999</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>252 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" AAPL GOOG IBM MSFT\n",
"Date \n",
"2016-01-04 NaN NaN NaN 54.799999\n",
"2016-01-05 NaN NaN NaN 55.049999\n",
"2016-01-06 NaN NaN NaN 54.049999\n",
"2016-01-07 96.449997 NaN NaN 52.169998\n",
"2016-01-08 96.959999 NaN NaN 52.330002\n",
"2016-01-11 98.529999 NaN NaN 52.299999\n",
"2016-01-12 99.959999 NaN NaN 52.779999\n",
"2016-01-13 97.389999 NaN NaN 51.639999\n",
"2016-01-14 99.519997 NaN NaN 53.110001\n",
"2016-01-15 97.129997 NaN NaN 50.990002\n",
"2016-01-19 96.660004 NaN NaN 50.560001\n",
"2016-01-20 96.790001 NaN NaN 50.790001\n",
"2016-01-21 96.300003 NaN NaN 50.480000\n",
"2016-01-22 NaN NaN NaN 52.290001\n",
"2016-01-25 99.440002 NaN NaN 51.790001\n",
"2016-01-26 99.989998 NaN NaN 52.169998\n",
"2016-01-27 93.419998 NaN NaN 51.220001\n",
"2016-01-28 94.089996 NaN NaN 52.060001\n",
"2016-01-29 97.339996 NaN NaN 55.090000\n",
"2016-02-01 96.430000 NaN NaN 54.709999\n",
"2016-02-02 94.480003 NaN NaN 53.000000\n",
"2016-02-03 96.349998 NaN NaN 52.160000\n",
"2016-02-04 96.599998 NaN NaN 52.000000\n",
"2016-02-05 94.019997 NaN NaN 50.160000\n",
"2016-02-08 95.010002 NaN NaN 49.410000\n",
"2016-02-09 94.989998 NaN NaN 49.279999\n",
"2016-02-10 94.269997 NaN NaN 49.709999\n",
"2016-02-11 93.699997 NaN NaN 49.689999\n",
"2016-02-12 93.989998 NaN NaN 50.500000\n",
"2016-02-16 96.639999 NaN NaN 51.090000\n",
"... ... ... ... ...\n",
"2016-11-17 NaN NaN NaN 60.639999\n",
"2016-11-18 NaN NaN NaN 60.349998\n",
"2016-11-21 NaN NaN NaN 60.860001\n",
"2016-11-22 NaN NaN NaN 61.119999\n",
"2016-11-23 NaN NaN NaN 60.400002\n",
"2016-11-25 NaN NaN NaN 60.529999\n",
"2016-11-28 NaN NaN NaN 60.610001\n",
"2016-11-29 NaN NaN NaN 61.090000\n",
"2016-11-30 NaN NaN NaN 60.259998\n",
"2016-12-01 NaN NaN NaN 59.200001\n",
"2016-12-02 NaN NaN NaN 59.250000\n",
"2016-12-05 NaN NaN NaN 60.220001\n",
"2016-12-06 NaN NaN NaN 59.950001\n",
"2016-12-07 NaN NaN NaN 61.369999\n",
"2016-12-08 NaN NaN NaN 61.009998\n",
"2016-12-09 NaN NaN NaN 61.970001\n",
"2016-12-12 NaN NaN NaN 62.169998\n",
"2016-12-13 NaN NaN NaN 62.980000\n",
"2016-12-14 NaN NaN NaN 62.680000\n",
"2016-12-15 NaN NaN NaN 62.580002\n",
"2016-12-16 NaN NaN NaN 62.299999\n",
"2016-12-19 NaN NaN NaN 63.619999\n",
"2016-12-20 NaN NaN NaN 63.540001\n",
"2016-12-21 NaN NaN NaN 63.540001\n",
"2016-12-22 NaN NaN NaN 63.549999\n",
"2016-12-23 NaN NaN NaN 63.240002\n",
"2016-12-27 NaN NaN NaN 63.279999\n",
"2016-12-28 NaN NaN NaN 62.990002\n",
"2016-12-29 NaN NaN NaN 62.900002\n",
"2016-12-30 NaN NaN NaN 62.139999\n",
"\n",
"[252 rows x 4 columns]"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.where(df < 100)#デフォルトはNaN"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"置換する値を指定できる。"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AAPL</th>\n",
" <th>GOOG</th>\n",
" <th>IBM</th>\n",
" <th>MSFT</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-01-04</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
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" <th>2016-01-05</th>\n",
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" <th>2016-01-06</th>\n",
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" <th>2016-01-07</th>\n",
" <td>96.449997</td>\n",
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" <td>10.0</td>\n",
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" <th>2016-01-08</th>\n",
" <td>96.959999</td>\n",
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" <th>2016-01-11</th>\n",
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" <td>10.0</td>\n",
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" <tr>\n",
" <th>2016-01-12</th>\n",
" <td>99.959999</td>\n",
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" <tr>\n",
" <th>2016-01-13</th>\n",
" <td>97.389999</td>\n",
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" <td>10.0</td>\n",
" <td>51.639999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-14</th>\n",
" <td>99.519997</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>53.110001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-15</th>\n",
" <td>97.129997</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>50.990002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-19</th>\n",
" <td>96.660004</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>50.560001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-20</th>\n",
" <td>96.790001</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>50.790001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-21</th>\n",
" <td>96.300003</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>50.480000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-22</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>52.290001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-25</th>\n",
" <td>99.440002</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>51.790001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-26</th>\n",
" <td>99.989998</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>52.169998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-27</th>\n",
" <td>93.419998</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>51.220001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-28</th>\n",
" <td>94.089996</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>52.060001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-29</th>\n",
" <td>97.339996</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>55.090000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-01</th>\n",
" <td>96.430000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>54.709999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-02</th>\n",
" <td>94.480003</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>53.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-03</th>\n",
" <td>96.349998</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>52.160000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-04</th>\n",
" <td>96.599998</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>52.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-05</th>\n",
" <td>94.019997</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>50.160000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-08</th>\n",
" <td>95.010002</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>49.410000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-09</th>\n",
" <td>94.989998</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>49.279999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-10</th>\n",
" <td>94.269997</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>49.709999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-11</th>\n",
" <td>93.699997</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>49.689999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-12</th>\n",
" <td>93.989998</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>50.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-16</th>\n",
" <td>96.639999</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>51.090000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-17</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>60.639999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-18</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>60.349998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-21</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>60.860001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-22</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>61.119999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-23</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>60.400002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-25</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>60.529999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-28</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>60.610001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-29</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>61.090000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-30</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>60.259998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-01</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>59.200001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-02</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>59.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-05</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>60.220001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-06</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>59.950001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-07</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>61.369999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-08</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>61.009998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-09</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>61.970001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-12</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>62.169998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-13</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>62.980000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-14</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>62.680000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-15</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>62.580002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-16</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>62.299999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-19</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>63.619999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-20</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-21</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-22</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>63.549999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-23</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>63.240002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-27</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>63.279999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-28</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>62.990002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-29</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>62.900002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-30</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>62.139999</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>252 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" AAPL GOOG IBM MSFT\n",
"Date \n",
"2016-01-04 10.000000 10.0 10.0 54.799999\n",
"2016-01-05 10.000000 10.0 10.0 55.049999\n",
"2016-01-06 10.000000 10.0 10.0 54.049999\n",
"2016-01-07 96.449997 10.0 10.0 52.169998\n",
"2016-01-08 96.959999 10.0 10.0 52.330002\n",
"2016-01-11 98.529999 10.0 10.0 52.299999\n",
"2016-01-12 99.959999 10.0 10.0 52.779999\n",
"2016-01-13 97.389999 10.0 10.0 51.639999\n",
"2016-01-14 99.519997 10.0 10.0 53.110001\n",
"2016-01-15 97.129997 10.0 10.0 50.990002\n",
"2016-01-19 96.660004 10.0 10.0 50.560001\n",
"2016-01-20 96.790001 10.0 10.0 50.790001\n",
"2016-01-21 96.300003 10.0 10.0 50.480000\n",
"2016-01-22 10.000000 10.0 10.0 52.290001\n",
"2016-01-25 99.440002 10.0 10.0 51.790001\n",
"2016-01-26 99.989998 10.0 10.0 52.169998\n",
"2016-01-27 93.419998 10.0 10.0 51.220001\n",
"2016-01-28 94.089996 10.0 10.0 52.060001\n",
"2016-01-29 97.339996 10.0 10.0 55.090000\n",
"2016-02-01 96.430000 10.0 10.0 54.709999\n",
"2016-02-02 94.480003 10.0 10.0 53.000000\n",
"2016-02-03 96.349998 10.0 10.0 52.160000\n",
"2016-02-04 96.599998 10.0 10.0 52.000000\n",
"2016-02-05 94.019997 10.0 10.0 50.160000\n",
"2016-02-08 95.010002 10.0 10.0 49.410000\n",
"2016-02-09 94.989998 10.0 10.0 49.279999\n",
"2016-02-10 94.269997 10.0 10.0 49.709999\n",
"2016-02-11 93.699997 10.0 10.0 49.689999\n",
"2016-02-12 93.989998 10.0 10.0 50.500000\n",
"2016-02-16 96.639999 10.0 10.0 51.090000\n",
"... ... ... ... ...\n",
"2016-11-17 10.000000 10.0 10.0 60.639999\n",
"2016-11-18 10.000000 10.0 10.0 60.349998\n",
"2016-11-21 10.000000 10.0 10.0 60.860001\n",
"2016-11-22 10.000000 10.0 10.0 61.119999\n",
"2016-11-23 10.000000 10.0 10.0 60.400002\n",
"2016-11-25 10.000000 10.0 10.0 60.529999\n",
"2016-11-28 10.000000 10.0 10.0 60.610001\n",
"2016-11-29 10.000000 10.0 10.0 61.090000\n",
"2016-11-30 10.000000 10.0 10.0 60.259998\n",
"2016-12-01 10.000000 10.0 10.0 59.200001\n",
"2016-12-02 10.000000 10.0 10.0 59.250000\n",
"2016-12-05 10.000000 10.0 10.0 60.220001\n",
"2016-12-06 10.000000 10.0 10.0 59.950001\n",
"2016-12-07 10.000000 10.0 10.0 61.369999\n",
"2016-12-08 10.000000 10.0 10.0 61.009998\n",
"2016-12-09 10.000000 10.0 10.0 61.970001\n",
"2016-12-12 10.000000 10.0 10.0 62.169998\n",
"2016-12-13 10.000000 10.0 10.0 62.980000\n",
"2016-12-14 10.000000 10.0 10.0 62.680000\n",
"2016-12-15 10.000000 10.0 10.0 62.580002\n",
"2016-12-16 10.000000 10.0 10.0 62.299999\n",
"2016-12-19 10.000000 10.0 10.0 63.619999\n",
"2016-12-20 10.000000 10.0 10.0 63.540001\n",
"2016-12-21 10.000000 10.0 10.0 63.540001\n",
"2016-12-22 10.000000 10.0 10.0 63.549999\n",
"2016-12-23 10.000000 10.0 10.0 63.240002\n",
"2016-12-27 10.000000 10.0 10.0 63.279999\n",
"2016-12-28 10.000000 10.0 10.0 62.990002\n",
"2016-12-29 10.000000 10.0 10.0 62.900002\n",
"2016-12-30 10.000000 10.0 10.0 62.139999\n",
"\n",
"[252 rows x 4 columns]"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.where(df < 100,10)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AAPL</th>\n",
" <th>GOOG</th>\n",
" <th>IBM</th>\n",
" <th>MSFT</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>2016-01-04</th>\n",
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" <td>741.840027</td>\n",
" <td>135.949997</td>\n",
" <td>54.799999</td>\n",
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" <th>2016-01-05</th>\n",
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" <td>55.049999</td>\n",
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" <tr>\n",
" <th>2016-01-06</th>\n",
" <td>100.699997</td>\n",
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" <td>135.169998</td>\n",
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" <th>2016-01-07</th>\n",
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" <td>52.169998</td>\n",
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" <th>2016-01-08</th>\n",
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" <td>131.630005</td>\n",
" <td>52.330002</td>\n",
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" <th>2016-01-11</th>\n",
" <td>98.529999</td>\n",
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" <td>133.229996</td>\n",
" <td>52.299999</td>\n",
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" <tr>\n",
" <th>2016-01-12</th>\n",
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" <td>726.070007</td>\n",
" <td>132.899994</td>\n",
" <td>52.779999</td>\n",
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" <tr>\n",
" <th>2016-01-13</th>\n",
" <td>97.389999</td>\n",
" <td>700.559998</td>\n",
" <td>131.169998</td>\n",
" <td>51.639999</td>\n",
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" <tr>\n",
" <th>2016-01-14</th>\n",
" <td>99.519997</td>\n",
" <td>714.719971</td>\n",
" <td>132.910004</td>\n",
" <td>53.110001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-15</th>\n",
" <td>97.129997</td>\n",
" <td>694.450012</td>\n",
" <td>130.029999</td>\n",
" <td>50.990002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-19</th>\n",
" <td>96.660004</td>\n",
" <td>701.789978</td>\n",
" <td>128.110001</td>\n",
" <td>50.560001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-20</th>\n",
" <td>96.790001</td>\n",
" <td>698.450012</td>\n",
" <td>121.860001</td>\n",
" <td>50.790001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-21</th>\n",
" <td>96.300003</td>\n",
" <td>706.590027</td>\n",
" <td>122.910004</td>\n",
" <td>50.480000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-22</th>\n",
" <td>101.419998</td>\n",
" <td>725.250000</td>\n",
" <td>122.500000</td>\n",
" <td>52.290001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-25</th>\n",
" <td>99.440002</td>\n",
" <td>711.669983</td>\n",
" <td>122.080002</td>\n",
" <td>51.790001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-26</th>\n",
" <td>99.989998</td>\n",
" <td>713.039978</td>\n",
" <td>122.589996</td>\n",
" <td>52.169998</td>\n",
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" <tr>\n",
" <th>2016-01-27</th>\n",
" <td>93.419998</td>\n",
" <td>699.989990</td>\n",
" <td>120.959999</td>\n",
" <td>51.220001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-28</th>\n",
" <td>94.089996</td>\n",
" <td>730.960022</td>\n",
" <td>122.220001</td>\n",
" <td>52.060001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-29</th>\n",
" <td>97.339996</td>\n",
" <td>742.950012</td>\n",
" <td>124.790001</td>\n",
" <td>55.090000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-01</th>\n",
" <td>96.430000</td>\n",
" <td>752.000000</td>\n",
" <td>124.830002</td>\n",
" <td>54.709999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-02</th>\n",
" <td>94.480003</td>\n",
" <td>764.650024</td>\n",
" <td>122.940002</td>\n",
" <td>53.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-03</th>\n",
" <td>96.349998</td>\n",
" <td>726.950012</td>\n",
" <td>124.720001</td>\n",
" <td>52.160000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-04</th>\n",
" <td>96.599998</td>\n",
" <td>708.010010</td>\n",
" <td>127.650002</td>\n",
" <td>52.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-05</th>\n",
" <td>94.019997</td>\n",
" <td>683.570007</td>\n",
" <td>128.570007</td>\n",
" <td>50.160000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-08</th>\n",
" <td>95.010002</td>\n",
" <td>682.739990</td>\n",
" <td>126.980003</td>\n",
" <td>49.410000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-09</th>\n",
" <td>94.989998</td>\n",
" <td>678.109985</td>\n",
" <td>124.070000</td>\n",
" <td>49.279999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-10</th>\n",
" <td>94.269997</td>\n",
" <td>684.119995</td>\n",
" <td>120.190002</td>\n",
" <td>49.709999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-11</th>\n",
" <td>93.699997</td>\n",
" <td>683.109985</td>\n",
" <td>117.849998</td>\n",
" <td>49.689999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-12</th>\n",
" <td>93.989998</td>\n",
" <td>682.400024</td>\n",
" <td>121.040001</td>\n",
" <td>50.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-16</th>\n",
" <td>96.639999</td>\n",
" <td>691.000000</td>\n",
" <td>122.739998</td>\n",
" <td>51.090000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-17</th>\n",
" <td>109.949997</td>\n",
" <td>771.229980</td>\n",
" <td>159.800003</td>\n",
" <td>60.639999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-18</th>\n",
" <td>110.059998</td>\n",
" <td>760.539978</td>\n",
" <td>160.389999</td>\n",
" <td>60.349998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-21</th>\n",
" <td>111.730003</td>\n",
" <td>769.200012</td>\n",
" <td>162.770004</td>\n",
" <td>60.860001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-22</th>\n",
" <td>111.800003</td>\n",
" <td>768.270020</td>\n",
" <td>162.669998</td>\n",
" <td>61.119999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-23</th>\n",
" <td>111.230003</td>\n",
" <td>760.989990</td>\n",
" <td>161.979996</td>\n",
" <td>60.400002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-25</th>\n",
" <td>111.790001</td>\n",
" <td>761.679993</td>\n",
" <td>163.139999</td>\n",
" <td>60.529999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-28</th>\n",
" <td>111.570000</td>\n",
" <td>768.239990</td>\n",
" <td>164.520004</td>\n",
" <td>60.610001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-29</th>\n",
" <td>111.459999</td>\n",
" <td>770.840027</td>\n",
" <td>163.529999</td>\n",
" <td>61.090000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-30</th>\n",
" <td>110.519997</td>\n",
" <td>758.039978</td>\n",
" <td>162.220001</td>\n",
" <td>60.259998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-01</th>\n",
" <td>109.489998</td>\n",
" <td>747.919983</td>\n",
" <td>159.820007</td>\n",
" <td>59.200001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-02</th>\n",
" <td>109.900002</td>\n",
" <td>750.500000</td>\n",
" <td>160.020004</td>\n",
" <td>59.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-05</th>\n",
" <td>109.110001</td>\n",
" <td>762.520020</td>\n",
" <td>159.839996</td>\n",
" <td>60.220001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-06</th>\n",
" <td>109.949997</td>\n",
" <td>759.109985</td>\n",
" <td>160.350006</td>\n",
" <td>59.950001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-07</th>\n",
" <td>111.029999</td>\n",
" <td>771.190002</td>\n",
" <td>164.789993</td>\n",
" <td>61.369999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-08</th>\n",
" <td>112.120003</td>\n",
" <td>776.419983</td>\n",
" <td>165.360001</td>\n",
" <td>61.009998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-09</th>\n",
" <td>113.949997</td>\n",
" <td>789.289978</td>\n",
" <td>166.520004</td>\n",
" <td>61.970001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-12</th>\n",
" <td>113.300003</td>\n",
" <td>789.270020</td>\n",
" <td>165.500000</td>\n",
" <td>62.169998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-13</th>\n",
" <td>115.190002</td>\n",
" <td>796.099976</td>\n",
" <td>168.289993</td>\n",
" <td>62.980000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-14</th>\n",
" <td>115.190002</td>\n",
" <td>797.070007</td>\n",
" <td>168.509995</td>\n",
" <td>62.680000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-15</th>\n",
" <td>115.820000</td>\n",
" <td>797.849976</td>\n",
" <td>168.020004</td>\n",
" <td>62.580002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-16</th>\n",
" <td>115.970001</td>\n",
" <td>790.799988</td>\n",
" <td>166.729996</td>\n",
" <td>62.299999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-19</th>\n",
" <td>116.639999</td>\n",
" <td>794.200012</td>\n",
" <td>166.679993</td>\n",
" <td>63.619999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-20</th>\n",
" <td>116.949997</td>\n",
" <td>796.419983</td>\n",
" <td>167.600006</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-21</th>\n",
" <td>117.059998</td>\n",
" <td>794.559998</td>\n",
" <td>167.330002</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-22</th>\n",
" <td>116.290001</td>\n",
" <td>791.260010</td>\n",
" <td>167.059998</td>\n",
" <td>63.549999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-23</th>\n",
" <td>116.519997</td>\n",
" <td>789.909973</td>\n",
" <td>166.710007</td>\n",
" <td>63.240002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-27</th>\n",
" <td>117.260002</td>\n",
" <td>791.549988</td>\n",
" <td>167.139999</td>\n",
" <td>63.279999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-28</th>\n",
" <td>116.760002</td>\n",
" <td>785.049988</td>\n",
" <td>166.190002</td>\n",
" <td>62.990002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-29</th>\n",
" <td>116.730003</td>\n",
" <td>782.789978</td>\n",
" <td>166.600006</td>\n",
" <td>62.900002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-30</th>\n",
" <td>115.820000</td>\n",
" <td>771.820007</td>\n",
" <td>165.990005</td>\n",
" <td>62.139999</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>252 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" AAPL GOOG IBM MSFT\n",
"Date \n",
"2016-01-04 105.349998 741.840027 135.949997 54.799999\n",
"2016-01-05 102.709999 742.580017 135.850006 55.049999\n",
"2016-01-06 100.699997 743.619995 135.169998 54.049999\n",
"2016-01-07 96.449997 726.390015 132.860001 52.169998\n",
"2016-01-08 96.959999 714.469971 131.630005 52.330002\n",
"2016-01-11 98.529999 716.030029 133.229996 52.299999\n",
"2016-01-12 99.959999 726.070007 132.899994 52.779999\n",
"2016-01-13 97.389999 700.559998 131.169998 51.639999\n",
"2016-01-14 99.519997 714.719971 132.910004 53.110001\n",
"2016-01-15 97.129997 694.450012 130.029999 50.990002\n",
"2016-01-19 96.660004 701.789978 128.110001 50.560001\n",
"2016-01-20 96.790001 698.450012 121.860001 50.790001\n",
"2016-01-21 96.300003 706.590027 122.910004 50.480000\n",
"2016-01-22 101.419998 725.250000 122.500000 52.290001\n",
"2016-01-25 99.440002 711.669983 122.080002 51.790001\n",
"2016-01-26 99.989998 713.039978 122.589996 52.169998\n",
"2016-01-27 93.419998 699.989990 120.959999 51.220001\n",
"2016-01-28 94.089996 730.960022 122.220001 52.060001\n",
"2016-01-29 97.339996 742.950012 124.790001 55.090000\n",
"2016-02-01 96.430000 752.000000 124.830002 54.709999\n",
"2016-02-02 94.480003 764.650024 122.940002 53.000000\n",
"2016-02-03 96.349998 726.950012 124.720001 52.160000\n",
"2016-02-04 96.599998 708.010010 127.650002 52.000000\n",
"2016-02-05 94.019997 683.570007 128.570007 50.160000\n",
"2016-02-08 95.010002 682.739990 126.980003 49.410000\n",
"2016-02-09 94.989998 678.109985 124.070000 49.279999\n",
"2016-02-10 94.269997 684.119995 120.190002 49.709999\n",
"2016-02-11 93.699997 683.109985 117.849998 49.689999\n",
"2016-02-12 93.989998 682.400024 121.040001 50.500000\n",
"2016-02-16 96.639999 691.000000 122.739998 51.090000\n",
"... ... ... ... ...\n",
"2016-11-17 109.949997 771.229980 159.800003 60.639999\n",
"2016-11-18 110.059998 760.539978 160.389999 60.349998\n",
"2016-11-21 111.730003 769.200012 162.770004 60.860001\n",
"2016-11-22 111.800003 768.270020 162.669998 61.119999\n",
"2016-11-23 111.230003 760.989990 161.979996 60.400002\n",
"2016-11-25 111.790001 761.679993 163.139999 60.529999\n",
"2016-11-28 111.570000 768.239990 164.520004 60.610001\n",
"2016-11-29 111.459999 770.840027 163.529999 61.090000\n",
"2016-11-30 110.519997 758.039978 162.220001 60.259998\n",
"2016-12-01 109.489998 747.919983 159.820007 59.200001\n",
"2016-12-02 109.900002 750.500000 160.020004 59.250000\n",
"2016-12-05 109.110001 762.520020 159.839996 60.220001\n",
"2016-12-06 109.949997 759.109985 160.350006 59.950001\n",
"2016-12-07 111.029999 771.190002 164.789993 61.369999\n",
"2016-12-08 112.120003 776.419983 165.360001 61.009998\n",
"2016-12-09 113.949997 789.289978 166.520004 61.970001\n",
"2016-12-12 113.300003 789.270020 165.500000 62.169998\n",
"2016-12-13 115.190002 796.099976 168.289993 62.980000\n",
"2016-12-14 115.190002 797.070007 168.509995 62.680000\n",
"2016-12-15 115.820000 797.849976 168.020004 62.580002\n",
"2016-12-16 115.970001 790.799988 166.729996 62.299999\n",
"2016-12-19 116.639999 794.200012 166.679993 63.619999\n",
"2016-12-20 116.949997 796.419983 167.600006 63.540001\n",
"2016-12-21 117.059998 794.559998 167.330002 63.540001\n",
"2016-12-22 116.290001 791.260010 167.059998 63.549999\n",
"2016-12-23 116.519997 789.909973 166.710007 63.240002\n",
"2016-12-27 117.260002 791.549988 167.139999 63.279999\n",
"2016-12-28 116.760002 785.049988 166.190002 62.990002\n",
"2016-12-29 116.730003 782.789978 166.600006 62.900002\n",
"2016-12-30 115.820000 771.820007 165.990005 62.139999\n",
"\n",
"[252 rows x 4 columns]"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df['MSFT']=df['MSFT'].where((df['AAPL']>110) & (df['GOOG']>750),0)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AAPL</th>\n",
" <th>GOOG</th>\n",
" <th>IBM</th>\n",
" <th>MSFT</th>\n",
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" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th>2016-01-11</th>\n",
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" <tr>\n",
" <th>2016-01-12</th>\n",
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" <td>132.899994</td>\n",
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" <th>2016-01-13</th>\n",
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" <th>2016-01-14</th>\n",
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" <tr>\n",
" <th>2016-01-15</th>\n",
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" <th>2016-01-19</th>\n",
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" <th>2016-01-20</th>\n",
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" <tr>\n",
" <th>2016-01-21</th>\n",
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" <tr>\n",
" <th>2016-01-22</th>\n",
" <td>101.419998</td>\n",
" <td>725.250000</td>\n",
" <td>122.500000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-25</th>\n",
" <td>99.440002</td>\n",
" <td>711.669983</td>\n",
" <td>122.080002</td>\n",
" <td>0.000000</td>\n",
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" <th>2016-01-26</th>\n",
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" <td>713.039978</td>\n",
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" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-27</th>\n",
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" <td>120.959999</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-28</th>\n",
" <td>94.089996</td>\n",
" <td>730.960022</td>\n",
" <td>122.220001</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-29</th>\n",
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" <td>742.950012</td>\n",
" <td>124.790001</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-02-01</th>\n",
" <td>96.430000</td>\n",
" <td>752.000000</td>\n",
" <td>124.830002</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-02</th>\n",
" <td>94.480003</td>\n",
" <td>764.650024</td>\n",
" <td>122.940002</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-03</th>\n",
" <td>96.349998</td>\n",
" <td>726.950012</td>\n",
" <td>124.720001</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-04</th>\n",
" <td>96.599998</td>\n",
" <td>708.010010</td>\n",
" <td>127.650002</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-05</th>\n",
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" <tr>\n",
" <th>2016-02-08</th>\n",
" <td>95.010002</td>\n",
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" <th>2016-02-09</th>\n",
" <td>94.989998</td>\n",
" <td>678.109985</td>\n",
" <td>124.070000</td>\n",
" <td>0.000000</td>\n",
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" <th>2016-02-10</th>\n",
" <td>94.269997</td>\n",
" <td>684.119995</td>\n",
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" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-11</th>\n",
" <td>93.699997</td>\n",
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" <td>117.849998</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
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" <th>2016-02-12</th>\n",
" <td>93.989998</td>\n",
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" <td>121.040001</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-02-16</th>\n",
" <td>96.639999</td>\n",
" <td>691.000000</td>\n",
" <td>122.739998</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-17</th>\n",
" <td>109.949997</td>\n",
" <td>771.229980</td>\n",
" <td>159.800003</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-18</th>\n",
" <td>110.059998</td>\n",
" <td>760.539978</td>\n",
" <td>160.389999</td>\n",
" <td>60.349998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-21</th>\n",
" <td>111.730003</td>\n",
" <td>769.200012</td>\n",
" <td>162.770004</td>\n",
" <td>60.860001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-22</th>\n",
" <td>111.800003</td>\n",
" <td>768.270020</td>\n",
" <td>162.669998</td>\n",
" <td>61.119999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-23</th>\n",
" <td>111.230003</td>\n",
" <td>760.989990</td>\n",
" <td>161.979996</td>\n",
" <td>60.400002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-25</th>\n",
" <td>111.790001</td>\n",
" <td>761.679993</td>\n",
" <td>163.139999</td>\n",
" <td>60.529999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-28</th>\n",
" <td>111.570000</td>\n",
" <td>768.239990</td>\n",
" <td>164.520004</td>\n",
" <td>60.610001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-29</th>\n",
" <td>111.459999</td>\n",
" <td>770.840027</td>\n",
" <td>163.529999</td>\n",
" <td>61.090000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-30</th>\n",
" <td>110.519997</td>\n",
" <td>758.039978</td>\n",
" <td>162.220001</td>\n",
" <td>60.259998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-01</th>\n",
" <td>109.489998</td>\n",
" <td>747.919983</td>\n",
" <td>159.820007</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-02</th>\n",
" <td>109.900002</td>\n",
" <td>750.500000</td>\n",
" <td>160.020004</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-05</th>\n",
" <td>109.110001</td>\n",
" <td>762.520020</td>\n",
" <td>159.839996</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-06</th>\n",
" <td>109.949997</td>\n",
" <td>759.109985</td>\n",
" <td>160.350006</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-07</th>\n",
" <td>111.029999</td>\n",
" <td>771.190002</td>\n",
" <td>164.789993</td>\n",
" <td>61.369999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-08</th>\n",
" <td>112.120003</td>\n",
" <td>776.419983</td>\n",
" <td>165.360001</td>\n",
" <td>61.009998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-09</th>\n",
" <td>113.949997</td>\n",
" <td>789.289978</td>\n",
" <td>166.520004</td>\n",
" <td>61.970001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-12</th>\n",
" <td>113.300003</td>\n",
" <td>789.270020</td>\n",
" <td>165.500000</td>\n",
" <td>62.169998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-13</th>\n",
" <td>115.190002</td>\n",
" <td>796.099976</td>\n",
" <td>168.289993</td>\n",
" <td>62.980000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-14</th>\n",
" <td>115.190002</td>\n",
" <td>797.070007</td>\n",
" <td>168.509995</td>\n",
" <td>62.680000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-15</th>\n",
" <td>115.820000</td>\n",
" <td>797.849976</td>\n",
" <td>168.020004</td>\n",
" <td>62.580002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-16</th>\n",
" <td>115.970001</td>\n",
" <td>790.799988</td>\n",
" <td>166.729996</td>\n",
" <td>62.299999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-19</th>\n",
" <td>116.639999</td>\n",
" <td>794.200012</td>\n",
" <td>166.679993</td>\n",
" <td>63.619999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-20</th>\n",
" <td>116.949997</td>\n",
" <td>796.419983</td>\n",
" <td>167.600006</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-21</th>\n",
" <td>117.059998</td>\n",
" <td>794.559998</td>\n",
" <td>167.330002</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-22</th>\n",
" <td>116.290001</td>\n",
" <td>791.260010</td>\n",
" <td>167.059998</td>\n",
" <td>63.549999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-23</th>\n",
" <td>116.519997</td>\n",
" <td>789.909973</td>\n",
" <td>166.710007</td>\n",
" <td>63.240002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-27</th>\n",
" <td>117.260002</td>\n",
" <td>791.549988</td>\n",
" <td>167.139999</td>\n",
" <td>63.279999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-28</th>\n",
" <td>116.760002</td>\n",
" <td>785.049988</td>\n",
" <td>166.190002</td>\n",
" <td>62.990002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-29</th>\n",
" <td>116.730003</td>\n",
" <td>782.789978</td>\n",
" <td>166.600006</td>\n",
" <td>62.900002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-30</th>\n",
" <td>115.820000</td>\n",
" <td>771.820007</td>\n",
" <td>165.990005</td>\n",
" <td>62.139999</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>252 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" AAPL GOOG IBM MSFT\n",
"Date \n",
"2016-01-04 105.349998 741.840027 135.949997 0.000000\n",
"2016-01-05 102.709999 742.580017 135.850006 0.000000\n",
"2016-01-06 100.699997 743.619995 135.169998 0.000000\n",
"2016-01-07 96.449997 726.390015 132.860001 0.000000\n",
"2016-01-08 96.959999 714.469971 131.630005 0.000000\n",
"2016-01-11 98.529999 716.030029 133.229996 0.000000\n",
"2016-01-12 99.959999 726.070007 132.899994 0.000000\n",
"2016-01-13 97.389999 700.559998 131.169998 0.000000\n",
"2016-01-14 99.519997 714.719971 132.910004 0.000000\n",
"2016-01-15 97.129997 694.450012 130.029999 0.000000\n",
"2016-01-19 96.660004 701.789978 128.110001 0.000000\n",
"2016-01-20 96.790001 698.450012 121.860001 0.000000\n",
"2016-01-21 96.300003 706.590027 122.910004 0.000000\n",
"2016-01-22 101.419998 725.250000 122.500000 0.000000\n",
"2016-01-25 99.440002 711.669983 122.080002 0.000000\n",
"2016-01-26 99.989998 713.039978 122.589996 0.000000\n",
"2016-01-27 93.419998 699.989990 120.959999 0.000000\n",
"2016-01-28 94.089996 730.960022 122.220001 0.000000\n",
"2016-01-29 97.339996 742.950012 124.790001 0.000000\n",
"2016-02-01 96.430000 752.000000 124.830002 0.000000\n",
"2016-02-02 94.480003 764.650024 122.940002 0.000000\n",
"2016-02-03 96.349998 726.950012 124.720001 0.000000\n",
"2016-02-04 96.599998 708.010010 127.650002 0.000000\n",
"2016-02-05 94.019997 683.570007 128.570007 0.000000\n",
"2016-02-08 95.010002 682.739990 126.980003 0.000000\n",
"2016-02-09 94.989998 678.109985 124.070000 0.000000\n",
"2016-02-10 94.269997 684.119995 120.190002 0.000000\n",
"2016-02-11 93.699997 683.109985 117.849998 0.000000\n",
"2016-02-12 93.989998 682.400024 121.040001 0.000000\n",
"2016-02-16 96.639999 691.000000 122.739998 0.000000\n",
"... ... ... ... ...\n",
"2016-11-17 109.949997 771.229980 159.800003 0.000000\n",
"2016-11-18 110.059998 760.539978 160.389999 60.349998\n",
"2016-11-21 111.730003 769.200012 162.770004 60.860001\n",
"2016-11-22 111.800003 768.270020 162.669998 61.119999\n",
"2016-11-23 111.230003 760.989990 161.979996 60.400002\n",
"2016-11-25 111.790001 761.679993 163.139999 60.529999\n",
"2016-11-28 111.570000 768.239990 164.520004 60.610001\n",
"2016-11-29 111.459999 770.840027 163.529999 61.090000\n",
"2016-11-30 110.519997 758.039978 162.220001 60.259998\n",
"2016-12-01 109.489998 747.919983 159.820007 0.000000\n",
"2016-12-02 109.900002 750.500000 160.020004 0.000000\n",
"2016-12-05 109.110001 762.520020 159.839996 0.000000\n",
"2016-12-06 109.949997 759.109985 160.350006 0.000000\n",
"2016-12-07 111.029999 771.190002 164.789993 61.369999\n",
"2016-12-08 112.120003 776.419983 165.360001 61.009998\n",
"2016-12-09 113.949997 789.289978 166.520004 61.970001\n",
"2016-12-12 113.300003 789.270020 165.500000 62.169998\n",
"2016-12-13 115.190002 796.099976 168.289993 62.980000\n",
"2016-12-14 115.190002 797.070007 168.509995 62.680000\n",
"2016-12-15 115.820000 797.849976 168.020004 62.580002\n",
"2016-12-16 115.970001 790.799988 166.729996 62.299999\n",
"2016-12-19 116.639999 794.200012 166.679993 63.619999\n",
"2016-12-20 116.949997 796.419983 167.600006 63.540001\n",
"2016-12-21 117.059998 794.559998 167.330002 63.540001\n",
"2016-12-22 116.290001 791.260010 167.059998 63.549999\n",
"2016-12-23 116.519997 789.909973 166.710007 63.240002\n",
"2016-12-27 117.260002 791.549988 167.139999 63.279999\n",
"2016-12-28 116.760002 785.049988 166.190002 62.990002\n",
"2016-12-29 116.730003 782.789978 166.600006 62.900002\n",
"2016-12-30 115.820000 771.820007 165.990005 62.139999\n",
"\n",
"[252 rows x 4 columns]"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"条件に当てはまらないものを別の列で置換する。"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df['AAPL'] = df['AAPL'].where(df['AAPL'] < 112, df['GOOG'])"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AAPL</th>\n",
" <th>GOOG</th>\n",
" <th>IBM</th>\n",
" <th>MSFT</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <tr>\n",
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" <th>2016-01-06</th>\n",
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" <th>2016-01-07</th>\n",
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" <th>2016-01-08</th>\n",
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" <th>2016-01-11</th>\n",
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" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-12</th>\n",
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" <th>2016-01-13</th>\n",
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" <td>700.559998</td>\n",
" <td>131.169998</td>\n",
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" <tr>\n",
" <th>2016-01-14</th>\n",
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" <td>714.719971</td>\n",
" <td>132.910004</td>\n",
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" <tr>\n",
" <th>2016-01-15</th>\n",
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" <td>0.000000</td>\n",
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" <th>2016-01-19</th>\n",
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" <th>2016-01-20</th>\n",
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" <td>121.860001</td>\n",
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" <tr>\n",
" <th>2016-01-21</th>\n",
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" <td>706.590027</td>\n",
" <td>122.910004</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-22</th>\n",
" <td>101.419998</td>\n",
" <td>725.250000</td>\n",
" <td>122.500000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-25</th>\n",
" <td>99.440002</td>\n",
" <td>711.669983</td>\n",
" <td>122.080002</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-26</th>\n",
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" <td>713.039978</td>\n",
" <td>122.589996</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-27</th>\n",
" <td>93.419998</td>\n",
" <td>699.989990</td>\n",
" <td>120.959999</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-28</th>\n",
" <td>94.089996</td>\n",
" <td>730.960022</td>\n",
" <td>122.220001</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-29</th>\n",
" <td>97.339996</td>\n",
" <td>742.950012</td>\n",
" <td>124.790001</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-01</th>\n",
" <td>96.430000</td>\n",
" <td>752.000000</td>\n",
" <td>124.830002</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-02</th>\n",
" <td>94.480003</td>\n",
" <td>764.650024</td>\n",
" <td>122.940002</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-03</th>\n",
" <td>96.349998</td>\n",
" <td>726.950012</td>\n",
" <td>124.720001</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-04</th>\n",
" <td>96.599998</td>\n",
" <td>708.010010</td>\n",
" <td>127.650002</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-05</th>\n",
" <td>94.019997</td>\n",
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" <td>128.570007</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-02-08</th>\n",
" <td>95.010002</td>\n",
" <td>682.739990</td>\n",
" <td>126.980003</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-09</th>\n",
" <td>94.989998</td>\n",
" <td>678.109985</td>\n",
" <td>124.070000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-10</th>\n",
" <td>94.269997</td>\n",
" <td>684.119995</td>\n",
" <td>120.190002</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-11</th>\n",
" <td>93.699997</td>\n",
" <td>683.109985</td>\n",
" <td>117.849998</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-12</th>\n",
" <td>93.989998</td>\n",
" <td>682.400024</td>\n",
" <td>121.040001</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-02-16</th>\n",
" <td>96.639999</td>\n",
" <td>691.000000</td>\n",
" <td>122.739998</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-17</th>\n",
" <td>109.949997</td>\n",
" <td>771.229980</td>\n",
" <td>159.800003</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-18</th>\n",
" <td>110.059998</td>\n",
" <td>760.539978</td>\n",
" <td>160.389999</td>\n",
" <td>60.349998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-21</th>\n",
" <td>111.730003</td>\n",
" <td>769.200012</td>\n",
" <td>162.770004</td>\n",
" <td>60.860001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-22</th>\n",
" <td>111.800003</td>\n",
" <td>768.270020</td>\n",
" <td>162.669998</td>\n",
" <td>61.119999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-23</th>\n",
" <td>111.230003</td>\n",
" <td>760.989990</td>\n",
" <td>161.979996</td>\n",
" <td>60.400002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-25</th>\n",
" <td>111.790001</td>\n",
" <td>761.679993</td>\n",
" <td>163.139999</td>\n",
" <td>60.529999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-28</th>\n",
" <td>111.570000</td>\n",
" <td>768.239990</td>\n",
" <td>164.520004</td>\n",
" <td>60.610001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-29</th>\n",
" <td>111.459999</td>\n",
" <td>770.840027</td>\n",
" <td>163.529999</td>\n",
" <td>61.090000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-30</th>\n",
" <td>110.519997</td>\n",
" <td>758.039978</td>\n",
" <td>162.220001</td>\n",
" <td>60.259998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-01</th>\n",
" <td>109.489998</td>\n",
" <td>747.919983</td>\n",
" <td>159.820007</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-02</th>\n",
" <td>109.900002</td>\n",
" <td>750.500000</td>\n",
" <td>160.020004</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-05</th>\n",
" <td>109.110001</td>\n",
" <td>762.520020</td>\n",
" <td>159.839996</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-06</th>\n",
" <td>109.949997</td>\n",
" <td>759.109985</td>\n",
" <td>160.350006</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-07</th>\n",
" <td>111.029999</td>\n",
" <td>771.190002</td>\n",
" <td>164.789993</td>\n",
" <td>61.369999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-08</th>\n",
" <td>776.419983</td>\n",
" <td>776.419983</td>\n",
" <td>165.360001</td>\n",
" <td>61.009998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-09</th>\n",
" <td>789.289978</td>\n",
" <td>789.289978</td>\n",
" <td>166.520004</td>\n",
" <td>61.970001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-12</th>\n",
" <td>789.270020</td>\n",
" <td>789.270020</td>\n",
" <td>165.500000</td>\n",
" <td>62.169998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-13</th>\n",
" <td>796.099976</td>\n",
" <td>796.099976</td>\n",
" <td>168.289993</td>\n",
" <td>62.980000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-14</th>\n",
" <td>797.070007</td>\n",
" <td>797.070007</td>\n",
" <td>168.509995</td>\n",
" <td>62.680000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-15</th>\n",
" <td>797.849976</td>\n",
" <td>797.849976</td>\n",
" <td>168.020004</td>\n",
" <td>62.580002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-16</th>\n",
" <td>790.799988</td>\n",
" <td>790.799988</td>\n",
" <td>166.729996</td>\n",
" <td>62.299999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-19</th>\n",
" <td>794.200012</td>\n",
" <td>794.200012</td>\n",
" <td>166.679993</td>\n",
" <td>63.619999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-20</th>\n",
" <td>796.419983</td>\n",
" <td>796.419983</td>\n",
" <td>167.600006</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-21</th>\n",
" <td>794.559998</td>\n",
" <td>794.559998</td>\n",
" <td>167.330002</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-22</th>\n",
" <td>791.260010</td>\n",
" <td>791.260010</td>\n",
" <td>167.059998</td>\n",
" <td>63.549999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-23</th>\n",
" <td>789.909973</td>\n",
" <td>789.909973</td>\n",
" <td>166.710007</td>\n",
" <td>63.240002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-27</th>\n",
" <td>791.549988</td>\n",
" <td>791.549988</td>\n",
" <td>167.139999</td>\n",
" <td>63.279999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-28</th>\n",
" <td>785.049988</td>\n",
" <td>785.049988</td>\n",
" <td>166.190002</td>\n",
" <td>62.990002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-29</th>\n",
" <td>782.789978</td>\n",
" <td>782.789978</td>\n",
" <td>166.600006</td>\n",
" <td>62.900002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-30</th>\n",
" <td>771.820007</td>\n",
" <td>771.820007</td>\n",
" <td>165.990005</td>\n",
" <td>62.139999</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>252 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" AAPL GOOG IBM MSFT\n",
"Date \n",
"2016-01-04 105.349998 741.840027 135.949997 0.000000\n",
"2016-01-05 102.709999 742.580017 135.850006 0.000000\n",
"2016-01-06 100.699997 743.619995 135.169998 0.000000\n",
"2016-01-07 96.449997 726.390015 132.860001 0.000000\n",
"2016-01-08 96.959999 714.469971 131.630005 0.000000\n",
"2016-01-11 98.529999 716.030029 133.229996 0.000000\n",
"2016-01-12 99.959999 726.070007 132.899994 0.000000\n",
"2016-01-13 97.389999 700.559998 131.169998 0.000000\n",
"2016-01-14 99.519997 714.719971 132.910004 0.000000\n",
"2016-01-15 97.129997 694.450012 130.029999 0.000000\n",
"2016-01-19 96.660004 701.789978 128.110001 0.000000\n",
"2016-01-20 96.790001 698.450012 121.860001 0.000000\n",
"2016-01-21 96.300003 706.590027 122.910004 0.000000\n",
"2016-01-22 101.419998 725.250000 122.500000 0.000000\n",
"2016-01-25 99.440002 711.669983 122.080002 0.000000\n",
"2016-01-26 99.989998 713.039978 122.589996 0.000000\n",
"2016-01-27 93.419998 699.989990 120.959999 0.000000\n",
"2016-01-28 94.089996 730.960022 122.220001 0.000000\n",
"2016-01-29 97.339996 742.950012 124.790001 0.000000\n",
"2016-02-01 96.430000 752.000000 124.830002 0.000000\n",
"2016-02-02 94.480003 764.650024 122.940002 0.000000\n",
"2016-02-03 96.349998 726.950012 124.720001 0.000000\n",
"2016-02-04 96.599998 708.010010 127.650002 0.000000\n",
"2016-02-05 94.019997 683.570007 128.570007 0.000000\n",
"2016-02-08 95.010002 682.739990 126.980003 0.000000\n",
"2016-02-09 94.989998 678.109985 124.070000 0.000000\n",
"2016-02-10 94.269997 684.119995 120.190002 0.000000\n",
"2016-02-11 93.699997 683.109985 117.849998 0.000000\n",
"2016-02-12 93.989998 682.400024 121.040001 0.000000\n",
"2016-02-16 96.639999 691.000000 122.739998 0.000000\n",
"... ... ... ... ...\n",
"2016-11-17 109.949997 771.229980 159.800003 0.000000\n",
"2016-11-18 110.059998 760.539978 160.389999 60.349998\n",
"2016-11-21 111.730003 769.200012 162.770004 60.860001\n",
"2016-11-22 111.800003 768.270020 162.669998 61.119999\n",
"2016-11-23 111.230003 760.989990 161.979996 60.400002\n",
"2016-11-25 111.790001 761.679993 163.139999 60.529999\n",
"2016-11-28 111.570000 768.239990 164.520004 60.610001\n",
"2016-11-29 111.459999 770.840027 163.529999 61.090000\n",
"2016-11-30 110.519997 758.039978 162.220001 60.259998\n",
"2016-12-01 109.489998 747.919983 159.820007 0.000000\n",
"2016-12-02 109.900002 750.500000 160.020004 0.000000\n",
"2016-12-05 109.110001 762.520020 159.839996 0.000000\n",
"2016-12-06 109.949997 759.109985 160.350006 0.000000\n",
"2016-12-07 111.029999 771.190002 164.789993 61.369999\n",
"2016-12-08 776.419983 776.419983 165.360001 61.009998\n",
"2016-12-09 789.289978 789.289978 166.520004 61.970001\n",
"2016-12-12 789.270020 789.270020 165.500000 62.169998\n",
"2016-12-13 796.099976 796.099976 168.289993 62.980000\n",
"2016-12-14 797.070007 797.070007 168.509995 62.680000\n",
"2016-12-15 797.849976 797.849976 168.020004 62.580002\n",
"2016-12-16 790.799988 790.799988 166.729996 62.299999\n",
"2016-12-19 794.200012 794.200012 166.679993 63.619999\n",
"2016-12-20 796.419983 796.419983 167.600006 63.540001\n",
"2016-12-21 794.559998 794.559998 167.330002 63.540001\n",
"2016-12-22 791.260010 791.260010 167.059998 63.549999\n",
"2016-12-23 789.909973 789.909973 166.710007 63.240002\n",
"2016-12-27 791.549988 791.549988 167.139999 63.279999\n",
"2016-12-28 785.049988 785.049988 166.190002 62.990002\n",
"2016-12-29 782.789978 782.789978 166.600006 62.900002\n",
"2016-12-30 771.820007 771.820007 165.990005 62.139999\n",
"\n",
"[252 rows x 4 columns]"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df.where(df < 100,other=10,inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"inplace=Trueで直接置換できる。"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>GOOG</th>\n",
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" <th>Date</th>\n",
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" <th>2016-01-12</th>\n",
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" <tr>\n",
" <th>2016-01-14</th>\n",
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" <th>2016-01-15</th>\n",
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" <th>2016-01-19</th>\n",
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" <th>2016-01-20</th>\n",
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" <tr>\n",
" <th>2016-01-21</th>\n",
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" <tr>\n",
" <th>2016-01-22</th>\n",
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" <td>10.0</td>\n",
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" <tr>\n",
" <th>2016-01-25</th>\n",
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" <td>10.0</td>\n",
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" <th>2016-01-26</th>\n",
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" <tr>\n",
" <th>2016-01-27</th>\n",
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" <td>10.0</td>\n",
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" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-28</th>\n",
" <td>94.089996</td>\n",
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" <td>10.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-29</th>\n",
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" <td>10.0</td>\n",
" <td>10.0</td>\n",
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" <tr>\n",
" <th>2016-02-01</th>\n",
" <td>96.430000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-02</th>\n",
" <td>94.480003</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-03</th>\n",
" <td>96.349998</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-04</th>\n",
" <td>96.599998</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-02-05</th>\n",
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" <tr>\n",
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" </tr>\n",
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" <th>2016-02-09</th>\n",
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" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-10</th>\n",
" <td>94.269997</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-02-11</th>\n",
" <td>93.699997</td>\n",
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" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-12</th>\n",
" <td>93.989998</td>\n",
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" <td>10.0</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-02-16</th>\n",
" <td>96.639999</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>2016-11-17</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-18</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>60.349998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-21</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>60.860001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-22</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>61.119999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-23</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>60.400002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-25</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>60.529999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-28</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>60.610001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-29</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>61.090000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-30</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>60.259998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-01</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-02</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-05</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-06</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-07</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>61.369999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-08</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>61.009998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-09</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>61.970001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-12</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>62.169998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-13</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>62.980000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-14</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>62.680000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-15</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>62.580002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-16</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>62.299999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-19</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>63.619999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-20</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-21</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-22</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>63.549999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-23</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>63.240002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-27</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>63.279999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-28</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>62.990002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-29</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>62.900002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-30</th>\n",
" <td>10.000000</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>62.139999</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>252 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" AAPL GOOG IBM MSFT\n",
"Date \n",
"2016-01-04 10.000000 10.0 10.0 0.000000\n",
"2016-01-05 10.000000 10.0 10.0 0.000000\n",
"2016-01-06 10.000000 10.0 10.0 0.000000\n",
"2016-01-07 96.449997 10.0 10.0 0.000000\n",
"2016-01-08 96.959999 10.0 10.0 0.000000\n",
"2016-01-11 98.529999 10.0 10.0 0.000000\n",
"2016-01-12 99.959999 10.0 10.0 0.000000\n",
"2016-01-13 97.389999 10.0 10.0 0.000000\n",
"2016-01-14 99.519997 10.0 10.0 0.000000\n",
"2016-01-15 97.129997 10.0 10.0 0.000000\n",
"2016-01-19 96.660004 10.0 10.0 0.000000\n",
"2016-01-20 96.790001 10.0 10.0 0.000000\n",
"2016-01-21 96.300003 10.0 10.0 0.000000\n",
"2016-01-22 10.000000 10.0 10.0 0.000000\n",
"2016-01-25 99.440002 10.0 10.0 0.000000\n",
"2016-01-26 99.989998 10.0 10.0 0.000000\n",
"2016-01-27 93.419998 10.0 10.0 0.000000\n",
"2016-01-28 94.089996 10.0 10.0 0.000000\n",
"2016-01-29 97.339996 10.0 10.0 0.000000\n",
"2016-02-01 96.430000 10.0 10.0 0.000000\n",
"2016-02-02 94.480003 10.0 10.0 0.000000\n",
"2016-02-03 96.349998 10.0 10.0 0.000000\n",
"2016-02-04 96.599998 10.0 10.0 0.000000\n",
"2016-02-05 94.019997 10.0 10.0 0.000000\n",
"2016-02-08 95.010002 10.0 10.0 0.000000\n",
"2016-02-09 94.989998 10.0 10.0 0.000000\n",
"2016-02-10 94.269997 10.0 10.0 0.000000\n",
"2016-02-11 93.699997 10.0 10.0 0.000000\n",
"2016-02-12 93.989998 10.0 10.0 0.000000\n",
"2016-02-16 96.639999 10.0 10.0 0.000000\n",
"... ... ... ... ...\n",
"2016-11-17 10.000000 10.0 10.0 0.000000\n",
"2016-11-18 10.000000 10.0 10.0 60.349998\n",
"2016-11-21 10.000000 10.0 10.0 60.860001\n",
"2016-11-22 10.000000 10.0 10.0 61.119999\n",
"2016-11-23 10.000000 10.0 10.0 60.400002\n",
"2016-11-25 10.000000 10.0 10.0 60.529999\n",
"2016-11-28 10.000000 10.0 10.0 60.610001\n",
"2016-11-29 10.000000 10.0 10.0 61.090000\n",
"2016-11-30 10.000000 10.0 10.0 60.259998\n",
"2016-12-01 10.000000 10.0 10.0 0.000000\n",
"2016-12-02 10.000000 10.0 10.0 0.000000\n",
"2016-12-05 10.000000 10.0 10.0 0.000000\n",
"2016-12-06 10.000000 10.0 10.0 0.000000\n",
"2016-12-07 10.000000 10.0 10.0 61.369999\n",
"2016-12-08 10.000000 10.0 10.0 61.009998\n",
"2016-12-09 10.000000 10.0 10.0 61.970001\n",
"2016-12-12 10.000000 10.0 10.0 62.169998\n",
"2016-12-13 10.000000 10.0 10.0 62.980000\n",
"2016-12-14 10.000000 10.0 10.0 62.680000\n",
"2016-12-15 10.000000 10.0 10.0 62.580002\n",
"2016-12-16 10.000000 10.0 10.0 62.299999\n",
"2016-12-19 10.000000 10.0 10.0 63.619999\n",
"2016-12-20 10.000000 10.0 10.0 63.540001\n",
"2016-12-21 10.000000 10.0 10.0 63.540001\n",
"2016-12-22 10.000000 10.0 10.0 63.549999\n",
"2016-12-23 10.000000 10.0 10.0 63.240002\n",
"2016-12-27 10.000000 10.0 10.0 63.279999\n",
"2016-12-28 10.000000 10.0 10.0 62.990002\n",
"2016-12-29 10.000000 10.0 10.0 62.900002\n",
"2016-12-30 10.000000 10.0 10.0 62.139999\n",
"\n",
"[252 rows x 4 columns]"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"置換はmaskのほうが感覚に合うので使いやすい。"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = df2.copy()"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>AAPL</th>\n",
" <th>GOOG</th>\n",
" <th>IBM</th>\n",
" <th>MSFT</th>\n",
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" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
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" <th>2016-01-22</th>\n",
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" <th>2016-01-27</th>\n",
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" <th>2016-01-28</th>\n",
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" <td>122.220001</td>\n",
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" <th>2016-01-29</th>\n",
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" <td>124.790001</td>\n",
" <td>NaN</td>\n",
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" <th>2016-02-01</th>\n",
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" <td>752.000000</td>\n",
" <td>124.830002</td>\n",
" <td>NaN</td>\n",
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" <th>2016-02-02</th>\n",
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" <td>122.940002</td>\n",
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" <tr>\n",
" <th>2016-02-03</th>\n",
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" <th>2016-02-04</th>\n",
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" <td>708.010010</td>\n",
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" <td>NaN</td>\n",
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" <th>2016-02-05</th>\n",
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" <td>NaN</td>\n",
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" <tr>\n",
" <th>2016-02-08</th>\n",
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" <td>126.980003</td>\n",
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" <tr>\n",
" <th>2016-02-09</th>\n",
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" <td>124.070000</td>\n",
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" <th>2016-02-10</th>\n",
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" <td>120.190002</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>2016-02-11</th>\n",
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" <td>117.849998</td>\n",
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" <th>2016-02-12</th>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-16</th>\n",
" <td>NaN</td>\n",
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" <td>122.739998</td>\n",
" <td>NaN</td>\n",
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" <th>2016-11-17</th>\n",
" <td>109.949997</td>\n",
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" <th>2016-11-18</th>\n",
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" <td>760.539978</td>\n",
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" <td>NaN</td>\n",
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" <tr>\n",
" <th>2016-11-21</th>\n",
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" <td>769.200012</td>\n",
" <td>162.770004</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-22</th>\n",
" <td>111.800003</td>\n",
" <td>768.270020</td>\n",
" <td>162.669998</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-23</th>\n",
" <td>111.230003</td>\n",
" <td>760.989990</td>\n",
" <td>161.979996</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-25</th>\n",
" <td>111.790001</td>\n",
" <td>761.679993</td>\n",
" <td>163.139999</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-28</th>\n",
" <td>111.570000</td>\n",
" <td>768.239990</td>\n",
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" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-29</th>\n",
" <td>111.459999</td>\n",
" <td>770.840027</td>\n",
" <td>163.529999</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-30</th>\n",
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" <td>758.039978</td>\n",
" <td>162.220001</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-01</th>\n",
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" <td>747.919983</td>\n",
" <td>159.820007</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-02</th>\n",
" <td>109.900002</td>\n",
" <td>750.500000</td>\n",
" <td>160.020004</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-05</th>\n",
" <td>109.110001</td>\n",
" <td>762.520020</td>\n",
" <td>159.839996</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-06</th>\n",
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" <td>759.109985</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2016-12-07</th>\n",
" <td>111.029999</td>\n",
" <td>771.190002</td>\n",
" <td>164.789993</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-08</th>\n",
" <td>112.120003</td>\n",
" <td>776.419983</td>\n",
" <td>165.360001</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-09</th>\n",
" <td>113.949997</td>\n",
" <td>789.289978</td>\n",
" <td>166.520004</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-12</th>\n",
" <td>113.300003</td>\n",
" <td>789.270020</td>\n",
" <td>165.500000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-13</th>\n",
" <td>115.190002</td>\n",
" <td>796.099976</td>\n",
" <td>168.289993</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-14</th>\n",
" <td>115.190002</td>\n",
" <td>797.070007</td>\n",
" <td>168.509995</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-15</th>\n",
" <td>115.820000</td>\n",
" <td>797.849976</td>\n",
" <td>168.020004</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-16</th>\n",
" <td>115.970001</td>\n",
" <td>790.799988</td>\n",
" <td>166.729996</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-19</th>\n",
" <td>116.639999</td>\n",
" <td>794.200012</td>\n",
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" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-20</th>\n",
" <td>116.949997</td>\n",
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" <td>167.600006</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-21</th>\n",
" <td>117.059998</td>\n",
" <td>794.559998</td>\n",
" <td>167.330002</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-22</th>\n",
" <td>116.290001</td>\n",
" <td>791.260010</td>\n",
" <td>167.059998</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-23</th>\n",
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" <td>789.909973</td>\n",
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" <tr>\n",
" <th>2016-12-27</th>\n",
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" <tr>\n",
" <th>2016-12-28</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>2016-12-29</th>\n",
" <td>116.730003</td>\n",
" <td>782.789978</td>\n",
" <td>166.600006</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-30</th>\n",
" <td>115.820000</td>\n",
" <td>771.820007</td>\n",
" <td>165.990005</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>252 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" AAPL GOOG IBM MSFT\n",
"Date \n",
"2016-01-04 105.349998 741.840027 135.949997 NaN\n",
"2016-01-05 102.709999 742.580017 135.850006 NaN\n",
"2016-01-06 100.699997 743.619995 135.169998 NaN\n",
"2016-01-07 NaN 726.390015 132.860001 NaN\n",
"2016-01-08 NaN 714.469971 131.630005 NaN\n",
"2016-01-11 NaN 716.030029 133.229996 NaN\n",
"2016-01-12 NaN 726.070007 132.899994 NaN\n",
"2016-01-13 NaN 700.559998 131.169998 NaN\n",
"2016-01-14 NaN 714.719971 132.910004 NaN\n",
"2016-01-15 NaN 694.450012 130.029999 NaN\n",
"2016-01-19 NaN 701.789978 128.110001 NaN\n",
"2016-01-20 NaN 698.450012 121.860001 NaN\n",
"2016-01-21 NaN 706.590027 122.910004 NaN\n",
"2016-01-22 101.419998 725.250000 122.500000 NaN\n",
"2016-01-25 NaN 711.669983 122.080002 NaN\n",
"2016-01-26 NaN 713.039978 122.589996 NaN\n",
"2016-01-27 NaN 699.989990 120.959999 NaN\n",
"2016-01-28 NaN 730.960022 122.220001 NaN\n",
"2016-01-29 NaN 742.950012 124.790001 NaN\n",
"2016-02-01 NaN 752.000000 124.830002 NaN\n",
"2016-02-02 NaN 764.650024 122.940002 NaN\n",
"2016-02-03 NaN 726.950012 124.720001 NaN\n",
"2016-02-04 NaN 708.010010 127.650002 NaN\n",
"2016-02-05 NaN 683.570007 128.570007 NaN\n",
"2016-02-08 NaN 682.739990 126.980003 NaN\n",
"2016-02-09 NaN 678.109985 124.070000 NaN\n",
"2016-02-10 NaN 684.119995 120.190002 NaN\n",
"2016-02-11 NaN 683.109985 117.849998 NaN\n",
"2016-02-12 NaN 682.400024 121.040001 NaN\n",
"2016-02-16 NaN 691.000000 122.739998 NaN\n",
"... ... ... ... ...\n",
"2016-11-17 109.949997 771.229980 159.800003 NaN\n",
"2016-11-18 110.059998 760.539978 160.389999 NaN\n",
"2016-11-21 111.730003 769.200012 162.770004 NaN\n",
"2016-11-22 111.800003 768.270020 162.669998 NaN\n",
"2016-11-23 111.230003 760.989990 161.979996 NaN\n",
"2016-11-25 111.790001 761.679993 163.139999 NaN\n",
"2016-11-28 111.570000 768.239990 164.520004 NaN\n",
"2016-11-29 111.459999 770.840027 163.529999 NaN\n",
"2016-11-30 110.519997 758.039978 162.220001 NaN\n",
"2016-12-01 109.489998 747.919983 159.820007 NaN\n",
"2016-12-02 109.900002 750.500000 160.020004 NaN\n",
"2016-12-05 109.110001 762.520020 159.839996 NaN\n",
"2016-12-06 109.949997 759.109985 160.350006 NaN\n",
"2016-12-07 111.029999 771.190002 164.789993 NaN\n",
"2016-12-08 112.120003 776.419983 165.360001 NaN\n",
"2016-12-09 113.949997 789.289978 166.520004 NaN\n",
"2016-12-12 113.300003 789.270020 165.500000 NaN\n",
"2016-12-13 115.190002 796.099976 168.289993 NaN\n",
"2016-12-14 115.190002 797.070007 168.509995 NaN\n",
"2016-12-15 115.820000 797.849976 168.020004 NaN\n",
"2016-12-16 115.970001 790.799988 166.729996 NaN\n",
"2016-12-19 116.639999 794.200012 166.679993 NaN\n",
"2016-12-20 116.949997 796.419983 167.600006 NaN\n",
"2016-12-21 117.059998 794.559998 167.330002 NaN\n",
"2016-12-22 116.290001 791.260010 167.059998 NaN\n",
"2016-12-23 116.519997 789.909973 166.710007 NaN\n",
"2016-12-27 117.260002 791.549988 167.139999 NaN\n",
"2016-12-28 116.760002 785.049988 166.190002 NaN\n",
"2016-12-29 116.730003 782.789978 166.600006 NaN\n",
"2016-12-30 115.820000 771.820007 165.990005 NaN\n",
"\n",
"[252 rows x 4 columns]"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.mask(df < 100)#デフォルトはNaN"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AAPL</th>\n",
" <th>GOOG</th>\n",
" <th>IBM</th>\n",
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" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
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" <th></th>\n",
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" <td>135.949997</td>\n",
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" <th>2016-01-06</th>\n",
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" <th>2016-01-11</th>\n",
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" <tr>\n",
" <th>2016-01-12</th>\n",
" <td>10.000000</td>\n",
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" <td>132.899994</td>\n",
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" <th>2016-01-13</th>\n",
" <td>10.000000</td>\n",
" <td>700.559998</td>\n",
" <td>131.169998</td>\n",
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" <th>2016-01-14</th>\n",
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" <td>10.0</td>\n",
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" <tr>\n",
" <th>2016-01-15</th>\n",
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" <td>694.450012</td>\n",
" <td>130.029999</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-19</th>\n",
" <td>10.000000</td>\n",
" <td>701.789978</td>\n",
" <td>128.110001</td>\n",
" <td>10.0</td>\n",
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" <th>2016-01-20</th>\n",
" <td>10.000000</td>\n",
" <td>698.450012</td>\n",
" <td>121.860001</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-21</th>\n",
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" <td>706.590027</td>\n",
" <td>122.910004</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-22</th>\n",
" <td>101.419998</td>\n",
" <td>725.250000</td>\n",
" <td>122.500000</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-25</th>\n",
" <td>10.000000</td>\n",
" <td>711.669983</td>\n",
" <td>122.080002</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-26</th>\n",
" <td>10.000000</td>\n",
" <td>713.039978</td>\n",
" <td>122.589996</td>\n",
" <td>10.0</td>\n",
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" <tr>\n",
" <th>2016-01-27</th>\n",
" <td>10.000000</td>\n",
" <td>699.989990</td>\n",
" <td>120.959999</td>\n",
" <td>10.0</td>\n",
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" <tr>\n",
" <th>2016-01-28</th>\n",
" <td>10.000000</td>\n",
" <td>730.960022</td>\n",
" <td>122.220001</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-29</th>\n",
" <td>10.000000</td>\n",
" <td>742.950012</td>\n",
" <td>124.790001</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-01</th>\n",
" <td>10.000000</td>\n",
" <td>752.000000</td>\n",
" <td>124.830002</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-02</th>\n",
" <td>10.000000</td>\n",
" <td>764.650024</td>\n",
" <td>122.940002</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-03</th>\n",
" <td>10.000000</td>\n",
" <td>726.950012</td>\n",
" <td>124.720001</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-04</th>\n",
" <td>10.000000</td>\n",
" <td>708.010010</td>\n",
" <td>127.650002</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-05</th>\n",
" <td>10.000000</td>\n",
" <td>683.570007</td>\n",
" <td>128.570007</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-08</th>\n",
" <td>10.000000</td>\n",
" <td>682.739990</td>\n",
" <td>126.980003</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-09</th>\n",
" <td>10.000000</td>\n",
" <td>678.109985</td>\n",
" <td>124.070000</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-10</th>\n",
" <td>10.000000</td>\n",
" <td>684.119995</td>\n",
" <td>120.190002</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-11</th>\n",
" <td>10.000000</td>\n",
" <td>683.109985</td>\n",
" <td>117.849998</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-12</th>\n",
" <td>10.000000</td>\n",
" <td>682.400024</td>\n",
" <td>121.040001</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-16</th>\n",
" <td>10.000000</td>\n",
" <td>691.000000</td>\n",
" <td>122.739998</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-17</th>\n",
" <td>109.949997</td>\n",
" <td>771.229980</td>\n",
" <td>159.800003</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-18</th>\n",
" <td>110.059998</td>\n",
" <td>760.539978</td>\n",
" <td>160.389999</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-21</th>\n",
" <td>111.730003</td>\n",
" <td>769.200012</td>\n",
" <td>162.770004</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-22</th>\n",
" <td>111.800003</td>\n",
" <td>768.270020</td>\n",
" <td>162.669998</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-23</th>\n",
" <td>111.230003</td>\n",
" <td>760.989990</td>\n",
" <td>161.979996</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-25</th>\n",
" <td>111.790001</td>\n",
" <td>761.679993</td>\n",
" <td>163.139999</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-28</th>\n",
" <td>111.570000</td>\n",
" <td>768.239990</td>\n",
" <td>164.520004</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-29</th>\n",
" <td>111.459999</td>\n",
" <td>770.840027</td>\n",
" <td>163.529999</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-30</th>\n",
" <td>110.519997</td>\n",
" <td>758.039978</td>\n",
" <td>162.220001</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-01</th>\n",
" <td>109.489998</td>\n",
" <td>747.919983</td>\n",
" <td>159.820007</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-02</th>\n",
" <td>109.900002</td>\n",
" <td>750.500000</td>\n",
" <td>160.020004</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-05</th>\n",
" <td>109.110001</td>\n",
" <td>762.520020</td>\n",
" <td>159.839996</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-06</th>\n",
" <td>109.949997</td>\n",
" <td>759.109985</td>\n",
" <td>160.350006</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-07</th>\n",
" <td>111.029999</td>\n",
" <td>771.190002</td>\n",
" <td>164.789993</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-08</th>\n",
" <td>112.120003</td>\n",
" <td>776.419983</td>\n",
" <td>165.360001</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-09</th>\n",
" <td>113.949997</td>\n",
" <td>789.289978</td>\n",
" <td>166.520004</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-12</th>\n",
" <td>113.300003</td>\n",
" <td>789.270020</td>\n",
" <td>165.500000</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-13</th>\n",
" <td>115.190002</td>\n",
" <td>796.099976</td>\n",
" <td>168.289993</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-14</th>\n",
" <td>115.190002</td>\n",
" <td>797.070007</td>\n",
" <td>168.509995</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-15</th>\n",
" <td>115.820000</td>\n",
" <td>797.849976</td>\n",
" <td>168.020004</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-16</th>\n",
" <td>115.970001</td>\n",
" <td>790.799988</td>\n",
" <td>166.729996</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-19</th>\n",
" <td>116.639999</td>\n",
" <td>794.200012</td>\n",
" <td>166.679993</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-20</th>\n",
" <td>116.949997</td>\n",
" <td>796.419983</td>\n",
" <td>167.600006</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-21</th>\n",
" <td>117.059998</td>\n",
" <td>794.559998</td>\n",
" <td>167.330002</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-22</th>\n",
" <td>116.290001</td>\n",
" <td>791.260010</td>\n",
" <td>167.059998</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-23</th>\n",
" <td>116.519997</td>\n",
" <td>789.909973</td>\n",
" <td>166.710007</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-27</th>\n",
" <td>117.260002</td>\n",
" <td>791.549988</td>\n",
" <td>167.139999</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-28</th>\n",
" <td>116.760002</td>\n",
" <td>785.049988</td>\n",
" <td>166.190002</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-29</th>\n",
" <td>116.730003</td>\n",
" <td>782.789978</td>\n",
" <td>166.600006</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-30</th>\n",
" <td>115.820000</td>\n",
" <td>771.820007</td>\n",
" <td>165.990005</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>252 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" AAPL GOOG IBM MSFT\n",
"Date \n",
"2016-01-04 105.349998 741.840027 135.949997 10.0\n",
"2016-01-05 102.709999 742.580017 135.850006 10.0\n",
"2016-01-06 100.699997 743.619995 135.169998 10.0\n",
"2016-01-07 10.000000 726.390015 132.860001 10.0\n",
"2016-01-08 10.000000 714.469971 131.630005 10.0\n",
"2016-01-11 10.000000 716.030029 133.229996 10.0\n",
"2016-01-12 10.000000 726.070007 132.899994 10.0\n",
"2016-01-13 10.000000 700.559998 131.169998 10.0\n",
"2016-01-14 10.000000 714.719971 132.910004 10.0\n",
"2016-01-15 10.000000 694.450012 130.029999 10.0\n",
"2016-01-19 10.000000 701.789978 128.110001 10.0\n",
"2016-01-20 10.000000 698.450012 121.860001 10.0\n",
"2016-01-21 10.000000 706.590027 122.910004 10.0\n",
"2016-01-22 101.419998 725.250000 122.500000 10.0\n",
"2016-01-25 10.000000 711.669983 122.080002 10.0\n",
"2016-01-26 10.000000 713.039978 122.589996 10.0\n",
"2016-01-27 10.000000 699.989990 120.959999 10.0\n",
"2016-01-28 10.000000 730.960022 122.220001 10.0\n",
"2016-01-29 10.000000 742.950012 124.790001 10.0\n",
"2016-02-01 10.000000 752.000000 124.830002 10.0\n",
"2016-02-02 10.000000 764.650024 122.940002 10.0\n",
"2016-02-03 10.000000 726.950012 124.720001 10.0\n",
"2016-02-04 10.000000 708.010010 127.650002 10.0\n",
"2016-02-05 10.000000 683.570007 128.570007 10.0\n",
"2016-02-08 10.000000 682.739990 126.980003 10.0\n",
"2016-02-09 10.000000 678.109985 124.070000 10.0\n",
"2016-02-10 10.000000 684.119995 120.190002 10.0\n",
"2016-02-11 10.000000 683.109985 117.849998 10.0\n",
"2016-02-12 10.000000 682.400024 121.040001 10.0\n",
"2016-02-16 10.000000 691.000000 122.739998 10.0\n",
"... ... ... ... ...\n",
"2016-11-17 109.949997 771.229980 159.800003 10.0\n",
"2016-11-18 110.059998 760.539978 160.389999 10.0\n",
"2016-11-21 111.730003 769.200012 162.770004 10.0\n",
"2016-11-22 111.800003 768.270020 162.669998 10.0\n",
"2016-11-23 111.230003 760.989990 161.979996 10.0\n",
"2016-11-25 111.790001 761.679993 163.139999 10.0\n",
"2016-11-28 111.570000 768.239990 164.520004 10.0\n",
"2016-11-29 111.459999 770.840027 163.529999 10.0\n",
"2016-11-30 110.519997 758.039978 162.220001 10.0\n",
"2016-12-01 109.489998 747.919983 159.820007 10.0\n",
"2016-12-02 109.900002 750.500000 160.020004 10.0\n",
"2016-12-05 109.110001 762.520020 159.839996 10.0\n",
"2016-12-06 109.949997 759.109985 160.350006 10.0\n",
"2016-12-07 111.029999 771.190002 164.789993 10.0\n",
"2016-12-08 112.120003 776.419983 165.360001 10.0\n",
"2016-12-09 113.949997 789.289978 166.520004 10.0\n",
"2016-12-12 113.300003 789.270020 165.500000 10.0\n",
"2016-12-13 115.190002 796.099976 168.289993 10.0\n",
"2016-12-14 115.190002 797.070007 168.509995 10.0\n",
"2016-12-15 115.820000 797.849976 168.020004 10.0\n",
"2016-12-16 115.970001 790.799988 166.729996 10.0\n",
"2016-12-19 116.639999 794.200012 166.679993 10.0\n",
"2016-12-20 116.949997 796.419983 167.600006 10.0\n",
"2016-12-21 117.059998 794.559998 167.330002 10.0\n",
"2016-12-22 116.290001 791.260010 167.059998 10.0\n",
"2016-12-23 116.519997 789.909973 166.710007 10.0\n",
"2016-12-27 117.260002 791.549988 167.139999 10.0\n",
"2016-12-28 116.760002 785.049988 166.190002 10.0\n",
"2016-12-29 116.730003 782.789978 166.600006 10.0\n",
"2016-12-30 115.820000 771.820007 165.990005 10.0\n",
"\n",
"[252 rows x 4 columns]"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.mask(df < 100,10)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df['MSFT']=df['MSFT'].where((df['AAPL']>110) & (df['GOOG']>750),0)"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AAPL</th>\n",
" <th>GOOG</th>\n",
" <th>IBM</th>\n",
" <th>MSFT</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
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" <tr>\n",
" <th>2016-01-04</th>\n",
" <td>105.349998</td>\n",
" <td>741.840027</td>\n",
" <td>135.949997</td>\n",
" <td>0.000000</td>\n",
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" <th>2016-01-05</th>\n",
" <td>102.709999</td>\n",
" <td>742.580017</td>\n",
" <td>135.850006</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-06</th>\n",
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" <td>135.169998</td>\n",
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" <th>2016-01-07</th>\n",
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" <td>0.000000</td>\n",
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" <th>2016-01-08</th>\n",
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" <td>714.469971</td>\n",
" <td>131.630005</td>\n",
" <td>0.000000</td>\n",
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" <th>2016-01-11</th>\n",
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" <td>0.000000</td>\n",
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" <th>2016-01-12</th>\n",
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" <th>2016-01-13</th>\n",
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" <td>700.559998</td>\n",
" <td>131.169998</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-14</th>\n",
" <td>99.519997</td>\n",
" <td>714.719971</td>\n",
" <td>132.910004</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-15</th>\n",
" <td>97.129997</td>\n",
" <td>694.450012</td>\n",
" <td>130.029999</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-19</th>\n",
" <td>96.660004</td>\n",
" <td>701.789978</td>\n",
" <td>128.110001</td>\n",
" <td>0.000000</td>\n",
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" <th>2016-01-20</th>\n",
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" <td>698.450012</td>\n",
" <td>121.860001</td>\n",
" <td>0.000000</td>\n",
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" <th>2016-01-21</th>\n",
" <td>96.300003</td>\n",
" <td>706.590027</td>\n",
" <td>122.910004</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-22</th>\n",
" <td>101.419998</td>\n",
" <td>725.250000</td>\n",
" <td>122.500000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-25</th>\n",
" <td>99.440002</td>\n",
" <td>711.669983</td>\n",
" <td>122.080002</td>\n",
" <td>0.000000</td>\n",
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" <th>2016-01-26</th>\n",
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" <td>713.039978</td>\n",
" <td>122.589996</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-27</th>\n",
" <td>93.419998</td>\n",
" <td>699.989990</td>\n",
" <td>120.959999</td>\n",
" <td>0.000000</td>\n",
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" <th>2016-01-28</th>\n",
" <td>94.089996</td>\n",
" <td>730.960022</td>\n",
" <td>122.220001</td>\n",
" <td>0.000000</td>\n",
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" <th>2016-01-29</th>\n",
" <td>97.339996</td>\n",
" <td>742.950012</td>\n",
" <td>124.790001</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-02-01</th>\n",
" <td>96.430000</td>\n",
" <td>752.000000</td>\n",
" <td>124.830002</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-02</th>\n",
" <td>94.480003</td>\n",
" <td>764.650024</td>\n",
" <td>122.940002</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-03</th>\n",
" <td>96.349998</td>\n",
" <td>726.950012</td>\n",
" <td>124.720001</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-04</th>\n",
" <td>96.599998</td>\n",
" <td>708.010010</td>\n",
" <td>127.650002</td>\n",
" <td>0.000000</td>\n",
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" <th>2016-02-05</th>\n",
" <td>94.019997</td>\n",
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" <td>128.570007</td>\n",
" <td>0.000000</td>\n",
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" <th>2016-02-08</th>\n",
" <td>95.010002</td>\n",
" <td>682.739990</td>\n",
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" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-02-09</th>\n",
" <td>94.989998</td>\n",
" <td>678.109985</td>\n",
" <td>124.070000</td>\n",
" <td>0.000000</td>\n",
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" <th>2016-02-10</th>\n",
" <td>94.269997</td>\n",
" <td>684.119995</td>\n",
" <td>120.190002</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-02-11</th>\n",
" <td>93.699997</td>\n",
" <td>683.109985</td>\n",
" <td>117.849998</td>\n",
" <td>0.000000</td>\n",
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" <th>2016-02-12</th>\n",
" <td>93.989998</td>\n",
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" <th>2016-02-16</th>\n",
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" <td>691.000000</td>\n",
" <td>122.739998</td>\n",
" <td>0.000000</td>\n",
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" <th>2016-11-17</th>\n",
" <td>109.949997</td>\n",
" <td>771.229980</td>\n",
" <td>159.800003</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-18</th>\n",
" <td>110.059998</td>\n",
" <td>760.539978</td>\n",
" <td>160.389999</td>\n",
" <td>60.349998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-21</th>\n",
" <td>111.730003</td>\n",
" <td>769.200012</td>\n",
" <td>162.770004</td>\n",
" <td>60.860001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-22</th>\n",
" <td>111.800003</td>\n",
" <td>768.270020</td>\n",
" <td>162.669998</td>\n",
" <td>61.119999</td>\n",
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" <tr>\n",
" <th>2016-11-23</th>\n",
" <td>111.230003</td>\n",
" <td>760.989990</td>\n",
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" <td>60.400002</td>\n",
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" <tr>\n",
" <th>2016-11-25</th>\n",
" <td>111.790001</td>\n",
" <td>761.679993</td>\n",
" <td>163.139999</td>\n",
" <td>60.529999</td>\n",
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" <tr>\n",
" <th>2016-11-28</th>\n",
" <td>111.570000</td>\n",
" <td>768.239990</td>\n",
" <td>164.520004</td>\n",
" <td>60.610001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-29</th>\n",
" <td>111.459999</td>\n",
" <td>770.840027</td>\n",
" <td>163.529999</td>\n",
" <td>61.090000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-30</th>\n",
" <td>110.519997</td>\n",
" <td>758.039978</td>\n",
" <td>162.220001</td>\n",
" <td>60.259998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-01</th>\n",
" <td>109.489998</td>\n",
" <td>747.919983</td>\n",
" <td>159.820007</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-02</th>\n",
" <td>109.900002</td>\n",
" <td>750.500000</td>\n",
" <td>160.020004</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-05</th>\n",
" <td>109.110001</td>\n",
" <td>762.520020</td>\n",
" <td>159.839996</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-06</th>\n",
" <td>109.949997</td>\n",
" <td>759.109985</td>\n",
" <td>160.350006</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-07</th>\n",
" <td>111.029999</td>\n",
" <td>771.190002</td>\n",
" <td>164.789993</td>\n",
" <td>61.369999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-08</th>\n",
" <td>112.120003</td>\n",
" <td>776.419983</td>\n",
" <td>165.360001</td>\n",
" <td>61.009998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-09</th>\n",
" <td>113.949997</td>\n",
" <td>789.289978</td>\n",
" <td>166.520004</td>\n",
" <td>61.970001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-12</th>\n",
" <td>113.300003</td>\n",
" <td>789.270020</td>\n",
" <td>165.500000</td>\n",
" <td>62.169998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-13</th>\n",
" <td>115.190002</td>\n",
" <td>796.099976</td>\n",
" <td>168.289993</td>\n",
" <td>62.980000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-14</th>\n",
" <td>115.190002</td>\n",
" <td>797.070007</td>\n",
" <td>168.509995</td>\n",
" <td>62.680000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-15</th>\n",
" <td>115.820000</td>\n",
" <td>797.849976</td>\n",
" <td>168.020004</td>\n",
" <td>62.580002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-16</th>\n",
" <td>115.970001</td>\n",
" <td>790.799988</td>\n",
" <td>166.729996</td>\n",
" <td>62.299999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-19</th>\n",
" <td>116.639999</td>\n",
" <td>794.200012</td>\n",
" <td>166.679993</td>\n",
" <td>63.619999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-20</th>\n",
" <td>116.949997</td>\n",
" <td>796.419983</td>\n",
" <td>167.600006</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-21</th>\n",
" <td>117.059998</td>\n",
" <td>794.559998</td>\n",
" <td>167.330002</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-22</th>\n",
" <td>116.290001</td>\n",
" <td>791.260010</td>\n",
" <td>167.059998</td>\n",
" <td>63.549999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-23</th>\n",
" <td>116.519997</td>\n",
" <td>789.909973</td>\n",
" <td>166.710007</td>\n",
" <td>63.240002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-27</th>\n",
" <td>117.260002</td>\n",
" <td>791.549988</td>\n",
" <td>167.139999</td>\n",
" <td>63.279999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-28</th>\n",
" <td>116.760002</td>\n",
" <td>785.049988</td>\n",
" <td>166.190002</td>\n",
" <td>62.990002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-29</th>\n",
" <td>116.730003</td>\n",
" <td>782.789978</td>\n",
" <td>166.600006</td>\n",
" <td>62.900002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-30</th>\n",
" <td>115.820000</td>\n",
" <td>771.820007</td>\n",
" <td>165.990005</td>\n",
" <td>62.139999</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>252 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" AAPL GOOG IBM MSFT\n",
"Date \n",
"2016-01-04 105.349998 741.840027 135.949997 0.000000\n",
"2016-01-05 102.709999 742.580017 135.850006 0.000000\n",
"2016-01-06 100.699997 743.619995 135.169998 0.000000\n",
"2016-01-07 96.449997 726.390015 132.860001 0.000000\n",
"2016-01-08 96.959999 714.469971 131.630005 0.000000\n",
"2016-01-11 98.529999 716.030029 133.229996 0.000000\n",
"2016-01-12 99.959999 726.070007 132.899994 0.000000\n",
"2016-01-13 97.389999 700.559998 131.169998 0.000000\n",
"2016-01-14 99.519997 714.719971 132.910004 0.000000\n",
"2016-01-15 97.129997 694.450012 130.029999 0.000000\n",
"2016-01-19 96.660004 701.789978 128.110001 0.000000\n",
"2016-01-20 96.790001 698.450012 121.860001 0.000000\n",
"2016-01-21 96.300003 706.590027 122.910004 0.000000\n",
"2016-01-22 101.419998 725.250000 122.500000 0.000000\n",
"2016-01-25 99.440002 711.669983 122.080002 0.000000\n",
"2016-01-26 99.989998 713.039978 122.589996 0.000000\n",
"2016-01-27 93.419998 699.989990 120.959999 0.000000\n",
"2016-01-28 94.089996 730.960022 122.220001 0.000000\n",
"2016-01-29 97.339996 742.950012 124.790001 0.000000\n",
"2016-02-01 96.430000 752.000000 124.830002 0.000000\n",
"2016-02-02 94.480003 764.650024 122.940002 0.000000\n",
"2016-02-03 96.349998 726.950012 124.720001 0.000000\n",
"2016-02-04 96.599998 708.010010 127.650002 0.000000\n",
"2016-02-05 94.019997 683.570007 128.570007 0.000000\n",
"2016-02-08 95.010002 682.739990 126.980003 0.000000\n",
"2016-02-09 94.989998 678.109985 124.070000 0.000000\n",
"2016-02-10 94.269997 684.119995 120.190002 0.000000\n",
"2016-02-11 93.699997 683.109985 117.849998 0.000000\n",
"2016-02-12 93.989998 682.400024 121.040001 0.000000\n",
"2016-02-16 96.639999 691.000000 122.739998 0.000000\n",
"... ... ... ... ...\n",
"2016-11-17 109.949997 771.229980 159.800003 0.000000\n",
"2016-11-18 110.059998 760.539978 160.389999 60.349998\n",
"2016-11-21 111.730003 769.200012 162.770004 60.860001\n",
"2016-11-22 111.800003 768.270020 162.669998 61.119999\n",
"2016-11-23 111.230003 760.989990 161.979996 60.400002\n",
"2016-11-25 111.790001 761.679993 163.139999 60.529999\n",
"2016-11-28 111.570000 768.239990 164.520004 60.610001\n",
"2016-11-29 111.459999 770.840027 163.529999 61.090000\n",
"2016-11-30 110.519997 758.039978 162.220001 60.259998\n",
"2016-12-01 109.489998 747.919983 159.820007 0.000000\n",
"2016-12-02 109.900002 750.500000 160.020004 0.000000\n",
"2016-12-05 109.110001 762.520020 159.839996 0.000000\n",
"2016-12-06 109.949997 759.109985 160.350006 0.000000\n",
"2016-12-07 111.029999 771.190002 164.789993 61.369999\n",
"2016-12-08 112.120003 776.419983 165.360001 61.009998\n",
"2016-12-09 113.949997 789.289978 166.520004 61.970001\n",
"2016-12-12 113.300003 789.270020 165.500000 62.169998\n",
"2016-12-13 115.190002 796.099976 168.289993 62.980000\n",
"2016-12-14 115.190002 797.070007 168.509995 62.680000\n",
"2016-12-15 115.820000 797.849976 168.020004 62.580002\n",
"2016-12-16 115.970001 790.799988 166.729996 62.299999\n",
"2016-12-19 116.639999 794.200012 166.679993 63.619999\n",
"2016-12-20 116.949997 796.419983 167.600006 63.540001\n",
"2016-12-21 117.059998 794.559998 167.330002 63.540001\n",
"2016-12-22 116.290001 791.260010 167.059998 63.549999\n",
"2016-12-23 116.519997 789.909973 166.710007 63.240002\n",
"2016-12-27 117.260002 791.549988 167.139999 63.279999\n",
"2016-12-28 116.760002 785.049988 166.190002 62.990002\n",
"2016-12-29 116.730003 782.789978 166.600006 62.900002\n",
"2016-12-30 115.820000 771.820007 165.990005 62.139999\n",
"\n",
"[252 rows x 4 columns]"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"条件に合うものを別の列で置換する"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df['AAPL'] = df['AAPL'].mask(df['AAPL'] < 112, df['GOOG'])"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AAPL</th>\n",
" <th>GOOG</th>\n",
" <th>IBM</th>\n",
" <th>MSFT</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <tr>\n",
" <th>2016-01-04</th>\n",
" <td>741.840027</td>\n",
" <td>741.840027</td>\n",
" <td>135.949997</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-05</th>\n",
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" <td>742.580017</td>\n",
" <td>135.850006</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-06</th>\n",
" <td>743.619995</td>\n",
" <td>743.619995</td>\n",
" <td>135.169998</td>\n",
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" <th>2016-01-07</th>\n",
" <td>726.390015</td>\n",
" <td>726.390015</td>\n",
" <td>132.860001</td>\n",
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" <th>2016-01-08</th>\n",
" <td>714.469971</td>\n",
" <td>714.469971</td>\n",
" <td>131.630005</td>\n",
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" <th>2016-01-11</th>\n",
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" <td>133.229996</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-12</th>\n",
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" <td>726.070007</td>\n",
" <td>132.899994</td>\n",
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" <tr>\n",
" <th>2016-01-13</th>\n",
" <td>700.559998</td>\n",
" <td>700.559998</td>\n",
" <td>131.169998</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-14</th>\n",
" <td>714.719971</td>\n",
" <td>714.719971</td>\n",
" <td>132.910004</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-15</th>\n",
" <td>694.450012</td>\n",
" <td>694.450012</td>\n",
" <td>130.029999</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-19</th>\n",
" <td>701.789978</td>\n",
" <td>701.789978</td>\n",
" <td>128.110001</td>\n",
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" <th>2016-01-20</th>\n",
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" <td>698.450012</td>\n",
" <td>121.860001</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-21</th>\n",
" <td>706.590027</td>\n",
" <td>706.590027</td>\n",
" <td>122.910004</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-22</th>\n",
" <td>725.250000</td>\n",
" <td>725.250000</td>\n",
" <td>122.500000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-25</th>\n",
" <td>711.669983</td>\n",
" <td>711.669983</td>\n",
" <td>122.080002</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-26</th>\n",
" <td>713.039978</td>\n",
" <td>713.039978</td>\n",
" <td>122.589996</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-27</th>\n",
" <td>699.989990</td>\n",
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" <td>120.959999</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-01-28</th>\n",
" <td>730.960022</td>\n",
" <td>730.960022</td>\n",
" <td>122.220001</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-29</th>\n",
" <td>742.950012</td>\n",
" <td>742.950012</td>\n",
" <td>124.790001</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-01</th>\n",
" <td>752.000000</td>\n",
" <td>752.000000</td>\n",
" <td>124.830002</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-02</th>\n",
" <td>764.650024</td>\n",
" <td>764.650024</td>\n",
" <td>122.940002</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-03</th>\n",
" <td>726.950012</td>\n",
" <td>726.950012</td>\n",
" <td>124.720001</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-04</th>\n",
" <td>708.010010</td>\n",
" <td>708.010010</td>\n",
" <td>127.650002</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-02-05</th>\n",
" <td>683.570007</td>\n",
" <td>683.570007</td>\n",
" <td>128.570007</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-02-08</th>\n",
" <td>682.739990</td>\n",
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" <td>126.980003</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-02-09</th>\n",
" <td>678.109985</td>\n",
" <td>678.109985</td>\n",
" <td>124.070000</td>\n",
" <td>0.000000</td>\n",
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" <th>2016-02-10</th>\n",
" <td>684.119995</td>\n",
" <td>684.119995</td>\n",
" <td>120.190002</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-02-11</th>\n",
" <td>683.109985</td>\n",
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" <td>117.849998</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-02-12</th>\n",
" <td>682.400024</td>\n",
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" <td>121.040001</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>2016-02-16</th>\n",
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" <td>691.000000</td>\n",
" <td>122.739998</td>\n",
" <td>0.000000</td>\n",
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" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>2016-11-17</th>\n",
" <td>771.229980</td>\n",
" <td>771.229980</td>\n",
" <td>159.800003</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-18</th>\n",
" <td>760.539978</td>\n",
" <td>760.539978</td>\n",
" <td>160.389999</td>\n",
" <td>60.349998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-21</th>\n",
" <td>769.200012</td>\n",
" <td>769.200012</td>\n",
" <td>162.770004</td>\n",
" <td>60.860001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-22</th>\n",
" <td>768.270020</td>\n",
" <td>768.270020</td>\n",
" <td>162.669998</td>\n",
" <td>61.119999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-23</th>\n",
" <td>760.989990</td>\n",
" <td>760.989990</td>\n",
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" <td>60.400002</td>\n",
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" <tr>\n",
" <th>2016-11-25</th>\n",
" <td>761.679993</td>\n",
" <td>761.679993</td>\n",
" <td>163.139999</td>\n",
" <td>60.529999</td>\n",
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" <tr>\n",
" <th>2016-11-28</th>\n",
" <td>768.239990</td>\n",
" <td>768.239990</td>\n",
" <td>164.520004</td>\n",
" <td>60.610001</td>\n",
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" <tr>\n",
" <th>2016-11-29</th>\n",
" <td>770.840027</td>\n",
" <td>770.840027</td>\n",
" <td>163.529999</td>\n",
" <td>61.090000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-30</th>\n",
" <td>758.039978</td>\n",
" <td>758.039978</td>\n",
" <td>162.220001</td>\n",
" <td>60.259998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-01</th>\n",
" <td>747.919983</td>\n",
" <td>747.919983</td>\n",
" <td>159.820007</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-02</th>\n",
" <td>750.500000</td>\n",
" <td>750.500000</td>\n",
" <td>160.020004</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-05</th>\n",
" <td>762.520020</td>\n",
" <td>762.520020</td>\n",
" <td>159.839996</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-06</th>\n",
" <td>759.109985</td>\n",
" <td>759.109985</td>\n",
" <td>160.350006</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-07</th>\n",
" <td>771.190002</td>\n",
" <td>771.190002</td>\n",
" <td>164.789993</td>\n",
" <td>61.369999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-08</th>\n",
" <td>112.120003</td>\n",
" <td>776.419983</td>\n",
" <td>165.360001</td>\n",
" <td>61.009998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-09</th>\n",
" <td>113.949997</td>\n",
" <td>789.289978</td>\n",
" <td>166.520004</td>\n",
" <td>61.970001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-12</th>\n",
" <td>113.300003</td>\n",
" <td>789.270020</td>\n",
" <td>165.500000</td>\n",
" <td>62.169998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-13</th>\n",
" <td>115.190002</td>\n",
" <td>796.099976</td>\n",
" <td>168.289993</td>\n",
" <td>62.980000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-14</th>\n",
" <td>115.190002</td>\n",
" <td>797.070007</td>\n",
" <td>168.509995</td>\n",
" <td>62.680000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-15</th>\n",
" <td>115.820000</td>\n",
" <td>797.849976</td>\n",
" <td>168.020004</td>\n",
" <td>62.580002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-16</th>\n",
" <td>115.970001</td>\n",
" <td>790.799988</td>\n",
" <td>166.729996</td>\n",
" <td>62.299999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-19</th>\n",
" <td>116.639999</td>\n",
" <td>794.200012</td>\n",
" <td>166.679993</td>\n",
" <td>63.619999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-20</th>\n",
" <td>116.949997</td>\n",
" <td>796.419983</td>\n",
" <td>167.600006</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-21</th>\n",
" <td>117.059998</td>\n",
" <td>794.559998</td>\n",
" <td>167.330002</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-22</th>\n",
" <td>116.290001</td>\n",
" <td>791.260010</td>\n",
" <td>167.059998</td>\n",
" <td>63.549999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-23</th>\n",
" <td>116.519997</td>\n",
" <td>789.909973</td>\n",
" <td>166.710007</td>\n",
" <td>63.240002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-27</th>\n",
" <td>117.260002</td>\n",
" <td>791.549988</td>\n",
" <td>167.139999</td>\n",
" <td>63.279999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-28</th>\n",
" <td>116.760002</td>\n",
" <td>785.049988</td>\n",
" <td>166.190002</td>\n",
" <td>62.990002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-29</th>\n",
" <td>116.730003</td>\n",
" <td>782.789978</td>\n",
" <td>166.600006</td>\n",
" <td>62.900002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-12-30</th>\n",
" <td>115.820000</td>\n",
" <td>771.820007</td>\n",
" <td>165.990005</td>\n",
" <td>62.139999</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>252 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" AAPL GOOG IBM MSFT\n",
"Date \n",
"2016-01-04 741.840027 741.840027 135.949997 0.000000\n",
"2016-01-05 742.580017 742.580017 135.850006 0.000000\n",
"2016-01-06 743.619995 743.619995 135.169998 0.000000\n",
"2016-01-07 726.390015 726.390015 132.860001 0.000000\n",
"2016-01-08 714.469971 714.469971 131.630005 0.000000\n",
"2016-01-11 716.030029 716.030029 133.229996 0.000000\n",
"2016-01-12 726.070007 726.070007 132.899994 0.000000\n",
"2016-01-13 700.559998 700.559998 131.169998 0.000000\n",
"2016-01-14 714.719971 714.719971 132.910004 0.000000\n",
"2016-01-15 694.450012 694.450012 130.029999 0.000000\n",
"2016-01-19 701.789978 701.789978 128.110001 0.000000\n",
"2016-01-20 698.450012 698.450012 121.860001 0.000000\n",
"2016-01-21 706.590027 706.590027 122.910004 0.000000\n",
"2016-01-22 725.250000 725.250000 122.500000 0.000000\n",
"2016-01-25 711.669983 711.669983 122.080002 0.000000\n",
"2016-01-26 713.039978 713.039978 122.589996 0.000000\n",
"2016-01-27 699.989990 699.989990 120.959999 0.000000\n",
"2016-01-28 730.960022 730.960022 122.220001 0.000000\n",
"2016-01-29 742.950012 742.950012 124.790001 0.000000\n",
"2016-02-01 752.000000 752.000000 124.830002 0.000000\n",
"2016-02-02 764.650024 764.650024 122.940002 0.000000\n",
"2016-02-03 726.950012 726.950012 124.720001 0.000000\n",
"2016-02-04 708.010010 708.010010 127.650002 0.000000\n",
"2016-02-05 683.570007 683.570007 128.570007 0.000000\n",
"2016-02-08 682.739990 682.739990 126.980003 0.000000\n",
"2016-02-09 678.109985 678.109985 124.070000 0.000000\n",
"2016-02-10 684.119995 684.119995 120.190002 0.000000\n",
"2016-02-11 683.109985 683.109985 117.849998 0.000000\n",
"2016-02-12 682.400024 682.400024 121.040001 0.000000\n",
"2016-02-16 691.000000 691.000000 122.739998 0.000000\n",
"... ... ... ... ...\n",
"2016-11-17 771.229980 771.229980 159.800003 0.000000\n",
"2016-11-18 760.539978 760.539978 160.389999 60.349998\n",
"2016-11-21 769.200012 769.200012 162.770004 60.860001\n",
"2016-11-22 768.270020 768.270020 162.669998 61.119999\n",
"2016-11-23 760.989990 760.989990 161.979996 60.400002\n",
"2016-11-25 761.679993 761.679993 163.139999 60.529999\n",
"2016-11-28 768.239990 768.239990 164.520004 60.610001\n",
"2016-11-29 770.840027 770.840027 163.529999 61.090000\n",
"2016-11-30 758.039978 758.039978 162.220001 60.259998\n",
"2016-12-01 747.919983 747.919983 159.820007 0.000000\n",
"2016-12-02 750.500000 750.500000 160.020004 0.000000\n",
"2016-12-05 762.520020 762.520020 159.839996 0.000000\n",
"2016-12-06 759.109985 759.109985 160.350006 0.000000\n",
"2016-12-07 771.190002 771.190002 164.789993 61.369999\n",
"2016-12-08 112.120003 776.419983 165.360001 61.009998\n",
"2016-12-09 113.949997 789.289978 166.520004 61.970001\n",
"2016-12-12 113.300003 789.270020 165.500000 62.169998\n",
"2016-12-13 115.190002 796.099976 168.289993 62.980000\n",
"2016-12-14 115.190002 797.070007 168.509995 62.680000\n",
"2016-12-15 115.820000 797.849976 168.020004 62.580002\n",
"2016-12-16 115.970001 790.799988 166.729996 62.299999\n",
"2016-12-19 116.639999 794.200012 166.679993 63.619999\n",
"2016-12-20 116.949997 796.419983 167.600006 63.540001\n",
"2016-12-21 117.059998 794.559998 167.330002 63.540001\n",
"2016-12-22 116.290001 791.260010 167.059998 63.549999\n",
"2016-12-23 116.519997 789.909973 166.710007 63.240002\n",
"2016-12-27 117.260002 791.549988 167.139999 63.279999\n",
"2016-12-28 116.760002 785.049988 166.190002 62.990002\n",
"2016-12-29 116.730003 782.789978 166.600006 62.900002\n",
"2016-12-30 115.820000 771.820007 165.990005 62.139999\n",
"\n",
"[252 rows x 4 columns]"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## インデックスとカラムの置換"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"252"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(df)"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
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" 252]"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(range(1,len(df)+1))"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df.index = range(1,len(df)+1)"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df.columns = ['a','b','c','d']"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <th>232</th>\n",
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" <tr>\n",
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" <td>750.500000</td>\n",
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" <tr>\n",
" <th>234</th>\n",
" <td>762.520020</td>\n",
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" <tr>\n",
" <th>235</th>\n",
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" <tr>\n",
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" <td>771.190002</td>\n",
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" <th>237</th>\n",
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" <tr>\n",
" <th>238</th>\n",
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" <tr>\n",
" <th>239</th>\n",
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" <td>789.270020</td>\n",
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" <td>62.169998</td>\n",
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" <tr>\n",
" <th>240</th>\n",
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" <td>796.099976</td>\n",
" <td>168.289993</td>\n",
" <td>62.980000</td>\n",
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" <tr>\n",
" <th>241</th>\n",
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" <td>168.509995</td>\n",
" <td>62.680000</td>\n",
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" <tr>\n",
" <th>242</th>\n",
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" <td>797.849976</td>\n",
" <td>168.020004</td>\n",
" <td>62.580002</td>\n",
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" <tr>\n",
" <th>243</th>\n",
" <td>115.970001</td>\n",
" <td>790.799988</td>\n",
" <td>166.729996</td>\n",
" <td>62.299999</td>\n",
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" <tr>\n",
" <th>244</th>\n",
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" <td>794.200012</td>\n",
" <td>166.679993</td>\n",
" <td>63.619999</td>\n",
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" <tr>\n",
" <th>245</th>\n",
" <td>116.949997</td>\n",
" <td>796.419983</td>\n",
" <td>167.600006</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246</th>\n",
" <td>117.059998</td>\n",
" <td>794.559998</td>\n",
" <td>167.330002</td>\n",
" <td>63.540001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>247</th>\n",
" <td>116.290001</td>\n",
" <td>791.260010</td>\n",
" <td>167.059998</td>\n",
" <td>63.549999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>248</th>\n",
" <td>116.519997</td>\n",
" <td>789.909973</td>\n",
" <td>166.710007</td>\n",
" <td>63.240002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>249</th>\n",
" <td>117.260002</td>\n",
" <td>791.549988</td>\n",
" <td>167.139999</td>\n",
" <td>63.279999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>250</th>\n",
" <td>116.760002</td>\n",
" <td>785.049988</td>\n",
" <td>166.190002</td>\n",
" <td>62.990002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>251</th>\n",
" <td>116.730003</td>\n",
" <td>782.789978</td>\n",
" <td>166.600006</td>\n",
" <td>62.900002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>252</th>\n",
" <td>115.820000</td>\n",
" <td>771.820007</td>\n",
" <td>165.990005</td>\n",
" <td>62.139999</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>252 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"1 741.840027 741.840027 135.949997 0.000000\n",
"2 742.580017 742.580017 135.850006 0.000000\n",
"3 743.619995 743.619995 135.169998 0.000000\n",
"4 726.390015 726.390015 132.860001 0.000000\n",
"5 714.469971 714.469971 131.630005 0.000000\n",
"6 716.030029 716.030029 133.229996 0.000000\n",
"7 726.070007 726.070007 132.899994 0.000000\n",
"8 700.559998 700.559998 131.169998 0.000000\n",
"9 714.719971 714.719971 132.910004 0.000000\n",
"10 694.450012 694.450012 130.029999 0.000000\n",
"11 701.789978 701.789978 128.110001 0.000000\n",
"12 698.450012 698.450012 121.860001 0.000000\n",
"13 706.590027 706.590027 122.910004 0.000000\n",
"14 725.250000 725.250000 122.500000 0.000000\n",
"15 711.669983 711.669983 122.080002 0.000000\n",
"16 713.039978 713.039978 122.589996 0.000000\n",
"17 699.989990 699.989990 120.959999 0.000000\n",
"18 730.960022 730.960022 122.220001 0.000000\n",
"19 742.950012 742.950012 124.790001 0.000000\n",
"20 752.000000 752.000000 124.830002 0.000000\n",
"21 764.650024 764.650024 122.940002 0.000000\n",
"22 726.950012 726.950012 124.720001 0.000000\n",
"23 708.010010 708.010010 127.650002 0.000000\n",
"24 683.570007 683.570007 128.570007 0.000000\n",
"25 682.739990 682.739990 126.980003 0.000000\n",
"26 678.109985 678.109985 124.070000 0.000000\n",
"27 684.119995 684.119995 120.190002 0.000000\n",
"28 683.109985 683.109985 117.849998 0.000000\n",
"29 682.400024 682.400024 121.040001 0.000000\n",
"30 691.000000 691.000000 122.739998 0.000000\n",
".. ... ... ... ...\n",
"223 771.229980 771.229980 159.800003 0.000000\n",
"224 760.539978 760.539978 160.389999 60.349998\n",
"225 769.200012 769.200012 162.770004 60.860001\n",
"226 768.270020 768.270020 162.669998 61.119999\n",
"227 760.989990 760.989990 161.979996 60.400002\n",
"228 761.679993 761.679993 163.139999 60.529999\n",
"229 768.239990 768.239990 164.520004 60.610001\n",
"230 770.840027 770.840027 163.529999 61.090000\n",
"231 758.039978 758.039978 162.220001 60.259998\n",
"232 747.919983 747.919983 159.820007 0.000000\n",
"233 750.500000 750.500000 160.020004 0.000000\n",
"234 762.520020 762.520020 159.839996 0.000000\n",
"235 759.109985 759.109985 160.350006 0.000000\n",
"236 771.190002 771.190002 164.789993 61.369999\n",
"237 112.120003 776.419983 165.360001 61.009998\n",
"238 113.949997 789.289978 166.520004 61.970001\n",
"239 113.300003 789.270020 165.500000 62.169998\n",
"240 115.190002 796.099976 168.289993 62.980000\n",
"241 115.190002 797.070007 168.509995 62.680000\n",
"242 115.820000 797.849976 168.020004 62.580002\n",
"243 115.970001 790.799988 166.729996 62.299999\n",
"244 116.639999 794.200012 166.679993 63.619999\n",
"245 116.949997 796.419983 167.600006 63.540001\n",
"246 117.059998 794.559998 167.330002 63.540001\n",
"247 116.290001 791.260010 167.059998 63.549999\n",
"248 116.519997 789.909973 166.710007 63.240002\n",
"249 117.260002 791.549988 167.139999 63.279999\n",
"250 116.760002 785.049988 166.190002 62.990002\n",
"251 116.730003 782.789978 166.600006 62.900002\n",
"252 115.820000 771.820007 165.990005 62.139999\n",
"\n",
"[252 rows x 4 columns]"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## loc,ilocの使い方"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ixではインデックスが列名か列番号かわからない(ixは列名優先)。列名として使いたいときはloc、列番号として使いたいときはilocを使う。"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a 742.580017\n",
"b 742.580017\n",
"c 135.850006\n",
"d 0.000000\n",
"Name: 2, dtype: float64\n",
"a 743.619995\n",
"b 743.619995\n",
"c 135.169998\n",
"d 0.000000\n",
"Name: 3, dtype: float64\n"
]
}
],
"source": [
"print(df.loc[2,:])\n",
"print(df.iloc[2,:])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"列番号で指定する。"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 135.949997\n",
"2 135.850006\n",
"3 135.169998\n",
"4 132.860001\n",
"5 131.630005\n",
"6 133.229996\n",
"7 132.899994\n",
"8 131.169998\n",
"9 132.910004\n",
"10 130.029999\n",
"11 128.110001\n",
"12 121.860001\n",
"13 122.910004\n",
"14 122.500000\n",
"15 122.080002\n",
"16 122.589996\n",
"17 120.959999\n",
"18 122.220001\n",
"19 124.790001\n",
"20 124.830002\n",
"21 122.940002\n",
"22 124.720001\n",
"23 127.650002\n",
"24 128.570007\n",
"25 126.980003\n",
"26 124.070000\n",
"27 120.190002\n",
"28 117.849998\n",
"29 121.040001\n",
"30 122.739998\n",
" ... \n",
"223 159.800003\n",
"224 160.389999\n",
"225 162.770004\n",
"226 162.669998\n",
"227 161.979996\n",
"228 163.139999\n",
"229 164.520004\n",
"230 163.529999\n",
"231 162.220001\n",
"232 159.820007\n",
"233 160.020004\n",
"234 159.839996\n",
"235 160.350006\n",
"236 164.789993\n",
"237 165.360001\n",
"238 166.520004\n",
"239 165.500000\n",
"240 168.289993\n",
"241 168.509995\n",
"242 168.020004\n",
"243 166.729996\n",
"244 166.679993\n",
"245 167.600006\n",
"246 167.330002\n",
"247 167.059998\n",
"248 166.710007\n",
"249 167.139999\n",
"250 166.190002\n",
"251 166.600006\n",
"252 165.990005\n",
"Name: c, dtype: float64\n"
]
}
],
"source": [
"df.iloc[:,2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### isinによるSeriesからの抽出"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"obj = pd.Series(['c','a','d','a','a','b','b','c','c'])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mask = obj.isin(['b','c'])\n",
"mask"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 c\n",
"5 b\n",
"6 b\n",
"7 c\n",
"8 c\n",
"dtype: object"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obj[mask]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- pandasのqueryによる抽出はSQLの説明の際に説明します。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
keras-team/keras-io | examples/vision/ipynb/knowledge_distillation.ipynb | 1 | 14201 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"# Knowledge Distillation\n",
"\n",
"**Author:** [Kenneth Borup](https://twitter.com/Kennethborup)<br>\n",
"**Date created:** 2020/09/01<br>\n",
"**Last modified:** 2020/09/01<br>\n",
"**Description:** Implementation of classical Knowledge Distillation."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Introduction to Knowledge Distillation\n",
"\n",
"Knowledge Distillation is a procedure for model\n",
"compression, in which a small (student) model is trained to match a large pre-trained\n",
"(teacher) model. Knowledge is transferred from the teacher model to the student\n",
"by minimizing a loss function, aimed at matching softened teacher logits as well as\n",
"ground-truth labels.\n",
"\n",
"The logits are softened by applying a \"temperature\" scaling function in the softmax,\n",
"effectively smoothing out the probability distribution and revealing\n",
"inter-class relationships learned by the teacher.\n",
"\n",
"**Reference:**\n",
"\n",
"- [Hinton et al. (2015)](https://arxiv.org/abs/1503.02531)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"import numpy as np\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Construct `Distiller()` class\n",
"\n",
"The custom `Distiller()` class, overrides the `Model` methods `train_step`, `test_step`,\n",
"and `compile()`. In order to use the distiller, we need:\n",
"\n",
"- A trained teacher model\n",
"- A student model to train\n",
"- A student loss function on the difference between student predictions and ground-truth\n",
"- A distillation loss function, along with a `temperature`, on the difference between the\n",
"soft student predictions and the soft teacher labels\n",
"- An `alpha` factor to weight the student and distillation loss\n",
"- An optimizer for the student and (optional) metrics to evaluate performance\n",
"\n",
"In the `train_step` method, we perform a forward pass of both the teacher and student,\n",
"calculate the loss with weighting of the `student_loss` and `distillation_loss` by `alpha` and\n",
"`1 - alpha`, respectively, and perform the backward pass. Note: only the student weights are updated,\n",
"and therefore we only calculate the gradients for the student weights.\n",
"\n",
"In the `test_step` method, we evaluate the student model on the provided dataset."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"\n",
"class Distiller(keras.Model):\n",
" def __init__(self, student, teacher):\n",
" super(Distiller, self).__init__()\n",
" self.teacher = teacher\n",
" self.student = student\n",
"\n",
" def compile(\n",
" self,\n",
" optimizer,\n",
" metrics,\n",
" student_loss_fn,\n",
" distillation_loss_fn,\n",
" alpha=0.1,\n",
" temperature=3,\n",
" ):\n",
" \"\"\" Configure the distiller.\n",
"\n",
" Args:\n",
" optimizer: Keras optimizer for the student weights\n",
" metrics: Keras metrics for evaluation\n",
" student_loss_fn: Loss function of difference between student\n",
" predictions and ground-truth\n",
" distillation_loss_fn: Loss function of difference between soft\n",
" student predictions and soft teacher predictions\n",
" alpha: weight to student_loss_fn and 1-alpha to distillation_loss_fn\n",
" temperature: Temperature for softening probability distributions.\n",
" Larger temperature gives softer distributions.\n",
" \"\"\"\n",
" super(Distiller, self).compile(optimizer=optimizer, metrics=metrics)\n",
" self.student_loss_fn = student_loss_fn\n",
" self.distillation_loss_fn = distillation_loss_fn\n",
" self.alpha = alpha\n",
" self.temperature = temperature\n",
"\n",
" def train_step(self, data):\n",
" # Unpack data\n",
" x, y = data\n",
"\n",
" # Forward pass of teacher\n",
" teacher_predictions = self.teacher(x, training=False)\n",
"\n",
" with tf.GradientTape() as tape:\n",
" # Forward pass of student\n",
" student_predictions = self.student(x, training=True)\n",
"\n",
" # Compute losses\n",
" student_loss = self.student_loss_fn(y, student_predictions)\n",
" distillation_loss = self.distillation_loss_fn(\n",
" tf.nn.softmax(teacher_predictions / self.temperature, axis=1),\n",
" tf.nn.softmax(student_predictions / self.temperature, axis=1),\n",
" )\n",
" loss = self.alpha * student_loss + (1 - self.alpha) * distillation_loss\n",
"\n",
" # Compute gradients\n",
" trainable_vars = self.student.trainable_variables\n",
" gradients = tape.gradient(loss, trainable_vars)\n",
"\n",
" # Update weights\n",
" self.optimizer.apply_gradients(zip(gradients, trainable_vars))\n",
"\n",
" # Update the metrics configured in `compile()`.\n",
" self.compiled_metrics.update_state(y, student_predictions)\n",
"\n",
" # Return a dict of performance\n",
" results = {m.name: m.result() for m in self.metrics}\n",
" results.update(\n",
" {\"student_loss\": student_loss, \"distillation_loss\": distillation_loss}\n",
" )\n",
" return results\n",
"\n",
" def test_step(self, data):\n",
" # Unpack the data\n",
" x, y = data\n",
"\n",
" # Compute predictions\n",
" y_prediction = self.student(x, training=False)\n",
"\n",
" # Calculate the loss\n",
" student_loss = self.student_loss_fn(y, y_prediction)\n",
"\n",
" # Update the metrics.\n",
" self.compiled_metrics.update_state(y, y_prediction)\n",
"\n",
" # Return a dict of performance\n",
" results = {m.name: m.result() for m in self.metrics}\n",
" results.update({\"student_loss\": student_loss})\n",
" return results\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Create student and teacher models\n",
"\n",
"Initialy, we create a teacher model and a smaller student model. Both models are\n",
"convolutional neural networks and created using `Sequential()`,\n",
"but could be any Keras model."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"# Create the teacher\n",
"teacher = keras.Sequential(\n",
" [\n",
" keras.Input(shape=(28, 28, 1)),\n",
" layers.Conv2D(256, (3, 3), strides=(2, 2), padding=\"same\"),\n",
" layers.LeakyReLU(alpha=0.2),\n",
" layers.MaxPooling2D(pool_size=(2, 2), strides=(1, 1), padding=\"same\"),\n",
" layers.Conv2D(512, (3, 3), strides=(2, 2), padding=\"same\"),\n",
" layers.Flatten(),\n",
" layers.Dense(10),\n",
" ],\n",
" name=\"teacher\",\n",
")\n",
"\n",
"# Create the student\n",
"student = keras.Sequential(\n",
" [\n",
" keras.Input(shape=(28, 28, 1)),\n",
" layers.Conv2D(16, (3, 3), strides=(2, 2), padding=\"same\"),\n",
" layers.LeakyReLU(alpha=0.2),\n",
" layers.MaxPooling2D(pool_size=(2, 2), strides=(1, 1), padding=\"same\"),\n",
" layers.Conv2D(32, (3, 3), strides=(2, 2), padding=\"same\"),\n",
" layers.Flatten(),\n",
" layers.Dense(10),\n",
" ],\n",
" name=\"student\",\n",
")\n",
"\n",
"# Clone student for later comparison\n",
"student_scratch = keras.models.clone_model(student)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Prepare the dataset\n",
"\n",
"The dataset used for training the teacher and distilling the teacher is\n",
"[MNIST](https://keras.io/api/datasets/mnist/), and the procedure would be equivalent for any other\n",
"dataset, e.g. [CIFAR-10](https://keras.io/api/datasets/cifar10/), with a suitable choice\n",
"of models. Both the student and teacher are trained on the training set and evaluated on\n",
"the test set."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"# Prepare the train and test dataset.\n",
"batch_size = 64\n",
"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
"\n",
"# Normalize data\n",
"x_train = x_train.astype(\"float32\") / 255.0\n",
"x_train = np.reshape(x_train, (-1, 28, 28, 1))\n",
"\n",
"x_test = x_test.astype(\"float32\") / 255.0\n",
"x_test = np.reshape(x_test, (-1, 28, 28, 1))\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Train the teacher\n",
"\n",
"In knowledge distillation we assume that the teacher is trained and fixed. Thus, we start\n",
"by training the teacher model on the training set in the usual way."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"# Train teacher as usual\n",
"teacher.compile(\n",
" optimizer=keras.optimizers.Adam(),\n",
" loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
" metrics=[keras.metrics.SparseCategoricalAccuracy()],\n",
")\n",
"\n",
"# Train and evaluate teacher on data.\n",
"teacher.fit(x_train, y_train, epochs=5)\n",
"teacher.evaluate(x_test, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Distill teacher to student\n",
"\n",
"We have already trained the teacher model, and we only need to initialize a\n",
"`Distiller(student, teacher)` instance, `compile()` it with the desired losses,\n",
"hyperparameters and optimizer, and distill the teacher to the student."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"# Initialize and compile distiller\n",
"distiller = Distiller(student=student, teacher=teacher)\n",
"distiller.compile(\n",
" optimizer=keras.optimizers.Adam(),\n",
" metrics=[keras.metrics.SparseCategoricalAccuracy()],\n",
" student_loss_fn=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
" distillation_loss_fn=keras.losses.KLDivergence(),\n",
" alpha=0.1,\n",
" temperature=10,\n",
")\n",
"\n",
"# Distill teacher to student\n",
"distiller.fit(x_train, y_train, epochs=3)\n",
"\n",
"# Evaluate student on test dataset\n",
"distiller.evaluate(x_test, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Train student from scratch for comparison\n",
"\n",
"We can also train an equivalent student model from scratch without the teacher, in order\n",
"to evaluate the performance gain obtained by knowledge distillation."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"# Train student as doen usually\n",
"student_scratch.compile(\n",
" optimizer=keras.optimizers.Adam(),\n",
" loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
" metrics=[keras.metrics.SparseCategoricalAccuracy()],\n",
")\n",
"\n",
"# Train and evaluate student trained from scratch.\n",
"student_scratch.fit(x_train, y_train, epochs=3)\n",
"student_scratch.evaluate(x_test, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"If the teacher is trained for 5 full epochs and the student is distilled on this teacher\n",
"for 3 full epochs, you should in this example experience a performance boost compared to\n",
"training the same student model from scratch, and even compared to the teacher itself.\n",
"You should expect the teacher to have accuracy around 97.6%, the student trained from\n",
"scratch should be around 97.6%, and the distilled student should be around 98.1%. Remove\n",
"or try out different seeds to use different weight initializations."
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "knowledge_distillation",
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} | apache-2.0 |
jbocharov-mids/W207-Machine-Learning | reference/firstname_lastname_p1.ipynb | 1 | 16782 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Project 1: Digit Classification with KNN and Naive Bayes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this project, you'll implement your own image recognition system for classifying digits. Read through the code and the instructions carefully and add your own code where indicated. Each problem can be addressed succinctly with the included packages -- please don't add any more. Grading will be based on writing clean, commented code, along with a few short answers.\n",
"\n",
"As always, you're welcome to work on the project in groups and discuss ideas on the course wall, but <b> please prepare your own write-up (with your own code). </b>\n",
"\n",
"If you're interested, check out these links related to digit recognition:\n",
"\n",
"Yann Lecun's MNIST benchmarks: http://yann.lecun.com/exdb/mnist/\n",
"\n",
"Stanford Streetview research and data: http://ufldl.stanford.edu/housenumbers/"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# This tells matplotlib not to try opening a new window for each plot.\n",
"%matplotlib inline\n",
"\n",
"# Import a bunch of libraries.\n",
"import time\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.ticker import MultipleLocator\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.datasets import fetch_mldata\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.naive_bayes import BernoulliNB\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.grid_search import GridSearchCV\n",
"from sklearn.metrics import classification_report\n",
"\n",
"# Set the randomizer seed so results are the same each time.\n",
"np.random.seed(0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the data. Notice that we are splitting the data into training, development, and test. We also have a small subset of the training data called mini_train_data and mini_train_labels that you should use in all the experiments below, unless otherwise noted."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data shape: (70000, 784)\n",
"label shape: (70000,)\n"
]
}
],
"source": [
"# Load the digit data either from mldata.org, or once downloaded to data_home, from disk. The data is about 53MB so this cell\n",
"# should take a while the first time your run it.\n",
"mnist = fetch_mldata('MNIST original', data_home='~/datasets/mnist')\n",
"X, Y = mnist.data, mnist.target\n",
"\n",
"# Rescale grayscale values to [0,1].\n",
"X = X / 255.0\n",
"\n",
"# Shuffle the input: create a random permutation of the integers between 0 and the number of data points and apply this\n",
"# permutation to X and Y.\n",
"# NOTE: Each time you run this cell, you'll re-shuffle the data, resulting in a different ordering.\n",
"shuffle = np.random.permutation(np.arange(X.shape[0]))\n",
"X, Y = X[shuffle], Y[shuffle]\n",
"\n",
"print 'data shape: ', X.shape\n",
"print 'label shape:', Y.shape\n",
"\n",
"# Set some variables to hold test, dev, and training data.\n",
"test_data, test_labels = X[61000:], Y[61000:]\n",
"dev_data, dev_labels = X[60000:61000], Y[60000:61000]\n",
"train_data, train_labels = X[:60000], Y[:60000]\n",
"mini_train_data, mini_train_labels = X[:1000], Y[:1000]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(1) Create a 10x10 grid to visualize 10 examples of each digit. Python hints:\n",
"\n",
"- plt.rc() for setting the colormap, for example to black and white\n",
"- plt.subplot() for creating subplots\n",
"- plt.imshow() for rendering a matrix\n",
"- np.array.reshape() for reshaping a 1D feature vector into a 2D matrix (for rendering)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#def P1(num_examples=10):\n",
"\n",
"### STUDENT START ###\n",
" \n",
"\n",
"### STUDENT END ###\n",
"\n",
"#P1(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(2) Evaluate a K-Nearest-Neighbors model with k = [1,3,5,7,9] using the mini training set. Report accuracy on the dev set. For k=1, show precision, recall, and F1 for each label. Which is the most difficult digit?\n",
"\n",
"- KNeighborsClassifier() for fitting and predicting\n",
"- classification_report() for producing precision, recall, F1 results"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#def P2(k_values):\n",
"\n",
"### STUDENT START ###\n",
"\n",
"\n",
" \n",
"### STUDENT END ###\n",
"\n",
"#k_values = [1, 3, 5, 7, 9]\n",
"#P2(k_values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ANSWER:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(3) Using k=1, report dev set accuracy for the training set sizes below. Also, measure the amount of time needed for prediction with each training size.\n",
"\n",
"- time.time() gives a wall clock value you can use for timing operations"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#def P3(train_sizes, accuracies):\n",
"\n",
"### STUDENT START ###\n",
"\n",
"\n",
"### STUDENT END ###\n",
"\n",
"#train_sizes = [100, 200, 400, 800, 1600, 3200, 6400, 12800, 25000]\n",
"#accuracies = []\n",
"#P3(train_sizes, accuracies)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(4) Fit a regression model that predicts accuracy from training size. What does it predict for n=60000? What's wrong with using regression here? Can you apply a transformation that makes the predictions more reasonable?\n",
"\n",
"- Remember that the sklearn fit() functions take an input matrix X and output vector Y. So each input example in X is a vector, even if it contains only a single value."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#def P4():\n",
"\n",
"### STUDENT START ###\n",
" \n",
"\n",
"### STUDENT END ###\n",
"\n",
"#P4()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ANSWER:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fit a 1-NN and output a confusion matrix for the dev data. Use the confusion matrix to identify the most confused pair of digits, and display a few example mistakes.\n",
"\n",
"- confusion_matrix() produces a confusion matrix"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#def P5():\n",
"\n",
"### STUDENT START ###\n",
"\n",
" \n",
"### STUDENT END ###\n",
"\n",
"#P5()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(6) A common image processing technique is to smooth an image by blurring. The idea is that the value of a particular pixel is estimated as the weighted combination of the original value and the values around it. Typically, the blurring is Gaussian -- that is, the weight of a pixel's influence is determined by a Gaussian function over the distance to the relevant pixel.\n",
"\n",
"Implement a simplified Gaussian blur by just using the 8 neighboring pixels: the smoothed value of a pixel is a weighted combination of the original value and the 8 neighboring values. Try applying your blur filter in 3 ways:\n",
"- preprocess the training data but not the dev data\n",
"- preprocess the dev data but not the training data\n",
"- preprocess both training and dev data\n",
"\n",
"Note that there are Guassian blur filters available, for example in scipy.ndimage.filters. You're welcome to experiment with those, but you are likely to get the best results with the simplified version I described above."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#def P6():\n",
" \n",
"### STUDENT START ###\n",
"\n",
"\n",
"### STUDENT END ###\n",
"\n",
"#P6()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ANSWER:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(7) Fit a Naive Bayes classifier and report accuracy on the dev data. Remember that Naive Bayes estimates P(feature|label). While sklearn can handle real-valued features, let's start by mapping the pixel values to either 0 or 1. You can do this as a preprocessing step, or with the binarize argument. With binary-valued features, you can use BernoulliNB. Next try mapping the pixel values to 0, 1, or 2, representing white, grey, or black. This mapping requires MultinomialNB. Does the multi-class version improve the results? Why or why not?"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#def P7():\n",
"\n",
"### STUDENT START ###\n",
"\n",
"\n",
" \n",
"### STUDENT END ###\n",
"\n",
"#P7()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ANSWER:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(8) Use GridSearchCV to perform a search over values of alpha (the Laplace smoothing parameter) in a Bernoulli NB model. What is the best value for alpha? What is the accuracy when alpha=0? Is this what you'd expect?\n",
"\n",
"- Note that GridSearchCV partitions the training data so the results will be a bit different than if you used the dev data for evaluation."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#def P8(alphas):\n",
"\n",
"### STUDENT START ###\n",
"\n",
"\n",
"\n",
"### STUDENT END ###\n",
"\n",
"#alphas = {'alpha': [0.0, 0.0001, 0.001, 0.01, 0.1, 0.5, 1.0, 2.0, 10.0]}\n",
"#nb = P8(alphas)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#print nb.best_params_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ANSWER:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(9) Try training a model using GuassianNB, which is intended for real-valued features, and evaluate on the dev data. You'll notice that it doesn't work so well. Try to diagnose the problem. You should be able to find a simple fix that returns the accuracy to around the same rate as BernoulliNB. Explain your solution.\n",
"\n",
"Hint: examine the parameters estimated by the fit() method, theta\\_ and sigma\\_."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#def P9():\n",
"\n",
"### STUDENT END ###\n",
"\n",
"\n",
"### STUDENT END ###\n",
"\n",
"#gnb = P9()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ANSWER:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(10) Because Naive Bayes is a generative model, we can use the trained model to generate digits. Train a BernoulliNB model and then generate a 10x20 grid with 20 examples of each digit. Because you're using a Bernoulli model, each pixel output will be either 0 or 1. How do the generated digits compare to the training digits?\n",
"\n",
"- You can use np.random.rand() to generate random numbers from a uniform distribution\n",
"- The estimated probability of each pixel is stored in feature\\_log\\_prob\\_. You'll need to use np.exp() to convert a log probability back to a probability."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#def P10(num_examples):\n",
"\n",
"### STUDENT START ###\n",
"\n",
"\n",
"### STUDENT END ###\n",
"\n",
"#P10(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ANSWER:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(11) Remember that a strongly calibrated classifier is rougly 90% accurate when the posterior probability of the predicted class is 0.9. A weakly calibrated classifier is more accurate when the posterior is 90% than when it is 80%. A poorly calibrated classifier has no positive correlation between posterior and accuracy.\n",
"\n",
"Train a BernoulliNB model with a reasonable alpha value. For each posterior bucket (think of a bin in a histogram), you want to estimate the classifier's accuracy. So for each prediction, find the bucket the maximum posterior belongs to and update the \"correct\" and \"total\" counters.\n",
"\n",
"How would you characterize the calibration for the Naive Bayes model?"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#def P11(buckets, correct, total):\n",
" \n",
"### STUDENT START ###\n",
"\n",
"\n",
" \n",
"### STUDENT END ###\n",
"\n",
"#buckets = [0.5, 0.9, 0.999, 0.99999, 0.9999999, 0.999999999, 0.99999999999, 0.9999999999999, 1.0]\n",
"#correct = [0 for i in buckets]\n",
"#total = [0 for i in buckets]\n",
"\n",
"#P11(buckets, correct, total)\n",
"\n",
"#for i in range(len(buckets)):\n",
"# accuracy = 0.0\n",
"# if (total[i] > 0): accuracy = correct[i] / total[i]\n",
"# print 'p(pred) <= %.13f total = %3d accuracy = %.3f' %(buckets[i], total[i], accuracy)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ANSWER:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(12) EXTRA CREDIT\n",
"\n",
"Try designing extra features to see if you can improve the performance of Naive Bayes on the dev set. Here are a few ideas to get you started:\n",
"- Try summing the pixel values in each row and each column.\n",
"- Try counting the number of enclosed regions; 8 usually has 2 enclosed regions, 9 usually has 1, and 7 usually has 0.\n",
"\n",
"Make sure you comment your code well!"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#def P12():\n",
"\n",
"### STUDENT START ###\n",
"\n",
"\n",
"### STUDENT END ###\n",
"\n",
"#P12()"
]
}
],
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"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
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"codemirror_mode": {
"name": "ipython",
"version": 2
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
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}
},
"nbformat": 4,
"nbformat_minor": 0
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| apache-2.0 |
napjon/moocs_solution | DataWranglingMongoDB/Lesson2/Lesson2_ComplexFormat.ipynb | 1 | 11800 | {
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{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"# %%writefile find_author_data.py\n",
"\n",
"#!/usr/bin/env python\n",
"# Your task here is to extract data from xml on authors of an article\n",
"# and add it to a list, one item for an author.\n",
"# See the provided data structure for the expected format.\n",
"# The tags for first name, surname and email should map directly\n",
"# to the dictionary keys\n",
"import xml.etree.ElementTree as ET\n",
"\n",
"article_file = \"exampleResearchArticle.xml\"\n",
"\n",
"\n",
"def get_root(fname):\n",
" tree = ET.parse(fname)\n",
" return tree.getroot()\n",
"\n",
"\n",
"def get_authors(root):\n",
" authors = []\n",
" for author in root.findall('./fm/bibl/aug/au'):\n",
" data = {\n",
" \"fnm\": None,\n",
" \"snm\": None,\n",
" \"email\": None\n",
" }\n",
" \n",
"\n",
" # YOUR CODE HERE\n",
" for key in data:\n",
" data[key] = author.find(key).text\n",
" \n",
" data['insr'] = [e.attrib['iid'] for e in author.findall('insr')]\n",
" authors.append(data)\n",
"\n",
" return authors\n",
"\n",
"\n",
"def test():\n",
" #withour insr\n",
" solution = [{'fnm': 'Omer', 'snm': 'Mei-Dan', 'email': '[email protected]'}, {'fnm': 'Mike', 'snm': 'Carmont', 'email': '[email protected]'}, {'fnm': 'Lior', 'snm': 'Laver', 'email': '[email protected]'}, {'fnm': 'Meir', 'snm': 'Nyska', 'email': '[email protected]'}, {'fnm': 'Hagay', 'snm': 'Kammar', 'email': '[email protected]'}, {'fnm': 'Gideon', 'snm': 'Mann', 'email': '[email protected]'}, {'fnm': 'Barnaby', 'snm': 'Clarck', 'email': '[email protected]'}, {'fnm': 'Eugene', 'snm': 'Kots', 'email': '[email protected]'}]\n",
" \n",
" root = get_root(article_file)\n",
" data = get_authors(root)\n",
" assert data[0] == solution[0]\n",
" assert data[1][\"fnm\"] == solution[1][\"fnm\"]\n",
" \n",
" \n",
" #with insr\n",
" solution = [{'insr': ['I1'], 'fnm': 'Omer', 'snm': 'Mei-Dan', 'email': '[email protected]'},\n",
" {'insr': ['I2'], 'fnm': 'Mike', 'snm': 'Carmont', 'email': '[email protected]'},\n",
" {'insr': ['I3', 'I4'], 'fnm': 'Lior', 'snm': 'Laver', 'email': '[email protected]'},\n",
" {'insr': ['I3'], 'fnm': 'Meir', 'snm': 'Nyska', 'email': '[email protected]'},\n",
" {'insr': ['I8'], 'fnm': 'Hagay', 'snm': 'Kammar', 'email': '[email protected]'},\n",
" {'insr': ['I3', 'I5'], 'fnm': 'Gideon', 'snm': 'Mann', 'email': '[email protected]'},\n",
" {'insr': ['I6'], 'fnm': 'Barnaby', 'snm': 'Clarck', 'email': '[email protected]'},\n",
" {'insr': ['I7'], 'fnm': 'Eugene', 'snm': 'Kots', 'email': '[email protected]'}]\n",
"\n",
" root = get_root(article_file)\n",
" data = get_authors(root)\n",
"\n",
" assert data[0] == solution[0]\n",
" assert data[1][\"insr\"] == solution[1][\"insr\"]\n",
"\n",
"\n",
"test()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Overwriting find_author_data.py\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# %%writefile post_html_form.py\n",
"\n",
"#!/usr/bin/env python\n",
"# -*- coding: utf-8 -*-\n",
"# Please note that the function 'make_request' is provided for your reference only.\n",
"# You will not be able to to actually use it from within the Udacity web UI.\n",
"# Your task is to process the HTML using BeautifulSoup, extract the hidden\n",
"# form field values for \"__EVENTVALIDATION\" and \"__VIEWSTATE\" and set the approprate\n",
"# values in the data dictionary.\n",
"# All your changes should be in the 'extract_data' function\n",
"from bs4 import BeautifulSoup\n",
"import requests\n",
"import json\n",
"\n",
"html_page = \"page_source.html\"\n",
"\n",
"\n",
"def extract_data(page):\n",
" data = {\"eventvalidation\": \"\",\n",
" \"viewstate\": \"\"}\n",
" with open(page, \"r\") as html:\n",
" soup = BeautifulSoup(html)\n",
" data['eventvalidation'] = soup.find(id='__EVENTVALIDATION')['value']\n",
" data['viewstate'] = soup.find(id='__VIEWSTATE')['value']\n",
" \n",
" \n",
"\n",
" return data\n",
"\n",
"\n",
"def make_request(data):\n",
" eventvalidation = data[\"eventvalidation\"]\n",
" viewstate = data[\"viewstate\"]\n",
"\n",
" #for managed session, use s instead of r\n",
"# s = requests.Session()\n",
" r = requests.post(\"http://www.transtats.bts.gov/Data_Elements.aspx?Data=2\",\n",
" data={'AirportList': \"BOS\",\n",
" 'CarrierList': \"VX\",\n",
" 'Submit': 'Submit',\n",
" \"__EVENTTARGET\": \"\",\n",
" \"__EVENTARGUMENT\": \"\",\n",
" \"__EVENTVALIDATION\": eventvalidation,\n",
" \"__VIEWSTATE\": viewstate\n",
" })\n",
"\n",
" return r.text\n",
"\n",
"\n",
"def test():\n",
" data = extract_data(html_page)\n",
" assert data[\"eventvalidation\"] != \"\"\n",
" assert data[\"eventvalidation\"].startswith(\"/wEWjAkCoIj1ng0\")\n",
" assert data[\"viewstate\"].startswith(\"/wEPDwUKLTI\")\n",
"\n",
" \n",
"test()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%writefile process_all.py\n",
"\n",
"\n",
"#!/usr/bin/env python\n",
"# -*- coding: utf-8 -*-\n",
"# Let's assume that you combined the code from the previous 2 exercises\n",
"# with code from the lesson on how to build requests, and downloaded all the data locally.\n",
"# The files are in a directory \"data\", named after the carrier and airport:\n",
"# \"{}-{}.html\".format(carrier, airport), for example \"FL-ATL.html\".\n",
"# The table with flight info has a table class=\"dataTDRight\".\n",
"# There are couple of helper functions to deal with the data files.\n",
"# Please do not change them for grading purposes.\n",
"# All your changes should be in the 'process_file' function\n",
"# This is example of the datastructure you should return\n",
"# Each item in the list should be a dictionary containing all the relevant data\n",
"# Note - year, month, and the flight data should be integers\n",
"# You should skip the rows that contain the TOTAL data for a year\n",
"# data = [{\"courier\": \"FL\",\n",
"# \"airport\": \"ATL\",\n",
"# \"year\": 2012,\n",
"# \"month\": 12,\n",
"# \"flights\": {\"domestic\": 100,\n",
"# \"international\": 100}\n",
"# },\n",
"# {\"courier\": \"...\"}\n",
"# ]\n",
"from bs4 import BeautifulSoup\n",
"from zipfile import ZipFile\n",
"import os\n",
"\n",
"datadir = \"data\"\n",
"\n",
"\n",
"def open_zip(datadir):\n",
" with ZipFile('{0}.zip'.format(datadir), 'r') as myzip:\n",
" myzip.extractall()\n",
"\n",
"\n",
"def process_all(datadir):\n",
" files = os.listdir(datadir)\n",
" return files\n",
"\n",
"\n",
"def process_file(f):\n",
" # This is example of the datastructure you should return\n",
" # Each item in the list should be a dictionary containing all the relevant data\n",
" # Note - year, month, and the flight data should be integers\n",
" # You should skip the rows that contain the TOTAL data for a year\n",
" # data = [{\"courier\": \"FL\",\n",
" # \"airport\": \"ATL\",\n",
" # \"year\": 2012,\n",
" # \"month\": 12,\n",
" # \"flights\": {\"domestic\": 100,\n",
" # \"international\": 100}\n",
" # },\n",
" # {\"courier\": \"...\"}\n",
" # ]\n",
" data = []\n",
" info = {}\n",
" info[\"courier\"], info[\"airport\"] = f[:6].split(\"-\")\n",
" #print info\n",
" with open(\"{}/{}\".format(datadir, f), \"r\") as html:\n",
" soup = BeautifulSoup(html)\n",
" table = soup.find('table', {'class':'dataTDRight'})\n",
" for row in table.findAll('tr')[1:]:\n",
" fields = row.findAll('td')\n",
" fields = [e.text.replace(',','') for e in fields]\n",
" try:\n",
" fields.index('TOTAL')\n",
" except ValueError:\n",
" fields = [int(float(e)) for e in fields ]\n",
" info['year'] = fields[0]\n",
" info['month'] = fields[1]\n",
" info['flights'] = {\n",
" 'domestic':fields[2],\n",
" 'international':fields[3],\n",
" }\n",
" data.append(info)\n",
" \n",
" \n",
" \n",
"\n",
" return data\n",
"\n",
"\n",
"def test():\n",
" print \"Running a simple test...\"\n",
" open_zip(datadir)\n",
" files = process_all(datadir)\n",
" data = []\n",
" for f in files:\n",
" data += process_file(f)\n",
" #print data\n",
" assert len(data) == 399\n",
" for entry in data[:3]:\n",
" assert type(entry[\"year\"]) == int\n",
" assert type(entry[\"flights\"][\"domestic\"]) == int\n",
" assert len(entry[\"airport\"]) == 3\n",
" assert len(entry[\"courier\"]) == 2\n",
" assert data[-1][\"airport\"] == \"ATL\"\n",
" assert data[-1][\"flights\"] == {'international': 108289, 'domestic': 701425}\n",
" \n",
" print \"... success!\"\n",
"\n",
"if __name__ == \"__main__\":\n",
" test()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Writing process_all.py\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
vreinharz/OpenNotebooks | HelloWorld/Helloworld.ipynb | 1 | 1832 | {
"metadata": {
"name": "",
"signature": "sha256:e6080c88002aa130af3b9002c09ff04ce3829d89fc38ae7194fa4023cc5cac3b"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Hello world!\n",
"##This is a Hello World program\n",
"\n",
"\n",
"Hello_World will print Hello World!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def hello_world():\n",
" \"\"\"I print 'Hello World!' \"\"\"\n",
" print(\"Hello World!\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If invoked, will print hello world"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hello_world()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Hello World!\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"help(hello_world)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Help on function hello_world in module __main__:\n",
"\n",
"hello_world()\n",
" I print 'Hello World!'\n",
"\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-3.0 |
GkAntonius/feynman | docs/auto_examples/Particle_Physics/plot_dchp1.ipynb | 1 | 3303 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\nDCHP1\n=====\n\nDoubly Charged Higgs Production\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"import matplotlib\nmatplotlib.rcParams['mathtext.fontset'] = 'stix'\nmatplotlib.rcParams['font.family'] = 'STIXGeneral'\n\nfrom feynman import Diagram\n\nfig = matplotlib.pyplot.figure(figsize=(10.,10.))\nax = fig.add_axes([0,0,1,1], frameon=False)\n\ndiagram = Diagram(ax)\ndiagram.text(.4,0.9,\"Doubly Charged Higgs Production\", fontsize=40)\nin1 = diagram.vertex(xy=(.1,.75), marker='')\nin2= diagram.vertex(xy=(.1,.25), marker='')\nv1 = diagram.vertex(xy=(.35,.5))\nv2 = diagram.vertex(xy=(.65,.5))\nhiggsplusout = diagram.vertex(xy=(.8,.7))\nhiggsminusout = diagram.vertex(xy=(.8,.3))\nl1plus = diagram.vertex(xy=(.95,.8), marker='')\nl2plus = diagram.vertex(xy=(.95,.6), marker='')\nl1minus = diagram.vertex(xy=(.95,.4), marker='')\nl2minus = diagram.vertex(xy=(.95,.2), marker='')\n\nlw = 5\nq1 = diagram.line(v1, in1, color='blue', lw=lw, arrow_param=dict(color='blue', length=0.08, width=0.02))\nq2 = diagram.line(in2, v1, color='blue', lw=lw, arrow_param=dict(color='blue', length=0.08, width=0.02))\nl1 = diagram.line(l1plus, higgsplusout, color='blue', lw=lw, arrow_param=dict(color='blue', length=0.08, width=0.02))\nl2 = diagram.line(l2plus, higgsplusout, color='blue', lw=lw, arrow_param=dict(color='blue', length=0.08, width=0.02))\nl3 = diagram.line(higgsminusout, l1minus, color='blue', lw=lw, arrow_param=dict(color='blue', length=0.08, width=0.02))\nl4 = diagram.line(higgsminusout, l2minus, color='blue', lw=lw, arrow_param=dict(color='blue', length=0.08, width=0.02))\nwz1 = diagram.line(v1, v2, style='wiggly', color='green', lw=lw)\nhiggsplus = diagram.line(v2, higgsplusout, arrow=False, ls='dashed', lw=lw, dashes=(4, 2))\nhiggsminus = diagram.line(v2, higgsminusout, arrow=False, ls='dashed', lw=lw, dashes=(4, 2))\n\nq1.text(r\"$\\bar{\\mathrm{q}}$\", fontsize=40)\nq2.text(\"q\",fontsize=40)\ndiagram.text(0.5, 0.42, r\"$Z \\ / \\ \\gamma*$\", fontsize=40)\ndiagram.text(0.8, 0.58, r\"$H^{++}$\", fontsize=40)\ndiagram.text(0.8, 0.42, r\"$H^{--}$\", fontsize=40)\ndiagram.text(0.98, 0.8, r\"$l^+$\", fontsize=40)\ndiagram.text(0.98, 0.6, r\"$l^+$\", fontsize=40)\ndiagram.text(0.98, 0.4, r\"$l^-$\", fontsize=40)\ndiagram.text(0.98, 0.2, r\"$l^-$\", fontsize=40)\n\n\ndiagram.plot()\nmatplotlib.pyplot.show()"
],
"outputs": [],
"metadata": {
"collapsed": false
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"file_extension": ".py",
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"pygments_lexer": "ipython2",
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"version": 2,
"name": "ipython"
}
}
}
} | gpl-3.0 |
DB2-Samples/db2jupyter | v1/Db2 Jupyter Extensions Tutorial.ipynb | 1 | 30379 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Db2 Jupyter Notebook Extensions Tutorial\n",
"\n",
"The SQL code tutorials for Db2 rely on a Jupyter notebook extension, commonly refer to as a \"magic\" command. The beginning of all of the notebooks begin with the following command which will load the extension and allow the remainder of the notebook to use the %sql magic command.\n",
"<pre>\n",
"%run db2.ipynb\n",
"</pre>\n",
"The cell below will load the Db2 extension. Note that it will take a few seconds for the extension to load, so you should generally wait until the \"Db2 Extensions Loaded\" message is displayed in your notebook. In the event you get an error on the load of the ibm_db library, modify the command to include the -update option:\n",
"```\n",
"run db2.ipynb -update\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%run db2.ipynb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Connections to Db2\n",
"\n",
"Before any SQL commands can be issued, a connection needs to be made to the Db2 database that you will be using. The connection can be done manually (through the use of the CONNECT command), or automatically when the first `%sql` command is issued.\n",
"\n",
"The Db2 magic command tracks whether or not a connection has occured in the past and saves this information between notebooks and sessions. When you start up a notebook and issue a command, the program will reconnect to the database using your credentials from the last session. In the event that you have not connected before, the system will prompt you for all the information it needs to connect. This information includes:\n",
"\n",
"- Database name (SAMPLE) \n",
"- Hostname - localhost (enter an IP address if you need to connect to a remote server) \n",
"- PORT - 50000 (this is the default but it could be different) \n",
"- Userid - DB2INST1 \n",
"- Password - No password is provided so you have to enter a value \n",
"- Maximum Rows - 10 lines of output are displayed when a result set is returned \n",
"\n",
"There will be default values presented in the panels that you can accept, or enter your own values. All of the information will be stored in the directory that the notebooks are stored on. Once you have entered the information, the system will attempt to connect to the database for you and then you can run all of the SQL scripts. More details on the CONNECT syntax will be found in a section below.\n",
"\n",
"The next statement will force a CONNECT to occur with the default values. If you have not connected before, it will prompt you for the information."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%sql CONNECT"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Line versus Cell Command\n",
"The Db2 extension is made up of one magic command that works either at the LINE level (`%sql`) or at the CELL level (`%%sql`). If you only want to execute a SQL command on one line in your script, use the %sql form of the command. If you want to run a larger block of SQL, then use the `%%sql` form. Note that when you use the `%%sql` form of the command, the entire contents of the cell is considered part of the command, so you cannot mix other commands in the cell.\n",
"\n",
"The following is an example of a line command:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%sql VALUES 'HELLO THERE'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you have SQL that requires multiple lines, of if you need to execute many lines of SQL, then you should \n",
"be using the CELL version of the `%sql` command. To start a block of SQL, start the cell with `%%sql` and do not place any SQL following the command. Subsequent lines can contain SQL code, with each SQL statement delimited with the semicolon (`;`). You can change the delimiter if required for procedures, etc... More details on this later."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%sql\n",
"VALUES\n",
" 1,\n",
" 2,\n",
" 3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you are using a single statement then there is no need to use a delimiter. However, if you are combining a number of commands then you must use the semicolon."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%sql\n",
"DROP TABLE STUFF;\n",
"CREATE TABLE STUFF (A INT);\n",
"INSERT INTO STUFF VALUES\n",
" 1,2,3;\n",
"SELECT * FROM STUFF;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The script will generate messages and output as it executes. Each SQL statement that generates results will have a table displayed with the result set. If a command is executed, the results of the execution get listed as well. The script you just ran probably generated an error on the DROP table command."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Options\n",
"Both forms of the `%sql` command have options that can be used to change the behavior of the code. For both forms of the command (`%sql`, `%%sql`), the options must be on the same line as the command:\n",
"<pre>\n",
"%sql -t ...\n",
"%%sql -t\n",
"</pre>\n",
"\n",
"The only difference is that the `%sql` command can have SQL following the parameters, while the `%%sql` requires the SQL to be placed on subsequent lines.\n",
"\n",
"There are a number of parameters that you can specify as part of the `%sql` statement. \n",
"\n",
"* -d - Use alternative delimiter\n",
"* -t - Time the statement execution\n",
"* -q - Suppress messages \n",
"* -j - JSON formatting of a column\n",
"* -a - Show all output\n",
"* -pb - Bar chart of results\n",
"* -pp - Pie chart of results \n",
"* -pl - Line chart of results\n",
"* -i - Interactive mode with Pixiedust\n",
"* -sampledata Load the database with the sample EMPLOYEE and DEPARTMENT tables\n",
"* -r - Return the results into a variable (list of rows)\n",
"\n",
"Multiple parameters are allowed on a command line. Each option should be separated by a space:\n",
"<pre>\n",
"%sql -a -j ...\n",
"</pre>\n",
"\n",
"A SELECT statement will return the results as a dataframe and display the results as a table in the notebook. If you use the assignment statement, the dataframe will be placed into the variable and the results will not be displayed:\n",
"<pre>\n",
"r = %sql SELECT * FROM EMPLOYEE\n",
"</pre>\n",
"\n",
"The sections below will explain the options in more detail."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Delimiters\n",
"The default delimiter for all SQL statements is the semicolon. However, this becomes a problem when you try to create a trigger, function, or procedure that uses SQLPL (or PL/SQL). Use the -d option to turn the SQL delimiter into the at (`@`) sign and -q to suppress error messages. The semi-colon is then ignored as a delimiter.\n",
"\n",
"For example, the following SQL will use the `@` sign as the delimiter."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%sql -d -q\n",
"DROP TABLE STUFF\n",
"@\n",
"CREATE TABLE STUFF (A INT)\n",
"@\n",
"INSERT INTO STUFF VALUES\n",
" 1,2,3\n",
"@\n",
"SELECT * FROM STUFF\n",
"@"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The delimiter change will only take place for the statements following the `%%sql` command. Subsequent cells\n",
"in the notebook will still use the semicolon. You must use the -d option for every cell that needs to use the\n",
"semicolon in the script."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Limiting Result Sets\n",
"The default number of rows displayed for any result set is 10. You have the option of changing this option when initially connecting to the database. If you want to override the number of rows display you can either update\n",
"the control variable, or use the -a option. The -a option will display all of the rows in the answer set. For instance, the following SQL will only show 10 rows even though we inserted 15 values:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%sql values 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You will notice that the displayed result will split the visible rows to the first 5 rows and the last 5 rows.\n",
"Using the -a option will display all values:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%sql -a values 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To change the default value of rows displayed, you can either do a CONNECT RESET (discussed later) or set the\n",
"Db2 control variable maxrows to a different value. A value of -1 will display all rows."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Save previous version of maximum rows\n",
"last_max = _settings['maxrows']\n",
"_settings['maxrows'] = 5\n",
"%sql values 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A special note regarding the output from a SELECT statement. If the SQL statement is the last line of a block, the results will be displayed by default (unless you assigned the results to a variable). If the SQL is in the middle of a block of statements, the results will not be displayed. To explicitly display the results you must use the display function (or pDisplay if you have imported another library like pixiedust which overrides the pandas display function). "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Set the maximum back\n",
"_settings['maxrows'] = last_max\n",
"%sql values 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Quiet Mode\n",
"Every SQL statement will result in some output. You will either get an answer set (SELECT), or an indication if\n",
"the command worked. For instance, the following set of SQL will generate some error messages since the tables \n",
"will probably not exist:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%sql\n",
"DROP TABLE TABLE_NOT_FOUND;\n",
"DROP TABLE TABLE_SPELLED_WRONG;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you know that these errors may occur you can silence them with the -q option."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%sql -q\n",
"DROP TABLE TABLE_NOT_FOUND;\n",
"DROP TABLE TABLE_SPELLED_WRONG;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"SQL output will not be suppressed, so the following command will still show the results."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%sql -q\n",
"DROP TABLE TABLE_NOT_FOUND;\n",
"DROP TABLE TABLE_SPELLED_WRONG;\n",
"VALUES 1,2,3;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Variables in %sql Blocks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `%sql` syntax allows you to pass local variables to a script. There are 5 predefined variables defined in the program:\n",
"\n",
"- database - The name of the database you are connected to\n",
"- uid - The userid that you connected with\n",
"- host = The IP address of the host system\n",
"- port - The port number of the host system\n",
"- max - The maximum number of rows to return in an answer set\n",
"\n",
"These variables are all part of a structure called _settings. To pass a value to a LINE script, use the braces {} to surround the name of the variable:\n",
"\n",
"<pre>\n",
" {_settings[\"database\"]}\n",
"</pre>\n",
"\n",
"The next line will display the currently connected database."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%sql VALUES '{_settings[\"database\"]}'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You cannot use variable substitution with the CELL version of the `%%sql` command. If your SQL statement extends beyond one line, and you want to use variable substitution, you can use a couple of techniques to make it look like one line. The simplest way is to add the backslash character (```\\```) at the end of every line. The following example illustrates the technique."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"empno = '000010'\n",
"%sql SELECT LASTNAME FROM \\\n",
" EMPLOYEE \\\n",
" WHERE \\\n",
" EMPNO = '{empno}'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The other option for passing variables to a `%sql` or `%%sql` statement is to use the embedded variable format. This requires that the variable be prefixed with a colon (`:`) in front of it. When using this format, you do not need to use quote characters around the variables since its value is extracted at run time. The first example uses the value of the variable."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"empno = '000010'\n",
"%sql select lastname from employee where empno='{empno}'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This example uses the embedded variable name (`:empno`)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%sql select lastname from employee where empno=:empno"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Timing SQL Statements\n",
"Sometimes you want to see how the execution of a statement changes with the addition of indexes or other\n",
"optimization changes. The -t option will run the statement on the LINE or one SQL statement in the CELL for \n",
"exactly one second. The results will be displayed and optionally placed into a variable. The syntax of the\n",
"command is:\n",
"<pre>\n",
"sql_time = %sql -t SELECT * FROM EMPLOYEE\n",
"</pre>\n",
"For instance, the following SQL will time the VALUES clause."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%sql -t VALUES 1,2,3,4,5,6,7,8,9"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When timing a statement, no output will be displayed. If your SQL statement takes longer than one second you\n",
"will need to modify the db2 _runtime variable. This variable must be set to the number of seconds that you\n",
"want to run the statement."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"_runtime = 5\n",
"%sql -t VALUES 1,2,3,4,5,6,7,8,9"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## JSON Formatting\n",
"Db2 supports querying JSON that is stored in a column within a table. Standard output would just display the \n",
"JSON as a string. For instance, the following statement would just return a large string of output."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%sql\n",
"VALUES \n",
" '{\n",
" \"empno\":\"000010\",\n",
" \"firstnme\":\"CHRISTINE\",\n",
" \"midinit\":\"I\",\n",
" \"lastname\":\"HAAS\",\n",
" \"workdept\":\"A00\",\n",
" \"phoneno\":[3978],\n",
" \"hiredate\":\"01/01/1995\",\n",
" \"job\":\"PRES\",\n",
" \"edlevel\":18,\n",
" \"sex\":\"F\",\n",
" \"birthdate\":\"08/24/1963\",\n",
" \"pay\" : {\n",
" \"salary\":152750.00,\n",
" \"bonus\":1000.00,\n",
" \"comm\":4220.00}\n",
" }'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Adding the -j option to the %sql (or %%sql) command will format the first column of a return set to better\n",
"display the structure of the document. Note that if your answer set has additional columns associated with it, they will not be displayed in this format."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%sql -j\n",
"VALUES \n",
" '{\n",
" \"empno\":\"000010\",\n",
" \"firstnme\":\"CHRISTINE\",\n",
" \"midinit\":\"I\",\n",
" \"lastname\":\"HAAS\",\n",
" \"workdept\":\"A00\",\n",
" \"phoneno\":[3978],\n",
" \"hiredate\":\"01/01/1995\",\n",
" \"job\":\"PRES\",\n",
" \"edlevel\":18,\n",
" \"sex\":\"F\",\n",
" \"birthdate\":\"08/24/1963\",\n",
" \"pay\" : {\n",
" \"salary\":152750.00,\n",
" \"bonus\":1000.00,\n",
" \"comm\":4220.00}\n",
" }'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting\n",
"Sometimes it would be useful to display a result set as either a bar, pie, or line chart. The first one or two\n",
"columns of a result set need to contain the values need to plot the information.\n",
"\n",
"The three possible plot options are:\n",
" \n",
"* -pb - bar chart (x,y)\n",
"* -pp - pie chart (y)\n",
"* -pl - line chart (x,y)\n",
"\n",
"The following data will be used to demonstrate the different charting options."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%sql values 1,2,3,4,5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since the results only have one column, the pie, line, and bar charts will not have any labels associated with\n",
"them. The first example is a bar chart."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"pixiedust": {
"displayParams": {
"handlerId": "dataframe"
}
}
},
"outputs": [],
"source": [
"%sql -pb values 1,2,3,4,5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The same data as a pie chart."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%sql -pp values 1,2,3,4,5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And finally a line chart."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"pixiedust": {
"displayParams": {
"handlerId": "dataframe"
}
},
"scrolled": true
},
"outputs": [],
"source": [
"%sql -pl values 1,2,3,4,5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you retrieve two columns of information, the first column is used for the labels (X axis or pie slices) and \n",
"the second column contains the data. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%sql -pb values ('A',1),('B',2),('C',3),('D',4),('E',5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For a pie chart, the first column is used to label the slices, while the data comes from the second column."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%sql -pp values ('A',1),('B',2),('C',3),('D',4),('E',5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, for a line chart, the x contains the labels and the y values are used."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%sql -pl values ('A',1),('B',2),('C',3),('D',4),('E',5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following SQL will plot the number of employees per department."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%sql -pb\n",
"SELECT WORKDEPT, COUNT(*) \n",
" FROM EMPLOYEE\n",
"GROUP BY WORKDEPT"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The final option for plotting data is to use interactive mode `-i`. This will display the data using an open-source project called Pixiedust. You can view the results in a table and then interactively create a plot by dragging and dropping column names into the appropriate slot. The next command will place you into interactive mode."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"pixiedust": {
"displayParams": {
"handlerId": "dataframe"
}
}
},
"outputs": [],
"source": [
"%sql -i select * from employee"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sample Data\n",
"Many of the Db2 notebooks depend on two of the tables that are found in the SAMPLE database. Rather than\n",
"having to create the entire SAMPLE database, this option will create and populate the EMPLOYEE and \n",
"DEPARTMENT tables in your database. Note that if you already have these tables defined, they will not be dropped."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%sql -sampledata"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Result Sets \n",
"By default, any `%sql` block will return the contents of a result set as a table that is displayed in the notebook. The results are displayed using a feature of pandas dataframes. The following select statement demonstrates a simple result set."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%sql select * from employee fetch first 3 rows only"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can assign the result set directly to a variable."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x = %sql select * from employee fetch first 3 rows only"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The variable x contains the dataframe that was produced by the `%sql` statement so you access the result set by using this variable or display the contents by just referring to it in a command line."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is an additional way of capturing the data through the use of the `-r` flag.\n",
"<pre>\n",
"var = %sql -r select * from employee\n",
"</pre>\n",
"Rather than returning a dataframe result set, this option will produce a list of rows. Each row is a list itself. The rows and columns all start at zero (0), so to access the first column of the first row, you would use var[0][0] to access it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rows = %sql -r select * from employee fetch first 3 rows only\n",
"print(rows[0][0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The number of rows in the result set can be determined by using the length function."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"print(len(rows))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want to iterate over all of the rows and columns, you could use the following Python syntax instead of\n",
"creating a for loop that goes from 0 to 41."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for row in rows:\n",
" line = \"\"\n",
" for col in row:\n",
" line = line + str(col) + \",\"\n",
" print(line)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since the data may be returned in different formats (like integers), you should use the str() function\n",
"to convert the values to strings. Otherwise, the concatenation function used in the above example will fail. For\n",
"instance, the 6th field is a birthdate field. If you retrieve it as an individual value and try and concatenate a string to it, you get the following error."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"print(\"Birth Date=\"+rows[0][6])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can fix this problem by adding the str function to convert the date."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"print(\"Birth Date=\"+str(rows[0][6]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Db2 CONNECT Statement\n",
"As mentioned at the beginning of this notebook, connecting to Db2 is automatically done when you issue your first\n",
"`%sql` statement. Usually the program will prompt you with what options you want when connecting to a database. The other option is to use the CONNECT statement directly. The CONNECT statement is similar to the native Db2\n",
"CONNECT command, but includes some options that allow you to connect to databases that has not been\n",
"catalogued locally.\n",
"\n",
"The CONNECT command has the following format:\n",
"<pre>\n",
"%sql CONNECT TO <database> USER <userid> USING <password | ?> HOST <ip address> PORT <port number>\n",
"</pre>\n",
"If you use a \"?\" for the password field, the system will prompt you for a password. This avoids typing the \n",
"password as clear text on the screen. If a connection is not successful, the system will print the error\n",
"message associated with the connect request.\n",
"\n",
"If the connection is successful, the parameters are saved on your system and will be used the next time you\n",
"run a SQL statement, or when you issue the %sql CONNECT command with no parameters.\n",
"\n",
"If you want to force the program to connect to a different database (with prompting), use the CONNECT RESET command. The next time you run a SQL statement, the program will prompt you for the the connection\n",
"and will force the program to reconnect the next time a SQL statement is executed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%sql CONNECT RESET"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%sql CONNECT"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Credits: IBM 2018, George Baklarz [[email protected]]"
]
}
],
"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
}
| apache-2.0 |
jeffzhengye/pylearn | pybasic/.ipynb_checkpoints/profile-checkpoint.ipynb | 1 | 3628 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# cProfile\n",
"\n",
"Python自带了几个性能分析的模块：profile、cProfile和hotshot，使用方法基本都差不多，无非模块是纯Python还是用C写的。cProfile效率更高"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# run profile\n",
"!python -m cProfile -o profile_test.out profile_test.py"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mon Oct 25 15:38:34 2021 profile_test.out\r\n",
"\r\n",
" 6 function calls in 10.031 seconds\r\n",
"\r\n",
" Random listing order was used\r\n",
"\r\n",
" ncalls tottime percall cumtime percall filename:lineno(function)\r\n",
" 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\r\n",
" 1 0.000 0.000 10.001 10.001 profile_test.py:6(func2)\r\n",
" 1 0.031 0.031 0.031 0.031 profile_test.py:2(func1)\r\n",
" 1 0.000 0.000 10.031 10.031 profile_test.py:1(<module>)\r\n",
" 1 10.001 10.001 10.001 10.001 {built-in method time.sleep}\r\n",
" 1 0.000 0.000 10.031 10.031 {built-in method builtins.exec}\r\n",
"\r\n",
"\r\n"
]
}
],
"source": [
"!python -c \"import pstats; p=pstats.Stats('profile_test.out'); p.print_stats()\""
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mon Oct 25 15:38:34 2021 profile_test.out\r\n",
"\r\n",
" 6 function calls in 10.031 seconds\r\n",
"\r\n",
" Ordered by: internal time\r\n",
"\r\n",
" ncalls tottime percall cumtime percall filename:lineno(function)\r\n",
" 1 10.001 10.001 10.001 10.001 {built-in method time.sleep}\r\n",
" 1 0.031 0.031 0.031 0.031 profile_test.py:2(func1)\r\n",
" 1 0.000 0.000 10.001 10.001 profile_test.py:6(func2)\r\n",
" 1 0.000 0.000 10.031 10.031 profile_test.py:1(<module>)\r\n",
" 1 0.000 0.000 10.031 10.031 {built-in method builtins.exec}\r\n",
" 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\r\n",
"\r\n",
"\r\n"
]
}
],
"source": [
"# 可以设置排序方式，例如以花费时间多少排序\n",
"!python -c \"import pstats; p=pstats.Stats('profile_test.out'); p.sort_stats('time').print_stats()\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# sort_stats支持以下参数：\n",
"* calls, cumulative, file, line, module, name, nfl, pcalls, stdname, time"
]
},
{
"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": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| unlicense |
hall1467/wikidata_usage_tracking | jupyter_notebooks/misalignment/expected_quality_versus_actual_quality_analysis/.ipynb_checkpoints/revision_expected_quality_versus_actual_quality-monthly-checkpoint.ipynb | 1 | 8346141 | null | mit |
xiaohan2012/snpp | iterative_vs_single.ipynb | 1 | 11366 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 1. single approach that predicts N edges using max_balance in one run\n",
"# and then predict using lowrank method\n",
"\n",
"# 2. iterative approach that predicts N edges in X runs\n",
"# and then predict using LR method\n",
"\n",
"\n",
"# result:\n",
"# plot of two lines: \n",
"# x-axis: N, minimum triangle count threshold (similar to embeddedness)\n",
"# y-axis: accuracy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"import _pickle as pkl\n",
"import networkx as nx\n",
"import numpy as np\n",
"import random\n",
"\n",
"from tqdm import tqdm\n",
"from snpp.cores.lowrank import alq_spark, predict_signs\n",
"from snpp.utils.matrix import split_train_test, load_sparse_csr\n",
"from snpp.utils.signed_graph import g2m\n",
"from snpp.utils.data import load_train_test_graphs\n",
"from snpp.utils.edge_filter import filter_by_min_triangle_count\n",
"\n",
"from snpp.utils.spark import sc\n",
"\n",
"dataset = 'slashdot'\n",
"lambda_ = 0.2\n",
"k = 5\n",
"max_iter = 100\n",
"random_seed = 123456\n",
"min_tri_count = 20\n",
"\n",
"recache_input = False\n",
"\n",
"random.seed(random_seed)\n",
"np.random.seed(random_seed)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loading train and test graphs...\n"
]
}
],
"source": [
"train_g, test_g = load_train_test_graphs(dataset, recache_input)\n",
"train_g_ud = train_g.to_undirected()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"confident_edges = set(filter_by_min_triangle_count(train_g_ud, test_g.edges(), min_tri_count))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 9%|▉ | 233/2645 [00:00<00:01, 2325.13it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"build edge2edges\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 2645/2645 [00:01<00:00, 2256.54it/s]\n"
]
}
],
"source": [
"from snpp.cores.joint_part_pred import iterative_approach\n",
"from snpp.cores.max_balance import faster_greedy\n",
"from snpp.cores.lowrank import partition_graph\n",
"from snpp.cores.budget_allocation import constant_budget\n",
"from snpp.cores.triangle import build_edge2edges\n",
"\n",
"common_params = dict(\n",
" g=train_g_ud,\n",
" T=confident_edges,\n",
" k=k,\n",
" graph_partition_f=partition_graph,\n",
" graph_partition_kwargs=dict(sc=sc,\n",
" lambda_=lambda_,\n",
" iterations=max_iter,\n",
" seed=random_seed),\n",
" budget_allocation_f=constant_budget,\n",
" solve_maxbalance_f=faster_greedy,\n",
" solve_maxbalance_kwargs={'edge2edges': build_edge2edges(train_g_ud.copy(),\n",
" confident_edges)},\n",
" truth=set([(i, j, test_g[i][j]['sign'])\n",
" for i, j in confident_edges]),\n",
" perform_last_partition=False\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from snpp.utils.evaluation import accuracy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# single iteration approach\n",
"\n",
"part, single_preds, status = iterative_approach(\n",
" budget_allocation_kwargs=dict(const=len(confident_edges)),\n",
" **common_params\n",
")\n",
"print(\" => accuracy {} (single)\".format(accuracy(test_g, single_preds)))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# iterative approach\n",
"\n",
"part, iter_preds, status = iterative_approach(\n",
" budget_allocation_kwargs=dict(const=200),\n",
" **common_params\n",
")\n",
"print(\" => accuracy {} (iterative)\".format(accuracy(test_g, iter_preds)))\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"to_scipy_sparse_matrix\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 77357/77357 [00:01<00:00, 52765.65it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ALS...\n",
"predict labels (SVD + Kmeans)...\n",
"eigen values: [ 190.59474185 90.81402874 70.1883324 47.81850672 35.17200069\n",
" 32.65883398 28.2857246 26.76476412 26.15166153 25.78369751\n",
" 25.28476371 24.56391419 24.03171128 22.94467161 22.06543332\n",
" 21.10618394 20.655356 20.19932586 19.36947917 19.19516865\n",
" 18.77874012 17.8812066 17.3571094 16.76504895 16.20612479\n",
" 14.92819051 14.42525262 13.9064831 13.53853552 13.20960812\n",
" 12.24701639 11.67489415 11.06718951 10.64686697 10.48079306\n",
" 10.09816838 9.45630068 9.06644391 8.75918222 8.48775764]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/cloud-user/code/snpp/venv/lib/python3.5/site-packages/sklearn/externals/joblib/hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
"descriptor assignment is deprecated. To maintain\n",
"the Fortran contiguity of a multidimensional Fortran\n",
"array, use 'a.T.view(...).T' instead\n",
" obj_bytes_view = obj.view(self.np.uint8)\n",
"/home/cloud-user/code/snpp/venv/lib/python3.5/site-packages/sklearn/externals/joblib/hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
"descriptor assignment is deprecated. To maintain\n",
"the Fortran contiguity of a multidimensional Fortran\n",
"array, use 'a.T.view(...).T' instead\n",
" obj_bytes_view = obj.view(self.np.uint8)\n",
"/home/cloud-user/code/snpp/venv/lib/python3.5/site-packages/sklearn/externals/joblib/hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
"descriptor assignment is deprecated. To maintain\n",
"the Fortran contiguity of a multidimensional Fortran\n",
"array, use 'a.T.view(...).T' instead\n",
" obj_bytes_view = obj.view(self.np.uint8)\n",
"/home/cloud-user/code/snpp/venv/lib/python3.5/site-packages/sklearn/externals/joblib/hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
"descriptor assignment is deprecated. To maintain\n",
"the Fortran contiguity of a multidimensional Fortran\n",
"array, use 'a.T.view(...).T' instead\n",
" obj_bytes_view = obj.view(self.np.uint8)\n",
"/home/cloud-user/code/snpp/venv/lib/python3.5/site-packages/sklearn/externals/joblib/hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
"descriptor assignment is deprecated. To maintain\n",
"the Fortran contiguity of a multidimensional Fortran\n",
"array, use 'a.T.view(...).T' instead\n",
" obj_bytes_view = obj.view(self.np.uint8)\n",
"/home/cloud-user/code/snpp/venv/lib/python3.5/site-packages/sklearn/externals/joblib/hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
"descriptor assignment is deprecated. To maintain\n",
"the Fortran contiguity of a multidimensional Fortran\n",
"array, use 'a.T.view(...).T' instead\n",
" obj_bytes_view = obj.view(self.np.uint8)\n",
"/home/cloud-user/code/snpp/venv/lib/python3.5/site-packages/sklearn/externals/joblib/hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
"descriptor assignment is deprecated. To maintain\n",
"the Fortran contiguity of a multidimensional Fortran\n",
"array, use 'a.T.view(...).T' instead\n",
" obj_bytes_view = obj.view(self.np.uint8)\n",
"/home/cloud-user/code/snpp/venv/lib/python3.5/site-packages/sklearn/externals/joblib/hashing.py:197: DeprecationWarning: Changing the shape of non-C contiguous array by\n",
"descriptor assignment is deprecated. To maintain\n",
"the Fortran contiguity of a multidimensional Fortran\n",
"array, use 'a.T.view(...).T' instead\n",
" obj_bytes_view = obj.view(self.np.uint8)\n"
]
}
],
"source": [
"# partition and cut approach\n",
"\n",
"from snpp.cores.joint_part_pred import single_run_approach\n",
"\n",
"_, part_and_cut_preds = single_run_approach(train_g_ud, \n",
" confident_edges,\n",
" k,\n",
" graph_partition_f=partition_graph,\n",
" graph_partition_kwargs=dict(sc=sc,\n",
" lambda_=lambda_,\n",
" iterations=max_iter,\n",
" seed=random_seed))\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" => accuracy 0.6170132325141777 (partition-and-cut)\n"
]
}
],
"source": [
"print(\" => accuracy {} (partition-and-cut)\".format(accuracy(test_g, part_and_cut_preds)))\n",
"\n",
"# k=5 => accuracy 0.6170132325141777 (partition-and-cut)\n",
"# k=10 => accuracy 0.71\n",
"# k=40 => accuracy 0.5024574669187145 (partition-and-cut)\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": 0
}
| mit |
QuantStack/quantstack-talks | 2017-12-04-DIANA-HEP/notebooks/bqplot.ipynb | 1 | 73208 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# bqplot https://github.com/bloomberg/bqplot\n",
"\n",
"## A Jupyter - d3.js bridge\n",
"\n",
"bqplot is a jupyter interactive widget library bringing d3.js visualization to the Jupyter notebook.\n",
"\n",
"- Apache Licensed\n",
"\n",
"bqplot implements the abstractions of Wilkinson’s “The Grammar of Graphics” as interactive Jupyter widgets.\n",
"\n",
"bqplot provides both\n",
"-\thigh-level plotting procedures with relevant defaults for common chart types,\n",
"-\tlower-level descriptions of data visualizations meant for complex interactive visualization dashboards and applications involving mouse interactions and user-provided Python callbacks.\n",
"\n",
"**Installation:**\n",
"\n",
"```bash\n",
"conda install -c conda-forge bqplot\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"from IPython.display import display\n",
"from ipywidgets import *\n",
"from traitlets import *\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import bqplot as bq\n",
"import datetime as dt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(0)\n",
"size = 100\n",
"y_data = np.cumsum(np.random.randn(size) * 100.0)\n",
"y_data_2 = np.cumsum(np.random.randn(size))\n",
"y_data_3 = np.cumsum(np.random.randn(size) * 100.)\n",
"\n",
"x = np.linspace(0.0, 10.0, size)\n",
"\n",
"price_data = pd.DataFrame(np.cumsum(np.random.randn(150, 2).dot([[0.5, 0.8], [0.8, 1.0]]), axis=0) + 100,\n",
" columns=['Security 1', 'Security 2'],\n",
" index=pd.date_range(start='01-01-2007', periods=150))\n",
"\n",
"symbol = 'Security 1'\n",
"dates_all = price_data.index.values\n",
"final_prices = price_data[symbol].values.flatten()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A simple plot with the pyplot API"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from bqplot import pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f9754b695fdd4bf99ec465aa44532185",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>VBox</code>.</p>\n",
"<p>\n",
" If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another notebook frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(1)\n",
"n = 100\n",
"plt.plot(np.linspace(0.0, 10.0, n), np.cumsum(np.random.randn(n)), \n",
" axes_options={'y': {'grid_lines': 'dashed'}})\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scatter Plot"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
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},
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],
"source": [
"plt.figure(title='Scatter Plot with colors')\n",
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Histogram"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
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" 366.60296034, 545.19000973, 557.881219 , 598.08015534]), scales={'count': LinearScale(), 'sample': LinearScale()}, scales_metadata={'sample': {'orientation': 'horizontal', 'dimension': 'x'}, 'count': {'orientation': 'vertical', 'dimension': 'y'}}, tooltip_style={'opacity': 0.9})], scale_x=LinearScale(allow_padding=False, max=1.0, min=0.0), scale_y=LinearScale(allow_padding=False, max=1.0, min=0.0)), Toolbar(figure=Figure(axes=[Axis(orientation='vertical', scale=LinearScale()), Axis(scale=LinearScale())], fig_margin={'top': 60, 'bottom': 60, 'left': 60, 'right': 60}, layout=Layout(min_width='125px'), marks=[Hist(colors=['OrangeRed'], interactions={'hover': 'tooltip'}, sample=array([ 176.4052346 , 216.42095543, 314.29475384, 538.38407376,\n",
" 725.13987278, 627.41208479, 722.42092654, 707.28520571,\n",
" 696.96332053, 738.02317073, 752.42752784, 897.85487854,\n",
" 973.95865106, 986.12615271, 1030.51247598, 1063.87990872,\n",
" 1213.28781603, 1192.77198966, 1224.07875982, 1138.66918589,\n",
" 883.37020431, 948.73206385, 1035.17568374, 960.9591817 ,\n",
" 1187.9346441 , 1042.49807664, 1047.07392837, 1028.35554336,\n",
" 1181.6334648 , 1328.56934179, 1344.06408436, 1381.88033632,\n",
" 1293.10176156, 1095.02211473, 1060.2308998 , 1075.86579671,\n",
" 1198.89486478, 1319.13284966, 1280.40016792, 1250.16989286,\n",
" 1145.31459636, 1003.31280264, 832.68578358, 1027.7633231 ,\n",
" 976.79810493, 932.99067476, 807.71113876, 885.46017434,\n",
" 724.07038959, 702.79636157, 613.24970545, 651.93995523,\n",
" 600.85944148, 482.79622306, 479.97800023, 522.81118728,\n",
" 529.46290952, 559.71009929, 496.27788993, 460.00377333,\n",
" 392.75772855, 356.8024124 , 275.48778419, 102.85952396,\n",
" 120.60213818, 80.42404456, -82.59579013, -36.31756458,\n",
" -127.04740102, -121.85286144, -48.94380522, -36.04551415,\n",
" 77.89455431, -45.58802773, -5.35386361, -73.8348727 ,\n",
" -160.91458762, -218.7995541 , -249.95480731, -244.33827309,\n",
" -360.85325717, -270.77060847, -224.2043645 , -377.82873313,\n",
" -229.00351375, -39.41459614, 78.46336097, 60.47087739,\n",
" -46.60438476, 58.84078793, 18.52309324, 140.76760027,\n",
" 161.59509808, 259.25900173, 294.89564145, 365.55295827,\n",
" 366.60296034, 545.19000973, 557.881219 , 598.08015534]), scales={'count': LinearScale(), 'sample': LinearScale()}, scales_metadata={'sample': {'orientation': 'horizontal', 'dimension': 'x'}, 'count': {'orientation': 'vertical', 'dimension': 'y'}}, tooltip_style={'opacity': 0.9})], scale_x=LinearScale(allow_padding=False, max=1.0, min=0.0), scale_y=LinearScale(allow_padding=False, max=1.0, min=0.0)))))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.hist(y_data, colors=['OrangeRed'])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Every component of the figure is an independent widget"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "9ea3e1b16a084cb2999cc3b4a212442a",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Figure</code>.</p>\n",
"<p>\n",
" If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another notebook frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Figure(animation_duration=1000, axes=[Axis(label='x', scale=LinearScale()), Axis(label='y', orientation='vertical', scale=LinearScale(), tick_format='0.2f')], fig_margin={'top': 60, 'bottom': 60, 'left': 60, 'right': 60}, layout=Layout(min_width='125px'), marks=[Lines(colors=['red', 'green'], interactions={'hover': 'tooltip'}, scales={'x': LinearScale(), 'y': LinearScale()}, scales_metadata={'x': {'orientation': 'horizontal', 'dimension': 'x'}, 'y': {'orientation': 'vertical', 'dimension': 'y'}, 'color': {'dimension': 'color'}}, tooltip_style={'opacity': 0.9}, x=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
" 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
" 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
" 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
" 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
" 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), y=array([[ -1.4449402 , -2.65548319, -3.44415245, -2.34951407,\n",
" -2.11469255, 0.01746086, 0.95390659, 0.91881141,\n",
" 2.18388925, 2.39538626, 1.69046491, 2.37043976,\n",
" 1.6741131 , 1.383716 , 2.7114987 , 2.61021721,\n",
" 1.80707582, 1.34273813, 2.36452872, 1.81198804,\n",
" 1.4251172 , 0.91482446, 1.09874995, 0.71326019,\n",
" -0.88857586, -1.7757568 , -2.70854584, -1.46522646,\n",
" -0.65255241, -0.06529304, -0.57065135, -1.38644289,\n",
" -1.8939605 , -2.9458406 , -0.44864021, -2.69396186,\n",
" -2.12995332, -3.41450562, -3.51884911, -4.50685105,\n",
" -5.68448002, -6.82467632, -5.06969016, -5.20267858,\n",
" -5.96838078, -5.41259381, -5.4022445 , -4.68221074,\n",
" -6.5064674 , -6.20286349, -5.43016866, -7.09176695,\n",
" -6.64357166, -4.94739009, -4.96224779, -4.14084186,\n",
" -3.47027141, -4.1777771 , -4.13801037, -5.70500508,\n",
" -6.15630812, -5.89062014, -5.16751965, -5.14290752,\n",
" -4.42292379, -5.52583 , -5.62752728, -5.6082479 ,\n",
" -3.75865665, -3.9728233 , -4.47183994, -4.45048872,\n",
" -5.36960216, -5.17684831, -5.54190353, -7.33323108,\n",
" -7.39181763, -7.70936072, -9.34178403, -9.40891818,\n",
" -7.91956222, -7.39825847, -6.78633128, -8.127828 ,\n",
" -7.65092963, -7.50248005, -6.97343481, -6.55080619,\n",
" -7.91058692, -7.95198773, -8.70985859, -8.75994268,\n",
" -9.65734361, -8.34487324, -9.20384563, -10.10278779,\n",
" -10.02820138, -11.10530045, -11.52996375, -12.35992835],\n",
" [ 1.41117206, 2.19697589, 2.13950637, 1.74828932,\n",
" 2.68920693, 3.09441101, 3.59246342, 3.56627118,\n",
" 1.87804115, 1.76557517, 1.23308525, 1.87814053,\n",
" 2.88998296, 2.23203191, 2.70041715, 4.43629615,\n",
" 3.76858343, 5.45050517, 4.59791932, 4.62087907,\n",
" 4.60973346, 4.62123236, 3.78355432, 3.19237122,\n",
" 2.52465093, 2.85161353, 3.18164864, 5.40759297,\n",
" 6.77858198, 6.26873874, 6.59360835, 7.59072633,\n",
" 7.62132816, 7.55168658, 7.60326152, 8.47053815,\n",
" 7.62221763, 7.29654816, 7.7669813 , 8.07842838,\n",
" 8.31801113, 7.94820997, 8.92074576, 11.054614 ,\n",
" 11.4610295 , 11.2678528 , 12.02359309, 11.48446045,\n",
" 10.7347701 , 10.76757885, 8.18478222, 7.03083186,\n",
" 6.68287 , 5.32948114, 4.29683804, 3.8600897 ,\n",
" 2.21712441, 1.81105261, 1.27578245, 1.30118766,\n",
" 2.45537169, 2.6278761 , 2.64893812, 2.74839258,\n",
" 2.97578536, 1.95904671, 1.84427138, 2.15302263,\n",
" 0.78226264, 1.64791557, 2.7292916 , 2.09791561,\n",
" 1.85657782, 0.97838748, 1.67776796, 0.61654567,\n",
" 0.39406866, -0.46485124, -0.41389697, -2.20812624,\n",
" -0.8816646 , -1.84627102, -1.78637634, -1.99889938,\n",
" -2.76101389, -3.64879403, -2.71239549, -3.23803608,\n",
" -2.9668659 , -3.76836278, -4.41554421, -3.94329706,\n",
" -3.01288857, -3.18820497, -4.61012484, -2.61216876,\n",
" -3.46871807, -5.01030547, -2.41588088, -2.81991318]]))], scale_x=LinearScale(allow_padding=False, max=1.0, min=0.0), scale_y=LinearScale(allow_padding=False, max=1.0, min=0.0))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xs = bq.LinearScale()\n",
"ys = bq.LinearScale()\n",
"x = np.arange(100)\n",
"y = np.cumsum(np.random.randn(2, 100), axis=1) #two random walks\n",
"\n",
"line = bq.Lines(x=x, y=y, scales={'x': xs, 'y': ys}, colors=['red', 'green'])\n",
"xax = bq.Axis(scale=xs, label='x', grid_lines='solid')\n",
"yax = bq.Axis(scale=ys, orientation='vertical', tick_format='0.2f', label='y', grid_lines='solid')\n",
"\n",
"fig = bq.Figure(marks=[line], axes=[xax, yax], animation_duration=1000)\n",
"display(fig)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# update data of the line mark\n",
"line.y = np.cumsum(np.random.randn(2, 100), axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "58bfae5cbdb94c21aaa19f97f79334d7",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Figure</code>.</p>\n",
"<p>\n",
" If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another notebook frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Figure(animation_duration=1000, axes=[Axis(label='x', scale=LinearScale()), Axis(label='y', orientation='vertical', scale=LinearScale(), tick_format='0.2f')], fig_margin={'top': 60, 'bottom': 60, 'left': 60, 'right': 60}, layout=Layout(min_width='125px'), marks=[Scatter(colors=['DeepSkyBlue'], interactions={'hover': 'tooltip'}, scales={'x': LinearScale(), 'y': LinearScale()}, scales_metadata={'x': {'orientation': 'horizontal', 'dimension': 'x'}, 'y': {'orientation': 'vertical', 'dimension': 'y'}, 'color': {'dimension': 'color'}, 'size': {'dimension': 'size'}, 'opacity': {'dimension': 'opacity'}, 'rotation': {'dimension': 'rotation'}, 'skew': {'dimension': 'skew'}}, tooltip_style={'opacity': 0.9}, x=array([ 0.80127342, 0.05029106, 0.42091014, 0.25697546, 0.2669759 ,\n",
" 0.79145373, 0.62386673, 0.43974531, 0.01058574, 0.96492794,\n",
" 0.96202325, 0.21755221, 0.04134637, 0.53019936, 0.95141081,\n",
" 0.91039585, 0.58466286, 0.30354885, 0.32996088, 0.89791355]), y=array([ 0.49178404, 0.13111623, 0.24842548, 0.2767949 , 0.12354668,\n",
" 0.46304438, 0.91605091, 0.66878254, 0.07247392, 0.00549482,\n",
" 0.27624767, 0.36269293, 0.77674967, 0.96700552, 0.38756717,\n",
" 0.68669003, 0.99490191, 0.74566658, 0.63618955, 0.07807485]))], scale_x=LinearScale(allow_padding=False, max=1.0, min=0.0), scale_y=LinearScale(allow_padding=False, max=1.0, min=0.0))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xs = bq.LinearScale()\n",
"ys = bq.LinearScale()\n",
"x, y = np.random.rand(2, 20)\n",
"scatt = bq.Scatter(x=x, y=y, scales={'x': xs, 'y': ys}, default_colors=['blue'])\n",
"xax = bq.Axis(scale=xs, label='x', grid_lines='solid')\n",
"yax = bq.Axis(scale=ys, orientation='vertical', tick_format='0.2f', label='y', grid_lines='solid')\n",
"\n",
"fig = bq.Figure(marks=[scatt], axes=[xax, yax], animation_duration=1000)\n",
"display(fig)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#data updates\n",
"scatt.x = np.random.rand(20) * 10\n",
"scatt.y = np.random.rand(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The same holds for the attributes of scales, axes"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"xs.min = 4"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"xs.min = None"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"xax.label = 'Some label for the x axis'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use bqplot figures as input widgets"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"xs = bq.LinearScale()\n",
"ys = bq.LinearScale()\n",
"x = np.arange(100)\n",
"y = np.cumsum(np.random.randn(2, 100), axis=1) #two random walks\n",
"\n",
"line = bq.Lines(x=x, y=y, scales={'x': xs, 'y': ys}, colors=['red', 'green'])\n",
"xax = bq.Axis(scale=xs, label='x', grid_lines='solid')\n",
"yax = bq.Axis(scale=ys, orientation='vertical', tick_format='0.2f', label='y', grid_lines='solid')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Selections"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ef3f9f2a4e80462aa69029e5441146fb",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Label</code>.</p>\n",
"<p>\n",
" If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another notebook frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Label(value='None')"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def interval_change_callback(change):\n",
" db.value = str(change['new'])\n",
"\n",
"intsel = bq.interacts.FastIntervalSelector(scale=xs, marks=[line])\n",
"intsel.observe(interval_change_callback, names=['selected'] )\n",
"\n",
"db = widgets.Label()\n",
"db.value = str(intsel.selected)\n",
"display(db)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f5cd09a2219e4cb8980dc5a5223e6539",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Figure</code>.</p>\n",
"<p>\n",
" If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another notebook frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Figure(animation_duration=1000, axes=[Axis(label='x', scale=LinearScale(), side='bottom'), Axis(label='y', orientation='vertical', scale=LinearScale(), side='left', tick_format='0.2f')], fig_margin={'top': 60, 'bottom': 60, 'left': 60, 'right': 60}, interaction=FastIntervalSelector(marks=[Lines(colors=['red', 'green'], interactions={'hover': 'tooltip'}, scales={'x': LinearScale(), 'y': LinearScale()}, scales_metadata={'x': {'orientation': 'horizontal', 'dimension': 'x'}, 'y': {'orientation': 'vertical', 'dimension': 'y'}, 'color': {'dimension': 'color'}}, tooltip_style={'opacity': 0.9}, x=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
" 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
" 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
" 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
" 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
" 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), y=array([[ -2.08285103e-01, 1.36608613e+00, 1.56507563e+00,\n",
" 3.55380755e+00, 4.67109101e+00, 3.10718638e+00,\n",
" 3.12581375e+00, 4.18013872e+00, 4.21068530e+00,\n",
" 4.17380177e+00, 5.44356652e+00, 4.73371234e+00,\n",
" 4.75122796e+00, 5.07485372e+00, 4.74106276e+00,\n",
" 4.72093366e+00, 5.49595692e+00, 5.92879454e+00,\n",
" 5.12007701e+00, 4.01595302e+00, 3.22685084e+00,\n",
" 3.22809930e+00, 3.06815951e+00, 2.23620202e+00,\n",
" 1.63805156e+00, 1.18012280e-01, 5.35865983e-01,\n",
" 4.95847257e-01, -7.63940086e-01, -7.35319582e-01,\n",
" 6.07302429e-01, -1.32633424e-01, 1.18250424e+00,\n",
" 8.59046769e-01, 1.05687494e+00, 1.15462574e+00,\n",
" 2.55614916e+00, 2.71458300e+00, 1.57268158e+00,\n",
" 2.61711212e-01, -1.27120984e+00, -2.98318001e+00,\n",
" -2.93704495e+00, -3.89541943e+00, -3.97623104e+00,\n",
" -4.68009008e+00, -5.45087438e+00, -5.93171972e+00,\n",
" -5.22813416e+00, -4.29898901e+00, -3.92781646e+00,\n",
" -4.91763901e+00, -4.27400774e+00, -3.58511107e+00,\n",
" -3.31046387e+00, -3.91408430e+00, -3.20522473e+00,\n",
" -2.78240615e+00, -5.89926274e+00, -5.25481071e+00,\n",
" -7.16855338e+00, -6.50499180e+00, -6.65906420e+00,\n",
" -5.46545252e+00, -5.56361373e+00, -6.45022799e+00,\n",
" -6.59758166e+00, -5.53777536e+00, -5.51152874e+00,\n",
" -5.62586390e+00, -4.88231039e+00, -4.67195102e+00,\n",
" -4.67787843e+00, -3.31181836e+00, -1.75670433e+00,\n",
" -1.14337810e+00, -1.42933725e+00, 6.75737404e-02,\n",
" 1.25069330e+00, 1.96959046e+00, 7.53513883e-01,\n",
" 8.94185786e-01, 1.50513611e-01, -8.49864040e-03,\n",
" 2.31558289e-01, 3.31717697e-01, -1.43457409e-01,\n",
" 1.12949634e+00, -5.66634927e-01, 1.63548604e-01,\n",
" -1.69393467e+00, -1.31133653e+00, -2.19824086e+00,\n",
" -1.31993710e+00, -1.23348458e+00, -9.85778197e-01,\n",
" -2.00405752e+00, -2.65862766e+00, -2.45141026e+00,\n",
" -1.86784034e+00],\n",
" [ 2.92909624e+00, 3.15195456e+00, 4.12799209e+00,\n",
" 2.57105816e+00, 1.24116630e+00, 8.85671522e-01,\n",
" -3.11756174e-01, 1.17464308e+00, 7.64424387e-01,\n",
" 2.14660628e+00, 3.63338875e+00, 3.67616847e+00,\n",
" 4.17796822e+00, 4.12186875e+00, 4.66030575e+00,\n",
" 5.14364760e+00, 5.01999798e+00, 5.52496796e+00,\n",
" 7.24866423e+00, 7.96168046e+00, 8.28748008e+00,\n",
" 8.41224960e+00, 7.39957647e+00, 6.37227960e+00,\n",
" 6.69563613e+00, 5.32624500e+00, 4.55991741e+00,\n",
" 5.84142875e+00, 7.75565844e+00, 6.08970237e+00,\n",
" 7.71635193e+00, 7.50491364e+00, 7.48990855e+00,\n",
" 7.37649692e+00, 8.45704105e+00, 6.84936447e+00,\n",
" 7.30552808e+00, 6.36065788e+00, 6.93144641e+00,\n",
" 8.47424275e+00, 8.47382542e+00, 8.84798051e+00,\n",
" 9.25753229e+00, 8.45793879e+00, 9.96957814e+00,\n",
" 1.16760464e+01, 1.23778298e+01, 1.24511152e+01,\n",
" 1.19892214e+01, 1.13627311e+01, 1.30735677e+01,\n",
" 1.44879828e+01, 1.44243213e+01, 1.28443908e+01,\n",
" 1.00123789e+01, 8.92895223e+00, 8.79833183e+00,\n",
" 1.01990209e+01, 9.54736466e+00, 1.00521801e+01,\n",
" 1.13553611e+01, 1.14838974e+01, 1.13414495e+01,\n",
" 1.00326860e+01, 8.83021070e+00, 9.24631033e+00,\n",
" 9.04540280e+00, 9.16793412e+00, 9.12065640e+00,\n",
" 9.78480045e+00, 9.00011304e+00, 8.66453240e+00,\n",
" 1.05607146e+01, 9.76092849e+00, 9.47935306e+00,\n",
" 8.88996635e+00, 9.33474772e+00, 1.03571400e+01,\n",
" 9.85892842e+00, 9.42751408e+00, 9.14853247e+00,\n",
" 9.67836626e+00, 8.93897096e+00, 8.56301099e+00,\n",
" 6.19081712e+00, 4.80907211e+00, 4.69662835e+00,\n",
" 5.59449253e+00, 5.88956831e+00, 4.79079985e+00,\n",
" 3.39054364e+00, 3.56522374e+00, 1.91242009e+00,\n",
" 2.97834691e+00, 3.04224311e+00, 1.43492295e+00,\n",
" 4.68969087e-01, -2.55342232e-01, -1.02853474e+00,\n",
" -2.51846775e+00]]))], scale=LinearScale()), layout=Layout(min_width='125px'), marks=[Lines(colors=['red', 'green'], interactions={'hover': 'tooltip'}, scales={'x': LinearScale(), 'y': LinearScale()}, scales_metadata={'x': {'orientation': 'horizontal', 'dimension': 'x'}, 'y': {'orientation': 'vertical', 'dimension': 'y'}, 'color': {'dimension': 'color'}}, tooltip_style={'opacity': 0.9}, x=array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
" 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
" 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
" 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
" 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
" 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]), y=array([[ -2.08285103e-01, 1.36608613e+00, 1.56507563e+00,\n",
" 3.55380755e+00, 4.67109101e+00, 3.10718638e+00,\n",
" 3.12581375e+00, 4.18013872e+00, 4.21068530e+00,\n",
" 4.17380177e+00, 5.44356652e+00, 4.73371234e+00,\n",
" 4.75122796e+00, 5.07485372e+00, 4.74106276e+00,\n",
" 4.72093366e+00, 5.49595692e+00, 5.92879454e+00,\n",
" 5.12007701e+00, 4.01595302e+00, 3.22685084e+00,\n",
" 3.22809930e+00, 3.06815951e+00, 2.23620202e+00,\n",
" 1.63805156e+00, 1.18012280e-01, 5.35865983e-01,\n",
" 4.95847257e-01, -7.63940086e-01, -7.35319582e-01,\n",
" 6.07302429e-01, -1.32633424e-01, 1.18250424e+00,\n",
" 8.59046769e-01, 1.05687494e+00, 1.15462574e+00,\n",
" 2.55614916e+00, 2.71458300e+00, 1.57268158e+00,\n",
" 2.61711212e-01, -1.27120984e+00, -2.98318001e+00,\n",
" -2.93704495e+00, -3.89541943e+00, -3.97623104e+00,\n",
" -4.68009008e+00, -5.45087438e+00, -5.93171972e+00,\n",
" -5.22813416e+00, -4.29898901e+00, -3.92781646e+00,\n",
" -4.91763901e+00, -4.27400774e+00, -3.58511107e+00,\n",
" -3.31046387e+00, -3.91408430e+00, -3.20522473e+00,\n",
" -2.78240615e+00, -5.89926274e+00, -5.25481071e+00,\n",
" -7.16855338e+00, -6.50499180e+00, -6.65906420e+00,\n",
" -5.46545252e+00, -5.56361373e+00, -6.45022799e+00,\n",
" -6.59758166e+00, -5.53777536e+00, -5.51152874e+00,\n",
" -5.62586390e+00, -4.88231039e+00, -4.67195102e+00,\n",
" -4.67787843e+00, -3.31181836e+00, -1.75670433e+00,\n",
" -1.14337810e+00, -1.42933725e+00, 6.75737404e-02,\n",
" 1.25069330e+00, 1.96959046e+00, 7.53513883e-01,\n",
" 8.94185786e-01, 1.50513611e-01, -8.49864040e-03,\n",
" 2.31558289e-01, 3.31717697e-01, -1.43457409e-01,\n",
" 1.12949634e+00, -5.66634927e-01, 1.63548604e-01,\n",
" -1.69393467e+00, -1.31133653e+00, -2.19824086e+00,\n",
" -1.31993710e+00, -1.23348458e+00, -9.85778197e-01,\n",
" -2.00405752e+00, -2.65862766e+00, -2.45141026e+00,\n",
" -1.86784034e+00],\n",
" [ 2.92909624e+00, 3.15195456e+00, 4.12799209e+00,\n",
" 2.57105816e+00, 1.24116630e+00, 8.85671522e-01,\n",
" -3.11756174e-01, 1.17464308e+00, 7.64424387e-01,\n",
" 2.14660628e+00, 3.63338875e+00, 3.67616847e+00,\n",
" 4.17796822e+00, 4.12186875e+00, 4.66030575e+00,\n",
" 5.14364760e+00, 5.01999798e+00, 5.52496796e+00,\n",
" 7.24866423e+00, 7.96168046e+00, 8.28748008e+00,\n",
" 8.41224960e+00, 7.39957647e+00, 6.37227960e+00,\n",
" 6.69563613e+00, 5.32624500e+00, 4.55991741e+00,\n",
" 5.84142875e+00, 7.75565844e+00, 6.08970237e+00,\n",
" 7.71635193e+00, 7.50491364e+00, 7.48990855e+00,\n",
" 7.37649692e+00, 8.45704105e+00, 6.84936447e+00,\n",
" 7.30552808e+00, 6.36065788e+00, 6.93144641e+00,\n",
" 8.47424275e+00, 8.47382542e+00, 8.84798051e+00,\n",
" 9.25753229e+00, 8.45793879e+00, 9.96957814e+00,\n",
" 1.16760464e+01, 1.23778298e+01, 1.24511152e+01,\n",
" 1.19892214e+01, 1.13627311e+01, 1.30735677e+01,\n",
" 1.44879828e+01, 1.44243213e+01, 1.28443908e+01,\n",
" 1.00123789e+01, 8.92895223e+00, 8.79833183e+00,\n",
" 1.01990209e+01, 9.54736466e+00, 1.00521801e+01,\n",
" 1.13553611e+01, 1.14838974e+01, 1.13414495e+01,\n",
" 1.00326860e+01, 8.83021070e+00, 9.24631033e+00,\n",
" 9.04540280e+00, 9.16793412e+00, 9.12065640e+00,\n",
" 9.78480045e+00, 9.00011304e+00, 8.66453240e+00,\n",
" 1.05607146e+01, 9.76092849e+00, 9.47935306e+00,\n",
" 8.88996635e+00, 9.33474772e+00, 1.03571400e+01,\n",
" 9.85892842e+00, 9.42751408e+00, 9.14853247e+00,\n",
" 9.67836626e+00, 8.93897096e+00, 8.56301099e+00,\n",
" 6.19081712e+00, 4.80907211e+00, 4.69662835e+00,\n",
" 5.59449253e+00, 5.88956831e+00, 4.79079985e+00,\n",
" 3.39054364e+00, 3.56522374e+00, 1.91242009e+00,\n",
" 2.97834691e+00, 3.04224311e+00, 1.43492295e+00,\n",
" 4.68969087e-01, -2.55342232e-01, -1.02853474e+00,\n",
" -2.51846775e+00]]))], scale_x=LinearScale(allow_padding=False, max=1.0, min=0.0), scale_y=LinearScale(allow_padding=False, max=1.0, min=0.0))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = bq.Figure(marks=[line], axes=[xax, yax], animation_duration=1000, interaction=intsel)\n",
"display(fig)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[22,\n",
" 23,\n",
" 24,\n",
" 25,\n",
" 26,\n",
" 27,\n",
" 28,\n",
" 29,\n",
" 30,\n",
" 31,\n",
" 32,\n",
" 33,\n",
" 34,\n",
" 35,\n",
" 36,\n",
" 37,\n",
" 38,\n",
" 39,\n",
" 40,\n",
" 41,\n",
" 42,\n",
" 43,\n",
" 44,\n",
" 45,\n",
" 46,\n",
" 47,\n",
" 48,\n",
" 49,\n",
" 50,\n",
" 51,\n",
" 52,\n",
" 53,\n",
" 54,\n",
" 55,\n",
" 56,\n",
" 57,\n",
" 58,\n",
" 59,\n",
" 60,\n",
" 61]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"line.selected"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Handdraw"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"handdraw = bq.interacts.HandDraw(lines=line)\n",
"fig.interaction = handdraw"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.72310049, 0.74771262, 1.46769635, 0.36479014, 0.26309286,\n",
" 0.28237225, 2.13196349, 1.91779684, 1.4187802 , 1.44013142,\n",
" 0.52101798, 0.71377183, 0.34871661, -1.44261094, -1.50119749,\n",
" -1.81874058, -3.45116389, -3.51829804, -2.02894208, -1.50763833,\n",
" -0.89571114, -2.23720786, -1.76030949, -1.61185991, -1.08281467,\n",
" -0.66018605, -2.01996678, -2.06136759, -2.81923845, -2.86932254,\n",
" -3.76672347, -2.4542531 , -3.31322549, -4.21216765, -4.13758124,\n",
" -5.21468031, -5.63934361, -6.46930821, -5.05813615, -4.27233232,\n",
" -4.32980184, -4.72101889, -3.78010128, -3.3748972 , -2.87684479,\n",
" -2.90303703, -4.59126706, -4.70373304, -5.23622296, -4.59116768,\n",
" -3.57932525, -4.2372763 , -3.76889106, -2.03301206, -2.70072479,\n",
" -1.01880305, -1.87138889, -1.84842914, -1.85957475, -1.84807585,\n",
" -2.68575389, -3.27693699, -3.94465728, -3.61769469, -3.28765957,\n",
" -1.06171524, 0.30927377, -0.20056947, 0.12430014, 1.12141812,\n",
" 1.15201995, 1.08237837, 1.13395331, 2.00122994, 1.15290942,\n",
" 0.82723995, 1.29767309, 1.60912016, 1.84870292, 1.47890176,\n",
" 2.45143755, 4.58530579, 4.99172129, 4.79854459, 5.55428487,\n",
" 5.01515224, 4.26546189, 4.29827064, 1.71547401, 0.56152364,\n",
" 0.21356179, -1.13982707, -2.17247017, -2.60921851, -4.2521838 ,\n",
" -4.6582556 , -5.19352576, -5.16812055, -4.01393652, -3.84143211])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"line.y[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Moving points around"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Label(colors=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'], interactions={'hover': 'tooltip'}, scales_metadata={'x': {'orientation': 'horizontal', 'dimension': 'x'}, 'y': {'orientation': 'vertical', 'dimension': 'y'}, 'color': {'dimension': 'color'}, 'size': {'dimension': 'size'}, 'opacity': {'dimension': 'opacity'}, 'rotation': {'dimension': 'rotation'}}, tooltip_style={'opacity': 0.9})"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "67536939ba414a2fab42819972b92bb1",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>Figure</code>.</p>\n",
"<p>\n",
" If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another notebook frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"Figure(axes=[Axis(scale=LinearScale()), Axis(orientation='vertical', scale=LinearScale(), tick_format='0.2f')], fig_margin={'top': 60, 'bottom': 60, 'left': 60, 'right': 60}, layout=Layout(min_width='125px'), marks=[Scatter(colors=['DeepSkyBlue'], enable_move=True, interactions={'hover': 'tooltip'}, scales={'x': LinearScale(), 'y': LinearScale()}, scales_metadata={'x': {'orientation': 'horizontal', 'dimension': 'x'}, 'y': {'orientation': 'vertical', 'dimension': 'y'}, 'color': {'dimension': 'color'}, 'size': {'dimension': 'size'}, 'opacity': {'dimension': 'opacity'}, 'rotation': {'dimension': 'rotation'}, 'skew': {'dimension': 'skew'}}, tooltip_style={'opacity': 0.9}, update_on_move=True, x=array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), y=array([ 176.4052346 , 216.42095543, 314.29475384, 538.38407376,\n",
" 725.13987278, 627.41208479, 722.42092654, 707.28520571,\n",
" 696.96332053, 738.02317073])), Lines(colors=['orange'], interactions={'hover': 'tooltip'}, line_style='dashed', scales={'x': LinearScale(), 'y': LinearScale()}, scales_metadata={'x': {'orientation': 'horizontal', 'dimension': 'x'}, 'y': {'orientation': 'vertical', 'dimension': 'y'}, 'color': {'dimension': 'color'}}, stroke_width=4.0, tooltip_style={'opacity': 0.9}, x=array([0, 9]), y=array([ 546.27495987, 546.27495987]))], scale_x=LinearScale(allow_padding=False, max=1.0, min=0.0), scale_y=LinearScale(allow_padding=False, max=1.0, min=0.0))"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from bqplot import *\n",
"\n",
"size = 100\n",
"np.random.seed(0)\n",
"x_data = range(size)\n",
"y_data = np.cumsum(np.random.randn(size) * 100.0)\n",
"\n",
"## Enabling moving of points in scatter. Try to click and drag any of the points in the scatter and \n",
"## notice the line representing the mean of the data update\n",
"\n",
"sc_x = LinearScale()\n",
"sc_y = LinearScale()\n",
"\n",
"scat = Scatter(x=x_data[:10], y=y_data[:10], scales={'x': sc_x, 'y': sc_y}, default_colors=['blue'],\n",
" enable_move=True)\n",
"lin = Lines(scales={'x': sc_x, 'y': sc_y}, stroke_width=4, line_style='dashed', colors=['orange'])\n",
"m = Label(value='Mean is %s'%np.mean(scat.y))\n",
"\n",
"def update_line(change):\n",
" with lin.hold_sync():\n",
" lin.x = [np.min(scat.x), np.max(scat.x)]\n",
" lin.y = [np.mean(scat.y), np.mean(scat.y)]\n",
" m.value='Mean is %s'%np.mean(scat.y)\n",
" \n",
"\n",
"update_line(None)\n",
"\n",
"# update line on change of x or y of scatter\n",
"scat.observe(update_line, names='x')\n",
"scat.observe(update_line, names='y')\n",
"\n",
"ax_x = Axis(scale=sc_x)\n",
"ax_y = Axis(scale=sc_y, tick_format='0.2f', orientation='vertical')\n",
"\n",
"fig = Figure(marks=[scat, lin], axes=[ax_x, ax_y])\n",
"\n",
"## In this case on drag, the line updates as you move the points.\n",
"with scat.hold_sync():\n",
" scat.enable_move = True\n",
" scat.update_on_move = True\n",
" scat.enable_add = False\n",
"\n",
"display(m, fig)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
},
"widgets": {
"state": {
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"1ffa1ac6658e4eef89e45cf62daae339": {
"views": [
{
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"42735872becb4c43b817ccff90973d63": {
"views": [
{
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"4b9a5b18593545faba761a719b2e6039": {
"views": [
{
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"7fb14b25d15240ec820c565619fe4f11": {
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"99604f1b70c54e19a508c95d705ce488": {
"views": [
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| bsd-3-clause |
kunaltyagi/SDES | notes/python/p_norvig/algo/Probability.ipynb | 1 | 239586 | {
"cells": [
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"<div style=\"text-align: right\">Peter Norvig, 12 Feb 2016</div> \n",
"\n",
"# A Concrete Introduction to Probability (using Python)\n",
"\n",
"In this notebook, we cover the basics of probability theory, and show how to implement the theory in Python 3. (You should have a little background in [probability](http://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/pdf.html) and [Python](https://www.python.org/about/gettingstarted/).) \n",
"In 1814, Pierre-Simon Laplace [wrote](https://en.wikipedia.org/wiki/Classical_definition_of_probability) (originally in French):\n",
"\n",
">*Probability ... is thus simply a fraction whose numerator is the number of favorable cases and whose denominator is the number of all the cases possible ... when nothing leads us to expect that any one of these cases should occur more than any other.*\n",
"\n",
"\n",
"<center><a href=\"https://en.wikipedia.org/wiki/Pierre-Simon_Laplace\">Pierre-Simon Laplace</a></center>\n",
"\n",
"\n",
"Laplace really nailed it, way back then! If you want to untangle a probability problem, all you have to do is be methodical about defining exactly what the cases are, and then careful in counting the number of favorable and total cases. We'll start being methodical by defining some vocabulary:\n",
"\n",
"\n",
"- **[Experiment](https://en.wikipedia.org/wiki/Experiment_(probability_theory%29):**\n",
" An occurrence with an uncertain outcome that we can observe.\n",
" <br>*For example, rolling a die.*\n",
"- **[Outcome](https://en.wikipedia.org/wiki/Outcome_(probability%29):**\n",
" The result of an experiment; one particular state of the world. What Laplace calls a \"case.\"\n",
" <br>*For example:* `4`.\n",
"- **[Sample Space](https://en.wikipedia.org/wiki/Sample_space):**\n",
" The set of all possible outcomes for the experiment. \n",
" <br>*For example,* `{1, 2, 3, 4, 5, 6}`.\n",
"- **[Event](https://en.wikipedia.org/wiki/Event_(probability_theory%29):**\n",
" A subset of possible outcomes that together have some property we are interested in.\n",
" <br>*For example, the event \"even die roll\" is the set of outcomes* `{2, 4, 6}`. \n",
"- **[Probability](https://en.wikipedia.org/wiki/Probability_theory):**\n",
" As Laplace said, the probability of an event with respect to a sample space is the number of favorable cases (outcomes from the sample space that are in the event) divided by the total number of cases in the sample space. (This assumes that all outcomes in the sample space are equally likely.) Since it is a ratio, probability will always be a number between 0 (representing an impossible event) and 1 (representing a certain event).\n",
"<br>*For example, the probability of an even die roll is 3/6 = 1/2.*\n",
"\n",
"This notebook will develop all these concepts; I also have a [second part](http://nbviewer.jupyter.org/url/norvig.com/ipython/ProbabilityParadox.ipynb) that covers paradoxes in Probability Theory."
]
},
{
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"source": [
"# Code for `P` \n",
"\n",
"`P` is the traditional name for the Probability function:"
]
},
{
"cell_type": "code",
"execution_count": 1,
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"source": [
"from fractions import Fraction\n",
"\n",
"def P(event, space): \n",
" \"The probability of an event, given a sample space of equiprobable outcomes.\"\n",
" return Fraction(len(event & space), \n",
" len(space))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
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"source": [
"Read this as implementing Laplace's quote directly: *\"Probability is thus simply a fraction whose numerator is the number of favorable cases and whose denominator is the number of all the cases possible.\"* \n",
" \n",
"\n",
"# Warm-up Problem: Die Roll"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
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"run_control": {
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},
"source": [
"What's the probability of rolling an even number with a single six-sided fair die? \n",
"\n",
"We can define the sample space `D` and the event `even`, and compute the probability:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
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},
"outputs": [
{
"data": {
"text/plain": [
"Fraction(1, 2)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"D = {1, 2, 3, 4, 5, 6}\n",
"even = { 2, 4, 6}\n",
"\n",
"P(even, D)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
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},
"source": [
"It is good to confirm what we already knew.\n",
"\n",
"You may ask: Why does the definition of `P` use `len(event & space)` rather than `len(event)`? Because I don't want to count outcomes that were specified in `event` but aren't actually in the sample space. Consider:"
]
},
{
"cell_type": "code",
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"Fraction(1, 2)"
]
},
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"metadata": {},
"output_type": "execute_result"
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],
"source": [
"even = {2, 4, 6, 8, 10, 12}\n",
"\n",
"P(even, D)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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},
"source": [
"Here, `len(event)` and `len(space)` are both 6, so if just divided, then `P` would be 1, which is not right.\n",
"The favorable cases are the *intersection* of the event and the space, which in Python is `(event & space)`.\n",
"Also note that I use `Fraction` rather than regular division because I want exact answers like 1/3, not 0.3333333333333333."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
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"source": [
"\n",
"\n",
"# Urn Problems\n",
"\n",
"Around 1700, Jacob Bernoulli wrote about removing colored balls from an urn in his landmark treatise *[Ars Conjectandi](https://en.wikipedia.org/wiki/Ars_Conjectandi)*, and ever since then, explanations of probability have relied on [urn problems](https://www.google.com/webhp?sourceid=chrome-instant&ion=1&espv=2&ie=UTF-8#q=probability%20ball%20urn). (You'd think the urns would be empty by now.) \n",
"\n",
"\n",
"<center><a href=\"https://en.wikipedia.org/wiki/Jacob_Bernoulli\">Jacob Bernoulli</a></center>\n",
"\n",
"For example, here is a three-part problem [adapted](http://mathforum.org/library/drmath/view/69151.html) from mathforum.org:\n",
"\n",
"> An urn contains 23 balls: 8 white, 6 blue, and 9 red. We select six balls at random (each possible selection is equally likely). What is the probability of each of these possible outcomes:\n",
"\n",
"> 1. all balls are red\n",
"2. 3 are blue, 2 are white, and 1 is red\n",
"3. exactly 4 balls are white\n",
"\n",
"So, an outcome is a set of 6 balls, and the sample space is the set of all possible 6 ball combinations. We'll solve each of the 3 parts using our `P` function, and also using basic arithmetic; that is, *counting*. Counting is a bit tricky because:\n",
"- We have multiple balls of the same color. \n",
"- An outcome is a *set* of balls, where order doesn't matter, not a *sequence*, where order matters.\n",
"\n",
"To account for the first issue, I'll have 8 different white balls labelled `'W1'` through `'W8'`, rather than having eight balls all labelled `'W'`. That makes it clear that selecting `'W1'` is different from selecting `'W2'`.\n",
"\n",
"The second issue is handled automatically by the `P` function, but if I want to do calculations by hand, I will sometimes first count the number of *permutations* of balls, then get the number of *combinations* by dividing the number of permutations by *c*!, where *c* is the number of balls in a combination. For example, if I want to choose 2 white balls from the 8 available, there are 8 ways to choose a first white ball and 7 ways to choose a second, and therefore 8 × 7 = 56 permutations of two white balls. But there are only 56 / 2 = 28 combinations, because `(W1, W2)` is the same combination as `(W2, W1)`.\n",
"\n",
"We'll start by defining the contents of the urn:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
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},
"outputs": [
{
"data": {
"text/plain": [
"{'B1',\n",
" 'B2',\n",
" 'B3',\n",
" 'B4',\n",
" 'B5',\n",
" 'B6',\n",
" 'R1',\n",
" 'R2',\n",
" 'R3',\n",
" 'R4',\n",
" 'R5',\n",
" 'R6',\n",
" 'R7',\n",
" 'R8',\n",
" 'R9',\n",
" 'W1',\n",
" 'W2',\n",
" 'W3',\n",
" 'W4',\n",
" 'W5',\n",
" 'W6',\n",
" 'W7',\n",
" 'W8'}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def cross(A, B):\n",
" \"The set of ways of concatenating one item from collection A with one from B.\"\n",
" return {a + b \n",
" for a in A for b in B}\n",
"\n",
"urn = cross('W', '12345678') | cross('B', '123456') | cross('R', '123456789') \n",
"\n",
"urn"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
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},
"outputs": [
{
"data": {
"text/plain": [
"23"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(urn)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now we can define the sample space, `U6`, as the set of all 6-ball combinations. We use `itertools.combinations` to generate the combinations, and then join each combination into a string:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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},
"outputs": [
{
"data": {
"text/plain": [
"100947"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import itertools\n",
"\n",
"def combos(items, n):\n",
" \"All combinations of n items; each combo as a concatenated str.\"\n",
" return {' '.join(combo) \n",
" for combo in itertools.combinations(items, n)}\n",
"\n",
"U6 = combos(urn, 6)\n",
"\n",
"len(U6)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"I don't want to print all 100,947 members of the sample space; let's just peek at a random sample of them:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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},
"outputs": [
{
"data": {
"text/plain": [
"['W2 B5 W3 B1 R3 W6',\n",
" 'W7 W2 B5 R8 W5 R6',\n",
" 'R5 W3 R9 R2 B1 R4',\n",
" 'W7 R1 R2 W1 W5 R4',\n",
" 'B2 R1 B5 R9 R3 R4',\n",
" 'W2 B1 R3 B6 R4 R6',\n",
" 'W7 W2 B2 R1 R9 R7',\n",
" 'W8 W2 R3 B6 R7 B3',\n",
" 'B2 R1 R8 B1 R4 R6',\n",
" 'B2 R3 R4 R6 W6 B3']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import random\n",
"\n",
"random.sample(U6, 10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Is 100,947 really the right number of ways of choosing 6 out of 23 items, or \"23 choose 6\", as mathematicians [call it](https://en.wikipedia.org/wiki/Combination)? Well, we can choose any of 23 for the first item, any of 22 for the second, and so on down to 18 for the sixth. But we don't care about the ordering of the six items, so we divide the product by 6! (the number of permutations of 6 things) giving us:\n",
"\n",
"$$23 ~\\mbox{choose}~ 6 = \\frac{23 \\cdot 22 \\cdot 21 \\cdot 20 \\cdot 19 \\cdot 18}{6!} = 100947$$\n",
"\n",
"Note that $23 \\cdot 22 \\cdot 21 \\cdot 20 \\cdot 19 \\cdot 18 = 23! \\;/\\; 17!$, so, generalizing, we can write:\n",
"\n",
"$$n ~\\mbox{choose}~ c = \\frac{n!}{(n - c)! \\cdot c!}$$\n",
"\n",
"And we can translate that to code and verify that 23 choose 6 is 100,947:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"from math import factorial\n",
"\n",
"def choose(n, c):\n",
" \"Number of ways to choose c items from a list of n items.\"\n",
" return factorial(n) // (factorial(n - c) * factorial(c))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"100947"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"choose(23, 6)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now we're ready to answer the 4 problems: \n",
"\n",
"### Urn Problem 1: what's the probability of selecting 6 red balls? "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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}
},
"outputs": [
{
"data": {
"text/plain": [
"Fraction(4, 4807)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"red6 = {s for s in U6 if s.count('R') == 6}\n",
"\n",
"P(red6, U6)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's investigate a bit more. How many ways of getting 6 red balls are there?"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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}
},
"outputs": [
{
"data": {
"text/plain": [
"84"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(red6)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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}
},
"source": [
"Why are there 84 ways? Because there are 9 red balls in the urn, and we are asking how many ways we can choose 6 of them:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"84"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"choose(9, 6)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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},
"source": [
"So the probabilty of 6 red balls is then just 9 choose 6 divided by the size of the sample space:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
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},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"P(red6, U6) == Fraction(choose(9, 6), \n",
" len(U6))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Urn Problem 2: what is the probability of 3 blue, 2 white, and 1 red?"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Fraction(240, 4807)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b3w2r1 = {s for s in U6 if\n",
" s.count('B') == 3 and s.count('W') == 2 and s.count('R') == 1}\n",
"\n",
"P(b3w2r1, U6)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can get the same answer by counting how many ways we can choose 3 out of 6 blues, 2 out of 8 whites, and 1 out of 9 reds, and dividing by the number of possible selections:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"P(b3w2r1, U6) == Fraction(choose(6, 3) * choose(8, 2) * choose(9, 1), \n",
" len(U6))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Here we don't need to divide by any factorials, because `choose` has already accounted for that. \n",
"\n",
"We can get the same answer by figuring: \"there are 6 ways to pick the first blue, 5 ways to pick the second blue, and 4 ways to pick the third; then 8 ways to pick the first white and 7 to pick the second; then 9 ways to pick a red. But the order `'B1, B2, B3'` should count as the same as `'B2, B3, B1'` and all the other orderings; so divide by 3! to account for the permutations of blues, by 2! to account for the permutations of whites, and by 100947 to get a probability:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
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},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
" P(b3w2r1, U6) == Fraction((6 * 5 * 4) * (8 * 7) * 9, \n",
" factorial(3) * factorial(2) * len(U6))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Urn Problem 3: What is the probability of exactly 4 white balls?\n",
"\n",
"We can interpret this as choosing 4 out of the 8 white balls, and 2 out of the 15 non-white balls. Then we can solve it the same three ways:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Fraction(350, 4807)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w4 = {s for s in U6 if\n",
" s.count('W') == 4}\n",
"\n",
"P(w4, U6)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"P(w4, U6) == Fraction(choose(8, 4) * choose(15, 2),\n",
" len(U6))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"P(w4, U6) == Fraction((8 * 7 * 6 * 5) * (15 * 14),\n",
" factorial(4) * factorial(2) * len(U6))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Revised Version of `P`, with more general events\n",
"\n",
"To calculate the probability of an even die roll, I originally said\n",
"\n",
" even = {2, 4, 6}\n",
" \n",
"But that's inelegant—I had to explicitly enumerate all the even numbers from one to six. If I ever wanted to deal with a twelve or twenty-sided die, I would have to go back and change `even`. I would prefer to define `even` once and for all like this:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"def even(n): return n % 2 == 0"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now in order to make `P(even, D)` work, I'll have to modify `P` to accept an event as either\n",
"a *set* of outcomes (as before), or a *predicate* over outcomes—a function that returns true for an outcome that is in the event:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"def P(event, space): \n",
" \"\"\"The probability of an event, given a sample space of equiprobable outcomes.\n",
" event can be either a set of outcomes, or a predicate (true for outcomes in the event).\"\"\"\n",
" if is_predicate(event):\n",
" event = such_that(event, space)\n",
" return Fraction(len(event & space), len(space))\n",
"\n",
"is_predicate = callable\n",
"\n",
"def such_that(predicate, collection): \n",
" \"The subset of elements in the collection for which the predicate is true.\"\n",
" return {e for e in collection if predicate(e)}"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Here we see how `such_that`, the new `even` predicate, and the new `P` work:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"{2, 4, 6}"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"such_that(even, D)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Fraction(1, 2)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"P(even, D)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"{2, 4, 6, 8, 10, 12}"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"D12 = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}\n",
"\n",
"such_that(even, D12)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Fraction(1, 2)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"P(even, D12)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Note: `such_that` is just like the built-in function `filter`, except `such_that` returns a set.\n",
"\n",
"We can now define more interesting events using predicates; for example we can determine the probability that the sum of a three-dice roll is prime (using a definition of `is_prime` that is efficient enough for small `n`):"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Fraction(73, 216)"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"D3 = {(d1, d2, d3) for d1 in D for d2 in D for d3 in D}\n",
"\n",
"def prime_sum(outcome): return is_prime(sum(outcome))\n",
"\n",
"def is_prime(n): return n > 1 and not any(n % i == 0 for i in range(2, n))\n",
"\n",
"P(prime_sum, D3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Card Problems\n",
"\n",
"Consider dealing a hand of five playing cards. We can define `deck` as a set of 52 cards, and `Hands` as the sample space of all combinations of 5 cards:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"52"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"suits = 'SHDC'\n",
"ranks = 'A23456789TJQK'\n",
"deck = cross(ranks, suits)\n",
"len(deck)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"['JC 9S QS AH 9C',\n",
" '5H 5C AC KC 3S',\n",
" '9H KS QC 3S 9C',\n",
" '9D KC 3C QH AH',\n",
" '9D JD 4H JH 2H']"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Hands = combos(deck, 5)\n",
"\n",
"assert len(Hands) == choose(52, 5)\n",
"\n",
"random.sample(Hands, 5)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now we can answer questions like the probability of being dealt a flush (5 cards of the same suit):"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Fraction(33, 16660)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def flush(hand):\n",
" return any(hand.count(suit) == 5 for suit in suits)\n",
"\n",
"P(flush, Hands)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Or the probability of four of a kind:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Fraction(1, 4165)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def four_kind(hand):\n",
" return any(hand.count(rank) == 4 for rank in ranks)\n",
"\n",
"P(four_kind, Hands)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Non-Equiprobable Outcomes: Probability Distributions\n",
"\n",
"So far, we have made the assumption that every outcome in a sample space is equally likely. In real life, we often get outcomes that are not equiprobable. For example, the probability of a child being a girl is not exactly 1/2, and the probability is slightly different for a second child. An [article](http://people.kzoo.edu/barth/math105/moreboys.pdf) gives the following counts for two-child families in Denmark, where `GB` means a family where the first child is a girl and the second a boy:\n",
"\n",
" GG: 121801 GB: 126840\n",
" BG: 127123 BB: 135138\n",
" \n",
"We will introduce three more definitions:\n",
"\n",
"* [Frequency](https://en.wikipedia.org/wiki/Frequency_%28statistics%29): a number describing how often an outcome occurs. Can be a count like 121801, or a ratio like 0.515.\n",
"\n",
"* [Distribution](http://mathworld.wolfram.com/StatisticalDistribution.html): A mapping from outcome to frequency for each outcome in a sample space. \n",
"\n",
"* [Probability Distribution](https://en.wikipedia.org/wiki/Probability_distribution): A distribution that has been *normalized* so that the sum of the frequencies is 1.\n",
"\n",
"We define `ProbDist` to take the same kinds of arguments that `dict` does: either a mapping or an iterable of `(key, val)` pairs, and/or optional keyword arguments. "
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"class ProbDist(dict):\n",
" \"A Probability Distribution; an {outcome: probability} mapping.\"\n",
" def __init__(self, mapping=(), **kwargs):\n",
" self.update(mapping, **kwargs)\n",
" # Make probabilities sum to 1.0; assert no negative probabilities\n",
" total = sum(self.values())\n",
" for outcome in self:\n",
" self[outcome] = self[outcome] / total\n",
" assert self[outcome] >= 0"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We also need to modify the functions `P` and `such_that` to accept either a sample space or a probability distribution as the second argument."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"def P(event, space): \n",
" \"\"\"The probability of an event, given a sample space of equiprobable outcomes. \n",
" event: a collection of outcomes, or a predicate that is true of outcomes in the event. \n",
" space: a set of outcomes or a probability distribution of {outcome: frequency} pairs.\"\"\"\n",
" if is_predicate(event):\n",
" event = such_that(event, space)\n",
" if isinstance(space, ProbDist):\n",
" return sum(space[o] for o in space if o in event)\n",
" else:\n",
" return Fraction(len(event & space), len(space))\n",
" \n",
"def such_that(predicate, space): \n",
" \"\"\"The outcomes in the ssample pace for which the predicate is true.\n",
" If space is a set, return a subset {outcome,...};\n",
" if space is a ProbDist, return a ProbDist {outcome: frequency,...};\n",
" in both cases only with outcomes where predicate(element) is true.\"\"\"\n",
" if isinstance(space, ProbDist):\n",
" return ProbDist({o:space[o] for o in space if predicate(o)})\n",
" else:\n",
" return {o for o in space if predicate(o)}"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Here is the probability distribution for Danish two-child families:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"{'BB': 0.2645086533229465,\n",
" 'BG': 0.24882071317004043,\n",
" 'GB': 0.24826679089140383,\n",
" 'GG': 0.23840384261560926}"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DK = ProbDist(GG=121801, GB=126840,\n",
" BG=127123, BB=135138)\n",
"DK"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"And here are some predicates that will allow us to answer some questions:"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"def first_girl(outcome): return outcome[0] == 'G'\n",
"def first_boy(outcome): return outcome[0] == 'B'\n",
"def second_girl(outcome): return outcome[1] == 'G'\n",
"def second_boy(outcome): return outcome[1] == 'B'\n",
"def two_girls(outcome): return outcome == 'GG'"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.4866706335070131"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"P(first_girl, DK)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.4872245557856497"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"P(second_girl, DK)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"The above says that the probability of a girl is somewhere between 48% and 49%, but that it is slightly different between the first or second child."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"(0.4898669165584115, 0.48471942072973107)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"P(second_girl, such_that(first_girl, DK)), P(second_girl, such_that(first_boy, DK))"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"(0.5101330834415885, 0.5152805792702689)"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"P(second_boy, such_that(first_girl, DK)), P(second_boy, such_that(first_boy, DK))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"The above says that the sex of the second child is more likely to be the same as the first child, by about 1/2 a percentage point."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# More Urn Problems: M&Ms and Bayes\n",
"\n",
"Here's another urn problem (or \"bag\" problem) [from](http://allendowney.blogspot.com/2011/10/my-favorite-bayess-theorem-problems.html) prolific Python/Probability author [Allen Downey ](http://allendowney.blogspot.com/):\n",
"\n",
"> The blue M&M was introduced in 1995. Before then, the color mix in a bag of plain M&Ms was (30% Brown, 20% Yellow, 20% Red, 10% Green, 10% Orange, 10% Tan). Afterward it was (24% Blue , 20% Green, 16% Orange, 14% Yellow, 13% Red, 13% Brown). \n",
"A friend of mine has two bags of M&Ms, and he tells me that one is from 1994 and one from 1996. He won't tell me which is which, but he gives me one M&M from each bag. One is yellow and one is green. What is the probability that the yellow M&M came from the 1994 bag?\n",
"\n",
"To solve this problem, we'll first represent probability distributions for each bag: `bag94` and `bag96`:"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"bag94 = ProbDist(brown=30, yellow=20, red=20, green=10, orange=10, tan=10)\n",
"bag96 = ProbDist(blue=24, green=20, orange=16, yellow=14, red=13, brown=13)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Next, define `MM` as the joint distribution—the sample space for picking one M&M from each bag. The outcome `'yellow green'` means that a yellow M&M was selected from the 1994 bag and a green one from the 1996 bag."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"{'brown blue': 0.07199999999999997,\n",
" 'brown brown': 0.038999999999999986,\n",
" 'brown green': 0.05999999999999997,\n",
" 'brown orange': 0.04799999999999998,\n",
" 'brown red': 0.038999999999999986,\n",
" 'brown yellow': 0.04199999999999998,\n",
" 'green blue': 0.02399999999999999,\n",
" 'green brown': 0.012999999999999996,\n",
" 'green green': 0.019999999999999993,\n",
" 'green orange': 0.015999999999999993,\n",
" 'green red': 0.012999999999999996,\n",
" 'green yellow': 0.013999999999999995,\n",
" 'orange blue': 0.02399999999999999,\n",
" 'orange brown': 0.012999999999999996,\n",
" 'orange green': 0.019999999999999993,\n",
" 'orange orange': 0.015999999999999993,\n",
" 'orange red': 0.012999999999999996,\n",
" 'orange yellow': 0.013999999999999995,\n",
" 'red blue': 0.04799999999999998,\n",
" 'red brown': 0.025999999999999992,\n",
" 'red green': 0.03999999999999999,\n",
" 'red orange': 0.03199999999999999,\n",
" 'red red': 0.025999999999999992,\n",
" 'red yellow': 0.02799999999999999,\n",
" 'tan blue': 0.02399999999999999,\n",
" 'tan brown': 0.012999999999999996,\n",
" 'tan green': 0.019999999999999993,\n",
" 'tan orange': 0.015999999999999993,\n",
" 'tan red': 0.012999999999999996,\n",
" 'tan yellow': 0.013999999999999995,\n",
" 'yellow blue': 0.04799999999999998,\n",
" 'yellow brown': 0.025999999999999992,\n",
" 'yellow green': 0.03999999999999999,\n",
" 'yellow orange': 0.03199999999999999,\n",
" 'yellow red': 0.025999999999999992,\n",
" 'yellow yellow': 0.02799999999999999}"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def joint(A, B, sep=''):\n",
" \"\"\"The joint distribution of two independent probability distributions. \n",
" Result is all entries of the form {a+sep+b: P(a)*P(b)}\"\"\"\n",
" return ProbDist({a + sep + b: A[a] * B[b]\n",
" for a in A\n",
" for b in B})\n",
"\n",
"MM = joint(bag94, bag96, ' ')\n",
"MM"
]
},
{
"cell_type": "markdown",
"metadata": {
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"source": [
"First we'll look at the \"One is yellow and one is green\" part:"
]
},
{
"cell_type": "code",
"execution_count": 41,
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"outputs": [
{
"data": {
"text/plain": [
"{'green yellow': 0.25925925925925924, 'yellow green': 0.7407407407407408}"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def yellow_and_green(outcome): return 'yellow' in outcome and 'green' in outcome\n",
"\n",
"such_that(yellow_and_green, MM)"
]
},
{
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"source": [
"Now we can answer the question: given that we got a yellow and a green (but don't know which comes from which bag), what is the probability that the yellow came from the 1994 bag?"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
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"outputs": [
{
"data": {
"text/plain": [
"0.7407407407407408"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def yellow94(outcome): return outcome.startswith('yellow')\n",
"\n",
"P(yellow94, such_that(yellow_and_green, MM))"
]
},
{
"cell_type": "markdown",
"metadata": {
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"source": [
"So there is a 74% chance that the yellow comes from the 1994 bag.\n",
"\n",
"## My favorite Bayes's Theorem Problems\n",
"\n",
"Answering this question is straightforward: just like all the other probability problems, we simply create a sample space, and use `P` to pick out the probability of the event in question, given what we know about the outcome.\n",
"\n",
"\n",
"<center><a href=\"https://en.wikipedia.org/wiki/Thomas_Bayes\">Rev. Thomas Bayes</a></center>\n",
"\n",
"It is curious that we were able to solve this problem with the same methodology as the others: this problem comes from a section titled **My favorite Bayes's Theorem Problems**, so one would expect that we'd need to invoke Bayes Theorem to solve it. The computation above shows that that is not necessary. \n",
"\n",
"Of course, we *could* solve it using Bayes Theorem, and if you only have pencil and paper, and not a computer, that's a great way to go. Why is Bayes Theorem recommended? Because we are asked about the probability of an event given the evidence, where that probability is not immediately available; however the probability of the evidence given the event is. \n",
"\n",
"Before we see the colors of the M&Ms, there are two hypotheses, `A` and `B`, both with equal probability:\n",
"\n",
" A: first M&M from 94 bag, second from 96 bag\n",
" B: first M&M from 96 bag, second from 94 bag\n",
" P(A) = P(B) = 0.5\n",
" \n",
"Then we get some evidence:\n",
" \n",
" E: first M&M yellow, second green\n",
" \n",
"We want to know the probability of hypothesis `A`, given the evidence:\n",
" \n",
" P(A | E)\n",
" \n",
"That's not easy to calculate (except by enumerating the sample space). But Bayes Theorem says:\n",
" \n",
" P(A | E) = P(E | A) * P(A) / P(E)\n",
" \n",
"The quantities on the right-hand-side are easier to calculate:\n",
" \n",
" P(E | A) = 0.20 * 0.20 \n",
" = 0.04\n",
" P(E | B) = 0.10 * 0.14 \n",
" = 0.014\n",
" P(A) = 0.5\n",
" P(B) = 0.5\n",
" P(E) = P(E | A) * P(A) + P(E | B) * P(B) \n",
" = 0.04 * 0.5 + 0.014 * 0.5 \n",
" = 0.027\n",
" \n",
"And we can get a final answer:\n",
" \n",
" P(A | E) = P(E | A) * P(A) / P(E) \n",
" = 0.04 * 0.5 / 0.027 \n",
" = 0.7407407407\n",
" \n",
"You have a choice: you can use Bayes Theorem to calculate answers like this algebraically, or you can use sample spaces to calculate answers exhaustively.\n",
"\n",
"There is one important question that Allen Downey does not address: would you eat twenty-year-old M&Ms?"
]
},
{
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"source": [
"# Simulation\n",
"\n",
"Sometimes it is inconvenient to explicitly define a sample space. Perhaps the sample space is infinite, or perhaps it is just very large and complicated, and we feel more confident in writing a program to *simulate* the situation, rather than one to *enumerate* the complete sample space. *Random sampling* from the simulation\n",
"can give an accurate estimate of the probability.\n",
"\n",
"# Simulating Monopoly\n",
"\n",
"Consider [problem 84](https://projecteuler.net/problem=84) from the excellent [Project Euler](https://projecteuler.net), which asks for the probability that a player in the game Monopoly ends a roll on each of the squares on the board. To answer this we need to take into account die rolls, chance and community chest cards, and going to jail (from the \"go to jail\" space, from a card, or from rolling doubles three times in a row). We do not need to take into account anything about buying or selling properties or exchanging money or winning or losing the game, because these don't change a player's location. We can assume that a player in jail will always pay to get out of jail immediately. \n",
"\n",
"A game of Monopoly can go on forever, so the sample space is infinite. But even if we limit the sample space to say, 1000 rolls, there are $21^{1000}$ such sequences of rolls (and even more possibilities when we consider drawing cards). So it is infeasible to explicitly represent the sample space.\n",
"\n",
"But it is fairly straightforward to implement a simulation and run it for, say, 400,000 rolls (so the average square will be landed on 10,000 times). Here is the code for a simulation:"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"button": false,
"collapsed": false,
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"outputs": [],
"source": [
"from collections import Counter, deque\n",
"import random\n",
"\n",
"# The board: a list of the names of the 40 squares\n",
"# As specified by https://projecteuler.net/problem=84\n",
"board = \"\"\"GO A1 CC1 A2 T1 R1 B1 CH1 B2 B3\n",
" JAIL C1 U1 C2 C3 R2 D1 CC2 D2 D3 \n",
" FP E1 CH2 E2 E3 R3 F1 F2 U2 F3 \n",
" G2J G1 G2 CC3 G3 R4 CH3 H1 T2 H2\"\"\".split()\n",
"\n",
"def monopoly(steps):\n",
" \"\"\"Simulate given number of steps of Monopoly game, \n",
" yielding the number of the current square after each step.\"\"\"\n",
" global here\n",
" here = 0 # The square number where we currently are\n",
" CC_deck = Deck('GO JAIL' + 14 * ' ?')\n",
" CH_deck = Deck('GO JAIL C1 E3 H2 R1 R R U -3' + 6 * ' ?')\n",
" doubles = 0\n",
" jail = board.index('JAIL')\n",
" for _ in range(steps):\n",
" d1, d2 = random.randint(1, 6), random.randint(1, 6)\n",
" goto(here + d1 + d2)\n",
" doubles = (doubles + 1) if (d1 == d2) else 0\n",
" if doubles == 3 or board[here] == 'G2J': \n",
" goto(jail)\n",
" elif board[here].startswith('CC'):\n",
" do_card(CC_deck)\n",
" elif board[here].startswith('CH'):\n",
" do_card(CH_deck)\n",
" yield here \n",
"\n",
"def goto(square):\n",
" \"Update 'here' to be square.\"\n",
" global here\n",
" here = square % len(board)\n",
" \n",
"def Deck(names):\n",
" \"Make a shuffled deck of cards, given space-delimited names.\"\n",
" cards = names.split()\n",
" random.shuffle(cards)\n",
" return deque(cards) \n",
"\n",
"def do_card(deck):\n",
" \"Take the top card from deck and do what it says.\"\n",
" global here\n",
" card = deck[0] # The top card\n",
" deck.rotate(-1) # Move top card to bottom of deck\n",
" if card == 'R' or card == 'U': \n",
" while not board[here].startswith(card):\n",
" goto(here + 1) # Advance to next railroad or utility\n",
" elif card == '-3':\n",
" goto(here - 3) # Go back 3 spaces\n",
" elif card != '?':\n",
" goto(board.index(card))# Go to destination named on card"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
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"source": [
"And the results:"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
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"collapsed": false,
"deletable": true,
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"outputs": [],
"source": [
"results = list(monopoly(400000))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
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},
"source": [
"I'll show a histogram of the squares, with a dotted red line at the average:"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"button": false,
"collapsed": false,
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"outputs": [
{
"data": {
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hkORgkh8meSbJ003baUkeS/JCkkeTnNq3/S1JDiTZn+SyvvbtSZ5L8mKS20epSZI0ulFH\nDu8AU1X1yara0bTdDDxRVR8BngRuAUhyIXAtcAFwJXBHjn1n553A9VW1DdiW5PIR65IkjWDUcMg8\n93E1cE+zfA9wTbN8FbCnqo5W1UHgALAjyWbg5Kra22x3b98+kqQxGDUcCng8yd4k/65p21RVMwBV\ndQQ4o2nfArzct+/hpm0LcKiv/VDTJkkak+NH3P+SqnolyT8CHkvyAr3A6Df35xHt7lueam6SpFnT\n09NMT0+PdB8jhUNVvdL8+2qSvwR2ADNJNlXVTDNl9NNm88PAmX27b23aFmpfwO5RSpakdW9qaoqp\nqal3f/7Sl7605PtY9rRSkvcn+WCz/AHgMuB54CHgM81m1wEPNssPATuTnJDkXODDwNPN1NPPk+xo\nDlB/um8fSdIYjDJy2AT8RZJq7uebVfVYku8D9yf5A+AlemcoUVX7ktwP7APeAm6oqtkppxuBu4GT\ngIer6pER6pIkjSjHXp+7rxdEC9X7BPBbLH6IIwPWD7PN6q9f7DnpDa66/xhWd30XaliZxzDacz3u\n9SvzO8bbBytxH914DINqqKosuME8vEJaktRiOEiSWgwHSVLLqNc5SFq2Ezn2CTJStxgO0tj8isEH\nIqXxcFpJktRiOEiSWgwHSVKLxxw6x4OUksbPcOgcD1JKGj/DQZJGsj5H+4aDJI1kfY72PSAtSWox\nHCRJLYaDJKnFcJAktRgOkqQWw0GS1GI4SJJaDAdJUovhIElqMRwkSS2GgySpxXCQJLUYDpKkFsNB\nktRiOEiSWgwHSVKL4SBJajEcJEkthoMkqcVwkCS1GA6SpBbDQZLUYjhIklo6Ew5Jrkjy4yQvJvnC\nuOuRpI2sE+GQ5H3AfwMuBz4G/H6Sj463qlFMj7uAIU2Pu4AhTY+7gCFMj7uAIU2Pu4ChTE9Pj7uE\nIU2Pu4BV04lwAHYAB6rqpap6C9gDXD3mmkYwPe4ChjQ97gKGND3uAoYwPe4ChjQ97gKGYjiMX1fC\nYQvwct/Ph5o2SdIYHD/uApbqlFP+5bztR4++yi9/ucbFSNI6laoadw0k+afA7qq6ovn5ZqCq6stz\ntht/sZI0gaoqS9m+K+FwHPACcCnwCvA08PtVtX+shUnSBtWJaaWqejvJTcBj9I6D3GUwSNL4dGLk\nIEnqlq6crTTQpFwkl+Rgkh8meSbJ0+OuZ1aSu5LMJHmur+20JI8leSHJo0lO7WCNu5IcSvKD5nbF\nOGtsatqa5MkkP0ryfJLPNe1d68+5df5h096pPk1yYpLvNX8zzyfZ1bR3pj8XqbFTfTkryfuaeh5q\nfl5yX07EyKG5SO5FesckfgLsBXZW1Y/HWtg8kvxf4OKq+tm4a+mX5J8BbwL3VtUnmrYvA69V1W1N\n4J5WVTd3rMZdwP+rqq+Mq665kmwGNlfVs0k+CPwtvety/i3d6s+F6vxXdK9P319Vv2yOP/4N8Dng\nd+lWf85X45V0rC8BkvwRcDFwSlVdtZy/9UkZOUzSRXKhg/1aVd8B5gbW1cA9zfI9wDVrWtQcC9QI\nvT7tjKo6UlXPNstvAvuBrXSvP+erc/b6oa716eyJ6CfSOxZadK8/56sROtaXSbYCvw18va95yX3Z\nuRexBUzSRXIFPJ5kb5J/P+5iBjijqmag90ICnDHmehZyU5Jnk3x93FM1cyU5B7gI+C6wqav92Vfn\n95qmTvVpMw3yDHAEeLyq9tKx/lygRuhYXwL/BfhPHAsvWEZfTko4TJJLqmo7veS+sZkqmRRdnGO8\nAzivqi6i90fZmeF7M1XzAPD55p353P7rRH/OU2fn+rSq3qmqT9Ibge1I8jE61p/z1HghHevLJP8C\nmGlGjIuNaAb25aSEw2HgrL6ftzZtnVNVrzT/vgr8Bb0psa6aSbIJ3p2f/umY62mpqlfr2IGxrwG/\nNs56ZiU5nt4L7n1V9WDT3Ln+nK/OrvYpQFX9gt4HFl1BB/sT3ltjB/vyEuCq5tjnfwf+eZL7gCNL\n7ctJCYe9wIeTnJ3kBGAn8NCYa2pJ8v7mXRpJPgBcBvzdeKt6j/DedxMPAZ9plq8DHpy7wxi8p8bm\nP/Ks36E7/fkNYF9VfbWvrYv92aqza32a5B/OTsck+QfAb9E7PtKZ/lygxh93rS+r6otVdVZVnUfv\ndfLJqvo3wLdZYl9OxNlK0DuVFfgqxy6Su3XMJbUkOZfeaKHoHbD6ZlfqTPItYAr4EDAD7AL+Evhz\n4EzgJeDaqnqjYzX+Jr258neAg8BnZ+dOxyXJJcBfA8/Te64L+CK9K/vvpzv9uVCdn6JDfZrk4/QO\nkr6vuf2PqvrPSU6nI/25SI330qG+7JfkN4D/2JyttOS+nJhwkCStnUmZVpIkrSHDQZLUYjhIkloM\nB0lSi+EgSWoxHCRJLYaDJKnFcJAktfx/2J5xsZKMszsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x116fc20f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline \n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.hist(results, bins=40)\n",
"avg = len(results) / 40\n",
"plt.plot([0, 39], [avg, avg], 'r--');"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Another way to see the results:"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"{'A1': 0.020935,\n",
" 'A2': 0.021575,\n",
" 'B1': 0.0226325,\n",
" 'B2': 0.02315,\n",
" 'B3': 0.022745,\n",
" 'C1': 0.026815,\n",
" 'C2': 0.023915,\n",
" 'C3': 0.0246325,\n",
" 'CC1': 0.018755,\n",
" 'CC2': 0.026415,\n",
" 'CC3': 0.0238025,\n",
" 'CH1': 0.0089675,\n",
" 'CH2': 0.01026,\n",
" 'CH3': 0.0083825,\n",
" 'D1': 0.027875,\n",
" 'D2': 0.0293275,\n",
" 'D3': 0.0308175,\n",
" 'E1': 0.028385,\n",
" 'E2': 0.02684,\n",
" 'E3': 0.0319175,\n",
" 'F1': 0.0269125,\n",
" 'F2': 0.026975,\n",
" 'F3': 0.0264975,\n",
" 'FP': 0.028675,\n",
" 'G1': 0.026605,\n",
" 'G2': 0.026425,\n",
" 'G3': 0.0250875,\n",
" 'GO': 0.0309775,\n",
" 'H1': 0.0221275,\n",
" 'H2': 0.026555,\n",
" 'JAIL': 0.0619775,\n",
" 'R1': 0.02977,\n",
" 'R2': 0.0286625,\n",
" 'R3': 0.0309625,\n",
" 'R4': 0.0245525,\n",
" 'T1': 0.023335,\n",
" 'T2': 0.0218375,\n",
" 'U1': 0.02629,\n",
" 'U2': 0.02763}"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ProbDist(Counter(board[i] for i in results))"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"There is one square far above average: `JAIL`, at a little over 6%. There are four squares far below average: the three chance squares, `CH1`, `CH2`, and `CH3`, at around 1% (because 10 of the 16 chance cards send the player away from the square), and the \"Go to Jail\" square, square number 30 on the plot, which has a frequency of 0 because you can't end a turn there. The other squares are around 2% to 3% each, which you would expect, because 100% / 40 = 2.5%."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Central Limit Theorem / Strength in Numbers Theorem\n",
"\n",
"So far, we have talked of an *outcome* as being a single state of the world. But it can be useful to break that state of the world down into components. We call these components **random variables**. For example, when we consider an experiment in which we roll two dice and observe their sum, we could model the situation with two random variables, one for each die. (Our representation of outcomes has been doing that implicitly all along, when we concatenate two parts of a string, but the concept of a random variable makes it official.)\n",
"\n",
"The **Central Limit Theorem** states that if you have a collection of random variables and sum them up, then the larger the collection, the closer the sum will be to a *normal distribution* (also called a *Gaussian distribution* or a *bell-shaped curve*). The theorem applies in all but a few pathological cases. \n",
"\n",
"As an example, let's take 5 random variables reprsenting the per-game scores of 5 basketball players, and then sum them together to form the team score. Each random variable/player is represented as a function; calling the function returns a single sample from the distribution:\n"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from random import gauss, triangular, choice, vonmisesvariate, uniform\n",
"\n",
"def SC(): return posint(gauss(15.1, 3) + 3 * triangular(1, 4, 13)) # 30.1\n",
"def KT(): return posint(gauss(10.2, 3) + 3 * triangular(1, 3.5, 9)) # 22.1\n",
"def DG(): return posint(vonmisesvariate(30, 2) * 3.08) # 14.0\n",
"def HB(): return posint(gauss(6.7, 1.5) if choice((True, False)) else gauss(16.7, 2.5)) # 11.7\n",
"def OT(): return posint(triangular(5, 17, 25) + uniform(0, 30) + gauss(6, 3)) # 37.0\n",
"\n",
"def posint(x): \"Positive integer\"; return max(0, int(round(x)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And here is a function to sample a random variable *k* times, show a histogram of the results, and return the mean:"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from statistics import mean\n",
"\n",
"def repeated_hist(rv, bins=10, k=100000):\n",
" \"Repeat rv() k times and make a histogram of the results.\"\n",
" samples = [rv() for _ in range(k)]\n",
" plt.hist(samples, bins=bins)\n",
" return mean(samples)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The two top-scoring players have scoring distributions that are slightly skewed from normal:"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"30.10648"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x116faeb70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"repeated_hist(SC, bins=range(60))"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"22.14147"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10516e828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"repeated_hist(KT, bins=range(60))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The next two players have bi-modal distributions; some games they score a lot, some games not:"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"14.01045"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ERE3CISIiahIOc9TAwEok1V4REd2QcJijxsaO0rqdxcTX2XZuLaAGBlb2oB8R0U25t9Ic\nNfk9lKDz+yWd/r2VJttGfk8Rs0furRQREV2RcIiIiJqEQ0RE1CQcIiKipqNwkHSBpK9IOiTpu5Le\nL2mJpMclPS/pMUkXVNpvkjRS2l9Xqa+RdEDSYUlbOulTRER0rtM9h8/ReqznauCdwPeAjcCTtq8E\n9gKbAMozpNcBq4EbgHv1xon59wEbbA8Cg5Ku77BfERHRgbbDQdL5wG/b3gZg+6Ttl4Abge2l2Xbg\nprK8FthR2h0BRoAhSQPAYtv7S7sHKutEREQPdLLnsAr4iaRtkr4p6QuS3gossz0GYPsEsLS0Xw4c\nq6x/vNSWA6OV+mipRUREjyzscN01wO22vyHps7SmlCZe/dTVq6GGh4dfX240GjQajW5uPiJizms2\nmzSbzY620fYV0pKWAc/Yvqy8/8e0wuEfAQ3bY2XK6CnbqyVtBGz77tJ+D7AZODreptTXA9fYvnWS\n75krpItcIR0RM3VWr5AuU0fHJA2W0rXAd4FdwMdK7RbgkbK8C1gvaZGkVcDlwL4y9fSSpKFygPrm\nyjoREdEDnUwrAXwK+LKkXwK+D3wcWADslPQJWnsF6wBsH5S0EzgIvALcVtkNuB24HziP1tlPezrs\nV0REdCA33pujMq0UETOVG+9FRERXJBwiIqIm4RARETUJh4iIqEk4RERETcIhIiJqEg4REVGTcIiI\niJqEQ0RE1CQcIiKiJuEQERE1CYeIiKhJOERERE3CISIiahIOERFRk3CIiIiajsNB0jmSvilpV3m/\nRNLjkp6X9JikCyptN0kakXRI0nWV+hpJByQdlrSl0z5FRERnurHncAetR3+O2wg8aftKYC+wCUDS\nVbQeGboauAG4tzwzGuA+YIPtQWBQ0vVd6FdERLSpo3CQtAL4feCLlfKNwPayvB24qSyvBXbYPmn7\nCDACDEkaABbb3l/aPVBZJyIieqDTPYfPAp/m1IcIL7M9BmD7BLC01JcDxyrtjpfacmC0Uh8ttYiI\n6JGF7a4o6Q+AMdvfltR4k6ZdfdL88PDw68uNRoNG482+dUTE/NNsNmk2mx1tQ3Z7f7sl/XvgXwAn\ngbcAi4GvAu8FGrbHypTRU7ZXS9oI2PbdZf09wGbg6HibUl8PXGP71km+p9vt71w1MLCSsbGjU3x1\nsp+FJqlPVutG26m3Md9+TxGzmSRsa/qWb2h7Wsn2nbYvsX0ZsB7Ya/uPgUeBj5VmtwCPlOVdwHpJ\niyStAi4H9pWpp5ckDZUD1DdX1pn3WsHgSV4REWdO29NKb+IuYKekT9DaK1gHYPugpJ20zmx6Bbit\nshtwO3A/cB6w2/aeM9CviIiYobanlXphPk4rtXamOp/mybRSxPx1VqeVIiKifyUcIiKiJuEQZ8C5\nSKq9BgZW9rpjETFDOeYwy83VYw5TtZ1vv7+I2SDHHCIioisSDhERUZNwiIiImoRDRETUJBwiIqIm\n4RARETUJh4iIqEk4RERETcIhIiJqEg4REVGTcIiIiJqEQ0RE1LQdDpJWSNor6buSnpP0qVJfIulx\nSc9LekzSBZV1NkkakXRI0nWV+hpJByQdlrSlsyFFRESnOtlzOAn8me23A78J3C7p14GNwJO2rwT2\nApsAJF1F65Ghq4EbgHvLM6MB7gM22B4EBiVd30G/IiKiQ22Hg+0Ttr9dln8OHAJWADcC20uz7cBN\nZXktsMP2SdtHgBFgSNIAsNj2/tLugco6ERHRA1055iBpJfAu4Flgme0xaAUIsLQ0Ww4cq6x2vNSW\nA6OV+mipzTsDAytrD8iJiOiFhZ1uQNIvAw8Dd9j+uaSJT3Pp6tNdhoeHX19uNBo0Go1ubr6nxsaO\nMvmDcyIiZq7ZbNJsNjvaRkdPgpO0EPivwN/a/lypHQIatsfKlNFTtldL2gjY9t2l3R5gM3B0vE2p\nrweusX3rJN+vr58EN/lT3/IkuIjoTC+eBPcl4OB4MBS7gI+V5VuARyr19ZIWSVoFXA7sK1NPL0ka\nKgeob66sExERPdD2noOkDwJ/BzxH62OigTuBfcBO4GJaewXrbL9Y1tkEbABeoTUN9Xipvwe4HzgP\n2G37jim+Z/Yc2qpnzyFiPmtnz6GjaaWzbapwGBhYWebr37Bs2aWcOHHkLPWsOxIOEXEmzNtwmOqP\n6lwaGyQcIuLM6MUxh4iI6EMJh4iIqOn4Ooez7eKL337K+yVLLpiiZUREtGvOHXOA75xSe9vb/im/\n+MURcszhzeo55hAxn7VzzGHO7TnAqXsO55xzbo/60b7Jzq6KiJhN5mA4zH2T3yYDcquMiJgtckA6\nIiJq+jgczq3d4VQSAwMre92xiIhZr4+nlV5msqmbsbFM3URETKeP9xwiIqJdCYeIiKhJOJxhebpb\nRMxF8zAc6geqz+RB6jdOW62+IiJmtz4+ID2V+oHqHKSOiDjVrNlzkPRhSd+TdFjSZ3rdn9M12fRR\nppAiYq6aFeEg6RzgPwHX07o/xkcl/frZ68Hk10QsWPC2Gdcnnz7KFNJ80enD3Ge7jG/+mRXhAAwB\nI7aP2n4F2AHcePa+/fhU06mv1177+9Oox3zW739cMr75Z7aEw3LgWOX9aKlFREQPzLkD0uef/4en\nvP+HfxjtUU8iIvrXrHieg6QPAMO2P1zebwRs++4J7Xrf2YiIOeh0n+cwW8JhAfA8cC3wI2Af8FHb\nh3rasYiIeWpWTCvZflXSvwYep3UcZGuCISKid2bFnkNERMwus+VspWnN9YvkJpK0VdKYpAOV2hJJ\nj0t6XtJjki7oZR/bJWmFpL2SvivpOUmfKvV+Gd+5kr4u6VtlfJtLvS/GB61rjyR9U9Ku8r5vxgYg\n6Yik/1l+h/tKrS/GKOkCSV+RdKj8P/j+dsY2J8Kh9xfJnRHbaI2naiPwpO0rgb3AprPeq+44CfyZ\n7bcDvwncXn5ffTE+2y8Dv2P73cC7gBskDdEn4yvuAA5W3vfT2ABeAxq23217qNT6ZYyfA3bbXg28\nE/ge7YzN9qx/AR8A/rbyfiPwmV73qwvjuhQ4UHn/PWBZWR4AvtfrPnZpnF8Dfrcfxwe8FfgG8L5+\nGR+wAngCaAC7Sq0vxlYZ4/8GLppQm/NjBM4H/tck9dMe25zYc2D+XCS31PYYgO0TwNIe96djklbS\n+nT9LK1/nH0xvjLt8i3gBPCE7f30z/g+C3yaUy/975exjTPwhKT9kv5lqfXDGFcBP5G0rUwLfkHS\nW2ljbHMlHOarOX22gKRfBh4G7rD9c+rjmbPjs/2aW9NKK4AhSW+nD8Yn6Q+AMdvfBt7svPg5N7YJ\nPmh7DfD7tKY9f5s++P3ROgN1DfCfy/h+QWum5bTHNlfC4ThwSeX9ilLrN2OSlgFIGgB+3OP+tE3S\nQlrB8KDtR0q5b8Y3zvb/BZrAh+mP8X0QWCvp+8BfAR+S9CBwog/G9jrbPyr//T+0pj2H6I/f3yhw\nzPY3yvu/phUWpz22uRIO+4HLJV0qaRGwHtjV4z51gzj109ku4GNl+RbgkYkrzCFfAg7a/lyl1hfj\nk/Sr42d7SHoL8HvAIfpgfLbvtH2J7cto/X+21/YfA48yx8c2TtJby14tkt4GXAc8R3/8/saAY5IG\nS+la4Lu0MbY5c52DpA/TOgo/fpHcXT3uUkckPUTrgN9FwBiwmdYnmK8AFwNHgXW2X+xVH9sl6YPA\n39H6H278trV30rryfSdzf3xXA9tp/Vs8B/gvtv+dpAvpg/GNk3QN8Oe21/bT2CStAr5K69/lQuDL\ntu/qlzFKeifwReCXgO8DHwcWcJpjmzPhEBERZ89cmVaKiIizKOEQERE1CYeIiKhJOERERE3CISIi\nahIOERFRk3CIiIiahENERNT8f2PQWW00LX+yAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x105a5ffd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"repeated_hist(DG, bins=range(60))"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"11.67614"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1051eeb00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"repeated_hist(HB, bins=range(60))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The fifth \"player\" represents all the others on the team; the distribution appears to be a cross between a normal and a uniform distribution:"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"36.308"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11f6b2908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"repeated_hist(OT, bins=range(60))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we define the team score to be the sum of the players, and look at the distribution:"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"114.27877"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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kjtE/FUnSMAbZQvgMcOUpbVuBR6rqMuBRYBtAksuBa4GNwFXAnTl5yuxdwE1VtQHYkOTU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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10592f7f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def GSW(): return SC() + KT() + DG() + HB() + OT()\n",
"\n",
"repeated_hist(GSW, bins=range(70, 160, 2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sure enough, this looks very much like a normal distribution. The Central Limit Theorem appears to hold in this case. But I have to say \"Central Limit\" is not a very evocative name, so I propose we re-name this as the **Strength in Numbers Theorem**, to indicate the fact that if you have a lot of numbers, you tend to get the expected result."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Conclusion\n",
"\n",
"We've seen how to compute probabilities. Just be explicit about what the problem says, and then methodical about defining the sample space, and finally be careful in counting the number of outcomes in the numerator and denominator. Easy as 1-2-3. \n",
"\n",
"\n",
"<center><a href=\"https://en.wikipedia.org/wiki/Count_von_Count\">The Count</a></center>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<hr>\n",
"\n",
"# Appendix: Continuous Sample Spaces\n",
"\n",
"Everything up to here has been about discrete, finite sample spaces, where we can *enumerate* all the possible outcomes. \n",
"\n",
"ButI was asked about *continuous* sample spaces, such as the space of real numbers. The principles are the same: probability is still the ratio of the favorable cases to all the cases, but now instead of *counting* cases, we have to (in general) compute integrals to compare the sizes of cases. \n",
"Here we will cover a simple example, which we first solve approximately by simulation, and then exactly by calculation.\n",
"\n",
"## The Hot New Game Show Problem: Simulation\n",
"\n",
"Oliver Roeder posed [this problem](http://fivethirtyeight.com/features/can-you-win-this-hot-new-game-show/) in the 538 *Riddler* blog:\n",
"\n",
">Two players go on a hot new game show called *Higher Number Wins.* The two go into separate booths, and each presses a button, and a random number between zero and one appears on a screen. (At this point, neither knows the other’s number, but they do know the numbers are chosen from a standard uniform distribution.) They can choose to keep that first number, or to press the button again to discard the first number and get a second random number, which they must keep. Then, they come out of their booths and see the final number for each player on the wall. The lavish grand prize — a case full of gold bullion — is awarded to the player who kept the higher number. Which number is the optimal cutoff for players to discard their first number and choose another? Put another way, within which range should they choose to keep the first number, and within which range should they reject it and try their luck with a second number?\n",
"\n",
"We'll use this notation:\n",
"- **A**, **B**: the two players.\n",
"- *A*, *B*: the cutoff values they choose: the lower bound of the range of first numbers they will accept.\n",
"- *a*, *b*: the actual random numbers that appear on the screen.\n",
"\n",
"For example, if player **A** chooses a cutoff of *A* = 0.6, that means that **A** would accept any first number greater than 0.6, and reject any number below that cutoff. The question is: What cutoff, *A*, should player **A** choose to maximize the chance of winning, that is, maximize P(*a* > *b*)?\n",
"\n",
"First, simulate the number that a player with a given cutoff gets (note that `random.random()` returns a float sampled uniformly from the interval [0..1]):"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def number(cutoff):\n",
" \"Play the game with given cutoff, returning the first or second random number.\"\n",
" first = random.random()\n",
" return first if first > cutoff else random.random()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.08430962257066954"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"number(.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now compare the numbers returned with a cutoff of *A* versus a cutoff of *B*, and repeat for a large number of trials; this gives us an estimate of the probability that cutoff *A* is better than cutoff *B*:"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def Pwin(A, B, trials=30000):\n",
" \"The probability that cutoff A wins against cutoff B.\"\n",
" Awins = sum(number(A) > number(B) \n",
" for _ in range(trials))\n",
" return Awins / trials"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.49543333333333334"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Pwin(.5, .6)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now define a function, `top`, that considers a collection of possible cutoffs, estimate the probability for each cutoff playing against each other cutoff, and returns a list with the `N` top cutoffs (the ones that defeated the most number of opponent cutoffs), and the number of opponents they defeat: "
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def top(N, cutoffs):\n",
" \"Return the N best cutoffs and the number of opponent cutoffs they beat.\"\n",
" winners = Counter(A if Pwin(A, B) > 0.5 else B\n",
" for (A, B) in itertools.combinations(cutoffs, 2))\n",
" return winners.most_common(N)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 37.1 s, sys: 317 ms, total: 37.4 s\n",
"Wall time: 40.5 s\n"
]
},
{
"data": {
"text/plain": [
"[(0.6100000000000001, 44),\n",
" (0.55000000000000004, 43),\n",
" (0.60000000000000009, 42),\n",
" (0.62000000000000011, 42),\n",
" (0.57000000000000006, 42)]"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from numpy import arange\n",
"\n",
"%time top(5, arange(0.50, 0.99, 0.01))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We get a good idea of the top cutoffs, but they are close to each other, so we can't quite be sure which is best, only that the best is somewhere around 0.60. We could get a better estimate by increasing the number of trials, but that would consume more time.\n",
"\n",
"## The Hot New Game Show Problem: Exact Calculation\n",
"\n",
"More promising is the possibility of making `Pwin(A, B)` an exact calculation. But before we get to `Pwin(A, B)`, let's solve a simpler problem: assume that both players **A** and **B** have chosen a cutoff, and have each received a number above the cutoff. What is the probability that **A** gets the higher number? We'll call this `Phigher(A, B)`. We can think of this as a two-dimensional sample space of points in the (*a*, *b*) plane, where *a* ranges from the cutoff *A* to 1 and *b* ranges from the cutoff B to 1. Here is a diagram of that two-dimensional sample space, with the cutoffs *A*=0.5 and *B*=0.6:\n",
"\n",
"<img src=\"http://norvig.com/ipython/probability2da.jpg\" width=413>\n",
"\n",
"The total area of the sample space is 0.5 × 0.4 = 0.20, and in general it is (1 - *A*) · (1 - *B*). What about the favorable cases, where **A** beats **B**? That corresponds to the shaded triangle below:\n",
"\n",
"<img src=\"http://norvig.com/ipython/probability2d.jpg\" width=413>\n",
"\n",
"The area of a triangle is 1/2 the base times the height, or in this case, 0.4<sup>2</sup> / 2 = 0.08, and in general, (1 - *B*)<sup>2</sup> / 2. So in general we have:\n",
"\n",
" Phigher(A, B) = favorable / total\n",
" favorable = ((1 - B) ** 2) / 2 \n",
" total = (1 - A) * (1 - B)\n",
" Phigher(A, B) = (((1 - B) ** 2) / 2) / ((1 - A) * (1 - B))\n",
" Phigher(A, B) = (1 - B) / (2 * (1 - A))\n",
" \n",
"And in this specific case we have:\n",
"\n",
" A = 0.5; B = 0.6\n",
" favorable = 0.4 ** 2 / 2 = 0.08\n",
" total = 0.5 * 0.4 = 0.20\n",
" Phigher(0.5, 0.6) = 0.08 / 0.20 = 0.4\n",
"\n",
"But note that this only works when the cutoff *A* ≤ *B*; when *A* > *B*, we need to reverse things. That gives us the code:"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def Phigher(A, B):\n",
" \"Probability that a sample from [A..1] is higher than one from [B..1].\"\n",
" if A <= B:\n",
" return (1 - B) / (2 * (1 - A))\n",
" else:\n",
" return 1 - Phigher(B, A)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.4"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Phigher(0.5, 0.6)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We're now ready to tackle the full game. There are four cases to consider, depending on whether **A** and **B** gets a first number that is above or below their cutoff choices:\n",
"\n",
"| first *a* | first *b* | P(*a*, *b*) | P(A wins | *a*, *b*) | Comment |\n",
"|:-----:|:-----:| ----------- | ------------- | ------------ |\n",
"| *a* > *A* | *b* > *B* | (1 - *A*) · (1 - *B*) | Phigher(*A*, *B*) | Both above cutoff; both keep first numbers |\n",
"| *a* < *A* | *b* < *B* | *A* · *B* | Phigher(0, 0) | Both below cutoff, both get new numbers from [0..1] |\n",
"| *a* > *A* | *b* < *B* | (1 - *A*) · *B* | Phigher(*A*, 0) | **A** keeps number; **B** gets new number from [0..1] |\n",
"| *a* < *A* | *b* > *B* | *A* · (1 - *B*) | Phigher(0, *B*) | **A** gets new number from [0..1]; **B** keeps number |\n",
"\n",
"For example, the first row of this table says that the event of both first numbers being above their respective cutoffs has probability (1 - *A*) · (1 - *B*), and if this does occur, then the probability of **A** winning is Phigher(*A*, *B*).\n",
"We're ready to replace the old simulation-based `Pwin` with a new calculation-based version:"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def Pwin(A, B):\n",
" \"With what probability does cutoff A win against cutoff B?\"\n",
" return ( A * B * Phigher(0, 0) # both below cutoff\n",
" + (1-A) * (1-B) * Phigher(A, B) # both above\n",
" + (1-A) * B * Phigher(A, 0) # A above, B below\n",
" + A * (1-B) * Phigher(0, B)) # A below, B above"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The exact analysis code is much more complex than the simulation code, relying on a lot of algebra and geometry and case analysis, and thus more likely to have an error. Let's define a few tests to check for obvious errors:"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'ok'"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def test():\n",
" assert Phigher(0.5, 0.5) == Phigher(0.7, 0.7) == Phigher(0, 0) == 0.5\n",
" assert Pwin(0.5, 0.5) == Pwin(0.7, 0.7) == 0.5\n",
" assert Phigher(.6, .5) == 0.6\n",
" assert Phigher(.5, .6) == 0.4\n",
" return 'ok'\n",
"\n",
"test()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's repeat the calculation with our new, exact `Pwin`:"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(0.62000000000000011, 48),\n",
" (0.6100000000000001, 47),\n",
" (0.60000000000000009, 46),\n",
" (0.59000000000000008, 45),\n",
" (0.63000000000000012, 44)]"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"top(5, arange(0.50, 0.99, 0.01))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is good to see that the simulation and the exact calculation are in rough agreement; that gives me more confidence in both of them. We see here that 0.62 defeats all the other cutoffs, and 0.61 defeats all cutoffs except 0.62. The great thing about the exact calculation code is that it runs fast, regardless of how much accuracy we want. We can zero in on the range around 0.6:"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(0.6180000000000001, 199),\n",
" (0.6170000000000001, 198),\n",
" (0.6160000000000001, 197),\n",
" (0.61900000000000011, 196),\n",
" (0.6150000000000001, 195),\n",
" (0.6140000000000001, 194),\n",
" (0.6130000000000001, 193),\n",
" (0.62000000000000011, 192),\n",
" (0.6120000000000001, 191),\n",
" (0.6110000000000001, 190)]"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"top(10, arange(0.500, 0.700, 0.001))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This says 0.618 is best, better than 0.620. We can get even more accuracy:"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(0.61802999999999531, 200),\n",
" (0.61801999999999535, 199),\n",
" (0.61803999999999526, 198),\n",
" (0.6180099999999954, 197),\n",
" (0.61799999999999544, 196)]"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"top(5, arange(0.61700, 0.61900, 0.00001))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So 0.61803 is best. I don't think we need more accuracy than that.\n",
"\n",
"0.61803 defeats all of the other cutoffs. But in other game-theoretic problems it might be that there is no one strategy that beats all others. Sometimes A beats B and B beats C, but C beats A. To understand what we are dealing with, it is helpful to draw a 3D plot of `Pwin(A, B)` for values of *A* and *B* between 0 and 1:"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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ZTAbP8wJ/n6ph0aJF7L77Ptx33zYkk/cRdrEDL6J1AtgtaEN8QOF5bwAfKfP3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\nXv7yl1vw+58j0VGVi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AyjH3Bfhc7/EiNG/B/bb799Uu+os6LhcDiZMXdSFjhdhqdmOpib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\nC3jsIh6PJ7uxhBD88MMP9O7dOxnwZOvEqhmjJXBCFiPdBllJx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u0s/GvMMAz0er3pPavV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\n4CGEEEKI16PAQwghhBCv93//gkyuIAylRAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1073d4978>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"from mpl_toolkits.mplot3d.axes3d import Axes3D\n",
"\n",
"def map2(fn, A, B):\n",
" \"Map fn to corresponding elements of 2D arrays A and B.\"\n",
" return [list(map(fn, Arow, Brow))\n",
" for (Arow, Brow) in zip(A, B)]\n",
"\n",
"cutoffs = arange(0.00, 1.00, 0.02)\n",
"A, B = np.meshgrid(cutoffs, cutoffs)\n",
"\n",
"fig = plt.figure(figsize=(10,10))\n",
"ax = fig.add_subplot(1, 1, 1, projection='3d')\n",
"ax.set_xlabel('A')\n",
"ax.set_ylabel('B')\n",
"ax.set_zlabel('Pwin(A, B)')\n",
"ax.plot_surface(A, B, map2(Pwin, A, B));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What does this show us? The highest win percentage for **A**, the peak of the surface, occurs when *A* is around 0.5 and *B* is 0 or 1. We can confirm that, finding the maximum `Pwin(A, B)` for many different cutoff values of `A` and `B`:"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cutoffs = (set(arange(0.00, 1.00, 0.01)) | \n",
" set(arange(0.500, 0.700, 0.001)) | \n",
" set(arange(0.61700, 0.61900, 0.00001)))"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0.625, 0.5, 0.0]"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max([Pwin(A, B), A, B]\n",
" for A in cutoffs for B in cutoffs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So **A** could win 62.5% of the time if only **B** would chose a cutoff of 0. But, unfortunately for **A**, a rational player **B** is not going to do that. We can ask what happens if the game is changed so that player **A** has to declare a cutoff first, and then player **B** gets to respond with a cutoff, with full knowledge of **A**'s choice. In other words, what cutoff should **A** choose to maximize `Pwin(A, B)`, given that **B** is going to take that knowledge and pick a cutoff that minimizes `Pwin(A, B)`? "
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0.5, 0.61802999999999531, 0.61802999999999531]"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max(min([Pwin(A, B), A, B] for B in cutoffs)\n",
" for A in cutoffs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And what if we run it the other way around, where **B** chooses a cutoff first, and then **A** responds?"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0.5, 0.61802999999999531, 0.61802999999999531]"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"min(max([Pwin(A, B), A, B] for A in cutoffs)\n",
" for B in cutoffs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In both cases, the rational choice for both players in a cutoff of 0.61803, which corresponds to the \"saddle point\" in the middle of the plot. This is a *stable equilibrium*; consider fixing *B* = 0.61803, and notice that if *A* changes to any other value, we slip off the saddle to the right or left, resulting in a worse win probability for **A**. Similarly, if we fix *A* = 0.61803, then if *B* changes to another value, we ride up the saddle to a higher win percentage for **A**, which is worse for **B**. So neither player will want to move from the saddle point.\n",
"\n",
"The moral for continuous spaces is the same as for discrete spaces: be careful about defining your space; count/measure carefully, and let your code take care of the rest."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
tabakg/potapov_interpolation | Dispersion_relation_hash.ipynb | 1 | 8717 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# This notebook includes the hashing approach only for solving the frequency and phase matching problem."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sympy as sp\n",
"import numpy as np\n",
"import scipy.constants\n",
"from sympy.utilities.autowrap import ufuncify\n",
"import time\n",
"from scipy import interpolate\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"from sympy import init_printing\n",
"init_printing() \n",
"\n",
"import plotly.plotly as py\n",
"import plotly.graph_objs as go\n",
"import random\n",
"\n",
"# import multiprocessing\n",
"# pool = multiprocessing.Pool()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## from https://www.andreas-jung.com/contents/a-python-decorator-for-measuring-the-execution-time-of-methods\n",
"\n",
"def timeit(method):\n",
" def timed(*args, **kw):\n",
" ts = time.time()\n",
" result = method(*args, **kw)\n",
" te = time.time()\n",
" print '%r %2.2f sec' % \\\n",
" (method.__name__, te-ts)\n",
" return result\n",
" return timed"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lambd,omega,omega1,omega2,omega3,omega4 = sp.symbols('lambda omega omega_1 omega_2 omega_3 omega_4')\n",
"l2 = lambd **2\n",
"\n",
"def n_symb(pol='o'):\n",
" s = 1.\n",
" if pol == 'o':\n",
" s += 2.6734 * l2 / (l2 - 0.01764)\n",
" s += 1.2290 * l2 / (l2 - 0.05914)\n",
" s += 12.614 * l2 / (l2 - 474.6)\n",
" else:\n",
" s += 2.9804 * l2 / (l2 - 0.02047)\n",
" s += 0.5981 * l2 / (l2 - 0.0666)\n",
" s += 8.9543 * l2 / (l2 - 416.08)\n",
" return sp.sqrt(s)\n",
"\n",
"def k_symb(symbol=omega,pol='o'):\n",
" '''k is accurate for omega inputs between 6-60.'''\n",
" return ((n_symb(pol=pol) * symbol )\n",
" .subs(lambd,scipy.constants.c / (symbol*1e7))) ## / scipy.constants.c"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"phi1, phi2 = sp.symbols('phi_1 phi_2')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## We need to find where ex1(phi1,phi2) + ex2(phi1,omega3) = 0.\n",
"ex1 = (k_symb(omega1,pol='e')+k_symb(omega2,pol='e')).expand().subs({omega1:(phi1 + phi2)/2, omega2: (phi1-phi2)/2})\n",
"ex2 = -(k_symb(omega3,pol='e')+k_symb(omega4,pol='e')).expand().subs(omega4,-phi1-omega3)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"diff_func_4wv_1 = ufuncify([phi1,phi2], ex1)\n",
"diff_func_4wv_2 = ufuncify([phi1,omega3], ex2)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"400 400 1000\n"
]
}
],
"source": [
"phi1_min = 30.\n",
"phi1_max = 34.\n",
"phi1_range = np.arange(phi1_min,phi1_max,0.01)\n",
"\n",
"phi2_min = -13\n",
"phi2_max = -9\n",
"phi2_range = np.arange(phi2_min,phi2_max,0.01)\n",
"\n",
"omega3_min = -26.\n",
"omega3_max = -16.\n",
"omega3_range = np.arange(omega3_min,omega3_max,0.01) ## 5e-5\n",
"\n",
"print len(phi1_range),len(phi2_range),len(omega3_range)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"eps = 1e-4"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def eps_multiply_digitize(y,eps):\n",
" return map(lambda el: int(el/eps), y)\n",
"\n",
"\n",
"def make_dict_values_to_lists_of_inputs(values,inputs):\n",
" D = {}\n",
" for k, v in zip(values,inputs):\n",
" D.setdefault(k, []).append(v)\n",
" return D"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"f_phi12_omega3 = lambda phi1,phi2,omega3 : (diff_func_4wv_1(phi1_range[phi1],phi2_range[phi2]) \n",
" - diff_func_4wv_2(phi1_range[phi1],omega3_range[omega3]) )"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"@timeit\n",
"def make_matching_dict_hash(phi1_range,phi2_range,omega3_range,eps=eps):\n",
" phi2_indices = range(len(phi2_range))\n",
" omega3_indices = range(len(omega3_range))\n",
" matching_dict = {}\n",
" for phi1_index,phi1 in enumerate(phi1_range):\n",
" y1 = diff_func_4wv_1(phi1,phi2_range)\n",
" y2 = diff_func_4wv_2(phi1,omega3_range)\n",
"\n",
" y1_rounded = eps_multiply_digitize(y1,eps)\n",
" y1_rounded_up = [ind + 1 for ind in y1_rounded]\n",
" y2_rounded = eps_multiply_digitize(y2,eps)\n",
" y2_rounded_up = [ind + 1 for ind in y2_rounded]\n",
" \n",
" D1 = make_dict_values_to_lists_of_inputs(y1_rounded+y1_rounded_up,2*phi2_indices)\n",
" D2 = make_dict_values_to_lists_of_inputs(y2_rounded+y2_rounded_up,2*omega3_indices)\n",
" \n",
" inter = set(D1.keys()) & set(D2.keys())\n",
" \n",
" for el in inter:\n",
" for ind1 in D1[el]:\n",
" for ind2 in D2[el]:\n",
" err = y1[ind1]-y2[ind2]\n",
" if abs(err) < eps:\n",
" matching_dict[phi1_index,ind1,ind2] = err \n",
" \n",
" return matching_dict"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#### e.g."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"'make_matching_dict_hash' 7.63 sec\n"
]
}
],
"source": [
"matching_dict_hash = make_matching_dict_hash(phi1_range,phi2_range,omega3_range,eps=eps)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAEcAAAAPBAMAAABElc8tAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nRU5ErkJggg==\n",
"text/latex": [
"$$2365644$$"
],
"text/plain": [
"2365644"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(matching_dict_hash)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
fluxcapacitor/source.ml | jupyterhub.ml/notebooks/train_deploy/spark/spark_airbnb/04_PredictModel.ipynb | 1 | 3923 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Predict with Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## View Config"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%bash \n",
"\n",
"pio init-model \\\n",
" --model-server-url http://prediction-spark.community.pipeline.io/ \\\n",
" --model-type spark \\\n",
" --model-namespace default \\\n",
" --model-name spark_airbnb \\\n",
" --model-version v1 \\\n",
" --model-path ."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Predict with Model (CLI)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"%%bash\n",
"\n",
"pio predict \\\n",
" --model-test-request-path ./data/test_request.json"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Predict with Model under Mini-Load (CLI)\n",
"This is a mini load test to provide instant feedback on relative performance. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%bash\n",
"\n",
"pio predict_many \\\n",
" --model-test-request-path ./data/test_request.json \\\n",
" --num-iterations 5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model [Dashboards](http://hystrix.community.pipeline.io/hystrix-dashboard/monitor/monitor.html?streams=%5B%7B%22name%22%3A%22Model%20Servers%22%2C%22stream%22%3A%22http%3A%2F%2Fturbine.community.pipeline.io%2Fturbine.stream%22%2C%22auth%22%3A%22%22%2C%22delay%22%3A%22%22%7D%5D)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%html\n",
"\n",
"<iframe width=800 height=600 src=\"http://hystrix.community.pipeline.io/hystrix-dashboard/monitor/monitor.html?streams=%5B%7B%22name%22%3A%22Model%20Servers%22%2C%22stream%22%3A%22http%3A%2F%2Fturbine.community.pipeline.io%2Fturbine.stream%22%2C%22auth%22%3A%22%22%2C%22delay%22%3A%22%22%7D%5D\"></iframe>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Predict with Model (REST)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import requests\n",
"\n",
"model_type = 'spark'\n",
"model_namespace = 'default'\n",
"model_name = 'spark_airbnb'\n",
"model_version = 'v1'\n",
"\n",
"deploy_url = 'http://prediction-%s.community.pipeline.io/api/v1/model/predict/%s/%s/%s/%s' % (model_type, model_type, model_namespace, model_name, model_version)\n",
"\n",
"with open('./data/test_request.json', 'rb') as fh:\n",
" model_input_binary = fh.read()\n",
"\n",
"response = requests.post(url=deploy_url,\n",
" data=model_input_binary,\n",
" timeout=30)\n",
" \n",
"print(\"Success!\\n\\n%s\" % response.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
JudoWill/ResearchNotebooks | RedcapQC.ipynb | 2 | 12312 | {
"metadata": {
"name": "RedcapQC"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"import os, os.path\n",
"import numpy as np\n",
"os.chdir('/home/will/RedcapQC/')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"raw_data = pd.read_csv('/home/will/HIVReportGen/Data/RedcapDumps/HIVAIDSGeneticAnalys_DATA_LABELS_2013-01-16_1211.csv')\n",
"raw_data.rename(columns={raw_data.columns[0]:'Patient ID'}, inplace=True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from collections import Counter\n",
"\n",
"def get_common(inser):\n",
" counts = Counter(inser.values)\n",
" return counts.most_common(1)[0][0]\n",
"\n",
"ident_cols = ['Gender', 'Year of Birth', 'Ethnicity']\n",
"ident_cols += [col for col in raw_data.columns if col.startswith('Race ')]\n",
"ident_cols += [col for col in raw_data.columns if col.startswith('Exposure ')]\n",
"\n",
"\n",
"#PatID, VisitNum, VisitDate, Field, ActualValue, ExpectedValue\n",
"common_fixing = []\n",
"all_count = 0\n",
"for pat_id, group in raw_data.groupby('Patient ID'):\n",
" \n",
" for field in ident_cols:\n",
" if len(group[field].dropna()) == 0:\n",
" common = np.nan\n",
" else:\n",
" common = get_common(group[field].dropna())\n",
" \n",
" for _, row in group[['Event Name', field, 'Date of visit']].iterrows():\n",
" all_count += 1\n",
" if row[field] != common:\n",
" expected = 'missing' if row[field] != row[field] else common\n",
" if expected == 'missing':\n",
" common_fixing.append({\n",
" 'Patient ID':pat_id,\n",
" 'VisitNum':row['Event Name'].split()[0],\n",
" 'VisitDate':row['Date of visit'],\n",
" 'FieldToFix':field,\n",
" 'ActualValue':'missing',\n",
" 'ExpectedValue':'a valid value'\n",
" })\n",
" else:\n",
" common_fixing.append({\n",
" 'Patient ID':pat_id,\n",
" 'VisitNum':row['Event Name'].split()[0],\n",
" 'VisitDate':row['Date of visit'],\n",
" 'FieldToFix':field,\n",
" 'ActualValue':row[field],\n",
" 'ExpectedValue':expected\n",
" })\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from dateutil import parser\n",
"from copy import deepcopy\n",
"\n",
"date_fields = [('CD4', 'Date of latest CD4 count'),\n",
" ('CD8', 'Date of latest CD8 count'),\n",
" ('VL', 'Date of latest viral load')]\n",
"\n",
"#PatID, VisitNum, VisitDate, Field, ActualValue, ExpectedValue\n",
"param_fixing = []\n",
"for _, row in raw_data.iterrows():\n",
" \n",
" for name, field in date_fields:\n",
" tdict = {\n",
" 'Patient ID':row['Patient ID'],\n",
" 'VisitNum':row['Event Name'].split()[0],\n",
" 'ActualValue':None,\n",
" 'ExpectedValue':None\n",
" }\n",
" date_dict = {}\n",
" for nfield in [field, 'Date of visit']:\n",
" all_count += 1\n",
" if row[nfield] != row[nfield]:\n",
" date_dict[nfield] = None\n",
" ndict = {\n",
" 'VisitDate':row['Date of visit'],\n",
" 'FieldToFix':nfield,\n",
" 'ActualValue':'Missing!!',\n",
" 'ExpectedValue':'Anything'\n",
" }\n",
" param_fixing.append(deepcopy(tdict).update(ndict))\n",
" else:\n",
" try:\n",
" date_dict[nfield] = parser.parse(row[nfield])\n",
" except ValueError:\n",
" date_dict[nfield] = None\n",
" ndict = {\n",
" 'VisitDate':row['Date of visit'],\n",
" 'FieldToFix':nfield,\n",
" 'ActualValue':row[nfield],\n",
" 'ExpectedValue':'Invalid Date Format!!'\n",
" }\n",
" param_fixing.append(deepcopy(tdict).update(ndict))\n",
" \n",
" vdate, fdate = date_dict['Date of visit'], date_dict[field]\n",
" \n",
" \n",
" if (vdate is not None) and (fdate is not None):\n",
" dif_date = abs((vdate - fdate).days/30)\n",
" if dif_date > 3:\n",
" param_fixing.append({\n",
" 'Patient ID':row['Patient ID'],\n",
" 'VisitNum':row['Event Name'].split()[0],\n",
" 'VisitDate':row['Date of visit'],\n",
" 'FieldToFix':name + ' too distantly measured!',\n",
" 'ActualValue':'%i months' % dif_date,\n",
" 'ExpectedValue':row[field]\n",
" })\n",
" \n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"paired_cols = [('Hepatitis B status (HBV)','Year diagnosed HBV positive'),\n",
" ('Hepatitis C status (HCV)','Year diagnosed HCV positive'),\n",
" ('Cytomegalovirus (CMV)','Year diagnosed CMV positive'),\n",
" ('Human Papillomavirus (HPV)','Year diagnosed HPV positive'),\n",
" ('Herpes Simplex Virus Type 1 (HSV 1)','Year diagnosed HSV 1 positive'),\n",
" ('Herpes Simplex Virus Type 2 (HSV 2)','Year diagnosed HSV 2 positive'),\n",
" ('Tuberculosis','Year diagnosed tuberculosis positive'),\n",
" ('Hypertension','Year diagnosed with hypertension'),\n",
" ('Diabetes','Year diagnosed with diabetes'),\n",
" ('Elevated lipids','Year diagnosed with elevated lipids'),\n",
" ('Asthma','Year diagnosed with asthma'),\n",
" ('Chronic obstructive pulmonary disease (COPD)','Year diagnosed with COPD')]\n",
"\n",
"\n",
"for pat_id, group in raw_data.groupby('Patient ID'):\n",
" \n",
" for data_col, date_col in paired_cols:\n",
" group[data_col] = group[data_col].replace('ND', np.nan)\n",
" group[date_col] = group[date_col].replace('ND', np.nan)\n",
" \n",
" for field in [data_col, date_col]:\n",
" if len(group[field].dropna()) == 0:\n",
" common = np.nan\n",
" else:\n",
" common = get_common(group[field].dropna())\n",
" if common != common:\n",
" continue\n",
" for _, row in group[['Event Name', field, 'Date of visit']].iterrows():\n",
" all_count += 1\n",
" if row[field] != common:\n",
" if row[field] != row[field]:\n",
" actual = 'missing'\n",
" else:\n",
" actual = row[field]\n",
" common_fixing.append({\n",
" 'Patient ID':pat_id,\n",
" 'VisitNum':row['Event Name'].split()[0],\n",
" 'VisitDate':row['Date of visit'],\n",
" 'FieldToFix':field,\n",
" 'ActualValue':actual,\n",
" 'ExpectedValue':common\n",
" })\n",
"\n",
"\n",
" \n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from operator import itemgetter\n",
"\n",
"sort_fields = ['Patient ID', 'VisitNum', 'VisitDate', 'FieldToFix']\n",
"ordered_fixes = sorted([row for row in (param_fixing + common_fixing) if row is not None], key = itemgetter(*sort_fields))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"fields = ['Patient ID', 'VisitNum', 'VisitDate', 'FieldToFix', 'ActualValue', 'ExpectedValue']\n",
"\n",
"df = pd.DataFrame(ordered_fixes)\n",
"#df[fields].to_excel('/home/will/HIVVariation/redcap_QC.xlsx', index = False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for pat, rows in df.groupby('Patient ID'):\n",
" rows.to_excel('PatsToFix/%s-%i.xlsx' % (pat, len(rows)), index=False)\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"num_pats = len(list(df.groupby('Patient ID')))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"num_pats/5"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"71"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"num_pats"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"357"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print all_count, float(len(ordered_fixes))/float(all_count)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"50540 0.163514048279\n"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
oakejp12/soccer_analysis | soccer_analysis.ipynb | 1 | 33319 | {
"cells": [
{
"cell_type": "code",
"execution_count": 454,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from sklearn import linear_model, preprocessing\n",
"from sklearn.metrics import accuracy_score as auc\n",
"from sklearn import cross_validation as cv\n",
"\n",
"from matplotlib import pyplot as plt\n",
"\n",
"# Load training times\n",
"train_epl00 = pd.read_csv('./data/EPL00_01.csv', encoding=\"ISO-8859-1\")\n",
"train_epl01 = pd.read_csv('./data/EPL01_02.csv', encoding=\"ISO-8859-1\")\n",
"train_epl02 = pd.read_csv('./data/EPL02_03.csv', encoding=\"ISO-8859-1\")\n",
"\n",
"# Load testing files\n",
"test_epl03 = pd.read_csv('./data/EPL03_04.csv', encoding=\"ISO-8859-1\")\n",
"test_epl04 = pd.read_csv('./data/EPL04_05.csv', encoding=\"ISO-8859-1\")\n",
"\n",
"# Concat all training and test files into one structure\n",
"df_train = pd.concat((train_epl00, train_epl01, train_epl02), axis=0, ignore_index=True)\n",
"df_test = pd.concat((test_epl03, test_epl04), axis=0, ignore_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 455,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1140 records read from multiple training files\n",
"760 records read from multiple test files\n"
]
}
],
"source": [
"# Number of records in training set\n",
"num_train = df_train.shape[0]\n",
"print(str(num_train) + \" records read from multiple training files\")\n",
"\n",
"# Number of records in test set\n",
"num_test = df_test.shape[0]\n",
"print(str(num_test) + \" records read from multiple test files\")"
]
},
{
"cell_type": "code",
"execution_count": 456,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"# Calculate points from FTR (Full time Result)\n",
"def points(set_type):\n",
" ''' Follows the league distribution of points in response to a result '''\n",
" set_type['Result'] = np.NaN\n",
" \n",
" for index, item in set_type['FTR'].iteritems():\n",
" if (item == 'H'):\n",
" set_type.set_value(index, 'Result', 3.0)\n",
" elif (item == 'A'):\n",
" set_type.set_value(index, 'Result', 0.0)\n",
" else:\n",
" set_type.set_value(index, 'Result', 1.0)\n",
"\n",
"# Calculate goal difference\n",
"def diff(set_type, col1, col2, new_col):\n",
" ''' Difference of two columns col1 - col2 = new_col '''\n",
" set_type[new_col] = np.NaN # Set all values to zero\n",
" home_g = set_type[col1] # Placeholder values\n",
" away_g = set_type[col2]\n",
" set_type[new_col] = home_g - away_g # Calculate the difference in fulltime scores\n",
"\n",
"# Calculate points - Result\n",
"points(df_train)\n",
"points(df_test)\n",
"\n",
"# Calculate full time goal difference\n",
"diff(df_train, 'FTHG', 'FTAG', 'FTGD')\n",
"diff(df_test, 'FTHG', 'FTAG', 'FTGD')\n",
"\n",
"# Calculate half time goal difference\n",
"diff(df_train, 'HTHG', 'HTAG', 'HTGD')\n",
"diff(df_test, 'HTHG', 'HTAG', 'HTGD')\n",
"\n",
"#: Calculate shot taken on target difference - SOTD \n",
"diff(df_train, 'HST', 'AST', 'SOTD')\n",
"diff(df_test, 'HST', 'AST', 'SOTD')\n",
"\n",
"#: Calculate shots taken overall difference - STD \n",
"diff(df_train, 'HS', 'AS', 'STD')\n",
"diff(df_test, 'HS', 'AS', 'STD')\n",
" \n",
"\n",
"#: Isolate Arsenal FC to review season performance\n",
"arsenal_str = 'Arsenal' \n",
"arsenal_train = df_train.loc[(df_train['HomeTeam'] == arsenal_str) | (df_train['AwayTeam'] == arsenal_str)]\n",
"arsenal_test = df_test.loc[(df_test['HomeTeam'] == arsenal_str) | (df_test['AwayTeam'] == arsenal_str)]"
]
},
{
"cell_type": "code",
"execution_count": 457,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#: These columns were dropped so that we could extract values!\n",
"drop_col = ['Date', 'HomeTeam', 'AwayTeam', 'FTR', 'HTR', 'Result', 'FTHG', 'FTAG', 'HTHG', 'HTAG', \\\n",
" 'HR', 'AR', 'HY', 'AY', 'HST', 'AST', 'HS', 'AS']\n",
"ars_train_del = arsenal_train.drop(drop_col, axis = 1)\n",
"ars_test_del = arsenal_test.drop(drop_col, axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 458,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train X: (114, 4)\n",
"Test X: (76, 4)\n",
"Train y: (114,)\n",
"Test y: (76,)\n"
]
}
],
"source": [
"#: Normalize values?\n",
"\n",
"#: Transform to numpy array - the X in linear reg\n",
"ars_trval = ars_train_del.values.astype(float)\n",
"ars_teval = ars_test_del.values.astype(float)\n",
"\n",
"#: Activate sklearn - normalize object \n",
"min_max_scaler = preprocessing.MinMaxScaler()\n",
"ars_train_val = min_max_scaler.fit_transform(ars_trval)\n",
"ars_test_val = min_max_scaler.fit_transform(ars_teval)\n",
"\n",
"print('Train X:' , ars_train_val.shape)\n",
"print('Test X:', ars_test_val.shape)\n",
"\n",
"# vector y in linear regr\n",
"target_train = np.array(arsenal_train['Result'])\n",
"target_test = np.array(arsenal_test['Result'])\n",
"\n",
"\n",
"print('Train y:' , target_train.shape)\n",
"print('Test y:', target_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 465,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Logistic Regression CV: 0.90\n"
]
}
],
"source": [
"#: Retrieve the results for cross-validation\n",
"\n",
"#: Collect whole data\n",
"data = ars_train_del.as_matrix().astype(np.float)\n",
"\n",
"#: Target data\n",
"target = arsenal_train.Result.as_matrix().astype(np.int)\n",
"\n",
"def stratified_cv(X, y, clf_class, shuffle=True, n_folds=10, **kwargs):\n",
" stratified_k_fold = cv.StratifiedKFold(y, n_folds=n_folds, shuffle=shuffle)\n",
" y_pred = y.copy()\n",
" for ii, jj in stratified_k_fold:\n",
" X_train, X_test = X[ii], X[jj]\n",
" y_train = y[ii]\n",
" clf = clf_class(**kwargs)\n",
" clf.fit(X_train,y_train)\n",
" y_pred[jj] = clf.predict(X_test)\n",
" return y_pred\n",
" \n",
"\n",
"print('Logistic Regression CV: {:.2f}'.format(auc(target, stratified_cv(data, target, linear_model.LogisticRegression)))) "
]
},
{
"cell_type": "code",
"execution_count": 488,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy score: 0.75\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\oakejp12\\AppData\\Local\\Continuum\\Miniconda3\\envs\\csc322\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if self._edgecolors == str('face'):\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0xd962b70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Preform Logistic Regression - multinomial\n",
"logr = linear_model.LogisticRegression()\n",
"\n",
"# Train the logistic regression model & predict\n",
"logr.fit(ars_train_val, target_train)\n",
"yhat = logr.predict(ars_test_val)\n",
"\n",
"print('Accuracy score:', auc(target_test, yhat))\n",
"\n",
"\n",
"plt.scatter(range(yhat.shape[0]), yhat, label='predictions', c='#348ABD', alpha=0.4)\n",
"plt.scatter(range(target_test.shape[0]), target_test, label='actual', c='#A60628', alpha=0.4)\n",
"plt.xlim([0, yhat.shape[0]])\n",
"plt.legend();"
]
},
{
"cell_type": "code",
"execution_count": 461,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\oakejp12\\AppData\\Local\\Continuum\\Miniconda3\\envs\\csc322\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if self._edgecolors == str('face'):\n"
]
},
{
"data": {
"image/png": 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W9Htiv+va3AlMK3DuOly0MauoiFAvW8vpi4BZkmZKmg4cBlzScswlwNsAJM0l\nTT1xV8Fz1+EnErMJqOS6Nm3P7XY/j7Uxs9JctDGz0pxIzKw0JxIzK82JxMxKcyIxs9KcSMysNCcS\nMyvNicTMSvsf9P4MqrMbDb0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xacaed30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Confusion Matrix\n",
"y_actual = pd.Series(target_test, name='Actual')\n",
"y_pred = pd.Series(yhat, name='Predicted')\n",
"\n",
"# Turn into a pandas data frame\n",
"df_confusion = pd.crosstab(y_actual, y_pred, rownames=['Actual'], colnames=['Predicted'], margins=True)\n",
"\n",
"def plot_cm(matrix, title='Confusion Matrix'):\n",
" plt.matshow(matrix)\n",
" # plt.title(title)\n",
" plt.colorbar()\n",
" tick_marks = np.arange(len(matrix.columns))\n",
" plt.xticks(tick_marks, matrix.columns, rotation=45)\n",
" plt.yticks(tick_marks, matrix.index)\n",
" plt.ylabel(matrix.index.name)\n",
" plt.xlabel(matrix.columns.name)\n",
" \n",
"# Normalize confusion matrix\n",
"df_conf_norm = df_confusion / df_confusion.sum(axis=1)\n",
"\n",
"plot_cm(df_conf_norm) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
google/data-pills | pills/Third Party/[DATA_PILL]_[Appsflyer]_Install_Report_Analysis.ipynb | 1 | 11940 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**PLEASE MAKE A COPY BEFORE CHANGING**\n",
"\n",
"**Copyright** 2022 Google LLC\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\");\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.\n",
"\n",
"\n",
"<b>Important</b>\n",
"This content are intended for educational and informational purposes only."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Instructions\n",
"\n",
"##### 1. Export Install Report from Appsflyer\n",
"##### 2. Upload csv to Google Drive\n",
"##### 3. Configure the locations below then run this colab."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import necessary packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-UI6voLWzyk3",
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"## Import Packages\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ABEgtUqQN8j0"
},
"source": [
"# Mount Google Drive"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Y1pSQ-boEoVV",
"outputId": "804ca21d-1100-4981-c3d5-6972b229225a",
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"## Mount to Google Drive\n",
"from google.colab import drive\n",
"drive.mount('/content/drive')\n",
"print(\"Log: Google Drive mounted on 'Files' tab\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aItyjyOaNiLk"
},
"source": [
"# Import Appsflyer's *Install Report* as csv from Google Drive"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NBbCBKDzEvMR",
"outputId": "88c5aeac-96e4-43b1-8753-46d17f59c755",
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"file_path = \"/content/drive/location/file.csv\" # @param {type:\"string\"}\n",
"low_memory=False\n",
"df = pd.read_csv(file_path)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Yjp69IBzOAY1"
},
"source": [
"# Prepare and check dataframe"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 478
},
"id": "DIJ4Eyi9bEYK",
"outputId": "ec9f123a-d438-4f5c-e501-88a3b01691d1",
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"# @title Select necessary columns and prepare dataframe { vertical-output: true, display-mode: \"form\" }\n",
"## Select necessary columns\n",
"df = df[df['Event Name']=='install']\n",
"df = df[['Attributed Touch Type'\n",
" , 'Attributed Touch Time'\n",
" , 'Install Time'\n",
" , 'Media Source'\n",
" , 'Country Code'\n",
" , 'Contributor 1 Touch Type' \n",
" , 'Contributor 1 Touch Time'\n",
" , 'Contributor 1 Media Source'\n",
" , 'Contributor 2 Touch Type' \n",
" , 'Contributor 2 Touch Time'\n",
" , 'Contributor 2 Media Source'\n",
" , 'Contributor 3 Touch Type' \n",
" , 'Contributor 3 Touch Time'\n",
" , 'Contributor 3 Media Source'\n",
" ]]\n",
"## Calculate time Touch to install time\n",
"df['Install-Touch Timestamp'] = (pd.to_datetime(df['Install Time']) -\\\n",
" pd.to_datetime(df['Attributed Touch Time']))\n",
"\n",
"df['Install-Touch sec'] = pd.to_timedelta(df['Install-Touch Timestamp'], unit='s')\n",
"\n",
"df['Install-Touch sec'] = df['Install-Touch sec'].dt.total_seconds()\n",
"df.rename(columns={'Media Source': 'Attributed Media Source'}, inplace=True)\n",
"df.head(3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 488
},
"id": "no8CNVvFdkLI",
"outputId": "94b62f1e-5571-4134-a709-6c39ea044af1",
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"# @title Describe data { vertical-output: true, display-mode: \"form\" }\n",
"\n",
"grouping = \"Attributed Media Source\" #@param [\"Attributed Media Source\", \"Contributor 1 Media Source\", \"Contributor 2 Media Source\", \"Contributor 3 Media Source\"]\n",
"\n",
"df_cont = df.groupby(grouping).agg(['count', 'mean','min','max','std'])\n",
"column = 'Install-Touch sec' # @param['Install-Touch sec']\n",
"min_entries = 500 # @param {type:\"number\"}\n",
"\n",
"df_cont=df_cont[column].sort_values(by=['count'], ascending=False)\n",
"df_cont=df_cont[df_cont['count']>=min_entries]\n",
"\n",
"##Affects next card\n",
"medias = list(df_cont.index.values) \n",
"\n",
"df_cont"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LaQSPiK7OS3w"
},
"source": [
"# Plots"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 503
},
"id": "ilciueUOf_eN",
"outputId": "90e83a83-ab82-4303-dab6-0e173efd4ad6",
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"# @title Use Violin Plots to compare distributions side by side { vertical-output: true, display-mode: \"form\" }\n",
"col_x = 'Attributed Media Source'\n",
"col_y = 'Install-Touch sec'\n",
"\n",
"sns.set(rc={'axes.facecolor':'white', 'figure.facecolor':'white'}, font_scale=1.15)\n",
"sns.set_theme(style=\"whitegrid\")\n",
"\n",
"sec_min = 0 # @param {type:\"number\"}\n",
"sec_max = 960 # @param {type:\"number\"}\n",
"\n",
"f, ax = plt.subplots(figsize=(30, 8))\n",
"ax = sns.violinplot(x=col_x\n",
" , y=col_y\n",
" , data=df[((df[col_y]<=sec_max))], \n",
" palette = \"tab20_r\",bw=.2, cut=1, linewidth=1, order=medias)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 611
},
"id": "pqV4kmqsWOGU",
"outputId": "6fc9d5b8-4e65-4b1f-990c-78ca25f11cb6",
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"#@title Plot histogram to compare distributions { vertical-output: true, display-mode: \"form\" }\n",
"\n",
"max_sec = 960 # @param {type:\"number\"}\n",
"bsize = 10 # @param {type:\"number\"}\n",
"#Change baseline to desired media source\n",
"baseline = 'googleadwords_int' # @param[\"googleadwords_int\"] {allow-input: true}\n",
"#Change media_source to compare\n",
"media_source = 'googleadwords_int' # @param[\"googleadwords_int\"] {allow-input: true}\n",
"\n",
"df_filtered = df[(df['Install-Touch sec']<= max_sec) & (df['Install-Touch sec']>= 0)]\n",
"df_filtered1 = df_filtered[df_filtered['Attributed Media Source']==baseline]\n",
"df_filtered2 = df_filtered[df_filtered['Attributed Media Source']==media_source]\n",
"\n",
"sns.set(rc={'axes.facecolor':'white', 'figure.facecolor':'white'})\n",
"f, ax = plt.subplots(figsize=(20, 10))\n",
"sns.histplot( df_filtered1['Install-Touch sec'], stat='density', kde=False, \n",
" color=\"slategray\", label=baseline, bins=range(0, max_sec + bsize, bsize))\n",
"sns.histplot( df_filtered2['Install-Touch sec'], stat='density', kde=False, \n",
" color=\"deeppink\", label=media_source, bins=range(0, max_sec + bsize, bsize))\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8T03cS-zDf1e"
},
"source": [
"# Contribution Ratio"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "mGsBk93gVbAb",
"outputId": "6c104fb6-1de2-40e5-9bbd-f3e0a1196ebc",
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"#@title Evaluate contribution/attribution ratio { vertical-output: true, display-mode: \"form\" }\n",
"\n",
"df_contrib = pd.DataFrame(df['Attributed Media Source'].value_counts())\\\n",
" .join(pd.DataFrame(df['Contributor 1 Media Source'].value_counts()),how='outer')\\\n",
" .join(pd.DataFrame(df['Contributor 2 Media Source'].value_counts()),how='outer')\\\n",
" .join(pd.DataFrame(df['Contributor 3 Media Source'].value_counts()),how='outer').fillna(0)\n",
"\n",
"df_contrib['Contributions']= df_contrib[list(df_contrib.columns)[1:]].sum(axis=1)\n",
"df_contrib['Ratio']=df_contrib['Contributions'] / df_contrib['Attributed Media Source']\n",
"\n",
"df_contrib=df_contrib.sort_values(by=['Attributed Media Source'],ascending=False)\n",
"df_contrib.style.format({'Attributed Media Source':\"{:,}\",\\\n",
" 'Contributor 1 Media Source':\"{:,}\",\\\n",
" 'Contributor 2 Media Source':\"{:,}\",\\\n",
" 'Contributor 3 Media Source':\"{:,}\",\\\n",
" 'Contributions':\"{:,}\",\\\n",
" 'Ratio': \"{:.2%}\"})"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [
"12bMVc1-N5TI",
"ABEgtUqQN8j0"
],
"name": "[DATA_PILL]_[Appsflyer]_Install_Report_Analysis.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
rjosest/WTC | Notebooks/NumberLongitudinalScans.ipynb | 1 | 1840 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"df=pd.read_csv('../Caselist/WTGoodCTCaseList_INSP.txt',header=None)"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [],
"source": [
"df['sid']=df[0].apply(lambda x: x.split('_')[0])\n",
"df['time']=df[0].apply(lambda x: x.split('_')[1])"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [],
"source": [
"df2=df.groupby(['sid','time']).count()"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [],
"source": [
"aa=df2.index.to_frame()\n",
"aa=aa.rename(columns={'sid':'sid2','time':'time2'})"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [],
"source": [
"bb=pd.DataFrame(aa.groupby('sid2').apply(lambda x: len(x)))"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [],
"source": [
"bb.to_csv('WTC_numberLongitudinalscans.csv')"
]
},
{
"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": "3.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| bsd-3-clause |
tmilliman/phenocam_notebooks | Reading_Standard_Files/PhenoCam_Summary_Files.ipynb | 1 | 273676 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PhenoCam ROI Summary Files\n",
"\n",
"Here's a python notebook demonstrating how to read in and plot an ROI (Region of Interest) summary using python. In this case I'm using the 1-day summary file from the alligatorriver site. The summary files are in CSV format and can be read directly from the site using a URL. Before reading from a URL let's make sure we can read directly from a file."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"alligatorriver_DB_0001_1day.csv\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"import os, sys\n",
"import numpy as np\n",
"import matplotlib\n",
"import pandas as pd\n",
"import requests\n",
"import StringIO\n",
"\n",
"# set matplotlib style\n",
"matplotlib.style.use('ggplot')\n",
"\n",
"sitename = 'alligatorriver'\n",
"roiname = 'DB_0001'\n",
"infile = \"{}_{}_1day.csv\".format(sitename, roiname)\n",
"print infile"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"#\n",
"# 1-day summary product time series for alligatorriver\n",
"#\n",
"# Site: alligatorriver\n",
"# Veg Type: DB\n",
"# ROI ID Number: 0001\n",
"# Lat: 35.7879\n",
"# Lon: -75.9038\n",
"# Elev: 1\n",
"# UTC Offset: -5\n",
"# Image Count Threshold: 1\n",
"# Aggregation Period: 1\n",
"# Solar Elevation Min: 5.0\n",
"# Time of Day Min: 00:00:00\n",
"# Time of Day Max: 23:59:59\n",
"# ROI Brightness Min: 100\n",
"# ROI Brightness Max: 665\n",
"# Creation Date: 2016-10-13\n",
"# Creation Time: 11:12:18\n",
"# Update Date: 2016-10-13\n",
"# Update Time: 11:12:21\n",
"#\n",
"date,year,doy,image_count,midday_filename,midday_r,midday_g,midday_b,midday_gcc,midday_rcc,r_mean,r_std,g_mean,g_std,b_mean,b_std,gcc_mean,gcc_std,gcc_50,gcc_75,gcc_90,rcc_mean,rcc_std,rcc_50,rcc_75,rcc_90,max_solar_elev,snow_flag,outlierflag_gcc_mean,outlierflag_gcc_50,outlierflag_gcc_75,outlierflag_gcc_90\n",
"2012-05-03,2012,124,2,alligatorriver_2012_05_03_120110.jpg,106.30031,115.73730,55.34694,0.41724,0.38322,106.39446,0.09415,115.55613,0.18118,55.95246,0.60553,0.41581,0.00143,0.41581,0.41653,0.41696,0.38285,0.00038,0.38285,0.38304,0.38315,70.15241,NA,NA,NA,NA,NA\n",
"2012-05-04,2012,125,0,None,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA\n",
"2012-05-05,2012,126,0,None,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA\n",
"2012-05-06,2012,127,18,alligatorriver_2012_05_06_120108.jpg,98.56315,114.36571,58.60785,0.42118,0.36298,99.56063,3.59571,112.30546,2.26816,57.38592,3.95619,0.41716,0.00405,0.41683,0.42104,0.42216,0.36983,0.01198,0.37316,0.37640,0.37951,71.00152,NA,NA,NA,NA,NA\n",
"2012-05-07,2012,128,22,alligatorriver_2012_05_07_120109.jpg,104.66830,114.42699,57.99294,0.41296,0.37774,101.61201,3.42694,109.60297,5.09935,56.93328,3.30283,0.40870,0.00760,0.41157,0.41206,0.41258,0.37906,0.00392,0.37819,0.38044,0.38412,71.27531,NA,NA,NA,NA,NA\n",
"2012-05-08,2012,129,19,alligatorriver_2012_05_08_120109.jpg,109.42668,117.97895,61.57828,0.40825,0.37866,103.80104,5.38061,111.59026,7.34960,58.74146,6.34041,0.40709,0.00670,0.40976,0.41134,0.41300,0.37905,0.00911,0.38013,0.38524,0.38747,71.54443,NA,NA,NA,NA,NA\n",
"2012-05-09,2012,130,21,alligatorriver_2012_05_09_120109.jpg,109.50769,117.67200,57.08726,0.41395,0.38523,100.64798,5.84903,112.13978,5.14872,57.89002,4.89616,0.41436,0.00287,0.41424,0.41590,0.41889,0.37191,0.01344,0.37685,0.38239,0.38647,71.80876,NA,NA,NA,NA,NA\n"
]
}
],
"source": [
"%%bash\n",
"head -30 alligatorriver_DB_0001_1day.csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"While the data can be read directly from a URL we'll start by doing the simple thing of reading the CSV file directly from our local disk."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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" <th>...</th>\n",
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" <th>outlierflag_gcc_50</th>\n",
" <th>outlierflag_gcc_75</th>\n",
" <th>outlierflag_gcc_90</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2012-05-03</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2012-05-04</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2012-05-05</td>\n",
" <td>2012</td>\n",
" <td>126</td>\n",
" <td>0</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2012-05-06</td>\n",
" <td>2012</td>\n",
" <td>127</td>\n",
" <td>18</td>\n",
" <td>alligatorriver_2012_05_06_120108.jpg</td>\n",
" <td>98.56315</td>\n",
" <td>114.36571</td>\n",
" <td>58.60785</td>\n",
" <td>0.42118</td>\n",
" <td>0.36298</td>\n",
" <td>...</td>\n",
" <td>0.01198</td>\n",
" <td>0.37316</td>\n",
" <td>0.37640</td>\n",
" <td>0.37951</td>\n",
" <td>71.00152</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2012-05-07</td>\n",
" <td>2012</td>\n",
" <td>128</td>\n",
" <td>22</td>\n",
" <td>alligatorriver_2012_05_07_120109.jpg</td>\n",
" <td>104.66830</td>\n",
" <td>114.42699</td>\n",
" <td>57.99294</td>\n",
" <td>0.41296</td>\n",
" <td>0.37774</td>\n",
" <td>...</td>\n",
" <td>0.00392</td>\n",
" <td>0.37819</td>\n",
" <td>0.38044</td>\n",
" <td>0.38412</td>\n",
" <td>71.27531</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 32 columns</p>\n",
"</div>"
],
"text/plain": [
" date year doy image_count midday_filename \\\n",
"0 2012-05-03 2012 124 2 alligatorriver_2012_05_03_120110.jpg \n",
"1 2012-05-04 2012 125 0 None \n",
"2 2012-05-05 2012 126 0 None \n",
"3 2012-05-06 2012 127 18 alligatorriver_2012_05_06_120108.jpg \n",
"4 2012-05-07 2012 128 22 alligatorriver_2012_05_07_120109.jpg \n",
"\n",
" midday_r midday_g midday_b midday_gcc midday_rcc ... \\\n",
"0 106.30031 115.73730 55.34694 0.41724 0.38322 ... \n",
"1 NaN NaN NaN NaN NaN ... \n",
"2 NaN NaN NaN NaN NaN ... \n",
"3 98.56315 114.36571 58.60785 0.42118 0.36298 ... \n",
"4 104.66830 114.42699 57.99294 0.41296 0.37774 ... \n",
"\n",
" rcc_std rcc_50 rcc_75 rcc_90 max_solar_elev snow_flag \\\n",
"0 0.00038 0.38285 0.38304 0.38315 70.15241 NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN NaN NaN \n",
"3 0.01198 0.37316 0.37640 0.37951 71.00152 NaN \n",
"4 0.00392 0.37819 0.38044 0.38412 71.27531 NaN \n",
"\n",
" outlierflag_gcc_mean outlierflag_gcc_50 outlierflag_gcc_75 \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN \n",
"\n",
" outlierflag_gcc_90 \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"\n",
"[5 rows x 32 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with open(infile,'r') as fd:\n",
" df = pd.read_csv(fd, comment='#', parse_dates=[0])\n",
"\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x108dd6a10>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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dZigjldBECE2Pe6cKD397mN3rO7Ux2mYrnYkZ8PYA5Fflw/IktWxoCrVa3ejf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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108dd6610>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot('date', ['gcc_90'], figsize=(16,4),\n",
" grid=True, style=['g'] )\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That was pretty simple. Now try to read directly from a URL to see if we get the same result. This has the advantage that you always get the latest version of the file which is updated nightly."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"https://phenocam.sr.unh.edu/data/archive/alligatorriver/ROI/alligatorriver_DB_0001_1day.csv\n"
]
}
],
"source": [
"url = \"https://phenocam.sr.unh.edu/data/archive/{}/ROI/{}_{}_1day.csv\"\n",
"url = url.format(sitename, sitename, roiname)\n",
"print url"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the `requests` package to read the CSV file from the URL."
]
},
{
"cell_type": "code",
"execution_count": 6,
"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>date</th>\n",
" <th>year</th>\n",
" <th>doy</th>\n",
" <th>image_count</th>\n",
" <th>midday_filename</th>\n",
" <th>midday_r</th>\n",
" <th>midday_g</th>\n",
" <th>midday_b</th>\n",
" <th>midday_gcc</th>\n",
" <th>midday_rcc</th>\n",
" <th>...</th>\n",
" <th>rcc_std</th>\n",
" <th>rcc_50</th>\n",
" <th>rcc_75</th>\n",
" <th>rcc_90</th>\n",
" <th>max_solar_elev</th>\n",
" <th>snow_flag</th>\n",
" <th>outlierflag_gcc_mean</th>\n",
" <th>outlierflag_gcc_50</th>\n",
" <th>outlierflag_gcc_75</th>\n",
" <th>outlierflag_gcc_90</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2012-05-03</td>\n",
" <td>2012</td>\n",
" <td>124</td>\n",
" <td>2</td>\n",
" <td>alligatorriver_2012_05_03_120110.jpg</td>\n",
" <td>106.30031</td>\n",
" <td>115.73730</td>\n",
" <td>55.34694</td>\n",
" <td>0.41724</td>\n",
" <td>0.38322</td>\n",
" <td>...</td>\n",
" <td>0.00038</td>\n",
" <td>0.38285</td>\n",
" <td>0.38304</td>\n",
" <td>0.38315</td>\n",
" <td>70.15241</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2012-05-04</td>\n",
" <td>2012</td>\n",
" <td>125</td>\n",
" <td>0</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2012-05-05</td>\n",
" <td>2012</td>\n",
" <td>126</td>\n",
" <td>0</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2012-05-06</td>\n",
" <td>2012</td>\n",
" <td>127</td>\n",
" <td>17</td>\n",
" <td>alligatorriver_2012_05_06_120108.jpg</td>\n",
" <td>98.56315</td>\n",
" <td>114.36571</td>\n",
" <td>58.60785</td>\n",
" <td>0.42118</td>\n",
" <td>0.36298</td>\n",
" <td>...</td>\n",
" <td>0.00954</td>\n",
" <td>0.37324</td>\n",
" <td>0.37641</td>\n",
" <td>0.37958</td>\n",
" <td>71.00152</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2012-05-07</td>\n",
" <td>2012</td>\n",
" <td>128</td>\n",
" <td>21</td>\n",
" <td>alligatorriver_2012_05_07_120109.jpg</td>\n",
" <td>104.66830</td>\n",
" <td>114.42699</td>\n",
" <td>57.99294</td>\n",
" <td>0.41296</td>\n",
" <td>0.37774</td>\n",
" <td>...</td>\n",
" <td>0.00329</td>\n",
" <td>0.37774</td>\n",
" <td>0.38016</td>\n",
" <td>0.38301</td>\n",
" <td>71.27531</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 32 columns</p>\n",
"</div>"
],
"text/plain": [
" date year doy image_count midday_filename \\\n",
"0 2012-05-03 2012 124 2 alligatorriver_2012_05_03_120110.jpg \n",
"1 2012-05-04 2012 125 0 None \n",
"2 2012-05-05 2012 126 0 None \n",
"3 2012-05-06 2012 127 17 alligatorriver_2012_05_06_120108.jpg \n",
"4 2012-05-07 2012 128 21 alligatorriver_2012_05_07_120109.jpg \n",
"\n",
" midday_r midday_g midday_b midday_gcc midday_rcc ... \\\n",
"0 106.30031 115.73730 55.34694 0.41724 0.38322 ... \n",
"1 NaN NaN NaN NaN NaN ... \n",
"2 NaN NaN NaN NaN NaN ... \n",
"3 98.56315 114.36571 58.60785 0.42118 0.36298 ... \n",
"4 104.66830 114.42699 57.99294 0.41296 0.37774 ... \n",
"\n",
" rcc_std rcc_50 rcc_75 rcc_90 max_solar_elev snow_flag \\\n",
"0 0.00038 0.38285 0.38304 0.38315 70.15241 NaN \n",
"1 NaN NaN NaN NaN NaN NaN \n",
"2 NaN NaN NaN NaN NaN NaN \n",
"3 0.00954 0.37324 0.37641 0.37958 71.00152 NaN \n",
"4 0.00329 0.37774 0.38016 0.38301 71.27531 NaN \n",
"\n",
" outlierflag_gcc_mean outlierflag_gcc_50 outlierflag_gcc_75 \\\n",
"0 NaN NaN NaN \n",
"1 NaN NaN NaN \n",
"2 NaN NaN NaN \n",
"3 NaN NaN NaN \n",
"4 NaN NaN NaN \n",
"\n",
" outlierflag_gcc_90 \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"\n",
"[5 rows x 32 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"response = requests.get(url)\n",
"fd = StringIO.StringIO(response.text)\n",
"df = pd.read_csv(fd, comment='#', parse_dates=[0])\n",
"fd.close\n",
"df[0:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If necessary we'll need to convert nodata values."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df[df['gcc_90'] == -9999.].gcc_90 = np.nan"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x108f14c50>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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RFbgAbM7ezI+FsYzs5S10I2ADnGIeCXfgFjSa4AgfwFyL2c5v/5T+ouukyBK+Rm/Wy7rS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EBkpOuiLYQ/UqnU7d8m1sB8+UrFUiy+bTHmDpqLHtoemNqfmSUrrSuF3qxH96juiIyMRKeIxqDj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\nAICjEGQBAAAAAI5CkAUAAAAAOMpf6JiQwrip4+sAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108ec9450>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot('date', ['gcc_90'], figsize=(16,4),\n",
" grid=True, style=['g'] )\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can look at other columns and also filter the data in a variety of ways. Recently we had a site where the number of images varied a lot over time. Let's look at how consistent the number of images for the alligator river site. The image_count reflects our brightness threshold which will eliminate images in the winter time when the days are shorter. But there are a number of other ways the image count can be reduced. The ability reliably extract a 90^th precentile value is dependent on the number of images available for a particular summary period."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x108f92610>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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2ztohmdRM65yX0Fwe9N5eHUR+v7lYUTqtZZ+P0p0Kzc0fvUYR5a0Kq1d3f++d\ndBp44olirF1rNXE8+mgxbr65zNTsncUnnwRx772l2d/btvnw3HNRTJ0axdq1Adx1Vyluu60MLS0a\n1q4N4M033ZWh7m+gAsiLL0Zw443lWLJE3Lja5yNN4WfPNt73Bx+EsHKldW5EwtbUqdFsUSF2Owu+\nWFFJSdcookuWhPDZZ0EpM//+eyNCXpxbCDz5ZLF1B0irD7YxOMVbbxXhm2+M686YEUE83ms+927F\np58GsWyZwU/sQnO//TYgNIKxczdmTCmWLw9lLfuJBPDNNwG88YYR2y/L0Vy6NIQxY0qtO3fce8yY\nUlPDbralVXW1P/s98PySNeRNnVqcDR9nwVb1BYD16wOYM0ecj0DpcO7csOm3G9TXa5g2LTrghTu7\n50+lgEmTipFIEIF60qRiz0LS7NkRW4PtmDGlWLfOnH0jqnz79NPRbJXJ5ctDWLWKEBflWxQffxxC\ndbXfdC4dhxOefTaKhgazZwEgSs7TT0ddG9qWLjWPad06P15/vYA5NX0MDQ0a7rmnFF9/bZ7njz82\n3tETT1hp68UXI6Y1ZeVKubz1xBPFqKvT8N13YjenrgPPPBNFW5uGF1+MoLbWZ2tIbmoy6OCuu8ow\nfTppqebWI7pxox9z54aza+/LL1vpb8UK+fOIDCf8+5GtqwMNb7wRxrffBrB8ubEmLVhQhIkTi6X8\n7ZNPyLufONFoGzVlSjGeeqpEfIIAHR3AjBlRk/fczrjhBEo/Xj2Ua9cG8cEHxrO/8koYU6aQ7+aL\nL8g39v77RVJjzZo1RPafNSuS5Vtvv12EMWNKMWdOGHPnhvHCC2Y6e//9EO68sxTffuu6u2afR489\n6bnntuDppw3CdKpm9eMfJ7FuncFcNU3P24O5775J1Nb60dREuOZnnwXx7rtiBZH2l2TBCn8jR5ab\nlOcrrqjMEiqLhgYN06cTQq6urslr/H0RhgDiw+TJxZg82axgGeXlgZEjSQ+qk08mSQJ//ztppsu/\nN17B++ijIEaOrMC77xZh4kSjv5k1R5T+pe1bjNVu772dTYBuq+aecQYZdy7VJQFg2rSokP4A4MIL\nK/Hee0X4/e8T2H13Y8zDhg0CQN5VdbUPV19did/+thOzZm0XXqc/4/jjSY85Sjd2xYqOOGIIHnzQ\n3BMPMFvkH364FA8/XIqyMkIvyaSGP/7R3HdUlFqQyQD//Ke8IfQRRwyR7gOAv/0thi1b/DjxxHbL\nokyKKhhWENMsAAAgAElEQVS/R42y0gsbOgwAtbV+DB9ehZNOsvKhdJrkkFFe5UWpHDGiAq+9FsGB\nByZxwAEDL0lGln7A0trzz0cxenQ5WluJ1f/WW8uRyQAXXNAKt5g7N4LLL2+W7n/4YavBg+bDU8ye\nHcFNN1WgttaPG25oxmmnGTyW8i0WJ54Yw0cfbTE93/z5ETQ0NKCiQk4k119fgTffDGPKFNKLlr6j\nu+8uw9dfBzFoUAYnntghPZ+Cfj/0Wz788J1Mvwca3nmnCA88YJ3nlSsNOebee8tQVZXBv/9tJJU/\n/7x53T355Bg2bRK/w7Fjy/DRRyG8+65YuVuypAivvRbB8uVFmD8/gp//PIH995dbVVhP2aJFYSxa\nRP53W6zohBNi2L7dn408mDXL2m9+0aIw/vlPWW9bK/h1/KKLKvHuu0X47W8T2HPPgeu2P++8Ksu2\nf/+byBYnndSOoUPdWZDuv19sfJVhwgSiG2zcaKgodpEXgwenEQrpaGnxobIyYzLuA8BOO5E5lCmi\n4bDuqi5NW5vYwnLvvaUYPdraL3TNmiDWrDH0gOrqGpx11iDLcUce2YnBg8m7POUUwuMeeaR0wPC1\nHnOR3H67edJY93ZVlfXDX7JkGyZMMBqqFzH64iOPEMHxwAMT+MlPiODjpkzz4sXb8NVXtaiursHv\nf99pIsT587c5nl9ZaXyEdXXmV0ma0lvR2DgwvVIUTmFcdA5ySY6noAsda4EXHWfXvuXMM60C4f/7\nfy0oKTHmXNYmw+043aKhQU4zW7cSqdLO0kf3sd61gQzW+i3yxNhVnBVBtE98jfwMZ3T+SH6X+Vp8\nagPPj3TduZgSi3Ta/FxeQsPpvXPt59zXIeJxmYw5J5eGe9XX+7Lea9ZT5BZei/rxtErzn3h6kaG2\n1uAhrCHYTSjkpk3GufQdbd1K7ptL+KeC+/l3883beeTjcR86Osw0sn59DX72s2R2DBs2kPndts0P\nv1/HzjuLmSZbSImFnSLKrrPbt/t3bJM/k917EdUC4SMLNm8m91C1PuSwW8+qq2s89+xkIVP4KIYN\nM+Sz6uoarFq1BR98sBVfflmLQw4xT+YttzQiwjm3S0vJgvaPf7ShuroG69Zttr3fWWe1ZnULEZqb\n8+NfA53Oeo1WxIaaySyrAcbIwSqa1JPV2allFzivHwEvPLhRZFni4UNRZPe3S+IfCHBSROmC6UXw\n5YUrOi8Bzt9vDc2lfzUEAuYchLIy6/wFg+Y8Uq+KqBvY5faJQL2sAz0M0gv4HCUeoncpaxEAuFc6\nC5UPl0xqlkJDRVwgB/+dJZPm9jIsZDSX63gpLx+o+TEijyibCwcY60AqZXzDuRiqvCpw/D3oOHLJ\nQWfPcaMQscdQZYAaA1UBmdzg9htzU+jF7loiuSUUIvNG97W0ECEoGNRt1yMZX7H3iHojELbADA8R\nrfHP3pX9zPsLnHhGPoqoiBbYebOLLuPXRhEtUkXU7RiLi3WLPMnCa1sYBTN6zWfGKn4VFWIKlymi\nVDnIRxG1KpLO57BFSXjmJiNar+Wj+xu6QxGlv/nFUxYqRzyiumlssoWXLaiQa6itV9gtwpRu7d6X\nKghiBvvdit6taOGy8yi4VToLVXCKtF4w39OJrySTmtQjKq7way5m4sXQQXlvTxSc6w3wooim0xpT\nEMr7vbwqojxdUh6Wi7eefT7ROPjnYRVRyreo0KgU0dzgtj2EzAvJwu57tVPIaJoArckQDBJvpWxO\nc1FEvX4bXosV8c+eS/X+/gYnnu8kV7hx5sjgRK928+LGOEMdDW4VyOJi3ZYG6bPmKmsNdEdCr1FE\nWcVRpoiyeVGsokgVUXahs7NeiKBpZuJ2+ohID0Hjt/KIuoN7RdT9CmD1iJJzeeFcVKyIhCxqrhgS\n8YiaabPQLRq8e0TJXzsm6bVkeX+HU2iuWBGVX8+t0lmo3siJhDWUx4mvkHOoR5QPWbcez3tEvSyU\nlHcqj6ixLZPRTMIuXcvSaXffsAz5K6LGvb0KUezziULpeBoVKaKUJpXnKTe4Lb7CR0yIYPe9BgK6\n1PBKeQ+ttREKEY+orB2ebG3vLkXUjUeU3m8gV6t3Chd19oja77crPuTsEZVf1803Qes7uFWWi4p0\nW2N0vsbXgV6Vvtew/2DQIIpcPaKJhEGsXi1ZPp/Z4uHkUY1EdNOH6ja0Nx8r0UAAFfi9LADW3E/y\n1yk0N5027uOmIlsoZA7NFV2zK2D3Ltx4UwaaQuCkQNkVKwLEi4Ld4ia6hmibqJJtLiDeTfN4nPgd\n28A+F0XUy/dIjToDPe/PziNKeRNbPCgXXpJvaC6riNqFM4rAjlck+PNeVpEhhvLqgex5ygduebub\nSCw7QdvnE8tEmsbSMvWIUkVUfK3u8Yh647UyRXQgt+Fzoi0n5clJ1rXzeuajiPJRAiI6pB5Rty1h\nNM1eycw3HUXliPYS+P0Go2OLAJmPMYiT9WCxOaIU3hVRM9NxItBIRDcd7zY0d6B7RJ2s7nQOvQhl\n1tBcsUeUX8xSKU2qtIoQDIJTRAu/SDkpTjxkFTpZDLQQSXYhEgkwdu1bAPc5osY9nIVwIP+CBsZY\nrAsXvzBbc0QNWucVAnHBJj401/3YKR/3Gh7XX0Dph31n6TSEHtFUSsvOXS5CbyE9ol7ni6Ub0bk8\njbLrM70vfUfKI5ob3Ibmusn55tudsfD7gbCkSw7vKSWhufI59aqIBgK6Z4+R1xxRa2guOWggKwhO\ncoOTccApyszum3dyBHW3RxSwN9TkGwU0kOkM6EWKKGAQbnm5t2JFtP9jR4eWs0fUa7Ei3iPKM2PZ\nh+Q1ZLi/wWkxpEJyIYoVWXNEzZNMFmex0ioCX6xI17vHI+quWJGc4AeaR5R9XrG30tjvtmpuIUJz\naehavmCVSgonjz5baTeXYkXeQnPJ34GqiNK550NzxTmibLEi7/cqVI5oJqN5rnLsVDXXTrji11tZ\nGKeCPdyG5qbT1gJnPOyqs/v9utQjys9lURGJLpPJYDKlUuYhC4V0R7mBR0uL5olnyYoVKY+oHEYe\nrfhFOxXIsuMPorlzW6zIzTdBx+alloxdag0db65G/4FMZ0AP9hEVgRIFn4dHwSpxLFHSAjL5TGYu\nimgmo2H0aNKjy26sLNi2Lhs3+jFpUjEOOiiBk05y7qHWl0AapRejqiqD/fZL4mc/I1zn2WeLbc8z\nFFFjQt58s8g21IZf2Oii5ZwjKldaRSChuXyOqPN5+cJOQHWTXyZjjnPmhHHggUnstVffrWY0fXoE\nRx7ZiVRKw7vvhtDY6DMJxawA8/LLEQSDusny/sMPAdx8c5npmk88QWg0ENCzIbB2i9udd5ZZtokE\np3fecZGo5QKiYkVOHqWbbirPKsLWYkUaADMh84roRx+F8NlnQRx4YBLV1T4sX16E3XZLIxgEfv1r\ng8A+/TSI5ctJOMny5UXYf/8kDj/c2K/rwKRJxTj99LasAbG/YfVqookvW2bMt6xY0ezZkez6JfqG\n33yzCO+9V4QLLmgR9uy79VZxf2EZZKG5r7wSQTxuENEdd8j7/o0ZU4otW/xYutR4PpY/L1xYhPnz\nw9h3X7mkaVVEjf+ffz6KP/6xAzvvnMHq1QFs2eLH0UfbWIJ2gNLWP//ZljUYvvZaGCtXhnDRRS0Y\nPDiDRx8txu67p3HCCX1zvV2zJoDvvgvA5wMWLCjC4sUSNyWHb74JoLPT2m+Txdix8jl/552wtB0L\nT7fvvVeEv/yl3UYRtR2GBW1tPrz4YhQHH5zAPvu4cx1lMhrWrAlkW8qw+OabICZOLMahhyawdasP\nRx/dafo2b7qpLNsqa+7cMA44IIlMhtDWGWe04Ycf/Ni0yY//+Z8UVqwI4dRTzf1KdR14+OES7Ltv\nCscd1/vobPbsCD77LIif/CSFhgYNw4a14tlni3Heea0mOchpvZo9O4L992/eEU1o3e8kQ9vJ66Lr\nsfRkX6zI9rame3updmunmCeTGh57rBirVtmHUn7/vVjQbGrS8OSTZA54vPJKGPvtl8Q++6Sxfr0f\nn30WzOoLH34YRHu7hsMO69thbz2iiB50EHlp553XghdeiCIY1PG733XiD3/oxKhR5dhlFzHDY63+\nI0c24c47y/Cvf7UhGAR23z2FSy9twdNPEyFS04Cdd06b+p4BpP/QM89YlSFR1dzLLmvGQw+JmfOv\nfkWY2PPPR9He7rMoPTIL3rx5RkOjOXMiePzxEsRi6X6niL71Vhg332wISdXVNdi40Y+vvrL/8mlI\nDbtYnXuutQEwC1mxIl65zGSI1VXXgV12SZvy5uj8X3RRC775hnwWF1zQgo0b/XjjDTJnoZA5fKgr\nPKIiBptv1VwZAx0+vArhcAbr1tV6GWKvwdatPlxzTSUOPDCBeNyHmhorO2MF70svrQQAPP30dtMx\nTz1VYvq9bh2h0VDInSIq2ieas0J5pkWhudu2+XDYYZ1Z5YCno7ffNgRWNzmifGguAPz3v2V46aXt\nOOOMQdl3BMDUdPv44wdn/yfN6sOm/YsXF+Hmm8vx9dcBjB3baP+gfRRjx4oNE6wn2ihWpGHqVLIe\nieaB8r633gpj6dKtAHIrkLbTTmls2eK38EqWR65YYQieEyaY1z1N07P3ffhh65rIegvOOceeX9Pr\nsaB8rLbWh2uvrcDBBycwb14cxx03BABcNXZfuJDQ1po1AdxzD6GtCy6oAgC8914IU6bU4fbby11f\nrzfiqKOG5HSeTI5hsXKlWfEYMiSd7VMNGD1ki4p0nHsuEZg1TRw2+9prEey5ZwonnNCOjz4KmeQw\nmSds113NxFlRkUEwqGPbNnLuqFEVjs8AAEOHplBTE8DcuRE88ID4uSkdAIQW2G/z6aeN9WDChFKM\nGtWMBQuKcMst5Vi/PpCVH/faK4X16wM44YR2U5/KzZt9uOuusuy1exsuuaTS9HvdugCef74YFRUZ\n/O//Gkr1FVdU8qea8MgjpbjxxmapUnj66W34/HOrYnbppc14+OFSW4WRylX/+79tAIhyyeeIHn98\nuzCN77rrmnH22QYP+stfDNl65MgmvPFGOHtv1iP6l7+0IxbLZPkxi7/9rR2trRpmzoyirs6XXfPp\ns2zb5pPSGotjjx0s3H7bbeX46KMQdtvN+AaoHnTRRVXw+3Vs2LAZf/rTYLS1+XDiiTXQNOCkk8j1\neiOdeUGPhOZOm0YEwdtua8LXX9fi88+34G9/68AJJ3Rg1aot0lwE1st47LGdWLx4W9aCsGLFVpx1\nVhsTmqtjzpy46fxXX92GE04wW68oeFe/3w+MHNmMAw8UWxpOPrkdq1dvwfjxDQCslUndhAf059L1\nojAv3jM3ZIhVW29sJCTZVX1Ef/GLBL77bjOGDk0jnbb2Cxs9uglTp9YBAP773yZMmlSfPb+kJGOy\noMkU0REjmjBhQl3296BB+Xkc7cKSRMWK+HdnpwDxTcr7Euhzbdvmz1qwKezKqbutIsyGFrkNgaPo\nypY5ovlMpzVMn75dcLQV/POLaDiVsj4D9TSzwqlX0FBSuzDA/ggSGmlfw8DOqLVtW+7v65e/TODG\nG5t2jMO8z21uptMa5ZXeZTnNlMbq670/Lz2XriEstm71u86n7O849FCzd/mKK5otxzz33HZ88skW\ny/bddkvhu+82Y/RoQk+aZr9WP/54PcaNI2vor36VwM47p4WK6yuvbLPkiJ5yShtWrbKOgYXIaXHk\nkZ0oLs54Kr7lZCSk9MhG29Bt7e1meuvNVepF3yldO3PlyTIecu65bVi0aKtl+w03NOOaa5qg65qU\ndiiNXHddE8aPb8AjjzRY7vnEE/VZgxOLP/7RoO+lS7fgRz8yHvqyy1owb148azhjo3KefLIed91l\nvd6cOduwxx5pXH99Mz76aAvWr9+c3Xf11c0oLc245ldtbT5hMdaaGjIHrEGvrCyTnS9q2KbVyftb\n3Y8ekQacwiDlZcLdu580zXodn0++oMo+JtnxNPRHFmPuhlD6c8lmN4qkKIegqSmXHFHze5S1bwGM\neaZhl9TK7yZHKRq15sk45TLIjpFBpPC4qZprzkfjr+n+/n0J9Dl9Pt3CUyhtiRVRd9dnC5bZ5YiK\n0JWKqGg+vdGY+bebqrmAYdhRhWW8gw/NlfVulSGfd+7zWSubGvsKE9Lhld55Hkmfj3rr3fS9dHtt\ngLzvgZ6HRcGnl9DCLSxk/EREh7K5N3q6k79GJVrxsXbh2jKI6ETTiHzmTRG130/pkpVZ6L35+/Tm\nwjOitYPKHLlE7NgVpQLkMjtdr2Xvio9UA8wymlt+KNM1qMInSwNkYVfXJRAgRm8vSrxovRXl+icS\nmrQGQH+rv9BLFVHxdjeFfthiRfx9fD5dyty8bqeKqCzG3M1HTZlff+xVJe7PaH4nYkXUt+NY9/dy\nW6yIHVMgAJNH1M2iV1ysu/KI8vBicBCHecqP56tPio4X0WJ/oDl2seIXPGow8NqKhQWb4+LdI9p1\nC4VoPr18L/w7kYXmysI4+2MER1eDr5oroks3Bqdc4PMZPSDtQnPt4HR/r/xEpnRQAdGp0IkIhlGR\n/jbvd+ofPFDAtyCjrSxYuFVE7TyidB5Yg7CmidczkcznhjZFdOLzEaOxF2HdqT2XnSLK36c3GzxE\naweVQ3MxWKfT9kZ8mcxO5SjZuzLa6hnb3BYrcnN/ymdofr79NeTHkLZG3rzJbnuMJ5NyhVPUt7kv\no0eexomIZAzIjUeUfhQiRdTv965wyrbTljGyhGy7Us8UlCn0x0VRtJDxVkdRk22qiHpR3ty2b2F7\nmxkeUde32aGIuqFBM93kO7/2OaLWQif8uxNZe7vSY9ddYHOB+W+dWuFFz+m20ii7iHn1VHflN51M\nahajTj73E+Uckh675u2U9r1EpigQ8B5Rt5WWKdh37nWuiaFGfN9CKaL5Ct/0mah3yUkR5fk2axSk\nMgBf9ZlVRAdyj1te+C4tde8RFdGBbK2m80D5KJ1j0fEiRTTXKAC/n6zVXnqJ2heiEXvqaSgxryyw\ndNbbopFEz0nXtlxCilMpex4ib2OoZ88XwYh2Eu93a5iT6RqGR9R5LbN7Pk0j66KXVAI7RZSll2RS\ny9IWzw+VR7QA6EqPKEWhQnNloLHlsn6jbvJRDI9o/yIqQPxMPBMUCRu0l5kXRYm/l7x9Cxuam4tH\nNGMKzbVbrAuriMr3iYoVWUNzvXlZ+woo0xYZnexCc90KoexciwQKu3fYm0Nz3ZwrCs2l71iF5npH\nJqNxHlHrMXZzyL7zXLyPdO74c7siR9SNN5O/L6+IOoXm8rRpVkTJXz4UmlWW+5sg5wW8Iipqlyf3\niJp3uPGIsmuUzHsm2u7G4CVSZHw+HSUlGY8eUfm+ZFITKqJyj6jxv5fw4O6A6DnpO8wlhzqd1mx5\ng2wOqSwvV0TJRVl644sVuYFTaK6byu1OekcwCDQ0uJ9nO/5N6SUU0pFIyPlhf+NfvVQRtSdeO9iF\n5hbWI0pDc2U5os6EQi1Q/dEjKnomngmKBBbqnfESCmn1iJK/PL1kMgbT9PsJE3SyvLFwG5pbaEXU\nTcgee4y70Ny+z8joc7GhhxR2obluFVF2rkURDnbvsCvDs7x6Z50g66VqzREl71QpolY4vX/eIyrL\ny5WBfedePRc+n7Gm8nRZqP6d7PO4ye/k11X67C0t5EH5aBme14r6PvKKKLve8B5Rrz1T+xN4mcWL\nRzSXHFG2V60MIjnLjXFY9C34fNQj6m6Odd25NUfHjsKrLF1SjygfJsmOyYtXtjsgek66trmJ4uOR\nStkb0Jw8ok6hubK1Jl9FlMoANLLR/hr2/CwU0j0VfbSrek5pKRrVkUxqWX7I89T+xr96KDQ3t/3u\nQnONv9aCCDr4fnnmffLr8aBCqkwRdZMjShfGgaKIuvGIGse6v5dM8RIVDTLChcyhuW4WvWjUGpor\nYir8tboyNJfei30H/PugAhm7YPQnjygbekhRiNBcs0fUm1e5K79pkcCQS0sPCrFSZG3fonJE5XD6\nnlhFNBgUt7ywUzDZd+61GIpdaK5bT7qX0Fx3iqiYj8pC0ZJJ89j54mGZjDePaG/zVPUk2Gq11Njk\nJUdUxnvosayxtFARaRSyfNNoVHctrCeT9vJGImHwXFYGpe+Nvw/7ffY2z5XII0rToXIpVpROa7b8\nKFePqLhYkfG/W3pxCs3lKzWL4OwR9WbMs5MNKC0VF2dMobk8T1U5onnCjTJZiNBc2XULVTWXQpTn\nCLjLDaAfvp0Qo+vAY48VY8wY+75LvQXt7RpuuaUMU6aYezGNGFGOO+8099ezm89vvrHvN7pkSQjv\nv0+0DVlo7htvhPHpp8Z12NBcv997aG4oZA7Fnj49iupqscmNvV6uSkJjo4abby7DK69ELPtefDGC\nyy+vwLvvEgJk3wH7/403lmd7pv3wQwAPPFCyQygzrrV5sw///W8Zvv0297YcPQH6/axdG0Q8bh47\nVSLzyxE1eNWyZdYP3c5AQBf3roCIHvIJzX3rLdIvq7MT2T6/otBcTQMefbRYWFjBK2pq/HjuuSgA\nYNMmP2bMsD5TX4KTIprJaJg0ifBEv1+XGOrk59fW+jFyZDmefz5q6vfpBpqmmxTRTIbMI1HGCiMo\n0+efOjXqqtWMzCM6axahg3nzIqbvlO35DACPPVaCiRONNea228owYYK5H7CdR3ThQkmPuF6MTZu6\nhj+zRjzKN70oojKIQ3PdXZce7wQ7jyjb69gO99xTZusNTCY1vPAC4VW33Wb0H2VzROfMCWPt2gBe\ney2Me+815Jy33/b2rXY1aE90FrTH6zPPFOPqqyuwfLkk30yAkSPLbb2BMhmPrq2yNZTSn8yj6bZY\nkVNobsTFsuOks8gKlspgp/A/8QThacXFOjo7Ndx6K6GlH34I4I47jD6lo0aV44EHDH535ZUV+O9/\ny1BX1zcV1G4ftRtLBks8p57ahrPOat2x3ZtHlE9Edhua+5vf2PdqYPtw5ROaSwUPO0Xl88+DuPXW\ncjz8cKmw0W5vw/jxJXjyyZKsQEvx3HPF+PhjM4MTMZOqKneuujPOiOGUU2IArEUp6If+xRdBHH/8\nYNM+dnF06xG9444GHHwwkWrY+Z41K4px46xNjAsVmjtqVDmeeqpEuO+KKyrx0kvR7G9ZRcjJk800\nc889ZVi5MmRSVt98M4zHHy/BpEnie/VW2DF0uxxRt1ZqdoH58EP3izOPE080ehcXotDPmjXW1T0f\nD+z115NG8VRRotfjrdULFoRx223lBekBumpVCCNGVKCuzofTThuEq6+uRLu4xXOfgFOoe22tD19+\nSQgqGBTTpZOCP3VqMa69tsLVeH7yE0OrZeslZDIaXnuNzOOdd5bmZcBgkckAW7b4MHJkhauwf1nU\nCGuAvO8+g7cmEmah9cEHS7MGNgB46qkSrFkTNF3bziOaz/fcU5g/vzDK8z/+0Y7KSoMA2XX4mmua\nUV6ewSGHkPXu3nvN/Ru9REPQY/fcM4UhQ9IYNarJ8djDDutEJEKIgdLRaae14Uc/ErvOTjutzbLN\n79c9tf+ZOLEE69fLreJNTZrQsGiE5moYPrwKRx45BBdcUIWlSw3lc8WK3kVnvDOAx4wZUZx2Wsz1\n9V591V6TkymiVA+Qe0RpVJs4R9QtHcoU0TFjGrD77imUl1sXzuHDm3HssR3Z304OMDZ649xzW9wN\nTALq6aS6y7ffGvxwwgSDH9bW+nHPPcZcvvBCFI8/XoIlS3qX4cMtul0RdUNALPE98EBDtmmtF48o\n7S142WXNpm1uFNGnnqoXjgUAPv98M2bMMBrHyxRR3lK3997WL86NR5S11PWFcCIveQY8kzjkkAQ+\n/3wLBg3yFjfKvr+ODrlQblZE9R1CmDiMl8WwYW2YNy8OwF0YhqbpOSmi551nZmKixuwy2OWIAmbj\nSmen+Rh6bl+rJGkXdWCXI+pWkWLnOh8v4MSJ9TjmGLKwHX54J6qra3D11dYm8m5QVZUWzlMhFApW\n2BKF5nYF2tu1rAetN7c9cIKTR5Q1Nvr94vnyEm618872N5w8uQ4jRhiCP+sRpbRcX++zjGOPPcSS\noZvQ3HwilkQ8kg15ZD2iIuGRhUwRZYXe3tzrUQandeTf/251vEZVVRo//WkKq1dvyW5j1+Hf/z6B\nL7+sRVUVIYwzzjArek6GtDPOsI4hEgE++WQLjj6609EjOn36dlx7LeGNlDbHjWvA/fc3WM7ZddcU\nfvxj60RqmrjlxpFHdli2UditfbL1gq4xzc3y77a9vXd7qE4/3arIA4VL3ZHRi6zFEoVz+xZ395d5\nTk88sQMrVmwVKqqjRjXjmWfqsr+d+BpVGi+5pBm3396EefO2uRucDSoqcrMse+133lvQKxVRmRXD\njQuc9YiKrusmNITveyU7DpBXzbXe21pQpT/miLqJuaewhvnkVgyFfX9tbT7bcA+ePoykeHfjdjPf\nvEfUrYDNV3CT9a+S5fQZ/1v38yHkZi8yObevCWd2HlG7HFG3iqjb1hZuQIWWfAv9VFToQsGHp4l8\n8zhFobky5KMEt7cbC31fSD2QweldsUaTYFAX8gQv+WRO/e9IgSLjN6uIsmFx/NyxedEs3FTN9UJz\nPL1Sowf7HtnxJxKGIioqrsNCXKzIyGXTND2nfLiehtN3ZtfvkIJdv+h6y/I5J0OrE/9ihXZv3lPr\nfVkaEY1LVBuAbhcpD3bj6ejQpEqTrDUHdTbE4+L9ZWXeKvf2BGTfUqHazshkdqP/uXi/KEeUhdu1\nuRBruNN3RXkm5cmFKOZXVpabUtAX+RrQSxXRQhUr4u9nnyPKJuxbryf77TZR2e+3PpfRR1Tez5Ld\n3heKhOSjiOaqEPFl+WVCIZujQooseBeivSame4FVERUfJ3o+kWLJgg9VEnlEc+kj1pOwU1zsckQL\npYh6qTiaaw9O/h6yBYqnY6ex8eOwKgbuFVFnb6B8X3u7L8sH+uoiCjiH5rKpGn6/2JjkJeLFic+S\ncKJLO9oAACAASURBVFzjt9G+RctuZ3PkKeQF5Ozvl8l4M6jy78vgQcY2lkYTCeOcsjLnZwes9ETX\niWhU91SVvbfAqdaAGyMpu34Z7ZiMbU7XcMoRtTPiy7bx1xUpKaJxEXlOrMC6UcpZdHRo0rVdtl7Q\n9UeWE11WlulVUWwiPizro1koXuxUe8Uueo0/3yzL55cj6gVuIz3ouyyMIpqbnNlXjbm9Mm7AqdKW\nG+TjEWXvXyiPKG+hBsyLbn9RRJ0s9SycKprJwDMvVhBubdVsmJsmVUTdvlu3853LXBUXmwcuY6JO\niqjYI8orHuawNaDveUTthEn6vCIFwW3zaWdLqKvLADAsw3RO3RpAeJ4n+754IdVJaOUt1fw35yU0\n10losVNUW1sNT0RfVkSdPaLm703Eo7x4T5wVUZ0T7g3DDNtT1OoRdT0EE3il1slgx9+Xvj/WqMjy\nUDY018lb4NS+JRLR+6TAVhiPqFUR9eIRdVrXzEZ897KAKP/PiZ54YwtFOq0J104nj6iM9uWKKLng\n1q3ihbqszH3l3u6AKGxTRjNdzYudQ3PJATJF1K18VQil0K3xmDoSCtESyyn9QIa+aGADekQRde/V\n5OGFqMQeUXc5ol3jEdUt42eJxskyRO7ddd64QiEfjyiFU16FrG8oQPKKZEIhqZpLmUVuiqibxZ6E\n5nqfK94jKmOAIgWB3eZGERV7RF0OtJegp0NzvXjHcw3N5WlAZsHmF3Qn7xRPxy0tZgOOl9Bcp7wU\nWSEtel/6ngsVDtYTcHpX7DuSeQ+9CK12ra8Ac+9QwByaa/xvjcSRXddNjij7TE7Cm4xeWVphvxW2\nfUtpqbMSTs4xK/9UySWKaN8T2JwUUTepS6yyRekjn9BcO3moOzyiIh5Nws/F95EhF48opVWZR7S8\nvHeF5op6msrWo67mxQZvEr+fdNrqqMglR7QQzhu3tER7khZC+S0vN57di5zRF/ka0MdCc3O5j1vi\ndXscv88tkRJvrJmg2EXXTnkSjbG3wklAYmHNa3N3Lvuu2JAtwFuOqKj3nBPceLJynSdRlWcRnDyi\nIiHXLjTX8Ij2AQJj4KZYUS7VSSmcy7Z7Cc2l13R9CgArf+G95hR2xhk3121t1UzvxYsi6rT4sXTF\n02ZrqxEq2lcXUSAXj6j1Wb0UK3LTi5v1OphDc41vo3CKKO/BEh9HjxHliBIF3bhR/jmi5ndOvaCR\niN4njR5OxiU3/EgUmmtu32J/vp1ywF8rd0WU0qdxsDhH1GrcJ+d5T4Egiqh4X329eNBOHtHy8kyv\n6vcoUopl61F3edZkNJ3J2K+VhdAR3MLtms23K8oHLI+T5e2L0BcjPYBeG5qb+7n5FisKBMxeUyeP\nqKa5IxRxaK43j2hfgBcljFcI3PbbNCueZg+oXY4or4iyIXJumYcbYTl3RbQwobkiIbc/hubae0Tl\noblu4UQTIsFFJgDlmiNqVURlXnLzbyejAi/YtbZqpnwmohi4e3dOQr3Z4Ga+ZlubL/tO+qJyQOEl\nR1SWf9vZqZkECTvFw0kp4cMW3YbmejEksqD9SSmcCq25yUk2e0QNenSbI8rSE3v9/usRdaOIGv8b\n64ueNQLnW6yIvX6uxYpE+YOyYkWi9J5cKji3t2tSOc4pR1SmtJWV6ZZvuifhRRHt6u/DqVhRJiMv\nZsme3x3wKs8VYmwsLXrRAVRorkt0tUfUTnEUeSX5e/IMzHo96/luFoBUyvpcn3ximB9nz47iq6/I\nzWfNimR7BfY1RdRLwYpc24U895zRP3P58iK8/rrRX23SpGLMmhU1Hb9+vT87tnxzRN0Iy3zVXLfg\nlQy5MJdLaK7xfzKpmZohx+Pk/ciUl6VLQ5Ym18kk8NBDJZg2LYq1a3Pv29DQoOHRR4vR3q7httvK\ncO+9pXjsseJsY+b33w9h8eIiZDLAxInFaGrSMGVKFDfcUG6iAx5UoH7ssdx77zpXiBQLRyLQhcWr\nkY1XXOU5oubfXj2ibW0+k6AyblxJtpm2Ex5/vAS1tT689JK4pxyrfE6dap4zkiNK/h8/vtTEE3Sd\nNPjevj3/ZWrBgiKsXEloeNs2X7Zn6uuvh7FqldgVkkoBDz9c4opPOb3vhQuND7Cuzo+77xa/W7Mx\nQH49Jz5rVzWXQhQ5IvOIOfGzOXOieP5541uTeZdGjKjArFkRi9ExnbYawlglY8KEEjz5JLm+G4/o\nM89Ecd11Rs/V1lYfFi0i60QkomPDhgCeecZMi6+/HsbHHwcxcWIxGhs1fPFFAHPneu/dqevAo48W\nY9KkYjz4YEnB1vDChOaywrxhrKPz7nQNJ3mI5Ym5pFKx/7sJzRXdI5XKpViRXI5btUr8Ubzzjj1t\n0FzmceNKsXp1ADfdVGZas5YtC2HZslCWz+RS2KilBbjxxvIsLc+ZE8aXXxLmvn69H88/b9xPrIiK\nn/n997u2/6lTsSI2coM/B3BfrKgnUAhFNNdWWA8+WJrl8TNmRPDoo4SXTZxYjMcfL8bo0WV59et+\n6qlijBpVjvZ2LSsD0utu3uzDN98EcNVVFbj99jJceaW7ntcAkEfnr9zgThGVE9mvf92Jk092fpPi\nsBD5dek+/hgnjyhAhN7WHe2zLr64BRMnlliO+eyzkG0CMm1UXl1dg8suq8z+39dCc90oolVVaYTD\nOm6+uQnHHON9ob/lFqOR+YUXVpn2rVhhbej7178Oxhdf1Ao8ohrgoo8oi9/8xp3bhr9eVVUae+yR\nNhkfeOy1Vwp77ZXC+vUBRKOZgobmsp6OKVOK8dZbxnt/9FFCrzKP6D//SRpcV1fXZLdNmxbFXXcZ\nwjS7zwtGjKjA/PkRJBJadhwAsHRpEZ59tg6nnELu/fzzcdx+ezlWrgzhjTfsm2gDwD77pLDnnim8\n/rr82EGD0ti+Xa4ZemlVQOH3i8Nj7EJzI5EMiotJJc/990/ivfcIDR92WGfWIEVx5JGdeOIJK3+h\ngv3ttzfgppsqHL3bPJ9rb9dMOUS6ruH7790tD08+WYJPPw1i5UpxM206lkQC+OAD8zEdHUZo7muv\nRTB+fAo33ED6CH71VQD/+U85Fi4MY/r07cgH//73IACETi+6qBLvvVeEww/vxPnnV2W385g9O4Ix\nY8rQ0ODDTTc1WfazcOJ7q1eLJfyhQ1P48587MGkSmdOODi2bH2R3zaFD04jF0lkjEg/WW8R6R1nP\noIiPyIRxN/zx/vuNhusyRWDGjChmzIji//v/zInFbA4ne8+dd06jttaP5ctDWLCA8Cw+l55HY6MP\no0ZZhaAXXyRCOa1jMGpUBYYNM/ooUloAgC+/DGYNmiee6I23rVgRwm23GWvUWWe1YdCg/Hu05eoR\nHTIknQ0fvf56o3/xvfc24vbby1BensGDD9bj/vtLLSkcPHKJEhHB79ex335JrF4dslxX5C0Teepv\nuaVJSMN2YZ3XX98kNAKx3x3F7runsGFDAOk0cPDBCenaPXhwGtu2WW9I5b0HHijFAw+Qb0PTdJx5\nJqG5008na9u4cfUYM6YMjY0aRo3y1l/61lv9mDyZjOvvf2/H8OEGP/vrXwejocGH009vg88nDv33\n+4Hzz2/BU0+Z15QVK7wpolVVaRxwQFKonB96aCeWLTPzfediRe5T6UQYM6Yh+73nirFjG/Dss+Jr\nnHtuCzo7ySCuuKIZn3wSwqGHJnaMzb2SvPfeKXz3nXmdLSvLmPin7B399redeP9965o7b14YJ53U\ngauvJnrERx+FMH++IQeVluq47jrvfcyrq4GbbyZ8LRrNYNdd0yYZcJ99Uhg9ujynfuB9ziM6e/Z2\n0+Ihu74oRNdNaK6T4ikaG/VkDRvWiptuaspamX760yReecVobkuvVVXlvltwX1NE3Vh/P/98C1au\n3Ir99kvhootapMedcop5nvfd1z7G5aCDxEoiDa0hxYrINiqkGR5Rd8yjokLH3Ln2DYtFHtEzzmjD\nvHlx2/OqqnQsW7YVZ53VipISvctCc2WWVy85orl6s3nQCrZ8mLbVC2afj8Nj993TWL58q7TC5uuv\nb8NLL1mVm9tvNxqnOwldovmRWZipJ4L/Pq65pgnffluLTz/dgq++qsXf/06MbCed1Ibp07ebxnDi\nie0YOtSed5x7bhsqK9OeQ5LTaWIx/93vxJWH9tvP/ttrapK/LKqIyuiWfWcij2AhPKIsqLfdSVmn\nY2ludn6XIqVxxAhDeaWL8z77mN/jBx9sxW23NeHuuwndsfTBX/Oaa4zrFRXpeO+9rdLx8FVzWX5E\naYOE5ho77rjDoH3r9aS7hHBSSPh3n8loAo8oEIulceyxHbj44tbsdidvFxUQZXDjbbCjZyfw9y90\nwZqNG2uySs7s2XGUlJD/RV7D2bPj+OSTLaiurkF1dQ1+/WtjjfzznzuwbNlWBIPAX//agUWLtrnK\nPWZhzRG1htiKjvf5gDfeMNZDJ0WUjQS59NJmVFfX4PDDO7PH7rZbCvffXw+ArGMiGtE04PLLDXmj\nuroGM2aQMZCqueZzVqzYiurqGqxatcV27V61agtGj260bOcVWxkaG8lDONGtCK3GZ2HhF1TuoYZR\nmUf01lubsPPOhDnvt18Sgwal0d6uYb/9kqZ0oYsvlstqt97ahIceEvOPGTOs66ybPqJearXwOOec\nNsyday9vOeHMM9swf774Grff3oSxY8mc//znKXz44RZUVTkXK+Llg+ee2579NtltrGwhe0cvvbQd\nV11lVSh5OW7TJrOg4rZYo/W6xv/19T4LvZI0udx4Xb/LEaUQeTjtFFFZbqmThxQwLLSUyIz+XOZw\nS3otuxwXnuj6miLqVQC2q7jJW2fd5EXZIZMxt2/JpViRG3gp0iA7n1h1xXQiswLb7XeTx9wTOaJ0\nrNbQUvMLo4q02/AlKmzKvASkgrZ9aK1T+I9I2JHxLrsqvqL7h8PWMWiaLs0R5a/hNJd8aGQqRd6t\nLPTXSfi3ey46l6JFivdeiIrGeQn3d4Ou+OZF1R9ZxYCG9PMKGl/ggv0O+DmqqDDPgR198jmihgdC\nM3lE2fvJQh15uAl7dDqGF4pJcSzzNp/PCNFjeaGTkuvEI7o73aVQiihdW2Uhil7DUXOBU7EiJ3lF\nVtBFRKvmKswkcsR6rNXrn0qJ+bCsbghgX6zIDUR8WWQEFY2BCvRO3mgRWB4jy+uk20V0yM9DJKIj\nnSbRMfxaYPfd+P3i9VQGJ48oW2CNPwfo3hxRr7BbV/hnEvHwYFDPyi/knYovSOVE0fksmpvNLyvX\nnGV2rhIJax62qCqzW/Q5j6jz9Q3GJLq3d0XUeWz0gzWYrJ49lg0jopYKasEUwSpA9gHtk4FXodHO\nCshbeJ0SsZ1oi4TmmkMe6Hi9Kor2+61tgrxc3+cTM2IKkbLPCnEipcCNF6AnqubScTu1H6HM1W2L\nCyq4yoQLv1/8LYss8zKI3qnsHDp++lyyBZgqLFQo4ZU0WiLeDj6fWOljIVL8W1s1qaLrZBy0ox3K\n00TKMd/zzymvsRDoCkVUNJ+sQECVSqfQV5bu+W+gstK8wcljYN5veOTZnp28Iuq0PgLei+KI0NJi\nHnwmY6UPmsfv85m/Naf7Oymidu+4EOAFvUL1khTRrd9vbM9HkXKLXPM+rddxzv/jDTGUN4l4BEsj\nmYy3bgbkHHn7FjcQhYuLUrEyGWvLJBoNlIsiytd+EIGuKW6KFUUiOlIpQrO8nGqniAYC3hxIsjmm\nSKc1WydQb3bK2L0nnu5F7ywUMuQXKgvKIE7DMv/mI3pyLWjE8udEQrM4SlparHm9btErFdF8+mXa\nhebaKaIyButGoaDhC3w5dJ9PNwmOdIG064PGM5O+5xH1djwbksnPO+/FcwpdcVok2dDcXIsVuTlW\nRGdeFVGymLr3iDrliLph4j3hEZUpZvwz0uPcWt2oICLzBBdCERV5rJ2q5jopiLxQwiuibj2iXkGE\nD5+0PYyTkJGrR5QPwRL9n2u4jwxuv3mZcdLumixEgq1MQBb11eO/44oKYwPLy8TXE3uZ2OvyVXPt\nPKLs+U4tPgBneuGFI5EiSg2FmmbmhSKFi107vCiihTZyANa0hUK18BDRmJceoIWA07rGGklFchzd\nZlcRVVbIhhr8RTyCeETJ/lTKKiTLwArOXtpk8BDxTVnkG2+ooFFhvALhBuy3KCtCY3hExTmigEFb\nVBFtayNGSXOEhnwcgYDOzIX795hr+5beLAvbe47tfwNmj2guiqim6Sa+xhv9clVEWbpNJq1jb2vT\ncjKmAC6KFU2YMAEff/wxysvLcd999wEAZs6ciYULF6KsjCSqnnHGGfjlL3+Z0wBEKExoLn9NuacU\ncJ8jKlZEzQySVUhZwZEKVHbFFuyqsvbmj48iH0WUt455V0TtPwJR+5auCdNz50mXn6tD1zXpOU6h\nuSKPqRtLVU+U/pYJgta+mGRsbkNzDY+o+LllPejy9YjmG5pLhVjK0HkDghslIBfFKZ3WssKHCE5h\nf7l6RPkQdPady1p95AvKZ7raIyqaKydlwc5bx1eLdSrmIfrm2aJA1tBc+di8e0Ttj7F6RDULDaVS\nRtESJ4UrGDTWTq+KaKE9ibzAX6jQXD6iByBzZnhEu14RdeKJ7Lv1FqJoPY+nf1pkiqVTeh1irCD/\np9Ni3mwXmgvkRweilAZZfYJk0pyPSuWaXFonsdeRGTzY0FwqW1Dw3zxRREl0jLfQXLkMLUJu7Vuc\nr9sbkK8iynpEqZwqg0jWS6XMLYP4+hu5huay8iHxiJr3t7YSRbRNXsJHCkdF9Mgjj8Sf//xnPPLI\nI6btJ5xwAk488UTPN+z60FzxfZw+ErbCoFcYljqzQioTHO3Kz/d9j6i3Qdopl7x1paPD/lpuPKLs\nsbnmiOYyD4UMzRV7ljTm/9zu3zM5omRgPN3zz2hUXnX3Ig2PqHi/3CPKCjn2goFoftyG5lLw82L1\niHoXTtwYHfjFLZmEUPigyM8jSv7KQnNlebl21V1zBWt8KmSuoEhZ9pJDLBLM+GvyRgL70EdxFAQb\nmptKaZb1xc07cWModuI3PN8XtW9JpbSsQGr2iMoiEdwZq9g1ivzv3oDpBrziWUhFlOcv3R+aa0+D\nueaIivL/eFqkPFGWI0p5vqxYkQgsLefnEbWeKytWlEgAxUxXMcrzczEksHMuozM2NDca1U3H8d9y\nOEwUURId48Uj6m7doZAZGyj6qyJqzY0WGdXMHlE7xVH0/pJJ+z6wuTocWCdZMmmNOmht9bkylIvg\nqPLtv//+KCmxtgvIFW6IqSs8ok6KqNscURF4jyhlgjKh1C40l/eImhl711s880Uhc0T5UJVC5IjK\nQnMLmfyeb2guTUK3qyhnt000B26eryeavNOx8nTPP4PX8Ewnj6isp7AsnFEEsaIhvh8VcpyUKqfQ\nXDdwM9c8bdGKd7nniNrtox44kQHFOYS0kIpoR4fx7IVsNM+/T03ThYqBU+grS/d8hIiXsCdZ1Vw2\nL57lf3RsbhTRrhAISVsZkUeUFisytou8XawAVOjQXK/011WKKNsHm8L8XvqOR9Tq2bWex9Mi9YiK\ncspJhIvBY93KkOYiWIVVRGW1QPh1lspAuRjG2PctozN6P1HEC/+e6DrV0qJZwo2dihV58Yg6FSsS\n9RHtO5C/AF4+EEV6mBVR3fa9i2S9REKzleVy94iaryHyiOZq3M25j+gbb7yBJUuWYO+998Y555xT\nYGU1dwKUKQB2RYzstruBPDRX/Bx2HlG+RyI/sffdV4rmZg2jRjV1iwUUABYtKkI4rOMPf3Duoeld\nEZXv4y2UTt5Wuzm85JIKbNwYwO9+l2CO1XIK08slR9QLU6XeWtm7vOYaa4+8pUuLMGhQBu+/H8Jn\nn1kJg2UafE8visZGDePHl+CSS1qktLVwYZEwhOiNN8I47rgOrFgRQnOzht12S+PLL4MIh3XE4z58\n8UUQxx7bgT/9yTzhVMjjjQy88OfVW2tUzRXvdxOa6yTMiNu3iI+l4/CaI+pFMabIRRG95x6SZpFr\naK7dc61eHcTUqVHst591Evn2LT4f8OGHQWzd6seuuxIioN99ezvw2GMlOOqoTjz+eDFSKQ0HHZTA\nRRcZPQwWLy5CIKDjsMPEvGrx4nC2PypPc5s2+fHWW0XYe++0a+8ghbXauThPTbYmuPGIerE2y3JE\ndR3ZnqWiqrn0dyCgm0Jlva7JXgWShx4qFVbSdVusiKXP9nb7D4Ad2w03lGPffVOmtiYAsj1LAcJ7\n6P3Xr/dj4cIwNm3yY/fd0ygq0nHWWUYs2pw5GsaONfepLFSOKCBeV+jz5OqN8AIvUUd2HlG70Fwv\nHlE2DYoNzXVrWC5UaK6Ib8qiS5JJDc88Y/SnpP0uR42qwMEHJ/GLXxBNobMTmDChBBdf3JKtoj5t\nWhS/+EUCU6cWY6+9Uia+e8MNRu/aV14x6DeZBL79NoBp04qx555mHszLJaxXORq1V4JYBIPejPlO\nobmiOTRHePReJTXX0NxgUEcySao3s84sr4rolVdW4qij5OGDH38cwoUXVqK4mBTVHD68BZ99FsR3\n3wVwzTXNqKnx4dVXI+jo0DBsWGs215lVRN97rwhffmn+YL7/3o8tW3LzIuakiP7pT3/CqaeeCgCY\nMWMGpkyZguHDhwuPXbBgARYsWAAAuOuuu+DzaYjFYrbXZxmC07E8QqFA9m8sFkNJCaFmv5/cdzvT\n0oi9dkkJeYH8+IqKzK9INJ7Bg8k9IpEoYrEiBIO+HecGEYvFcOqpaRx0kI6bbgrsOD5iuQbF6NEG\nM4nFYigtNb6+zz4rxcyZZJy//GUY551XuOSpQCAgfdf/+hdZ4To7nRXRSIQ8+09+omPtWutK9OCD\nKdN97r5bw1FHkf+DQfMYysuLTedeeWUa48fLCb2oSL6SzJ5Nm5kXIRaLIRr1Q9eB8nKi1JWVlSIW\nc2dMqay01whKS0ssYTnFxVHEYsbiMHlyCrNm+fCb32QwahShC/rsZGwaiorEzZQ//dQqcaxYUYQV\nK6wK5m9+k8GWLRpOOCGK666zf66ODh/Gji3D7rtHceGFVtqKxWI45xxy7zvuMC9o551Xhc7OBE49\nVS4NbdgQwZlnkvMMeiPPrml8I2y/iRaiUWsTcoC0tGhosM7HTjtVIhYDiovFLG7w4CqhoHTSScW4\n/HLyf3Gx8Z3usYeOCy9MZ+eKjCmIK69MY948H779llwsFDLo8+ijM/jb3zKIxWIYNIjs9/tDiMVi\niET8O65hpotrrwWWLNFx1llRxGJR0/Uo7YbDuinv47e/zeDCCzPZ9xUIGOf8/OcZrF5NvsnddtOx\naRM1vJhXedpbbNAg8zdnwF7KtcsRvf56q+Eke9VQBMXFxrklJVGcdBJp/r50KRHIdN2HWCyGO+/0\nYezYAMaONc5/5ZUILrgggp13Jr/PPtvKqwKMFvN//1eV/T8cNsYVi8VwxBHB7DwCwP33p3YcF0Ys\nZi+lVlRYn7+qqtSyrajI/B7pnJWVkfdfXk7oFjCML/vtl8EeewD771+JnXfWUVur4frrg6bvY/To\nFG67zXjOwYNjKC8n1wwGQ6iqIs3Ndb0UX3xBjvP5AigtNb6r8vJSFBWR5zj//Awee8ygo4ce0nH2\n2cChh2Zw7bUZnHyyvcDh93sXLaiCfOihGSxb5kMgEAHgQyRShIoK4739+tfW9xoIuJOCg0Edd93l\nwxFHkN8vv+zc9L6iIgZqZ//Vr4KorTXP9eWXR7O85LTTDDrZd18d33yjQdOKTd94rgiH/dA0QjPa\njhvGYpXM/+WWcw47rAz5+ghOOSWDWbN8O8ZQJJWPKip0/OMfkez3GQoFLTIF5U2BgM+0b/DgQYhG\n6f2Au+/WMWqU+fzS0sCOvwbPrKrSstc97rhS7Lmnjttv92PzZvG3F4vFcMEFaZSWYgdfBnN9Y/38\n+98zlrFfdlkac+b4sGGDef5jsRjKyoBDDsngF7/Q8fvf65g82YdddpHJrlUYNUrMTx56aBBmzyYf\n/vjxPtx7bwAVFVGMGJGBrgPXXWfmH6efbsgZNTXGXFx0kcHnotFKHHUU2VdS4sOjj6Zw0UX0dxli\nsRI89lgGN96o4cc/Nt7BkCHRLG0BwJVXhrDrril8/LEP8+aZv7dBg8qx8846DjoogxEjrO+O4rLL\n0ojFYigvJ9ctK6tALGbVtEIhP4JBM41QWR4ASkvLXMtr3Y3SUuPbB4A//CGDDRs0FBUBe+yhYdEi\n49ghQwZljQyTJ6dx553Aj340CHV1VF4wnCUU552XRiJB6O6qq4CnniJh4H/+cwYzZpDva9EiOb9p\naSGKJsUrr0SyUSRXXVWE888P4PPPybuuri7GU08Rg3A6beb5fK9llv68IqczKyqMBfzoo4/G3Xff\nLT32mGOOwTHHHJP9resZxOP2jWZJufNdAMDxWB7J5CAARUilUojH42htLQFQBk3TEY/H0dDgB7CT\n5dodHaUASqHruml7MlkFwJhU0Xh0vRhAOerq2hGPNwEYAiCAdDqJeHw7HniAHHfTTUMBAOl0KwDr\nosGDjLcIwKAdY0wAIATU0NCCeDyHrGAJYrGYzbsemh2PE1payLuYP78Whx8+BJs3+3HAAQl8/nkI\nZ57Zin/8oxHsZX76U2D27BBOPjmGZDK14x7kfq2tLQAMWttrrybsv3+JxRJDkU4nwM4VRVVVGnV1\n5CNKJDoQjzeis7MUul6C+voGAIPR3NyEeNzGPcugoSEIYLDNO2hBY2MKgMFE29raEI+3ZJ/tmGO2\ngn4Wo0aZ329nZxkymShaWtoB5Mdsf/7zNrz8ctMOZW0XV+fU1NCxUrDjI/83N7cBMCuH7H4ROjpS\n2Wek9JZIkG+lqcmgbQBIJjOm69XXNwOoslzzwgubUVPjx7PPmhWoRGI74nF9xzmEJi6/vBkPPkgE\nlMbGuh1HEg2GNpQmYStDd1yjDUApbrihCZdeSt7HPvuEcPrpZF4zmQRGjKjDiBHA7NkRXHJJRrQD\nTQAAIABJREFUJYAUAEKfU6bU7ngvQHs7+Y47OhKIx+vQ1kb4jUEXBIMHAytWIHuersdAlcDOzk7E\n4w3QtJ3Bhv/MmmXcBwB0nbxTTdPx4otb8LOfkXl///3N2HVXyoMyAKzKRHt7M4BKy/ZkMgnAaug4\n88xWvPlmOKvI/t//teCJJ9zTbGtrx45xkDnq6CDvHAC2byffJqWFxsbS7D4W8XgdAgFqOLHyKiLM\nWBXprVubQHlrPB7Hpk3m91pfT/h0RwfhGXaorw+A8H0DbW3G9Sl4HkXH2doaBlCFurp6xOPpHft8\nAHbGsGFNOPvsNjQ2Ah99xD638bz/P3tvHm5HUef/v8+5+725N7k3NyxBRGQPa0BQQFkiRCSyqUQZ\nkAwgEAQkBmQVGFmGiCQsMoCjAdEHUBFEFmWJII7wdUQdl5/oiDOuDJCc5OYu5y5n698fnTpd3V1b\nd1cv55x6PU+ec3NOd1V1d3VVfeqzLV++Htdf77x7mzYVMDFhl1kulzAyMgpga8+YWsHYmPNeFYtj\nmJrqBtCLrbaawIsvzuDww+1r2n//DXj9dWfB+OUvd+Pcc/3vI6FcroDc8xdeWI/f/a4DF1zg71cs\nzjlnBH/60xwUi9OoVLpQKpUwNTUDYBCzZ9fQ18caZ9j9mXDSSZP47nd70ddnYeedN+DGG3tx1VX8\nDRKat97aiOlpa8vf/jH0rbcKlMbWbtfVV49i+fIitt9+W4yPT6JQ8CeeD8rkZD/y+VkoFAqwLLuv\njo2NbHnfgWLRfl8I//Ivo5ieLkpjK8g4//x2PPqoXUe5PINCYaT+W6lkr7cA4JVX3sTISA5kTK1U\n7PUPTbU6D7ZHmHt837ix4Apy8tOf2p/0WqFWGwTQg6mpIgoF2wpibMx+72q1KkqlAl56yT72v//b\n7vs0pZLd9s9/3il7bMxZDxaLMwBsafjmm9/aMn84XH65/e+HP+zC6ac77zV5h7/3PefYD37Q/X7S\n/OUvo6DXBjSbNjn3bMMGe+26fv0UCoXxLRopd3mjo45vNI8NG0ZRLtv15XIVHHdcAc88Mwff/W4v\nNm8eR6EwhQMPBJ57Dvj3f7fXb/axE7CsAQA5vPDCegwNVXDuubZlypNP2u3Yffcy/vCHDkxMbMbG\njWU89RS5J95W2MdfdtlbKBSA8fFOAMPYvHkUhYJfwTE1NQeW1ekaxyc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o47nnNuDKK2fj\n8ce7XYKoNyejfT5bu68CT5OYz/snSdJ/ALHZrkyg23bbGl5+eT33d57G68knfdm80d0NrFu3wfe9\nDNLGJUumsPPO9gNQF0z8i/9vfMOO98/zU8rlgKVLp7B06RS2285Jns4ziReNp7xn/Nxz67FgQQWL\nF8+rm+Yec8wUfvnLTqxfzx/vRJqlYjHv6p/ETJyYFllWDuefP45cDrjzTrEmlLBsWRF77FHBBRcM\nBtIAXn31KJYvL+K119zT64oV43j9dfv6zj9/Ah/7mLMI1OEjesst7sU4S4DwLvx4gqhMG0E2RRcv\nnsJ99424+spDD230mWxut10Vjz22ASeeOA9e1qzZjHe9y72wsU1znY1N3obaPfdswvLlQ8jlgNNP\nn8Rdd83C3//e7no3yfv44x/Tc7Tz+9Klk/j2t+XjKhnffvWrNzFvXg0vvtiFf/qnuSiV7PFw661r\n+OtfqygUnPdfb7Ai5yEcffQMjj7aFpg++MFpfPCD07564jHNVf9ed/0sv8aeHgvPP+8fV2fPtvAf\n/2GP3TvuWME55wwxy6T7+YEHlnDggfb4J2JoqIaXX34LO+88X3icl8HBGh56aCNOOWUu95hly4q4\n/373Bm2xmGMKCITly4s4/HBHeP7Sl2Zh1aoBLFo0je22q+Ib3/Bv+O6wQxUvveSf2+j3gpeDGwC+\n/e2N9b/DzGu89TZBlvqkEYTU/fcv48UX3feGbA59/OOT+Kd/4m/OdXQATz/tjCPHH88XWmnuvnsE\nd99tbySff769cX3jjaP18e3f/m0zuruBSy4Zq1sciTblfvjD9fjgB+fVN23POGMCN9xgC8P0mE9b\nnhmNKKMef+49dR8dupwgkJeZZzLBStpeb53nFFoL5c4xF7xdSUBrRFV3nVW10bQPE+D3w2NNhrwF\njGN2mRO2ISxxmOaGMdOQpSkQmYnTGiHA3VeLxRxl5mbBsnLMAEY0Xr+xIHi1CuQdkpnmioIsRDV7\nYfVv3f2IPD+3n4nayy8KYhFkQQmouyyoBP8iC8q2NqtumksHN+OhGjSGbgdtXu31Z5XR1UVfh/jB\nqgQroi06eH1RZ9RcGkcjyt8Mos2sZT58oufsjW4uy3/L0xyzghV56/NGqJdZIxHotqimQiP+e+S9\nIouvctke+1jzvf5gRervtI6o64DYx02kERUJp1HWVUHvp2itFFZrHMYiraNDHmSSpP2hod2zWHiD\nz/Ai3wLydaPXHN95z/UuOGXBimT+0I2KzN0nDlh5vun7Lg5C5M0nyz6u4Uxz44Y30KkHK4re20lZ\nvIUg3QbRDrWXxhBEc/VBy8mdGO6esqI6utPZyAVR73P3fialEQ1SPs9/I8xALJswiSDK0ox6tVN0\ne4rFXH1SJb67tEaUBX1+cI2o+/+0r5dokqQXud46oy7w6f5Hxo24JmzZdyx0CqJBfUrF/mHOs7NN\nc+2/RZYTliUfR+gJlSzC6YmfDnqjQkeHJfVlotvnxStgeaPS0ujQiKrgfQd5/r2yDSyxIMrWXshS\nG9F4gxXxNhR547qs/XSdqjEbikV7bvMGPrIF0RxTiNCpEZWZK3p/01WvyiYYyxJNNBaGE0Ttz6Cm\nzqJ3N2yqwDDndXZaUkHUu5kxb15VOq96x3k6QCGrT4rgjY9RUiqykJnmWlauKQRPL7LrjgNZMEFR\n0L62NsvVh3jPhN7MC3NtTSyIWp7/uz9556l+L4LszPMWgqw8WLz/0zSGIEprRO1Ghp0QWffeHbDJ\n/RtrkvRGjfUKpqRtQRZ7qn4wsrbxIMd6Hc3D7N7KFt5dW6zCWVqBDRv4prlTU3kqObz93GUTpjs6\nqvBQH5VKzrWwIe3O5y1hACDRgjTqRCcScnWhqmlgwQpEJSuDt3jkjUu8IDaiNtL+ftWqnbesrc0S\nLnZmZoJpRB0hzqr/n6d15V1zEL8XlcAUtLmyikY0arAib90Ay0fUaYjIesCLSBD1mst7tWb+49nj\ntkqwIu/7oaoRDSuI9vY62l6iBSCmuaz5XrdGNMiYFcdiV6YRZf3m/s5yfQbBm4tZB2EFrHDWSf6I\npV68Y/a8eTVMTIgr8/Y7+l2RBTLy4n1vogaD4mE0osnXCbDXQyJBtL2dn0+WhjbjbgiNaNKmuQRZ\nsCLyO8tcKSjTW8y5ebtRdMcIK4hmFZZpri5B1Ot3pjJQORGM2RsTcQQrsssK/7B4i8You8g8yCKM\ntRhbv55vmgu4k8NXq3ITIvr84IKoe5KkNaKqkZTj8lmiCVqH7Hiv6WGQOkSLB964xitbFKwoyPeA\n3zRXRVs5OZmXLkDdGlGnHbbmL+cy8VSho0Puy0RgjUteoamtjb+wY5kA6xybeKa5/Ki54vJkGlH6\nfGcTUF3bQmtEaQ2r/765BRvaZF8E3RaZcAA44xu9KPNqRNmmufGmbxGha7ErGntEprm8vsH7TUbU\njW0WugUsWV0yjaj3vmy1VVU6r/oFUWdeluUY9cIbH+PSiPIsEO2+7m57mDkwa8gE8Dig7xXr+YqC\n9rW3W64+xDfNdVuHqbo71MsNdLQGdJuu+ct3fxJYCzrWeTp8RIlfJ99H1Plb5iPK+y2rQikdrIgM\numF3hFlaA3pylfkx2ce4Tbq8n2F8ROM2zSXHerWAYSZNVdNc1kSzYQPfNBcApRFV8xENqhGl+zjR\nmpH7oSqI6p5Aadymufan7gnS2UCjdyWjv/zBfUTZF8b3HeS3kW+ay29vsZgLqRF1pyRh3TveM7NN\nc8V1ElhjM8svnTfekPsVt4+o9x6GFURl0ctZ+SWD+IgSAY5sUpD6eO+ztw7Zgp8uR2XRNDnpFzbJ\neeUytpjmsjW7ujSiQLC+kISPqP97/3OXlakKmW+CCqJxjc1ByeflprlebI1oUEGU1Of/Tb7xydug\n1LvgVHF54I2RjYxjoZNOZ2QLouLj3RpR9jPwKjJUrUwITa8RFe3Y0fBU5lEEUToPk7tM53u/RlS1\nwmxuCdF+LNE1ov5BVKQRZQcrch/r3UGPK32LStt40D6iUQUpmdDiaDX9x3lNg/2CqKNlVjHNpc9X\n6RNewbW9nc4HiHq7xaa58nrCQg/qcS12WBtoOgSTdH1EnbrsYEU5ZhoemmIxJ+0zvGBF9DseLFiR\nf6OBB0swY/m48wR11kZoHIKoV2D2jqeq/TiMj2hQ01w6aq4syJO3Dtl7T/+uMkYUi/ZGG70oc0xz\n7d9YUfI7Oixt6VuCprRIwjSXINJ+yr5TJWywoqyQz6tp32m23roqXRN64zvQ1l9RNaJxmeZ6LdK8\nZFXREhXZdccNaz0o0oiq+oh6+40oGwOLJvYRVfve+7vq9yJkPqIi01wRDzzghOGO+0UdGcnhllv6\nAwuRtVrOZx4V1qeD9QzpQdm7mBMJorygRc79V7+hcWtESVsrFbaJWxBUfaVU2uftq7Rpbrmcw6pV\nA4yz3Offc08ffv3rDtx7r3z1t2LFnPrfDz1khx/vtnPLu/qYaKGnK5cei6BpiWiC5hFNShBVNc3t\n7q5tOT6YiS/gPK+2Ngvr17fhzTfbtkQ/5t8UWyPKL/SJJ3pw/fVO/6ODFdECXTCNqLppLr0xyDcj\nTS9qLkFkmkv/P5ogajHHraDBiv70p466SbXX59epi/0pezfpclQsDK64YjZ+/esOl7BJNuLuv78P\nmzaxo+bqTd8S7Pg4THN576jM9UlUpipRN7bTJpeTa+q9zJsnf4jee+nWiAbrBLx3IS5B1PiIJgtr\nPfjf/81PgajqI+p9Nk2jET3hhElXguq461NfcMi58MJx7LFHGbvu6s6NtnLlOM45Z8L1MI85xp0f\nKGiahLi48so5uPXWflcSbxVoH9GVK8ex++5lV44rFnvtZScyv/Zafh48wG06BgDveEeVe/wBB5Sw\n555OomXeRJlUsKJggqiz87vTThXst18JN900Wi/jPe8R308a2YJMliiexisI0MGKVBgZacP118/G\nscf6cwmyePRRJ7ffxEQenZ0W7r57BO95zwwGB2nTXDWNqI6JbOHCMgYG7Jf0wx928s1lSSP62c+O\n4ZxzJur/P/FE/1iqohH9+MeLWLCgjA9+cKo+Lh177BT22aeEHXd0cppef73z3oruQ3u7hYMOmqkv\nrtragL/+1e6A8+dXhfduZkauEV23rrv+Ny240AJdEI3ookXTyvMC6/ehoRoOOmgGw8PVev1Bouaq\nRGI+66wJaZJzunx604YliHqP/9KXRnDMMf68ikccMYM99ihj5Uo7Z++nPjXuOtd9fX7zchpe+hZC\nPm9h553tcfCGG9hzRBSNqOx9yuct/PGPHejttbBokTP2zp1bw3veM4ORkTy2266KQw7x27jpDVYk\njyS6ePEU5s61+1uSPqKsjX7RPBhmnDzppCnstlsZZ55ZDHSebGxevHgKX/rSSOD2fOhDU1i9Wnwe\nXS5PI3r44dM499wJfOYz4/jwh6ewYEEZCxeW8IEPTKG/3/8QzzrLHtv33ruEj33MPyjSPtm77VbB\nnnuWfcfw8I6Pa9duwsEHz0S2LLrhhs348IedvJkqvpKiPvK+96mvgbLEWWcVseuuZZx0kjhXrW4+\n/OFJvP/90/X7fsopk9htt7JvY+nqq0exYIHTX1TTt3i57bbNeNe7BDa/HkJkQ4qG6gB0113RhFDe\nAJmERvSMMyZxxhn+ZLUXX2xP1v/4h/NWDw3VcNhh0/jxj+1FlKqAGbcgunGj3eOCan1oQXTXXSv4\n4Q834IEHxMnCe3osPPusPykyWyPq/L+jw8KZZ07g3ntnbfndbuuppxZx883uBQtPIxqHAJHL+X3L\nwgi6lYpt4kASsj/xRA8A4N3vLuGRRzbixz/uxCmnDAvLkk0g5PmqXH/ZM5+JzHrjYMX0vWAPAAAg\nAElEQVSKCRx22AwOO2wGH/3o3HrdIo2obtPc3l4Lv//9m77v44uaq67BI6xYMeH6/7/9m38s5Y93\nTn2rVzvv0GGH2ZsHl1wyjt12q+AjH5lbL+fMM4u4+urZ0vIPPXQGDz64qf5/+p340Iem8Mwz3f6T\ntqCSvgUAli+fwNe+1ucSWt2CKPtBeRftH/94EXPmWL5xQtQ+L21twHe/uxEXXjgHjz7a6xJEdWlE\nr7tOLoRuaaGrzPZ2yyeI1mp+4ffDH55ybbgQZs+2XInsr7pqHHfd1V9vb5CouTyNKH1+dzfw1FMF\n33F+01xry/nsulh1kr/33ruE3/7WlhR22qmM//mfDjz2WAEHHsheUHV2Ao88stHz7SxfPXqDFYk7\n4n33jeCGGwZw992zYvFDC2JpplsQnTevhuef968TVOHVed99wYVQAPjyl+XnffjDU/jzn9uxZk0/\n2tr8GtFPfnICn/+8+x1+7jnnGp980j8mXnrpeP29Hx4eRsHzWtDjxrx5NTz77Ab8/OcdOOEE+Qaw\n971ZvHgGixdHF/q8a2IV01ze8/rIRyYxe3Zj2u5uv30VL7wQvg+H5Utfcq8Btt3WfpcOOGBrvPmm\nPQDeeecITjppCsuXF7HddvMB2Bpy2sxWda138MElfO97BQDzlY5vIdNc8YJbpyAqQxSAJi2VvRcS\nqU2UAoIFHawoKv57bwnvj8gPmOdLRLRpwZ6z/J7w+p8KjiDK3gEnizQVIUt1QabyzLw+o0QjqnKu\njkADtJkRabdliTVlSQnJYQVRWb9jm+bGd02yYEW8fI6EID5F7kTb4mNtQVReZnu7tSXth9MOWjvK\nTt/iFxa89z2IjyjvmQbViMYTrIg8R399XsE0Sl0sDT7vGYvSbtHn8+qiP8mxYUxz3WnV7AKDmlJ6\n0Z2+RaUv0CmLdKAyN6pqRFsRevzx9ifZM2K5d8n6JMv8X3VDVkcgPBVkGlHW9zqtFg029LzHevZ0\nXA5Av4l2vdx4iuWT1ODEF0h5xwf3dQoLa7eYkBWN6NRUOEE0aEAFEayok96FE2viE+3EeheYSQUr\nCmaaa3/awYqcC/ZGjlSZXGQTC1mwqfQnryBAJkSVwUklSINMWKVNROhQ/iLT3CCmmDrIgmluFHjt\nJ/3D2/eibOB5tVKicyxLTbNEAiCRd8Wy1IIV8VIlqT5PlfeHNhPmbVTR6QziEEQdVwTLlzrB1oiy\n2xcEHVFzowqiYUxzWXmBowqiOjWiqnOrTNsUlLCmuSJrjlYSUul33jsPyuJnsAVRtfpE60weSc2X\njlDJbliz+ohmDXpsYj37fN7d3/ixJaKNk00niDoDncX8nqfNSlIjytsNB9Sj5sYtiBKNaNDrp4MV\nRUVmmss7XkUjGkUQlR2raqbEg7S1XM65+goZNJw8jPKyZJMWKUtl0eLViNIpVGSopEiQRVpjOc1X\nq2JNmW4fUR7xpW/xlxunIMrXiLp/5/lcB7kPdF1qGlF5oXZKmFx9HK3V1IIVecddvwm/uG6V8TiX\nc8rhBd2JSyPqLZPOaUoIEqxIRC7HDrIWJI+oN60Mvy53PySfMo0oyzSXtekXNMqpl/b24DmTeajm\nEaU3MnUTZJ0kamsrCRXu8cf9m+wZ9fb6J2bZeBAl0m1SGlGyDm+1YEVZQyZv5HLuzbi4shA0sSDK\nrk9Wv448ojJ0PMykBNEwkfriMs2lF3KAf7ASmebyFixhFnsqgmiUQZTWXtDtcrRS6r6Zco2oU5cM\nvyAK5XaoaBZk2neWIGoLHfxzkjbN1a8R9Qt8cY6hqoKozKRbZYfUG5BGfF3yYEV2mXa9JDdapZJz\ntV1VI+qMJWr9R5RWimUm7IXlI6oSrEgVZ0yx/yCCaBy5qfN5d9RcmUaU9Y6680XzG+a916oaUbdp\nrv8cRxDNjmkuEMzSQN8awa/ddv7PnldZx6aF8x6lVb/9yer/YUxzZXjdKIKQlEZUJXpsksqhVoWe\n93hpXOjNON5YHPWZNJ2PKEFmQiI73vle/0JWJPRkJWouEUSDolcQdV+k3zQ35/vdPo9VFjmGTJzE\nj0bfYo+uK5pprmN26taI2oUE0YjKfUTd90GEN1gRnUdUhopmoUsSoJlOnUCb5ooWeo0+aSWtEeW9\nB36NqL9d7nLkdQX3EZUXStpFcjnTZrr5vMXUlOVyfEGUrl/WPpW2pe0jSq6T3Hv3xl5O24YKSyAJ\nYporEuxZ9ZAx0wlWpK4R9aYbA+igTsJipOgPViQ/zvER1TP4BREuVY9t9HE5CHQqKS8y01xeLnpx\nfeDWJyN501z27ywLFNk5huDQY9P0NPsYejOuaXxE40a0Q8f6f9jvoyASerLykk1O2l1DZpLmRXWy\nDIOtEfV/R/AKmbKygHCLBDWNqF+IDlp+peI2zaUjXgKqgqiaRjQLprmyY1hhxG0fUf45jb7gYWnm\n4tTy8u6Xd8MmDtNc0TmqPqKkLxJBlNaI5nL8vuo3zXXfY7kgqiYk646aq4pfEGVrRHX5iLo1ouJx\ngvW9WyMqrsuuw/3/ID6ibNNcPRuUuoMVqTwXr7WPToJu8PPOj2ODP6vwcuAC8jEtnEbU/gzzDmcp\nWJExzY0fet4jc6YX2pqNbwEVrR1NZ5rrrYfvK8o+XvX7KIh8RMfH80oLzaQEVlY9vAnunnv68N3v\n9mLzZj3dinWf3KZr7B1YkUaUZ5qblMl4kGMrFffEQC8iAVXTXNnvZPdc3q5bb3WnJggSNVfFNFd2\nDNs0VzyZhwnYEIaw5l+y41mmuWn4iHo1eTLT1SAmhHZ54mevGjWX3C8yqd53Xx9+8QtHI8Ha8S+V\ncrjoojmu74JGzVUNVpS+RtQxzWXVp8tHNEiwIvYCPUf9Lq6L9RkmfQsrWFHUe683WJE8jyig30dU\npOX0rrNkGtGggqsOnD6djvArWmPI5l1Z3ARRfY2gERWnb4nHDNTgQI8RMzPsG+s2zY2nHYnnEY0b\nmUAp+z0JAU/2MEW+REnDE0RZpmvXX2/nEtQliLL8Uch96emp1XMGeo8XDVS8BWYYQTHI76JzLr54\nzKVtJG2cmcm5Jobjj5/CH/7QgX33tW1k6YXTvvuW8Otf++1fZRML7SN6xRVjGB3N4Y9/7MARR0zj\n0Ud7cdJJU/UckdPT7udKBEeVXVQV09zVqzfj3nv78MYbbZiezuE3v3GfRE/Kl1wyhr/9rQ1HHjmD\n3XevYMOGNhxzzLQrRy/gv++XXTaG8XHny6VLJwMlXuaxaNE09tmnVM8VLGPZsiJeeKEbH/uYP98w\nDWusuO66UVxxxRwcddQ0NmwI/64dc8wUnn66BzffvBmXXjqHWx9gJ2a//fZ+DA/bL6D3XTvvvAkM\nDtbw0Y9O4umnu7F48QzWrOl3lfG5z7nz5b3tbc4sqGKaKzNjo9tPJtWZmRxOPLGj/tsuu5QxOFjF\nyIi7wuefd+fr815fFEGUHmOuumoMo6N5HHSQu88RYcwdNZdv0qfCQQeVsN9+JVx1lXPf3VFznbbt\nuGMF7353CV/5Sl+9rWGxo+Y6//eaz3qRaURFVh3e57Ro0QzeeqsN733vDLbZpsrd3BoeruLgg2ew\ncGGJMuu13+Nly4ro7bVw221Ofw9LV5fF1TQERSWPKGAnq//BD7qxbFlRS73uZykWDoxprp+zzy7i\nP/+zEx/5iJ2P99RTi9hxxwqeeKIHF10kni/yeTvH8kknTeGss4aU6jv++Cl861u9OO88J5/07ruX\nceCBM7j2WnHe4aQ1ojyMRjQZyOburFm1ev8EgNtuG8HLL9u+Uh/84BQuuYSsD9j9Y9myIjZuzKOt\nDbjwwgnmMSISF0ST0jxl2TRXxWxItvsf1GQ2LKoa0aTMgEh71q3bgLe/3b3lq+J76/VrJIvb+IMV\n8Qf4lSvdLy5py8RE3uUXefTRMzj6aCcZMi1kPvVUAW97mz95sHr6lhwuuMDdDpKAur3dwhVXOBqj\n7m57cUWES5V3RGR2e9NNm3H66XZd73qXk3iZJFVmlbHzzlV8//t2Ju+BgQqefNKf7B7wP9dPf9p9\njbfe6k70HJaBAQs/+AG7DSzmz7cTjctg+WLutVcFTzyhXhePtWtHAIzgtdecjsR7loceWsKhh27k\nHkcLmc88U8CDD/a6fj/11CIWLHAPascdN1UXVtvb2XVfffUorr9+tnKOSzK2ev2ZAftezp9fwzPP\nFHDQQVsLy4lHIwosWMDvq95yaKExDL29Fp56yq6rWLQvhPZ9pO/pU09tcFkc6DTN9WrRvch8REV+\nct7n9KlPTeBTn5IvhLq6gO98x+7PTz7pbEJ84xub6n8fcshG33lB6e21MDmZ0+KyolrGVlvV8PTT\n0ccHFkFMc7OiEU2b7baruuaGm28eBQCcd57aRsGXvzwSqL6hIf9c1N0NPPaYvD8nrxHldwReX8uK\n+1ozQOSIZ57Z4Np0O/nkKZx8si2Yzplj4dhjp/D97/dwx/Btt4025qRgmptMLwrqy5DkwCi7Byr3\nKKmXkSVgskx+khJEycBFBkyWySLbBMZtkuY1X9L5/NmCqPr59Msu8hGhF6i88lVN1FTNWwFHMxks\nWBH/OlQnvzCpFBrdF0mWr1MHbv9TtXOCBitilUtvksii5qqOd6RMrz8zqcNum7oPeRhBlHcdKr6O\n9DjqNcWPgmPu72y8sXxCdUQYzeXYJvF801z/d/R9EPvJRX+/9UeZdejrs31ES9GNLmBZ8Zrl81Dp\nC1nWiMYV0bwZiSs9hx+yAc75lakRbey5PMuo5kvm9Y+oY2fT+YhGFTiTMAdQMc3NCqwOxtLGxiGI\neu+DO7gJ3ydF5IvhFWCTyiMaTOPq3HQ6QI8XlUlDNX2L6Pl56/EKoirPXiREqvadMJOk6sIoqzjR\nTeOrgxVYRoZ8nHWXw3p29AYEL1gRLQiqah0BtiAqE4ho/AKa+HiVPqyyMGf5iOoURJ0yLY4gSs4I\n3+FyOXb6Ft51iDYOAXYuRe+5Ud7tON8xIkQTjXQU4gwEKEI0hvrvv3hzNE1hohHH/6RJ6h6FCVZE\nMM9RP7KMBQRZDImwZEjk0UtYE9wk8ojqEETTDFbE0p7F0R62RtT+2xEo3VoV1nl0+7wCbFy7pVE2\nNOhjRWZpOtO3iDWi7jYQQZQIlyrPXqQR5TnJ6yBLmzphSMIciWVGKT9HrA33fs/aEPEKojSkv9Dl\nBDHNZbk2yExEo6DiIyqCrREl2ksdD9/9nnvvt87x0GsREjQnK+C+D6IxUFd746Kvz74QEoU+CmkJ\nojRB1lVJ5XA2NB7ejbEg5xj0o6oR5b3TURVRTawR5S2UeM72Yid8nYii5gJqAkbWoubG47Pqrpz2\nEWUtbINpRIk2L7gpmlwj6jczDG+ay3/DdUTNJfdD9Py8/bWnx60RVXn2RhANhxMdOJl7pNpPZaa5\nXljHuU1zxZYAwX1EWaa57k8RQTWiKqa5QTWicZjm0lFzRRrRKPOe91nKNKIsVE1zdZkSxwXpx82i\nEQ1yrMjCIQ3TXEN2UNGIGpJD1e2paTSiSQ1Auga8NDSiKnUm96KqmeEm4SNKRxNm+YjyzgNo31L3\nBoXjI6p+Q4P4zBCCCET0sVFNc2X1kvsRxkeUDF4qz56kemExMyM/PyyNvotKp6mJizAaUZmZq/e+\ns8Yrr7aePodswATViJLNGV6wIvpThF8gi7cjsRZmOk1z/WVasKycT5DTMa94fUQJQcZAuh1iQZTU\nGcWUOPSpUnSb5qZBENPcLAqi3roN6SPTiGZB+99KqGtE2d83nEY0KeS+DOLjZd9HQVamSgjtNIMV\nsbQzyZjmWr7FmVcDzjoP8GsX/IKohgYL6g+7qyxahKkJouIHoyLoeAefri6vRlTeDpFGVFd6AxZJ\n5RGNCyetR3x1hAlW5Jyrdhwr9YponCP93hGQHKFJhFgjSsoMvukk14jy26ZmLeK3LtBpmkuug5gs\n8zSi3uPD4I2aS3+vCj0eiXxEdbpWxOkjOjGhRxBN28JD1xhqNKKtjUnfki1UNzv5wYqiPZym04iK\nduhE9dMLHvf3yY9iaU82NKIIua+/nscXv9gPy0omWBEgTjTOEkq953k1qc8+2+36vwpy01y2EK1K\nmKi5KmWxcIIViUxz3fUQ7WawYEXpmOY2eqQ9lu9gXHUA6vdL5vfn7f+scUSsEfX7ckdN3yLT4tJ4\ng/boCVYkepf95dBCY1S8prneqLne69TpI+ptgwr0eKSmEVUv20ucftg6fURrtVwq41mzaEQN2cHR\niLI7gmXljCCaIOouNuzxx5jmaqpf5fuLLxYnA9ZFloIVidK3nHfeEG67rR+vvtruOm7ffd2x6pcs\nmcIBB5QCJ7plmbfed98mLF48VTcPpSc2FR9Rb7Ci3/ymk3sOH/nNjzKI0i/7XnsxVtRb8C5Qly0r\nYs0aJ+fYUUdNuwJ9XHihO3n2ddeNCpPF89pABFBWsKL77mPnKmOZ5t5ww2a8+90zOPvs4AmQVaHv\n+8c+Nql0zkUXjeNznxuNqUXBiDO1BCGM1ljVwqS3t4Z99inhmGOmfcfwAml1dFj4whc245BDZrDf\nfvZYoiKI3n77SL0/l0osjaj7U4UwPqJhYJmqed0QdJQv8zu97rpRHHzwDBYuDJ9vJJezuOUfe+wU\ndtjBlrD337+EpUvZ7+RVV9lz7ZFHTjP9l/7lX0Zx/vnjmU/NQdquw/0gCz6isvdd5Vj7N/vBXXPN\nKC64YJx/oAYWL57Gu95VwsqV8dYTN+eeO4GbbtKT91rEaacVcfvtwXKXBke8wWf3dX+cAINe1q7d\nhJNOUlsXAe5546ab7Dn6kENmsM8+0fJTJZS+1iHrGlHe9/PmVbFyZTwL5jBCS3KCqL8xZIFEJtdq\nNedaQK1Y4R7w58yx8Pjj0RNs53LAYYeVcNhh/k6vKogSgSjO3Ta2RlT9fHqhvP32fOdN72LvX//V\nFp5WrhwEANx//ybX75dfPo7LLx/HdtvNBwCcdVYRr7wi91LfaSd3G4h202uau3z5BBYvZq+4yDkf\n+tAUnnyyBwBwxhmTOOMMtUFQRWBmQe7lokXTroTNIi69NDsLFrJxkjUfUVU/9333LeM732FvTnif\nKTnn3/99E/bcs4KHH96I3/3OnqJkgujrr/8fAOCZZ2wLB5aWPe1gRSJYPqJxmOZ6N+S817nnnhXu\n8wpSF8/s+itfGcE11wxg7dpZOOGEKXzyk0XmcbvsUqk/UxZnn22f99JLYTYS/e0F4plTneeqxzQ3\nHYGb7/bitUJS1YgSzj2X/fx1MjBg4Xvfi77+SJtrrklGEfKFL8S/CRslfYtBH8ccM83cJOZBP5PT\nT5/E6aerC7EiMmQEqgfdgqjq7zrJko8oO1WLfTPc/oX6b5B38S0z9RE9IyJQs/KPAuG0JEF+12n6\nS9BhsqfqpO4+x/4kwiUv8jAtaBDNwFZbCaIiCYgqiMYpyMUJz2VAJ0mkb2HB67/090FNc0l/jiqI\nsuoXETV9C0sjqjNqLqtMkY9oFPJ5PVpcFbKevkXVtFuVtBfnXi2VaPNXrBHV2ChDw8Ea73jH8P5v\naB6aThAlyHwZZMernhcHWfcRJd+Rdlar7gFF173yTt4iv1D6b5FGlCyQZCl0omCX5W582Ki54uOi\nr27CCaJs01xRe8g58+aFkwjDLmxVJrwsk0TU3DDBimTpW1TeJ947yDIVDiqIsgJgBQlWFFRwiBw1\nkHHfdUbNdUxznU3EuARRkUZUN1kPVqRT25pWsKJ4THOjt8vQuMg0oiyMaW7z0nQ+onGZ5iarEU2u\nLhmiVC20tikJQVRVI8o6zpt/lLezq4KKRlSXaa4IHf1ENX+U+xy3aS559qJrJscODiYriOo0jUuD\npNO3BH13owiiQdqjKoiK+jNL0OURJa1JmIAyzoaJc+NooTEqjiBqf7a3W9oFUXLdRiPqL1vHIjqt\nzTSxQOmP1eD9jVWWEURbG9l7ITLNNX2n+ZBOF3fddRd++ctfYvbs2Vi9ejUAYGJiArfeeis2bNiA\nefPm4TOf+QxmzZoVe2NVYPkq0P8PK4gmiUqdSS2sWT6ijkbUMc1MSxClfxMtAOWBP9RXCirXF0UQ\nbRzTXPtTZTFI2koHTwpCWA0L6RONqhFNIn1LtKi5cbTH75MWVCPKLtf9qYIO01y1evzP2TvORivf\n/qxUclvKjFsjqq88Ed4NAx1l6US3RjTtRXiQdZLRiBp4RBFEjWa0+ZBOyUcccQSuvPJK13ePPfYY\n9t57b9xxxx3Ye++98dhjjylXmNQAJBNIecfzy9PRKjXUBNH42wGo+YhWq9GDdbBxFyTbYRXV6xVE\n4/Q/YE/KQQRdtWN1tDmMIKqqEaUhfYWkMwhKq5rmJp2+RVVIk2/shdcKssz71DWiegTR4MGK+J0/\nCz6i3vLjdE3g5RGNA+c5hZ8QdQqLcZbNSmmRBCJzW5EFWlDh1NA6OPMau0Ow3hfTd5oX6XSxYMEC\nn7bzlVdeweGHHw4AOPzww/HKK68oVxh3HqygJrnq5WVrGybN9C1ewSMuH9EgwYrkZRHh2TEfo9Ed\nrCgJ01wdhDHN9eYR5eV2pa+ZBBvq7U1WI5olf+swOKa58c3CcaRviQJvU0tFY8VKE0QIo8UNoxEN\nY13D8plyxixJIwMQp2muQ3JzZdbTt+jWiOrQjgdFJlzS36scK/vN0AqILX1YGlGjCW1eQi3TRkdH\nMThop4iYM2cORkfVwz0npxEln9F6b1Z9GpJ6KW+/vd/3nXenftOmPC68cLD+e5KmufTzFdVLykpC\nIwpYDSOIqmpE6ai15Byy6CcCglgQtT9FyelFhBGY6TY1ukY0zvc9XLAi/RYkoo0my8pFNs3lWcmw\n8As44pPo/hXmWZF6brhhAG+9lccvftGBtWv7kMv5x5Io0IHmbEFUn2krKSPJ8UuPIBqf+btuH9G0\n1yFB5jWjETXwiJK+xfSh5iNySIFcLoecoGesW7cO69atAwCsWrUK7e3tGB4ejlotl+7uti2f3Rge\n7kB/v93jOzr89dL/nz07x/y+t9cur729LZZ2Dw8Po7PT/Rja2+Vb4P39/Rge7tPWDt5z+f3v/SqG\n2bMHMTxsobvbbverr87Gf/5nG/X7AIaHo8+8/f3ufjV37iC8TezttZ9vLgf09fVu+a4Xw8NdruNy\nObt9w8NzMDxsuZ63XfaQr2welYr49zlzZmNoyH39g4Ozle/JnDnsviiCPu5f/7UCy+Kfe9ddFfzl\nLzkMDw+jt5ddhpfvf7+CxYs7cNFFVRx3XDcmJ6vYemv7+M98BvjFL2pYsaILw8NduOyyKt75TgsX\nXeT0iVNO6cEvflHDwQfPxg03VNDXl1e+trlzLVx9tfq9oDnqKODoo2tYtUq9viwxZ47dv9vbO2Nr\n/wyV+nV4WO09IONiT08fhod7fL+T96ujo0Op3cPDw+josMeTgQHnXdm4JaVlf38/2tvzGBy0MDLi\nn29IHUUqLeGuu1r44x+dY+fNc8o99dQqHnjAvobjjqvh0ENrePLJPH7yE/t+d3XZ8wd5F/v7xWNa\nX1+e+nsWhoedF6urq23LdQ1geJgdS4EIshMTeVx77VZ44gny3C1tzz2Xs+pa1t7eTuTzefT02O2c\nN09f3xoenos5c+j/u8u+6irgf/6nhnPO6cHQkL/vBOG444D3v7+G1avDv9/HHmuPETffHH2M8M6j\nE1tSj8+aFX2+7uhoRz4fbhyMAr0JOGuW+30n662ODv/6qLe3B8PD7h1Estbp6opvPGsl4l5Px0Vp\nSyr4np5eDA93+37P59vR3e1+H8lavqurqyGvuZEh7+3AQD93DotCKEF09uzZGBkZweDgIEZGRjAw\nMMA99qijjsJRRx1V/3+1WkGhEF9y4VJpDoBezMxMo1AYxcRED4BBlMt0vfMBwNWO8fEuAHN9309P\n9wPoh2VVNbfbaUOpNATAeRkrlSpkj2ZsbByFwpS21gwPD3uubz732I0bN6NQKKNWs9s9NjYNwJlk\nx8fHUCjMcM9XZXTUeSYAsHnzCAoFt9Pq1NQsAAOwLKBYnAQwgMnJSRQK467jyuV5APIYHx9BoVDB\n+Hg3gKH67yMjm9DerqY2GxnJA9hG0O5RbN5cBbB1/buxsVEUCiWl8sfHOwHYA628z/n78rJl2PId\n+4wTTnB+t4Vqfxle9twTeP115//XXeeUn88DDz7olPnpT9t/X3TRtgCAz39+FH19Rdx9NzA9DZxx\nBqu/8a/te99bjx13rHKvR8bXvua0rdEoFu1+OjNTQqGwKZY6ymWA3OvNmzchn5e/B9PTAwBmYXy8\niELBn5SevF/lchmFwkZBSU7fq1TmAujC6Kgzfmze3AZga4yNjaNU6sM++9Twz/9cxBlnzMWuu5bx\nxz921M8HbEGOvJs77zyNz39+EqecYo8h1ar97gPAzTcDDzxg133PPW8CAD7xCeD++3tx5ZVzMDVl\nzx+jo/a7SLeJxfh4LwBb+pqcnECh4CT6np4eBNCDsbExFArs5OH0mFKpzACwF/ttbSpjgBq53LZ1\ngbdcnkGl0rVlzOzXVIf9vo+MbESlYoE3rnR1AQ88YAvfOqr9+texpZ7wZegaI7zj2ugo6b/uPhGG\nUmkuLAuS90k/ExM5kGc7Oel+32dm7PVWteqsj3K5bWFZOUxPT6FQGHOVVS7b73i5HN941kqozaPZ\ng6w7JiYmUShMMH7fCjMzZRQKI/Xvxsfttfz09AwKhc2JtdUAlEpkDhvnzmEs5s/nyxE0oYxo3vWu\nd+HFF18EALz44os48MADlc9N2jQ36vFJmOaGKTtNe3mvj6hfQ6incUGi5srw5uRLPlhRkPOTe7jx\nRrdUN4UUkaV0RkmThGmu25QyWNRcXrt0meYSWCaYrONp09x83v1/Ff9kvyml2v0IEpCI/ZtTQE+P\n83dJbe9KiVzOH6zINoHT27mM6RyNPrPfLETNDYLIlSZrMTcMyaJism5Mc1sHqd9j2FUAACAASURB\nVEb0tttuw6uvvorx8XEsX74cS5cuxYknnohbb70Vzz//fD19S9bwCxrhfJpa3UeUhSPU2Y0ol903\nJ65gRSzfI3cwE1FZxI/Rcn2KyuYRJlhRkPKT9LFKYoPFCKLhIe9Y1qLmxiGIEliBf0hgHbpcVltp\nM0KvIKrin8zboIo/fYvzNy0w604pwooGrGsMSMNHNOvoD1YUvZyguDd/2BfijdUg61dZW08ZkkX2\nXsSxQWbILlJBdMWKFczvr7nmmlAVJh+sKNjxjUKagigd8ALwa0Tju5eiQCTi+nmRXVXKDkrUqLlZ\njdQcFF3XkUakyKwgC3OvA5lwJzpHZ7tE6ZlYgqhMI9rWZrkE0zCBsnQIoipjNX3fe3stdHVZmJnR\n+8xtAcEJLMa6p7rqMdjoDlYUNo1VFETvnCharizdmqF1oYPQsTB5RFuLxPfX4h6AeAOdihYrSHmt\njDdKaqkUj0bUO0iJo2qKy/KapOkQFEW/+49RHz3DpJnIMkYjGp4kTHNZ9cmPE5scxqURZbWBhk7f\nksu5BVPa5JVHeI2o/IJVNUS9vZZSW4Pi3XCITxA1K0WCXo1oOnlEVQjqjpLV6zAkg7ORKT/G0Pw0\nnRGNV3CMqhFNw0c06zs+SWlE/QtP/zGqdSUpiLIIY5rbLAOxEUTD4+QRTaY+1WclS4ujy0c0qEY0\nn3dSDXlNc1X6kVdwSMo0lx4fenqSEESt2OaZZhm3dKDbNDeNexteIyouy9DaiMYgk76ltWg6QZQQ\nJGec6Lismkm2oo9olIlNpyAqwy7LYnynBtH0NPqAq2sTp7VNc+PLcciuL9jxWfMRBRzhs60teP5Z\nHYJoVCG8t9dCb6/+nQfevdPtI9ro45ZO9AuiaYyFbv9PmrCCqOkjhlyOv5GZdWWMQS/GNLcOzwlf\n9fxk0R3EIgheoc7ryxSXRjSKqQ/xZSMTuXcRG2wBLg985W1PmCATjR70Q9e708oa0aRNc1UXurIF\ndpgFM6tMuu94TRN57wf5Pp+3XBrRIAQVRKNqrOlraWuzlCL8BsUrLBgf0fjRLYimPScE0VKZfmAQ\nQdwDWDRahGhDNBJ3fU+qczWSaa6M/fcv4Ze/dG/tp7ljRARPMinaeQgddN2rRYtm8P73T+OHP+zm\nlqs60X/1q5vwla/0Yeuta8yydGtEo5QfxDT3058ex047+fLnBGLlynG8/e3RyhBhBNHwJG2aGzRq\nbtR2XXPNaD0Ay/XXj+LznwcOPtjJ1+nXiFp43/tmcNRR0/j0p8dx/PHzfGX29looFu3Np3nzaliy\npIadd/bnOr3pps0YG3NfcNi+SsafAw4o4aST3PmdP/e5MczM5PD+94tyK9ORctVSzUQhDkH0m9/c\niIce6k0loE5W0TmvZNk0l/Wd0YgaRJAxyJB9Pve5MVhWF448UjSHhafppoywduVZFkRPO62YKUF0\nctItiMYVrKinx8LXv74J++yzNTZubIu067pwYRl33eUkQY7TRzTq7nAQQfSyy8bVC+Zw8cXRy2Bh\nTHOj4wiiyQxAQTfuoprmnnuuIyC+851V3H+/O8m9U0+uvhDv6QHuv38TNm1iV3LOOUXceOMAqlU7\nyuijj1ZQKPj7+OmnT3LbRSxOVK+D3IcHHtiI/n73Tdlhhyq+/vVNjLMc6A2A+ARRC0AOuZxFCaL6\nAuAcfHAJBx+sMfFpE0DurY73N4uCqOiYKBZMhuZHJIgajWi22GGHKh57rIJCIZ61WNOb5kbXiKbv\nr5e2OQ6hr89WfxSL9s0gPqJxmeaqEFWDEaYclWP9prnqL7DTdxtbADOmudEhfSC7GtG4X3a3j2wQ\nH1ESVC0I5H77TXPVrjNsX/eaIMcZrIhYbDiRiBt7nMky3v4UhSxoj1R8RHnHqv5maA1yOStU+hZD\n85GCIJrMaKrb9DLOl8Jbtkq02DgnJV7ZZKe/WLQbxDPN1b2wUbnWoM9HJRBSeKxI5ZmouW5aWRAl\n1561YEVxpm9hlRMmWFGlErwR/nlDTZCQ5ykW49WIdnXFK4ja9eTMgi9mdPuIGo2ooVnI5cTBikwf\naR2aUCPq1mDq8hFNk6QFUd7gMGuWWyMat2muCmF3nP2CaHCNpej3aKa/6WvhdaBLs5sVi4A00OWL\nqUrQ8VJn+hZROUEEURIpN4xGlOAdT+TBioKZ8npxa0TDlaFaBz0+mQVfvOgWRNMYC91CpjcavOX7\n3rEm8F+06WsGgjhYUXZz5hr007RLPF2CaBqomHWmIYh2ddk5+pIWROPYWY1imiuDVVYr5hHVZZrb\n6PchCjKBLy2SFkQB/jvrXfBGMc311qUqSDi/hxuYvRrROMf3fN69gdfK71fc6BREa7Vc6u4aUU1z\nm8XtxBAduUbU9JFWoQk1ouHqMRpRB97g0NZmoa/PqgcrIj6iJU98Ct33LI5rjdNHNLpG1P5sFk1g\nFt6hRsUxgc3WTZT1zSQ1ol7T7c7O6Ka5YQVRHRrRWi2eMY/nI2rez/hoNtPcKMdEOd7QfJAxSPQ7\nTRZ8pA3x0PSCaNC8eKrf60TWxqQFEp4gms/bAYu8PqJJmebq1Ix6g6zoFERZxwQpv1k0ooRmuY40\nyKpGVGYyrGsBLhJEifbROz4S09ww94zXV+MWRL1lxREEygiiyaNTEKXLSxKRjyhr41/0LuiykjE0\nPrI8oobWoUl0Ln6CqvWzpBFNP1gR+6JzOaCvzzHNJaTrI2p/RvcR1dMeUpbfvFr9fCOIGgi6F7K6\ncNrFHyt0whaa3C4ChCjBiuj6APXriBqsiFeeTpxr4UciNuhF571Ny0c0KCrvgulzhqAaUdNnmpfE\n84hmwTT3vPMm8J73uBOzJi2I3nijk9PyuuvG8OyzPdxjiQlsUog0op2dTroWMojELYh+4xubcP/9\nfRgc9DdMh49oPm8FisyqYpob9ByaRlhsqBA16NIDD2zEj37UpbFFjQfpC3ELot/8ZgE//GG38vGy\nIGHxmOa6A1h0d1s49dQili515wONEqxoyZJpPPPMFK64YsxXvwgyZka57lNOKeKhh/pcPqL0PBEV\nnvYqrjnuzjtH8Pe/t3DIa+j2EY1eRhiCakR5x9rfZWxHzZAaJn2LgZC4IJoUogHyc58bY5zBHiDj\nehn++Z+dxdP221exzTZVvPkme9JmtSENH1EisJHf416EEvbbr4z99mMvyMIKffQ19vZGS7fCqt9f\nXpAHRkwOG3vSjmqGdcQRMzjiiBn5gU1MUlFz3/e+Et73vpL8wC2omgxHfa/8QpPl+u3mm0d950QJ\nVtTba+GrXx3x1a863ka53uuvH9siiNppVXbdteyaJ3RhByuK3zT3pJOm4im4gXDek+g3OQs+oir1\nG9Ncgwq5nDhYkcp3huag6XxEw9abJdNcL2zT3PgaJtKI5vPO77zJNe3JUgX6GoMmj1frS+4yjWmu\nIQxZ9RGVt0vPqkHsI8qmfcv2ahTTXFb9IvSYudqFkHuq2zKC7SNqXs440akRzYKWKMg6Ke22GrKN\nzEfU9J/WoUmMAB28O26qnTltQVQ0UWUpWBEtiPI1osltXYWd6OkFWHe3XkGUdUwY09xGH4jN7nd0\nHBPYbN1EmcmwrgW41xdVpS8RVwYdwru6j6h6+2R12cGK4ntvaEHUEC9h81yzyKJGNLhpLv83Q2sh\n04iaPtI6NK1GNKggwBdEo/m5qSKaqFj+i2kEKwLsBWi1av+eBS1N2OcSRSMqQ9fucKMPxM6iw6x4\nw5KUaW5QVAXR6DgLedXFCRkvK5XotXsFYR46fES92l/d0HOZ2zTXvJ9xoVcjmkvdXUNlw96xDuC3\ntdHnNkN0RH3ACKKtRdMJos2oEWUN6Gn4iOZytt9i0j6iIsLWRbddv2mu3+c0iFZbdwTOtDATSXR0\nLmR1IvN90/XswwhnRCNKNsyioabR0iHQ0cJ9HAsxUl7c9RgcdL6/cWrJVTEaUYMuwprmmr7TfDT4\nUtcPb6ALqxHNAkmnbxEJom1tTt3ZEETD3YhoGlH58VFMc3VoV7JEowvUaZJU1NygEM1MslFzg2lE\nwwQr4qEmiEarg/a79UYI1g19X5tlnMkiujeS0n5WvPlWV7o8Q2vBN801HaSVaDqNqLceXYuDuAnq\nI5qGIArY94Ms8LIgiIYlbtPcKIJo1oSOqDRCf8gqWTXNTSqIUpRgRToEUXUfUb1zjZ0zMh6XAcdi\nI2cE0ZhptmBFXljrLLWouU02yRkCk8/z07cA/L7ebOsjQyqCaDK9qJlMc7MSrIhoRIk5Xhai5uow\nzY0jWJH/nCB12BWk7Q8UFWOGFR0jiNqfwTSi9nuTdNTcqOM0qeuWWwZQLOrXiNLvo53DL5vCTTOh\n895mYQEe1TRX5TdDayAKVmT/noEOb0iEptOI8hzlwwcr0tAoBUi7d965jIsuGue24cwzJ1zHx9MW\nvoCZz1tNoRE94YTp+t9Z04huu20Vp55axNe+tklru5LGCKLRyWpf+OhHp3DCCZP47GfHmb+naZr7\njnfY92zt2uj3LIggqlMj+v/+X1fMgqiulDMGEc2mEVVZJzVLjANDvBgfUQOh6YYK7yAYVSOatI/W\n3XePYO5c/jbRpZeOx94evkbUcvmIijSnSRG2rp4eCzfeuBmAfo1oVEE0nwduvnkUe+6pIexnijRL\nGpo0yWpf6O21cNddmzFvXrwqUfcCV01LSO7Z7rtHv2dpCaKs/0eFlGeCFSWHYzkQ/SbXauwI+kmi\nohEVpTIym5MGgkgjasal1qJpNaLe3TiZmp/3uywohy7odosWJEm8nCIBs1l8RAGgVLIbqj9qrv+Y\nVtwdbm9PJvWRoXnxalqSNtdSF0TjM6XVDT0+mQVfvOjUiNZq2U3fEvRY0+cMoj6QBTN0Q3I0nSDq\nrUf3LnVckF1EliDKitKaXvoWZ3c3C4JolLqmp8MJojKiakSbBbJ734rXbtCDdyGfdF8KohFViaYd\npm7d5XnNnQ3xQvxxo5KFTQP/vMbfbGRtGqXdfkN2oN0DvGShrxuSo+n0NGH9E7Jij84SYry/x404\nWJE8j6juBZmIKPdjaso+OYlgRa2oEXVMc81qt1WJugB3hKZsR3jVEazISxI+onGniTGIF9xBiKOP\nBSXIOintdYwh28hMcw2tQ9NpREnHJiYsjeYj6oTWp7/z/x1nniVxsCLnHjeyjyhgNKJxQ6KXGloP\n3f09LX9GVY1orRaf4BgHjiBqNoriRpcgGkcfi0rQ9hgfUQNBlL4ly5uOBv00nZ7G6yivOsmmLYiq\nanJ1+pzwEIXUpgVRkcCaBkHvSVwaUfv3YFGbmxFjmtu66BufHB/9NHbJVftuHAsn3QKiE6zIfU/N\n+xkvOgXRrGlERYKlCVZkECHSiJLfDa1B02lEeb5EasKD+ve6EQUr8hzpOj4OZD6i1WpOelxSBDUJ\noiGCaBLBilpR60AWTWkvngzpoctHP32NqLjieARR3eU5VkImWFFy6Lq/WdCI8tdJxh/UEAzjI2og\nNN0SUbePaFJRc93tkFeWlkaU9hHNqiCqysxMOI2oHDMpA8HN4w0GL2kLokE2/nS3La4NHMdH1G09\nZIgHfT6iWYiay65fXSNq5gSDTdg8oobmoz3pCpPSiPrTt4jPk2lE00zfwmpPEm1h1S0zpyDHJUWU\nuq69dhS9vRaOOWZaa53GR9TGmOYaopK2INpMPqLeYEWAfCw3RKeZghXxMMGKDEERvxcmiFor0XSC\naHzBiuJteFBBNF6NKN/3M2vBiqKw7bY1rFmzWXu5bW3+e5DVBUScOFFz022HIT30Rc3NuiCavrZK\nhuMj6hZEyYaRIR7szdvoHTeLprkin0/WnBc0doeheZGb5po+0io07fJYl1N80j6irKi5rPak4yNq\noa0N0vQtyWpEkx+sZNfH2kxIewGRBkYj2rroeuZuQTT5XfIgGlHdAl28GlH7gqrVXEtukiWLnjyi\njSaImmBFBhEi6zpjmttapBCsKBnBQVewouTTt6j9nl6wIqu+uyvSnCZFGoOViiAa9JxmhKRvacVr\nN+ghK0G/VATRRskjSlOtIvOa3EanmUxzg/RJ0btq5gSDLH2LoXVouqi5jmlu0PrYPT+pgV89aq77\n+DiQRc3NkkY0i7C02mkvINLAuWYzq7QausanRjHNjUNIiDtYEZAN4abZEQVlCUIzpW8xGIxG1ECI\n5CN6/vnno7u7G/l8Hm1tbVi1apX0nKSCFenSiCa1A5+tYEViH9Fq1f4/P9BF9sxlk4T9DFtPGHNM\nFTP0cAyJoss1wrJyqQqiMuIJVhRPHlG6nbZGVGs1Bg/6NKK51P3mZEEdDQZVdL0XhsYncrCia6+9\nFgMDAzraogUiRAU1qZUNsEma5qYtiIo0orSPKI80THOzNKAZH1EbYppLNi4MrYfuYEVJk2aworhM\nc/N5x2KjWjXRKeNG14K7GTSiKr8ZWgNRXzYa0dai6UxzHc1isJFf5iMaN9mKmsv/ze0jyj6m2X1E\nZbD6TNoLiDQgGlEjiLYecbyXWdeIZt1HlC6XjprbimNTkuh6jllYnAfRiBpB1CDCmOYaCJE1ojfe\neCMA4Oijj8ZRRx0lPT4t01wZ6Qcrym1ph78ium7aVC0uRIJoLgeMjubx0592ZsJHNIuDldGI2tCL\nXYMhLLmclXkf0TgEurgEW79pbobMSZqQ1tCI+i/QRM01iBC9F6Kx3vSd5iOSIHr99ddjaGgIo6Oj\nuOGGGzB//nwsWLDAdcy6deuwbt06AMCqVavQ2dmF4eHhKNUK6ey0L2lgoB/Dw33o789t+b5TWO/Y\nmPM3fdzs2cTUty3WdpMXct68uVi/3nnTzj23isWLZ9f/P2+e3Yaenl4MD3dpq7+9vb1+feSeeenv\n78SsWfbfH/nIMBYtciSMbbe18MYb9nlz5w4hxlvlaZM9M+dyQG9vLwD7U+e9EXHJJdW6n9Xq1bYK\ncHh4CHPnuo+bN28Y7Yln7U2Xnh77gvv6BjA87J5x6P5maD7IuNnR0RH5Oedy9ngH5NDb24Ph4c5A\n5+voa729szA83Mv9vbOzDe3tea19WjZnBaVti4lCe3sbZs3q3fJdJzo7Yd5FTbD6Wj6fQ1dX8H7r\npVbLoa8vubmNxezZ7rG8t9fuU93d/nUdWYPR9PS0bTkv+v0wNPY82tnZjrY29thjWf6+PmuWvdbr\n6opXhjCwibOvRVoaDw0NAQBmz56NAw88EH/60598guhRRx3l0pSWSjMoFEaiVCtkenoQQA+KxXEU\nClMYG+sCMBelUgmFwibueZs3twHYGgBQKBTq309MdAMYQrVadX2vn20BAJs2bcTYWAcA+4Ffc81b\nGBkBgPlU2+ajWJxEoTCurfbh4eH69W3ebN8zL+3t05iZsQDY0mipVAZgDxQ77VTCG2/Yf4+MbEJH\nRzJqsImJHgCDsCxgcnISwAAmJ/XeGzb28zj22I3YbbcKAGD1avu7kZGNsCyrfgwAbNpUSH03O2mq\nVftd3Lx5HIXCtOs3ur8Zmo/R0U4AwyiXyygUNkYqK5fbFsXiJGq1PkxPT6FQGJOfRBGlr23alAew\nDSYmiigUitzjpqbmwLI6NfRpZ8wol8VzVlBqta0AtKNWq2JycgrAAKany6jVcuZd1ASrr1nWNpic\nnEahMBq6XHujej6mp5OY21jY/XJsbBSFQqn+7dTUAIBZW9Z1m13HTkzYazCa6ek5AHpDvccGP408\nj1arc1EqgTM/zPet48hab3qa7muGpAjT1+bPny8/CBF8RKenpzE1NVX/+ze/+Q3e/va3S89Lzkc0\n2HmyqLlxm+a6252uqRTPlLKnx3L9Rv9NAtMArWWay6rf5BG1IffB+Ii2LrrGzbRMc8lY3BzBipy8\nvrTZfCuOTUlCTMujEDb2RdwEzRVqTHMNBJ6PaFj3OkPjElojOjo6iltuuQUAUK1W8d73vhf77bef\n9Ly0BNGw6VuS8hENkkdUx8QmgieI9vZaKBadxtFtcFJ1pCeIxuk3q1I/wQiiNqRPkOBWBkMYHF+i\n5CO8NlOwIloIoKPmtpqlRtLo8BElc3La80gQvz2R0Jx2GhpD+sjeC+Mj2jqEFkS33nprfPGLX9TZ\nFi04uynBNJlpd3pvu0XEnX+Jp8Hq7bUwNuasWlhBlLx/J0Xa9dOYhZ2NSd9i0PEu0gGDkl7AqgYr\nsqw4ghUlk0e0o8MIBXHSzIKorqCQhtaDN17K1sJZStVn0EPTpm8JPmCye3fSQoUsjyg5Js6XkVd2\nb6+FSoV9XNqmuWkNTmzzIzNSAsY016AHMt5lPWpuXBpM3eRyluu6zMZZvOTz0eensC5HujHpWww6\nMaa5BqApBVES5TbYeY1kmht3e3imlL29lkuocPuI0kcmJ4ilL/T56097sZAVnDyiZkYxhKdRBNHG\nMs11rBXMgi9edNxfMtemPbfw07fIj5Udb2gteBs0srHW9J3mo+mWzLoH7KSCFTn1hfdn1QXfR7Tm\nEipon8wsmcamTdqLhazQ3m5Mc1sdHeOmLYjmUrF6UBdEGyFYkf1Jb3YaH9H40WHB5Gywp7vxGlUj\nmv7GsSEr2MGK/J3EaERbjxQ0oskMREFNc9P2ESXao2wLom7TXJ5G1ETNTb4dWYTkTTU+Ha2H3vfS\nSl0jKiMOjahuWNqoRmh3o9PMPqKi741prkEE772QCaKm7zQfkfKIhiHpqLmqkVTlprnxNvyJJzbg\n+9/vqS/eRcTtI8oSRJcsmcIRR8zgscd66t9lwUc0bYwgyufSS8dQrQIf/ehk2k0xNDBpmuYS0glW\npLc8XrCitLVsrYAuH9G051ZjmmvQRT5vCTWiXk48cQq//GUnLrssjTy6hjhpWkHUq3mVaWLT9hHd\na68K9tprXNgWQhrBilatGkVHB1Cp5JjH0YumVteIpt2mrDA4aOHmm8MncTc0LjrHp2z4iIorbqRg\nRXTZxjQ3fngmiEHIqo+o873/hTcaUYMIXh/gbbp0dQFf+IJZTzQjTTsFBY2mmiUzALVgRfE1jD1p\nWlt+o9vg/N2qgqjBYOCj4/10BNEs5xHVL9DFda20j2gcArTBjY6831kxzfUG5wurETUYZNGkTV9p\nHZowaq796V0UhPW7TGMHMos+ouQ+lMs55nHGR9RgMHjRF6zI+TtJgkXN1R2sKL48orQgmraWrdnR\nG6xIQ4MiYHxEDTox6VsMQBNqRMOasMhMTpIMuCJfgETfYRXBGhwcUy6qFRnzEU0zqqbBYHDQ+V6k\nKYgSDVBzpW+xXOO58RGNFz2CqP2Z9rMK5iPKN9c186aBrxG1O4fpI61D0wmi3p3DqKa5ae9Askgj\nWBG5D+48os5No+9fK2lEDQZDvLjHlmQX4qoa0Tj8V/ULttaWT+c74yMaPzITRBWyYpobJBuB0Yga\nRPB8p41GtPVoWtPcIAOm6PcsvgxpBCsiixUSrMjr95Il09wkF6tZ7B8GQzNhL1icv5OuG0hHI6ob\nY5qbDjr6bFYFUdH3YkHUaOFbHZ7vNC/gqKF5aVpB1OtUr3Am89ukoubSqAUriq9+1i4VvXAh/+eZ\n5ga/9+HJ6sRsMBj0oCPqaFTkgqgJVmRgo2PjOKtRc4MGKzLaLgOBZylg8o63Hk0riOryEc2iIJpG\nsCKvWZd3ck0rai4NEYbT1sgaDK0OeS9oS4koNIZGNJ7gQnGUR8Zz20dUbz0GP/p8RKO3JSnM3GgQ\nwdugMZsVrUcDDWtqhO3EfEE0jWBF8t/T8hG97bbNAICFC8uoVPy/k/YlBV3XOecUcfrpRZxzTjG5\nBgj43OdMzitDa3LAASWcffYEbr99REt5WRdELauRghXRGtFc6gFwmh29UXMbKViR2neG1kQmiBpa\nh6bViKouIAhZGiCzKIiSNm23XRXve98MLMudyiU9H1HnRvT1WbjpplH09WXDNPi887IhEBsMSdPW\nBvzLv4xhu+0Yg0lAaF+itARRGXGYuMYl2LqDFTWWlq0R0WFanlUfUZEfnzHNNYigff95vxtag6YX\nRKPWm4Zprozk/Gwd6MVKW5uFWg0oldKPmps+GeoYBkMTkoVgRTLiSd8SXx5RQrXaauN18vCCsgQh\nq4Ko6HsTNdcgQuYjavpI69B0e6G6NaJZ9BEF0gtWBNj3pFoFymXnOzqtixlADAaDLmiNUlZNc+Mw\ncY0vWJFFXZdJ3xI3ekxz7c+0n5V3cySosGmEDAPBpG8xEFLQiMYr0fEG7LABgLIoiKYRrMitEbXz\nz9GmuWkJomkPVmnXbzA0O1nQiBIfPR7xaET1lkeXS8o2prnx00xRc3mw+2ows11DayFP35Jsewzp\n0YSmue6d86ga0TRehiz7iAK0aS7Q1WX5zjGCqMFg0EU2BFHxcY0giLKCFVUq6QfAaXZ0PMesm+ay\nYL0PRsgwEIxproHQhIJo/a9A5/EF0eQnaTXT3PhupGzR5Zjm5tDTYx9cqbD9ReMm7cEq7foNhmaH\n3nhLK8m5iiAaZ7oVneXRgqgxzY0f3oI7GNmMmut872+X8RE1iOArVEznaDWabgoKu3MoN83Nzsuh\nI/iBCFmEv7Y2WwitVnPo7k5XI2owGJqbNDWihGbTiMZZj8GNTtPctJ9VkL5j0rcYRMjSt5i+0jo0\nnUbUW08jmuaqaHOTNs2laWuzMD1t3xgiiNI+okmS9mCVdv0GQ7OjI/1FtPrlG3+NFKyI1ogC2fU7\nbCaMIGpjhAwDgZe+JW3rF0PyNO0UFHzXl93p05iksxKs6OqrR5m/5/PA1JTdCJZpbpKkPaGlXb/B\n0AqkuRBXjWKefY2oVS/XCKLJ0UzBivSlbzFCRqsjey/M2qp1aLopyLtgacb0LUkFK/rkJ4vM39va\n4NOIyrSocWEGK4OhuXH7iKZbP484fER1w/IRBdL3O2x2dGj0s5O+Rfx/2W/eYJKG1iWfF6dvMbQO\nTWea2yrpW+Jsj2zSY5nmpqURTRszoRoM8UKbxmZZEE1bSJDhCKKWSyOV1hcMcAAAFzhJREFU9XY3\nOjpiOmRVEBV9b+ZGgwiTvsVAaFpBVJdGNGj03SSIXyMq3rVsa3P+dkxz42uPiLQHq7TrNxianbSD\nFanUGYcgqnuMJ9cRdJPWEA09prlkTs5W1FzjI2oIC2+8NH2k9WhPusLk0rcEo5E0onFjm5lZSoJo\nq5vmpl2/wdDs0KaN2Y2aqz9YUVxjqvERTRYdfTYrwYqCbczzj03/OgxpIwtWJMOyLExPT6NWqyFn\nOlTsvPXWW5iZmfF9b1kW8vk8uru7Qz+HxAXRuCE+CGRRoHpfGkkQTcJHVLQ4aWtzKica0Wo1rYEg\nexprg8Ggl7Q1orLxNo5gRXFpRL330Aii8aJjvs6Kaa4Xk77FEJao6Vump6fR0dGB9vamE2MySXt7\nO9poLRRFpVLB9PQ0enp6QpXddKa53h2WqBNAVgXROJEJovRvjiAab5uyiplYDYZ4cQcrSn7jya5f\n/KI3QrAiGhOsKDmaOWquqM+z2mrMLg0EefoW8fm1Ws0IoRmhvb0dtQgmPE0niPLqCSvcZdEnKYlg\nRWKNqPN3q+cRNRpZgyFesuAjmkawojg1osY0Nzl0CqJpz3dB6g+qLTW0FlE1osYcN1tEeR5NNwWF\n3XGTmeZmDR2LlKmpHD7xiSH86U9udXutlhNqHmhBtK/PPi6tjSkzFhkMzQ1v5zw55INtHIKo7ms2\nwYrSQY9pLnE50tCgCJhgRQZd5PMmj6jBJoVgRfFqkHgDXXiNqOUqNwlUXkCZqZgKL7/cieef7wYA\n/OAHzveyRVVXl3MzjjxyBpUKcNZZRey//zaR2xSUtAcrWf0PPrgRGzZkdDfDYGgA0g5WpOYjqj9Y\nUamk92LJuG00osmiYtotI03TdBpd6VvSnrcN6WOnb2F1hHQD0wXh+OOPx+OPP552MxLjW9/6Fg4/\n/HBss43etX7TRs0NOrmaYEUOMkGUaEEBoL+/hiuvHI+vMRlH9qwOP9wfZcxgMAQjTR+5tExzJyf1\nTpa9vUQQtYwgmiA6NPrZ9RENFxk3bYHakD48jWhWNl1UaCUhFAAefvhh7L777kYQleHPIxqtwnR2\nZcQvYNwvaK3mNr/10tvrzKq0UJoGae+apV2/wdDsuIMVpVs/j0YQRMlY7dWI0lHQDfqxNT/Rysiq\nj2hYrWfa12HIBlGCFdFcc80AXn21Q0+jtrBgQRnXXTcmPGaXXXbBa6+9hpdffhmrV6/GwMAA/vCH\nP+C4447D7rvvjrVr12J6ehpr167FO97xDjz77LO44447UCqVMDg4iDvvvBPz5s3Dxo0bcf755+Ot\nt97CAQccgB//+Md4+umnMTQ0hEceeQT33nsvSqUSFi5ciJtuuokbvfaFF17AqlWrUK1WMTQ0hG9/\n+9sYGRnBxRdfjL/97W/o7u7GzTffjAULFmD16tXo6+vD8uXLAQCLFi3C/fffDwA47bTTcNBBB+Hn\nP/85ttlmG9x777340Y9+hF//+te44IIL0N3djccffzx0lFwvTbcXyhvwwwpvzawRLZftirxlWZa4\nDbTw2eqCqMFgiJe0gxUB6QiiU1O6BVH7Jubz7vtoxtB4aeaouaLvG0GjZUgPmUa00Xj11VexatUq\n/OhHP8IjjzyC//3f/8VTTz2FU045Bffeey8A4KCDDsITTzyBZ599FieccALuuusuAMCaNWtw6KGH\n4oUXXsCSJUvw+uuvAwBee+01PP7443jsscfw3HPPoa2tDY8++iiz/o0bN+Kzn/0svvKVr2DdunX4\n8pe/DABYvXo19tprL6xbtw6XX345LrroIum1/PnPf8ayZcvwwgsvYGBgAN///vdx3HHHYd9998Wd\nd96J5557TpsQCqSgEa1U4p31vKa5utK3ELv1JEhKEC0W2RXJkrPPmuX8RmtH0yDtRVTa9RsMzU4u\nZ7Vk1NzJSb0F0puGxjQ3OXT02azkEY16LY0qZBj0I0vfEgSZ5jIJ9t13X2y99dYAgB122AGHH344\nAGD33XfHyy+/DAB44403cN5552H9+vUolUp4+9vfDgD42c9+hrVr1wIAjjzySMyZMwcA8JOf/AS/\n/e1vceyxxwKwc6cODw8z6//FL36B97znPfUyBwcH62V/5StfAQC8973vxcjICMbHxe5022+/Pfba\nay8AwD777IO///3vIe6IOpEE0V/96le47777UKvV8P73vx8nnnii9JxKJUqNchxTXHdvDjuAZlXQ\n0CmIeq8xiI9oV1f0dkQh7eeTdv0GQ7OThWBFMuIIVhSXaS6xhCGkLdw0Ozqj5qY935hgRQZdRE3f\nkjU6Ozvrf+fz+fr/8/k8KlsEn6uvvhrnnHMOFi9ejJdffhlr1qwRlmlZFk4++WRcccUV2tvb1tbm\nyv05M+PEM+miFvZtbW2Ynp7WXj9N6CmoVqth7dq1uPLKK3HrrbfipZdewj/+8Q/pebojAXrx+4hG\nLlFTOeokpRHlLXRkgiitBW20wcJgMDQWbh/R5FUqKmNcraZ/LNRvmmvfu8lJd3ouI4jGi948ommr\nFNXrN+lbDCJaMX3L2NhYPdDPww8/XP/+wAMPxBNPPAEAePHFF7F582YAtgbzySefRKFQAACMjIxw\n5awDDjgAP/3pT/G3v/2tfiwAvPvd766b87788ssYGhpCf38/tt9+e/z2t78FAPz2t7+tnyeir68P\nExMTga9bRmiN6J/+9Cdss802dVX0IYccgldeeQVve9vbhOfFrxG1P4Omb+GhT6ANXmfY31UpFu0V\nSLXq/t6y1DWiaZP2YJV2/QZDs2OCFemB+Ih6y9WtyTX4ia4RtT/T3jQwGlGDLnjpW5p5s+Liiy/G\nueeei9mzZ+PQQw+tm7yuXLkSn/rUp/DII4/ggAMOwFZbbYW+vj4MDQ3h0ksvxSmnnALLstDe3o4b\nb7yRKWfNnTsXN998Mz75yU+iVqtheHgY3/zmN7Fy5UpcfPHFOOqoo9Dd3Y3bbrsNAHDsscfiO9/5\nDo488kgsXLgQ73znO6XtX7p0KS6//HLtwYpCC6KbNm3C3Llz6/+fO3cuXnvtNS2NikJPj92LSVAp\nEhGQ0poHggz8JPR9EqgIos89140jj5wXqR6S3/JnP+vEfvsB1apd3ptvtgmvN4uCaH9/Or6qzThY\nGgxZIpeD9oiIQet/9NFe/OQnfD+EajWnfSzQHc2WHtPNuJUc+TzwX//VEWm+nphoDtPc9nZxOYbW\nIZ8HZmZyvveCWE02Qh8hMs8hhxyCQw45pP79d77znfrf9G8f+MAH8IEPfMBXTn9/Px588EG0t7fj\n5z//OX71q1/VzWNPOOEEnHDCCUrtWbRoERYtWuT6bnBwsB4siaanpwcPPfQQs5znn3++/jeJqgsA\nS5YswZIlS5TaEoTYgxWtW7cO69atAwCsWrUKK1d2cJ1tdfDYYxYefLCC/fcfRC4HLFsG/O//VnHJ\nJe0YHBTXu3p1BYcdZvna96//WsGSJbVY200zNARccEEVe+zhtOVrX6tgm23s/69cCbz4IgAIcqwo\n8o9/1PC2twG5XA6WZZe3117AoYfada1bV8Zf/pJzXfvgIHDeeVXsu6/7Xr30Uhn/9V+5xO4TALzv\nfcA//3MVH/pQcs8HANasqeD113N4+9vn1gfMb32rjM5OJNqORqW9vd3cJ4MSK1bk8NxzFrq6ajju\nuD4MD/cFOj9qX7v00hp+/vM8ROPtvvtWceqp3RgejuY0//jjZUxMAH/9aw7HHOOfi6KwZAnwiU9U\ncdppeey22yycemoVlQpw8sk9GB7WFwGxlWH1tQsuyMG2wos2X8+eXcWhh85Gb2+kYkJxzz0V/OY3\nOey555BLK3vyycD//E8Vn/xkV/26v/71Cl58MYeDDprjUwBcdFEO7e1VnHBCL4aHU7iQJqOR59FT\nT83hzTerqNX878Uhh1SxZIl4rH/rrbfQ3p54vNVY+Nvf/oazzz4btVoNnZ2dWLNmTSavTdSmrq6u\n0H0xZ1nhjEb++Mc/4uGHH8ZVV10FAPjud78LADjppJOE5/3f//1fmOoMMTM8PFy3QzcY4sb0N0NS\nmL5mSArT1wxJ0sr9bXJyEr1p7MpkgA996EOu4EIAcMcdd2CPPfaIrc729vZ60CUWrOcxf/58tbLD\nNmqnnXbCG2+8gfXr12NoaAgvv/wyPv3pT4ctzmAwGAwGg8FgMBgMHJ588sm0m6CV0IJoW1sbzjzz\nTNx4442o1Wo48sgjsf322+tsm8FgMBgMBoPBYDDUCWnMaYiJKM8jkhHy/vvvj/333z9KEQaDwWAw\nGAwGg8GgBMnPmUVfylajUqkgHyGkt3mCBoPBYDAYDAaDoSHo7u7G9PQ0ZmZmkGuEELsNTldXl88v\nFbA1ofl8Ht3d3aHLNoKowWAwGAwGg8FgaAhyuZy2PJYGOXEGxko5PbLBYDAYDAaDwWAwGFoNI4ga\nDAaDwWAwGAwGgyFRjCBqMBj+//buLrap+o/j+KfrLDDGHlpGMHM4EEZ8QEKArOgGKHMmA7wgBp0x\nukBMZCQGbnQxUZegRgWcIXTpUAjEixm9MUFFLzSBRAyZINnU6R4sPjBwgw1wc91y2uMF2fLnb9Bt\nnJ7+Ot+vO7ac08/3t29O+u359QAAAAC4ymPzDGQAAAAAgItcvSNaU1OT8NdoaGhI+GtMlMnZ3Pjb\nTJSp62ZqLsnsbJK5/WbyupFtYui18SPbxJjaa5K562ZqrhEm5zO130xeM7JNzER6bazHTLqtuUuX\nLk12hOsyOZvJTF03U3NJZmczmcnrRrbJxeQ1I9vkY+q6mZprhOn5TGTympHNPJNuEF22bFmyI1yX\nydlMZuq6mZpLMjubyUxeN7JNLiavGdkmH1PXzdRcI0zPZyKT14xs5vHW1tbWuvmC8+bNc/PlMA78\nbeAm+g1uodfgFnoNbqLf4JaJ9NpYjuFhRQAAAAAAV026rbkAAAAAALOlJzsAEuPChQsKhUK6dOmS\nPB6PysrKVFFRof7+ftXV1amnp0d5eXnavn27MjMzdfbsWdXX1ysSiejRRx/VQw89JEkaHh7WSy+9\nJMuyFIvFFAwGtXHjxiRXB9M41W8j4vG4ampq5Pf7jX0yIJLDyV7bunWrpk6dqrS0NHm9Xr322mtJ\nrAymcbLXBgYGFA6H9euvv8rj8WjLli0qKipKYnUwjVP91tXVpbq6utHzdnd3a+PGjVq7dm2ySoNh\nnLy2ffTRR/riiy/k8XhUUFCg6upq+Xy+MWdx/TuicMfQ0JCKiopUWVmplStXqqGhQYsWLdKnn36q\ngoICbd++XX19fWpubtbdd98t27ZVVFSkzMxM+Xw+LVy4UJKUlpamkpISVVRUaM2aNWpsbFRBQYEC\ngUCSK4RJnOq3ER9//LEsy5JlWSopKUlSVTCRk732ySefaMeOHVq/fr3KysqSWBVM5GSv7du3T4sW\nLVJ1dbXKysqUkZExrjdrmPyc6rcZM2aovLxc5eXlKisr05EjR/TEE09o+vTpSa4QpnCq13p7e7Vv\n3z7t2rVLFRUVOn78uCzLUmFh4ZizsDV3ksrNzR39kvC0adOUn5+v3t5eNTU1adWqVZKkVatWqamp\nSZKUnZ2t+fPny+v1XnMej8ejqVOnSpJisZhisZg8Ho+LlSAVONVvknTx4kWdOnVKa9asca8ApAwn\new34J0712p9//qnW1lbdf//9kqT09HSGAvxNIq5tLS0tmj17tvLy8hJfAFKGk70Wj8c1PDysWCym\n4eFh5ebmjisLW3P/A7q7uxWJRDR//nxdvnx5tElycnJ0+fLlfz0+Ho/rueee0/nz5/Xggw9qwYIF\niY6MFHaj/Xbw4EE9/vjjGhwcTHRUpLgb7TVJeuWVVyRJDzzwAHdFcV030mvd3d3KyspSfX29fv75\nZ82bN09VVVWjH/IC/8+Ja5skffnll7r33nsTFROTwI30mt/v1/r167Vlyxb5fD4tXrxYixcvHtfr\nc0d0kotGo9q9e7eqqqqUkZFxze88Hs+Y7m6mpaVp586dCofD6uzs1C+//JKouEhxN9pvJ0+eVHZ2\nNo+kx79y4tq2Y8cOvf7663r++ef12Wef6fvvv09UXKSwG+21WCymSCSi8vJyvfHGG5oyZYo+/PDD\nREZGCnPi2iZJlmXp5MmTCgaDiYiJSeBGe62/v19NTU0KhUJqaGhQNBrVsWPHxpWBQXQSsyxLu3fv\nVmlpqYqLiyVdvb3e19cnSerr61NWVtaYzzd9+nTdeeedOn36dELyIrU50W8//vijvv76a23dulVv\nvfWWvv32W+3Zsyfh2ZFanLq2+f3+0WOXL1+ujo6OxIVGSnKi1wKBgAKBwOhuomAwqEgkktjgSElO\nvm/75ptvNHfuXOXk5CQsL1KXE73W0tKiWbNmKSsrS+np6SouLlZbW9u4cjCITlK2bSscDis/P1/r\n1q0b/fmyZct09OhRSdLRo0e1fPnyfzzPlStXNDAwIOnqE3Sbm5uVn5+fuOBISU7122OPPaZwOKxQ\nKKRt27bprrvu0jPPPJPQ7EgtTvVaNBod3f4djUbV3NysOXPmJC44Uo5TvZaTk6NAIKCuri5JV9+8\n3XLLLYkLjpTkVL+NYFsursepXps5c6ba29s1NDQk27bV0tIy7hnBY9u2Pf4SYLoffvhBL774oubM\nmTN6a72yslILFixQXV2dLly4cM2jmS9duqSamhoNDg6OPqDozTffVE9Pj0KhkOLxuGzb1ooVK/Tw\nww8nuTqYxql++9+tId99950OHz7Mf9+CazjVa3/88Yd27dol6erWyZKSEm3YsCGZpcEwTl7Xzpw5\no3A4LMuyNGvWLFVXVyszMzPJFcIkTvZbNBpVdXW19u7d+7ctl4CTvfb+++/r+PHj8nq9Kiws1NNP\nP62bbrppzFkYRAEAAAAArmJrLgAAAADAVQyiAAAAAABXMYgCAAAAAFzFIAoAAAAAcBWDKAAAAADA\nVQyiAABMUCgU0nvvvZfsGAAApBwGUQAAEqy2tlaff/55smMAAGAMBlEAAAAAgKvSkx0AAIBUEYlE\nFA6Hde7cOS1ZskQej0eS1N/fr71796q9vV3xeFwLFy7UU089pUAgoMbGRrW2tqq9vV0HDx7U6tWr\ntXnzZp09e1YHDhzQTz/9pKysLD3yyCO65557klwhAADu4I4oAABjYFmWdu7cqdLSUh04cEArVqzQ\niRMnJEm2bWv16tWqr69XfX29fD6f9u/fL0mqrKzU7bffrk2bNundd9/V5s2bFY1G9fLLL6ukpETv\nvPOOtm3bpv379+u3335LZokAALiGQRQAgDFoa2tTLBbT2rVrlZ6ermAwqNtuu02SNGPGDAWDQU2Z\nMkXTpk3Thg0b1Nraet1znTp1Snl5ebrvvvvk9Xo1d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"text/plain": [
"<matplotlib.figure.Figure at 0x10ac8e810>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot('date','image_count', figsize=(16,4), style='b')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One possibility would be to filter the data for summary periods which had at least 10 images. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10ad8bbd0>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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D9mdKFwQnGh7CGNlw/3C80OkFjGgxAvc2u5dvZwtD9qI+V3gOgDgxwMQ2E/FP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4/p9DqTkfUtV7bAXeQOF6ELfrr2E3669hMiFBHYPHFzg0UpCDHWR1k3O2zd+HU0eknsoqHe5JYu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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ad7fc10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df10 = df[df['image_count'] >= 10]\n",
"df10.plot('date', ['gcc_90'], figsize=(16,4),\n",
" grid=True, style=['g'] )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This looks a little cleaner especially for the 2013 season. "
]
},
{
"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": 1
}
| cc0-1.0 |
QuantStack/quantstack-talks | 2019-06-26-GeoPython/notebooks/4.0.xcpp.ipynb | 1 | 8737 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Xeus-Cling\n",
"\n",
"## Repository: https://github.com/QuantStack/xeus-cling\n",
"## Installation\n",
"`conda install -c conda-forge ipyleaflet`\n",
"\n",
"A Jupyter kernel for C++ based on the `cling` C++ interpreter and the `xeus` native implementation of the Jupyter protocol, xeus.\n",
"\n",
"- GitHub repository: https://github.com/QuantStack/xeus-cling/\n",
"- Online documentation: https://xeus-cling.readthedocs.io/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Output and error streams\n",
"\n",
"`std::cout` and `std::cerr` are redirected to the notebook frontend."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#include <iostream>\n",
"\n",
"std::cout << \"some output\" << std::endl;"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"std::cerr << \"some error\" << std::endl;"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#include <stdexcept>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"throw std::runtime_error(\"Unknown exception\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Documentation and completion\n",
"\n",
" - Documentation for types of the standard library is retrieved on cppreference.com.\n",
" - The quick-help feature can also be enabled for user-defined types and third-party libraries. More documentation on this feature is available at https://xeus-cling.readthedocs.io/en/latest/inline_help.html.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"?xt::xtensor"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using the `display_data` mechanism"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For a user-defined type `T`, the rich rendering in the notebook and JupyterLab can be enabled by by implementing the function `xeus::xjson mime_bundle_repr(const T& im)`, which returns the JSON mime bundle for that type.\n",
"\n",
"More documentation on the rich display system of Jupyter and Xeus-cling is available at https://xeus-cling.readthedocs.io/en/latest/rich_display.html"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Image example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#include <string>\n",
"#include <fstream>\n",
"\n",
"#include \"xtl/xbase64.hpp\"\n",
"#include \"xeus/xjson.hpp\"\n",
"\n",
"namespace im\n",
"{\n",
" struct image\n",
" { \n",
" inline image(const std::string& filename)\n",
" {\n",
" std::ifstream fin(filename, std::ios::binary); \n",
" m_buffer << fin.rdbuf();\n",
" }\n",
" \n",
" std::stringstream m_buffer;\n",
" };\n",
" \n",
" xeus::xjson mime_bundle_repr(const image& i)\n",
" {\n",
" auto bundle = xeus::xjson::object();\n",
" bundle[\"image/png\"] = xtl::base64encode(i.m_buffer.str());\n",
" return bundle;\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"im::image marie(\"src/marie.png\");\n",
"marie"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Magics\n",
"\n",
"Magics are special commands for the kernel that are not part of the C++ language.\n",
"\n",
"They are defined with the symbol `%` for a line magic and `%%` for a cell magic.\n",
"\n",
"More documentation for magics is available at https://xeus-cling.readthedocs.io/en/latest/magics.html."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#include <algorithm>\n",
"#include <vector>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"std::vector<double> to_shuffle = {1, 2, 3, 4};"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%timeit std::random_shuffle(to_shuffle.begin(), to_shuffle.end());"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# `xtensor`: N-D arrays in C++ \n",
"\n",
"- NumPy-style API\n",
"- Idiomatic STL-style C++\n",
"- Lazily evaluated"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#include <iostream>\n",
"\n",
"#include \"xtensor/xarray.hpp\"\n",
"#include \"xtensor/xio.hpp\"\n",
"#include \"xtensor/xview.hpp\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xt::xarray<int> arr\n",
" {1, 2, 3, 4, 5, 6, 7, 8, 9};\n",
"\n",
"arr.reshape({3, 3});\n",
"\n",
"arr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# `Symengine`: Symbolic Computing in C++\n",
"\n",
"- by the creators of Sympy\n",
"- now a possible engine of sympy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#include <symengine/expression.h>\n",
"\n",
"using SymEngine::Expression;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rich rendering of mathematical expressions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Expression x(\"x\");\n",
"\n",
"auto ex = pow(x + sqrt(Expression(2)), 10);\n",
"ex"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"expand(ex)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Combining `xtensor` and `Symengine`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#include <xtensor/xarray.hpp>\n",
"#include <xtensor/xbuilder.hpp>\n",
"#include <xtensor/xio.hpp>\n",
"#include <xtensor/xview.hpp>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Expression y(\"z\");"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xt::xarray<int> e = xt::linspace(0.0, 100.0, 6);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"e + y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#include <xtensor/xreducer.hpp>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"auto s = (e + y) * xt::view(e + y, xt::all(), xt::newaxis());\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xt::sum(s)()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Widgets"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#include <xwidgets/xslider.hpp>\n",
"#include <xwidgets/xbutton.hpp>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xw::slider<double> slider;\n",
"slider"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"auto button = xw::button_generator()\n",
" .description(\"Click me!\")\n",
" .finalize();\n",
"button"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "C++14",
"language": "C++14",
"name": "xcpp14"
},
"language_info": {
"codemirror_mode": "text/x-c++src",
"file_extension": ".cpp",
"mimetype": "text/x-c++src",
"name": "c++",
"version": "-std=c++14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-3-clause |
neurodata/synaptome-stats | collman15v2/201710/Check_Annotation_IDS.ipynb | 1 | 7901 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import argparse\n",
"from intern.remote.boss import BossRemote\n",
"from intern.resource.boss.resource import *\n",
"from intern.utils.parallel import block_compute\n",
"import configparser\n",
"import requests\n",
"import numpy as np\n",
"from numpy import genfromtxt\n",
"import shutil\n",
"import blosc\n",
"from IPython.core.debugger import set_trace\n",
"import sys\n",
"import os\n",
"import itertools\n",
"from functools import partial\n",
"from multiprocessing import Pool\n",
"from multiprocessing.dummy import Pool as ThreadPool\n",
"from multiprocessing import cpu_count\n",
"import csv\n",
"import datetime\n",
"import toolbox"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def main(COLL_NAME, EXP_NAME, COORD_FRAME,\n",
" CHAN_NAMES=None, num_threads = 4, CONFIG_FILE= 'config.ini'):\n",
"\n",
" bf = [5,245,245] # in z,y,x order\n",
"\n",
" config = configparser.ConfigParser()\n",
" config.read(CONFIG_FILE)\n",
" TOKEN = config['Default']['token']\n",
" boss_url = ''.join( ( config['Default']['protocol'],'://',config['Default']['host'],'/v1/' ) )\n",
" #print(boss_url)\n",
" #'https://api.boss.neurodata.io/v1/'\n",
" \n",
" #intern\n",
" rem = BossRemote(CONFIG_FILE)\n",
"\n",
" cf = CoordinateFrameResource(str(COLL_NAME + '_' + EXP_NAME))\n",
" cfr = rem.get_project(cf)\n",
" anno_res = ChannelResource('annotation', COLL_NAME, EXP_NAME, 'annotation', datatype='uint64')\n",
" \n",
" ex = {'x': cfr.x_stop, 'y': cfr.y_stop, 'z': cfr.z_stop}\n",
" \n",
" blocks = block_compute(0,ex['x'],0,ex['y'],0,ex['z'],\n",
" origin = (0,0,0), block_size = (512, 512, 16))\n",
"\n",
" rid = []\n",
" for b in blocks:\n",
" rid = rid + rem.get_ids_in_region(anno_res, 0, b[0], b[1], b[2], [0,1])\n",
"\n",
" u = np.unique(np.asarray(rid))\n",
"\n",
" ## bounding box for annotation_i\n",
" #bb = [rem.get_bounding_box(anno_res, 0,ui, 'tight') for ui in u]\n",
"#\n",
" #for i in range(len(bb)):\n",
" # bb[i][\"id\"] = u[i]\n",
"#\n",
" #A = [(rem.get_cutout(\n",
" # anno_res, 0, bb[i][\"x_range\"], \n",
" # bb[i][\"y_range\"], bb[i][\"z_range\"], \n",
" # id_list = [bb[i]['id']]) != 0).astype(int) \n",
" # for i in range(len(bb))] \n",
"#\n",
" ##Bmeans = [np.int32(np.round(np.mean(np.asarray(np.where(A[i] == True)),1))) for i in range(len(A))]\n",
" #Bmeans = [np.int32(np.round(np.mean(np.asarray(np.where(A[i] == 1)),1))) for i in range(len(A))]\n",
" #\n",
" #Bglobal = []\n",
" #for i in range(len(bb)):\n",
" # ad1 = np.asarray([bb[i]['z_range'][0], bb[i]['y_range'][0], bb[i]['x_range'][0]])\n",
" # Bglobal.append(Bmeans[i] + ad1)\n",
" # \n",
" #ColMin = np.asarray(bf)\n",
" #ColMax = np.asarray([ex['z'] - (bf[0] + 1), # The z index is inclusive\n",
" # ex['y'] - (bf[1] + 1), \n",
" # ex['x'] - (bf[2] + 1)])\n",
"#\n",
" #m = [Bglobal[i] >= ColMin for i in range(len(Bglobal))]\n",
" #M = [Bglobal[i] <= ColMax for i in range(len(Bglobal))]\n",
" #mm = [np.all(m[i]) for i in range(len(m)) ]\n",
" #MM = [np.all(M[i]) for i in range(len(M)) ]\n",
"#\n",
" #Bcon = []\n",
" #con = [np.asarray(mm[j] and MM[j]) for j in range(len(mm))]\n",
" #for i in range(len(Bglobal)):\n",
" # if con[i]: \n",
" # Bcon.append(Bglobal[i])\n",
"#\n",
" #loc = np.asarray(Bcon)\n",
" return(u)\n",
"## END main"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def testMain():\n",
" COLL_NAME = 'collman' \n",
" EXP_NAME = 'collman15v2' \n",
" COORD_FRAME = 'collman_collman15v2'\n",
" CONFIG_FILE = 'config.ini'\n",
" OUTPUT = 'fmaxTest20171214.csv'\n",
"\n",
" CHAN_NAMES = ['Synapsin647', 'VGluT1_647']\n",
" #CHAN_NAMES = ['DAPI1st', 'DAPI2nd', 'DAPI3rd', 'GABA488', 'GAD647',\n",
" # 'gephyrin594', 'GS594', 'MBP488', 'NR1594', 'PSD95_488',\n",
" # 'Synapsin647', 'VGluT1_647']\n",
" #CHAN_NAMES = ['synapsin', 'PSDr'] \n",
"\n",
" Bcon = main(COLL_NAME, EXP_NAME, COORD_FRAME,\n",
" CHAN_NAMES=CHAN_NAMES, num_threads = 6, CONFIG_FILE= 'config.ini')\n",
"\n",
" return(Bcon)\n",
"## End testMain "
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Bcon = testMain() "
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"236"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(Bcon)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n",
" 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27,\n",
" 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,\n",
" 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
" 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,\n",
" 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,\n",
" 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,\n",
" 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105,\n",
" 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118,\n",
" 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131,\n",
" 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144,\n",
" 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157,\n",
" 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170,\n",
" 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183,\n",
" 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196,\n",
" 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209,\n",
" 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222,\n",
" 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235,\n",
" 236, 237])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Bcon"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
colour-science/colour-ipython | notebooks/appearance/cam16.ipynb | 1 | 764 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# !!! D . R . A . F . T !!!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CAM16 Colour Appearance Model"
]
},
{
"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": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| bsd-3-clause |
keras-team/keras-io | examples/vision/ipynb/mlp_image_classification.ipynb | 1 | 22794 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"# Image classification with modern MLP models\n",
"\n",
"**Author:** [Khalid Salama](https://www.linkedin.com/in/khalid-salama-24403144/)<br>\n",
"**Date created:** 2021/05/30<br>\n",
"**Last modified:** 2021/05/30<br>\n",
"**Description:** Implementing the MLP-Mixer, FNet, and gMLP models for CIFAR-100 image classification."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Introduction\n",
"\n",
"This example implements three modern attention-free, multi-layer perceptron (MLP) based models for image\n",
"classification, demonstrated on the CIFAR-100 dataset:\n",
"\n",
"1. The [MLP-Mixer](https://arxiv.org/abs/2105.01601) model, by Ilya Tolstikhin et al., based on two types of MLPs.\n",
"3. The [FNet](https://arxiv.org/abs/2105.03824) model, by James Lee-Thorp et al., based on unparameterized\n",
"Fourier Transform.\n",
"2. The [gMLP](https://arxiv.org/abs/2105.08050) model, by Hanxiao Liu et al., based on MLP with gating.\n",
"\n",
"The purpose of the example is not to compare between these models, as they might perform differently on\n",
"different datasets with well-tuned hyperparameters. Rather, it is to show simple implementations of their\n",
"main building blocks.\n",
"\n",
"This example requires TensorFlow 2.4 or higher, as well as\n",
"[TensorFlow Addons](https://www.tensorflow.org/addons/overview),\n",
"which can be installed using the following command:\n",
"\n",
"```shell\n",
"pip install -U tensorflow-addons\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"import tensorflow_addons as tfa"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Prepare the data"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"num_classes = 100\n",
"input_shape = (32, 32, 3)\n",
"\n",
"(x_train, y_train), (x_test, y_test) = keras.datasets.cifar100.load_data()\n",
"\n",
"print(f\"x_train shape: {x_train.shape} - y_train shape: {y_train.shape}\")\n",
"print(f\"x_test shape: {x_test.shape} - y_test shape: {y_test.shape}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Configure the hyperparameters"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"weight_decay = 0.0001\n",
"batch_size = 128\n",
"num_epochs = 50\n",
"dropout_rate = 0.2\n",
"image_size = 64 # We'll resize input images to this size.\n",
"patch_size = 8 # Size of the patches to be extracted from the input images.\n",
"num_patches = (image_size // patch_size) ** 2 # Size of the data array.\n",
"embedding_dim = 256 # Number of hidden units.\n",
"num_blocks = 4 # Number of blocks.\n",
"\n",
"print(f\"Image size: {image_size} X {image_size} = {image_size ** 2}\")\n",
"print(f\"Patch size: {patch_size} X {patch_size} = {patch_size ** 2} \")\n",
"print(f\"Patches per image: {num_patches}\")\n",
"print(f\"Elements per patch (3 channels): {(patch_size ** 2) * 3}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Build a classification model\n",
"\n",
"We implement a method that builds a classifier given the processing blocks."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"\n",
"def build_classifier(blocks, positional_encoding=False):\n",
" inputs = layers.Input(shape=input_shape)\n",
" # Augment data.\n",
" augmented = data_augmentation(inputs)\n",
" # Create patches.\n",
" patches = Patches(patch_size, num_patches)(augmented)\n",
" # Encode patches to generate a [batch_size, num_patches, embedding_dim] tensor.\n",
" x = layers.Dense(units=embedding_dim)(patches)\n",
" if positional_encoding:\n",
" positions = tf.range(start=0, limit=num_patches, delta=1)\n",
" position_embedding = layers.Embedding(\n",
" input_dim=num_patches, output_dim=embedding_dim\n",
" )(positions)\n",
" x = x + position_embedding\n",
" # Process x using the module blocks.\n",
" x = blocks(x)\n",
" # Apply global average pooling to generate a [batch_size, embedding_dim] representation tensor.\n",
" representation = layers.GlobalAveragePooling1D()(x)\n",
" # Apply dropout.\n",
" representation = layers.Dropout(rate=dropout_rate)(representation)\n",
" # Compute logits outputs.\n",
" logits = layers.Dense(num_classes)(representation)\n",
" # Create the Keras model.\n",
" return keras.Model(inputs=inputs, outputs=logits)\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Define an experiment\n",
"\n",
"We implement a utility function to compile, train, and evaluate a given model."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"\n",
"def run_experiment(model):\n",
" # Create Adam optimizer with weight decay.\n",
" optimizer = tfa.optimizers.AdamW(\n",
" learning_rate=learning_rate, weight_decay=weight_decay,\n",
" )\n",
" # Compile the model.\n",
" model.compile(\n",
" optimizer=optimizer,\n",
" loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
" metrics=[\n",
" keras.metrics.SparseCategoricalAccuracy(name=\"acc\"),\n",
" keras.metrics.SparseTopKCategoricalAccuracy(5, name=\"top5-acc\"),\n",
" ],\n",
" )\n",
" # Create a learning rate scheduler callback.\n",
" reduce_lr = keras.callbacks.ReduceLROnPlateau(\n",
" monitor=\"val_loss\", factor=0.5, patience=5\n",
" )\n",
" # Create an early stopping callback.\n",
" early_stopping = tf.keras.callbacks.EarlyStopping(\n",
" monitor=\"val_loss\", patience=10, restore_best_weights=True\n",
" )\n",
" # Fit the model.\n",
" history = model.fit(\n",
" x=x_train,\n",
" y=y_train,\n",
" batch_size=batch_size,\n",
" epochs=num_epochs,\n",
" validation_split=0.1,\n",
" callbacks=[early_stopping, reduce_lr],\n",
" )\n",
"\n",
" _, accuracy, top_5_accuracy = model.evaluate(x_test, y_test)\n",
" print(f\"Test accuracy: {round(accuracy * 100, 2)}%\")\n",
" print(f\"Test top 5 accuracy: {round(top_5_accuracy * 100, 2)}%\")\n",
"\n",
" # Return history to plot learning curves.\n",
" return history\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Use data augmentation"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"data_augmentation = keras.Sequential(\n",
" [\n",
" layers.Normalization(),\n",
" layers.Resizing(image_size, image_size),\n",
" layers.RandomFlip(\"horizontal\"),\n",
" layers.RandomZoom(\n",
" height_factor=0.2, width_factor=0.2\n",
" ),\n",
" ],\n",
" name=\"data_augmentation\",\n",
")\n",
"# Compute the mean and the variance of the training data for normalization.\n",
"data_augmentation.layers[0].adapt(x_train)\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Implement patch extraction as a layer"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"\n",
"class Patches(layers.Layer):\n",
" def __init__(self, patch_size, num_patches):\n",
" super(Patches, self).__init__()\n",
" self.patch_size = patch_size\n",
" self.num_patches = num_patches\n",
"\n",
" def call(self, images):\n",
" batch_size = tf.shape(images)[0]\n",
" patches = tf.image.extract_patches(\n",
" images=images,\n",
" sizes=[1, self.patch_size, self.patch_size, 1],\n",
" strides=[1, self.patch_size, self.patch_size, 1],\n",
" rates=[1, 1, 1, 1],\n",
" padding=\"VALID\",\n",
" )\n",
" patch_dims = patches.shape[-1]\n",
" patches = tf.reshape(patches, [batch_size, self.num_patches, patch_dims])\n",
" return patches\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## The MLP-Mixer model\n",
"\n",
"The MLP-Mixer is an architecture based exclusively on\n",
"multi-layer perceptrons (MLPs), that contains two types of MLP layers:\n",
"\n",
"1. One applied independently to image patches, which mixes the per-location features.\n",
"2. The other applied across patches (along channels), which mixes spatial information.\n",
"\n",
"This is similar to a [depthwise separable convolution based model](https://arxiv.org/pdf/1610.02357.pdf)\n",
"such as the Xception model, but with two chained dense transforms, no max pooling, and layer normalization\n",
"instead of batch normalization."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"### Implement the MLP-Mixer module"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"\n",
"class MLPMixerLayer(layers.Layer):\n",
" def __init__(self, num_patches, hidden_units, dropout_rate, *args, **kwargs):\n",
" super(MLPMixerLayer, self).__init__(*args, **kwargs)\n",
"\n",
" self.mlp1 = keras.Sequential(\n",
" [\n",
" layers.Dense(units=num_patches),\n",
" tfa.layers.GELU(),\n",
" layers.Dense(units=num_patches),\n",
" layers.Dropout(rate=dropout_rate),\n",
" ]\n",
" )\n",
" self.mlp2 = keras.Sequential(\n",
" [\n",
" layers.Dense(units=num_patches),\n",
" tfa.layers.GELU(),\n",
" layers.Dense(units=embedding_dim),\n",
" layers.Dropout(rate=dropout_rate),\n",
" ]\n",
" )\n",
" self.normalize = layers.LayerNormalization(epsilon=1e-6)\n",
"\n",
" def call(self, inputs):\n",
" # Apply layer normalization.\n",
" x = self.normalize(inputs)\n",
" # Transpose inputs from [num_batches, num_patches, hidden_units] to [num_batches, hidden_units, num_patches].\n",
" x_channels = tf.linalg.matrix_transpose(x)\n",
" # Apply mlp1 on each channel independently.\n",
" mlp1_outputs = self.mlp1(x_channels)\n",
" # Transpose mlp1_outputs from [num_batches, hidden_dim, num_patches] to [num_batches, num_patches, hidden_units].\n",
" mlp1_outputs = tf.linalg.matrix_transpose(mlp1_outputs)\n",
" # Add skip connection.\n",
" x = mlp1_outputs + inputs\n",
" # Apply layer normalization.\n",
" x_patches = self.normalize(x)\n",
" # Apply mlp2 on each patch independtenly.\n",
" mlp2_outputs = self.mlp2(x_patches)\n",
" # Add skip connection.\n",
" x = x + mlp2_outputs\n",
" return x\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"### Build, train, and evaluate the MLP-Mixer model\n",
"\n",
"Note that training the model with the current settings on a V100 GPUs\n",
"takes around 8 seconds per epoch."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"mlpmixer_blocks = keras.Sequential(\n",
" [MLPMixerLayer(num_patches, embedding_dim, dropout_rate) for _ in range(num_blocks)]\n",
")\n",
"learning_rate = 0.005\n",
"mlpmixer_classifier = build_classifier(mlpmixer_blocks)\n",
"history = run_experiment(mlpmixer_classifier)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"The MLP-Mixer model tends to have much less number of parameters compared\n",
"to convolutional and transformer-based models, which leads to less training and\n",
"serving computational cost.\n",
"\n",
"As mentioned in the [MLP-Mixer](https://arxiv.org/abs/2105.01601) paper,\n",
"when pre-trained on large datasets, or with modern regularization schemes,\n",
"the MLP-Mixer attains competitive scores to state-of-the-art models.\n",
"You can obtain better results by increasing the embedding dimensions,\n",
"increasing the number of mixer blocks, and training the model for longer.\n",
"You may also try to increase the size of the input images and use different patch sizes."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## The FNet model\n",
"\n",
"The FNet uses a similar block to the Transformer block. However, FNet replaces the self-attention layer\n",
"in the Transformer block with a parameter-free 2D Fourier transformation layer:\n",
"\n",
"1. One 1D Fourier Transform is applied along the patches.\n",
"2. One 1D Fourier Transform is applied along the channels."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"### Implement the FNet module"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"\n",
"class FNetLayer(layers.Layer):\n",
" def __init__(self, num_patches, embedding_dim, dropout_rate, *args, **kwargs):\n",
" super(FNetLayer, self).__init__(*args, **kwargs)\n",
"\n",
" self.ffn = keras.Sequential(\n",
" [\n",
" layers.Dense(units=embedding_dim),\n",
" tfa.layers.GELU(),\n",
" layers.Dropout(rate=dropout_rate),\n",
" layers.Dense(units=embedding_dim),\n",
" ]\n",
" )\n",
"\n",
" self.normalize1 = layers.LayerNormalization(epsilon=1e-6)\n",
" self.normalize2 = layers.LayerNormalization(epsilon=1e-6)\n",
"\n",
" def call(self, inputs):\n",
" # Apply fourier transformations.\n",
" x = tf.cast(\n",
" tf.signal.fft2d(tf.cast(inputs, dtype=tf.dtypes.complex64)),\n",
" dtype=tf.dtypes.float32,\n",
" )\n",
" # Add skip connection.\n",
" x = x + inputs\n",
" # Apply layer normalization.\n",
" x = self.normalize1(x)\n",
" # Apply Feedfowrad network.\n",
" x_ffn = self.ffn(x)\n",
" # Add skip connection.\n",
" x = x + x_ffn\n",
" # Apply layer normalization.\n",
" return self.normalize2(x)\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"### Build, train, and evaluate the FNet model\n",
"\n",
"Note that training the model with the current settings on a V100 GPUs\n",
"takes around 8 seconds per epoch."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"fnet_blocks = keras.Sequential(\n",
" [FNetLayer(num_patches, embedding_dim, dropout_rate) for _ in range(num_blocks)]\n",
")\n",
"learning_rate = 0.001\n",
"fnet_classifier = build_classifier(fnet_blocks, positional_encoding=True)\n",
"history = run_experiment(fnet_classifier)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"As shown in the [FNet](https://arxiv.org/abs/2105.03824) paper,\n",
"better results can be achieved by increasing the embedding dimensions,\n",
"increasing the number of FNet blocks, and training the model for longer.\n",
"You may also try to increase the size of the input images and use different patch sizes.\n",
"The FNet scales very efficiently to long inputs, runs much faster than attention-based\n",
"Transformer models, and produces competitive accuracy results."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## The gMLP model\n",
"\n",
"The gMLP is a MLP architecture that features a Spatial Gating Unit (SGU).\n",
"The SGU enables cross-patch interactions across the spatial (channel) dimension, by:\n",
"\n",
"1. Transforming the input spatially by applying linear projection across patches (along channels).\n",
"2. Applying element-wise multiplication of the input and its spatial transformation."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"### Implement the gMLP module"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"\n",
"class gMLPLayer(layers.Layer):\n",
" def __init__(self, num_patches, embedding_dim, dropout_rate, *args, **kwargs):\n",
" super(gMLPLayer, self).__init__(*args, **kwargs)\n",
"\n",
" self.channel_projection1 = keras.Sequential(\n",
" [\n",
" layers.Dense(units=embedding_dim * 2),\n",
" tfa.layers.GELU(),\n",
" layers.Dropout(rate=dropout_rate),\n",
" ]\n",
" )\n",
"\n",
" self.channel_projection2 = layers.Dense(units=embedding_dim)\n",
"\n",
" self.spatial_projection = layers.Dense(\n",
" units=num_patches, bias_initializer=\"Ones\"\n",
" )\n",
"\n",
" self.normalize1 = layers.LayerNormalization(epsilon=1e-6)\n",
" self.normalize2 = layers.LayerNormalization(epsilon=1e-6)\n",
"\n",
" def spatial_gating_unit(self, x):\n",
" # Split x along the channel dimensions.\n",
" # Tensors u and v will in th shape of [batch_size, num_patchs, embedding_dim].\n",
" u, v = tf.split(x, num_or_size_splits=2, axis=2)\n",
" # Apply layer normalization.\n",
" v = self.normalize2(v)\n",
" # Apply spatial projection.\n",
" v_channels = tf.linalg.matrix_transpose(v)\n",
" v_projected = self.spatial_projection(v_channels)\n",
" v_projected = tf.linalg.matrix_transpose(v_projected)\n",
" # Apply element-wise multiplication.\n",
" return u * v_projected\n",
"\n",
" def call(self, inputs):\n",
" # Apply layer normalization.\n",
" x = self.normalize1(inputs)\n",
" # Apply the first channel projection. x_projected shape: [batch_size, num_patches, embedding_dim * 2].\n",
" x_projected = self.channel_projection1(x)\n",
" # Apply the spatial gating unit. x_spatial shape: [batch_size, num_patches, embedding_dim].\n",
" x_spatial = self.spatial_gating_unit(x_projected)\n",
" # Apply the second channel projection. x_projected shape: [batch_size, num_patches, embedding_dim].\n",
" x_projected = self.channel_projection2(x_spatial)\n",
" # Add skip connection.\n",
" return x + x_projected\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"### Build, train, and evaluate the gMLP model\n",
"\n",
"Note that training the model with the current settings on a V100 GPUs\n",
"takes around 9 seconds per epoch."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"gmlp_blocks = keras.Sequential(\n",
" [gMLPLayer(num_patches, embedding_dim, dropout_rate) for _ in range(num_blocks)]\n",
")\n",
"learning_rate = 0.003\n",
"gmlp_classifier = build_classifier(gmlp_blocks)\n",
"history = run_experiment(gmlp_classifier)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"As shown in the [gMLP](https://arxiv.org/abs/2105.08050) paper,\n",
"better results can be achieved by increasing the embedding dimensions,\n",
"increasing, increasing the number of gMLP blocks, and training the model for longer.\n",
"You may also try to increase the size of the input images and use different patch sizes.\n",
"Note that, the paper used advanced regularization strategies, such as MixUp and CutMix,\n",
"as well as AutoAugment."
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "mlp_image_classification",
"private_outputs": false,
"provenance": [],
"toc_visible": true
},
"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": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | apache-2.0 |
marcinofulus/teaching | Python4physicists_SS2017/Wyklad_notes.ipynb | 1 | 19590 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(\"asdf asfd\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\"asdf asdf asfd \""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(2+3)\n",
"2+2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a = 1230"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(a+1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(a+11)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a = input()\n",
"\n",
"\n",
"print(int(a)+1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## 2.3.2017"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"xx\n"
]
}
],
"source": [
"print(\"xx\")"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(range(10))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ValueError",
"evalue": "invalid literal for int() with base 10: '1a'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-3c0bffb342e5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'1a'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: '1a'"
]
}
],
"source": [
"int('1')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Zen of Python, by Tim Peters\n",
"\n",
"Beautiful is better than ugly.\n",
"Explicit is better than implicit.\n",
"Simple is better than complex.\n",
"Complex is better than complicated.\n",
"Flat is better than nested.\n",
"Sparse is better than dense.\n",
"Readability counts.\n",
"Special cases aren't special enough to break the rules.\n",
"Although practicality beats purity.\n",
"Errors should never pass silently.\n",
"Unless explicitly silenced.\n",
"In the face of ambiguity, refuse the temptation to guess.\n",
"There should be one-- and preferably only one --obvious way to do it.\n",
"Although that way may not be obvious at first unless you're Dutch.\n",
"Now is better than never.\n",
"Although never is often better than *right* now.\n",
"If the implementation is hard to explain, it's a bad idea.\n",
"If the implementation is easy to explain, it may be a good idea.\n",
"Namespaces are one honking great idea -- let's do more of those!\n"
]
}
],
"source": [
"import this"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.5"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1/2"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.1"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"0.1"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.1"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1/10"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1//2"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1%2"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1/2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.5"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1./2"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bę=1"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bę"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# asd as asfd "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# asd asd fasdf\n",
"a = 12 "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a\n",
"b\n"
]
}
],
"source": [
"if False:\n",
" pass\n",
"print(\"a\")\n",
"print(\"b\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# \n",
"```C\n",
" for(int i=1;i<10;i++) { asdf asdf }\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"2\n",
"3\n",
"4\n",
"5\n",
"6\n",
"7\n",
"8\n",
"9\n"
]
}
],
"source": [
"for i in range(1,10,1):\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"l = ['aa', 1 ,2, 2323,'ble'] "
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['aa']"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l[:2:2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 aa\n",
"1 1\n",
"2 2\n",
"3 2323\n",
"4 ble\n"
]
}
],
"source": [
"for i in range(0,len(l),1) :\n",
" print(i,l[i])"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 aa\n",
"1 1\n",
"2 2\n",
"3 2323\n",
"4 ble\n"
]
}
],
"source": [
"i = 0\n",
"for el in l:\n",
" print(i,el)\n",
" i+=1"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 aa\n",
"1 1\n",
"2 2\n",
"3 2323\n",
"4 ble\n"
]
}
],
"source": [
"for i,el in enumerate(l):\n",
" print(i,el)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(0, 'aa'), (1, 1), (2, 2), (3, 2323), (4, 'ble')]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list( enumerate(l) )"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"l.append(\"$Asdfasdf\")"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['1', '2', '2323', 'Asdfasdf', 'aa', 'ble', '$Asdfasdf']"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"l.reverse()"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['$Asdfasdf', 'ble', 'aa', 'Asdfasdf', '2323', '2', '1']"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"l.sort()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"l = list(map(str,l))\n",
"l.sort()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['$Asdfasdf', '1', '2', '2323', 'Asdfasdf', 'aa', 'ble']"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"l.sort?"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"l = ['a',1,2]"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"l.sort(key=lambda x:str(x))"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 'a']"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy \n",
"import math"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'sin' 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-78-93e746b069d9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msin\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[0m",
"\u001b[0;31mNameError\u001b[0m: name 'sin' is not defined"
]
}
],
"source": [
"sin(1)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"600"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(dir(numpy))"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"54"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(dir(math))"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.8414709848078965"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"math.sin(1)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.8414709848078965"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numpy.sin(1)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"numpy.sin?"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.84147098, 0.90929743, 0.14112001])"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numpy.sin([1,2,3])"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.8414709848078965"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"math.sin(1)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "a float is required",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-84-fa0f50f7ee9c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: a float is required"
]
}
],
"source": [
"math.sin([1,2,3])"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from math import sin,cos"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy,math"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.sin?"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from math import *"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on built-in function sin in module math:\n",
"\n",
"sin(...)\n",
" sin(x)\n",
" \n",
" Return the sine of x (measured in radians).\n",
"\n"
]
}
],
"source": [
"help(sin)"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def mojsin(x):\n",
" \"\"\"\n",
" To jest mój Narodowy sinus!\n",
" \"\"\"\n",
" return sin(x)\n"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.sin?"
]
},
{
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
scotgl/sonify | ver_0.5.1/2. Full_or_Empty_Training_Module.ipynb | 2 | 32115 | {
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"hideCode": false,
"hidePrompt": false,
"hide_input": true,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The UTDallas ArtSci Lab Sonification\n",
" \"Half-Empty/Half-Full\" Training Module.\n",
"\n",
"\n",
"Here are the instructions for those unfamiliar with these sonification training modules\n",
"\n",
" Basic Instructions for each cell :\n",
"1. Press \"Shift + Enter\" at every new cell to initiate display/execute that cell\n",
"2. Hit the Space Bar to go to next section (cell)\n",
"\n",
"\n",
"These are the two main actions that you need to move forward, cell by cell in each unit\n",
"This is how this system functions\n",
"Do these now\n"
]
}
],
"source": [
"from ipywidgets import interact, interactive, fixed, interact_manual\n",
"import ipywidgets as widgets\n",
"from gtts import gTTS\n",
"import matplotlib\n",
"import numpy \n",
"from traitlets.config.manager import BaseJSONConfigManager\n",
"path = \"/Users/scot/anaconda3/envs/py36/etc/jupyter/nbconfig\"\n",
"cm = BaseJSONConfigManager(config_dir=path)\n",
"cm.update(\"livereveal\", {\"autolaunch\": True,\n",
" \"theme\": \"sky\",\n",
" } \n",
")\n",
"\n",
"#Supress default INFO logging\n",
"# The UT Dallas Art Science Lab Training module \n",
"print ('The UTDallas ArtSci Lab Sonification')\n",
"print(' \"Half-Empty/Half-Full\" Training Module.')\n",
"print(\"\\n\")\n",
"print(\"Here are the instructions for those unfamiliar with these sonification training modules\" )\n",
"print(\"\\n Basic Instructions for each cell :\")\n",
"print('1. Press \"Shift + Enter\" at every new cell to initiate display/execute that cell')\n",
"print(\"2. Hit the Space Bar to go to next section (cell)\")\n",
"print(\"\\n\")\n",
"print(\"These are the two main actions that you need to move forward, cell by cell in each unit\")\n",
"print(\"This is how this system functions\")\n",
"print(\"Do these now\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"hide_input": true,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## As Before, Please put on your best headphones on and Set your system Volume to 50%.\n",
"## Words are not sonification, exactly, but listen in this next section use your ears!\n",
"## Use Space Bar or arrows at bottom right to navigate to next section"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import random\n",
"from gtts import gTTS\n",
"import time\n",
"from IPython.display import Image, display, clear_output\n",
"from ipywidgets import widgets\n",
"import os\n",
"import platform\n",
"speechflag = 0\n",
"if (platform.system()=='Windows'):\n",
" speechflag = 2\n",
"if (platform.system()!='Windows'):\n",
" speechflag = 1\n",
"\n",
"\n",
"display(Image('dep/images/glasses.jpg'))\n",
"tts = gTTS(text=('In this sonification,we try to represent the emptiness or fullness of something. For instance a drop of water falling into an empty glass sounds like'), lang='en')\n",
"tts.save(\"dep/audio/num.mp3\")\n",
"if (speechflag==1):\n",
" os.system(\"dep/audio/afplay num.mp3\")\n",
" os.system(\"dep/audio/afplay empty.mp3\")\n",
"if (speechflag==2):\n",
" os.system(\"cmdmp3 dep/audio/num.mp3\")\n",
" os.system(\"cmdmp3 dep/audio/empty.mp3\")\n",
"\n",
"\n",
"tts = gTTS(text=('Or the same drop of water falling into a full glass might sound like'), lang='en')\n",
"tts.save(\"dep/audio/num.mp3\")\n",
"if (speechflag==1):\n",
" os.system(\"afplay dep/audio/num.mp3\")\n",
" os.system(\"afplay dep/audio/full.mp3\")\n",
"if (speechflag==2):\n",
" os.system(\"cmdmp3 dep/audio/num.mp3\")\n",
" os.system(\"cmdmp3 dep/audio/full.mp3\")\n",
"\n",
"tts = gTTS(text=('Notice, as how the saying goes, it is difficult to say if a glass is half empty '), lang='en')\n",
"tts.save(\"dep/audio/num.mp3\")\n",
"if (speechflag==1):\n",
" os.system(\"afplay dep/audio/num.mp3\")\n",
" os.system(\"afplay half_empty.mp3\")\n",
"if (speechflag==2):\n",
" os.system(\"cmdmp3 dep/audio/num.mp3\")\n",
" os.system(\"cmdmp3 dep/audio/half_empty.mp3\")\n",
"\n",
"tts = gTTS(text=('or half full '), lang='en')\n",
"tts.save(\"num.mp3\")\n",
"if (speechflag==1):\n",
" os.system(\"afplay dep/audio/num.mp3\")\n",
" os.system(\"afplay dep/audio/half_full.mp3\")\n",
"if (speechflag==2):\n",
" os.system(\"cmdmp3 dep/audio/num.mp3\")\n",
" os.system(\"cmdmp3 dep/audio/half_full.mp3\")\n",
"\n",
"tts = gTTS(text=('Ok. Hit space bar to go to the next section where you can explore this sonification (before taking the test)'), lang='en')\n",
"tts.save(\"num.mp3\")\n",
"if (speechflag==1):\n",
" os.system(\"afplay dep/audio/num.mp3\")\n",
"if (speechflag==2):\n",
" os.system(\"cmdmp3 dep/audio/num.mp3\")\n",
" \n",
"\n",
"\n",
"\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Time to move onto the Exploration Module\n",
"\n",
"Now that you have the general idea of this straight forward sound representation.\n",
"It is time to move on to the next phase of exploring the sonification. \n",
"Go back to the list of available notebooks and select \"Full or Empty Training Module\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"hide_input": true,
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"## Time to move onto the Exploration Module\n",
"\n",
"Now that you have the general idea of this straight forward sound representation.\n",
"It is time to move on to the next phase of exploring the sonification. \n",
"Go back to the list of available notebooks and select \"Full or Empty Exploration Module\"\n"
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"hide_code_all_hidden": false,
"hide_input": true,
"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"
}
},
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| gpl-3.0 |
yashdeeph709/Algorithms | PythonBootCamp/Complete-Python-Bootcamp-master/GUI/3 - Widget Events.ipynb | 4 | 11496 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Widget Events\n",
"\n",
"In this lecture we will discuss widget events, such as button clicks!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Special events"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from __future__ import print_function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `Button` is not used to represent a data type. Instead the button widget is used to handle mouse clicks. The `on_click` method of the `Button` can be used to register function to be called when the button is clicked. The doc string of the `on_click` can be seen below."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Register a callback to execute when the button is clicked.\n",
"\n",
" The callback will be called with one argument,\n",
" the clicked button widget instance.\n",
"\n",
" Parameters\n",
" ----------\n",
" remove : bool (optional)\n",
" Set to true to remove the callback from the list of callbacks.\n"
]
}
],
"source": [
"import ipywidgets as widgets\n",
"print(widgets.Button.on_click.__doc__)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since button clicks are stateless, they are transmitted from the front-end to the back-end using custom messages. By using the `on_click` method, a button that prints a message when it has been clicked is shown below."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Button clicked.\n",
"Button clicked.\n",
"Button clicked.\n",
"Button clicked.\n",
"Button clicked.\n",
"Button clicked.\n"
]
}
],
"source": [
"from IPython.display import display\n",
"button = widgets.Button(description=\"Click Me!\")\n",
"display(button)\n",
"\n",
"def on_button_clicked(b):\n",
" print(\"Button clicked.\")\n",
"\n",
"button.on_click(on_button_clicked)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### on_submit"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `Text` widget also has a special `on_submit` event. The `on_submit` event fires when the user hits return."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello\n",
"press enter\n"
]
}
],
"source": [
"text = widgets.Text()\n",
"display(text)\n",
"\n",
"def handle_submit(sender):\n",
" print(text.value)\n",
"\n",
"text.on_submit(handle_submit)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Traitlet events"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Widget properties are IPython traitlets and traitlets are eventful. To handle changes, the `on_trait_change` method of the widget can be used to register a callback. The doc string for `on_trait_change` can be seen below."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Setup a handler to be called when a trait changes.\n",
"\n",
" This is used to setup dynamic notifications of trait changes.\n",
"\n",
" Static handlers can be created by creating methods on a HasTraits\n",
" subclass with the naming convention '_[traitname]_changed'. Thus,\n",
" to create static handler for the trait 'a', create the method\n",
" _a_changed(self, name, old, new) (fewer arguments can be used, see\n",
" below).\n",
"\n",
" Parameters\n",
" ----------\n",
" handler : callable\n",
" A callable that is called when a trait changes. Its\n",
" signature can be handler(), handler(name), handler(name, new)\n",
" or handler(name, old, new).\n",
" name : list, str, None\n",
" If None, the handler will apply to all traits. If a list\n",
" of str, handler will apply to all names in the list. If a\n",
" str, the handler will apply just to that name.\n",
" remove : bool\n",
" If False (the default), then install the handler. If True\n",
" then unintall it.\n",
" \n"
]
}
],
"source": [
"print(widgets.Widget.on_trait_change.__doc__)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Signatures"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mentioned in the doc string, the callback registered can have 4 possible signatures:\n",
"\n",
"- callback()\n",
"- callback(trait_name)\n",
"- callback(trait_name, new_value)\n",
"- callback(trait_name, old_value, new_value)\n",
"\n",
"Using this method, an example of how to output an `IntSlider`'s value as it is changed can be seen below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"int_range = widgets.IntSlider()\n",
"display(int_range)\n",
"\n",
"def on_value_change(name, value):\n",
" print(value)\n",
"\n",
"int_range.on_trait_change(on_value_change, 'value')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Linking Widgets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Often, you may want to simply link widget attributes together. Synchronization of attributes can be done in a simpler way than by using bare traitlets events. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Linking traitlets attributes from the server side\n",
"\n",
"The first method is to use the `link` and `dlink` functions from the `traitlets` module. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import traitlets"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create Caption\n",
"caption = widgets.Label(value = 'The values of slider1 and slider2 are synchronized')\n",
"\n",
"# Create IntSlider\n",
"slider1 = widgets.IntSlider(description='Slider 1')\n",
"slider2 = widgets.IntSlider(description='Slider 2')\n",
"\n",
"# Use trailets to link\n",
"l = traitlets.link((slider1, 'value'), (slider2, 'value'))\n",
"\n",
"# Display!\n",
"display(caption, slider1, slider2)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create Caption\n",
"caption = widgets.Label(value = 'Changes in source values are reflected in target1')\n",
"\n",
"# Create Sliders\n",
"source = widgets.IntSlider(description='Source')\n",
"target1 = widgets.IntSlider(description='Target 1')\n",
"\n",
"# Use dlink\n",
"dl = traitlets.dlink((source, 'value'), (target1, 'value'))\n",
"display(caption, source, target1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Function `traitlets.link` and `traitlets.dlink` return a `Link` or `DLink` object. The link can be broken by calling the `unlink` method."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# May get an error depending on order of cells being run!\n",
"l.unlink()\n",
"dl.unlink()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Linking widgets attributes from the client side"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When synchronizing traitlets attributes, you may experience a lag because of the latency due to the roundtrip to the server side. You can also directly link widget attributes in the browser using the link widgets, in either a unidirectional or a bidirectional fashion."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# NO LAG VERSION\n",
"caption = widgets.Label(value = 'The values of range1 and range2 are synchronized')\n",
"\n",
"range1 = widgets.IntSlider(description='Range 1')\n",
"range2 = widgets.IntSlider(description='Range 2')\n",
"\n",
"l = widgets.jslink((range1, 'value'), (range2, 'value'))\n",
"display(caption, range1, range2)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# NO LAG VERSION\n",
"caption = widgets.Label(value = 'Changes in source_range values are reflected in target_range1')\n",
"\n",
"source_range = widgets.IntSlider(description='Source range')\n",
"target_range1 = widgets.IntSlider(description='Target range ')\n",
"\n",
"dl = widgets.jsdlink((source_range, 'value'), (target_range1, 'value'))\n",
"display(caption, source_range, target_range1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Function `widgets.jslink` returns a `Link` widget. The link can be broken by calling the `unlink` method."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"l.unlink()\n",
"dl.unlink()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conclusion\n",
"You should now feel comfortable linking Widget events!"
]
}
],
"metadata": {
"cell_tags": [
[
"<None>",
null
]
],
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
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"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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"version": "2.7.10"
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| apache-2.0 |
sorig/shogun | doc/ipython-notebooks/pca/pca_notebook.ipynb | 1 | 49717 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Principal Component Analysis in Shogun"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### By Abhijeet Kislay (GitHub ID: <a href='https://github.com/kislayabhi'>kislayabhi</a>)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook is about finding Principal Components (<a href=\"http://en.wikipedia.org/wiki/Principal_component_analysis\">PCA</a>) of data (<a href=\"http://en.wikipedia.org/wiki/Unsupervised_learning\">unsupervised</a>) in Shogun. Its <a href=\"http://en.wikipedia.org/wiki/Dimensionality_reduction\">dimensional reduction</a> capabilities are further utilised to show its application in <a href=\"http://en.wikipedia.org/wiki/Data_compression\">data compression</a>, image processing and <a href=\"http://en.wikipedia.org/wiki/Facial_recognition_system\">face recognition</a>. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%pylab inline\n",
"%matplotlib inline\n",
"import os\n",
"SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')\n",
"# import all shogun classes\n",
"from shogun import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Some Formal Background (Skip if you just want code examples)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension.\n",
"\n",
"In machine learning problems data is often high dimensional - images, bag-of-word descriptions etc. In such cases we cannot expect the training data to densely populate the space, meaning that there will be large parts in which little is known about the data. Hence it is expected that only a small number of directions are relevant for describing the data to a reasonable accuracy.\n",
"\n",
"The data vectors may be very high dimensional, they will therefore typically lie closer to a much lower dimensional 'manifold'.\n",
"Here we concentrate on linear dimensional reduction techniques. In this approach a high dimensional datapoint $\\mathbf{x}$ is 'projected down' to a lower dimensional vector $\\mathbf{y}$ by:\n",
"$$\\mathbf{y}=\\mathbf{F}\\mathbf{x}+\\text{const}.$$\n",
"where the matrix $\\mathbf{F}\\in\\mathbb{R}^{\\text{M}\\times \\text{D}}$, with $\\text{M}<\\text{D}$. Here $\\text{M}=\\dim(\\mathbf{y})$ and $\\text{D}=\\dim(\\mathbf{x})$.\n",
"\n",
"From the above scenario, we assume that\n",
"\n",
"* The number of principal components to use is $\\text{M}$.\n",
"* The dimension of each data point is $\\text{D}$.\n",
"* The number of data points is $\\text{N}$.\n",
"\n",
"We express the approximation for datapoint $\\mathbf{x}^n$ as:$$\\mathbf{x}^n \\approx \\mathbf{c} + \\sum\\limits_{i=1}^{\\text{M}}y_i^n \\mathbf{b}^i \\equiv \\tilde{\\mathbf{x}}^n.$$\n",
"* Here the vector $\\mathbf{c}$ is a constant and defines a point in the lower dimensional space.\n",
"* The $\\mathbf{b}^i$ define vectors in the lower dimensional space (also known as 'principal component coefficients' or 'loadings').\n",
"* The $y_i^n$ are the low dimensional co-ordinates of the data.\n",
"\n",
"Our motive is to find the reconstruction $\\tilde{\\mathbf{x}}^n$ given the lower dimensional representation $\\mathbf{y}^n$(which has components $y_i^n,i = 1,...,\\text{M})$. For a data space of dimension $\\dim(\\mathbf{x})=\\text{D}$, we hope to accurately describe the data using only a small number $(\\text{M}\\ll \\text{D})$ of coordinates of $\\mathbf{y}$.\n",
"To determine the best lower dimensional representation it is convenient to use the square distance error between $\\mathbf{x}$ and its reconstruction $\\tilde{\\mathbf{x}}$:$$\\text{E}(\\mathbf{B},\\mathbf{Y},\\mathbf{c})=\\sum\\limits_{n=1}^{\\text{N}}\\sum\\limits_{i=1}^{\\text{D}}[x_i^n - \\tilde{x}_i^n]^2.$$\n",
"* Here the basis vectors are defined as $\\mathbf{B} = [\\mathbf{b}^1,...,\\mathbf{b}^\\text{M}]$ (defining $[\\text{B}]_{i,j} = b_i^j$).\n",
"* Corresponding low dimensional coordinates are defined as $\\mathbf{Y} = [\\mathbf{y}^1,...,\\mathbf{y}^\\text{N}].$\n",
"* Also, $x_i^n$ and $\\tilde{x}_i^n$ represents the coordinates of the data points for the original and the reconstructed data respectively.\n",
"* The bias $\\mathbf{c}$ is given by the mean of the data $\\sum_n\\mathbf{x}^n/\\text{N}$.\n",
"\n",
"Therefore, for simplification purposes we centre our data, so as to set $\\mathbf{c}$ to zero. Now we concentrate on finding the optimal basis $\\mathbf{B}$( which has the components $\\mathbf{b}^i, i=1,...,\\text{M} $).\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Deriving the optimal linear reconstruction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To find the best basis vectors $\\mathbf{B}$ and corresponding low dimensional coordinates $\\mathbf{Y}$, we may minimize the sum of squared differences between each vector $\\mathbf{x}$ and its reconstruction $\\tilde{\\mathbf{x}}$:\n",
"\n",
"$\\text{E}(\\mathbf{B},\\mathbf{Y}) = \\sum\\limits_{n=1}^{\\text{N}}\\sum\\limits_{i=1}^{\\text{D}}\\left[x_i^n - \\sum\\limits_{j=1}^{\\text{M}}y_j^nb_i^j\\right]^2 = \\text{trace} \\left( (\\mathbf{X}-\\mathbf{B}\\mathbf{Y})^T(\\mathbf{X}-\\mathbf{B}\\mathbf{Y}) \\right)$\n",
"\n",
"where $\\mathbf{X} = [\\mathbf{x}^1,...,\\mathbf{x}^\\text{N}].$\n",
"Considering the above equation under the orthonormality constraint $\\mathbf{B}^T\\mathbf{B} = \\mathbf{I}$ (i.e the basis vectors are mutually orthogonal and of unit length), we differentiate it w.r.t $y_k^n$. The squared error $\\text{E}(\\mathbf{B},\\mathbf{Y})$ therefore has zero derivative when: \n",
"\n",
"$y_k^n = \\sum_i b_i^kx_i^n$\n",
"\n",
"By substituting this solution in the above equation, the objective becomes\n",
"\n",
"$\\text{E}(\\mathbf{B}) = (\\text{N}-1)\\left[\\text{trace}(\\mathbf{S}) - \\text{trace}\\left(\\mathbf{S}\\mathbf{B}\\mathbf{B}^T\\right)\\right],$\n",
"\n",
"where $\\mathbf{S}$ is the sample covariance matrix of the data.\n",
"To minimise equation under the constraint $\\mathbf{B}^T\\mathbf{B} = \\mathbf{I}$, we use a set of Lagrange Multipliers $\\mathbf{L}$, so that the objective is to minimize: \n",
"\n",
"$-\\text{trace}\\left(\\mathbf{S}\\mathbf{B}\\mathbf{B}^T\\right)+\\text{trace}\\left(\\mathbf{L}\\left(\\mathbf{B}^T\\mathbf{B} - \\mathbf{I}\\right)\\right).$\n",
"\n",
"Since the constraint is symmetric, we can assume that $\\mathbf{L}$ is also symmetric. Differentiating with respect to $\\mathbf{B}$ and equating to zero we obtain that at the optimum \n",
"\n",
"$\\mathbf{S}\\mathbf{B} = \\mathbf{B}\\mathbf{L}$.\n",
"\n",
"This is a form of eigen-equation so that a solution is given by taking $\\mathbf{L}$ to be diagonal and $\\mathbf{B}$ as the matrix whose columns are the corresponding eigenvectors of $\\mathbf{S}$. In this case,\n",
"\n",
"$\\text{trace}\\left(\\mathbf{S}\\mathbf{B}\\mathbf{B}^T\\right) =\\text{trace}(\\mathbf{L}),$\n",
"\n",
"which is the sum of the eigenvalues corresponding to the eigenvectors forming $\\mathbf{B}$. Since we wish to minimise $\\text{E}(\\mathbf{B})$, we take the eigenvectors with the largest corresponding eigenvalues.\n",
"Whilst the solution to this eigen-problem is unique, this only serves to define the solution subspace since one may rotate and scale $\\mathbf{B}$ and $\\mathbf{Y}$ such that the value of the squared loss is exactly the same. The justification for choosing the non-rotated eigen solution is given by the additional requirement that the principal components corresponds to directions of maximal variance."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Maximum variance criterion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We aim to find that single direction $\\mathbf{b}$ such that, when the data is projected onto this direction, the variance of this projection is maximal amongst all possible such projections.\n",
"The projection of a datapoint onto a direction $\\mathbf{b}$ is $\\mathbf{b}^T\\mathbf{x}^n$ for a unit length vector $\\mathbf{b}$. Hence the sum of squared projections is: $$\\sum\\limits_{n}\\left(\\mathbf{b}^T\\mathbf{x}^n\\right)^2 = \\mathbf{b}^T\\left[\\sum\\limits_{n}\\mathbf{x}^n(\\mathbf{x}^n)^T\\right]\\mathbf{b} = (\\text{N}-1)\\mathbf{b}^T\\mathbf{S}\\mathbf{b} = \\lambda(\\text{N} - 1)$$ \n",
"which ignoring constants, is simply the negative of the equation for a single retained eigenvector $\\mathbf{b}$(with $\\mathbf{S}\\mathbf{b} = \\lambda\\mathbf{b}$). Hence the optimal single $\\text{b}$ which maximises the projection variance is given by the eigenvector corresponding to the largest eigenvalues of $\\mathbf{S}.$ The second largest eigenvector corresponds to the next orthogonal optimal direction and so on. This explains why, despite the squared loss equation being invariant with respect to arbitrary rotation of the basis vectors, the ones given by the eigen-decomposition have the additional property that they correspond to directions of maximal variance. These maximal variance directions found by PCA are called the $\\text{principal} $ $\\text{directions}.$\n",
"\n",
"There are two eigenvalue methods through which shogun can perform PCA namely\n",
"* Eigenvalue Decomposition Method.\n",
"* Singular Value Decomposition.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### EVD vs SVD"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* The EVD viewpoint requires that one compute the eigenvalues and eigenvectors of the covariance matrix, which is the product of $\\mathbf{X}\\mathbf{X}^\\text{T}$, where $\\mathbf{X}$ is the data matrix. Since the covariance matrix is symmetric, the matrix is diagonalizable, and the eigenvectors can be normalized such that they are orthonormal:\n",
"\n",
"$\\mathbf{S}=\\frac{1}{\\text{N}-1}\\mathbf{X}\\mathbf{X}^\\text{T},$\n",
"\n",
"where the $\\text{D}\\times\\text{N}$ matrix $\\mathbf{X}$ contains all the data vectors: $\\mathbf{X}=[\\mathbf{x}^1,...,\\mathbf{x}^\\text{N}].$\n",
"Writing the $\\text{D}\\times\\text{N}$ matrix of eigenvectors as $\\mathbf{E}$ and the eigenvalues as an $\\text{N}\\times\\text{N}$ diagonal matrix $\\mathbf{\\Lambda}$, the eigen-decomposition of the covariance $\\mathbf{S}$ is\n",
"\n",
"$\\mathbf{X}\\mathbf{X}^\\text{T}\\mathbf{E}=\\mathbf{E}\\mathbf{\\Lambda}\\Longrightarrow\\mathbf{X}^\\text{T}\\mathbf{X}\\mathbf{X}^\\text{T}\\mathbf{E}=\\mathbf{X}^\\text{T}\\mathbf{E}\\mathbf{\\Lambda}\\Longrightarrow\\mathbf{X}^\\text{T}\\mathbf{X}\\tilde{\\mathbf{E}}=\\tilde{\\mathbf{E}}\\mathbf{\\Lambda},$\n",
"\n",
"where we defined $\\tilde{\\mathbf{E}}=\\mathbf{X}^\\text{T}\\mathbf{E}$. The final expression above represents the eigenvector equation for $\\mathbf{X}^\\text{T}\\mathbf{X}.$ This is a matrix of dimensions $\\text{N}\\times\\text{N}$ so that calculating the eigen-decomposition takes $\\mathcal{O}(\\text{N}^3)$ operations, compared with $\\mathcal{O}(\\text{D}^3)$ operations in the original high-dimensional space. We then can therefore calculate the eigenvectors $\\tilde{\\mathbf{E}}$ and eigenvalues $\\mathbf{\\Lambda}$ of this matrix more easily. Once found, we use the fact that the eigenvalues of $\\mathbf{S}$ are given by the diagonal entries of $\\mathbf{\\Lambda}$ and the eigenvectors by\n",
"\n",
"$\\mathbf{E}=\\mathbf{X}\\tilde{\\mathbf{E}}\\mathbf{\\Lambda}^{-1}$\n",
"\n",
"\n",
"\n",
"\n",
"* On the other hand, applying SVD to the data matrix $\\mathbf{X}$ follows like:\n",
"\n",
"$\\mathbf{X}=\\mathbf{U}\\mathbf{\\Sigma}\\mathbf{V}^\\text{T}$\n",
"\n",
"where $\\mathbf{U}^\\text{T}\\mathbf{U}=\\mathbf{I}_\\text{D}$ and $\\mathbf{V}^\\text{T}\\mathbf{V}=\\mathbf{I}_\\text{N}$ and $\\mathbf{\\Sigma}$ is a diagonal matrix of the (positive) singular values. We assume that the decomposition has ordered the singular values so that the upper left diagonal element of $\\mathbf{\\Sigma}$ contains the largest singular value.\n",
"\n",
"Attempting to construct the covariance matrix $(\\mathbf{X}\\mathbf{X}^\\text{T})$from this decomposition gives:\n",
"\n",
"$\\mathbf{X}\\mathbf{X}^\\text{T} = \\left(\\mathbf{U}\\mathbf{\\Sigma}\\mathbf{V}^\\text{T}\\right)\\left(\\mathbf{U}\\mathbf{\\Sigma}\\mathbf{V}^\\text{T}\\right)^\\text{T}$\n",
"\n",
"$\\mathbf{X}\\mathbf{X}^\\text{T} = \\left(\\mathbf{U}\\mathbf{\\Sigma}\\mathbf{V}^\\text{T}\\right)\\left(\\mathbf{V}\\mathbf{\\Sigma}\\mathbf{U}^\\text{T}\\right)$\n",
"\n",
"and since $\\mathbf{V}$ is an orthogonal matrix $\\left(\\mathbf{V}^\\text{T}\\mathbf{V}=\\mathbf{I}\\right),$\n",
"\n",
"$\\mathbf{X}\\mathbf{X}^\\text{T}=\\left(\\mathbf{U}\\mathbf{\\Sigma}^\\mathbf{2}\\mathbf{U}^\\text{T}\\right)$\n",
"\n",
"Since it is in the form of an eigen-decomposition, the PCA solution given by performing the SVD decomposition of $\\mathbf{X}$, for which the eigenvectors are then given by $\\mathbf{U}$, and corresponding eigenvalues by the square of the singular values.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### [CPCA](http://www.shogun-toolbox.org/doc/en/3.0.0/classshogun_1_1CPCA.html) Class Reference (Shogun) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"CPCA class of Shogun inherits from the [CPreprocessor](http://www.shogun-toolbox.org/doc/en/3.0.0/classshogun_1_1CPreprocessor.html) class. Preprocessors are transformation functions that doesn't change the domain of the input features. Specifically, CPCA performs principal component analysis on the input vectors and keeps only the specified number of eigenvectors. On preprocessing, the stored covariance matrix is used to project vectors into eigenspace.\n",
"\n",
"Performance of PCA depends on the algorithm used according to the situation in hand.\n",
"Our PCA preprocessor class provides 3 method options to compute the transformation matrix:\n",
"\n",
"* $\\text{PCA(EVD)}$ sets $\\text{PCAmethod == EVD}$ : Eigen Value Decomposition of Covariance Matrix $(\\mathbf{XX^T}).$\n",
"The covariance matrix $\\mathbf{XX^T}$ is first formed internally and then\n",
"its eigenvectors and eigenvalues are computed using QR decomposition of the matrix.\n",
"The time complexity of this method is $\\mathcal{O}(D^3)$ and should be used when $\\text{N > D.}$\n",
"\n",
"\n",
"* $\\text{PCA(SVD)}$ sets $\\text{PCAmethod == SVD}$ : Singular Value Decomposition of feature matrix $\\mathbf{X}$.\n",
"The transpose of feature matrix, $\\mathbf{X^T}$, is decomposed using SVD. $\\mathbf{X^T = UDV^T}.$\n",
"The matrix V in this decomposition contains the required eigenvectors and\n",
"the diagonal entries of the diagonal matrix D correspond to the non-negative\n",
"eigenvalues.The time complexity of this method is $\\mathcal{O}(DN^2)$ and should be used when $\\text{N < D.}$\n",
"\n",
"\n",
"* $\\text{PCA(AUTO)}$ sets $\\text{PCAmethod == AUTO}$ : This mode automagically chooses one of the above modes for the user based on whether $\\text{N>D}$ (chooses $\\text{EVD}$) or $\\text{N<D}$ (chooses $\\text{SVD}$)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PCA on 2D data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 1: Get some data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will generate the toy data by adding orthogonal noise to a set of points lying on an arbitrary 2d line. We expect PCA to recover this line, which is a one-dimensional linear sub-space."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#number of data points.\n",
"n=100\n",
"\n",
"#generate a random 2d line(y1 = mx1 + c)\n",
"m = random.randint(1,10)\n",
"c = random.randint(1,10)\n",
"x1 = random.random_integers(-20,20,n)\n",
"y1=m*x1+c\n",
"\n",
"#generate the noise.\n",
"noise=random.random_sample([n]) * random.random_integers(-35,35,n)\n",
"\n",
"#make the noise orthogonal to the line y=mx+c and add it.\n",
"x=x1 + noise*m/sqrt(1+square(m))\n",
"y=y1 + noise/sqrt(1+square(m))\n",
"\n",
"twoD_obsmatrix=array([x,y])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#to visualise the data we must plot it.\n",
"\n",
"rcParams['figure.figsize'] = 7, 7 \n",
"figure,axis=subplots(1,1)\n",
"xlim(-50,50)\n",
"ylim(-50,50)\n",
"axis.plot(twoD_obsmatrix[0,:],twoD_obsmatrix[1,:],'o',color='green',markersize=6)\n",
"\n",
"#the line from which we generated the data is plotted in red\n",
"axis.plot(x1[:],y1[:],linewidth=0.3,color='red')\n",
"title('One-Dimensional sub-space with noise')\n",
"xlabel(\"x axis\")\n",
"_=ylabel(\"y axis\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 2: Subtract the mean."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For PCA to work properly, we must subtract the mean from each of the data dimensions. The mean subtracted is the average across each dimension. So, all the $x$ values have $\\bar{x}$ subtracted, and all the $y$ values have $\\bar{y}$ subtracted from them, where:$$\\bar{\\mathbf{x}} = \\frac{\\sum\\limits_{i=1}^{n}x_i}{n}$$ $\\bar{\\mathbf{x}}$ denotes the mean of the $x_i^{'s}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Shogun's way of doing things :"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Preprocessor PCA performs principial component analysis on input feature vectors/matrices. It provides an interface to set the target dimension by $\\text{put('target_dim', target_dim) method}.$ When the $\\text{init()}$ method in $\\text{PCA}$ is called with proper\n",
"feature matrix $\\text{X}$ (with say $\\text{N}$ number of vectors and $\\text{D}$ feature dimension), a transformation matrix is computed and stored internally.It inherenty also centralizes the data by subtracting the mean from it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#convert the observation matrix into dense feature matrix.\n",
"train_features = features(twoD_obsmatrix)\n",
"\n",
"#PCA(EVD) is choosen since N=100 and D=2 (N>D).\n",
"#However we can also use PCA(AUTO) as it will automagically choose the appropriate method. \n",
"preprocessor = PCA(EVD)\n",
"\n",
"#since we are projecting down the 2d data, the target dim is 1. But here the exhaustive method is detailed by\n",
"#setting the target dimension to 2 to visualize both the eigen vectors.\n",
"#However, in future examples we will get rid of this step by implementing it directly.\n",
"preprocessor.put('target_dim', 2)\n",
"\n",
"#Centralise the data by subtracting its mean from it.\n",
"preprocessor.init(train_features)\n",
"\n",
"#get the mean for the respective dimensions.\n",
"mean_datapoints=preprocessor.get_real_vector('mean_vector')\n",
"mean_x=mean_datapoints[0]\n",
"mean_y=mean_datapoints[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 3: Calculate the covariance matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To understand the relationship between 2 dimension we define $\\text{covariance}$. It is a measure to find out how much the dimensions vary from the mean $with$ $respect$ $to$ $each$ $other.$$$cov(X,Y)=\\frac{\\sum\\limits_{i=1}^{n}(X_i-\\bar{X})(Y_i-\\bar{Y})}{n-1}$$\n",
"A useful way to get all the possible covariance values between all the different dimensions is to calculate them all and put them in a matrix.\n",
"\n",
"Example: For a 3d dataset with usual dimensions of $x,y$ and $z$, the covariance matrix has 3 rows and 3 columns, and the values are this:\n",
"$$\\mathbf{S} = \\quad\\begin{pmatrix}cov(x,x)&cov(x,y)&cov(x,z)\\\\cov(y,x)&cov(y,y)&cov(y,z)\\\\cov(z,x)&cov(z,y)&cov(z,z)\\end{pmatrix}$$\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 4: Calculate the eigenvectors and eigenvalues of the covariance matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Find the eigenvectors $e^1,....e^M$ of the covariance matrix $\\mathbf{S}$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Shogun's way of doing things :"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Step 3 and Step 4 are directly implemented by the PCA preprocessor of Shogun toolbar. The transformation matrix is essentially a $\\text{D}$$\\times$$\\text{M}$ matrix, the columns of which correspond to the eigenvectors of the covariance matrix $(\\text{X}\\text{X}^\\text{T})$ having top $\\text{M}$ eigenvalues."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Get the eigenvectors(We will get two of these since we set the target to 2). \n",
"E = preprocessor.get_real_matrix('transformation_matrix')\n",
"\n",
"#Get all the eigenvalues returned by PCA.\n",
"eig_value=preprocessor.get_real_vector('eigenvalues_vector')\n",
"\n",
"e1 = E[:,0]\n",
"e2 = E[:,1]\n",
"eig_value1 = eig_value[0]\n",
"eig_value2 = eig_value[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 5: Choosing components and forming a feature vector."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets visualize the eigenvectors and decide upon which to choose as the $principle$ $component$ of the data set."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#find out the M eigenvectors corresponding to top M number of eigenvalues and store it in E\n",
"#Here M=1\n",
"\n",
"#slope of e1 & e2\n",
"m1=e1[1]/e1[0]\n",
"m2=e2[1]/e2[0]\n",
"\n",
"#generate the two lines\n",
"x1=range(-50,50)\n",
"x2=x1\n",
"y1=multiply(m1,x1)\n",
"y2=multiply(m2,x2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#plot the data along with those two eigenvectors\n",
"figure, axis = subplots(1,1)\n",
"xlim(-50, 50)\n",
"ylim(-50, 50)\n",
"axis.plot(x[:], y[:],'o',color='green', markersize=5, label=\"green\")\n",
"axis.plot(x1[:], y1[:], linewidth=0.7, color='black')\n",
"axis.plot(x2[:], y2[:], linewidth=0.7, color='blue')\n",
"p1 = Rectangle((0, 0), 1, 1, fc=\"black\")\n",
"p2 = Rectangle((0, 0), 1, 1, fc=\"blue\")\n",
"legend([p1,p2],[\"1st eigenvector\",\"2nd eigenvector\"],loc='center left', bbox_to_anchor=(1, 0.5))\n",
"title('Eigenvectors selection')\n",
"xlabel(\"x axis\")\n",
"_=ylabel(\"y axis\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the above figure, the blue line is a good fit of the data. It shows the most significant relationship between the data dimensions.\n",
"It turns out that the eigenvector with the $highest$ eigenvalue is the $principle$ $component$ of the data set.\n",
"Form the matrix $\\mathbf{E}=[\\mathbf{e}^1,...,\\mathbf{e}^M].$\n",
"Here $\\text{M}$ represents the target dimension of our final projection"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#The eigenvector corresponding to higher eigenvalue(i.e eig_value2) is choosen (i.e e2).\n",
"#E is the feature vector.\n",
"E=e2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 6: Projecting the data to its Principal Components."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the final step in PCA. Once we have choosen the components(eigenvectors) that we wish to keep in our data and formed a feature vector, we simply take the vector and multiply it on the left of the original dataset.\n",
"The lower dimensional representation of each data point $\\mathbf{x}^n$ is given by \n",
"\n",
"$\\mathbf{y}^n=\\mathbf{E}^T(\\mathbf{x}^n-\\mathbf{m})$\n",
"\n",
"Here the $\\mathbf{E}^T$ is the matrix with the eigenvectors in rows, with the most significant eigenvector at the top. The mean adjusted data, with data items in each column, with each row holding a seperate dimension is multiplied to it."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Shogun's way of doing things :"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Step 6 can be performed by shogun's PCA preprocessor as follows:\n",
"\n",
"The transformation matrix that we got after $\\text{init()}$ is used to transform all $\\text{D-dim}$ feature matrices (with $\\text{D}$ feature dimensions) supplied, via $\\text{apply_to_feature_matrix methods}$.This transformation outputs the $\\text{M-Dim}$ approximation of all these input vectors and matrices (where $\\text{M}$ $\\leq$ $\\text{min(D,N)}$)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#transform all 2-dimensional feature matrices to target-dimensional approximations.\n",
"yn=preprocessor.apply_to_feature_matrix(train_features)\n",
"\n",
"#Since, here we are manually trying to find the eigenvector corresponding to the top eigenvalue.\n",
"#The 2nd row of yn is choosen as it corresponds to the required eigenvector e2.\n",
"yn1=yn[1,:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Step 5 and Step 6 can be applied directly with Shogun's PCA preprocessor (from next example). It has been done manually here to show the exhaustive nature of Principal Component Analysis."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 7: Form the approximate reconstruction of the original data $\\mathbf{x}^n$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The approximate reconstruction of the original datapoint $\\mathbf{x}^n$ is given by : $\\tilde{\\mathbf{x}}^n\\approx\\text{m}+\\mathbf{E}\\mathbf{y}^n$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x_new=(yn1 * E[0]) + tile(mean_x,[n,1]).T[0]\n",
"y_new=(yn1 * E[1]) + tile(mean_y,[n,1]).T[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The new data is plotted below"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"figure, axis = subplots(1,1)\n",
"xlim(-50, 50)\n",
"ylim(-50, 50)\n",
"\n",
"axis.plot(x[:], y[:],'o',color='green', markersize=5, label=\"green\")\n",
"axis.plot(x_new, y_new, 'o', color='blue', markersize=5, label=\"red\")\n",
"title('PCA Projection of 2D data into 1D subspace')\n",
"xlabel(\"x axis\")\n",
"ylabel(\"y axis\")\n",
"\n",
"#add some legend for information\n",
"p1 = Rectangle((0, 0), 1, 1, fc=\"r\")\n",
"p2 = Rectangle((0, 0), 1, 1, fc=\"g\")\n",
"p3 = Rectangle((0, 0), 1, 1, fc=\"b\")\n",
"legend([p1,p2,p3],[\"normal projection\",\"2d data\",\"1d projection\"],loc='center left', bbox_to_anchor=(1, 0.5))\n",
"\n",
"#plot the projections in red:\n",
"for i in range(n):\n",
" axis.plot([x[i],x_new[i]],[y[i],y_new[i]] , color='red')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PCA on a 3d data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step1: Get some data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We generate points from a plane and then add random noise orthogonal to it. The general equation of a plane is: $$\\text{a}\\mathbf{x}+\\text{b}\\mathbf{y}+\\text{c}\\mathbf{z}+\\text{d}=0$$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"rcParams['figure.figsize'] = 8,8 \n",
"#number of points\n",
"n=100\n",
"\n",
"#generate the data\n",
"a=random.randint(1,20)\n",
"b=random.randint(1,20)\n",
"c=random.randint(1,20)\n",
"d=random.randint(1,20)\n",
"\n",
"x1=random.random_integers(-20,20,n)\n",
"y1=random.random_integers(-20,20,n)\n",
"z1=-(a*x1+b*y1+d)/c\n",
"\n",
"#generate the noise\n",
"noise=random.random_sample([n])*random.random_integers(-30,30,n)\n",
"\n",
"#the normal unit vector is [a,b,c]/magnitude\n",
"magnitude=sqrt(square(a)+square(b)+square(c))\n",
"normal_vec=array([a,b,c]/magnitude)\n",
"\n",
"#add the noise orthogonally\n",
"x=x1+noise*normal_vec[0]\n",
"y=y1+noise*normal_vec[1]\n",
"z=z1+noise*normal_vec[2]\n",
"threeD_obsmatrix=array([x,y,z])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#to visualize the data, we must plot it.\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"\n",
"fig = pyplot.figure()\n",
"ax=fig.add_subplot(111, projection='3d')\n",
"\n",
"#plot the noisy data generated by distorting a plane\n",
"ax.scatter(x, y, z,marker='o', color='g')\n",
"\n",
"ax.set_xlabel('x label')\n",
"ax.set_ylabel('y label')\n",
"ax.set_zlabel('z label')\n",
"legend([p2],[\"3d data\"],loc='center left', bbox_to_anchor=(1, 0.5))\n",
"title('Two dimensional subspace with noise')\n",
"xx, yy = meshgrid(range(-30,30), range(-30,30))\n",
"zz=-(a * xx + b * yy + d) / c"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 2: Subtract the mean."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#convert the observation matrix into dense feature matrix.\n",
"train_features = features(threeD_obsmatrix)\n",
"\n",
"#PCA(EVD) is choosen since N=100 and D=3 (N>D).\n",
"#However we can also use PCA(AUTO) as it will automagically choose the appropriate method. \n",
"preprocessor = PCA(EVD)\n",
"\n",
"#If we set the target dimension to 2, Shogun would automagically preserve the required 2 eigenvectors(out of 3) according to their\n",
"#eigenvalues.\n",
"preprocessor.put('target_dim', 2)\n",
"preprocessor.init(train_features)\n",
"\n",
"#get the mean for the respective dimensions.\n",
"mean_datapoints=preprocessor.get_real_vector('mean_vector')\n",
"mean_x=mean_datapoints[0]\n",
"mean_y=mean_datapoints[1]\n",
"mean_z=mean_datapoints[2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 3 & Step 4: Calculate the eigenvectors of the covariance matrix"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#get the required eigenvectors corresponding to top 2 eigenvalues.\n",
"E = preprocessor.get_real_matrix('transformation_matrix')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Steps 5: Choosing components and forming a feature vector."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we performed PCA for a target $\\dim = 2$ for the $3 \\dim$ data, we are directly given \n",
"the two required eigenvectors in $\\mathbf{E}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"E is automagically filled by setting target dimension = M. This is different from the 2d data example where we implemented this step manually."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 6: Projecting the data to its Principal Components."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#This can be performed by shogun's PCA preprocessor as follows:\n",
"yn=preprocessor.apply_to_feature_matrix(train_features)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 7: Form the approximate reconstruction of the original data $\\mathbf{x}^n$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The approximate reconstruction of the original datapoint $\\mathbf{x}^n$ is given by : $\\tilde{\\mathbf{x}}^n\\approx\\text{m}+\\mathbf{E}\\mathbf{y}^n$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"new_data=dot(E,yn)\n",
"\n",
"x_new=new_data[0,:]+tile(mean_x,[n,1]).T[0]\n",
"y_new=new_data[1,:]+tile(mean_y,[n,1]).T[0]\n",
"z_new=new_data[2,:]+tile(mean_z,[n,1]).T[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#all the above points lie on the same plane. To make it more clear we will plot the projection also.\n",
"\n",
"fig=pyplot.figure()\n",
"ax=fig.add_subplot(111, projection='3d')\n",
"ax.scatter(x, y, z,marker='o', color='g')\n",
"ax.set_xlabel('x label')\n",
"ax.set_ylabel('y label')\n",
"ax.set_zlabel('z label')\n",
"legend([p1,p2,p3],[\"normal projection\",\"3d data\",\"2d projection\"],loc='center left', bbox_to_anchor=(1, 0.5))\n",
"title('PCA Projection of 3D data into 2D subspace')\n",
"\n",
"for i in range(100):\n",
" ax.scatter(x_new[i], y_new[i], z_new[i],marker='o', color='b')\n",
" ax.plot([x[i],x_new[i]],[y[i],y_new[i]],[z[i],z_new[i]],color='r') "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### PCA Performance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Uptill now, we were using the EigenValue Decomposition method to compute the transformation matrix$\\text{(N>D)}$ but for the next example $\\text{(N<D)}$ we will be using Singular Value Decomposition."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Practical Example : Eigenfaces"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The problem with the image representation we are given is its high dimensionality. Two-dimensional $\\text{p} \\times \\text{q}$ grayscale images span a $\\text{m=pq}$ dimensional vector space, so an image with $\\text{100}\\times\\text{100}$ pixels lies in a $\\text{10,000}$ dimensional image space already. \n",
"\n",
"The question is, are all dimensions really useful for us?\n",
" \n",
"$\\text{Eigenfaces}$ are based on the dimensional reduction approach of $\\text{Principal Component Analysis(PCA)}$. The basic idea is to treat each image as a vector in a high dimensional space. Then, $\\text{PCA}$ is applied to the set of images to produce a new reduced subspace that captures most of the variability between the input images. The $\\text{Pricipal Component Vectors}$(eigenvectors of the sample covariance matrix) are called the $\\text{Eigenfaces}$. Every input image can be represented as a linear combination of these eigenfaces by projecting the image onto the new eigenfaces space. Thus, we can perform the identfication process by matching in this reduced space. An input image is transformed into the $\\text{eigenspace,}$ and the nearest face is identified using a $\\text{Nearest Neighbour approach.}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 1: Get some data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here data means those Images which will be used for training purposes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"rcParams['figure.figsize'] = 10, 10 \n",
"import os\n",
"def get_imlist(path):\n",
" \"\"\" Returns a list of filenames for all jpg images in a directory\"\"\"\n",
" return [os.path.join(path,f) for f in os.listdir(path) if f.endswith('.pgm')]\n",
"\n",
"#set path of the training images\n",
"path_train=os.path.join(SHOGUN_DATA_DIR, 'att_dataset/training/')\n",
"#set no. of rows that the images will be resized.\n",
"k1=100\n",
"#set no. of columns that the images will be resized.\n",
"k2=100\n",
"\n",
"filenames = get_imlist(path_train)\n",
"filenames = array(filenames)\n",
"\n",
"#n is total number of images that has to be analysed.\n",
"n=len(filenames)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets have a look on the data:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# we will be using this often to visualize the images out there.\n",
"def showfig(image):\n",
" imgplot=imshow(image, cmap='gray')\n",
" imgplot.axes.get_xaxis().set_visible(False)\n",
" imgplot.axes.get_yaxis().set_visible(False)\n",
" \n",
"from PIL import Image\n",
"from scipy import misc\n",
"\n",
"# to get a hang of the data, lets see some part of the dataset images.\n",
"fig = pyplot.figure()\n",
"title('The Training Dataset')\n",
"\n",
"for i in range(49):\n",
" fig.add_subplot(7,7,i+1)\n",
" train_img=array(Image.open(filenames[i]).convert('L'))\n",
" train_img=misc.imresize(train_img, [k1,k2])\n",
" showfig(train_img)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Represent every image $I_i$ as a vector $\\Gamma_i$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#To form the observation matrix obs_matrix.\n",
"#read the 1st image.\n",
"train_img = array(Image.open(filenames[0]).convert('L'))\n",
"\n",
"#resize it to k1 rows and k2 columns\n",
"train_img=misc.imresize(train_img, [k1,k2])\n",
"\n",
"#since features accepts only data of float64 datatype, we do a type conversion\n",
"train_img=array(train_img, dtype='double')\n",
"\n",
"#flatten it to make it a row vector.\n",
"train_img=train_img.flatten()\n",
"\n",
"# repeat the above for all images and stack all those vectors together in a matrix\n",
"for i in range(1,n):\n",
" temp=array(Image.open(filenames[i]).convert('L')) \n",
" temp=misc.imresize(temp, [k1,k2])\n",
" temp=array(temp, dtype='double')\n",
" temp=temp.flatten()\n",
" train_img=vstack([train_img,temp])\n",
"\n",
"#form the observation matrix \n",
"obs_matrix=train_img.T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 2: Subtract the mean"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is very important that the face images $I_1,I_2,...,I_M$ are $centered$ and of the $same$ size"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We observe here that the no. of $\\dim$ for each image is far greater than no. of training images. This calls for the use of $\\text{SVD}$.\n",
"\n",
"Setting the $\\text{PCA}$ in the $\\text{AUTO}$ mode does this automagically according to the situation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train_features = features(obs_matrix)\n",
"preprocessor=PCA(AUTO)\n",
"\n",
"preprocessor.put('target_dim', 100)\n",
"preprocessor.init(train_features)\n",
"\n",
"mean=preprocessor.get_real_vector('mean_vector')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 3 & Step 4: Calculate the eigenvectors and eigenvalues of the covariance matrix."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#get the required eigenvectors corresponding to top 100 eigenvalues\n",
"E = preprocessor.get_real_matrix('transformation_matrix')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#lets see how these eigenfaces/eigenvectors look like:\n",
"fig1 = pyplot.figure()\n",
"title('Top 20 Eigenfaces')\n",
"\n",
"for i in range(20):\n",
" a = fig1.add_subplot(5,4,i+1)\n",
" eigen_faces=E[:,i].reshape([k1,k2])\n",
" showfig(eigen_faces)\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These 20 eigenfaces are not sufficient for a good image reconstruction. Having more eigenvectors gives us the most flexibility in the number of faces we can reconstruct. Though we are adding vectors with low variance, they are in directions of change nonetheless, and an external image that is not in our database could in fact need these eigenvectors to get even relatively close to it. But at the same time we must also keep in mind that adding excessive eigenvectors results in addition of little or no variance, slowing down the process.\n",
"\n",
"Clearly a tradeoff is required.\n",
"\n",
"We here set for M=100."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 5: Choosing components and forming a feature vector."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we set target $\\dim = 100$ for this $n \\dim$ data, we are directly given the $100$ required eigenvectors in $\\mathbf{E}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"E is automagically filled. This is different from the 2d data example where we implemented this step manually."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 6: Projecting the data to its Principal Components."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The lower dimensional representation of each data point $\\mathbf{x}^n$ is given by $$\\mathbf{y}^n=\\mathbf{E}^T(\\mathbf{x}^n-\\mathbf{m})$$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#we perform the required dot product.\n",
"yn=preprocessor.apply_to_feature_matrix(train_features)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Step 7: Form the approximate reconstruction of the original image $I_n$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The approximate reconstruction of the original datapoint $\\mathbf{x}^n$ is given by : $\\mathbf{x}^n\\approx\\text{m}+\\mathbf{E}\\mathbf{y}^n$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"re=tile(mean,[n,1]).T[0] + dot(E,yn)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#lets plot the reconstructed images.\n",
"fig2 = pyplot.figure()\n",
"title('Reconstructed Images from 100 eigenfaces')\n",
"for i in range(1,50):\n",
" re1 = re[:,i].reshape([k1,k2])\n",
" fig2.add_subplot(7,7,i)\n",
" showfig(re1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Recognition part."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In our face recognition process using the Eigenfaces approach, in order to recognize an unseen image, we proceed with the same preprocessing steps as applied to the training images.\n",
"Test images are represented in terms of eigenface coefficients by projecting them into face space$\\text{(eigenspace)}$ calculated during training. Test sample is recognized by measuring the similarity distance between the test sample and all samples in the training. The similarity measure is a metric of distance calculated between two vectors. Traditional Eigenface approach utilizes $\\text{Euclidean distance}$."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#set path of the training images\n",
"path_train=os.path.join(SHOGUN_DATA_DIR, 'att_dataset/testing/')\n",
"test_files=get_imlist(path_train)\n",
"test_img=array(Image.open(test_files[0]).convert('L'))\n",
"\n",
"rcParams.update({'figure.figsize': (3, 3)})\n",
"#we plot the test image , for which we have to identify a good match from the training images we already have\n",
"fig = pyplot.figure()\n",
"title('The Test Image')\n",
"showfig(test_img)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#We flatten out our test image just the way we have done for the other images\n",
"test_img=misc.imresize(test_img, [k1,k2])\n",
"test_img=array(test_img, dtype='double')\n",
"test_img=test_img.flatten()\n",
"\n",
"#We centralise the test image by subtracting the mean from it.\n",
"test_f=test_img-mean"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we have to project our training image as well as the test image on the PCA subspace."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Eigenfaces method then performs face recognition by:\n",
"1. Projecting all training samples into the PCA subspace.\n",
"2. Projecting the query image into the PCA subspace.\n",
"3. Finding the nearest neighbour between the projected training images and the projected query image."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#We have already projected our training images into pca subspace as yn.\n",
"train_proj = yn\n",
"\n",
"#Projecting our test image into pca subspace\n",
"test_proj = dot(E.T, test_f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Shogun's way of doing things:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Shogun uses [CEuclideanDistance](http://www.shogun-toolbox.org/doc/en/3.0.0/classshogun_1_1CEuclideanDistance.html) class to compute the familiar Euclidean distance for real valued features. It computes the square root of the sum of squared disparity between the corresponding feature dimensions of two data points.\n",
"\n",
"$\\mathbf{d(x,x')=}$$\\sqrt{\\mathbf{\\sum\\limits_{i=0}^{n}}|\\mathbf{x_i}-\\mathbf{x'_i}|^2}$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#To get Eucledian Distance as the distance measure use EuclideanDistance.\n",
"workfeat = features(mat(train_proj))\n",
"testfeat = features(mat(test_proj).T)\n",
"RaRb=EuclideanDistance(testfeat, workfeat)\n",
"\n",
"#The distance between one test image w.r.t all the training is stacked in matrix d.\n",
"d=empty([n,1])\n",
"for i in range(n):\n",
" d[i]= RaRb.distance(0,i)\n",
" \n",
"#The one having the minimum distance is found out\n",
"min_distance_index = d.argmin()\n",
"iden=array(Image.open(filenames[min_distance_index]))\n",
"title('Identified Image')\n",
"showfig(iden)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[1] David Barber. Bayesian Reasoning and Machine Learning.\n",
"\n",
"[2] Lindsay I Smith. A tutorial on Principal Component Analysis.\n",
"\n",
"[3] Philipp Wanger. Face Recognition with GNU Octave/MATLAB."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| bsd-3-clause |
obulpathi/datascience | scikit/Chapter 7/Text classification.ipynb | 2 | 253825 | {
"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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Text Classification of Movie Reviews"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from helpers import Timer"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.datasets import load_files\n",
"\n",
"reviews_train = load_files(\"aclImdb/train/\")\n",
"text_train, y_train = reviews_train.data, reviews_train.target"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of documents in training data: 25000\n",
"[12500 12500]\n"
]
}
],
"source": [
"print(\"Number of documents in training data: %d\" % len(text_train))\n",
"print(np.bincount(y_train))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of documents in test data: 25000\n",
"[12500 12500]\n"
]
}
],
"source": [
"reviews_test = load_files(\"aclImdb/test/\")\n",
"text_test, y_test = reviews_test.data, reviews_test.target\n",
"print(\"Number of documents in test data: %d\" % len(text_test))\n",
"print(np.bincount(y_test))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Words can't describe how bad this movie is. I can't explain it by writing only. You have too see it for yourself to get at grip of how horrible a movie really can be. Not that I recommend you to do that. There are so many clichés, mistakes (and all other negative things you can imagine) here that will just make you cry. To start with the technical first, there are a LOT of mistakes regarding the airplane. I won't list them here, but just mention the coloring of the plane. They didn't even manage to show an airliner in the colors of a fictional airline, but instead used a 747 painted in the original Boeing livery. Very bad. The plot is stupid and has been done many times before, only much, much better. There are so many ridiculous moments here that i lost count of it really early. Also, I was on the bad guys' side all the time in the movie, because the good guys were so stupid. \"Executive Decision\" should without a doubt be you're choice over this one, even the \"Turbulence\"-movies are better. In fact, every other movie in the world is better than this one.\n"
]
}
],
"source": [
"print(text_train[1])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n"
]
}
],
"source": [
"print(y_train[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bag of words reminder:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"bag_of_words.svg\" width=80%>"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"74849"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.feature_extraction.text import CountVectorizer\n",
"cv = CountVectorizer()\n",
"cv.fit(text_train)\n",
"\n",
"len(cv.vocabulary_)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[u'00', u'000', u'0000000000001', u'00001', u'00015', u'000s', u'001', u'003830', u'006', u'007', u'0079', u'0080', u'0083', u'0093638', u'00am', u'00pm', u'00s', u'01', u'01pm', u'02', u'020410', u'029', u'03', u'04', u'041', u'05', u'050', u'06', u'06th', u'07', u'08', u'087', u'089', u'08th', u'09', u'0f', u'0ne', u'0r', u'0s', u'10', u'100', u'1000', u'1000000', u'10000000000000', u'1000lb', u'1000s', u'1001', u'100b', u'100k', u'100m']\n",
"[u'pincher', u'pinchers', u'pinches', u'pinching', u'pinchot', u'pinciotti', u'pine', u'pineal', u'pineapple', u'pineapples', u'pines', u'pinet', u'pinetrees', u'pineyro', u'pinfall', u'pinfold', u'ping', u'pingo', u'pinhead', u'pinheads', u'pinho', u'pining', u'pinjar', u'pink', u'pinkerton', u'pinkett', u'pinkie', u'pinkins', u'pinkish', u'pinko', u'pinks', u'pinku', u'pinkus', u'pinky', u'pinnacle', u'pinnacles', u'pinned', u'pinning', u'pinnings', u'pinnochio', u'pinnocioesque', u'pino', u'pinocchio', u'pinochet', u'pinochets', u'pinoy', u'pinpoint', u'pinpoints', u'pins', u'pinsent']\n"
]
}
],
"source": [
"print(cv.get_feature_names()[:50])\n",
"print(cv.get_feature_names()[50000:50050])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<25000x74849 sparse matrix of type '<type 'numpy.int64'>'\n",
"\twith 3445861 stored elements in Compressed Sparse Row format>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train = cv.transform(text_train)\n",
"X_train"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This movie is terrible but it has some good effects.\n"
]
}
],
"source": [
"print(text_train[19726])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 9881, 21020, 28068, 29999, 34585, 34683, 44147, 61617, 66150, 66562])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train[19726].nonzero()[1]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X_test = cv.transform(text_test)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Elapsed: 7s\n"
]
}
],
"source": [
"from sklearn.svm import LinearSVC\n",
"\n",
"svm = LinearSVC()\n",
"\n",
"with Timer():\n",
" svm.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.99995999999999996"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svm.score(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.84575999999999996"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svm.score(X_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def visualize_coefficients(classifier, feature_names, n_top_features=25):\n",
" # get coefficients with large absolute values \n",
" coef = classifier.coef_.ravel()\n",
" positive_coefficients = np.argsort(coef)[-n_top_features:]\n",
" negative_coefficients = np.argsort(coef)[:n_top_features]\n",
" interesting_coefficients = np.hstack([negative_coefficients, positive_coefficients])\n",
" # plot them\n",
" plt.figure(figsize=(15, 5))\n",
" colors = [\"red\" if c < 0 else \"blue\" for c in coef[interesting_coefficients]]\n",
" plt.bar(np.arange(2 * n_top_features), coef[interesting_coefficients], color=colors)\n",
" feature_names = np.array(feature_names)\n",
" plt.xticks(np.arange(1, 1 + 2 * n_top_features), feature_names[interesting_coefficients], rotation=60, ha=\"right\");\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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RP42IayJi0wGf2T4iboiIH0fEjyLi+ZPMgyRJkiQtdJMekTsVuLaUshtwfX3e7y/AC0op\newH3A54bEXtMOB+SJEmStGBNOpA7Eri4Pr4YeEz/B0opvyulfK8+/j/ATcA2E86HJEmSJC1Ykw7k\nlpRSbqmPbwGWDPtwROwE3Av4+oTzIUmSJEkL1uJxF4iIa4GtBrz1ivaTUkqJiDIknQ2BS4GT6sjc\nSpa1Hi+tf5IkSZK0EC1fvpzly5d3+myUMmesNbaIuBlYWkr5XURsDdxQStl9wOfWBj4DXFVKOWuO\ntMbOWQDt9YkIViWN+Sw/LWkslG0x6fWYRBpui4W3LTzX1/x6TCKN1XGOSJK0JkUEpZQY9N6kp1Ze\nDjytPn4a8KkBmQngQuDGuYI4SZIkSdLcJh3IvQk4NCJ+Cjy0PicitomIK+tnDgSeCjwkIr5b/46Y\ncD4kSZIkacGa6NTKSXJq5XRNMVoo6zGJNNwWC29beK6v+fWYRBpOrZQkLTS359RKSZIkSdJqZiAn\nSZIkSTPGQE6SJEmSZoyBnCRJkiTNGAM5SZIkSZoxBnKSJEmSNGMM5CRJkiRpxhjISZIkSdKMMZCT\nJEmSpBljICdJkiRJM8ZATpIkSZJmjIGcJEmSJM0YAzlJkiRJmjEGcpIkSZI0YwzkJEmSJGnGGMhJ\nkiRJ0owxkJMkSZKkGWMgJ0mSJEkzxkBOkiRJkmaMgZwkSZIkzRgDOUmSJEmaMQZykiRJkjRjDOQk\nSZIkacYYyEmSJEnSjDGQkyRJkqQZM7FALiI2j4hrI+KnEXFNRGw65LOLIuK7EXHFpL5fkiRJku4o\nJjkidypwbSllN+D6+nwuJwE3AmWC3y9JkiRJdwiTDOSOBC6ujy8GHjPoQxGxHfBw4L1ATPD7JUmS\nJOkOYZKB3JJSyi318S3Akjk+dybwYuDWCX63JEmSJN1hLB7nwxFxLbDVgLde0X5SSikRsdK0yYh4\nJPCvpZTvRsTScb5bkiRJkpTGCuRKKYfO9V5E3BIRW5VSfhcRWwP/OuBjDwCOjIiHA+sBG0fE+0sp\nxw5Kc1nr8dL6J0mSJEkL0fLly1m+fHmnz0Ypk7nfSES8BfhDKeXNEXEqsGkpZc4bnkTEQcAppZRH\nzfH+2DkLoL0+ETH23VTaacxn+WlJY6Fsi0mvxyTScFssvG3hub7m12MSaayOc0SSpDUpIiilDLyv\nyCSvkXsTcGhE/BR4aH1ORGwTEVfOsYy1pSRJkiSNaWIjcpPmiNx09UwvlPWYRBpui4W3LTzX1/x6\nTCINR+QkSQvN7TUiJ0mSJEm6HRjISZIkSdKMMZCTJEmSpBljICdJkiRJM8ZATpIkSZJmjIGcJEmS\nJM0YAzlJkiRJmjEGcpIkSZI0YwzkJEmSJGnGGMhJkiRJ0owxkJMkSZKkGWMgJ0mSJEkzxkBOkiRJ\nkmaMgZwkSZIkzRgDOUmSJEmaMQZykiRJkjRjDOQkSZIkacYYyEmSJEnSjFm8pjMgSdLqEms6A5Ik\nrSYGcpKkqbSqQVgpZSL5kCRpGhnISZJWMIlRLIMwSZJWLwM5SdJtJhFAGYRJkrT6GchJ0gLjdWGS\nJC18BnKStIA4GiZJ0h2DPz8gSZIkSTPGQE6SJEmSZszEArmI2Dwiro2In0bENRGx6Ryf2zQiLo2I\nmyLixoi436TyIElrUszjb1XTkCRJd0yTHJE7Fbi2lLIbcH19PsjZwGdLKXsA+wA3TTAPku6gbu8A\nqj+NUsq8/1Y1DUmSdMcTk2oERMTNwEGllFsiYitgeSll977PbAJ8t5Syc4f0xs5ZsOKF/hHBqqQx\nn+WnJY2Fsi0mvR6TSMNtMfltMR8GMJIkaaGLCEopAxtLkxyRW1JKuaU+vgVYMuAzdwX+Z0RcFBHf\niYj3RMQGE8yDpNvZmhrJkiRJuiMbK5Cr18D9cMDfke3PlWxlDWppLQb2A84rpewH/Jm5p2BKuh2s\n6emEkiRJGt9YvyNXSjl0rvci4paI2KqU8ruI2Br41wEf+zXw61LKN+vzSxkSyC1rPV5a/yRNjgGV\nJEnS9Fi+fDnLly/v9NlJXiP3FuAPpZQ3R8SpwKallJWCtIj4IvCMUspPI2IZsH4p5aUDPuc1clN0\nPdVCWY9JpDEt14V5bZkkSdLCNuwauUkGcpsDHwN2AH4JPLGU8qeI2AZ4TynlEfVz+wLvBdYB/gE4\nvpTybwPSM5CbogBomtZjPiaZxnyX78+HJEmSNMztEshNmoGcgVz/8pIkSdIdybBAbqxr5KRV5Q8Y\nS5IkSavOQE63G0fWJEmSpMkwkFNnjqZJkiRJ08FATp04miZJkiRNj7F+EFySJEmStOYZyEmSJEnS\njDGQkyRJkqQZYyAnSZIkSTPGQE6SJEmSZox3rbwD8ecDJEmSpIXBQO52MIkAalXT8OcDJEmSpIXD\nQG41m0QAZRAmSZIkqc1r5CRJkiRpxhjISZIkSdKMMZCTJEmSpBljICdJkiRJM8abnYzgLfslSZIk\nTRsDuSG8W6QkSZKkaeTUSkmSJEmaMQZykiRJkjRjDOQkSZIkacYYyEmSJEnSjDGQkyRJkqQZYyAn\nSZIkSTPGQE6SJEmSZoyBnCRJkiTNmIkFchGxeURcGxE/jYhrImLTOT73soj4cUT8MCIuiYh1J5WH\ngd835p8kSZIkTbtJjsidClxbStkNuL4+X0FE7AQ8E9ivlLI3sAh40gTzsIIbbriBUsrYf43ly5ev\nch4WShrTkIdpSWMa8jAtaUxDHiaRxjTkYVrSmIY8TEsa05CHaUljGvIwLWlMQx6mJY1pyMMk0piG\nPExLGtOQh2lJYxryMMokA7kjgYvr44uBxwz4zP8G/gJsEBGLgQ2A30wwDytYKDtwGtKYhjxMSxrT\nkIdpSWMa8jCJNKYhD9OSxjTkYVrSmIY8TEsa05CHaUljGvIwLWlMQx4mkcY05GFa0piGPExLGtOQ\nh1EmGcgtKaXcUh/fAizp/0Ap5Y/A24B/Bn4L/KmUct0E8yBJkiRJC97icT4cEdcCWw146xXtJ6WU\nEhGl/0MRsQtwMrAT8G/AxyPiKaWUD42TD0mSJEm6I4v2NWGrlFDEzcDSUsrvImJr4IZSyu59nzkK\nOLSU8oz6/BjgfqWU5w5IbzIZkyRJkqQZVUoZeE/GsUbkRrgceBrw5vr/UwM+czPwqohYH/gv4BDg\nG4MSmyvDkiRJknRHN8kRuc2BjwE7AL8EnlhK+VNEbAO8p5TyiPq5l5CB3q3Ad4BnlFL+MpFMSJIk\nSdIdwMQCOUmSJEnS7WOSd628XUXEzOa9ERETmz66prdHsy6TXKcxv/9OEbHtmt4O02BN7YNBmv0x\nTXmStDBFxN4Rsemazse0mES5u1DKbtsG0yMiDq/30hCr3k6auQO7/v4cwLoTSGvDCaTRBDBjb8tS\nh0MjYr95fnez89ctpdw6nzRWVbP+9U6lUVZhiHcVK4znkb9f+MhVrcgjYsf55isiFtX/q3xuzXd7\ntI6rQ1Y1D3356bxOrc/eqZ2neXzno+az3JD8zKxJNagiYtF8t8fq6Hyab5qz3sBsrf86azAPq2Ub\njnt8tcrNHSPiwPkcnxGxD7AMeFJE3LPVVhhbRNx1nstF6/GO9f+4dwffNSKOX5Uyq/Wdi+vlLati\nldta8zWhQPSEiNi+aSNNqG5eI/VJc56s6vIRsddkcjSvPGxJ/s70qyLicfM819dq/59nPiZxbK30\n/WO2k5o8rBcRd55vO2mmGjd1pXepTz8aEY+bRxp3iYhNIuKZ1J9NGHeH9n1+fYD5BlIRsRuwLCJ2\nqM8775NSyq0RsT1wc0QcOO7yrTwsiYjDx12uak6o5wPP7Eu303ZtB4P1+WbjZqKU8mbgo8DzgTdF\nxAGRN9UZS0TsTRYwW7TS7nxylVL+Xw0kb6gNi3G//7aCej4ndaugfihwTUR8rz5u3u+6T9ZqPV5S\n89P5GG999vSIOLMGD2NVQrUBcnZEfD4iDhpn2QGeEHkd77xFxJ59z8ctN7aKiLXnu3zr/HjgOMu1\nv78+PBrYbT5pNB02EfGY+n/cfbpoxaex/rjHeSuN9SNiu3GPjVYjYLeI+JuIuN84yw9I71H1+B6n\nw2etWn5vCDy9Xd7U98c9ttaLiLVj/E6spiPy4RFxYkQcEvkzQeN8d7M914+IXSJi41ajeeR6RESU\nUv5fffp64HrgjBrQdN4OpZQfABcADyDr9ifGGAFZE/xExOOBi2IegWDrHD2S/KklSil/HTOZs4BN\ngT3q9pxP4/2+tdx+F/DCcReu53ZTXp4WEQ+ZRx7601yVzu5DI2KjeXznOsD+wFUR8fKaZudjs36u\nqVMXRcTmEbF4Hmk058hmEbFRRNxlHusStX2xeUR8tLZVxll+UV1+R/Knv3YaNw996T1mnov+CTgT\n+BVwPPC6edRpTZ3xooh4RYw5KFO3Ranb8tERMd/Oiub4fF1EnAFjH18H1v9vIAcj+vPZ6fiaqUAO\n2BI4ISJuIG+qcsU4C9eNcijwRuClwLdhhcKi00Zrff7tZBD27Yi42zh5abkF+DHwmohYZ9yAsJTy\nK7LyO6g+v3XcRgCwB/DeiHjVOCdEbYz8v4jYGXgOcGV9/aiIOGiMBlpTyB0TEW8jT+zjxsjH2gCl\nlPeQPT2/Bd4CvKQ2BsapkM8CPl9K+X1kz/BbI2LfMfISpZQ/AdeQDbRN6usjz7V2gyYi3j1uQdsq\n6BeR++M44FrgAxFxWUTs0GWf1ELu1tpIfj9wbkR8NyIePU5+qvOBdYBdW421Tkopvy2l7Ax8Brgw\nIj4YfaOlXUTEW8mfRvljRCyO7DwZN429gde3K+B5BNpnkY3M+S7fdPy8OHq9/Z3K8IjYFjg4Il4P\nnA78ZJzl++wDPBq40zj7tO/4Po9sbF84buDQ+s6LgBOASyPilDGWvzUiNgA+CewKfDUiLol5zIyI\niOcAL6x5Wisi1o3xOpDOBnau5c3GEXH/msdxj403kkHMOyJi/455b87z/YDTyDr1YuDvauOm00hO\nTeNA4Cry2Lo2Il44xno05f+JwJ/JcmtH4D3AyVE79obVa9HrILmJ/K3b9YBnACdGxEMj4s4d1qMJ\nuF4NvKKU8teIeF5EvDMidh+27AA/Au4d2Ym1YVSjFqpl99VkW+dyYIvWOdM1aFgbuDtZBz2CPE/G\nHRncEfhg5G8HP7iUckNNY+zR44h4YkS8gNwXnbdj9AKo48gb5P33uN9dSvnvUsqzyE7m+0bE1yN/\nBuu2TqkReWh3MnwIOBX4fEQ8uUmjy3rUc+RuwLnA/yDbOEvGXJ0mr4cAewIfjohzo2MHZWs93gGc\nW0r5ZUQ8IiIujYjDOmciLSE7zF9Ry7xOHVl1W/w3GcwdDPwBuAtwTES8sLYlR6WxuO67Xcm26xPJ\nNs5Tuq4DebNFgHeS5e//HWPZJh9NMHgX4M5kbPLPEfE0GH181XPpgIi4pa7DW1uv06TRKTOllJn6\nIwvo68iC8mXAPvX1LYGjOiy/GDgH+Efg5cDDgC3re48DNhixfHODmGOBT5AH0j8DWwMbAht1yMOi\nvucbkAfUG8lGbwBrjbH81mShfzHZuBpney6q/3eq2+MprXWcMw99abwSeHF9/Brg88C/AId0WLb5\nriV1nz4AuJG86ynAds1nRqSzBdkYeUB9vivZsPkq8LiO67EX8EVgE+BE4LK6X84D1h6x7Fp9z+9E\nNkReO8a+WFz/Pwe4oj5eHzigy3HVSue5wOdazzcCvgn8Bvi7MdL5EPA6smH0N8A/AMu67M/28UWO\nkv4YeOig43eu7dlOq54jbwV+QQYA63dch62B79XHO9Zj4hqy8T3OeXJD65g8Avg4+RuYXZd/KfCh\n1jF9NvDYcfLQ2pevBz4CbDLmckcD/4tsKB4MbNt6f8cx0tq0bscvAnsM2u9z7dP6/zSyQfMM8udn\n1qnn76ZjpPFwsuNoXeDrwL5kufnA/nNxjnTOJuuPbYCvkT3E/xd42xjH1jpk4LALWWacQ9ZNT+i4\n/K7Ad+vje5J3ff4c8NIxj4nnAe8HHgz8E7B5zdvI7VmX/yjZODy8nhuvJjvDTmdIuVO33VPq4wvI\ncmtr4L5kffTiMdZhbeArwINar51ANvjePer4bB0XnwCeWh/vQpbdvyPrtpF1IxkAXVK34Vn1uz8I\nvJcs0+ccnE7XAAAgAElEQVQ8zunVZU2dujHZaL7/mPtze+Dv67H1TOCAcZZvpfNCcnTzo2R7ZaO6\nDuczop1Tl9+UvAP5/wae3np9MzqWffWY/ApwUj2mdq/n6TojlrutDULWXXvV500duUWH715pXwFP\nAL5EduLct0Mazfe9CLiwHts/A75FlqMHjbE/rgSOJNtIl9fXtgbW67Bsc3w/iGzTPLBu23cB3wNO\n6JiHjch24tZkPXIOWRZfSQY04xxfu5Lto73mcWy+G3hZfbwjWRd8nywDt+mYxtfIMuJw4Ciyw/cC\n4N4jlmvOz3sC3xzw+o5jrsuVZHtx27p/f022FzptF/In274PLAce1eSFHIwYeWyUUmZnRK7pnSml\n/BfwdLIhsg3w9shpkh8jT7K5lm8i40XAB8idvwg4huwNeCvwmlLKfwzLR6lbmRz5OZkMfD5SSvkX\ncpj0pBHrsU7JUZONI+J/RMSTyN6V68nGzCNLmnNkrvR66F4aEfchD6AnA/9JNnDG8fcR8U6yh/0u\nwFLgSfV75sxDX0/+F4FnRMT3gf9dSnkoeRDuOXDhwR5JFkq/AH5fSvlY7Vk8ieztGOXO5PY7NiJO\nBv5asifudWTB28WN5Ml0I3ksvRh4U3089FwpveH0h0fEPcne7ZcBB0XECfW9tYb10JTsAV6X7H08\nsfaUn0H2dL9ljB7VrwG/jZxGvFYp5d/JToKrgEfVnqyhai/TNsB5pZTflVI+Th7zW0VO4xq4Hs35\nETn3/WjgrmRD4sXkaPhtx+8wpZRbSyklIp5We7geU0o5hTxOHwR8N7pNtdkEuCkiziIDiH+q/+9R\nR2VGihz9+gvZE3sKeX78ihzxHZmHWnbdC3hn5DTA15ANqldEh9HB9rlW9+Wr63qc2vTexZCRtdpz\n+O9kx8TfUo8DsgfxvhHxDnK7jhQRG5VS/lTPrY/Qmwkwsne7ZK/0esB+pZTnAfchg9v/JsudkXlo\nlUk7koH9M4EfllK+D+wMnEIG/cPWYTHwe7Kj5W3AG0spLwDeB2xVSvnPUfmoNiXLvnuSox7/TAYB\nx3Tsbf8v4DsRcSl5fnyODDjuGxF36pKBus3vQZZzDwLeV0r5I1kPvLjD8ncmG4LfJMurY0opp9fn\nv6rHzVy2I2dyfJ8sfy+t9eC3yPL/XhGxacdRj7+QdeDSZoShlHIh2TjbjJz5MucUw9KbpvoHYOeI\nWK+U8g+llOeQgf6fSil/nuP7b7veppTyE7Ksv4ash04ky86dSil/btX/g/JQ6ojTUyPiWWTA8idy\nmuYR7e8apuQsm/PJ/bEz8JSIeHrHsqKZHroP8ONSysFk0HI02Wh/H3CXYe2cWk8tKjmz5HjgocBJ\nEfGTyNHeN9bXungaWYffCHyplHIz2fk99Nqo1nbekZy11Fx+0YyanhIR9xjx3c204YdExN9GxMPI\nY+xIMlC+LEZcK9aqkw8FXkIGDm+tz7ckj/ORImI7ep1oB5HnK+Q5+vhRy7fKvcOAz5ZSvlzX4VVk\nJ+kJEfG2GDBFsO+82Z4MNK4m68bX1LJ4G+D/dFmXJs1Sys9qHj4ZdXp6l+O7fuZnZBuJUso/lVLe\nS3bqXV1K+W2HNHYB/rOUcmEp5Wqy7LyWbAufFEMua2m1PzYEbomIrSNi7douXwK8vGPborlcYWMy\nMP9NKeVy4LFkB/zlEfHEOZZrztPdgKtKKfuSdcdbIuJj5KjttjXeGW2cyHNN/dHrjdiA7GV6Br2R\nl/tTp9H1f36OtM4Dntl6vit5gfTrGK+H/XiyV/eLrdeuAk4cssxiMgjdB9ibHDn5ONnzeQPZELiJ\nOrI413qQFc2WZAF9FvBhsuL9Cjn94Ikd8t/0eL255vtospfk1+Swc9de5dfU/3sDf1Mf70z2MNx1\nxLKLWo/3By4FfkodyQNeAHymy/Kt1x5LVlhXAM8CNuy4HpvU42uvmv/16uuXAS/qcoySheTXyODr\nq2Tj7jPk6OSBI5Z/QOvx+WTP0rVkD+Li+njvjuuyHtmD/Bmyw+IwsrDfuR7/x3ZIYxEZxL6u9dpW\n5KjpXTosfwJZWZwDfAH4LvAfZIfL5l2Oi5rGJ4G31+P7zq3PjNwW1F5nsqJ8F3B4fX4S8NEu27KV\n1mvJivg8sjdz/bpOnXrM6n78F+DL1F7cun0eMGK55jjcjAycTiR7Yp9KjsIM3ZesOKp5Ab3ZBw8k\ny7zzyXN1syFprF3/35WsbN5Bjkq+gQwCTml/T4dt8SpyxOYLrde+RXZiDVvuUGpvKdkY/DZwS+v9\n9wOnNefjkHQOIyvxdcke5ReTMwI+C+w+5nHxXHLE5kmttK8a8vnbRhTIhuYR5Ajzverry4ALxszD\nE8jy7vut166mjqJ02Tf1eP5QzcteddtuPNe2bL9Wz6c/k3VYU1fvRY7gr9vl2KzP9yHP9xeRU43e\nSHYCrV/Pm5VGB8nOgPe0nu9PdtQ+uj7ekyw75lyX1rInAsfXx815sgUZrD++Ph9U5+xEnelAzvA5\nvea7Ccz/FxlADR2FaqV3Z7LzsBlZfTJZjr6d4edp+1z/MnBw+726TZ7IkDqRXrtgbbJjYO/We88G\nflCPk7X6v3NAWouAvyPLzh9RZwCQjdQzhiz3SGCX1vPT6jZdQp6zjwC+PmIbNnVIMyL4OrLzpp3u\ntiPSOLL1eE+yo/sq4O71tXcCe45xnp5MnlcX1eebAT8Eth4jjYeSAc++rdfOINtK55MdDv3L3AO4\nN9kx8Jb62s70yqKLgDd3/P4tyXrv9WR5+6D6/31d16HZ9mR99kayzNyCDO62LyPO0/r+RuTI6tnU\nGRR1H32U7Jw7aY7lXk5rpgLZLngu2WGwMdkGPmPUsd2X5hnA21vPt6/74qnU9vGgc6w+/hqttiHZ\njnxWzdPIUfPblhtn46+pP3qFy9nkiMm5ZGPiNOAefZ8dVOk0yx8CfKs+XkIWjC/vmId2BfyIetB8\nmeyZP7zm5boRaexNBgZvIwvU9fvevyc5RHwWAwp9eoXTovZ61cd71b8n1hNr6LQaspBtKre3Aa+s\nj+9B9soOW3bd+n87suLduPXeemSh+6YRaWxBFghHtV57ERmwvIicCnIjcwxP933nGdQh6fp8U+BT\nZJC88bB8tPJyaT2+LiF7p9at++PDI5btn1LZ3icHkyMxLyN7rnaYI4371Ly+mrwJxZZk47jprHgM\nrQ6DIcfmjuSocBOwHEs28M4lC/qt6zYfOHWMXiH6OLLXsmnQ/bDukyupU0UZ3KBpzrOgr9FFFvgH\nkFPYjuiwT4IsqNcmGwJvrK8/hI5Tlchzrb982IG8Pmynjmm0C/0lrcdXAKd0WH5rclrqpvU4axoz\nLwSuHHWekQX6vcmy41hyWswryYb6z+u+aaa3DSr7mrLidGplS44G7l/T35zaaJ0jD9uRZduudZmH\nkp0/LyfPuxvJoOzgIWk0ediw7s/tyU6Gy8gZEacDnx6xLXYmG4CnUQM+sty9mRw9eQdww6DzsHVu\nHFuP7Y+3Xj+MbPhfB3ygyzFRlzuAnEp4D3rn3y5kI+vAQecIK5YNX6KvEUhOO/sB3adEblL/diI7\nGN5bj40zgeUd9sfedRtuVp/flww+vgY8d9A6tNJoGvKvBO5X9+0nyOmpV5J1ymPrZxaPWI8DyZGb\nu5KdRa8jy6x3kWXxGczR0Kyf/3ty2t6z6muPJEdbLyen+T972Lq0zrWn1eXeBTysvn4v4OwhywVZ\nT/yeDBjbDey7kUHZfmSj9QPMXfY2x9BhZMfXB8nOjTeS58w9gUd0PC5Ooga3ZOfHZ8h6tfN0RLIN\n8hWyjHkHK0553WjU9mx99u5k8PMBsp47lCwzmsb3oDLrqXW7HU+Wn1vU5T9U9+l15DXPXbbFR8hz\n9XBq513dH4eOWG6PejxsAhzWev319dh+B/DVjtvyIWSHzZ5kmfd6Mgj8NGNeqlBfO5ns8P4AWT/8\nor7+ZeCBAz7/kHpM/wvwtwPW892DvmdIng4g66TjySDwbeRlG5cyR/BBr8zZk6xHDiVnDbyIbGNc\nQe/ynKFlTuv5dvSms7+2Hq8Pr8fPewedY/QujziTrPt2rsfIR8iOvI/SOxfnHEjpe74VWR83l318\njZwp8jBal7gMOC5eAryrPj6o7s93k2VRp8uabktznA+viT96FcaO1J5Kej2ZryQbNk/qmNZLyAbI\nXvVEOpMs4I7ssOxSskfrGmqvb339pJrGc+g2SrAh2WB/P1lBHUpO52l/5p8Z0NhtHQBvq8tfQPYi\nbtz3uWtpzWmfIx/NCMEpZOPmq3VbDu0dIhsuJ5I3r4DseWj3KNyF7nOcjycDi8+SFeaGZMPuDWRF\nMmeDn6zsHlS/7zlkQHkOvbn0b6ZW4B3y8eF64p0CXFtf26b+DR3Ro1dAnUoGgn8AjhvwuQuBZ8yV\nBtn4eC1ZMB5DbcyRhdU36BC81G35QXLU61O0Rpjrd7yYOa6RI3u4/oasZP6p/X1kA/g08pwbeP0k\nvcJvL7JA/3DdR8/u+9zTyAJ3zp76+rmNa15eQKv3lez1H9rRUD/3bOCT9fGSug5PIK+lGnntZutY\nvoZsiJxCNlg3IxvOK1UUc6RxVN0fryZ7h+9M7/rJZhRmropr57od30lWTv3n+R7kuftOhvTe1e/7\n+/r4AfW7/50sQ4ZeD1bX9TKy0fHU5nygN0q3DTnL4JMMCQjrZy+hNgjJxtSTa9onAtt12JYHkp0i\n55Hn257kaM0x5AjjdnNtz7ovLyKnM57T996D6jHSdXT1MWQZ+3pyBkQzerN9c2wyuAHW1GWvaI4f\nMng7lSwLd2+OiY75+BR1RLYeKyfVtJ5Jb+SyP5hs8rAVORL7LbIhdCLZybExK458D1uPLckOqO1a\n7+1Hdi58bUTemzQeS/bEn13/v6V9TNb9cg1DOmjr48PJoOOb1HqYPO6XDFuX9utk/bMfWXZ8mCyP\nd2zlddBxtWHr8QuBP5KB7Ep1IBmkDz3O6/54eM3L1uQ5+uK51rv1WnNtfZBTt88jO0jOIzt3T2NE\nO4fseDyLbIA29eBmNZ3LyTp16CybVlpbUa9Bq9v0XLIMuYjWdUBDjovFdZkfUGdQkXXLvahtj1HH\nVn38AnrXXzXnxMXAG4Ys354p9CgyQDqbDMy3Is/7k6kjcyPyEvTaJ3chy9NnkOfqwzos37Qvjibr\noGPJc30r8lx5PjmS/XjgmgHLL63/T6bXMfACaodDTa9re23X+j0vY8D12WQQstL1nK19uh45EPM2\nsk2zlAxaFrXTY3iH5FZk+/Bkssy/B9m5eAZ53typHjP3GJBGc55vXI+BbwNPq69tT9Zlm891bPat\ny0b1O5fR60h9MBmYHlHX6bvkjYIG7ley3n4VGcecX/fvm+ZaZui+GXeBNfVHFib/l7xWpnltG7IS\n36a9o+bY8HuTQdN3yR75h9fXzwSe3+H7DyQbkf9GVuTb0WvMDJ0q1ncgHkWelDuRhesH6s4/qB6E\nG9Aaph2wfDOl6Ih6Ep5DBnRNT8M69aAYeSOEevC+iKz8zyGnVL5mxDKPpjd6dXDdL+8mp1A085SH\n3lykfz/Vg/kfyRO8S6/hLmQwsDdZ4W5H9lg213ZcQRbcXW56sDnw/vr4cmrwSBZWx3VZD7IB9F2y\n5/QbrRO7aViuRfaI7TEgjefRm6qxQT0+zq378AlkRTDndK/WMXg/er07W5N3fruybottyYJjVKN9\n35r/n5KNwXYv7H3m2n99aVxDreTInsDP0Tq/yGtKR05VrZ99BDkl9J1kg+IohoxMtpZbi+whO6ye\nJxeQlfGZdDhXW+m8jTxHH0ZeF3Fu3S5703GaVGvfvKHm4xRy5ONOw7ZlPS63r+tyFFlOvKNuk+36\nPvtrWr3GA9LalDxnP0c2ph5dX7+aOUaJB6Tx2HosfYKsMDdmxQbTLQwIQuiVW8eS13ZA9rAfR61E\nu+zP1uN1ycDrLWQj9en0RpKH9irTm61wNXmtzMH19Vcz3rT668hG5bPJa0gh65b2aMxc+3UdslF3\nGNmge0/dJ+f279cReTh00LnQYRu0bzjTTAd8TD0+LqzHWtcpRaeT9eGgUYAt6v+VRuNYMQA7kzo6\nQpZTF5GzXF7a+syw6Zlr9R0fp5DB1MeonZJD9kVTfh9GXrfZvL6E7PD9KqPrgOvJ+q+Z3bK4Pv93\nWqOIZHk4Z/BQP3NncmSgmU4d5OjHx8kgYNi00FfSmymzJb1rq5fU175At0DudLKu+iJwt9Z79yY7\nCYcGUfWzdyXr4S+TwfnSQfuyf7/QuqEYGfxsQra7riADgC4d7s0+XZcMrA+r+TivbpcHk9M8h40I\nvo4sJ5qR6r3J8vsqsvwf64ZyNY0zyPK38w1F6J2r25HTlM+qf2eTnWpNZ+9GZHC2S9/ye9T8rkO2\nLxeT5cZZZH12KfDtMfLzdbLt+kfg9AHvX82AzurWPnkTeX7uTu8GZBvXY3xUudWkcQk5MnsxWWae\nXNezqWcOpo7M9y2/aev7XlC3xUHkIML11DbrGPl4OznS/VyyDHs/9b4brfN9zsusWvvnDLI+aWKY\nr9BhxtJKaY27wO35x4oF/iZkYXUTWfltNUY665DBTxNt363+f3h9vevdGQ8he2PeXf8/kBwmvpoh\nU/hYsZfpfGD/1nsPrOuzUvA2R1pvptfruwnZ2/VcsqHX9AwP7VkmK+u/I4OVZqrXIrL3aeScb7JS\neS5Z6X6BrPDeRDawRs73phd8bEGv8tmkbtebgVePWH4Dslf7+2Qh35xc69ObEtH1erIgA9kfAZ9q\nHS8/6rIt6udPJht1h1Cn15KN54/Ta2SudG1D3eYPIBsjb6XXwN6+pndR3a5Dp7CQldaXaY001HXY\njez5GzXfvH2e3Zfe9U9n0RshnfOaMjIA3IgsJD/T997hdb+OvBNgaz9uSx0VIEcqmil872HEtYat\ntJ5ONky/TW8U6IuMmFLTWn43svHQVAAbkCNIH2JEAd1K48l9z48hR9vPYPRdUJ9Tt9sT6ndvTjZM\nLiEbmYeQvfbrM7hSHXQX1RdRG6zkCMyc13INSqO1P24gK68D6rG3PvXaoiFpXU4G8Q8hA9rPkJ1H\n249xXNyL7BFuepMfTVbmZzPHdESyF7gZRfxwPbaCLP9urOfNN0floclHXddXkw3Mr9MLWD7OgAbE\nHOk8hWwUf4FaxpDTcR7UZfn6+WdQp2XRuxZ0Z7IsGnW+b0c2Xl7dem1tsj44bYw8bE82+psbCC1u\nvdflurwnko2Y42h1jNRj5Mn18Vw94+27LT+ZrA+a/bwO2Ui9rON6vIzsxLyYXgP/7mTjbO1h60OO\nUnyM7Bx+Xuv13cjrRz/XnEtzrUt7ncgy/8v02igPJG8SMmy55nq2LcnOp92b72yt39Bp3K20lpCB\n/afJsu5Y+gKXUfuWbOSeUh8fS878uZ7ebJm5tmXTQfMmWtdckWXMU8i239NGbcP6/+/oXYv2LLLz\n5GP1uPib9mcHpLE7WfZ+r2+fHkK2sy5lyHTh1nbfENit9frJdBg06N/O5EyaY1p5O4Esf8+jXq/e\nv49a58Fa5GyfvydHarcg6+knkEFe1zbOseTU9kVkubdVffwcetdyHj5sXep6PIzsrGgGHp4DfHDU\n8V3/35O8sWDz+iPrdvgg9Y7YQ9I4hpx6fm1rW0Y9to6r+3ro9dGt/XFXcrS+OdbuQbY5P0q9G3ZN\ne6U7eNf/W5Kdu/cly4916usvYcg9IYbmbT4L3R5/rNi43L2u8FZ1I55Djhq8pP+zc6R1EvWarXpg\nLyYr5BfR19gasGxzUj6G3kjLnmRl/kGyl2bOC3f70no1OY92pe+kN+w/sIFX870t2Rvyr6wYDG5J\n7Y1h9PUIj6gn4ilkUPgDslHXZfSq2RZH1v/7kD14HyQLy07BU7O+5IjRdWSh+bz6+kHDTmxWHJm8\nhixUX16X63w79prGzmQhtAPZ6G9uxfshhlwXMUc6Z5JBQ7MfT6FeX9fh+LwTWch/gmwYNY27+zEk\ncKnr3ATCy8jez3fQGnVqba8u1zPcg2yo70WOgD2VDDA/Rx2x6D9OyEbhu8lC+n5kZXlm6/3dyN7Z\nUT/r0eTzkHpM/KEenzuSFeImdLxNc/3s+nX77FxfP5rWNVQd0jmcbJx9iRWnqG7PkBsOtD63O9nb\ndwmtKTRk47C5dmhYD/vu5GjtOWQgvbS+fve6j8/suB7HkA3dpfSC0h3IRtFKI8QD9scudf++ndrZ\nRDb2zqGORnXMx6PIivRGsqEeZKX6mI7LP5nsXDmHvB7jE2Rnya70bo40aEbGfvW8+Cd6F/o35dim\nNV/7dl2PutyL6/HZXC/6eFo92/35aG3Ldeldf7sPvSDw+YxZgdf1upHWdVNkI/P0OfKwhF6Hxl3J\nKVbLyXKjPRo15/UhrFgnb0PWO5uSAfYlZMfa0zrkvWkQPYAsa68jR3y36P/eQfu07/2vkg3S35Cd\ngKe03msagHNez9t6vjHZUP0TOSLzFXqNsrmulWlPwXso2Vj+KiteU7XZiDTa1xVvTO9mB78gp87e\nwJDAg+xA26/+X0LvWp/nkO2FzcnrnbtOiWw6Ezer2/U8sk48op3fIcvvRpZxR/W9/i46NNjJ9sR/\n0Hdtej3Ohnbet7blJuQ0wvYN8Latx/16/Z8fkt7BZCf9F1vrfyeGl5vtDolTyA6ea+ldtvBv5AyT\noR15rTR2IGc7fK4v7QMZcnMoVjxX1yeDhjPrsXE8Y8woqWnsR07jfxe9nw04gKwfu7QdNyXbF9+p\n+6Zpt3yLOqI/Kh2ynfdvtMqYes6cwIg6uZ5fl5A/pbHCT8yQdWpzns45et86vs4mO37aQf4mZHm2\nxbB06nufrNvxRno3V9qW7JzrdJ6ulOZ8Fro9/uhVfi+qO+Cr9BoSa5EFZ9Mgmmvjr0UWDp+vG/45\n/el3zMsGZMDTzPm+G9nTu4RuUw2aA2C7ugP/gRzR6tJrOahx8op6QH6IDjfz6Fv2Olas/A8lG1ij\nRvGa/fFIsnJpXxvwMLIBP/AasHYa9HrqziJHwvYmR4DOouM1bXX508nG6dZksPE+suF7367blSxE\nmuuU9iUL21eRjfihAXFfWovrcv9Fjpq8vKa941zHGoMbSruSvWZXkIX+nIEpWbAeX4/Dg+prS8ig\n+kZalVjH/XokOa3yneRvB72XLJzWptdLPehYvBM5sn1GXfbl9Vz5Yd0m11GnUnY558jg/jBy1Pfi\nmqcX0nE6Cxn8fZAc4bhPfW37mtZYv8dEnucvJkeT3sD4vy+zA9kY+nA9P06l3mxpru05II196jZ9\nF/lbdE2Q3/TErtQgaO3TZ9D7bZ3vkhXYI8hAYqeO63B5XYdz6P0OXjOzoTkuBk2fu+2udmRluxEZ\n6DczAB7LiGmy5Dn5bnLK2dnAQ1rvXQC8u+/zc9UD967nxO9o3eWTLPs6V5zkqMBj6uOnkjfY+BRZ\nMTc3xxjWU//+emz+M72gavt63oysRwakdyQZ3H6x7tsvz7UtyPqyuUnXbvW1B5FBy7vJc3UThncu\nNMfVcWSdfD29mxCtTXaWjKoD2r8P1gRah5IN5gvJIL9T3UyOEny8Pv56ff4bcjr2bh3WY3OyHn5Z\n3bd3IuujM4Cjhx1XrfXYl9alBOTPe9xINt6XDFuXVj7a1xXfQI5UbkSOOI+6s+Jx5HWmz6A3lfQh\n9bi8hOxk6NqJtpTsoPgMvY6jPcjRh6F3lG2l9Td1X15Kdsq1f6tyzmsNW5+5J1n3vZ2sR15QX7+I\n0Xf4bfbJ/vR+ouUt9I36D9qf7fwNeHwiGaR/hhHlJllPHFbPh+bmPc8kz89mFsBZI9LYhfpbiPX5\n/es2/RaDr7/vP9fbQdxOrHi9+yPrcfZxRlyn3lrmZPInd75M3tRnMdmRcw29QGRQPdTUVfuSZd9i\nstw8h+wouZw6i2jIPjmX3kyzO5OjVp8ly/97dtmvrfcPIzsEzyJH4E6kdx380EEAcobUDq3nx9Vz\n5Vo61CGtY/8oeh387bt03puOv186MP35Lnh7/NUdd2N9fDW9u1Lty4q9YV0aREfUE/vrjN8Dewrw\n+vr4meTIy59o9WR2SGPT+reY7Jm4jiwcht6Jil4he0A9CJs5780PO97KiB9AbKW1Edkofl7f659n\nyF3n+j77VWqDihV7NdZi9I983o2sXC6rJ3LTqFu37p8bGFIBN9uD3q3x30CvJ+VRZOH0tI7r8QpW\nbgh2/SHgptf6IWQD4Fiysbon2Sg7kRE/fk2vEfMIshK+LbAne9vez4iKq372OLJAfBG9G9A8gOz5\nGufWyNfRmhpB9k5fxfCGafuHb68lG5QfpHfjl3fS4dq61nrfnyyk79p678FkEDK0gVg/+3CyUF6P\nrHCaBusiOoyitdJ5Chk0HUZ2vjyArHw+Rrfex3uRDboHkR0L96R3A4IDRhwX7UbmLvTOtSPIBs77\n6NCoIs/Hr5CN8zfU/flWsgJ7RcftsCv522CQ533TkfVKRlyQ3dqnLyOD80+SPac7kY2bsxlx5zmy\nkr2SHBld4RofsnPtOlrX8QxJ5xnkdRFnkw39G8nOp5sYcFH8HGnsRJZd36zb865kuXV3hnSCtfbn\nk6nTgshrwLYlpyNtxHjXW76QbOS+sB5nzc1eljLiBif18cvpTYVct/4dTXaAjBwlqN93MzlKfg29\nUatd5/rOAduiP3B5NHmOvpaOI801nYeTbYEXUGdQkA2lz9LhZw/IqVAXkiMVy8jz8859nxk1SnA8\nWe69pXV+bFDTHHm9d/18/3XF19L3A88M7+F/XF2P88lAqplq+yzy/Jlzv9JrYG5GnqOn1uP7GzXN\nrbvmo/WZdck20wfq/4eQZfJc5X9zXDTnw+ZkW+nAum1+wug7/LavkfpezcOBZJn3KbJDbtS+bLbF\n0+n9WHZzLfPmZFAxqtxr2gbX1+/esC9/i4btj+ZcImeTPJgVA7rHk221b9O6Cc+QbfFyctT98+TI\n2X6mCYgAACAASURBVNLWvr7nsDy00tqRendOsj1+JTny/j7g3GHnGFmu/IHs8GquR74T2U7agyzf\n5xw1r68/it5oWnPPgT3I83U5IzqsW9tiu7rcjmTn2YPJOuUmejP7hnViNYNGy2h1CJOdPn+lV/6M\nCiafWf/eTL1mluxk+8pc26DTfprvgrfHX93YbyQLguWtA+TLDAnGWifkruTc1WOpwQ7Z4L2VDtcz\n0CvMjyV7+C8nA4ityN6VoSNI9E7qh5KVyyfIQqXpxTiZ4b+n0qzHTmQv4xfq3yn0Lo4cGvi00tqU\nbMw9nuwJ+AjZ2/9Yul8jsiFZOB/U9/p76BAI0pse+niyovgBK/ay38wc19j1nyB1fV5L68YZZAXa\n9c5zp1ODA2pvDFloHDViueaYWI+8Qcu7yB6eSxj8kxLDKuB7123wBHK04Af05o4PK1Sa4+rBdVse\nQlbiF9bjvT21cuT1cWSldwF1Wk3rvU/SraH8Xnq3Dr4PWUjdQDYImul8Iy8iJqdA31DX4+Cu+7KV\nxnnkaOpJ1CCdDMY+RPepLGeSDeVP0ytz1iErsZ06LP9gsuf0feS5/jqGTMUZks4VZEX8e3q90puT\nZVHTATLs2NqNDCa3pk77q+txLUPugFqP62bO/gZkmfEF4B31tZ3J8mPYTxY05dbB5MjsXcgG7zvr\ncbY2rbsJDjvP6uMDyalFP6r7cxsysP1Bh/P0KWTQcG49ntciG6ufpTVDo8P+uJbsDX4BGeR/lJy+\ntlOHfbGYOqJI1mfN7xQ9hqxTul6jvQcZhJ5Sz61zyGmZdxuWB+rvaLWeNzeJ+g6960XmvPlDX1oH\nkUHL7sBXWq9fRcc7bpKdsk3g8lAyID+hvtdMuZqrYdfs1/vQ66k/nuxAam7v/rhhadT3dmXF0fFd\nyXLsVV32Z9/zncky43N1v+zUem/UlMotGXxd8XvJoHlYPtqd2ZuQHYiX1P1zUH2962/XvYneTxYs\nqsfa1+sxcgJZPg+rk9ataXwAeGd97YHk+d7pMgVyyvjyeny8lt6Uu3sz+m7azfZ8L72pqOuS5d9j\nyY7ROQNreg3++9d1vhd5c70/0/0nqtrXQO1Aljc30XdN9Yh92u50OYTsdLmMFe/E+twhx1VT9q5P\nBsHN9ZLPJtuQV9DxLpV1uRPqPmnf/XVPsvN2aBBW33sBOQPhF7R+37imu8WIbdG+k+VHycuKzmi9\ndjj1EqNB6bS2xabkoMk36vnxOnqjhXdvfX5UG2V9sm1yPTni27TBd2LIzVJo/SZz/ezH63HRXA51\nBWPMRhuYt1VZeHX/1RPxrfVAaK5Pexb1tuIdlv8C2aA6n6zIm2l9mzH+lMSj6F1cvh7ZM9vp4nSy\nB+OR9H5D42L6frR7xMnwRno/7vpQereYbwrYlS6sHJDGC+mNKm5C9iTcRDZcR94Gt5XOc8mG0cPI\n3rMD6RAI0lfwkI27U8nC5stkYPmyDumcQI7QPKTm5ac1P0MvpO5L42Fkb9G19KaoLSZ78obemp5e\nYX0k9cJlstH8hHqMXcaQKXjUO6LVx1eRDdNj6nH6ZOB/kj1oA4MYVmzgXkSvp2sdctrCh5njdwhH\nrNfT6r64f83jo4DvdFhubbIR86q+1z/NHD91MGR9gmwgNr8j9Xxq0NFxvz6ALGC/R+8uch+ke0V8\nF3rB24X0GpdPofvIzYeoBTfZ4fOmmp+hgUt7Hcng/JNk+fcN6l256vqNbASQFc56dZmt63F+ABn8\nLB+Rh7eTo2jNlI8HkeXo+TVfl1FH9BjRg0iOer+29XxzsrE78oYzrW3RPj6OJRsjPyUDzGZa9LBR\n4y+T5dRr6PWabk/HKdj183vT+s0oslf3/Lour2OOBiKt3nsywL+I1m356zkyznTy17eOre3IjqM3\n17zcechyB5ANmX9kxbs+P5JstL57jDxsRE4T+yW9jrAnMeDW53Msvy19v61ENsguJBupXaZfr0eW\ncc0MnY3JIOAShvyWK1nGN+fIXcgAtz0tck8ysJ5zGje90a71yfpnq9Z7jyA7Xt7W5diuj/djHtcV\n06uHNiQbhzvU59uQ590V9I3qDUjjLq3HjwLO73v/JWT5df6wbVI/+9Z6LhwN/Lgvj03H07Df+H0Y\nvQ6nB5OB/scZ78eydyPbBj9kxes+16c3HXxUR8V1ZJ18bD3G9gf+QnZeDdsf29X9sBHZIdt0th5B\nLyAeeWdceh0qr6bX0H8Dvbt6t38aZNj2PI5sZ96ttR82JTvdO90VkQyg30TWH28lp0CPvMawlYe1\n6M0geyzZjv8S2Yb7/ojvfgQ56+1vW6/tTLaNfsV4AzGn0euguX99/pH2MT1kXQZdFrMX2V67kuws\nHXiTJ7I90/xcxZ3o3Sn/cDIwfX/dth/reozPua6rmsDq+qP3o5P71pV+a13p5dRG1aCN3Fr+CcAV\n9fFmZE/i5+g1fLs0iO5GNl4eQOsHjuvB3enHY8mK67LW8/XIhuGHyIBqVGPormRD6i19r/8tcGrH\nPGxXD7wPs+I835HXHbVOhvaFts00pZ/Q6gHtsD3XrifSSfSmHOxLVmRdp609imzIXUT2WL2LHGHt\netfPB9L7jZwzgP9F9iJ+jI4NGjLQuRX4RN9+vTd9o1oDln0POZq7hCys1yGnrTUjxmdRfzdnRDon\n1OPi8L7Xt6V3Y5Iux/hm9TgPclrO1+q2uJI67ZfRN9C5Nxl4HE+O8jZB8U7t7xqwXFPBNNeSvYJe\n47y52+XJHbbF+mRD4Gyy4f5pcrrWebRGDTqksxU5deINzTFSX/8BHTpt6r5s7rrW7k38CK0pph3S\nOY4sr06lNxK2JzmKNGwaX3Ouvo0st5rnzycrv+WMvrvXwWQD6pNkoLAlOW3vZPJcf0r/9w3Jx/3J\nMvcQelPW3kffj9IOOS72JDux3kIGHc1vDr0euLjDdmymlT4d+Ebr9c8z4rbyfelsRjasX0BvtHL3\nuo0+2d4mrWV2J0eXzyOn1q5dj4sfkyNqH6T+HEPHPOxY9+F3WLEMvwe96+0G9Qa3R22eSv5UxZdY\nsSf6tp9JGZGHpgf+CLIueQ1ZN3+FOg2cwQ2f/tkUH6VVXtPxhkh9aexB9oxfWvfzFtQ759b3B123\n+fK6HZv1OJps7L6abLC/l9Z1+HN873X1+w4gR5jPIc+Tpjx9P73p0KPKvSPqcfAysrP3h+S05WsZ\ncV0xvXPsXWQA+1myPntwfX1/hl8n2NyGfRE522Bj8rz4GtmIvis5inI38tqwOTt7yTLihvr4U/Tu\nOHoS8PiO+/M4+kbu6r4ZFYweTuuuvfVYOpOsv15FK1gdkkaz7zYh2wfrk+duU4++ltHXfu5Vt92v\nqdfAsWID/9mMnkq+A9mB+YZ6PKzX996nqVPdRxwTW9fj4UtkfbYLY85uqel8muyov1/dBmeSAwJD\np2W2tud9yTZFu6w5ve8cGdYJdzBZtnyd1uUE9fj8Vzp0rjbHJn2DBGQn+sPH2BbPJ8/1Y+hNYX9Y\nzd/ImQhkOX0rWbduQdYp+5Btp4F3XB5rX61qApP8o9eL8Yh6El9fV3ZHsjFxGL1pJMOG+dcjA7nL\nWLEH4yWM+C2XvnS+QgaQ/0TvVuDb1fwM/aHoVhrrk42ZT9Jr2O4PXD9kmXZUvzXZwL26nuAP6v/c\nsG1R3z+d7FX7dD0R79fl4KFX4exL9tR/l2xwb1UPxl0Yr6A8h96PsP+RFW9/Pedwf2s9d/v/7Z11\nuF3F1f8/ExcSElwCSXASNPgPl0JxAhQnQHEori8ESXAJklDc3a24uxcvlNLi8Ja37qUU1u+P79rs\nuYdzzt7n3nuuZb7Pc57c7LP3nNkza2aWLyRwrIoY9j3QQbQrJetAISbwpOj/I72dRRuY0xmcHn+N\nmPy4IHrvuM8Vz/VDzM8UFFeRuUucjRjVlRFDU4ZZXxcdHK9QI7NltT5UfL8c8re/zmljNaRFm5UG\nA2+9P6f6vDyKpzEvok2/5wHE3B1Lrv2bHTFmZegrY2QmI0b3T0ggXhdYosH32AsxVDshJuZ4SmjM\nfNyOd9q8DB1gayFh5sNGxhPteb8A/hhduxmvrVVtTKM1thZVtJ1oH6pbsLvi/h18Li6iSqxmjT6E\nKv05wuniTMR0vliGJvzZZxGz/bx/ziEXWkplY0WKn/cQ090HWdIbEe7nRW53KyAm63yksHjE29oJ\nF7arjPfS/v73IkE4SxN/iPdrVIO0OQpZIt+iwgJeOf4VYzSUlkzhKSh25Wa0f5Zx414BnQE3IiXB\n7kiAmUCufKkl2GeC03jECB2E3GTfRMJVYUIk8n1vaWR9W8/7fgaySg6t1wf/LnPVuwTxGLOhfXcy\nUqZNisasGn1vjXiK2fyZcci9dhrag66gQDgnXxeZpfwZxNhmtVzPo2VG6nouY6v7871QArWpiEE9\nheLC48OQa+sC5Jan/sgr5B3Ef+3jNPd6Cdo8DinSroquvUWecKqmksFpa00keOyJx0j7mO5b4rfn\nQmvqsujaGmjNP0NFfbUadNUXnYOZZfNYtHZ3wGvE1mkji8Ue4+/wIRLIM5rcjpLu9T4WHyDr1TIV\n3/UnrzFYz4gxAfGKZyLh/CZ/j0bqVP4QuCv6/8zI8n4xdcJoojldBnmTXIi8jE7PxoBifrXFnoT2\n2F8ipc3CFfcWtbUq4q9eRXx05rn1C/IyNrX2rWxfWwjtVRPRGj/Xx2Jovd+npcJmfcS7no9qTJ5Q\nr9+NftqtoXbtlDalDZBG5ddooymsNxQ9P94X5b1IgNkVMd/PE/mr1ng2W9h74q4GiBGYhMzr51GQ\npaaSMJCm5wBf5LcgS8q28WTX6MPGOJOODo1j0SF0ii+sMkzyVjgjirRN5/ni2oeCjFhRGw85MZ6L\nsmW+jKyKpVyTosXwqhPz7Ugr/b4v8prZBKOxmMXp4Akflw2RJa00o44OrVuRtWgnGqtFuBhiztam\npbXmMKQdegoJHkWMZV/ExPzNN4PgfXkMufUcVpau/NoEZKG8knIxXDOTx+Gd43Q5Jzps7vUxzWiu\nFMMdtT0YCYGjKVAykDOIC+GuSEjQHev0/TblXPAGokMia28pxBB9TMQQlWhnQSTIZoqTLCnPRZSI\nKfA5zAqyTyBPjnIHecxGVe0jVZhHFAP0on8uJnJHq7fu0J55bDae/u98SLAsc+gMRozhDEjw2R8J\n5lMpKB4etTEe7TGnoUQMeyHhZ1fKB9lviqcrR/vGD5HA/wZRXG2Jdvr43ExB+/hVlE/sNDda1/cj\nN/Sj/B3uQoLMAMTkVTJcGaOdKbvW8Dm8G9i4kTXl7cxIlLgIKQduRQzjwtXogZZC9VR/75uz8UeK\nknuow9xVtPEmsmxOROfACVQovmr0I4tjmwExqJcjF7xj0Rq7nJZucEXKp/2QEvADdJZOQS5YFxXQ\nd+wVchgSHk+OxqM3xXvW0kiQfRM4wK8NRYrR3XxMMkVz0TlwCbnwGscVHxKNWVEbxyMBaGfcQwhZ\nSq8mipOu8txop8ng9HkSEnoOIxJ6kOBwMr5/FfRlXZRI6B50Vl5CHqdcTxgdjJSJs3sbpyGr3t3U\nUXZXjg/a455AiTV2jua07hkS9WNvcu+HgPiLy9G5WtMa5/N/EtoLlkOK4bl9HN5EfNZnFFiPyGND\nF0J7y15ov7oJKc6n4edLiXcZ5+8zEZ1l05BQXbfUVtROFi7xnrcTG0NGlWzjTuSJsR46Py5HvN7+\n1An7oOWeszwSbOdAvMEUn9/TK++t0dYS5Hz2Bk5b7yP++6SCZwf52F+HhLcsW/FcyKU6Sx5W6EWA\n1mScBXe00+rvaUVx+aq/0R6NtEtHciZgIJEZFMVVXAb8nZLuSUjbNNknbnt0iD1WNHnR84PRxja7\nT9Z+fv0cCtzvovdYGLlqXOmfH/sE7k557cy+aHO8lLw+xaZoAy8b3/E8FRp1dFhcVGtBVSymHyJG\npj8S4GZCh/C3ZTcGb2dtJFSuTB6HtIYTdCHDjTbJtZGG5R6fn18igaiMdbGXv8NMaDM5D21y61Eu\nW9v6vgl8SZ7BKBMeBjidFFqP/P4dEWP9nNPlkmizKhMTsbCvh6v83xX996/G08MX/PYK/g6P0LJm\nXT+kfZ2EJ9foiA/aVH9OdNAhgXgNInfmOs9v6+++PXnSml5IMK2pia1oY38fz1/h7rJI69631hqp\neH5mpHyaEl2bxduIM7sWuSJOQYfGWcjaMxAxN4uTx3IWZbtcAyVVWoBckLucOgqCinbORgLsjb5G\n5nC6uIY6hzg5EzEGMdkb+pqYSgNJRaL2VkYH8XbkyRNW9L6VSlxT0d4MPh+lD05fXzsihuJttA8+\ngzwz+iMh9XuWsej5SbhrMGIMtkbCx02UT8o0h//uo75ONo2+q1dHKpuP/dH5szJSbsyMzrd4j69F\nU1kb69LSjXxu8hIpRW7XTyCm/nDymNOlEIN4lNNbFr9UKk7F18MuaO1n8VSH1moDKZcGICXRHX5t\nhNPmg+g8LVujbH8kQF+HmMPvWdprvUf0fT/ESxxVcf0mH9e6XkPRvMztdDiRPIHaVCqyUld5/lGn\nqZPJ9/+1kEJsmo/ncCQIzVfQ1qZIuP4h2vMuRefzodSJS4vG83jgzOj6MkiwXZvyiubYIyY7p1+j\nZdr9eiUkZvR+303LmMdZi+bS75sJCWC3ohJIWUzgRkgpWWQ8GOK0tAha4z/w64N8Pl70vmXxZoW1\nFpEwuhp53P7RlKvDmo3JkohPvAC5/q5GyZJM5IXpZ0T8Z5ZZ/GEKQiXIz7H9yRWh10ffL0weJ1y0\nzjZCsbxZQqcFkJL1LB/XTavRpt+bZZffl9zleMHo+3F4WEu9fiBFy0dUUSjQipIzNX+nvRpqUyfy\njWketJm94wQYayHHlmxrf5+wRdEm/SCSovvVmrQqbWyAGKr5kctAVuvifkomBkGb5eFOTFsi7ePa\nFfeU2SRmRULLh+QCZeaqUmRWHoQYsI0rrp9JnVguomQdiAFaGDFmd/u1+dHhXDqmwZ/rhzb8y5GQ\nfCJRAdcq92eLeseILq5CwtcyPsel/JwRE/VatAksRe6vvUjBs9lhtwNyZXyTyHcfHXzfCRENjsl+\n6OC5gxLudxV0tTU6PFqUg6DAPcn/3gsx3JfRMsvlgGg9NvQuDbxzbHlaxOf2TWCbBtfGVshCc7/T\n+c5OE6UZffIYxUwLeZxfX4nytesyLf/n6PCZv+L7eht9dnCOQYqS9X1uforcxlavNxe0tDTMgJiC\n85Ci5gjEML5cby6jPmyPrAJjEDN1KN8v0VG050ykZczKikgIqhubV2ucUHzyL9EecB95gotWp2ou\nOadz+FwO8THJ3IJej2gkVPajgrYXQ0zAzeTM3aKUdAP3+y9Fyr+VEUPwnvdnuaI5QQqNCxCTeWzU\n782pky25yvqYivaK/citRasRuV7VeX5+xNS9T5SkzMduHQriqyv6cTE63/d32tyXCnfhamOBGLYr\nUcKXEyq+WxkpPuoVeY73zUt8PHdDQupUJOg3ehYuixjsXZBg2gedT0sha8b3vH7I13rw8ehNzrD/\nBwmBb9dbG0iB+Jj3+Uh0Fh+IhLDBaA8tW57kIKTwOZ4ayStqzEe8Rq5HSuGDGxm/6PkRiDe6kZaW\n3ckUWLCie9dA+9ZtiN/7IQ3kD4j+v5nT6MV4LcCSvz+Y/Px4A+05sSv0UHI3zXpzuzXyYBsfXRuG\nPKpWKdmXLN40y0OxIuLTriRKlFTluXG4lTq6NouP6Y5o33uMPPdFvfNoKPCu/30nOd+7CtHZXjn+\nNdpaCZ2l2f47IKL9Q2o806KeIDIiXICE6UOpWOv1+oGs+Nf6ujy8LE00vA6a0WirOyMG5ALkcnaC\nL4i9yLXRdbMzIveoXyM3mB2R9P0Z0oKW0u54O8ORtnEfZEW7DTFDt5R8fjQt3e+GosPnfOrEJJBr\nLmZCwmhMtLug2J8rGhzT7dEmtxkSaDckCvyv8czuKPXuTtG1eX0cj0SMb10tOy0Pv3mJmBckJF+G\nDvcybmvPIS3PScjV6WWfk6qxYTXamBkJ1HcgxmoJp6eiIqNLoYOuD8ra1BcdnC+RC1UftJHuB1Ei\ne10VuhridHWB96vIFSezIK6N3FPPRub915ErcVOZ44q+DPRxXdj/vzESkuuWFqlo4zzy9Mo7Iqbk\nfKJ6YyXamA9pTdcHXqygucLsXv4eWT2uEUir/SoSoHpR4rDxdtYkP7BmRAzm4b5W6mUkzJi7c8hd\nVPv6uj/S57VUvUsUP5tp9vv5+9ySjWe9dyEXBjdCmtdlyd2FzqQgwUnUzqxO0zug/aoXOg+uJ2LM\nyo5rG2l0ENp37kYMQW8kTBbFH/VH59YySLjeEzGKjcadjkDupIOQ8Da/t/cpVeLyKp7NlGDbIAvJ\n69F3D+OF0SlWEPTyfhyM9pnTkCXtXio8Ewr6s7qvqeeIEmBEdFPLGhczVRshz5aTkQX8H0i4LVMi\n5QhU4uUyxBBmyXfGkccd1bUIIgvg5dH12VD851tEgnUD87se2nuyskL7I8VWXaHIx/9JxKD+yOlj\nThS7WOTCtxlSXl6NrEfr+/q8BAkf/SgoAeHfDfb37o+Y/KxEynjqhEpUtLE+YpIzt7tfUJAQpEY7\nA5wm3iIqIF05fxXX5vexXhNXPDmNHo/OxWMpsJhEdDESCS1DffwmIMXiRTQWl3Ymctu+zWkti+k9\nreTzmRv5s97GGGRpfbyBPtyG1vhjPpaLers/Ii84X03hNov/3ha0zH2whb/Ta+Q1J4v4lDHeh1WJ\nsiyj0KSqtEVLfnMQEqCy2MWp/l5xkqiZqJETIZrXI5FQH/z+DZAl7wlKxF1WXMtKityOZ6Bvz0+7\nNtaqDrQ05Z5Nbj5eFB3mV1Aii1/U3obIDHolYrhPRdr+ssWeM4veGLRZ3oaY+bmo48JXOXn+7JHR\n+y3k12qllR+KNJcD0Ub9FGLGMg3oILTpZ4HApZhuX4S7kvs8X09Bin1/Liv++F3aXKTZPJYSGSLJ\nhYbD0abwuC/EZdFmNzcl4h6R0HG23/+6X1saHeCl0uhWjMXc5Cnqz6IcE5K5UV2JXL2yjJt7Ig3e\n8vE7N2N9tJauoucyJmAOxARNQwf5NMScvkeUXbXZHyRIX4UOix+TaxyPLzOviKn7Bk8A4tdmR+5s\nZdMrj3Z6nOzvv6lf3wZ4rGQb05wuricP7F8GKQuK3LWyA2NTH4s/EVn8fa4yS1C9OJMxiIkZhpia\nE9GarxknU6UPi5ArvmKN7h1E/v1F9OnXTvLPQU5j71AnjTgSFjLX8ZuRhfY55L62Dbk2tbBuUZNo\ndU8k/LyMu4FRX6O8ObIyvIyYqguQRjaLY2oktngW5KnykNNrP8QkZmNSaRH8LsGJ/zsAuVBfgfaq\nqRTEHkVtzUtUiB25ZZ+JlC1nURArXqW9Pkjh8rR/5iwYx14V/YgTfc3htFXTDZyWzN0QpOjdC52t\nJyEm85+UKw0yo9Px8VW+K1WapEa7g9G+NR86+++hipcJLYWGF3wujkTCwmTEcNYsgl7R1jXAv8nT\noo9AAsB1lEjrHo3nUUjQeCi6/ix1Ylij99gOKSmy+o4BKQu+pVhJnNHFWFpmRFwQWbS+xBOJ1eoD\nUsycg5KKHBV9NwgJUacQZR6u04eR5PXZzkE82yCnz7qZLqvQaMbbrYvW+yXI1XJC5b0Fbc6EFEdf\nIm+Vsufh9j4XI53Wz0C89CnU4Z9pqWxZESm5nyMXguagZexlGdfjU5GSeV+/tjsFHgDkZ8huTuMv\noL33cFS0+8x6z1fM6yif11H+/xUQz7II0flYh75Ho/PvTnIXzP7oLDmlzHw08mnXxtrUES3ifxFp\nGv3FVyA3x9bTCK+OfHqXRFrdI8hrUJXNRhhb9HZAjM2nSCtdN/4pmsD1fUFc5m3djg6c+8gryFfT\nEJ3kiy9L0b8tcgu9HDEzNxNpA1sxvlmMSM3NqcZzSyHG/+oGxnF5tMHPjhigsYhZ+4mP7U00Vh9m\nEHKxuhcxA6tSIh7Mn13D5yDWxszq41oUS5AJo/MjbeVePg5TkFtQQ7XaWjlvGfPaKrqK2skEv0Oz\n90bC+iqIuZuVXEvdFEaZ7/v2D0UCx4U+rpvVW+MVz/ZDm+Lb6PAplUSjoo2b/PfnRwfeXegAeIxy\ncZuV8UfD0Z41Q3RPUUHgXkhb+QO0B/4KCXWlmWTfK45BAt25KLj6Ae9fmRIUcyPGoa/Pwe2IoTwO\nr+0U97nGe4xDB+hq6PCegPa0KdSJ5fJnN/Nx3we4M7q+s/flWuowZs3++JyOQ9rtbE+opOVKgWp1\nxByvg4SX0+J3K/GbQ/Egf///0U5rz5HXw6s3t7cgRmQxxLjuSJ6sZUS1PldpYxh5cperyZUKmyAm\n74LWzAtiNA+jpAs0yir8LFrrd9LShS6zHlVz4cvO5NWRsmRtciZ7iq+Vnf2eemnQgz93Mkq0chFV\nrOT15qPke/aigOlGTPUp0f/HIe+l6yhZ6BkpmiagfeIX/vcA5OI5rARtjUV772nIc+dUv34wJdxt\n/d5nEE9yHHmSkTkRv1cmlqsfUvCejqyAmcJ7DeDKEs8PRvzWb/Eaw3g8INqLi1LsxzF+ByBBfHck\nzJ1ORVmgGm1k9DkWrdcbkMJ7M7RmN6GkS2SdMSpUUkT3H4UElcOjObkR8Z714vfjWNqMX9kdKUev\nI3fTLFPveChSaCyL9phLUIzgg0SJiao8Nx4pAY6Lrg1Ce/DKSB54mjqhPBXtneXPzIrkii/QOTRf\n5XvXeP5upKC+3Pv1Ern3ULvzje3aWCsILSuuuR7a3Ff0wX4Rr2HVQFsbIVet55HwcAey2pSu3+Tt\nNGzRo6W1433EGB+ETKkPOTHHhVirMUTbo1iQz8lr3Q1AB9456BCZJf69Dp6rbZEf/o4l7p3ghHsl\nco8dGY3RTEg7XLqGR/bOPg73+RiXjY0b4ovpfWSJG4gE9mcoWRQeMdaZVnoUOgBuQJt4Q+5SkCPM\nKgAAIABJREFUDb5zdjgNaS1dVbQ3AmXYvL/i+nep7ZtMQ9nhtzeR+ySyKj6FmPZSCWOiZ4cjIeYN\nn+c+RePgzy2BmOK4KO5maA8aWeZdqB1/dGoDY7EVHnvq/x/q7dZN7oQ02gtmdOLv/wywg187hALN\nX9SHy8jdXn6A9rxPkDtNlo67Wl2ujOkbjBReU5y+7qIkU+nPL4vir6ahvXbD6LvhPr4N0UVH0HGN\n705Elp7xiNE9hAb3baetq5Ew/SgS0BdFMTDbUUN4IRcyV/Q5fAz4C9pvh5bsf6VA2hdplw9H5+IZ\nfm0x2sFFqNaYkJ8Xm/s6nc/X2jGIQTyJOvEmEW0PRYrIm9FZNAUxnH2q3V9tLMiF6d7IW+dCZCmZ\nWDSe7Ux3w5A75Z8QkxmHXpRyR6/S5ro+vm9SUnnk9JR5L2yPzqLXkTKmTI3fWvUdH6eAv4joIjg9\nHIYEsYm+Np4ht4BUY/hj61E/xAtsgBj3C9Ee9EaZOUX71uvkKe17ISX8cZSwxkXtPIjO9YWRsuiR\nyuebTWNor98U8Udn415biMfI/q42ntkaWQXx7bNE3/VFZ9mVBb+d7VtZbopbfF4XQfvMKtRI9FLR\nzqI+/x9XoyNkWHiccuE8m3jfn0R7+CAUtlEoCKIz9DGnh+fQ/nkjEuh2aMr8NZM4Cl52FqRx3QgF\nUsfB27shRuLaVra9qRPCg8C6JZ9ZnVZa9Khu7RiONAE/paVZuR4hXorq+zxOy+xkA8s83wFz1j9e\nqCXm9yjkXnQqEoAazjZX0eZwZO0rTJzg9w/GExIgYfIWxIw8QBUXmRptrIs0MZWZP1fF0/g3cbyf\nQBqpicD+raWrijazIpuvkCcHeZySNV7a8C5ZIpYNkWY9s/hk8a/H0MqAd39+MSqCrQvu39bH4H5K\npqOv0sZWyEpcGX+0k/9dJFj3933mPcRALB59t3Cd5waSxycdTF5weg6/tjxytSwsr4EUK4+gPfMg\npKA4BDFaNWPz/NnJiAE4nJYJTs5GAsT1ZcbB7+mHXKbPRgzh/1BR0LhZtNlO9J0xNAciYeE4p42v\nfC4aKYVxAlISjXPaCP4pdQ4gK0vGzPZDyq8/NLK+iDI5IoZkGGJsbqGdUmaX7Md3SQl8vQxFjPvD\n3p+q7oTkDP/u5GfyCsh6MtXprGyW4ZORFe5icqvAGkiAKV0WqZ3Gox8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+RpN/ty0lUjLB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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xa49b470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"visualize_coefficients(svm, cv.get_feature_names())"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Elapsed: 12s\n"
]
},
{
"data": {
"text/plain": [
"0.84592000000000001"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.pipeline import make_pipeline\n",
"\n",
"text_pipe = make_pipeline(CountVectorizer(), LinearSVC())\n",
"with Timer():\n",
" text_pipe.fit(text_train, y_train)\n",
"text_pipe.score(text_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Elapsed: 4m 21s\n"
]
}
],
"source": [
"from sklearn.grid_search import GridSearchCV\n",
"\n",
"param_grid = {'linearsvc__C': np.logspace(-5, 0, 6)}\n",
"grid = GridSearchCV(text_pipe, param_grid, cv=5)\n",
"with Timer():\n",
" grid.fit(text_train, y_train);"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'linearsvc__C': 0.01}"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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NjSqDKCLDT19W5PYl9/IF4EV3rzazucCvzOwsd98PkE7t/Bj4VLrH30lNTc1b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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1f1da6d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from figures import plot_grid_1d\n",
"plot_grid_1d(grid)\n",
"\n",
"grid.best_params_"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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IbSIbGFZrmb/rysfzgCOAFwCnRTZyTFoh+h/ghPp4b5s36t9aVdX9VVVdADwL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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2f583908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"visualize_coefficients(grid.best_estimator_.named_steps['linearsvc'],\n",
" grid.best_estimator_.named_steps['countvectorizer'].get_feature_names())"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.88680000000000003"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.best_score_"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.87587999999999999"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.score(text_test, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Text Classification continuation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TfidfVectorizer"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Elapsed: 3m 52s\n"
]
},
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x2c23e470>,\n",
" <matplotlib.collections.PolyCollection at 0x2c23e6a0>]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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H6K4Npay2Fp54wnclM4M1a7xXcNttfrxzJ5xxRtxrEBHpLTMjhJCz4feecgZP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\nRFAwEBER4P8DJsbXA/UA7Y0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1fb9beb8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"tfidf_pipe = make_pipeline(TfidfVectorizer(), LinearSVC())\n",
"\n",
"param_grid = {'linearsvc__C': np.logspace(-3, 2, 6)}\n",
"grid = GridSearchCV(tfidf_pipe, param_grid, cv=5)\n",
"with Timer():\n",
" grid.fit(text_train, y_train)\n",
"plot_grid_1d(grid)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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DGmy/NNHA3JIo744gGoubNfn8nMbqxfefTQQfl+afE8PLzBfnz7sj7/8qxd8O\nyfli1Yb7cS9TOyjnAvsQwUmjBny+Pt5H1Gs/KF6/CFi/5vqq6uBli3z6baLs/cWw70G3sT4nn4/d\niODl+0S74ihysNnwexwFzO957QPABQ2335Lo4Dgw59UFRAfQC8v9HbJ9ItoVdxOjlSsUfxvlOtuF\n6CRo3LHH1E64lXIaVYfaJTmfbNH73iH7sBnwveL3VYkZVvcTdWNtMEPUp8cS09ZXIq79nwP7lnln\nwLYvA/4P0R74Ct1OraoT6gtEZ9nQ70KUe9+hqG+IoO77RLurSefH8kQn8Y7E6GlVH95KUZYNyRNL\nEdf7vUT9tUH+23bApTXbV9fXhsDc4vUzgbf3O/+D0sg/b0B0hJ1OtAveS7QBn2qYL7Yiyt0X5O9y\nLXGt/y015WdPHl0t56n35XP5LqKOvqThNfL8nLfOJdp7byau2+9S01H6bBpN3vTH+JcP/EnF75sD\n84lG28AeKPoUQESvzSeJ3rCTGaHXhiikLge+Xbx2L/BnI6SxDBF83UUEc/sQDYJN8/c8ZkimXpaY\nnnIi3ZG+ncjTL/tl4OK1pYhK4wU5M5wGfJkoZL5F9Br8jDzK1i+NnvRWzv+qoeSHiEKitkFRpHFI\n/s7l8byLmkKiJ40diAZyedGemC+UzzAgCKBbIa+VL+ZP0Q1YzyeP7DTch/Py/88Hjsg/b0oUxpsM\n2W7n4uer8z48SPT8LJV/btpZUDUoDiOC5KqX9wzg1iHbvZfo2HhR8d3/o8pjRCN46LEgj6zmn7cj\nCrkH6PbaHwz8sCaNvYhRpFWJ3sCriF6oC4iG0d8BH6zO0YA0qtHGal92IBqZ9xCdFnMoKoWa/VmO\nCGAuoNugOSKfoyuBUxqm8QXgLXQb3UcTQeE1wOHDvgvdhskfiOlUEA29x2g2qrQZeeSHGB3bKP98\nLvDFEfL3mUQZ8Wvi+jyV6DgY2sNJTDWvPnNrYvro3xd/vwlYkH/uV2ZdSNFrma+La4gOpD2JxupN\nNfvQO3JwJlH5HpPTuBX4SINjUB3TfYgOitPIATUxInwk0ZDt29AjOjbKTrX35nz5vvz7xkR5scyQ\nfdg+5+E9iPLix8S1+zViSubDdGdM9Due84p8eDExE+AyIjh8BzHb4agmx4LoUNyfKPMfpVvuvRa4\nu+k5yefyp0Sj85XF67WzXvL7ViDqwNuJcvyA/PoJ5JHhAdtVwdSZRB1+JdEoWpFobO0ObN1wHy6i\nGywsTZSOspaMAAAgAElEQVTBjxDB0NCZGn2u118RAUPVgH+EHMTQvz2zPt2RmDcTC5fdks/Ju4he\n+CtHvEYOJ8rNxtdZT95YLuenxh17OV9XI8wn5GPwCFFGvJeoky5rsg85jRVznvpg8drmRIflJ4C3\nDNhuY/JMJaJD6nyijLiP6OD8b3nfhtVDZVvkDOB/EeVn1Y7blgimmtZFH6No6BNBydXEFPbzBmzz\n4nwO1si/vxv4J+D8/PvBwF8MygP5taV6ft895697iLZzbQdpz3f4OVFe3UaUOX9L0a5vcK1+BHh/\n9Vr+jufkY3FEg+2PZ2qQvjpRR50AHD/oGutJay7RPjkpb1tOpXxrcY77tsHz/xvmPLB+3v4w4nq7\nGzi38TFt+sZx/KNbSO1FbswRjedLqpNSs33Z8/8x4KDi95WJ3r0vMyQaLQ7g6sTIzTZEBPvXRDC1\nAHioZj+qNDYiGldVQ/kYIii8gqgU1gEeB+YNORaXEyNhVxCFygJ6eq36ZYT8+vOJSuti4HW9n0M0\nho8kKuiBhU1+76vyRXlbPo5VZn47eaSr7tzkzLgh0Zj4B6JwuYKeEYUGaS0LfI6Ydngc0Uj463w8\n76Cn0Uv/gmd1ovL4PXAdEZiuU/O5c/P/6xO9gWV+W4YoyC8csv1Lc/77ADHyuybRe18F2odQBKoN\njsPtffLChkQws/GAbZYmCubziOD1RKJi3JkYGbmS3GCs+exLiEbivPz7h4nC9ySiEnwMOHDI9rOJ\nAGxh3oeqB26lfCzXJUabvkSDaQVEMHhG8fuBREX8JRpMy6Ib3G5OBGTXEhXyE0Rj7XLgnTVpVFMQ\ndiQ6DC4nKvYf5/x/HXDqkO1fTzR2VyJ6W68jOk0+BRzWME+cQjQe3ka3wbgRMb1qaP7O7y0bFisT\nnUEP5/Q2rNl2U2LUaEF17oky8+f5/HwC+FbNdbku0dN/d3WsiMbiJ4lr+xYGjICU+1/k6SNynn9l\n3v6anPeH9c5Wlfm84ud9c754mmjMX5PT/SkDAiqiM+Fe4DXF9/g1ERyfQVxvA4N8YvTlaaJRuF2V\nT4lAdyMi0L+A3AAekMbh+Ro4Jn+fzYg67ViisXNJPj8HN8lfxTl6E9Eh9zfEVO2ty2M3YLvl6E4Z\nnUv0eD9G1CkDz2mfdD5FBKqnENfLeUTnw1MMHo2p8sUqRKP/IKLT5KNE/Xgk9dN5q7xwGPBIkUf2\noNu5tVHDNFbN5/AlxHV2FdEB9UWK2R8D0tiV6ND9cvl5RMfri4iA6lvE9bbIbAumBse7EGXlDkRj\n/jJidGfodZa3XbHIp1vkvPRvRGO3tmMvn4Nn8n6W5c4h+XucTJQ9C+qu9+L3relOj3tbzptHEGXr\nDf2ORf6sfyI6K15Y/G0zouzfPh+Pz9GnrdaTv84hyv/lc77+N6Isuxh4bX5Pv3PS+z3WJkb2fkMs\navcoUbfuRzG63LPNIUSg8km65e9xROfe7US768Ah+1C1W7fJ2y1V/O1s4L9T03nDooH+RkRd9iqi\nPDyfKBMHTpuk2/bdlihfVyPaC+8hrr2mM6lmEfXey/Pvc/L/S/e+r2Y/LiXaA58hZgdcX6U5LI1i\n+5WJTpfriTJzAdEeSIPy08DvNMqbx/WPiOzfn0/IlfmAPEiu2IZs9zAx3WkNokf3qzlzbpv/fhED\nekmKNPYgGqwPUBQERAX6YE636QjG40SvwJeIxsCOxd9mE71Zi9xDRvfi3gj4VHVyiUbBOUSPxesb\n7sPy5N4uIkjdm56pV0Sjb9+adJ4kCu5t8gX2WXqGzhleGV9NMaqY0/oEcU/ZFjWfXRUU2xMF0oH5\neFRB4kVEhbotfUZl6FaCRxGVxoeJwmsVYjh7N+orn+2IXpLNi++zS/H3NaiZnpfP+YFEwXQD0aBa\nOf9tffKN2Q3P61uAr+af1yIaXIcT9+kMvYctb7MJEaRfQTRwht5P07PtnHzMHyIahFsRjbSjidGh\n2/vl6yHX2+1ER8WRREFVjiqcC9zWIJ0DiWlHjxILnEAU5C8edj7y/3vm83FPPn7PIxooB+fz/hLg\n0Zq8uT8RgN1NVOTLE9faYTnf7j4ojTItYhZAOa2jdppkfl9VZqxLNEA+kvfnjUSFsErN9tWxOChf\nH++n2xu4dT4/xzfYj12Ikepqask2RKP1aKIhuX75ef2+Q/75MCJQeIyi04GaCoxuJXhdzp83EaN0\ntdN8+qR1JdHgP5eY879F/j4vzd/paopOgN7jSQT3byaC8k8Q94NsQFw776W4D3fIPmxHBNj3UYzQ\nFX9fIR+nQYFIFXx9nKgLy9HH6ljtzYCp0kX+3o1oVN5PlDUvy9/vUuDE3vM3IG/uSzQOP0meQpyP\nybU0KLPy+7ckj9wQAd5BRJl+JTXtg7zNfOBDxe8vJgLD62h47xXRyN+fqG8uJ+rGy+ryZk8adxL1\n8s+BE/Jr6xCBWjVrY1h9unLe/sf53K3Z8/c5ROC5yBRppo4Ufj6/r7qXeXWibh04apvftxYxMnoC\n0clQ5ZPXEB3Yl9NgOm9O5y5iwZ1F7nvO5/jb9JlqXeSrlYk24/FEWb0dMdJ3PVHnL020xRaZas3U\nqdBnEbcN3E2fupwI+BdpJxT7sSYRJK9f/G37fI4Glv3F9isQbbT5dEdAdyMC5X2JMuVxBtTV+Zy/\nmCgnrifq5g2JsmpLhoxuEWXarfnnI/L2FwG7V3mGqKcH1iPF91g6p3EQ0UmwbM/7TiCu+aG3ZhAB\n+XuIa+JjRBv6JzQoN/P2KxJB9HL596q8u5nmU3E3oTs4dB8RB1xFdNS+vWbb6nicSMz8mJ3z5yeJ\nzrh3lvvVaH+avnGm/4qdfz5RQTxO9PRXN+BdypB7c4hG1Pfy9l8kGrgvIiLyPycaFE9SPy1vF6J3\n6V+IRvv6dCPr2jnyxXt3BK7JP69D9DjcnfdjvXxy6hoX5xM9LIcUr1UN33WHnUy6he6RRKG0MRGZ\nf464wHcnCrtlqZkDm/f39uL3ZYiG3ueJ3vy6Id+9gD/PP69NVKBDRxuKbcvevF/lz7yGaGjtWVxk\nSxMX72v6bU9c1D8lph/9kig4Lyd6zpvcz3Iw0XN4ef7c84lC5XTiQn2QIffjEUPbW+afl83n5Qqi\nMj2cqARrbzyurhWiYbQPUVB/iqgELx2WR5ka6FSV/gZET/O1OZ3a6TZFGlsShdNDRLC6cX69rqDt\nNzJycE7naqJTpSpEj2NI732+jsqG/J/lfPIFhkyv68lXTxIVyAVEQXshxbTTfJwXaTAWeWtWzlOv\nz/niX/JxKSv7/Wg20rcd0QC5bti57LMPy1A0Wogg+aP5uw2937VIY1liROZ1RNl7G1EpN5mCVZ6D\nufkcfjQfhxPojoAOKq/K81H2zJ5KNNbuouYGbrp1yLx8bVV5aC+iV/YJ4BU1aVTlZjWb4eX5XNxK\nVKwb5r+vABzb4LjsSgT3C/I+nEazBalm9xyTXYkOxltzXlqVKDNWAL5Zk9aLch5+O3GNf4Sih5co\nfx6sSeOXRH14GlHOXJ33YbnimDVZcGbNvA9PEQ3PRvdOFNs/nM9jda/RIosCDdl2GaLe+A+Kxn/O\nr6OUe28h6rAniY6DVYkO3P0bXmfHEfXYOjmNqtH88rwvtaO3xe+bETM9nqKoU4mA+Yoh6cwlT+Ui\nrtPzinxWe98vUd++mRghfYhody1X/L1uavMsptZHryTKnl9SlLVEu27QiFB1PK8nAtzbiI6PUylm\niBDXf9+ROqJhfC3d0b6l8u//ClxUvG9Lau7ro1v279rnb9X9mwNHLHOeuqu4xm4iZhjMLvah760y\nLDrCtj0RsN9IBJpb9Htf8f6riGBj15y/dyE65a6l27lVV49U5e/lRAfnffnfB4AdivedBHy5wXXy\nUmIG04/pdlq8s24/inSWyt//YeCl+bXDGW0W0s5EZ+9OwDfyazvkczO0Tst/24qoR44q9mllojPj\nvKb78Wx6o24wk3/ERf4jckOE7vzi/fPrw+6hWJYYVn0yH4DqpM4jeniPpfnI1l5EL/G1+f9diQLu\nfhpUIERh913gkz3fbYuc6WvvBck/r0SMiP2MKHRH7S1fiqg4X1b8bdecVqMbEYtjeB/RINg4v/ay\nKoM22L4a8dyOqSOetdNk6Pa8vZru3PTq5shPEw3vDYjGycCVGolC4QyiV/dRovKrAvWm9w2sQRSW\nN+SL/BGi8f4KhkwHIxpXOxOV0Mer7533+y05vQtpsHJRkeYJREH5I3Ijk2jI791g208RvV1PkoN9\nYmTobOor0up8lKuKvYgYBf4ysdJiXUdDVcG8jugFu5Do+Fgh78PTFAX4gDSqBtnBROWxAt1r/kSi\nEN+vwT7sC9xcvL5ePq5/U31GwzSuLl5fiZji8kxdGvn9OxGV2AuIBtYaxDSsgVM++6RxJPFIi3N6\nXr+TIaOEPe99DzG6tRZxL8yexJSPe6jpUSyO/YuJRmp1D97BRHB0OcOvzzIQ+mK+Jt6az+tcone0\n6ayAA4kGxmvpBmVziGu3tmeUKDe/WOYfouf/LqL8qrspfxuiTNiD7gyH1fLvVxB1wz51x7L43DfS\nvcZPJcqdSymmZ/ZJY00iCHtZPnaziIbAPsQ1djO5cUd08g0rv17N1EU4Vs7HZ+BUyfy+tene//EK\nioY+0SH4T0Q52qRDrMpf2xPlzI+b5oc+aR1CNDC/Oew8FO8vOxvmEKNIryfPeKHB6HdPem/N5+Vs\n8r2M+fcvMaQzi6kzZ35PscJyzlvfBa7Lvy/NgPuViLpyQyIQO4api6I8Sk29TJTxGxMjEPOJ8vtS\nYorgBkT5O2xq9rOrGRKN2w2Kv51MLIxxS/59WfLCRr3XaP5/a4r7pYly8JNE+fHK4vsOCkQ2z8f9\nF8Bbi9e3IAL/+6pjT33H8wZEZ/FfER0wS/V+5yHHYhNi5kxVDm5HtHFuJXceMGDhnSJfvIxu5+oW\n+di9Ou/T0cPyVc7PX877Xs0oW4cIlM+k5lov0tqMbuf7A8SaD18l2ntVULIyfeoCppZ7s4h6cA7d\nBWJeSnQ8DGwHF8dzOaL8WTXnx0eI6+P+Il/0vdYGHOMtyKsU53N77bDzWpyvFxCzLH5GvnWpyPuN\nF297drtRN5jJP6LBfGFxQpYigoF3MGSYssjEW+dM8BUiANidPhfzoEyZ/z+E7nDxNkRD/maid3Po\nvVL586r7jOYTPfWfoOjpLva135SdMkNuRRQWaxMX6yeJ3qN312WEIo1Flqgv/rZR/r/psscr5fPz\nHeLCfbi4wIZNrdiO7ojnL2k44tmTxhpEj9V1xWsrEkHO0IKm59ysRDQ435Bfey8NeimKvFHdC/IC\nojfsZqICaRroL0f0Ut9GFJLb5Nd3pGYp2nJf8veYl79TNfXnKIr7c/pstyNRCBxBFE4rEZXXU3l/\nlqLpSj9R2d9A9D59jtwwIoLEr9O8UfFDorH5U6Y2foeufkd3iuEp+fMeJMqIajrp8QxZ9p9ub+gy\nxPX5NNFIWqd4z0uLn/sV0NVU0xXy8fsBMU1mjeI92zQ8ni/O3+cWorf3aaIA/18Mn2bSO19/J6Ln\n/ftEQ+tUGtyfSW6wEAHMusT0kGPy395DcZN8TTpvyPv+SSKgvY2ofDcn93oPOJZVntiGKK9ekc/H\neeSVbUe5RvM+fIc8tZua5cP7pLVDzlM/Yup9yLPJo9iD8ng+ltsS9xv8Y++xIxqyh9BsJb/35PN5\nNcUIFlEe7lqzH5sRDan/Qp6OVvxto5w/Bj6+gAiqP1b8fgvRQKwWntib+nudriXKx1cSDd5zidG9\n6vp7L3l12ibnl6krAL6GKD++y5CVcek23DcgOk6qWQpzic6w3zGkDunJW6cR5fZn6d4zt14+NkM7\nT5g6GrQ58RiKPxSv3UkOCKgZaSRG2L5F3If9Q4pOH4YH1uVCYXOJAOpndAPzN1DTyZqvgdfk7c8i\nd/YSZcdlOa/+HUNWNCz24zIiKP4Xohx+dsSBmg65Iq33E6srH1y8thbRVqnrXCzPyauIcvMRiiCd\nPEWv3zlhanttXaITZGWiPP8C0eF57JDPfzbAIjqsnmFqULgS0cZZvffzivcsV+TxXxGd31cQ5WgV\n7G9Gd+bWsADiBuAviRH0UR7/k+jeg70f0cbYjVz3EJ1AX6P+tpCq3Hsr3Zk3HymO0fsZcn97cRxe\nQXQ4X00EtKvnY7kb3bKnSfv5tcQ1UtWFN+bjehc5KByQL/q17Q8npiffQsP2Yt99mu6GI31IFLZz\n8sX5DEXvSr8vNySd84meonWIkYgb88ndoeEJWJZooO5QZORliAu87tk72xMNwWXozrtdi2i0/5Rm\n86mrDPkO4oJ+hDySlY/Rq+jeGFr7fBJmvkT9lkTD4sb87wQiODyJBqNK+ZxOd8Rzdr6wqsL7BGIE\n4JsUS5XSnXo38MIgKpoTiGDsE0TjeX8iQHxhzXeo0jiQqAR7p6F9iXwPxaC83ee1zYnG5teJEdhG\nnQZ524tynnqU7jD8BkQj4eUDtlmTGA28iijgTu75+xfrjkN+XxWQnp7z597EwjefJd9LQ/NVpC4h\nRsheSp5ylffzz6ipPIjK77h8LBYQHQ/3k1fPI+5TGXbv2GVEAb1U/sxTiZGPc4nKY9nivf0qwdn5\nvcvmnzfN+3FjTuvFFPPWB32P/Pdlip+XJa7zFxDX2KsHbdeTxrbEtbI1cc0fQFxf19Nwrnz5XYmp\nSH9OBPE/Yfiqti8kGt7VwiflqnmfIvckDjuexd9OY+o0oa2IwKrxSEjehxfnn99CTC25hLhWh332\nwURjprreNyQCok8R9cqOdfvfk97xRGD4ZaKBVPU6Hzcsbxbbrwo8nX/+CvneZ6IcKkcUhn2n3ufv\nVDf070yz+0yXy9fTD4jG0DuJBsr5RN1Uja73a4AsTZRxFxNB0xlEvXY5cb2+i6hrG63alj//80SD\n5hi6Dav5DLj/mW7dUd17cyExXfFqunXROv32v08aO+ZrYkPgfzM1CGr6fME1iZkmyxPX6P3EbRbX\nMHUEsl+ZU12bBxH3VVYjx2fn/ZkyRbpmP6rndC1N1Ic3E6Pgd9I8EFqDuF7uIsrCagXm7Ybl75wn\nTyVmNVRTJtcmyqof5rwxZbpunzQ+RDdQ2pCYav4gcZ0/r+e9dSNTL6S41YDo5PtpTm+tmrxRlRXH\nEfXhN+gGQXOITtJhbYO3UCyUlNP55/zZA1dq7kmjqreOII/G59fXI9orA1ci7Mnf2xDX5vr5OllI\nlH+bU9NpTyzwcwpT22U7A/fkn99HXvWzwflYhZiVsUXOW6fk1xvNYsrvfTTns2uAT+TXtqRBO6s4\nHmcRZc0HmLoy5YrkjqF+eYOpQfpVdDvu5xLX28VE51SjdtIi6U9no5n8I3qx/or8INGG28zO/y7M\nF2d1sR5EVIjHNkznncCH888nEY2a/05+MGrDNI4jCrayx37nnMlqe8yJhs1P88/3033g5QuZ2qNT\nN21mHEvUf4ModA8kIvwv0HMfzLD9YNERzzk0GPHM79+JuFduLYob1olA+5/zvgybilDlgUOJiv8C\nYvpnNYLwFRouPJHTeYTuUPe84vVZDF8OtwouDiCGzp8NjIlK6SZqHjpcpLU/MQK8DDHdp5oXPpvh\nN9suRxR0Z+fraiEx7bNq1Dza4HxsQlRU9xP3QlZLaa9AjFjdQf0KY2VhdThRkXyP7gpl8ymmDw5I\no+oFOyjvy4+IQu7MvP0RDLkfhAh6npevjYfoNipflLf/LMU9mwPSWJ0IgtbM5+9V+fU9iAb855uc\nU6Jw/yJRcF9IXGdNRynLaZ8PEI2ZK3Ier0bphzbQijR2IhppF9Lt8LmEaDwOXXGTaJjdTVQyD1Pc\nw5mP9UMMedYLU0extyWu1dfQXbHyIzR//toBwP9l6jPDNiXKjLoFoeYQFeZ8okFUNdh3JRqAX6R+\nFdbe+ziWI4Lkc4gZFjcTgUFd2T0r57GLiPLrgeJvjzXJW/m9/Z6/cw3RYTHsnPROHbqMmKHw25xP\nTqBmVKk4pj8hFko4kKlTJj/VJI2czvbEaMNaxOjLHcTI/MHDjiXdsuICYjrYqsQ0tJvy9zmP+tWF\nq7L6KqLMOZTudLpdiJkeTWeZzMrXWDVDY0WivNqBbn1VNy3uVLrP76w6I88h2hf3MXzktfouRxFt\nrGo/diRPlW6Sv/N5vTnn7Z3zMbiTaDPV7f8JdDsIbmPqjILdiXp2aOO7yjdEMFk98/KlxBTMbzJa\nvX48MdX/o3Q74pfN32n1BtvPI0Y+lifK4Wp64eY97+vXMVuVtfOZek/nx4D/BC7vvR77pLFjvha+\nQwRgLy2OyYnk5z82+B4XU6z4Siw+UQWZQxfyINq8NxHX2YFE2bUUUQ/+Jh/fgSNKPekdSLQPn8fU\n5epv7j2mPdtV6zJsTvd5k4/RHXW9muYPgV6emBI9l+hkrZ4x+1pqYgG6deoCoj4+kaiblyvOy0j3\nzU5Jf7obNvzi1QW+OTEd4hi694a8gxgtG/h8qt6MShT4H6S4gTNfXHUrBlUF1TFE4/ROouBcmxgN\nqFuZsVyJ6jDiHrSriXtRjmXqlMW6DLkb0Qh5Jd3nECWil7FuNKfajxktUZ/fuwlTp8msSPSOXkXP\nQgoDtp9DFBDTHvHM7z+aaKSdS3eFtnXz9+s7tY2YFvSXRDB5Dd0e89WJKXILaFiJ5u2WJwq93Xte\n/3STi5wo3J4igpA/5J9f1yQ/9KRzFTEn/Ay6c5j3IQq+vt+HaDRUU0vnEAXXlUTlXVWMQ4Ognjxw\nCvGIge9RLLJATH0Z1jNaNvJ2onsj/OPE/XxbE423TerySb4eHqc7fWwfosF8H81Xd9yMGAn6EtE7\nW93zdADd57jUNZy3JHoRryUqoy2IiuhoahagyNvfSASS78vH79qcN2vvaynSeHYVQmKU7CaKUaaG\naXw7H4tvMfWZL0Ov855zuks+/k/n87Eu0bn2VMN9OJfo7T6RaJBcQfRO1j6OokijWlH2N/k4bDzK\ncchprEGMWv84n9N5RKBWNdaazLQ4jgjsLyemJ1YjmB9gyKgn3Xqoeojrh4jpXNU5eQdDboYvrxvG\n8/ydMljeiLgX5B8pHisyLH/kvx9NTI3+PhGcDxxtHZLGR4kRztcS1+saREfM7dQ0mvO5u5Koy2+q\n9j3//NWabcuHXx9EXOuP013Y5Qrg4qbHMP/+ipyn390kL/VJb6t8LA8rXruaCBQvbXp8iTL3Khou\nJFV+F6IOqIKFpYhRxoOJaWlN7mHehij3HiLaWXvR00arOzZEIHBnzo/l81gPo7vK7qDRmN5nbW1K\ntwPqAqYuCFKXv3fP+XOrnuvtXpqNhM8j2onfIMqHarG2jckjd0O+R1n+7peP/+fz9XZQ/j6H1X2P\n/B1+3+/c0XAwgugoeIyYxfRBulNZ1y6+U9/yhqnlzKr52P2U7vM0T6XolOqz/SEUz5Ukru3f0X3+\n2s5Em6tRW4voZH5/PoZlZ9j3yJ2vDc5ptWrpNXRnD72evDjJdP9Ne8ORPiR6Vm8kCpYr6C4HuQrN\nFtF4M1FQvpIYgfglMQJSTRMZ5f6BI4H5+edliGkKA1fm6rkobiA30ImK4E1EQ7H2OV9FGnOJHt3f\n0b2X7WRqKo+eNGa0RH3PeXkv3YbCFvm1oQFuTxrTGfGspnvtRzQktid6SO4jGhpz674HMWrzOfrf\nx1G72lqf9E7LeWq/fMHuQr55dcD716LbkLqXaKQenfP5G/J+fXPEY7kzUWg/QXd062aGPKOP6Gma\nR1TYl9Cd5rg70bh5kp5pHn3SOJeoNKsKeZOc13+Z/58PXFqTxq5Ew/RY4K782vOJUenHcx6t7mWo\nayiulY9puYT3OkQv4e5Dtqvy8cvpLtW9CdHQvYO492n58r190ui3StYORG/7dUTv/7xhaeS/bURu\nXBMjfYcTDflfAKc1yAvVg3q/SlFJEIHQnfRZJnrAsTiUbo/ik3SnF7+d+nswFlnhjujU+knOG9fT\n7QwZdl/hGkS5/6b8+6uJRt851K+KWO1DucjM+kRHw5NVvhxyPst9L6cjb0uUHz+hWO5/SDrViOnG\nREC4HzHCdSVD7mcstp9HtzPyfiJ4mkN0CN5KNFC+TPceqKHT7JjB83f6pVf8fBBxr8qnGXHaTf4u\nvySmIjV9CPQRRENzWSLAPSi/fgGDV86bm/NTud8rENPv30rU67cz/N6zzenOlplNlDl3010Q6t35\nGDda6p6oB+fmtNYjOh2qB3oPKydm9b6HaOf8hijvrqK7LPdj9Jm2XqTxonz+qs6FDxOdMAPv9+qT\n1spEHXYnUxdpmUvNvb/Fe6v7H59PNN4/QZTBL+n9rj3bVZ0N69AdtXgpEZz/gjziVPPZ1b1O84g6\nfe3ibwcQM0+GBtk96a1AlDV/SbdOeT3DA4h+U922zcf1bqLsG7ogSHEsqudZzcr/Tsnn9BcMmS7Z\nk9bGROf9P1CzlHvPduUK1k8RddpuOW9/j6gThz7SqOe7HEu0je4jRsKvJ8qMJxh+rX6NaKeeRXR4\nPI/8PL98br5JniVHzUIe+TxUC439G90ZXmcBX6v5HuXMqXOJW1IeLl77ISMs1NX3M2ayccOTejjd\naHIVoqF4H93ApkmP5EF0G4d7EVHpM9Qv514VVJsRPSs7M/XG4QuBzzX8Hm8mgpVX97y+Hg3vP6A7\nN/WFRCX8cWL6zEK6veB1jdUZLVGft9kvb3Md8YyR2/OFcQ/dRUWGNQimNeKZ37c60TA9kKh0yuVS\nDyUKm+sbnpOliR73XxMVyF5EYFS7OmRxgS5dvHYm0Sj4RT4mw5a5/3Q+ZmsRQdTSxHSM6nhcRoMn\n1uf3zsvn5HJipPRrRI/kVRS9cn22K5fLPZMoIK8gCpeN8t92b/D5axENiVsopo8Rhe8PialMa9ak\nsRPRkPtfvcet97qou07ye44nGsyH5jyzK0MWNenZdu+ct95KVGbVAjHn06DjJJ+Py4iGxNnVdZXP\nyY4E18QAACAASURBVPkN9/+VROWxGXBH8foDDH/w8d4UPZnEPXefJ3oJV8vH+YmGeXtuviY+RFTI\n1X1K+1LMm6/JW9sQI/ofJa7ZqtH5YeCzTc5HTmcPIvhpdA569uGFxOjAE8TocRVU70zNoj1FGifk\n/PQ0U+8pOZz6RR/mEGX1R/J+fKD428tymgfVpLEK0SD7HVNXvVstH9P1ivNW12s/4+fvDMozxe+P\nUxP0D0hnWWrqgJ73X0m3AX9SPj4fJaaJLdJZS7dO35foQCwXgtqfqFcfon6kcRUi+NiLqHN2JEaD\nziM6j+bTfArU5kQ5+TWikfkdYgT259Tc4F98n/OIDutP0B0BOijn8bWI+5EGPq8xX5NXEA3UJ4gy\n4wyi0XnDCOdjI2L66x15n15Nw8c45P93JzoXPkEEZImo266jZnStyMefIabBPr/429HENNShjy/I\n534lIqi+kuiEe11xnG+ie2tC7SygIq99MR+PW4lgZOfyew/Y/m3584+mWx/vl7dvujJu9cDizxDt\nrRVy3r2A7nS9ftMlq+9bTQ2vHlVyF9GOPbzJ5xf58IGe1xYQ5d7QYJ9Fby25iGirfJEYNXwXA+6N\nL9LYgehc/ie6ccMmeb8+SLFAV4PvcgPdVbDfSNTHTxDl6cA2ONH+v4LuYj8bENf7bcTAzPnUBHSN\n9m+mCdR8+WWICu92pk5/ejf1z3woR21SzkzfJRoos/MBajp8/z2iQv2rnMEPJ3pZN6L+PoxqP/Yh\nes9+yIAV8+jf01FNM6wWR/gGMed/I6KRsg/d+xmaPOdlpkvUr0V3dcgziQv9AXKjb9h3Kf5WjXhe\nxYgjnkQvz5uIoe+n8nlYsfj7MnTvX2raw7sK0UD8t3yOh16gTG3oXUI0Pk4iht9XJ3pgBs63Z+oN\n7d+mu2zrpUSjdRdi5LXR6BjR03NPvqj/AvhvRECwD0N6jort7yamVMzOefuifF6OHXYeq++S/9+A\nKOC+RwSW5RKuTVcTPIAo8H+d93+l/PpnadCT1ucYn0T3eUAPUvNw857tt8vHoJoi+ezDUAflK7qV\nx0X5exxMVKjfBI7rOV7Dpoi8gWgMzCEaqE8QDbXrqWkcEeXbH/Ix2zhfL6cTwfpTRMfDwOX+e9I6\nNKfzFWLhn+NyHv8exZSomjS+R9yT8mj+dxndJdprV5SlmDJFjO6dz+ij1w8QjaLLiXt+nyCPttVs\nVzVKlic6WXYiyp7fU3SC9e7zgLReQXdVxB8T13mVny5lyHMX8zGveu7vIMqoz9C9R3Q7GvZ25/fP\n+Pk7w44ZEai8Z6ZpNfisg4hOvHOK1w4jOkr7BrgUy2nn/PQjYsXPN+fXNs/Hs+nqyxvn6+tOoo3Q\ndPps71TFRIwkrEJ0nhxLBPADO42L62czorPi9UTgdR1R7u2S/74cEWwucv8YU0fW5jD1HtyDiQZr\n3TL3s4vvUJVvWxNB6SeIDsfa+0Tzd7+DaBdcRYwgnEN0INS1C6p92DPn6/cx9VlytffXE7OfbidG\nss8nZt6cSpThXyAa4/fUXVv5/5fnc3BLzpMnEYHdMXRnBfRr71V/24Ior8/Jn3t5Pr8r9uadIcfz\nBCIQ2ZGoE6pnhh3CkM6bYvvV83moHgK9KhGYnQR8ZYTrdIW8H2+hW5++lvwc3ppr6y9Z9NaSNYhg\n6CPULA2f8+TsnK/uz/nxLPosjT8kX1THaiciEHw9U6+bLRuksVH+Dg8Q5cW6xK0qxxNtt5OpWWWy\n0bGeaQI1B/O1RAV2N9H782aicnyUIRF6cQBXJxoAC4kC5gAikm3SSK3SOJnulJ1fEJH9rygaa3Vp\n9Lx2DDFadyMj3MNANIj2J4Y6qwZr0ykAvb2X01qiPv/9nXSX312FCB6upJjWxvDG5mFMc8Sz5yI4\ng2jIPEQ0dFYhAsRPzyC/bccIc3iZ2tD7H0QQ9cZh36HYdg7RQ/Sv1QVOVMLfJCr3pgsVzCN6hKuK\n4EVEBfJXDFmalm4FtiPRM14Op2+Yj2XdUs3P9qIRvZrVYhGnEPdB3EnNCmNFGuUUjLWIgu/nxM39\njZ/h0yf9lYjRg4HXCt1rffmcn6tRyuq+gS3L9w1IY3+iV/Ysooyo5sWvQpQ799F8ifYf0O1tO5To\nlf0J0bBp0tu8fM4DvyF6EFckGskbDzsO5fkojt35RKPsTUTFfDfw3obf42Dy/YdER8G++Tg8SbHa\n4pDzsTZRtvyEmLN/MjEV9SmGPAaCqeXEvnmbucT1uWo+js+Qy7wG3+O1wFU9r12Q06jrvOkte7cl\npuxclvPXO4ipZMOWIz+b6OTZmLyAEVHm/JZosD1MzfLwxTGd9vN3Ju0f0elyMlHWfJVmz5CrVjEr\nO4z2J+rXR+h2FjRZpTjRLfO2JkaS7yUCwrqV56py78Scl559nlR+fTbdRW/qHih9Dt3bF1YjgoF3\nEo3gtQd9H7p1wPOJcvbneV9qV9jsl7+Jxv5XiPbNG4nAfy8iUB34jMFi+8vorhq9Jt3Vlr9K7kyj\nvn3y5Xy9rkVc76vQnaY9dB+I1W9vIcqX6r6eFYk68kSiHNxs0H70HIuniIDonHytfpCezvvec5LP\n961Em/cGugtKrUtMn7w+H8smdUDKaW1ClOEX0r0P+3KGTxGv8vetRBD1EfIIF90l9pvOUqgC9EOI\nNvOFdDulqsW6ho0SDru15HHyfWQDti0XpTqV6DQ+jCgDrszfbel+18aA9C4i6ubPE8FU0xVg96Ub\niO5KBP1fItoFjdJofD2OM7E+X2TlnJkuIi7wzxIN1g833H6HfEKrodYriULnX+suzrz9cnmbtfKF\ncHp+/TJ6lmsekhm2JAqqz+b/dyQasDcBn2+YxjyK1cyIyvQ64H/SfDRnRkvU5zTWJ+YR39vz+i00\naKTlzD+tEc+edFaiu7DCC/IFchtTVzpsvBjGCJ/btKE3dEXCIo2j6T7365vEiNuzveEN0zgq56U3\n0h1RmkWMsvS994tugbsM0XP0EyKIevbhyaMcD2JqxUd7Xpub82jtvP38/tOJ6/x0ugHQzvl7bZR/\n/6M2FIlC+zqigXoz0cv6H8BlDbffhwg4ft9zvVb3hVR5dliHxU7EaN6riEr8a/laPYvmq7VVleY2\neX9+2DRPFmmcQfS8V89uvI+ae8b6pLFLvj7fQHdZ4x3z9V/7XfL5fzUxgn8hMfp3B/C3DH+O0UbF\nz8sTZd8BwJ35tecRU4YHXmd0FwhaOe/vYzkvrl+8p7YOKd57KFEHbZuvz4OJOuH7NJuuuDRxnS+g\ne2/RVvmaOa1477BZFmN5/s6k/cvH51yiEXw9EQgMC6hOIoKy25g6AvsB4Fc1n1VOrbuOCJjKa/3V\n5HtdG6SxNXm0lhgx/TLRUCufd/gd+qxOS5TdiaiT/w8xVXPz4u/r0Z0eVTeispAoY15M3GtzR12e\nLLatAsuTiM6auUSb6zGi53/WsOuEokFKdLhf0vP3C4nOpUtrzmki2mvl8yovIxrAF9G932/Q7Iay\nLvst0abYnz73AA7aj+JY7EMxPTSfi+oRNsMCoWrF69OI9sCjPed0e3JHwpB9KEcCNybq9C+S21tE\nwPzqcn/7fS8iCPxK/vlOugHx2dTMjmBqefPxnA9eR4wGHUHUB6/qtw8D0hv51pKe73IT3Q7/6hif\nTLEKZ4P8vQ9Rbr49H8P7iTp5m2HfgegguSVveyTd6fLHEfX8ZxlhymTtsRpXQn2+yNvoNgjuz//W\nzSdn6POp8v9H073Z+bNEQfmSnN7QHqcirf2JnornEVPMPplfv5fm037KpeGPzCdzyvLo9O9tqTLC\nBsX3uIJi+hej3Ww7oyXqi/fsSUxD+mHOVFsTc+hrC3+i5+pmRhzx7Dmv+xGV2C1EsF6t1rM53amK\nf5RGBWNo6A1J+3Ri5PWrNL8R/HXEaMO9RC9SdT6a9s6eSjQU30JUyhcRvaW1n8/U6Vy3EcuJD32m\nyZBzejD5xlpiuuVNxP1bG42S3jTPaW+lVH2vvYjRxpOJEYgFQ9KoruM1iYbS8USD7xZqnk84IL0z\niRGlc/Lv+9NwlJBogDxABA/VlLiDiSDm1IZpbE5Ufj8jytFjiCDzy9SPsPULCHYmOsKOJk/PKM//\ngDxxNNGQ+ClRZhxIt8Oh7gGiJxHTj48tXtsw7/97iQ6QgceC6Kmu7utcngjKTs/78V4i0Fx10Pcd\nkObGRGPmG8QoxEo57aFTJ5nasNiZaKDenK+P9cv39OblPmlN+/k7z4V/REPrjCF/fz/dYHYu0VP+\nW2LEspoS2miV31wmnJGvt7uIOunIEfd3PrnXn+6UxWspHltAn04QIqg/o+e1i4n7b69o8LnP5hei\nznqIqc9WrNpOTRdWSXm/T+t5/QaG3AOXr6sT8n4sQwQu3yE6jF9BdET/Mp/Xe8j3CvakMavYhzlM\nXfny4Hx+HyveV/cA508T0wVPJOrETxAdQk2PRfXstt8QZUb1oPTdGHKPEFNnJswiBhOuJgKQd/Z+\nPsOD02qBmQ1yWpcS5ffHgB8N2a585uU8opPjW3QXl5pNzOpp2oH/a2Jk7K15Hy6hZ9rssO/RJ72R\nbi3J27yOuFZP6fl+y9MtP+vKzbXzNfJdop1VPULnW+RytGb7avXpzxPXaRWMrkCDR66M8m8sifT5\nAsMaBI1uFCYq3TXzCfw2MYpxG0OmugzJAKcSo0m35XSG3vBbbN+7NPwKRKB5NVF4NFlA44r8/tcT\nPebXEg3oZ5fersvUffZjpCXq+6Q3m2j8/x3R0Bv6LKJiu2mNeDK1wPwCEUTsSfep82dTrNA4ykU+\n4veeUUOvQfrLUvMIhT55o1ra/Wiid/iqYRc43QbvmhSLXOQ8cnnO340bFvlcbk0Uun9BBCGj3u/1\ndSIQfAsxins0Mf3vY4ywyuQ0jneZr84iKv0Lid6wcgrlcrkMWaTsydf0/sRoxc/p9sQtQ1RCf0te\niWmE/ZpLtydtFjFtp1EHUJFPv0o0vMuVJoc+KqDn9wOIBvyNRGPiQmIEokmwvkYuX95EdKLNIsqv\nL1DcNzDsOmVq+f0w0QF0Dw3Lb6Jn8u6cJ6tFkw4mAq26xZxWIgKoDYkOtCOJntWtc568gwb3sfU5\npvOIEcOricZe3UhK1dO8Rj4WVZn/KmKK2S11x4MxPn/nufwvX8M3Er3S76I7irxZPsd/oGY0n26j\nfiei0V2WH4cR96IdX5PGlkU6uxEjz6sXf/8U3dV3B42CrEwEUvsRDbxqevQGRHD4TM5nA+9n6fn9\nSqKTYNMi/z9OzSqZPd//1URHQTnz5TsMeWYjsZz8TjlvvjNfb5sS7a1fE7MDzsrfq+/jMYpjeSFR\nf/2OqYvmfINcnzF4Bb2qTjyKYkGwfM1dSEyvG/pA7GI/ZhGjlmfla+sioiPgbrrPFeu3Em+1D+8l\nZt9UAfr+xAjhQmpWOy73hZhmeFTx2p8RgwrbDNmHTxOzZap7U19CdO6dR7S1PkdNAFIch1dRtJNz\nnr2FCPJmNHuJBreW0O102Cnv9zeJAYHVRv38fFzekX/egbhF5GGivTJwleDezyFmJLyLqAffQ80C\nM9M6NuNOsNj5mTQI9iQaQ+uRVxQjhuP/koY39tMdodsmH/zbiB7zdRk+BN9bCY+8NHzx3mp1sOq5\nM1sTjZwbqFmBbxz70eAYrUz0FjxJVCQDR2aY5ohnmbGJIO7e4vXV80X2SYY8v2fM+XLaDb0x78eB\nwP+juHme6Blb0CSPEwX2g8TUm/IxAXtSM6pDt/LYgXw/YP597fz5f0+DoI6oOJYnKsKV8/5Uozpf\novsstj9WgF3lq7fl/LgHcR/IjeRFOPLfNwPuG5DGckQh+zdEL/kKTO2J24zuqkwjfQ8iCNgZOLvh\n99i4eG0VovPjn/L1MfCBsD1p7U7c1/RCIhB4D937lgZO78jnsmrkfonoYX+E/9/eeYfZUZV//POm\nURK6Iv4SICBFCBCaQAQCUoIRBEPoIgEUaQLSFaQHDV06SJUWQJHemwHpKFKkd6SjqHQU3t8f3zPc\n2Zu7987d3Vt29/0+zzx7d+7cmTNnzpzz1u8rq+BmJGWWEvNYrbyBSvP3C0XGdtm5lkb5QedVa38n\nv/0aUm4ztrWMJGENaqxDlObZryNhM3tnBqM15XKK13y8FVljT0chM8PRvDmJKvXs6OH6O711S33+\n1fR5TBqbVwOb5Y5Zn2I5aAOQAe5xUoRH/jo1fvtVEm04pbDs49L4nIzWsqcpeVU6JVxIn5dI93It\n8jRl79aYGu3IDItbp/8XRELnL5FB7DqK5zAPR8qHIQ/IA8hwfA6pfEm19yN9XjHdx+EopG0+SiGI\nM6R3b7roC0rz3mLIYDMEyYxPoXnvC3bEAvcxW3qmB1f4rhDzaGr3NJJBNN3X0cizciydcA7k7mMk\nUohG5n6/FJpDChkLcv9n9ez2pz4v1BEopWcKMuQsita345ARvpqMl3mZByMP0q1IZsvCJccBFxVt\nS1e3XH8OQeugIRntZuTQWIPinvAhKPR1v7L9lyLvZac1PSmtAZsjhXgfSgSDJyBD0P/Ve39V29uA\nzuySQFBpcKAF51ok2KxKjZyt3G/zHrotkYfuFTQR16pWn02Klajhf0JBavj0/R4oPvyE3L4Z0oua\nUeDXqsnR7XYU6K9aoSIL0UWPJx2LZm+JCqHeQG5yo6Mg2pQcCLoh6PXQ9bOE9seQcFfI2pIbF1ul\nd2Nqek/qyh9L57gBuKXC/iXJlYeo8H3FPBfk4bsUCZoPNuOZokVnKh1rda2HvFLzp/9nokZYF1p4\nMxrbzSnR3B/Q3fso8n4ir/fZyAK5SG7/4RQsA5G7958iwebmNLZepHau6veQJXInOtL0b53mnAuo\nQeFddr4uz9+dnG9z4FNqU9RPx8aGhJI9kLB6HKXQySIh3lugue8eEkkOmsOvpspiTEd23bORtXwL\nStT521FaazpbA7pdf6cvbGjdGJf6MDMabJrG91nUQWKRfjsYKUDT0ntfNMx7GDJajUYKy8ZIEVgS\nKSQHUBLmqxbJRet65kWfiDxjl5DL/ao2PikZFh9ABoKhKPJkewqGniPhcnGkyGShl2sgj9B4qoSc\nUyJ72CBdd0fk0T8TKRHrpTlgEIlkqcq5pqD1bDylvKfzkaewSJ0rS9f6JYrMOJ0Kho5a7zsyKv4w\nvXfnUUrjWB951k+legjnsUje/TKSgV9D8+aCuWNqGa87rWdX43cZOc3a6Rk8i/gCtqrjvdgCKeMX\np3G0durLvdPYvoOSl7BbMmeVNuQZsK9I188ISYYiI2WhnPDcOZdHa9g26F0dhDzIS6drTKdo59qx\nJvIUb4Z0iIVyx1T1unbp/hvQoV0SCDo51wCk3V6H8nMK5Y6l39btoaNk2ZqFLlLDU7IyrIMWkJXS\nA70PWLfofae/83S1HQ14rl3yeKYX4SuUFNCMYOVRNIHWVXy0AfdVSNBr4PWzhPaHqZLQXr4PCQf5\nQpFHUDw2PBNUN0QK4d0UqFdWYXyui7wHo5HSMyK9r1dk7yoNmrjL7mMPJKTm6Wtvo3Z9k7yVN2NR\nGpfesTOQUjepUv834F5mQkLmZLTw74AW9ptq3UeVc26AFtMbgXE1jl0e5UycmN7rdXPfzYE8yFWN\nWZXGCV2cvzs53wzkwsOqjMusEPWRyOM7Mu1fjQKFUSktxt8nkRUhYSUrmHw1BWqwIaPLHeTCmCmF\nB0+uNabowfo7vXmjpLgehkK610ZC+EyIBe92qrCd0THMe020Jg9N42QKipYoxNiZzvM1RNxwElrD\nVqtwTDVWxFVQ2GmWx5gpN3tSkMk1d85lUL7TWdSglq9yjjlTv1adI3LHZ3LSMCTbZbTqByAD0sXU\nqA9Ydr4vI5nrKFI0AZrTN0ufOyPyyPoz67+ByHh8Gpq7sxzeaortwLL/B6d3bB8k7xyV9hUJs1sf\nzd3T0rOcOfVrpyUxyn7f5Xp26fcjkQc98yavnP5/mgLKQ2rvFYhsLvMEfwsZL86lQKmRntpQXv0E\npNxflN1f9owqPbsa51sHGcOmpW1X5Ll8uMbvbkY5n7uQDKPIS1+INKfu+25wpxYWCKqcYw6UlLlG\nweNXo4seOhTnewaa5HfNXb8QNTzVix7/KL0cFxS4h0zQ7BZFfQ89wy73ZzpmZmRFfB1Z3rJJdJn0\n0nWZ5r4H77GqoNekNtTyUmb9tjWykl+JlN2vIyX3UmoIFZQE1oySeYY0rnZEBoPfU9vjmZ1jaeQx\nnZomuEPTM62bEKULfVUe3jEbEo4ORKEWRwB/rHGOrD9HIU/vVCTYfS/1z/pUSELv4fvI2jAWeZ4H\npuc5CQlY91Oj1kvB6xRldxySnuFxyEL9c8os1PXON9Q5f3fzPrN5M/M0HoSUyzsQgcPQ3DFF2MHu\nI+W2Isv1eemdW4kqhiRKxqc5kfHsP5SFrVKDsZMeqL/TFzZKwteaiMxqv9Snv6BEu1205tiViNDq\nfORhzIxGy1Glzlb+OaHc0pnS81kIeTBPT+9Kti7WUrSvpcR6t3t6vsen/zPPal2GLLpgWETreUak\ntS4yCtYsGExJTtqHUv23pdE6chBScr/e2X3Q0RCWZ2lcHSlzB6Kw+bnLj++kPb9Mz+A3uTGxOjJm\nFS0tdDClEgNZLb5T0dpQmNocGdWyEPfZUQjj/PkxVGlc5fuGOuvZ5X77DcrqiyEZ/C6q0MuXHb8q\nkjsvQ+v6ymgeOp9S6GKjZc75KSk/91Oq83o0BVmfOznvUCQrLZje4WvoxEhIRxl8Q6ScZ5EVF5Ki\n03r83hvZsbmbKyQQ9NC1uuyhQ5avY1AoW93U8FQuejxb7vuZKNUnquW67hZFfZv0Zz58aAwlNquJ\nuf2zlh8b23T9mE0OcyCL2ThkTftRmjRrhjSk32dCxc+RwH85pTyvEciyWCj8BwntE9PnrAjzlUh5\nb0r4FBIiDqDkodsGLaz7UaXeTNk5bkRC0aIoXOcWyor00njv2B/pmPM2BIUwzkVBxs4eGFszIqU6\nK469LvIwnU8TLaM9cD+LkpTx9G6Mo0SFXU9pjnz5gslIMfoRNXI6kFX3QDrmIY6lpAz8oNaYoqSo\n90j9nd665earmZFynLGhroTCwy6lYAgr8gCfkT4vh7yUxyNFd/6C7VgG5ROdlX6b5SB/EykFR1Aj\npzu91+cjhfK8dB9rIyNCVaWwQH8VNiwiYf9uFHVzMzI4nIG8wLXqs2Zy0jPA5bn9lu5lw4LPZLt0\n33+m5A3bAoXLbZA/tsoz+VEaB2uhNIq5kUdrxtwxtTxsy6HyFU8gATwzApxLciRQQYbN/X4CUkTz\nhdpnTGProDruo7v17GZGMtaJuX0/JxFa1DmWBiFl8LHUL1Oq9WV3N6aPADonPZPsuqOQp7BwuHmN\n6w2gQj4zOYNd6oPxSJ6/Pe3/DjnWzx7vh0actF02uuihQ16h+5D7eBIlaviMvaYW9Wqlosd7UKP2\nWYVzdpmivtX9mZus5kICRWYR/g4KEbk77e/TQkUP9/86pDycXP/uRSnMo0gx1OWRJXRutIBlhVQL\n16hCXpw/IZbNfI7gBtQgq+mBPsgWsAlpgp4C/Ast7HWF7KAY/RvK9q2DhJSZm/F+pfchi5EfTEkY\nyCzMjVYGs/5cDxla/kKpqGrmPV2pGW3poftZDAlYK5CMWcjiejKl/KOiRAHl5QvGA3dXOX5oOmYW\npLg9l58nkbHuEarkxdBxHely/Z2+tCFPzKkoAiVPirE5NfKTcsden3/XkfIyDhlvCvVlasO+SMHe\nNT2fw1CI24xIUJvOw4QUtjx506ppPE5O/y+CBN+mGF9y/w9H6/MkFFp3EDVCuMp+vxpax++mo5HV\nKl2v7LfDEPX5Ksir9DLKaV60Wptz+wdRoutfOL2rR6Tvxqd7qXb9bN4bQaqriAg4svprJwCP1epL\nZAh8DHlLnyQVeEeK1Hy54yrJjN2qZ1fhec6I5uw/pL49HeUHVlWwa40ZNJcO7Ow+emhs5jkTtkQG\n3jfTs9gdvb+75o9tcDt2pmQUWCGNs5fT36p13Lp1/UaduJ02uuCho0QN/wpiNNou7a/l2erRose5\ndtRFUd/K/qSjUJGFtJ2PJv0sROIQuhjz3p82Ogogs6QFY+vcvp8A5xU4T7YwbI8EmTVJxS+RC/+C\nas+j7JkOQxa93yHDw3TUyI2YuMvacCIdKep/m97TQgVEc/9fQi4cBAlHD9DA0EskuGcCwXwohGnu\n3PdjgfuaNbaQ9XEaCgnbHHmD/kgdeYWt3CjlTKyBFvSBKP/2ciTgTCXVdyp//jXOW1f5AmRR3z+N\nxxnSu/k0imrIPLYzFWkHPVB/pzdvZfPeqUjA3Is6vO9l88WGSDi9k7QOp/1V3/PcvLkeMvpkLLLz\npPF2HMmjTqrPV+EcMyOlbSvkWcqH6Q1AwmaWmtBIYTMz9nwXzd87dncMIcXoB6lf70RMlNVqmWZz\nzhwk5SX33WRE5FGYLCFd+zJyyhOaT3cqHwOdPNezKFGij0YGvnuRYW5kdo9Vrv9TFJWxKMlYk573\niRTMuaXr9ewy5eGHSAk7A3nUh6B1bGkKhmwWbGej2ZIzzoQTkVPjDKSkn0Yu5LIJ7VgsteNLZd93\nyojbY21o9AV6+0bJStwpIxYdtfuGFD2mDor6Vm+5ye4AJOgPQsySu1KWE1Nt8o6tw9jaEQkVv0bC\nya2pP5+kBkMjJSFw+fRMLkTekCw2+1jglILt+RGlsKuRaUyejiy+X23UZFnWhvHp/g8jl1OTFqCt\n8mOwSn9OQIv57sjC+Qiylt9CaYFuFJPUbki5znKNTknX3R55jW+jiSQzKNTqV+nzIGQ1vgZ5hw6m\njcPjkIIyESlCr5DL+0OJ2NeiHIzsHah7vqFA+QIkWO+PhIcXSKG/SDk7HhnTqtZrzI9beqD+Tm/e\ncu/pxii87g/IE34ddZROQMaPVdLnGdKYuB8J8bUKlOcVum2RsnBu2TEjKeV9VZINMrlhFrQGDBn4\nWQAAHiBJREFU3p7eq++n/XPRsd5Uoz3iy6W5bmNk5H2MOpj4qpx3ThQ9U41WPRN4R6P8rifT/LJ0\n7phO2X3T9xsCH1Jis5wVGULOQcrRZOCuIv2Z2nwLiuzYHRnn9kzzRRHyoCHI+75Lup/MQLgZnZRZ\nyZ2jW/XsytrwJ7SWbYTm8vOQ17Ph+dw9NCYrcSbMibynJ9GR2bAZ8sWBwCFZ/6a/cyOPcENTa7KO\nCBSAmQ1w98/L9plnI8XsIkSM8G808c2HJr1r3P3h8uO72IYl0It/fFfP0QyYmaH45Tfc/ey0by7k\nyTjd3a9uZft6A8xsDnd/18wmUGIIehkJGUshi+T17v5IlXPMgtzvnyHB5idIMPguIjuYE+UPjHX3\nfxdo04ZIEXoFJbY+YmaroNChpoxJM1sKeRAWQ/kPDwBPufv7uWOme8+y99fM5kEe6z8iQflTFIb5\nMfKk/KWzc/RA24eg/v8rCn+6BoW2rYAW9k+Ah9z94J68boV25OetNdBi+AN3/0fadxDwX6ScHezu\nbzWyPV2FmWWhfKeh8KFjUBHav6bvlwaedPePK83fdV5roLt/VuX7WZDXeHGkZN/s7n9O3y2JQs0v\n7Wxc5cbnEPSO/wuRy/wEKXkXA9OqtaEvIDfvbYgMm9eieW8MUmBWRGGkp1Q5R9aX30YC+++A49z9\neTMbjkLaLnX3W6qcY6C7f2ZmqyFilnVRtMrsKDfo5PLrVTnX74Cz3P0GM5uIDGOvIcHvviLn6CrM\n7CuIfOOXZnY9qTYqipK4ERkMnkCK7kc9cL1afXE2omV/Hc27IPKLO9z9xQK/3xyFFN6N5sx/oPzh\nedM5r3X3Z8xskLv/r0ZbJyAP06cop+1z5CFb291frnZ/ZnYO6r+JKPR8bWSU/Dmwi7vfU2nOMLOv\nojqfvzazRd39KTM7Dr3rU5En/ghkSHirmtxpZruivKrDzGwwinZZBq3vJ7n7PdXuv11gZiOQoeLP\n7j4+t/9iVMtyShPbsh56z3cD3nT3/5nZocD/3P3Qhl47FLLuIfdyfh8lv49P+7+EYsXXQArZja1s\nZzOR65NxaOI8Fpjq7u+Z2d0o7HJaIwTevgIzG4kUhlPQJP8bd3/CzFZHyv5sqMbdlFp9aGYLI8vy\nrCi88J60EH0JLYjnuvujdbRtEMqJ3AFZrg919/+k7xolVGRjakbgc3f/1MxWQguxI6XskrxSVuEc\n2SK2G/Afdz8nCexjkCf8yyh05J8NVMZWQQvuYKTYzpTafoe7P1pL6O9pmNlod3/YzI5Fi/hUlLN6\nCgp7uQaRYXQquLYKOYF5TmTouQEpkINQDvCCyPq+RRPbtB2ijV4IeU8eBv7g7n/PHVPJWJDdy2hK\nxdkXcPdxZjYUKe+zuvtPm3QrLUHZvLcgMt49ZGZzI4F3IRRW9rm7/7fgOedG79oYNF8dm81XVX6T\nzTfDESHLNSiMbQtkaL0MuNfd1ytwjmWRB2bbfJvNbAp6pjsVuY+uwszOQF6xS5EXfgrq413c/c9m\n9msU8ndmA9uQzb3rImVkq7R/KcSIuCxiCLymyjmGpHn//1B/roWij05FRGd1z5tmNhB5Tt3dPzKz\nM4F33X3vanOxmX0D5QCuk/7fA3my7wBecPczqxhehiHP/leQV+865GGbD5G9PIZy+a7qrA1mNgCF\nZt+EPDc7uftpufMPd/en6u2PVsLM1kQGmBmQZ+w+NA/slGSfhsgWZW0YAXyAGB2fQ4aD2ZDndHV3\nf6OR12+5u7I3b7Rp0eMW9kcWZjIjmiyGIcvRgWhBuIQyWtbYqvbnmijk83VyleaRhXZTEtlCwXPt\niKyJd6OQxYyOvGqRRUqhEfMiy1s+n+V7iBjkrAb3QznL2dkodHM5FHO/Zb5/apxrBPAG07ODrUUN\ndrAeuI/voHCSfSiRdqyG8gVOQ+GghclVeqA9Y1J7tkEhGUsgD//hyJC0Grnwn3bd0KK9Tfq8OArl\nPRoJR1noYMPC/XLjMwtLs/SObo48dhdQIzwud64erb/TG7c0750PvE3Krcl99wiwcpXfzkMp1GtV\ncsW703h+B0UaFCXyOAGFoI0mR+iSnnOWhlCLyfVA5HnZt8J3A4qcoxt9OQR5A49J70MWpn4cyotb\nGRmEqrJD9mB7dkx9UU7PPpZSeFit/MqHKBEufDnd19vAjkV+38k5DeX5HZprRzkl/cKkvFpU7uCv\nyJOWfT+o7PhafANdqmdX4ZhvIw/6/dRIX2j3jRZyJlCitx+IQkqPQ2vLkVTIk29IG1r9AHrzRpsX\nPW5hv5yKXPmnpEE9HgnPYyglyfdpoaIH+3IIEtQfRyQFo7t5vi+huP0XUAx/pyQFZb/bHXlOdgPm\nS/tmQ4UzMxKbhj5Tpmc5Ox95E4ZTEmyK1JdaKy3idbODdbP9A5GV/XQkeP8YeT8HoYT/86ij5k03\n2pHd5wIoXOdoJLCtmTtmKLLWFk6wb+aWu4dFUSjf1mXfD6eHKJJrtCPLH1o2PdOzkGV1WHreo0j5\nLgXONT8NqL/TG7fcvPcscFV6Z8cBt9b43W+QF+hbwFMoNHA8pfIqP6OMTKLKuQYgBWIHlPu1cto/\nmRqFwcvHHMrZuhN5NMY0uS8HI6/Le0i5NJRjdFvq270bfP3MUDscyUgLoRzgt4Gdq/Vb+XfIkHgZ\nHWuxLoDCWgsZ5Wr1VfbsK3x3WxpLiyKPyelIYd8GWLDomEp/u1TPLjffLJye4VaUqPb3RMruDkXv\nt103msSZUP6cEaHI8rn/myrDR8hiN2BmM6PJ7kn0II9whZ0sgxSyv7v7dq1sY7OQC7nZCE1Q+yIL\n0CJoMTygpQ3s5TCzOVAuyUZIMdrea8TH1zjf7MDC7v5AtWu6cjnGImvVPEghG4hCXr4H/M3dd2lU\n+Gku1OU76N53dPdPTHlgi6O4+0fd/aw6zzsIeTB+nHZtguLFGxISYWaD3f2/ZrYzUig/Q3kpHyEh\n7TYkuHzYiOvn2pGFUQ32FD6VQqrWQc/zXhSi+KaZzenu/2xke7qLNN9sR8r/Q2OhsWElldtxLTJA\nfQUZOTY2s1HAa+7+bjqmUqhih30pL2UxVEvtZ+kclwEruvu/+1uYd5r39krbG8iA8mAnxw5BAu2s\nyIP1O2TsmA/lGX2EyA/GuPsHBa+/CFLyQMLvR0g529Ddn+4kvyd7xzIymM/T9f+FCCT2BK5y9z2L\n9UL3YWY/QO/I7rm/b6LQ7YbNObm+GIEMJ+8jY8URSLk6EnjA3b9bxzkPQ893C3d/x8xWRvP41mmt\n6PHQNlP++8mIPOTHaBzNiebMr6H5/EZ3v7nKObK+WAbJi0+h/pjq7vea2TeR13IgylH8uMq5piGj\n6odofL3o7ken9+UzrxGS21tgDeZMyMkXG6D38yBktL6AUsmmvd391UZcfzo0U/vrSxtR9LhSnxiy\nuG2e/h8ALIkWsEKemNhq9vESpDpRDb7ObEhJ2QoxMubZ67ZAE9dOJAsSDWaAS9eqFOoykiosZwXO\nW5MdrAfvYViaIzKP+ijkUXmaXDHQJrRjPiQM7FC2/8JmjK0eaH+5VXMIEnLPR4L7aqSwowa3YzQS\n/ociZXBm5Nkanb4/njLPXYVztEX9nXbf0ry3bYHjBiOCiH8i4XZ25FnbH1na62YvRQaUI1E40+8p\nsbB2Vloj84L8GnmC7kYe6J8gBXEhSgzMTWfORKHrz6CIi2YVnj8rrSXfSNe+MY3ttSjVd61VPHkA\nJbbUQ1D46QXpuWzb6P5EIW3vI69tPnR/RRTmvVTB83S1nl3WlxsBV6fPc6T57gZKLI99OjWmB59n\nNq6Gpvf6mDRPfILyRjekyd7G8JB1ATlv0FxoEf7Y3d9OVvzJyAK1KfKQ9ZsOTuQRJyNBZXd3vyjt\nvwnlGV3SyvYFiiNZd8ejcKlPURjQq+7+Uvr+K+7+ZvrcKCKP7D0bDvwPKTS/QUr+IZ5jOeuh6zU0\nadhKLKPXI5KWzEN1M2KsfKhR167Qlo2RpdcQHfvtZvYAouOuaPlvB+SszDMho8BwZOXf2cT2+QM0\nH+/W4HaMQLlBzyDv5iRETnClux+QPFu/R3meFT1b1pH1804kVD2LlI95kSHkIne/Ix3fr7xjXUXy\nBn2KFNr3ESPjvd0858xImRroJY9nNe/Y/MD57j42/fZbSBG6yN3P605begKpTVt5IoJo8LXmQaF9\nP0QG24yk4THgd+5+SJXfZv25KFJwXwbeQkrNh2gteMk7YUTsaZjZb5GXdG2kDE52RT58EXHQye8y\nT8x6SNDfycX8mkV6fBdFm5xpZtu7++md/H5GZGjYAtXGzVhx9wFmd/f9GnDbfRppLfwcpdP81sw2\nRXVBT2x6W2J+rw/5RdHMpgL/hyaJ94Bj3P05Mzskfe4TbuNqKF+UzGw2NOGMQ678+xD5yffT9yFU\ntDlyitAMSOh1lOv0PPIEr4rirCc1sA1FQl3ud/f1G9WGnoCZ7YJo2Kel/9dGnscHkGdsUZSg/p0G\ntyN7pgORV/PD9Hx/hHLFnkaUw3u0qzIGHcbF0ciS+RgSuEflvhvu7q828j5MzIerIUF7GKoNOBF5\nli9GoWqXu/vJ1jlTWiZk7YWUyJNMjJGLo1yfX7v7s/ljG3EvfRnp/dsFje9NvMEhwbnrbo/Cuyd4\nYrszs3XQ+7aNV2GD7YtICuAw4ExUwuU+tJZs7+6v1HpXzexURJo2DZV8GY1o0i9z9xfMzEBUiQ28\nhwEoL/VdEzvkUUj+O9Ldz+/sHS2TGbdFfXCeu2+dO2YkMuD/r9q7bmJH3gh5fl9G6QuXoPIvx7j7\n73vshvswcuvh99Fc+wKlckDzoqLUe7r77U1tV8zx9SG3iB6AhKmtEWHFisDi7r5D7ti2FWx6Crn+\nGI9CNx9A8fIfoL5ZChFS7Ofu77WsoYG6YaJLvtXdLzazFVHO1UzIgn+Iu9/UQO9YNq7OQovwE8BF\nSCn8HIUXvJy8OU2liq8HJhrzx1D42h9RPtBEVMR6dSTIH+7ujzewDXmB4GgkzDyDBIkX0/6FgefT\nItXW85aJxvxSd1/dzK5Iny8ylTP4u7tf1uDrZ4v5rMgLNiMSjh5BAtpHiJzjmXR8NQGrberv9FUk\nZeAH5V6HBlwnGxdLIXlgXVTL7zpUTmInxIK3VX9VsJOimikU09x9r87mm9waMBYZsXZL/TsvCvfe\nANHDN8zLZ6mWmYmuf5PU7j97qkdlZlsB87v7YVXO0a16dvl9ptzvPRA1/CPI8D0vcI+7799T991f\nYGZ/QiyVe6IUoz3NbDHkVDjGq+TxNaQ9/XBO6DaSNabfFz3OTTTjkIB8A5ocXgYudve/mBJut0D0\ntJu1s6AX6LAIjkKT1KXufkPu+yVQnZZXGy1UWDdCXdoBucV8Y5Qn8AxSzg7MeT5mabShIveeHohy\n5q5CORwvodpEU3qLN7/MIDYn8r5PSt89irwPDzZD4DXVLHrK3Y8y1SXaCFgJ5QxN8YIkHNYG9XcC\n3UOZ0eNalLrwGgptWwcpaH9DRBTvtrMRqZFIXvlRSBm5I82PtbxjB6FIjXPdfdu0bwDKxXvJRfLU\n6HDzJ5H35BTgTnc/1MzmdfdXcsdUC2Htcj273Ll2Q2ySg1H4JohA7R1UtDjmiDpgiubaF4WJ7+Du\nK6T91yPPa8Pq8nWGAc2+YG9HesEcuYr3MbMfJ6HqH0hAyArkWivb2QzkFpSNUb2IvRFF8HvABWY2\nj7vfhRanfWLCaH/khMexKOxqkpktlBZS3P0xT4xDjRZ4XWx5WyOv3HvA7GkhfhnVIssW5rZDEtAy\nFszVEYX5Uojq+QYzO8HMZm+kMpabgz43FaqfgJLht0Feum3RgnRko9rQUzCzWdLHJZMF83nksZ3T\nzJZIytE9TVTGBqO5fkYAd3/A3fdFTF2vufu/0/6a7XD3W9H7dhIK57oVuMndn0jfx7zZ/jD4Iizx\nf+5+jyvf9mL0TE9GYZN7m9ki/VEZA3D3T9z9L+5+m5fC8yp6x3K/OQTRvM9vZq+Z2Rbu/rm7P+3u\nn6RjGpLDnP6ORQQobyASn6PSIVNMDLVZO6drQ27fvigf+3Lt9hdcYezrotDWL67XSVsWRqG3VwPL\nIwKmUSjy4ssxR9SPNEffg57NA2Y2yMzWR2GpTVfGIBSywsi9LEPS57uR63ge4C4zuwQtxNOg8cJq\nu8AUyrYIsJmZzefu/0ru++dQLgTu/rqn0KhA+yKv3Lj7qShHJkug3jKFVzUV7v6hu7+FLIOHIu/B\n414g76CVyFnLv4tqGW2Z9u+GLKSjUchNQ5GMIu7u7yASj1kQM+WNLrKIcxADWjsrt0OB1Uxh0RcD\nw939QhT2+RYSeB9HtXwgCceNhCuB/0JgWTPbxsyWMhHhzI+Eprr6090/c/dzEeX9UWg+PT0pfoE2\nR/KCDEEC9mJmtqOpbMT77v4XFD1zI6I075T8ob+hkpyUGb3NbCYz29SUd7Wgu6+JDHQnm9mVjWxX\nasNnKUrjcKSIPYhIWT4ys28DC6RnW/NcKOR+duTZ2jvtn4y84M9BBwN39ru8UvoMUtz+hQqnv4hI\nRRZFjKKBruF6VKLqY+TBnohqF7YEEbJYJ0zJpQsiheMTVEPoLUS9/Ki7v9+fwhHMbHFEXTsGhWO9\njUI1fuXuS6Zj+mW8fG9CLrRiBiS4fwURaZyALHG/Ap71XI5kk9tXd6hLOyAJadsAP0UW8oO9SWyK\nZvZ14CHgRHffJ7f/BFQ3Zw5gZnffpBnt6SpMzGIbIlZDR+E+ryZFHTP7sru/nT43dUyYwrXXAFZB\n68Gf3P2g7rbDGlx/J9BzyM2dA1AO4SbIo/MycBdwd+Ytz4/VQGXk+vM0VBPqAyRjDQb2TSGKi7v7\n441633Oh0XuiUOKbUSTBdWhtHINknGsshabXOF/d9exyv10NecVuQbLVtmgNvMfMhnk/I4hpBMxs\nGGJM/8AL1ihsSDtCTq4Ni6LHHWAdk0yHuvsHZrYksi6si+roXNEqt2+gfuQWwaOABZDlaClgZWBn\nd7/PzBbzNslp6W1KvnUs7P0XVMz4syaE1g0C9kNeuv3d/ewk7E8A5gaOc/fne4MRycRGOBKFCT6G\n8jGWAtZy951a2K6haDEfhgq0NqQ4baB9YWbzoTp4R7v71Sly5LvACLQWXtHSBvYS5BShEciLPyrt\nH4U8S39z5Ww2/P1KbXgIkY9sZGYrIMVoDuA+d7+lzvONRZT1qwKvohDrY2rdi4kqfyFUSun9tC2D\napVVLJIe6J0Ihawgkvv4SlRVfWqyho1CHoQj3f36ljawScgpp4sgV/5rKMfnrCS0j0HV62dALuBz\nalmPAq2Fmc3mIiCYG+WvrOiJHtrMtgMWdvd9epsS1I5olefDRDp0HiLX2dpzrI7trDyUeW5nd/c3\n0xyzNWLbXAkJwRe2830E+jZM7HcTgfURPfsvEVnABOQ1fT3mz+JIyssewC/d/f60b1kUTjbJm0RC\nZGZroaLNnyEyptvKvq/rmVrBenZVfr8BcgSshVgAbyp67UD7IxSygrAoetwBJrrpq5DFei0Ug/tn\n4ERUlHNz4Gl3v7ZVbQzUhom6+yBE9vARcBqy6GZ5MMNJVO2eyDwCvRcmGv4bEGnEpHYWEnPK2DLI\n8PU0iTAIeRmzHNXHWtfKQH9FuVc55fuNQCRXE1C5jkPc/aMWNbFXIslak1F4sgP3IzbYXYE53P3H\nzZy3TJwBW6Kohs9R3ao3Wmn8sRqFqAO9E6GQVUG55cKi6DEAZrY0cIC7TzSzB1DO0dIopGCqV6nJ\nEWgvmNnhSBE7DlgBhUbsj3Ijj0fx6rO7+w/7y/juDzCzr7mK2Le9Vynl7b6EiJSWBr6BCnlO9cRE\nGGMz0CqY2cHAae7+RoqcmRXl3H4JeXOaUoy6N6P8/TWznZGM9QTK2doUuBP4mbv/pxXzlqlo+w9R\nwfZQhgI9jrZk1WojZExp483sUERN/BQwBSXrLg780xIlcz8SCF4A9k9x8i+6SAruTNux0L6MbYHp\n8Diy/j0BDHD3M1De2CBEw/0RypmEJrDXBZoDLzF7taUylkLEs/yJGZAQdAeylJ+D2CLHZMf3o7k3\n0AawEiX6cqiw7O0px3Ggu/8LhfGf4e4fWhU688AXyN73iabSAecjorTdEfnF8sAurVLGANz9n+5+\nlLv/N+SbQCMQHrJOYFH0uANy/fFNZK1+DVkApyLmofEo0fUXvcHqHijBVMNpAvAIcGwuXHHWLFY/\nnmmgWchby02U12cC57n71rljRgJ/91Ito1jIAk1BLpR2BAql3RSVKzgTFel9FljD3ZdoYTN7HUxs\nqpcg0osjgUdRztiMwI7ufk8LmxcINByh5XcCj6LHX8BKNTnmQBPlXEkA+idKvB0JXOXuv0g/CeGo\nFyBn5TsdWBYVWz7YzC42s1H5xOm+PL4DbYcB8AXd80OI7n4VM3snhTLh7i9mZEGhjAWajGy8HQLc\nlcLXDLgNmBPVTFwPvmA5DRSAu3+MGGH/iGptDUI1MGdvYbMCgaYhPGRVkELyjgReAfZz95fT/qsR\nXfRt1X7f12CqC/K8ux9pZt9BhXpvQQprZtEOT0ovQM7KOy8wF/CUq+Dll4C9UK2Utd39by1taKBf\nITcuhyNW22tQIe0tgPkQwcy97r5eC5sZ6OdI+USXIhKrBRHb5/0oauQYVyH2QEGYygZshqI0bgMm\nAR+6CsAHAv0CoZBVgUXRYywVHkzhBCcBf0X5G/MBV6Mk10Pc/ZEWNjNQB3JC7zeAc1F5giWAU9z9\npHTM17I8o0Cg2TAVr74b5Tae6u7fTPtXBN5JhCRtXzst0HdhZhPQ+vcpJQa+e5Eh6+VWtq23wVRn\nbC2Uv7wIUszGIuX2xDD0BvoDQiErg0XR4y+QWCXXQ3Hdn6G6a6cij+Gu7v6OmT0KbJKxnQV6D8zs\nROARdz8j1Vs5CMXr/8Ldb0zH9GmDQ6D9kEJpt0ehYZui8XiXmU0G5nX3SS1tYCDAF8QeM6Co2Y9S\nLu677r53GAu6jmR0GQMsB7yUS4UIBPo0QiHLwaLocQeY2URUFf5hVFfsSnd/Pvf92cBH7r5zWLB6\nB3LesZVQTuTvgWtzIad7AcPc/eAWNjPQz5Hm4N+kf7dCbJ+3Axu6+9Mx3wTaBYkRdCSwDTDZ3T+N\n8dl9mNmgTL6K/gz0B4RCVgEWRY9JDFJ7AK8jpXQJRHf/IFLQ3kTW64tbSUUb6BrM7HhKobgnAs+6\n+3tlx8QzDbQMZjYWeehXBV4F7nH3Y2JcBtoRlor1xvgMBAJdQShkZYiix4KZDQVWR3HcA5BAtAAw\nDHgOUdLe5O6fxALUu5DKNawPfILYFQcBN6btqQi1CbQLzGxmND4Huvu7aV/MN4FAIBDoUwiFrAwp\nb+qrwGzAXu6+sZkthBSyPVJOWZ8WCHKhm7MCfwAGo/DNh1Au2ZIotvugFjYzUCeyfLDk/VwTGIGe\n55zAKsD97v7TVrYxEAgEAoFAoL8h6pDxRXIuqejxMOAp4HlgDjPbAxFZvN0flDHoUIPtWOBGd18N\nFceeC9gQ9c0F0KGWVaCNkVPGZnP3v7v7b1FYrgOjgWnAWenYeKaBQCAQCAQCTUK/F7yi6HFlmNlg\n4D+IdQ93f9Dd9wX+gZiknkn7+7Ry2leQlLEFgIfN7Gdp36PufgQq5/CKuz+a9sczDQQCgUAgEGgS\n+r1ClhM+f4UUr0dS0eP7UDHS3dz9RPhCeesXCpm7/xe4EFjWzLYxs6XMbBCl+mPhSellcPcXgG2B\nFczsT2Y2MeUKLglcB/FMA4FAIBAIBJqNfp1DFkWPa8PMxgFroByjT4A/uftB/SF0s68iKdabAIcB\nLwHT3P2QeKaBQCAQCAQCzUe/Vcii6HFxJC/KzCi/7sUU/hbCex+AmQ0HXotnGggEAoFAINAa9GeF\nLIoeBwKBQCAQCAQCgZZiUKsb0Aok2u+VUdHjFVHR43nMLF/0+F7g4pY1MhAIBAKBQCAQCPR59EsP\nWRQ9DgQCgUAgEAgEAu2AfqeQRdHjQCAQCAQCgUAg0C7odxTXUfQ4EAgEAoFAIBAItAv6aw7ZdEWP\ngQfN7Cqi6HEgEAgEAoFAIBBoEvqlByiKHgcCgUAgEAgEAoF2QL/LIcsjih4HAoFAIBAIBAKBVqJf\nK2QQRY8DgUAgEAgEAoFA69DvFbJAIBAIBAKBQCAQaBUiTyoQCAQCgUAgEAgEWoRQyAKBQCAQCAQC\ngUCgRQiFLBAIBAKBQCAQCARahFDIAoFAIBAIBAKBQKBFCIUsEAgEAoFAIBAIBFqEUMgCgUAgEAgE\nAoFAoEUIhSwQCAQCgUAgEAgEWoT/B143wAyHzZfjAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2bf03390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"visualize_coefficients(grid.best_estimator_.named_steps['linearsvc'],\n",
" grid.best_estimator_.named_steps['tfidfvectorizer'].get_feature_names())"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.89283999999999997"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.best_score_"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.87719999999999998"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.score(text_test, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# N-Grams"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Elapsed: 81m 36s\n"
]
}
],
"source": [
"text_pipe = make_pipeline(CountVectorizer(), LinearSVC())\n",
"\n",
"param_grid = {'linearsvc__C': np.logspace(-3, 2, 6),\n",
" \"countvectorizer__ngram_range\": [(1, 1), (1, 2), (1, 3)]}\n",
"\n",
"grid = GridSearchCV(text_pipe, param_grid, cv=5)\n",
"\n",
"with Timer():\n",
" grid.fit(text_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar instance at 0x000000001F215648>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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1vRX438DXbL9d0lSKwcNQTMX9a9s3SnpTuf+6dnH0eorSVcDxQ4t1tGN74Ugu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\nhCMiIvokSTgiIqJP/j+1B+X+Tge1GwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x3dbacd30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"scores = np.array([score.mean_validation_score for score in grid.grid_scores_]).reshape(3, -1)\n",
"plt.matshow(scores)\n",
"plt.ylabel(\"n-gram range\")\n",
"plt.yticks(range(3), param_grid[\"countvectorizer__ngram_range\"])\n",
"plt.xlabel(\"C\")\n",
"plt.xticks(range(6), param_grid[\"linearsvc__C\"]);\n",
"plt.colorbar()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'countvectorizer__ngram_range': (1, 2), 'linearsvc__C': 0.01}"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.best_params_"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Td1GdTnQIrJTXaamay78hl4kpwGPAhvnzC4Ata6ZxBvDe/Posovf534Adaiy7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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x5067ab70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"visualize_coefficients(grid.best_estimator_.named_steps['linearsvc'],\n",
" grid.best_estimator_.named_steps['countvectorizer'].get_feature_names())"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.89539999999999997"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid.score(text_test, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Look at the Natural Laguage Tool Kit (NLTK)"
]
},
{
"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
}
| apache-2.0 |
Kebniss/TalkingData-Mobile-User-Demographics | notebooks/data_exploration/position_exploration.ipynb | 1 | 823307 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"import pickle\n",
"import numpy as np\n",
"import pandas as pd\n",
"from os import path\n",
"import seaborn as sns\n",
"from scipy import sparse, io\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.basemap import Basemap\n",
"from dotenv import load_dotenv, find_dotenv\n",
"%matplotlib notebook\n",
"\n",
"dotenv_path = find_dotenv()\n",
"load_dotenv(dotenv_path)\n",
"\n",
"RAW_DATA_DIR = os.environ.get(\"RAW_DATA_DIR\")\n",
"\n",
"train = pd.read_csv(path.join(RAW_DATA_DIR, 'gender_age_train.csv'))\n",
"events = pd.read_csv(path.join(RAW_DATA_DIR, 'events.csv'))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.00 968711\n",
"1.00 76362\n",
"104.00 27977\n",
"116.39 14751\n",
"116.33 6721\n",
"Name: longitude, dtype: int64\n",
"0.00 968955\n",
"1.00 76375\n",
"30.00 28466\n",
"39.91 19289\n",
"34.74 6964\n",
"Name: latitude, dtype: int64\n"
]
}
],
"source": [
"# EVENTS\n",
"\n",
"events[['longitude', 'latitude']].describe()\n",
"lo = events['longitude'].value_counts()\n",
"la = events['latitude'].value_counts()\n",
"print lo.head()\n",
"print la.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sea_lo = lo.ix[[0,1]].sum()\n",
"land_lo = lo.drop([0,1])\n",
"land_lo = land_lo.sum()\n",
"sea_lo > land_lo/3"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sea_la = la.ix[[0,1]].sum()\n",
"land_la = la.drop([0,1])\n",
"land_la = land_la.sum()\n",
"sea_la > land_la/3"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\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": {
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N2aJMQcI5JNMfD1oDnONNYK1hoWUcbM/o/ypu/aygl7xTPEslLlmpoPGhPNFH4888ki79dZbY6axcplxtwKXBZOqDEhHN4w77m3BFHsUI7Y99mA/F+wRiWnDs3F4cNIhylyxYxzncijtAzlDBLinyRQB7gn4jprtnQAjVCNM8bCHFt9w/EO1/tJHEeCOVmrNZqpYa53cdUXMaI9AS3mCP6617uZMWdxD+Rn+vyYstW6DZHB2ualLImPime/qDHOtwU7xTRDddLojh4Ozv3fcUS8lZda6ZU3l7ftZU8C64RnICoqEkgaCXrd/RVM+judnw/GgUMtKPzj0ZT7kd//QsSvrX5l13clwWT36vn0EUnulCHD7EEfVKAIcBdPYFuqVAIfBh4o0vUPeiIdIgNvWFmetbtpAqHJN8RDdpnjBe07pIT+hPAe41qaFejCm/37WEm285xKODfbT9rghwHlnl2PbqqKUiK1T5ZojwDPMGnOLbFaNVfc7J15YkrOsxiiA0jlei0bizwTPQZZ1ylNXZaUsa7ovet7ZcbSKeUqnIe7TZStXJLgMoXrf443BXp6ngEXucuz9aFq9lnRXVQRQuBEgcs8lAlwVwJbKiwC3BORAq+mVAKOtL3OpHLrrVlWBsIt10ERhgPWTF2KRkIcw5e65lOMlihDb1vnQphjxYse9i36mz041rXuU92OhQYAPCQFjQJgPU8VwHjkdPXeU/QrrhqhzFrjqxTyg1YbMM6Z06puq9al8uwhAjHh+WXsoZPKe5ar7HcQXd/Gs+S6yNOeNDsEQpRB7TvpsPPesXbvWbrvttszn3t8ljLEuEfRxiAS3u/7KahMJLkOo+ve8w1G6lnn0uGs0LUwTEXYX5LKjGdWRj7uDlG571r0IcBxkrZcSAW4d0kFV2BsB9jy/ZVp5tzS2GYG2zRk4+OCD7c4772yzysZ1Ncn36BYWiE4W5gizbpH0CLlDy9HYZs7eKpMBMfXAVVlRgWPPD0M+wB/BBEE9K2JulX61WRbLW91nkXmBBMv1uc0ZaV5XOt1cUY1VXaAhq3kRslnbRJSuQkZdieKeECjs2LN4XkhlhJU5Kzq6C7OUZx3iyVCl3RAT2vP3Vt06ms9avRrG2ZW7TFaoh8j03oUXkR9riUHB1/04Kn9ixudlUC6jROdZqZsPPrY9MHVlPTKxH1NIvfNFgGMBbbmcCHDLgA6sut4IsLu+lWl2+T7MPzkw/GxoBNjxKrOsF+HoUQjZkBEUIW5uwePFwAuQc3tDsfj6WMI+dr1O0rmEUQ4gjLvCgBcbax6hIzaAEjgj2EOswb2rtEdl2NVNV5NlzS5rS9+PHgFfmzFkrioBxnuh6Ex9USC4rJF7CjDPSc2zxHN2xBFHJN4TeRHf/dnheSKInOe1rovuOJNg5qPJ+6EuZrpvWAh4GjLeSzHXOLvRx4zPy/gRHfYKPqOwALOPOdFOe8fwfgXrQFEsAlxlAlssKwLcIpgDrKoXAozFEPdnv1yL7wKYWxTZACAMuFwONRXC0AiwY+qbKpjGCLbptelBGLgXV0MnclWD1nS15n2tdBXAKj0uBEosUFlXKDjUySEM5pBgtMJDsJ5iAYbw17EE92Wd72odjks7zB/PtXuLxAp5CMsoY2LzHm/durXQ3Z0+VE3Nk1Z0MZZTTz21MA8w6w53bJ5TFHx12s2a2zIF7hDXQxtK0iGOS32qhgDKKRTcsR5cTTzLqvWs39Iu+/A+b1vJjyzgR7QqHM8SAe5pSYgA9wR8R832QoCLXO7Q1Luwj9DibqV1SFwXGA6VAPvY6whoCIgIuFkWx6pBa7qYA9rg5cwLvcJLpfWuxWIDSc8K0tN6h0ZYIc8lwlOVy89vtpVGqkrbKrsYAZQXoRtw7P6KIpL7ggAtudDyLHKOrejyozB15gflKNbf0047zSDaRXsdiiesw16mSbt1+jq0e8CBfUh5X4c2M930h70Y+aqK8mncCbDHAEC2QcbBsJIVgA+5BxJ80EEH1TIeZM0g7zzqrRm7QwS4m8diSSsiwD0B31GznRNgLA1o1vIELl7MbE4eXXiUOLRxpmjoBLgqfrwkECazribnP6v2o2r5VNTEqre3Ur6KUO1pgNAG90na6w4cwRkrWpXUKghcuJeJANdFvb37UGAwf3XO8x111FGJtbUskBnznOeS7CPhPYCgGXshvHIP5A1h8thjj00EeZ4jCDHrMk+oD6PC1zl/HNvHcSnn79oqz/C4jE39zEaA9yREjGcBL7wqz78rTMYFW/YnJ7uMN/SeSnts8Sx4bnqPfk18gjYucMPdvGy/LGhLBLiNiahRhwhwDdDG6JbOCTCCBwQ41uWubSzZjJx0IAA2dXEZdwKcPsuGUImrYPrC6oPQPNSrKNhOV32uEyE5iPTYVTdbacfdsmOiiNKgRw6PPf/cSidVSS4CzB9EkQ+CoUfmLosI6xWuW7fONmzYUIiwxxIoKlSVALslF8GdiM9+7pdzve6WXbRXhX0ahYvjuC05DzIZBvXyYzPpn4wt1lNg3HCY9P4i90B8ff+to8weNwLMnpD3vsH6y/vaL/cm8b/Tx/Tqro+WUjGKANedgIb3iQA3BHDgt3dOgMEDS5mnzumSCCPo3XvvvYkmLgy+4gEJEP4QrPg7tl/jToCZD3cNAh+3TIbrNv2yGNqaHkKU5LqKnfSLd2jYxvQnJBIQKeaDZzx8hlhjuJXVdAGL6YbKVECAfY6jKPxkzliHKLhiUlwddthhiWXXo8HnNRtz3rtOKiQnukcffbTdfvvtST/Y18Mr7zgCexzrkL5hAYs9/1gB2rErCvll/j3qrQ8gzzOFvY55C6PWjt2gp6TDIfFlvlz2ipVvQpjGiQBj5MCim3eF8h8K//R7CfkUJUGTK2b/i6xfBDgSqLaLiQC3jeiw6uuFAAMBL1GELQSQLoQQhDw0gmj9eAkgRGH95UWA8MTLnn6wyWMZZgOM0XZPAgEOl6RbBDxSKJg0tZKPcsnTX84j9u1Wi/IEa1QdrOg7L2AEeU97NErM2q7b87HywmfdgAXPdvj8MD4EknF0924bryHUx3ONgOeCMHMXmx83NhJ0mRAKDnW8JrjvyCOPtI0bNy5AmVYkZaUv8eBPjJXx1wniNoS5G0UfwAZcYt979MFdqF1hrPPEo5iZenWGnm5ubKji7pzVKu+noQYjTRP1shgb7rmE0idLKcu7nPda3SvPk65mfSLANYFrepsIcFMEh31/bwTYYYEwYHngE0M468DJixqSGwp8Xg8vb4hxaM2gH5TNCpCQbn/SCHAdfPu8xwNW9NkHb7uqO2dWn1mnZZa1IYy1qA8QYCcYLnShcIKkiAAPY/bYbyGfdRQ2hxxyiG3ZsqV0IKxjBEGIVd68Vz1aQT3szSi9eN68/yiR+F/YTlm8idIBTFkBsAPHOmsCwuXnsyFcdSyMUwb3wnCrZmzwecqTl/ysa9tnu8eBAIMN5DcmWwL4pN+1/A/yGyP7MYHMHc9MaMQZgUwiAtzT5iAC3BPwHTXbOwEetRsaGyIEl40qTL0U4ksZXPrCIAVshLzE+RRpAsk/yYsfQZ8XEhtqmfaxo7mdimaYt76tvyHQTc8VluVNDcn2kMkkQjSCMEQL4jvupH7SHib2NQ+C43EAYi1EsQTYMeNYBWsg62LPhASjNGH/zeqDe6W4hwFKx82bNy86xkLdocU5ywI8aXM4ivGwlzYhr9zPs17FkjyKcYxLnax5P2vtzyH/y1NCuKIB5dWoDAZZ2HmMgC689ZrMXZY7c0x9yHvukZhXHjmPchBc5sFjJ1DeFXkxXi8x/UmVEQGuAVobt4gAt4HicOvonQCH50s9NUObbja+IWa52rnAVXaGNLRS0Efu8w/1ukaWaeZcHS8LzpnpGj0CQwsiVZTiKwaNGELPmuMZQbEz1AvhzF0iXXhqEAVzqMMc6375UQ/mJ9biwYA5A8zZ2ypXWZA6PwrjqbVC0htakP34Cm2zxvzoAaTNPX0QTDnjN3RhvQp+XZXNU0JUab+JJblKO+Ne1hWYaSLrruWhBdflDsqiUPJ4JeOOQZv9r3vmNlQGhv0JnwXkSD8ml3e2GIUi5SDHLV8iwC0DGludCHAsUuNZrncCjECDlYjNywWdAw88sJEW2qfCE5rzdzrqIZsbLnMuOJW5zFDOw+OHU03/IT1caAZ5aa1fv76ygDiey6ffXvNCKssz2nUPY3MBh/3ys4m8OLMicIdlEYB4PhCCioJ8dD1utTd+CPg5uDAaat4oWKMe6M3dDKuMOMtFOX0/hJx9lvcASskyDwd3gV550NG2z6svsJWXvMd2br0l6afIb5XZ2Vu2DQJMbcw38xDrVVCvt+N7Vx759RGFZ7LdSiws8+c7Zn/JuzsMiEUZV6TxPkZuZD/ifYtsx//y9iX2rhF5o4kA9/SoiwD3BHxHzfZOgLPG2UbU4XQQAlyK+HhkvzbPabi7Ev1ms8RFDw1tGJmUnHJskLzEqlpPOloLY9dMUc7iPgZTJ6Jt2M+YXMZhG5MQQbqPeVKb8whUCfTiAZKquj871qxtPBbKFI3pufF88LjVhoJnqCTddu6nbffBx9qyO39kqz/3Ok1vTQTcQ6MtosVc1zlPXLP7Y3NbGfn1gTAfEK62z/KODVBmC3l8kaeK1lLRMYui8WbJmnXrGiGuIsAjBLeoahHgnoDvqNlBEuCmRCLPpRlNHy8fTwTfNsahq3ToJhimDWEjx+KdTtvRdl+mob4YwtglDk3Ps8eOx92sQ011l+NUW+OPgFtzsVhAeIqES0+PhoW2DgGukwvTz4+7a7Z7SVAX/YUA+zGTnY8/3Hae9S5bccm7bcX9m8d/cnoYga+DNs+VuiuvKzF6GNbgmowlv4PreMsdcjd59hbkPX7y8awTvg7DmBiUQ35ib0gradgXUBagqIuNN5GX6gjPwKqKupbhSVcnAjxigPOqFwHuCfiOmh0kAWbsWEzzAqKUvaSzAiEg4HHfCM5nLJoqz6XJRk5AJDZSNmUX1mif/JW33XZbR1M8uc1k5f/sa7Rt5A10gZHzknlX2mpXdn69LzzU7vARYH/1lFVZvXWLIGuMPQzvmaoE2F1qY70VEHJpp8gKiYdN2Zni4aM/nB5WDYJWpefUTWAg9mrejbGptqq0MU5l/SjBOPW5jb56mj8ULchEYTwI1gRyUfg/T1HpOb+9D+wLHqk5fczBUxqx3squvHO/A32figCXTeiIvhcBHhGwA6l2kASYjY2zlLiisHFiWfXAWGxunFlz96Cs4C1sYp5+ow+cXYgjXyZ94ZyqW1kYE/1HiLvjjjv66N7EtNnk3E/bIDSN/kx/UJKwrrPyEvI9ChwIMB+u0Cqm88Btz+jk1+d7JySYT3ilvQvYr9jP2L+KFDRp1FjT7H2x65M+sfcXXVVJ+OTPZLMRhpGIm9WUfzcEGCUIikLPGz6qtlTvsBBoMycushMfj8LslmN+lsUMCFHJ89YaqGJNBLinJS0C3BPwHTU7SAKMMAZxDPPj4fYJscQ9hRcoghUuzXmue2yIuM9U2RTbxBwNI5YMyDybNf1FsONlgJCHMMlmG+uu02bfJqkucPVgOAjnWKq6vpq67DuZDb0TWP+46vuFEoi1nBWwaGhnobvGf9LbY317ahp+99yTTcfNPusB/NJ1pWMkVMlxjWLKUxYV5QDO6j/jxL1ZBLjp7Mbf35VVknWLd5anFizz5IofgUoOFYEheWmF71K8EdJX7BGkHrAWAe4BdJoUAe4J+I6aHSQBzhs7Ahskh4sNzFNmpMsP5WxkmOMSyzXab0gOAiJEBhIsK3Czle5niVCE9Om+xNpkPbYl1GExY2woTDyFF2sn7faFUElZ14g3Q1N3DxGBNFFty0rBWkVJl/aiyRMEUeCx/sqCJDXdf/OOv/jcyALc7irtigDTa9riHc47EE+WtvbLdhFRbW0gwBxjhBhS+rsipV/dHMJtYFVShwhwByBnNSEC3BPwHTU7VgQ4xCQvgikvWKyCMedAOtPYCGYAACAASURBVMI4ETA9iilEDQGT/x1xxBGV3AO76u+4tsPcb926tTerv0cZbyPyKZYzyAYvZUhKnqDImD190sACd4zrMhpcv0NlX5vuhD5QPGpCt3oC9OXFSogN9MZz2ETwda8K3y/DScEd+8477xzcPI1rh7okwI4R64sPhEQkeFxXTn6/myrBRoEIewn7V9Z6Y6+6++67e5MdRIBHMePN6xQBbo7hkGsYWwKctgC7ZhnXvRHlYmt9HiHBxx9/fELadLWDAKlW+nCD9t7HuHDGjNSF0hjhFOKLMMkZO12ThUAYqGqUZ94hnAiBZUqUdD71PLQh0U2Od6xduzYJIkifeKaoD8shSiHctjkKo2u8EXBXed7lIsHjPZfp3scGveti1OwZ7G+ss6Ijc3hSpYNuddG/iDZkAY4AaRRFRIBHgepw6hxbAhy6svAiRUAaF+IbTj/WDM68oX3U1RyBUVjIqvaqzIWzan2x5Ycw9ti+qlwcAqHFNa3cgRh2sedxdMOVSrEEuIo7IYpA2nC3f5BJKzhxv/YUKLfccksceCo1eARQkqB0yTqTOfjOq4OZCOB9Fxv0bpQQolRBtio7shH2AYUbR0xGnS2k4rhFgCsC1lZxEeC2kBxmPWNLgD3n7jiTX5YEwt/JJ59sW7ZsGar7zTBXbk6vhhDIguBsHrW8K/CwFEOQBqrB7gqGiWvHj3q4qzvCnH9G7bbHGt5///0Tks3a4ipyIwzB5xhKGMQtb2LC9GGhAid0yw7vPfLII23jxo0TN8/TPCAs+7wHqxCVacZryGPvMw5HGpeiODF5GKaD/w0EaxHgniZCBLgn4DtqduwIMFYPjwg97uSXOXaLyuGHH26bN2/uaNontxmEKaxPCNa8AFkv/N0lMXTlTB8o+/j7in7ex5gnuc2iCOOjsvhDRFjDPENcaWsupDgrGnk4DwS/ibGiYNkNg3BhfWFfZ1/Mclf0FDpDsDBN8rrrcmxhIMMu21Vb7SIwtKBXdVITVvFcaRe9wtpEgDsEO2xKBLgn4DtqdqwIMAIZghkCGpstwlLZmbWOcKzdjEdiJSI0rkMe5bp2hVN+Iy51nPlJnymDLIQulqOEyaM2j7KNvLp5LuRO3wfy7bdZFlQtlmRW7Vk6Fy/rKXS1LnMtjPXCYB/HRTF8VmNy0ioKdNUZHX555l0W4OHPU14PUWKwHw1FHqubmnBIFuwAaxHgnh4NEeCegO+o2bEhwKHVYVLIbzjHjO/EE09MXKGbBI/paN2MZTOsmy6swWkC0RVYjA8lypAioHc19klsB+8FD4KVtrhiXUXgHMUVxldAsEVxhAIpvIqUPLHuz3UVRX2nj3PvCgVuamf1Cc92cOyzlr6DT6bHXtcLq+9MEjlzKALc0+IWAe4J+I6aHTwBRgD0/JNggqaYgFdD0TS2OU8Insccc0ziEkiU0y7ddtscx9DrGrU1uK72uSluPBNE34xxPW3alu7vDoG0mzABqVDkNEkzVNT7NDFNny9nTyY4FwQ960pbjPPaQul33HHH2UEHHZS5n7MPciwknSsdPPCU6dNbJk3a+Nv/x888bLpbNePTUkyk+/EZzfT1FAXZ0JSusenasmZrSBGs9/RPBLinx0oEuCfgO2p28AQ4tKZNMvllvhEsCaDEhZsfhAY3b13tI4C1lBfdKKztacLSfu/zaxyoC1eXEExUW+wJnIf1SLkoNxA2IcGjOuedzgvMcxKmFqNPRDoPLz+Ti1U0JvgV9+LxQlubNm3KnTMUghxpuO666xbKYAFet25dgsPVV1/dmzLUFRBuCfafzAvzxd9t5ASfqAWtwUwMAqxzFHF9KqLywESpVtVDgz0MuYA9tovo+hUWgghwBbDaLCoC3Caaw6trkASYyKd+zhfIsKjxGWB4+lZnFMsLgiZCEySKTfyuu+5qtQ1VthgBhGhcNtsiE325P/uoEMoh9ayjIQomWn/VEGA9MacE/gvJlOc9RwBt+3L3waLI4pzddStn3XPnsVFaaYvozxBx7gEPiC/XqaeemvQDofWaa65pG4pG9dFPWYMbQTi2N0+6VZu1zfM4RC819gP2jKrXnXfeWfWWrsqLAHeFdKodEeCegO+o2cERYNyAcY1zDTqbLL8j/E2i23M4z2gseXEyViIYYmXZunVrR0thepthXUEk2rAG14k8OQrkhxaRcxRjnIY62QsQ6FCIpS/2CtyN23aF9sj0KFE8/VG6bQg5ezVX3byfYQqksrmk7EknnWSXX355ZlFwOv300+373/9+63iU9a3oe51vbYLeeN7r6cnw5sJ7YdIuT4s21KM2dZXQIsCTtlKbj0cEuDmGQ65hUAQYIQbyy8aKG11bVrkhT0BW39jAweGII45IgmLp6gaB0LXUz/S5FSemB3m5S2PubbsMpJ6z8ihVBubO1fZQJ6Y+5opnn7WHMoa/WX9FltLYgFNVQHIX/qI0S56LmHpZX1X3asYGOTjllFOSIFuMo+gCg+OPP97+7d/+LbcYfUIBJaVhldm2xNMojMDMe/j2228flCKh2oj6Le3PDQrsSYts7ceyhvxOqRtcD2+7qvtYRytNFuCOgE43IwLcE/AdNTsYAoxAhJAztGAKHc3DomawrBx88MGJ9cVzcfbRD7U5j0BZWhfmC7f9IZ33o88IXxAoPn6WfKAv+KlfapBBLK8QOAQx/oYEM28oVtz1F8GTeXXLUtnarAMs525xgyYQX9H53Dp1c48faWEsWJJR9jHuW265JbNK9sCnPe1p9t3vfre0SfbNAVtySvvfZQH2LZQdWPpRloUXLucbN27ssjsT0xbR2Vnb6XPy4z5ADBPsR217nLSNS+zRinS7IsBtz8T41ycCPP5zWDSCwRDgyYa52uhQBiDIIdwqmmg17EZROo9kuKV+SMQ3PX4/i+ZCy1DPbY1i3sapToigW+JCt2NXXPhZO/YGyGK4L0Be2nDfd7w8/zDEE3IEEa5zUQ/CqKfn8jog+jw7YZCaoty+kN8rrrgiqgsiwPkwoTgheJintGJu84IsMvcoJpRTPGrZLRTiOeR57CsTQLXeVisdHqtx5Wq1GropXQd79lf23YEqiGUB7mbpLGlFBLgn4DtqVgS4I6CrNhPmPa5676SV7zugSB4B5kWLBaVqtMm+5odxIPzyGeiLvi9oem8XsnjGGWck7rshAU67OGedmx1FRHM/44tHDsdRPNCUAwWBRdjPWkd8hxsiP/3CzZmykF+eGZ6d8MKiTRsbNmxYMhdVCPDhhx+epE7SNY8ARGX9+vWJhwr4X3XVVVEWPOaCVDIeG6HI6sfc4TUQRuqeVvw9hU5dK+TQcfN3sRP92P52GcMlK0p9Xj9RArG/DfU8855+iwDHLrSWy4kAtwzowKoTAR7YhITdGTeCNSooiWaM4N8X0SxyMx0HQQeChMCCEMuLXtGhR7VSq9ULSeQZ98B3EAn+B/nkuuGGGzJTCqEcw301fb4QQa7sLG1MD3nO0iQVCwlkiPVO2xAkP2fOuvIjLHxf5BFRpMzCZdQj/UNkIWBctB1LrujXYYcdNhLX7Rjs+iyDwsHT6NEPV07UcQnH7Z51xsVaIMI265O595RXfIflnvn3c8N9jr/vtj34Fbh7JPW++zSq9t1iyv7F7yhY2Lu4sryMWJdpN/tR9Y16y85f8x70AKuj7EdLdYsAtwRk1WpEgKsiNl7lRYAHPF9uhWGzRsDoiwD2BZG7dXoU8LTVqKt+FRHguikXuuo77XhEdXebraq977Kv09QWzzPpOrIC5RTNkVs45l7/ZTPI8uyszVx4dkJE2ggARb941iBBftaYurP66cFw6K9HhW4yhwTFYp3ecccdtd1vCR542223NenGWN3LXDFmPAdGQTIgwCggeQ/RFnPDHK1duzZRNLAGjjrqqOT/bbrij9UkmCXKJ5RQEEGenUl+X3uQPkikBy/19wsKABRZ4Vpg3bBOugqeBeEukhdQAtP3MblEgHuaKBHgnoDvqFkR4I6ArtMML1CsILghoo2f5BdqFj7h2ca6kR3r4J6+pyzQUJm2uY0+1KkDQQTikj5Hzsuf7yA3YCx36DroNr+nyHsAskEwnazL03zMvfXb+Lia7dplq/7kPyTndevkBebcLOd8Pf86LvK4KiPAskbK9h3OLjspaorKoYcemtTV5KK/WIEhI3lpnJrUP7R7sTYydzHRtJv0HSLMXgEZhlykA6SBOXhPYyBLTxmGEoj5KHtmmsxDeK8rw/g56vlP99lzzmcF6gxJMFiwPsGoK1fjME1bFtZFKd7ampsW6xEBbhHMKlWJAFdBa/zKigAPfM78RTqpOQXz4E9rkSFxBP/pSrAI+1VGgD1tzJCWUmz+0bbcZoc09nHpC0TWP2m34aw1xzMAIfXgUjvO++oiCzBzTiTT2Iu6EExpi3OiTmLzrL159bYZeMoF+jpuu94/9gkIGgS+bgCvWAyHUo7gVsy9B7gaRb8gOhA8lCx5Z4JZC1j/PGjbKPoxtDohda5I7OLYErKAn693t2t+uiIEBWdecLMusfM85TyPPNcoRyCeXVzsa1jh8y7W5xAwisRCBDgSqLaLiQC3jeiw6hMBHtZ85PYG4ZcX3zRFheYFimDhWmOs4e6S2eW0lRHgcQ5YBkFgfLr6QQDBkOe6jAAjWGOVLcor6gKnK4ny3A35nrKhm2ATy2tRBOeqqPJ8o1Cq68qNSzkkEMXONF24ICP0o8ToW7CfpkBkEE88Nfjp6cpGqaSFTKLYKfL0wLPEPTi6VgCBA3sLHzwBILyeDoo+j1JBEz7vWcECIb186CPvvK6s0S3sQyLALYBYpwoR4Dqojc89IsDjM1eJ9h3Xnr6jIncFWdoNtEyrO8p+paPxhm35vIyy/VHUjSCAVn6az+2NAtfYOiGzkD1+phVbkNcwBQ1lIMlhkKOidtgjEH6zSDAKGwRRJ9+sXz51iVObBLgJEXc82qgjdg6HUq5NK3zTMUG+IBhtBGRr2pdR3u9eShBenqku0uFVyc+MlZhn89Zbbx0lDIvqdvLL3uIRxCHtXF16GzEX3q53sOgd3hlA9RoSAa6HW+O7RIAbQzjoCkSABz09izvHSxZBGWtQkSVojIZU2tVQa4x1yCPDlt44ggJ5LlxZL9sRNN96lZAjSI8swK1DG1Uh69mjKqetRlkBrapGls0S+DzwlltCOFvPBwKcd+a4bDDci7DdNKgM+xoKvqa5Z0MC7NYw3IO7CsBThlfb37P/YGVrena6zX5NuhUYosd6x9LYZYYC5pmzvkWR/Od++U1mT3uJ2RVfsSd+79PJtHapjHASTLtghJKPC6VbnRgFddclsQnCfbXr9uv2O+M+EeAWwaxSlQhwFbTGr6wI8BjNmWuaEeqm5YIIYMmC8EPUcHHsyw08K7ol85DlbjUu8zPGQsG4QJzZT55lCG06cjJrDHKKgOvn5Vj7WNWqREEPU7KEHdj3jFfazMveZXbpBTZz7d8nXyEoevm6oLZhBWZf41mqa4mm7yjIsPy4oM36Rgif1MjQBJ5iX6xy9rvuHFe5r425rNJe3bKsC9ZIFeWPvwdQ+uCV1PVVtpbD4Hgz7392YgXesmVLZ910N2je0zyH7HNcXZ+7ZR9wq3zeu7szUJo1JALcDL/ad4sA14ZuLG4UAR6LaZrv5LhaGptC7KQAC2zf5239zJdbk9wtdRwt8riYQzbyAto0nTfdn48A68Wtr5Bh5gJBHNKbng/OFlI2tGZ47t2884Z55+3WfPgGW758hdmunTbz/l9K0tcgHDcNWtSWkO3WWw/oEwbk4neIXl7UcogBZ4ezxoJlGaLY99lgCJOfj0ShAeEP8+pWfWbAayhBj9J9H7oVGGuqK0r8aAFEOGv9MGc8o3zHM8r+D8HqWhkbnpFPp0Fz/EML8Mw3PmRdHgngGeXsr69zvDk8p3hZLI2qa7+sPEpDj1Dd5fnjsn7V+F4EuAZobdwiAtwGisOtQwR4uHOT2bNQqzlmXW/UXQQO3LgQRHix9XkhsPKSR/jB0tGHFaDJ+F2hgFAAserSPa5JvyftXkgwFmCE6jI3dIgua40PZXkemLv0hQCKgM4aTRNFBNHH/eJr7IFfPM/2/1+ftLl/+e/zZwRfdIHZEw41u+8Om/n0q2rB3BYB5owjRIO1mSarHiE6JP1Ydz2wTpGgj+cIAfX6dIP24xvMEXPn5J4+MWd1Ld/sP+yJkI2mioxak59zE8pBxjw06zTdZS2xZm+//fZFvc+LIM4coWggXoK7Pfeh9MTi731OW3rz5g5LNUq0KlbuOuuAtce7hHlnffvfnsKwrTzlsX1jzDwbE+DlJAIcO+ktlxMBbhnQgVUnAjywCSnrjp8ZjMnPWVbXOH6POzRC1SgjbZbhgrDKSxXyghWnaytAWf+yvvc+I8hBoMAPQoSQ0rdVrM54pvEeT32C4F2WazRtbWG/gAQy3/yOcEh9rN9bX/OFhXzCuEzWudoKwuRupZDVmAvLrisCWMd5Zwyd2PR1ThZyXpSnFQsk5KpIGYX3C3tOXoTskBzFYNdFGe/T2rVrk7HVJflt97XMjZh1yJpJE+S2+1GlPk8F6AqFPAtwVp2jtAKjaOP94mnaPMo8ChnPQ8/ew8Vz3VXQRZeVYveSKnPRcVkR4I4B9+ZEgHsCvqNmRYA7ArqtZni5uOAangXukxC2NbaYerD4IPTqikcAMoT2P8wN7HmlsWQ3ccGM74VKNkGAZ93P0hXVg3CJcgZrFfsEhOngn32hbf2l37flF73NVt1x9cLtrAE+15z9SbMVq8x2breZD5xZuZsQBQRcyCdrrCnJHJWwzh5J3fSzK68H2sSqDSZlgj+kzC2MmzZtSixpuBGjtIBcQPB5Xjmz7d/jxgvmKEUgIaO28lVdHPT72GOPtY0bNy64o1KHu6byk++aHsPAAs4zEhvsLEZZ0JZXQ1XM8so3cSln3XPUIe/4QN0+ujcKirRQBkm/V1CQcXVpjeX5YV01XVt1sWnxPhHgFsGsUpUIcBW0xq+sCPCYzJlbfRCGsOIhKPHSx5rjAtA0kGDG75awMZm6XrvJyx8NeOhiiwIBYlSUKqfXTqvxRQiw3nnW/aww5C3Mt+lRV9PCrc/z3Bv/zmyf/c0e+anNfPjFi+pGqL5lxcG2+2XvsWVfeqet3vj9KPSdcFAYS4/n1GSfWrdund1yyy21he0YchLVyZxCoyLYWc1VSVvD/SeffLJdc801hsWU+YYcpi8w9u83bNiQfO1Wtya4jOpeiIjvPyhxeHeFUYyrYhT2k30MjyAUozwXKARojw9KgrwrZg1wPj4rhRDv3K5zyHr8jzvvvDNzSAvWYL6dnbWZC89eVI71AQltKxgWe40r2bI8oEJLL22jtOHq2g16VGu643pFgDsG3JsTAe4J+I6aFQHuCOg2muFFEgbe4CXswXPQwOIiN+nXtORATs8jAmTZOdGsuQ+jCfv3fo6c75qmrpn09db3+MIgax5gJtZl3QPGbT/0FNt1zvvs8H/+qP348n9IhuRkuaqViz3omGOOSVxDsebMrT/D7KV/ZHbxH9vMzd9ZIGOnn356YvWk/5SNtcL4ecE8Qb+N+cDqHSrRsBzefPPNbVSd1AHuKAiwxqKEaGoRb61jA63I3fOr4gSJhUxzLCZ9QcrwSshbRxz/4N1Z5AnAWkcZE6b1oR3a9IjsrOu6a5U62IvpR1nqrzKl0MJ54HmWaRxlmDvtbLPnv9Hs6x82e9a5Zu45lUGQqy4Nx84Vc+n7WfuhUgzFhF+4wQ/prHrVsfdQXgS4B9BpUgS4J+A7alYEuCOg22iGFzLafwSGMBAUAjGCpluAZSFtA+3h1IEAhkBRhwBnjcLzIyL8IaQg0LXtGjcc9Ma7J57GA6+PKnPEHgD5SgfqwXLoZ8AhpqeccopdffVet+gYtELSPPfmb5qtXG22Y5vNXPDchduxzLmVL9bCxz30uS6hiOl7Xpmjjz46IeptCOaODyQLwf/6669v0rWpuJcz6RDCmPOaKDBQWjBfRc+E73O8F9nj3D2ctrg/7yx1FcAh2m7dLDrjna4TRY8rR1wBQBme99tuu23RuHjX006Re3vaArxAdpcvTwhxcvF7QJCrjDMs62d+i/Ifo5Tw95UrI7wOKV4rIy8CXBmydm4QAW4Hx6HWIgI81Jkp6RfRDXmR+4VWFTKD4MvLlZeqrslAAKIaa/UrG7ELIxAqPzcoK3AZasP93qNIhxbW9N6Q1XtIBGdG/WxglfUFwYOkJtHEMyzA6fZYc1iwNm/enERlzTpzzr6FK2tVK2CbM+OWYQgTn7oBkGLca9vs96TUxfyzDnDTZS54v4UuyKwdSCMkOXShjhk/70MPnoiSI4Zox9QblikLrOVleX7Yf7MIOGsH4njb2RfOW2xnZ+2JX3jNEkv1tiOfmXtsIYwOnbTZsgUY/Hj2/Rx3Fk4hAU6nb5QbdNWVZSLAlSFr5wYR4HZwHGotIsBDnZmIfoV57nip8NLhp4f/j6hirIugeR9iio02QUWp0VZgG4gIZ/BCDwEPzAMB4nuEGz9f2uY4VNdoEGA+sWYxjxBazy0b0xrulxAD1gPzXuV8IBZTzvnGXPQPwZ7+0Q6u0yjpQus26w+FzFACslV1DXccUEjwGUq045j5GVIZT3XlChkP/sVeT2AlcsEP9aKvkGDWeEiyIYqeZ5i+Q3zzvHlYdxtXH2a7f/UDiyKzL7LwOgBYdLc9YjMffP4iSBbKuvX32sts5tL3JGVQ7KAI4NkDa5SrVZ853kmhx1lVAgx5RunKfqArCgER4CiY2i8kAtw+pkOqUQR4SLNRsS+8zNCK80KCDCMEo9k+7rjjEqE46+oq+mnFoSwqjlBepF32wlgDcBdDMKpyeS5QBIChBg5DSEAIdPd2CANEBXLqeUOrWEF8rSCcp4OWIIzxv3T6iiqYqmw/CLAmcLVtevEsECSHdRBzZpdyuFN7BF+34LImQ/LnUYrDM470F6HbSQCknX3M3VqbjqWN+8vOXOa1IetvG+jP18EcsF/xPrjuuuvaq7iDmljTbnGG6MVYnD0F2JZzv2C2ep/5Xvp53rd+e68Ls/cf1+bNV9mqr7zHlj94z6JRpc8EJ19mnP3lWQTjWM8LPz5RlvqP593fs2595z3Nx/NgV/E66WDKhtyECHBPsyMC3BPwHTUrAtwR0KNsxhPd86LhpUIQFl644Tlhbx9Bc+hXlRcjUWx50WL9jonM6dErSYUC4QMzyGUM4e4KN6wH9A8tuxPfdNuu7IjtU+gtUHSP0iLFItp9OdaDEwIsYR4QjmA/6bO+dXtXhcDx/Jx00kkJ4aVfBJLiJ2uNvkECEIRjFDWQaVykY4Nl1R1fzH2MC0tcLCkI66yCX0xfprmMY1nXGj9u2Pl4E/fml793PjXZpR+wmWv/3pZYdbfPme3Ybrbv/mYPzdrMR16yMNy53/qC2eMPWbAgp8/+7jjwKbbz5e+1FV98u628+8bkHYiFusibygPwuSK1CFveX6ESjH2A+sPAWBBoJ8jjNk899FcEuAfQaVIEuCfgO2pWBLgjoEfdDNpyBGReLAifpMfwICCjbnsI9aNt98ic9Aete9oNEddLPycdutKBGe6gfVuDEQiwjNF3d23NwxZNfFb006zyjA9scI0v09yjfECAKctZOoQ5n6Y+QCxZE75GWcf87pb7trCIJXAIzew5fkYzTHVDXyAtkN88V09PVwNxZwy4hZYRZYg+7TaJvBuLU10LMN437L16fmKRXloOpSbvMFzymWsUvOzPQ8xzXH+US+8se/bSxDU867vyU6+xFfdvTipdcgbYXaH3WIDn/q8vm+33hMS6vPyv35DkBs9TMiQBr377kuQ88vKI6NFZARs9jdukH1dqcy2k6hIBHiG4RVWLAPcEfEfNigB3BPSom/GUJwgMCJ2cscG9yc/Zjbr9odWPRZfxQxLABNdvF0r5HTKBhcf/B36QDA+A08d4sPpC0rOstYzBz+bSZ8YQGxUYguE5o8vG5WeOh5xXtGwMk/h9mgCPaoxlQri3GxPZ+cQTT0zIoBPbMGJueA4SARlCDBlm/aG4CskwfWI94iJ9443zFiuUeyhqytLH1MUpFoes+uuS57p9nbT7IGO3/sdPLASBSsa3JyDU0Zf+brJnj2re+8LS3Z/TrtI897zPs7yb5l77l2YHrt1r6Z2dtVWf/03b/hufNdtnT4BMj/zMwHCZvvQDZi96y1536p3bbeYDZyZn9EOPBxQOXLR9129dvOg8chFGvL94j4UX72I8mWLcwPvCf+DtigD3NEEiwD0B31GzIsAdAT3qZsKzgFgSH9y+22aeebbN3PAtW7bt4VE3P9j6IY0oA7LSm/DSd0uDD8DdtfoYEKSWeQwt2WE/GAtuZCg1qqRrgdx7oCPIAx8IBRYVxo+QxU93c6NuypRZ5PrAaFrbZL48VUxZAJq6GGF5m5eT59OmpIko/0OQRZkCWeX8fdbl54M5S0x5VyzlWW6pCxdoLH6sPT+X6HUTcZp70/liPe951SA+efjgpklfaQtPiLr1sq/E5HatO0+TfB9KDva/ra//8l5ix4D3WDHJb+sW4bpRuoeIH2vbSWfoicQezLGlvGdtyVnfO280O/S4ebIbpD1KxuyW4DQpnr3XbM3PJGeE117y28n6B1uey+Rdw1zsUUDMXHh2Lny8Q1BMpI8x8EwxZ8o2UHvliQDXhq7ZjSLAzfAb+t0iwEOfocj+8dLEisJPCPCOp55t9qI328qv/5mtuOLiyFomqxg4IMQX5c9F2EJghVhCQNM5C7tCBKsGAgdunvQXwQOBIS1MIGTQzyqBv9LWXHedpR208x6UxMePdZy+4GKtc1pdrYD4dvzsOkHgylzaY2s96qijEoUHboo7H3+47TzrXXbUlZ+zVbN3JGuR72gXCw/PFOXDFDVhO5SHJMe66BfVle4/9bJe3crUIWDWPQAAIABJREFUprW1idU33U/2FXfbleUrdhXagiUyJF02M4Pmxeyhh2zmIy9IKkNJwp5ZJV5EfC+GVbLIhXju1Z8yO/Qpi8luVu5fH1JIjPlfmCMYL6M95dzdedd+T7TV//HNtu2rFywJtJVGKS9dn3uvZJHjYSE92N6IAPc0NSLAPQHfUbMiwB0B3UUzWDDcerh9+WpbftqLbPm1X59KC7C7UoYEErKAhSErEjZniLGwEdXWFQldzBltOMn0fKlYwSC4ROVNp5ppO4UEZAUiBSa06xY72pHg3tUKqNdOTL7fmJpRuvjZc8pvO/fTtvvgY22f2U12yFf/MLHqQDb8nD0kuMiawzrmGYpJqwSR8fUe01fKYKnGXZNnu879We2gDIJoEBuAfaCteACeZ9mPKyS5k/fkUQUnPkOORh87J1XLzZ3xOrOff5Xtd/Ul9oQrvrCQE3f9+vVJMLXwgoTtPPN3bcVlH1tEwlgHBE6bhquI8C8KkOWkNrT2ZlmDQ9D2lIX8LgTT2xN9evtZ59uuE55jy6/7lq265PxcqD0QZdaxHN65KFWz3KOnYe5aGKMIcAsg1qlCBLgOauNzjwjw+MxVaU/dgtiW8Fba4EALIBgjZELuEGg9N7KnhoJcglV4VglBlPJYlHBHS59jGtVQXWBgziAitOvuzUXRT8M0E1l9Y6zUU9WCSz8QtsBIeRpHNevt1NtW8La0FdUtwCsueXcSWOfkk09OlCF4SUCEcVeGAKefEU+lhJV206ZNmYqm9MjrWHA9oJsraMI6ILJYq8tSqVGOsXjAN/rOs9h23ASeYerFpRQCwCft1YEXRlbE/phVwvPtpIV22ooGXtY2CkZwZs/0T7ptj1KeVVcYqGnmz3/dDp3ZkRRLPBB27ixrPvl+GqJDs45ZG2CMgvuaa65Z8NDw53ORG3QecmnLb1a5FFHG3fyJ6463u079T7b8Gx8ttACzH+QdmUGpykdeRVHLOquQCHBt6JrdKALcDL+h3y0CPPQZqtC/tnKCVmhy8EURdHnxZgmGHgAL4cK/5yXO2UIIJK6ebbmYpoHyM7eh9SdNBooEvDClRLpuT2shF+bBL8/GHUSwZK3WDXZHMCksybgzO/HwdXjMMcckljksoumUQLSJdZMLZYkHm+O8Lv9nXSMUF+Ud5znjvqrKJgijK7P4nf6jEIJkuhs/fcb1Mn0xRk8zFn4Hjn6muPGkBBVgNWcPuv7663OrZe78/Gds204u3c3ccehC+en5yfEc4Z3jQZroU2gBzMo57uMLSduyO39kqz/3utihL5TDQ4b1NmkXc8lzyfuJD89ReIxn7ve/YbZ6xmzbnM188Jdt7ve+ZvaYfedhCF2ai/520MLzwP4/6tgT8TnmWIAHTsybB55PlKk8e4xNsSUqr1gR4MqQtXODCHA7OA61FhHgoc5MjX7xckEo6UIIqtG9Qd/iVmNPy4LgitDK31icPHgUP/OsLKE1t2iwCDN+DtfLIfBg0QqjfRYRYOrAbTUdDIt6Eaa9DUh8bLToQU+QOpeLAESD577ORTRn3EhZf5BcyC+EFhfmm266KSG/rLOs/LxZwjGkGRdW1hx1Olmib7hBQjR5hiB91Bt7TjgcmwfVoW/uAkv/qMv76UotFFhenmcFgpwVUbcta3rWHPBcF6VFquPKzvPNWCAW7j3ShfWXtcGnyFIesw8uuO3u3Gkr//zchRQ+VdbwJBJg1iqW3qIAX0vSHG3fbrZq1dJzwE6A00Gv0meEs6zDe4j0/j/4a5v71mdyp8WPymQ9U34Tz7t/n06XVmW+p7isCHBPky8C3BPwHTUrAtwR0F00MyorRhd976MNhGVcEt3lGQ21v6wRiCEHWBgoE16QjSzrEmWKhL+iaJpZ59kgEJCEdPu0gUCN9QVtursi0nc/xwvx8POGfWCrNrtDoA6BSveOlEUcF8Aa6wQY62/RhVDLuvX8nrhqsv7C9QqRDs+64hrNBdH2s8VVkWKfg/wQGZfnADJI37OC3UEOId48rzxLecHjslKPVe2Xl2dfcYsXe0qZxSuM3RDTJsTX9y7KQ3zBedQXY2HOynKUx/QjrTxZ2Bu5eY/1Me/sr9c/aQR4nwOPsO3P+R3b9ndLA06FWGx/1WfmozLPT/5euNPW3/RE5BHddHToVGAsXKHzLk/dFzPnKlMbARHg2tA1u1EEuBl+Q79bBHjoM1Shf54LuMItU100/fJ2SwrEEUEZQR4SmnbxohwW4jy307QroLs773rHP2fmU8yy/vrEcN7SSQP/Q5iG3DLX7p6d7gffF7meTvWkT+Dgm5whdTiOPvroJQHX8qByKyCE6KSTTloIvIenRLhW0/dDerEyu5KItY2lK/bMp9cH4Tv22GMXlENYqrMsUDwXKAdcWeXWKj8WAZGGFDexoGdhFPP8oaziok/0kTks8txB6UZ/3fMDxVxIrPmuzeBdeXOPogG86G+dC8sx/eZn6FafTucD6SoLwDQJBBgcUGRybf0Pb7BH1v1vmQGnsrDIDX4VTkyBdTcplpUaif/7fXuCYWXNNfMYk9qIfaHIA6LOOpqye0SAe5pwEeCegO+oWRHgjoDuopk2rRhd9NfbmFt/htlL/8js4j+2mZu/00nTaVKLsIxQhxDpl7/g/UwkhNOFdT9nW+Zuzj1YeCh329kXLuRTPOqr5yXCr+fjxVU56+J7+opljf7wOwKFR252l1Lu9aA+WJ+KUj91ArAa6QyBphZggq85qY3pdHj+nOfFXW85l8uzgnUydLtnDXt0ZZRKYeqappF8Wf9YqvMUPll5vXmGKc93PPd5ebdjsEiXYdxlZ+9j9w7G5q7tEA3myFPNcMwh7T7u6XLK9qQ64/J7wM5zMsfWg7LO99W7VzzBHnz5h+ZddmfvtZkLX5ZUk7YAr7j4LbbznD8xu2ejrbr0T5IATHO//Cazp73E7Iqv2Mw3PpScA8caHY7Xo+SXWd1j+95muXR+aD8f7iSyyOKd992SPMBOass67lbedPmA+CZVXP63NnPZR5bUxnsI1/6Y4zXsL6zbIc5JGUwD+V4EuKeJEAHuCfiOmhUB7gjoLprhheqWhS7aa6uNuTd/02zlarMd22zmgue2VW1hPR6RlRc5AjzYIUiF5+ggnRDTkAAjMPu5W4TQsuBDWLlIrVRXKHWBziMyI0giGHt9kF4EY35mndPsBEw10isCRS75MR1jHSOEF53jC+sJI766Usgj0ENIWLNpQopyDivzv//7vy+y+JID2N2Y81yudxz4FNv58vfaii++3VbefeNCV3gO6bO7+2eNNXQPhryliT7PkQe9i8GqrAzPKWQ/76I9iGpMrIasGAB554k9enubZL5srLHfr1u3LrG0oziZO+8iswP2uGtDtu7cYDP/z2/YknOtu3ebrVixKPfvosjRBW65WFU9iGH6+Ehsn9suhxLUFZgQQuaLdevkd+5ZrzJ79uvMvv1pm/nXz9vcS99n9pSfN/vxbWZPOsLsX//GZr79Cdu9+rG27bVfmFekzj1i9sjc/O95ltz0QMqiQTsx3nydzXzhvEwYeM+wDmOUrIwTDyf2BJ6LdMyKtnGe0PpEgHuaWBHgnoDvqFkR4I6A7qIZzoF1EQil7bH0YQFmDC5MZlmJ+D5NgF3I58XPCz1P2PQUKyeccEJCfqteCNF8INcIcG51Dl3cIQ1+FjBGEKnaB5UfHwTaUHw95SlPSSJBQyhZb/zMc3N1K2QaIbdE50WOTbvzh/fjVotiB5KUvhZI00+22swnzln4uuhMb1gHhNOfpTyLlZPSpvtnUURc6kbZ4H0oizCfRYAhEen0ZCjoXJE3xFUbxj5ILLuv/vjeoE27dtnqP3uhbfu9v196njUM1hQObI8FuGysToRZU32ndCvzdEiT+0UKgT044Ba+82kvtR3P/729Q9+53WzFqkUuy4tw8TPCoWU3K/IzNwXRn4uwzVqDeeVRyLM/ccUEzCqb0yn9XgS4p4kXAe4J+I6aFQHuCOgumuFMlK44BNBiu8UpiwDzssb6CxlAOPb0DUXRdj3oFALC6aefviR1TFnPaIsPVoGsc5Hu4k7fEeqqnp0sa1/fjycCWFjqehiEI4YEX3311cn6Yy1DGiGlkNOwftYdazxtMfYI5CjiIByhqzPtYAXDgyFLKeQpldLplrhv55OPtV3nvM8O+vaH7DH3b0wUU6x/ns82LUoQVCzYdc+3OpZlUZ9jPEeoK4sAZ5F+9qfTTjvNtmzZEmWV63qVL7Hu0gEiF3P29b6ttvIHX1hM6oo66BbKG79rMxe/Lbdk6DJ8+AEzlffitjFKn38P61/k/s0Xs7Nmd1+32ALM/7D08vOAA+at41wxxNYbC92e+V9OdOiioFd4ULAGY6/0+XreXewLKHDZXzz7Qmx9U1pOBLiniRcB7gn4jpoVAe4I6FE3gzUB1ypdcQiErop55wR5OfOSJvIpAjov/hXPfLnZi95sdukFtvqaS5OXObj7uUd+IpByPs9zlcb0CCuuB97KK099WIHlShaD6PiVYR3yHFc5K1cnh2wWMm6V9IjOYZms2ALu5pyOquznMiHBfBeSYA/klEVyibhOeSxGfjaePkCq+UB23Z2V5wurEmR9FMF1/Dmrq1Rg/hhLnks5SgCs5WVXFgFmvNRL39iPwMpTOOFivmHDhmTPQpnhwZVoh/7ceONe9/Gyttv8fkmwJq/83k1mjz/c7Mq/Nzv2l8zI2/zII2YE2Fq2LJugBWSuiKiFQaMO/uePJeuwTyvw3AvfbnbyL5td/Q8287X3LYJ30TnePaSWsUHit//mX5nN7Mln7Rbx73zO7Ixz5+vISmmUJrfpyUwT4Z07zFaszAzQGN7K2kO5U+UqW+tF+eyrtDPhZUWAe5pgEeCegO+oWRHgjoAedTN5bryjbndc6w8jQIduWlnj4cW/++ifNXvFn5itnFkQFHb+0TMTYoxgGbpWIniGwYFiMArPVRaVx0qls74xiI5PGeYUxYZHK4bghHMcrq307yhfYlLtcB8kABLJ+qQt9gyIFMqXojPARXtLVhoUDy61fv36pD3IF4TP+5oVKRrSibU5Ni9wkatxGzNPX5/61KcmZNKjuF911VVRVVMeEgw2WVfsXl2UB5x6sX4zf+xfriggKjftsyeFygHO4dInokc3vbDkp4lQUYo32nvcyb9o97zg/HliywXRhfAG7r1hv+aOe67ZWe/ITvNDwQWL6N02c+ErklvnTn+F2ZnnmT36kBmuwT++ZSGAVp5bflMsYu7nWZt9w9fyx/r6L89bdzn37Pjs3Dlv5U1baRML8P7zhDXrKkuFFN5D2R1ztuxL77Tdzz7P7MlHmm3daDOf20Ou95RlPXnwuth3D3sMa5JnuuhcetteHDHzMYZlRIB7mjQR4J6A76hZEeCOgB51M22n8hh1f/uuP3QlLCPAuCze/mt/bbZ6n70uZ48+ZKs++PzkTF9VrXh67KE7dt+4qP1uEXC34bLzoEW9gpDw/Bfl1c1ynYV4I6hiGcs6GxtGGs5z22Xt0m7W/YyJ/MIQYKzIWH4ZL2eBcddNW2892FzZDED4sRzFCuNl9RV97xYssDr55JMTayvtXnnllYXVQhjyCDA3+pnnoqCFZQS46rie8YxnJLf84Ac/qHrrQnksy5s3b07SGHExj8nRj7d+u9CCeMQRRyTz//AfXLbXannFVxZFdU53avuhp9iu/+OD82dcnRA+8hOzuW1ma560pL0lltSf3mMzH/+VpFqwxLuhizUTjgOvB1zqNz31N8xOeV5A3Gdt5sKzl8zDkjF4CchqMvbdi3MAVyG8S188e+didtYOvug37Ic//GGirOLjGQrYO6oetwnTO6WbpS6eDxQyVeutvXDH+0YR4J7mTwS4J+A7alYEuCOgR91MG7lAR93HIdUfukCjpcYCl3UhMHG2evM+a233r35gQWDY9RevtwN+cmtifUm7gVYdZ0ze0Kp1qvz4IFC0/qqMArfjkEhTLy6xCJlYVusES4MAEvTKXZuz+lO0funPMccck3hEkPMXss4n7Y6LhQ7rZBlBqRKBtgp2RWXTykXI/HOe85yFs5Bgc8kllyyqoiwatBdGAeFkMt2HNAGGkODmTN0QiLzUaUVjwapNJO6yi35xtjxcM7SL1Tl0a/fz5xtf8slFKd6oH6WBBwyEREF45l7zGbND1pttudlm/uo3M7uRWH9f8jazr7zPZm74ZlImkxgmQZsyLMBuNf3xRpv5zK8ttAEJL4t0Tp9RzMR6IuThyBphXfBuCHPlZikK5n79s2YHHWV2161m92wwO+nMgPAHlnIaC8/87t5FaKnFQbC8Q0X5f7MiRu/aZQ//51MX3MRdMVOGV9b4GTtWX7DkfvYADy6HsgTlVZ29qGzNTvD3IsA9Ta4IcE/Ad9SsCHBHQI+6GYQjBOC659ZG3b+h1Q8pwHLFVZQ/GcsXlotE6Hzem+yAX3y1Lb/iYtvnso8mwg2Kh6aYV4mqOTQc1Z92EIgNjFSlNc8XjQCK9QtrXdWLte1nct2ala4jJvct90B8SXtEeQLQ8QxiaSLwFAqo66+/vrR7CM4QsCrnpEsrLSkQs7eec8459s1vfnMhBZIH0Ss7o+yKBebK8zF7GqWQAKPM4NwzeGGl42+IM3tYlUBgkMDbbrutFJLjjz8+sfSGZLeOGzHKg+OOOy6KdHunlgTNwu330fvnSWJoAb79Bpv5/G8vGsuCK/b2bbbys/+nrbh/c+lY0wWYC55HrjSBTZelHMoC5tu9IFgvnrc5XT50FU/cnv1KR7xecPG+az5tFO7Qe6I0L9yXvicrqFXYgQJS/NDsrM2d/6zKWGXdgBwCyQVHnlPwYN2yZlHcVAmi1UqHxr8SEeCe5lAEuCfgO2pWBLgjoLtoBgFziHkguxh7nTacACNYZqU/QdjmZX7TTTclFiyEVQRCtNphLt46bS+WS+bzKnJJM94UzfG7391rw5zSrANfn22MKC91UdW6ITSQ1fTzQn8hYpBalEVZro0IwqQGw3KJJRPhmOcJgZh9i+cwj6SjaOJZ5NmjrWuvvTbKfZJnFoJCf2nfo6znpUPKwyNmb33hC19o//Iv/7JAguknbtBFJBjvEsYN6YUcQDLBkflyAoyCgDFkrYeqbtJYbME6jwQztxBrynharKprJCwP/swbdYXX3IveOW/pvP16s8fsZ/bEw5Oct8u+9bG9njZ+A4EFC/L+Nulf2b30HwUpF3PpCgFwxEqKpbhqcK0l55vDoFTpFEVp0lqWxzeS8C4Um5212Xf/XKnnRRlO4fesZ1cChBZkPz5Q9dmr0vaElhUB7mliRYB7Ar6jZkWAOwK6i2bKIi520YdxaoOXM6Qjz4KLsIzwygsdgopgWjW4VRkekAAEA9rhdxHgMsQm83usf6H7Mn+z3orOkcYgwbpCaK9iJSyrl346KcsqC1HLO9vHfZCKLGsiZI7nDLLJxXMJwYBocPY0dN0ti0gLgYPMZZ2x5RmDcMY+a9TFWWZINB9cubMucCHwF+QVIg+5xa27KPAUfWQskCjqZq/xiPNY3MN6stqsSoCpgzbBNlxbtE2uWpQR/CTCNmUgw3VjHHCmm3WX5U68yKU5dOvdMTcfayEgvwS8yjovW7ZO2/7eo5iDFR4MacVGUSCw3asfa7tOer4tv/brtu2Nl+5NYxSOM8uim5z93eMG7WXz8vimB5xj8Q2VCUXPal38eL793SmyWxfFRfeJALcCY/VKRICrYzZOd4gAj9NslfRVBHj4k8kZRoi3fxAQJSQMf95G3cMwly9WVohHVrqg2H6wviBidYkvwj7rsoiA4wXBnpNWIDlxzyLBbkllfCh8+PA/LLyQVfocEi4IoUerhnSEpBWyxt+0z3Pl54fpU1ku3zAfadE5R+qhPrfOQ3440xymOKMPWLWpk+89bRrED+VDWdoy6vZxMVawYT2wDiBZ9IGz3FkXLrhhFOg8cp6+l0BktMkHXEPXaF9/uK3S/zTuZWsQi6mf+U7yvP6nj5itPcVs09U28/+9Ibl9kZvzPZuzLcAQuC+902Zu/k5Zk7nfh/mAlz94T+168m5MUhWdc4HZgUfNF8mIaj13xuvMfv5V89/t2D7vyrx6Zr58SFL3uDhD9pcoCIK6F0WGLhtROuVRSpnA3Jaduy9rIv29RyZn/bgCrmodKr8IARHgnhaECHBPwHfUrAhwR0B30QzCYlFU0S76oDayEUBQevQXX28/ueg9Zj/ZKpiEwCIEsBb6VeesZVgZBLROgCTqCKNSx9QDecOqmz56gVDtgY/CvuEO6+7TuENzVpK+hm6ktIvgzIf+UD8k2c9VQpr9zCXkECLeJIo2dblrddhXT+eUXqqec90tu4yJsboVN038PQhZnqKL8eH+DBn1oxZ14gqAK32pkuoIay+4giH9pA6IC2c4XfmBlZnvsUbnBfyDQDuJB4vw7PCSM70OaF4KJI8o7QSxgQV47pWQ79PM7rzJbM2TbflFb7NVd1zd2u4z96qPmh1x6uL60qTTyauXCi3ebs0N/xfi4xjEWn3DNjgvvc8BudG5y9ZlXZBcEc96oI26e1Hd9ifwPhHgniZVBLgn4DtqVgS4I6BH3QwCE1YDXcNE4JEXv8OWn/w8m7viUnvoc28cZifVq14QcOLijddxa90r9+5KXE6bWHVCazREKCYSbJg6hb5A9lwAhkjRn3CPgkTSjlu5PbgV7rkQydDyjEWS/9GGk18n6xDjrPP7o55Id2/2drCoZuU39u+Lgnd58EIwazqWOsoTrMjg7UoGXJeZdw/GBUmGoIeWecbvJB0lCIQ3j+jkRnAGnA2X28xFf7BouuZe9n6z9XsCMuWQ5LL5XZRCKYx67OS0AakO2557y2XzUZjT5Lasg1WJbYY7s3sc5DW1ctMVtuNxRy5E5w7dyD1P9Si8j8L9Qyn+YhZCaRkR4FKIRlNABHg0uA6lVhHgocxEC/0YRSTZFrqlKszskZX72fYzf8ceuvhPZAHWiliEQDoPdZpcVYELa17TCMnhPlIlfRLjwHXaCRHuzJAk/uckGIKLNTEdjRhBHNdhT5mC9Zf/eYRdrLFYloZycdb35ptvXugO0a1xP85TFoAFJDJLMYHnTpgqpskYcZ9mHnBtjb2YL6zx11xzTaKUgISHVum0WzVlsPIynvQVnnVdtm3++0wL8MYrbea/FysCi87Ulo1t7k3/w2zmsXuLbZszW7lq3hrq5NO/bUCG5879rNnBR8+7MnN59OZ0O2lLb9kAsr4PSLCvo0KFyYP32cxHF+caxiLLs1j3aERZt1lLHgTLy8Yq0crqnuLvRYB7mnwR4J6A76hZEeCOgO6iGSwkQxISuxjzUNtAENx50vNs+TVft+XbH0kEDrmCDXW2+u1XeHaf4FdYPIusiXm9bWuNhRGPSZ9UxUrk+chDoRchHYHdg2dhUfRzs+FYsvIJ+/lWiF0dt+BRzSx9wdoKucdSCkYI/1i0IRl+btitqGXBhrjXU+807TNnlDmH+/3vf7+wKrAFV3f/xvPgRz/60cJ8sxbTkacpz1x6btu5V37C7PATzB6cNZubtWU3/E/bfcZrbeXX/8xWXHHxPAE+8w1mp/+K2eV/azOXfaTp8KLuLzxDSw2plEHLb/i2rbjsY1blnPDCuAIr9SKy3wbpzRhtFPnlvu1zNvOnv7xQA14UzFuV5zkK7FShtByCsiTPdb5O/VN4jwhwT5MuAtwT8B01KwLcEdBdNONn07poS23kI4CA8ujJL7YVL/nPtuvvPmCPufqriUthbNRZYTtdCITnYiEYENk6AmMWgayDJKQOEs56zYrgW1QnxBDixL150YPZp7JSj0HEuCdLQAcjxocFeQgX4yQyNdZRt5iT6gfrPYSfvkKCUWSUuYF6tGUn+22Mjz4885nPTObvlltuyawSK/bGjRuTfkLUwdjPUjMHkCXPTYwlngsFR1jfEvfmu26ylVf9XRLt2C3AbYynah1zp51t9vw3Lpx/tSu+Yva0l1jy84inzgfdyjt/+/UP28yVXy5scseBT7Gdr/3UojpWfPa3bOevfXJvhOeylEbeQoXURq584Bktsv4m7tGf/z2buf3fF8bRVb55lC9hEDoR4Kqrd0l5EeDGENarQAS4Hm7jcpcI8LjMVGQ/FQgrEqgRFFtw2SOf7+ysPeYHf2kP/csXbefD8wFydAmBNAIIipC6MIhTnXOc1FuWGigWfdyNPTBU7D1hOYRzCBMCd14OXL7HzdbHnRc0y+tt00JaZ0xZ94TzxLlZyC8k/sorr0yKY+km325ZFGiv2xUPbfWPeiC1uGeTNzkMTMV3KDnoc16Eae8Ha9T3r/T59EUWz3s3mu1abvakI8zuutVm/vK1bQ6lcl1FLtThXp1UnLIIF6VeWrg3fc8DPzY74MmLIzuX9boG+Q2fG6r3s8D83Hn/nbZ8xUp7+M/Psydu+/Gi1lmbKDVGqYhFmeNn2r3xMu+HMoj0vYkA97QIRIB7Ar6jZkWAOwK6q2YQXj2PZldtNm2nyVmvpm23eX9oDUEY2ef9z04ER3eDbLMt1TU5CKTP7tcNguURV0cp4Mag7sGU8nIBex3huCHcWL0haVkWYBR7WMZxrxzKVaaowHrKfnz55ZdHuZ2SWglyM4rr1FPnIxVfddVVi6oHT5QQ6XRX/C9r/hhzktpoz5GOuRe8zeyU55ld/Q8287X3LTrvG+abHcWY2qpz25HPtN2/+oGlJJgGMs4HL7F670lftOLit9jOV3/cbNWeoFhVIzcXDGjB7fz8Z9maD/9okfXXXaJnL3q32T9/Pqklnb7Lq2ZeiRFQN7dzGebpNQzpxvtJVyMERIAbwVf/ZhHg+tiNw50iwOMwSxX7OApLQsUuVCoeWhHGRWjKGiBEfteaNclXD8zO2uM/9qJEiBQBrrQcpq5wWmisQ4BHldKkbDI8eFMYxAlSWxsKAAAgAElEQVRX3qL8wV4nVl3cb+k7liP+3r3+52zXOe+z2c/8jtkNe/O/QrwIuoTleChngcsIMOOkrxCAPEt4iG+4DggUFrp7g1H6PG7Z3KS/h+yedNJJiVt2mCopPQ7mBOUEyoi8NrPW6EK+WxoegAW4Cj5LSHCYlmj2Ppu58FcWqltiAd61y1Z9/OXJ+eElZ4A9OFYDMrxAcAmwde3Xbc0v/GrSF3eBTsjxG49d6F/WEQOP2s16ZP6rHm2IxTKMH4Dyl2d2VAG3Yvs0AeVEgHuaRBHgnoDvqFkR4I6A7rIZXnK8BHFxG5LFJA+DSbEA+/gQSAh4BRHg5V9mCetybait4SEQEh+eVwRbzs1Vubr0NAhzBYd9xHIL2WP9x6Rhcm8VT5mUWK7f8b9s+ep9bNfcwzb75sX5VcEGrJrk/K2CaVlZ3J7TEZKz7iECdNr9OKsc+zXKS4gvFryQfHI2nO+5iPDcxMr/sz/7s/a9730vqYt6najwNxhDvmkDYkwQtKz9yyN+h0Rq3BWZC++h3bvNfvKT+fRBewJcLRDc1395/v/zDHTvNO6xAi/cU7Z4anyfFfxqgRz/v283+9cvJrWeeOKJSTAznhOUGUceeWTyPgqjw7cRLT49hLTiHStzjOKnBhTTdosIcE8zLgLcE/AdNSsC3BHQXTeTBFY58jhb9vw3Vo5u2XVfJ609JwIQGQkAkza77Y8njJpa1fqLAAzB6lrRAklysu6IVCXhHjDLLVmM45Ejnm6Pe+1HbPbPf3eRBZg2IL+Qw74swBAKLFz89Jy9CPllFi7IKgqNsnLMPZY6CGdRGiPOWEKQIadldfrcYDnHJZv1wpngf/qnf0q+chLP94wLfKmXC8LN/yFLEF7a5X72NMZDOiTOOPu1YAH+7udt5jufbv9BaaHGKsrW0NV5+XXfsl0P3jcfzTpNfJ0MVzjPW2coRXl/01Zg6mfutp3+cjvgnHdZQtUDS3adAHdhn91jAxdnnl8CX6HQCi9Id+z6rIPHFN0jAtzTZIsA9wR8R82KAHcEdB/NPOH1nzQ78bnGy3vVJef30YWpbHMoZzGnEvwxHDREA4KBUMpPrIUIlPxedjUVMhFkEZRp262KtAvB4uOBcyiXdTYXUgoZxn25TpRZtzgiRMfcDwmjn7RXmAO1DLiI77GeeVo5t3pfd911CQHkb6IogxX/i7nAknPOeXmaPYVUTF2Ugch6lOmie5ycOKmmz5BhLp9jz18cukbzPaTcideWLVuScRP9mgBf//iP/1jq3VKFcMaOu0q5dF7iMit1Et35lf/VbGafvTl9wwazIjt77l//6eUbuD1njrGAYGcRYOpYdF541y4Ljxg1sQLzXODe7BHMs7wyYp7nKnM5xWVFgHuafBHgnoDvqFkR4I6A7qOZlU84xA54xbts5WUfr5TfsI++TmKbCJwxrqCTOHaNqRkC6XN8kC73JnAiiqIlj0zROnVAdiCL/HRC7XVBICGv4VnCkFj5CLDs0k5RJHOIFNaeqjlG3ZqL+yTEFitw0UU52kD4hnzz8cvz8WLFZMzpC8UCGDBe2jr66KMXioT95nv+pi98wHHt2rWJtTPtegxB5DmvcsyBshBh8KJu+spPLGgxSo9wXLi3Uk+R9b3orLLnaM7D/OSTT04w8LXnKZ3ABwswfQ6twOl6yghns6ek+O7wrK7nJS4j5HPnXTQfydmvkHSG53mDM8LL//oNSWnOri+/6G2268Xv3us+nXyxa7HVeASDziPAK8/4Ndv/ZW+fbzF1lpk1WPWohXfdvQlYu1keGawPXPj1/mtlskWAW4GxeiUiwNUxG6c7RIDHabZq9DUdTKVGFbqlBgJYVBAu5AJdAzzdkiCAZRbrHYJqGTEMIctKrRQLaZYLdlN3yZi28yzMefcSpInzpxBixutBwLw8lmU+1IsQDvkNcytz3wknnGDk7i27sLQWlYs9C1zWTpPv81zniSoNDnfccUfl6iHO7GNFwbfyokh7Y70S4Ld+e+EM7+o/e2GSl3jn4w+3nWe9y1Zc8m5bcf/mJZgsye8bg9rOnWa7dpqtWm22fZvZipWLcwxTR9vW4D39WjgDfO8dZu95TmZvWQOhosgLVT2y4PehpCEqe9GFgiomEF4MvCqjNEh9rQER4L6Q76ZdEeBucO61FQKdIEzr6gYBCINHv2wSrKab3qqVISOA8BorSGJFROHFp+6FizGBrEKLZl8RpmPGgGAPSXMX3vAed8vMss5i2eQZ5VN2lZ3LZn+FhENG/PxsWZ1tf5/uI/OIdQ4lXBXlSdivI444otC662WPOeYY27hxY+YclFlc28YhrC+LfG8799O2++BjbdmdP7LVn3tdZvOlOYL9Lie1acsw3+8JnpUUHSH5nf3Gp8wu/WAhjFj5URiljwzUJcC4P2cF19y13xNt+6s+abbmSWa3/MDu++irRzm901S3LMA9zbYIcE/Ad9SsCHBHQPfZTFZahD77M+lte1oXXDJlAZ702R7d+Dw4VjqKcDpHa2jtbNobLKO41KZdq7Gg1iVSTfvU9v3g52dgy+rGgkxgKs6/ll0xaZHK6qj7PeeRmTN3h8ZK1zT9WhXLNgQLd2iUL1iMOV/a9TW3/gyzl/6R2cV/bDM3f8fmznzDfNCqy//WZi77SNKdtAU4fY/3ee7Nl5mtXBXnvpx2kx4R4Q3xTFyer7rM7LPnlcKcR1iLzuiyflAusf7dxdl/+jGCdMPbzzrfdp2wxwpNJoQgNVNpJ1WgCAER4J7WhwhwT8B31KwIcEdA992MXKG7m4HYtCfd9UgtjSMCkDS3YmKt8fOiWPaIPswzjbIF62NbVxj5N6yzCzfotsYQUw8W0pggWkR9hvyXRbNlDlBEYD3v43ILMCQUi3QblmhILW7jMVZyxozyhDUKFnVcrpviNvfmb5qtXG22Y5vNXPDcqOry7lmSzzeqtm7O+9KVvDO/Wd1Mr/XEY+LXPmXLjjjRZjdebfbhcxbdFqNQ44xvePaXfWhu9f62+zc/a8vXHGiz13/X7MLXxqKmcsUIiAD3tEJEgHsCvqNmRYA7ArrvZqpGGO27v+PavgfoqRtcZFzHrX63i0DM8wpZ5YxwjHUytncclcBiFBI5hFsId5VAT7Ht9VVu3bp1iYtwmVW7zP3Z+9+n9Zc+xPYzFm9POZUm0p6rOB34iL/9vDBYcD67a++XPGtu0ZgX7rnnDrMD15ptvs5mvnCezT3/LWanvXD+VndnLrLsdhDoyokvP2cverfZP38+ajo5RuFR5lHmMFf7XnBtogAKiTTKC5QYYTojvndvEJ4XP0qFhd+PXCR1zM4mdU3SHhEFbjeFRIC7wXlJKyLAPQHfUbMiwB0BPYRmeLEhWPMSy4qSOoQ+jnMfsBJBfHXud5xncRh9jz233zbxghhu2LBhAQSEWsjvpK1pgvgQQbnIUomFHSt8TKCstuehyipkHLfffnurc5Q3nliijdURQkyarnG48oJ1zZ3zp2brTo8nwh0Mtor1l+6gzIDELorQ/saLbM2RpyxYgJENUKZBiv0ID2SZtrgPD5TQlZr6+nBz7wDeITYhAtzTrIgA9wR8R82KAHcE9NCagQCTLkTBsZrPjCI+N8dQNexFIMb666VjCUkMvuR2hRCGqUsQhouiAMfUO7Qy7HmcmUXYz3JZxrIFrrj+Qv7zLncDxU2askUpqUaJQZvkGzKN5Zb3Q1Z6I0gQ740YF2fOTqfzCo8ShyZ1z73yE2aHn7BgAU7XtUCQ+SJIgZSU6+DMb9gft7ja+c9qMuRF92LZZ9/hmcArgr+xFPOTC7LrxyNYH/wta29r8JdVJAJchtCIvhcBHhGwA6lWBHggE9FXNxBmmkSN7avfQ2lXmvChzMT49wOBk+cxK2VJ3uggGbipVs2/m1UfVlHOfKaJHNZfPBvKzsGOywyceOKJ866fGYGaIL5cMW7ljjnzhvJg8+alaXW6wITx/PCHP2ylKd4FWAOLAmjFEu7TTjvNrrzyylb61Xcl0ZGh04TYI0S3TJST9fv9S8y+8JZiaB6zn8084yyb+8ElZo8+uKSsBy5DKUS0eZ59t/RCcGmH9Y2iiJ+cD9bxns5Xowhw55DPNygC3BPwHTUrAtwR0ENuJi9P4JD7PIS+ifwOYRYmow+c0eP8XdWjCVhscNNtI+gRSBYROYRliGFWyqFxmQUCO61du3aJBROhH7dzSG2W5TNvfG4FRnHBXPThFhqbrqhsjjxnMGMIcyaH97E+URLEYIQ1mfRIQ7m2HflM2/2y99iyL73TVm/8fnS35s56j9lxZyy19Ibpj7y2dEqkEVqHY1yhZ854le17zvn20EXn29x3Fp8ZhtDyPPAe87RGzD0WXv6P+zq/+1nhaMBUsG0ERIDbRjSyPhHgSKDGtJgI8JhOXJvdRqjBJTC0BOPudMstt7TZzETVhctjbH7WiRq4BtM6AjFRV4sabdMNmnOAEFzO/6Uvd2n14EYI0PyOAF2HFPsxDK+Hc4Z16qkyISgZjj/++ISoOlmF9EP4EPhjrZveJgTYXcYhDVjvsZYxlq6utlyNY9cRY8RboIwE8w5BqRDjLt0FVnO//3Wz1fuYbXvEZj74/Ogml0SEziK+YW0dBsSa3XyD2QfPyh9LiQWYecSiixs/F88f65dnc9LO/UdP+PAKigD3NCciwD0B31GzIsAdAT3kZtyFLxQ+EXIQhrmwLum8z94ZRFiWG9iQV/T49M2j6jbpcSxxiW0DQoW7cxjACAs1ltObb745sxr2B56JGOKHRQmLa9rVGzK5devWVty5szrJ2d5nPetZiXDP2VQsnux9EDR3Z+Z/kIEwEFgMbpAFiDB1Y5GnvqLzwzF1xpZpa/6rEGlwO/roo3PXw9Of/vQE4xhX8thxNi3X2AJMB/IiQndEetMYtHEemGcbIsyFUjc27VXT+dD90QiIAEdD1W5BEeB28RxabSLAQ5uRnvqTDryDQIfAiKBz0EEHJb/zPwTUogvh0QNrIQSSDmOSNMlpYtDTdKnZCUEAwRMBNPaiPM9qGKgKC2zRmc3YusNyhx12WBJZmAsrHm2WkZnYgFlYvCHAWRd7BvUkeUXn5nJdceuMCTdlxoXSIRwLhBzyx3X7Sa+wbaedZftee6kd8L2/qExi3SW6yzPBTQkwru1Yr8E7No8x6xDrf54isKolvc58cs88qX3v/O0rV5ld9gmbufxv6la3cN/cC95mdsrzzK7+B7Nbv2921jvigl31QIQTEvyRV5ltuLzWuP0IFPUQ8K6NeAK1OqKb8hAQAe5pbYgA9wR8R82KAHcE9Dg0g+UCosuFlRM3SNyisdQgJGLdIL0FF6Q2y10RITM894UgiFDVV4TUNnFHKGcsIflos37VNV0IhJaX2JF3Rax47ukfbsJVyEyMBamIAIc4oEwjBRECubsaQ7ryBHSPUstzihUr9FphLFgssWJD2nyPghBTzqMVh+6uaz7ygsQazN5V1ZrblJTGrgfKQV7Zm6so51BouNsrmKKorHKFCpKs+8CZekcdBXrBrdk7sWuXzbz/2VWGkll2kduz7TZbPv9eXLhiiG6Zq3TjXgbdeWjWZv/wGbVq9Hc63hs61lMLwlHfJAI8aoRz6hcB7gn4jpoVAe4I6HFoJhRM08IU7tAITX5BlJ0sh2NzV8DwfwjQRYFVxgEb+ojgnBccZlzGoH4OBwEsb05CYnsFsdi0aVNs8UblnPhWJXMoiNg/IED8TB+fCPOJFnUwi3hDfqkTUupRqVHQQdjD/cjPKaO443PUUUclFm36Eo7HrZ9+TnXu2eeZPesVZv/6Nzbz7U8k3YNYVyXCBKbChTzrLHWjSSm4uYyU+q0oOsGhKqkPmy5rC8yZD+YQa7vPR9tjTyzAv/qBvdbZ7dts5k+f26iZHQc+xXa+5uNmq2fM7rvXbM3j46y/6VZjSHKjns7fnFiA/+1Ss796U63amCe8tuT6XAu+Lm4SAe4C5Yw2RIB7Ar6jZkWAOwJ6HJrxiLK4PfNSDd2deUHiCln3QsvsVuW6dbR5H9Zrd/FmvDFXFQtLTH0qM90IsP4gIzEXVj4UUDyToyIT6X5AXpzENjnzDlnFsgR5hXDichkT7bqMeLsnBvvWUu4xn74lfLYZD/2gfDge93Ips4JWJcJVLOcxa6CoDBZu1lJMwKmm/crKF13UN+YR62J4przpeMP7537+XLMzzjXbucOWffFtlSI8Z/Vj7ryLzA548ny+X64RRnJuC4eYiNBttaV6OkdABLhzyOcbjJMMe+qcmm2MgAhwYwgnqwKipCLocSFsh+6+kNgsYTMWgaaCV2w76XJYpRHkGUvoSsnfbjVCIMcCzs8wv2dY16iD9NQdn+4bXwT87GneCFA6YeHEWtf1MQLyy2JtbsOKicURpVPoRVI2a2UEuOx+nvuQaFOfB70K7+XYBgQy1iLqgcvK3HvZ7yB9HJtgDsP9pqzvVb+vknKoyT7M2V/WYVVPGDBmrT/wwAPJHFBHG4Q4sda+/L224otvt5V331gVtszySZ2v/VR+wKtWWmm3EhHgdvEcWG0iwD1NiAhwT8B31KwIcEdAj0szCIK4+yGkpKMdI8SEqZKqjgkLBcF6/OwwbeAqCDn19AtV6vSAM9yDAODknN8RuMPgQvyNwFUUkAsC7OOmTty+sViFF67cXRORKpio7PggUORVwdpjPULK+nJNbEpAm85EE6JG2zynrszj7zQBRrnA91WCP/mY8iImswetW7cumTOfOwILuTWaPTDW4yQWP+rEqo6igr00i8h7MEP2MxSbMdG60+3HWsqz+g0mnLt2hSrvEpStZamU8jBISOq5/81s2fJ5ovrAfTbzsbMXis+d+Qaz03/F7PK/tZnLPhIL5d773/rtdghwnht0y+7RIsCVp3icbhAB7mm2RIB7Ar6jZkWAOwJ6nJrBSoNwgtCEUIXWnqtO0J70uHGfoz7qhfx6uhEEOITHKtFsIegIcrz8IbZ+7gyyCzlmDJAMyrgVuIr7KPWhDAitSAjVnj90nOZUfR0WAjxbWNPSrsA8B5ADnoM2LK9NRj3uBBiFF1g6xmAOEfN9A++PWKtvGkfIORd7i1u1mS/m76YXfMRszRrDgTbZK85/1sLtTZWIefPpZ8PZ77LOlbv3St0AfuyFnPvNI6zbDz3Fdp3zPlt+0dts1R1XL+omnkPgnFY+UifvA1zPqyp5FtyUvSUI5ZfeaTM3fyf5TxjE6oD/+r8nmQwg/rHKy4X7qz5AIbF1F2rqqONGXYEkiwBXnaixKi8C3NN0iQD3BHxHzYoAdwT0uDUD2cVC4ufl3OWtatqWrHETBAdyunnz5kVfI3AjKHlwmyLMILmhNTmvLEKuB+HBglHVdY/7GXNoXS5LBTVuc63+dosAa4lnK/RQ4JmAPHE2tcl52zZHAmnAelmXNDXtS1MCDo54ljghZB9wJRlEtA1FFkTYXXrdc+bqsy9cIDxpYhLGWWiKT3i/Ky3J2T6Kq8zFeu6Nf2e2z/5mj/zUZj784kVdKAuYxfl2cKmyry6yADvB3LHNZi54bvJs/fQXftseOv55ts8P/4cdePnnkjWMR0VsfufaBDgL/ApEts7cJWvsW581+8r/Xed23TN8BESAe5ojEeCegO+oWRHgjoAex2awnCBMQAI9vQm/I+hlRYBuY4xlghYafKwukOSu8hWG6aEYI5alGJLeBh6qY7IQ4LnhmUpbfolSTMqfoV1NSWiT8TRtG2LA+VsUX3x4Zj1HeZN+pe+FvOFa7G7FIXnKssyNygqMoo7xEXW5zb0Rko+Vtsh7psgC7ArPInd+XNE9iFfVvPFz688we+kfmV38x7bukQ2ZGQd47ni33HLLLUkqP5QfRXv43Bu+ZrbvvtUCYYVBs9KW4DoW4IhFmqyvlJdBxG0qMl4IiAD3NF8iwD0B31GzIsAdAT2uzXjgK6wnWIO4EN4hwU0CYuXh4bmGs6KZ9nH+ljF6nkTvM+56ypc4riu6+35jIYSY8MFSl/XcNCV7oxpV03O4TfrVBiZYgCFeHrwJ/EPLe5P+5d27EJUYV9zbrrOHPvTSRUVHZQWmEdbaMcccYzfeWC0gVBj4L/SUQVnD3t/Gfsc7g2BgBA/LI7mel5n3AO36e4f5g7Bi1cdtOvRKQCGJYpR9mu/z3JxpH88j9m+eRc8FPff6Lycu6zY7azMXnm0Lf0NaIbI7d5itWhwLYsncp628I7b6hu3L/XkUT/Gg6hQB7mk6RIB7Ar6jZkWAOwJ63JpBAMGqgduYW3uxArjggqCFoIR2v+3Lz+2FUUJDAt52e2X14ZqK4OZXn30p66u+HxYCPEPHH398qccEzxh5aod2tUFC646prbaJOQAp8qMQEKF0cLu6fSy7Ly+4H/MNGWdfiUkJVdZO+H1V3ELyizsy/aF/ECvONbftkk//UAKw3mmLTxiUi3eOn3/3VGHMF++eDS/++DxZ/emsrf7zV9qybQ/bCSeckFhBY/pK3AnOMdMGLv5YhB99yz/uDXjlQIYW29h0SG2T3qz6Uv9LyO/F7zf7n39RZYmo7HghIALc03yJAPcEfEfNigB3BPQ4NoOQEBJcBEhcCl37jhadaLVtRzUFq7Tlqc/gU4wxnb4FHDya9TjOrfrcDQIoiVDolF0nn3yyXXPNNWXFOv8+L9pxFx2pSuTy+pRWWEG4sCiOYt/K6gPkDoKW55Ycpp5L35+Xkq0If/faKUvTRB1h/ez37Ol1A4NVWRMQYAgoeyjvE94zeP1k7amUhZjzLF1/zl8snK9e+fU/sxVXXJzcS5kiKzXu1T7vV189H6QLUr3iuP9gsy94x+KIz1mBrGJcmNsmwCWAyv25yoob67IiwD1NnwhwT8B31KwIcEdAj2szED8nf5zTQgsP+XPBCYEJAa5td2gE7zAPcZ00SW1hjhXCzxF6nR7Juq02VM/kIQDBQnCPOS/PGWAEWo4ZxEaq7QKxtkhonb622TYB89JWxlF4r+SNk/2LPrgVOl0O8ulp2PiOfRZXXT70s2r6uVjX9TCVHK7F7LlVIuXXmde8e3B/pj8EKgwDlKFAAjfm7+Hf/NK8BfihWVv71d+35dsfSaqjz7yX0hckl3cJwcGyzvzO/cE3zFbNzLs6u8szZ2ppw0lv+F0REe6QACfk909/xez2H7Y5BaprmAiIAPc0LyLAPQHfUbMiwB0BPQnNuCUUQSRMV4SAz3dtn61bv359Igg54R5VhNOYucF6AJnxq4/zyDH9VJlhIMB6wXU+1tXWyR6WKpRJfa51RxDihdLLz/53jWybBDgrP667+3Y1rnRe9XS7KExYN/wMSWh678nrr8dP4HuUKOERkrx7QgJMGQJFVUkXNArscAvn2eECCwgweKAUqJo3GFKfzjYQ9nnu1z9jdtD6+X+lCa8XjHWBHgUYGXXK8tsR0MNpRgS4p7kQAe4J+I6aFQHuCOhJaAZhxANCISSFLn0ESxlFhFXqxF2R6/rrr6+cxqhN3MNo0JCCPGtOm22qrvFEAPfOtIst5CIv8mzo4s+a5znjzH2f1mA/L9nXDLRJgBlDGMOAv1FOZOXMHdV4PZI+7Xru8qK2UCqihID0xZxbjrX6hm2mXaxph//FkOdR4ZRVL9ZhXKRZkxD9m266KXm+ytzYizBZ8ZRfsIdf+l8Wuz+3QXpHHAFaQa+6XHmDaEsEuKdpEAHuCfiOmhUB7gjoSWnGSWDaAoqWHs19mUBSF4chBJ5ibLh7I4AhTOsSAlkI5BGrqudp3X26L2sw1l+e6S7OhGbh2DYBzoreng5wN8oV7cc4IJl40eTmJH/L12zNIets+daN9rgv/JZt2rQp2VfxsGHv4af/Hu63eXhBaLGesofyk/UZeiakrcC4YjPvfVn+s+aAZ4e+s++iXIp5JsCDcll5rBnf/eddstf9mUaz3Jtjrb8dRoEWAR7lUzrIukWAe5oWEeCegO+oWRHgjoCelGbcbTDtBj0vPyxP3NUQnNu80ueO26y7al1Y5xA6w/OEVetQ+clGAKsdJCJ9VSHAkCTWGkQFa/C1117bi/cDlre+olO3TYD9jHXoueJeLaNS3KXXAIpD9siiPLRrPvyj+ZgKu3bZY//0zEL8QzKMco7YBOyXIeFNE8DQxTgvyFYda/Konmr6yDsFd2Ysv/Qf6zhXOJd4JYVeOXlj4H7mYNPTX2d20pnZxDd2ME6QQwI94rPAzOejG6+2xxx5is3+26Vmf/Wm2N6q3HgiIALc07yJAPcEfEfNigB3BPSkNBOmPkq7QTPGUURYzTq/Nyl4ahyThwAu+1nn4asQ4DT5w/OCq2urHGSBtokonOe+PaoZbJsA009S+qQtr3nzNYpxQdBw473yyiszc+FCfNd86PoFt9wV7/2F1r1NIPsoVrB+83tWAMMhEWAI39FHH2233nprMiX8ne4zQeQ46xvmF84bg6+rubd+uxn5HcUCKajTFRmPzM7avk94wgIWs288tuOeqLmOERAB7hhwb04EuCfgO2pWBLgjoCepGYQnrA0QU4TJdOqKttMjIXgP7UzaJM2nxtIOApAJLFN5Z+GbEGB6iBUMazBnQrPcOtsZRXYtWKTdHRpX4i6uKnjF9gfc2EvCPYv5ggzGROuObaeoHG1xthvXZs+5iyWTD9/t+t1L56MQz87azIVnZ5L2NvrBHs5ejlcPa8v7QN1Dy0vNHKF8xR2ffoYEOCS/nlOZ9UrZkBA7Zo/9uZfb/WecZ7Zs+dLzv1hvH3nQbJ/94l2iY92kG05a4vr8rc/amue81pajCDCTBbghpmNyuwhwTxMlAtwT8B01KwLcEdCT1kxiqVizJhH2s3LitkmCRYAnbfVM5niK8rky4iqErsj6iVsyrq5FeU9HhXCXlsEqeFUZb1YKMz/fn873XaXeKmUhaA8++GBylCLL/Xrt2rUJgaMMF4SZfZBPW8oPSC7EF4s0SjRXEwAAACAASURBVIGu3MCr4BSW5ZkgEBbX7sc9yXae+bu29oaL7f7bbkzeRXgpUAbSiweGl023N/fWfzRbvmI+9RGXpz/yn35DeCY4dHX2NEl+Lz9H6Pbs842785qnv2iB/OsscN2VNHb3iQD3NGUiwD0B31GzIsAdAT2pzeA+iDUlSxjHuoBFrGmO4Kzzxn3hiZAYnjvrqx9qd1gIFKWqYc1AWnlGcMGNucrcf3musMjmCfkxbVQtA8kg2nvoCr1ly5aF54H+eFTlKmPN68eoCDCkEhKcviCEWEO7IoJFyoSi+c8K5lV1LsPy7NPMa1fjrttXLL3uBr39rPNt1wnPscfe+l07/Hv/LfFGYi3GrJm5455r9pK3mX3lfWZnvaM4/VGTwFh1Bxrct5Dv94F7bN+X/qHN7HjU7PSXJn3e/tCsPfCHz2ihFVUxcAREgHuaIBHgnoDvqFkR4I6A/v/bexfg2667vm/pSvL1S9KVbCTLvrpXLyPrYct4kngKk4xbQzMhpTEJaQIEGvIgITStoaG0mQKmHcg4EOq4PEsGWgJNQgiGziRAKAkdShjHGca1JdmSrizdq7dk6V5JluVrWdedz5F+1/tu7X3286y1zzmfNfOf+/jvvX5rf9ba56zv+v3Wb+2qGSZP7GtrOxKIlXgmxVMmVyUEcJzJWc28ishBzCP4mXDhlamHf+9qP/tc6wm0HQNGQizCWocmkuoSwLSG8YiIYi/+JpOyIQwR8CRuqi90cbQTmXkRjiwMEdZLQSyTCAwv5tijnPqImaHjkuRHPA9HqjUtZHV58YfaW3c94exNYdd8tvC5iqBrK3Gk0lztwR4LGHyO17NEz2Vjaj2MMc6fx8N75rWvT19493em8/71B9PRS1+1GmO8A+EJ7mvr7D5gPLjh2Q1P8KP3pnTFNS8Phc4Q8nzW60ub3vfO9Jq//IF08B1/On3xgTvSeW98Szpz6uF06ie/LaXHXtwXbdlpAgrgQt2rAC4EPpNZBXAm0Ptshkkn+7fGlhIh0Hi2mZwy0WRi1RTSzQQabwzeJL3CY3t3++9bd54siz+MD0TNkIIYoV5EZ1fhWJjIlN517dDf8x4w9td5mrmGbRBNop3fEWbLcTRDF4vmFsB4zeMIM3iFmKoyYcErzh0fymqu6+McaPZ6txU+b2DetMd1TDsiMRbPz2d1NWqnLVP0GDtT7qHvWGiK5It48slBcenXvTed9+7vSBf94T9Lr7vt11ah0H3L6e/5nZQuuPDFEOZTJ1M6dOnZkOhbfvWvp9v+7M9+KUSaSkMcP/lgSpe96UUzTV7ivg1ouG7l9X1J+J799SVXrDzAF779T636xvDnCYC371YFcKE+UwAXAp/JrAI4E+h9NsMXNplkx3qBm44v2STPmAQSrhor8dUJYniFI+EK1yByciUH2uSzW/dwAuuyCCNi8QKPOUu3jxc4WotooR3hgR3+FOfegQjjnaXdfcf1uvYi0nlfENJ9F4sIeR3q2YYDXswI0w5bcSxQNZkev+P5qsKc95y2li54hxHo6xZO+P1QPm3PxWc04rIpezmLgCzINP0uBycYsBhJvzLGPvWpT63M8v/0XfXYqCt/9s8OSph4+m/82osJx6r7gKn8+efTm3/529Ldf+qDZxOSrYxWkpPxz7kySVf3dZ86+XBKP/iuZrRf9c3p0J///nTqn/9PKf3+L+XAr43yBBTAhfpAAVwIfCazCuBMoPfdzFQvcFPimjFMmYST6IbJb0ygxtTDPUyWmRQS6olgoD5E81BP11j73leeABPzOKKorTVjPZlDBHDYRjQSbj1lDNJeJuR42oaUPgmyuAYhg0e4q4zlVq8XcYcXun78EdcROlv3smO3dOli2dTuqW1uy2KOp5nPuFwJwuI5+IxmISP2azMmG/vx3X8zXfZ135UOffSfp+d+6ycGYWgUsBHi/MAn0h/9+E+lj33sYy+r8/Tb35PSn3zvuWHTgyx/6eKVN/eeP0zpf/3GkTV4244TUAAX6mAFcCHwmcwqgDOB3nczbXsk+3JhksBewzEZUPHCMXFCpNb33DH5HbtHMdrOBJv9afzJpA3xgceJeoeGvvbl4XXLIMAeWPYdrjtCh3E7RpCOEcBQueqqq1bhsUPHNd5TQrYRp2PGLe8490Xm4rYeghUik2vXnWs8lwBeN1LoF1hVC97XqYn7po7OdQJ4ymdhn3YRKo7YxQ5jiEW9qZ/ffezWrxmy9YVFKCIt+kYrhK2zHuC68Zc8wtf+4jevFoL4POf9iEWUc/YOf/Q30pmXQpO7nhOmfEdUo6FO/b//LKVf+f6uW/39/hJQABfqewVwIfCZzCqAM4HedzMIxAgZHsuCyUffLLpVG0zqEcH1wqQGcTJHwROMDSaOYYsJHO2da5/eHO20jnkJsLBC/05N9NbUqrECmLq4F89ZU7bjJlu0H9E39X3gSJ11e1ertlmQ4nMB0dJ0zncpAYxAR/BN/bwaO9LoB0R4015Wxhp9Olfoc1sb+Rzj8ytCyBnnLPTkLCyQDNk6wBYA+mzsVptrr712FV59+pt+MqWrbkrp/jvSwf/zb60SzUUINmOV0PPwAD/3az+cnvvd/yOl7/5QOnTkphWe+uLJOeHN9b29OYFqa1sJKIAL9ZwCuBD4TGZ3QgCf9x2/kC654Z3p1J0fTumnvjUTOs30JbAuSVDfOriOyRgT9LaM0011MRliMtk0KZpzD13YDu9JtS1MWvE0j/ECDuHjteUIkOmZfZJzlikCmHYgLiORVlP4b7SV9wOx0yfhVtfzsdWB97Sv8Ka+OD6JxSL2myJiWFCi/X3FdFe72n7fFuq9bk/sWFt972vr902EPfdt0ybG9zrbfMYjfod81jNuCNVGrPctjDMiJviM5v7YX9x0P+OUMRn77LmHNp4TlfTWr0mH/soHUzrvwLnn9Sp8+3aJ172cgAK40KhQABcCn8nsTgjgSIJhZsRMo2aEGcLHmBz3LWdD006dSgd/5j1nbxs6CWwT30ysENN9E/L0aTcTKH6oM+rlTyZK8W/aEyKBvyNMaAtjF4/HkAlfnzZ5TR4Cc0Q41Fs6VQBHfXF+b9PkHu8WwnOIYF1HdEqbeS8QWoSy4v1kwagrnHqO3iXku+lzgEUzPMFN0SNz2K3WgYcxkm/xGdfkEW87v3jutjTV1xZFM7ftCLuOz8Ux9fMZjDd4nSf49Df9w5SuujUdeODj6cJf+tsrM7wLsVBZ3wJAtAIMENf33HPP6jO9MVrikivSJf/9b6XzEeGV7NDOTcb0pPe8REABXGgoKIALgc9kdicEcPqOX0iX3fDO9Nyxf5+e+/FvyYROM0MJMHlggtsnRK26x+rg+8/NiMnEo29Y8TrPxZB6hj7ruuvxJNCuulhgwo8YGbP/cs72WdcwAhGyOuyu7quniMk2Mc1eyWpoL1EQQ/cKb0oAR71Dwqi7KXZfse5zoI+Y6rbQfgV9zOdhn8RgJQVwDg8w45AIgKmLkoRv4wlel7W6+v1y4U98QzrwmU+vckTww0JM/dxuwvGri69t23Eu+nsfTRc2HPmnAJ7yluz9vQrgQkNAAVwIfCazuyGAX8rIO/WLMxPzvTbTdJ5uE5A2DzDXxt5aJo3r+jyEZr1+JiORSKZEZzAxw5uAx+Ghhx46pwmIYPaZMdlyPJfonXE2l+wB5olYfIp9rXMdldREag7RnmPvb7XtXRnm8UgjqMaWOFu4uvDHu82Yue222zoT+7EgxmcenwmlIkTaPkvHMqnf15SMbGzdcEYEr9uzfPqbPpjSVW/70vFHL0Ua8f1EXzftPWYcsBCAl5jP7aZtBWePZKo1XgE8tje9L6WkAC40DBTAhcBnMrszAjgTL83MQIAJIROJKQlLaAZeYEIFmRRGAiomPkyAmDQymal7mxEBhBuS+bUuPqc8GrbieZis1vf78ryIECa+sWeMvWcR6sk9eBJpGz+I4HX7Nqe01XvnJzD1mK+mFjFWET1zemg3LS63UQDDnveN6Iu2UGjE6rpM3039h2iMzNhN2a4RWnxmrNt7nfsM9LY3I0T8/G9OWn1+D93v26cdhJQ3RRvdcsst6c4770ynD16Snv/OXzkrgok0iuzb9HV8VkeGf75r+Fw/fPjwykNMn9YXJJoE8Er8fuLfpfQz39an2Z3X8FnDd0Vsm9l0QrTOBnnBpgkogDdNuKV+BXAh8JnMKoAzgdbMywkwOYkjioYkLqnWFGKy6dgSJm2EfMakFpHKJJdjLRDAc3pYsV+dmOBVqk+OsM//hecaDwVtnLstjrX8BOhbxM7cZQ5BWW3T3PXVn3eO+jct0tv6iAR2d9xxR+OvWVjj930L1zMeuhbZQkzV6+UzArGFuJlzAaRv+5uuaxOUU+rk+Qh7HnO8XZfdtqOb+H/ssuhRjzRCALMtgAgdCgsU999//2qxlSPP7r333pWoJkz/7rvvfvle7fd9eOV5ju+jlfidOQEW+5sjvLtkWHwXf38/GwEF8Gwoh1WkAB7Ga9uuVgBvW4/taHtJkkWIWZ/9wX0RMJlhlT68sTE5H3q8Rl97Y67jeecU4mPa4D3TCSiAX2S4jQIYbx+ChtBwEmK1hRmHoGJRjXuaPqsQVngKEUv33Xdf58BCzCBi6l48FsWWljW+Krw6H6zjgsgUvskol6bQdQQp4pToG4RtvSB86dsmjz1tZpxwH3UjjhkzL3uGl0Qwdc8tfqkzxh9/R8SzgGDZaQIK4ELdqwAuBD6TWQVwJtCa6SbAxANPMD9znMF56623pk984hNnk0rF5HxoJunulnvFvhOYuk+0jd8cgrJa99z1VevmvSXsd8jZrU3PndMDjBBCFD344IOrpqzzqEWmZjyhbZ5dnh/GZAruu7AVYbfYR/SyHWKurNxzvpdzJsJijGw62V/VA3z6r/1iSq87nJ5//P702p/7ltU+X6KPHr72q1N6999K6Xd+Mh38D7/cias6Nhk7EQrdNyljp4EeF1Q98W3HePWoxku2h4ACuFBfKYALgc9kVgGcCbRmhhGI0GhCCZvCm/vWFiFtrJQzeWHiincl54Slb1u9bnsJsNhSHacs4DDWpoZ2XnPNNauwy7lKVWzNVWfU09fr2WU3pwCuLwjg/W3y/kWb3/rWt64+P+YqCF7GCmG3CMK+onku+0PqmSMRFu8DAn+ObM9dbed863gnI+sz9l/4vj+yEsAI2GPf9I/P2QNcr/PMa1+fXnj3f5XO/50fX2WKJmQdD3B4/xnz8Z2ySW92tAu7cVwW/6cHuGsU7MTvFcCFulEBXAh8JrMK4EygNTOOQGT0xLtEmGkUJopMHpk0sh+q+juuYaIf5/HGJOHaa69Nn/zkJxs9VNiJxCelMq2OI+RdpQkwmcbbVA9ZJXkOwmbKYssQj22Ip7ZtBPx/TODnZnb06NF04sSJWQRcLgHM5wb7Nevnua7zTs6d7XuJoc5tYwOhzvOPLQjfpvONx9a37j6EL1EZ9DGf5ye/45fTocuPpFOPnUjph79mJSLJ2fD5P/JfpKf/2Le0eoCf/zPvS2du+k/SgTv+Tbrw19+3+o6AAckL4z277rrrVqIYAczY2dQiBgsQbBWK6KhNnGW/ib6wzskEFMCTEY6rQAE8jtu23KUA3paesp2ryQdCF6FRFxuIEH6YlDBBQJA0eXK4H68ye/2oLzIvV0UD+/EIhVzaHjyHwDIJMCllMt1UQhgT0spkHEHL5Jn9lBQmtUycY/zVJ8/UfezYsd4Pzv1tAnhT5+viaSO53NRkTQhfCs/A+7np0uYNX+dVq+6/nNI+npHPmXUZoKfUv6l72SNLdM6QwgIQn6csVuZeXGx7rxizvA/1837rz1X3APN76oRDRAIwbhn/vMdt5wMP4dV2bT0RW44w8jnabR2TCSiAJyMcV4ECeBy3bblLAbwtPWU7OwkwMcOjw8Qm9vRVb+J3CGCyQPcpK8/ByZMK4T6w9vQaIhPYG7kueRsimN8jdvE2IrzYIzpXYdzjlUJUsahTFxkhigk7nTtMk2fH5lTPXuz9n1rPEKZt3nV44a3k3a+XKQKYehGBLBQgfjflKRzCYOi1Y5K9LTFTMRELfB9UF0lPf813p/SO/zylP/y/0sHf/rFWNPQb73J1waeaVIt3fErUR5thPkMQ7vzp3t+hI3err1cAF+o+BXAh8JnMKoAzgdZMHgJ4dG+44YZGz0pb5s9oGRN59nRFRlYmOLfffnuehmtl6wgwEe17PiwTbrx9c2ZsDeFLpEJM5IeETE8Fjn082V1H/fSxs8m9yW32gxWiju0RhLGyeBDH2PBceO5ZNIi93GPOe+ZeRCCfJ1P3hPdhuelrhmaD3qRXdOyz8gxvfvObz8nUHfuE05kziTOB140bsj/HUUQsArHIFXv1ec+nRkO02cbWC3/7V9MrXn84PfP4A+n5H3r3WATetz0EFMCF+koBXAh8JrMK4EygNZOXQDUkGst4bpiw44Gph0ZH9mnETDXhUK5QzLxktDYnAcYOk9+urOVzC1PCLhnjnEVaLTmFJO8Lk/05tgrkbHfwQgAhzggzhyP9yDvPM/EnixaEqxMSTbgp4pXPkDgjts84QlDj1d6ER7CP/U1cM/RM5Jx7f/s+LwsZ/PAeEQb9hcu/PL3wTR9I6eCrU/rcs+n8f/Jd6YLH7lqNCby91YULxgvjgX3EjBXGB9fEQtCmBf+hD9z5YnIvFlb+u7eutvSwULONEQV9+2vPr1MAFxoACuBC4DOZVQBnAq2Z/AQQJ0wOmOiQEIVJAqGWTGqYyCJ2w5vBBLWesGhJ5wXnp6fFvgSY/N54442ryxFHjCOiCKrZguc6IggbhEHiUWwKZ2ZcM6lnC0CT6IqjfKgnQqPZvzjm2J3YcrAua3IfhryD2Eco5Sx9FiVIGkY/0r5IcFTNLlxvL8wjOR+fH7skfKvPOiQZWFdm7Zx9Hrbw3uJNJSqI/n322/9JSpdc8aWmPPVouvKf/o3Vv1nAOH3L16b0p78npX/5I+ngbf9qFYaMCI3tBtWxtOnvjVf+j7+TXv36w+nZTz+QLvrpv7BqA+81Ij3nFoIS/banNhXAhTpeAVwIfCazCuBMoDVTlgATFibs/DBRwGPH/918883pU5/6VGPjNr2SX5aI1ucigEgKLzAeIf7Owgvjp5rMCeGEt2iM2KSteCQZuwjWrlDqL//yL0933XXXyx6x6VglPFmINtocBVHL/zGxxjvLJDs8TLQBAYHQDw8n7xWCYN1e6DbeJby/RIT09czGUWowRwSzoMaiRxS48DsE/C6EOPd5L1jQIVS8Wk7/xQ+mdPRtKR3/WDr4T//rc/jkSGrWp93Va+JYJ/r3U5+9MJ359v/9rGf1/J//9nTkgs+c9eqe/t5/m9KB81M680I6+P7/+Kzw5f2gVDOX59yfyzvHD15p7I79bBnKzuuzElAAZ8X9JWMK4ELgM5lVAGcCrZnlESBZFhN2VuybSnjyltdyW7QkAniSwsMbYbS0D7Fbz/I7RewNuRexjPeRvYjhFWKsx1nYdX6EU+NVjoJAROQi7up7fJlwh9CLc7oRwni9xwhgxDoCCWGfoyB88NgNya4Nt8jkfd999636GzZwoJ/bPkNyPE8pG/WEYG17aBkXTQnFSrU77MaZujEWnvquf/3i/u9Tp9L5P/Gfrfb303beoboHmH6PhdS6AGZckAgrd8ZrxDg/+zgWS4+lDdtXAG8YcFv1CuBC4DOZVQBnAq2Z5RFgIk/Iatv+Tbxgm5zEsGqPSMkd+rm8ntjuFl166aVnz52uis1bbrnlnDBonpJQ/AilJfkS/0Yc1M+irRMhVJP7EFtDCmeUxiQescqe1jn3CsZeWUQlZ2yPLX3CkcfWXb2Pd56FBBJeDS033XTTyqvOooaetheP8GLxJ0qbB3ipC4lxrnFkZa+LdD6f3/KWt5yzABTvDu8UUQ+xpYBoCd5PSunIIT6PaBvt2dUQ/KHv7pZfrwAu1IEK4ELgM5lVAGcCrZllEmBCjNcuMnpGKze5bw3PA54GvEhMwk6cOLEyG/uUl0nKVq0jwDjCi8o4iokxns1PfOIT59xG+OwqgU1KZ73DEUradCYs1/N7EvWMCa9FtN1xxx2JSTEh2FNE6rrnR8BSxmaEziWAuzLBtz0jYeIIpBA3YxateO8RVfy5CwK6Txb0nOHAQz+h+OxlAZQxwTvStLjEIlYIW+qPPd4sXvE+8ZnN53dVaMaZwEPbM/R6+PN50+TxZV89Y3QXxtlQLjt4vQK4UKcqgAuBz2RWAZwJtGaWS4CJRCTGilbOnbkU0YMwoiBqmDwxcbnzzjtXE2ImUkxmqgl0xgie5VLej5YhNDmGCw9R3/OmIUPIMkerNJUhoc9xP+GbjKfq2b9j6unqNcYue6AR7ywA4GEeU4aeA3z6m386pcNvSemBT6aDv/Q3e5scK7SDXZwB25Y3IBoCF7yDkSyJdx+PKZ8DvOPVBGm9G7/AC+lzPrvayiYXEufAQRg3795HPvKRxurqWb/pO8YAx+WxGFLfix+JqObIjN7n+QjN53sE8V49eim+T0yK1Yfi4q9RABfqIgVwIfCZzCqAM4HWzLIJRLhbdQLLGcBTz3NkAoXgpVTPjWRizCSYiQuTyPAKhn08Cl1hsVWi1MePornsOKOP8SoN3QtLFmkEcHihEEtMogkxbcqQHEf4VJ+Wa1m4wTvFntp6SOdY8beOKCHW99xzz+qSaiKgIb0QScMQwVFX1/19z2yt1oOnj/dqjEgPDzeC6aMf/eiKM3wjJJb3PLLxIj5isavpOfhMafL2dz3zEn/P5xZM2sb7ko+S4z0hQoL3bp1QZOsA7xbPyp/hyef/73zm/PTCN/xQOvArfzc9f9//t3pXN/EZHN8ddWHNQiqLbYxHhHDVdnWv/hLHjm3qTUAB3BvVvBcqgOflubTaFMBL6xHbU4RAJESpGmeyjEgdu2eSCQieuLq4xWNGSCuTFmwgvtsmyuFBiiM3mGDFJAjBECG1JDBCPG1yz3KRjtlCo/QZE9OhIhgxSd/Sh4wNwm4Ze4Qt1/u1SWwyRhB5TIIJRa7fM7cHGOGDkIsQTLxiJIgaUq699tpVW5nErwsFr9c51AMMS97lqXsiSZwXWxboK9rNYkVkA+7z7LlCZPu0ZY5rWPBpEvyMXcbglM/QOdrXVgdhwrwTH/vYxzo/47mOfiZsmoVJ+vsLV92aXvhLH0wHDpyfXjj5cHrqB/7ExprL5zyf7/XxG1FDcfzaJsT3xh7KivsSUAD3JTXzdQrgmYEurDoF8MI6xOaUI8CEPjxoMaGdktCkniSGJ4tQT/aVMTFkwrLOg9JEA8HBRKwqrPk/wvFo95j9ieWo76Zl+gfPPv1NP+FBpK/pH/q9KbIgwqC5p/p7/h+PLt6nCKulrqajZUIYNx13NHb/a1sPIQoQr4hCxl8s0vQ98oaFIBLNVfcwzt3GaDsCuG+71o1IvLv07ZSsxptOrpf7jWoLg47PziV6Ivls5nOX92y1VeGVr00H/+ifSac/8uspfe5L2dCDZSQsZNGDRU3K57/rX6XzXn1xOnPmhXTqR/9cSg/cvhH0jLfqMWR1I3OMyY003ErnIqAAnovkwHoUwAOBbdnlCuAt6zCbuzkCEUbMn+zlZHWdSRIZY+shclwTGVDxAvFTL00COLy/XEudiFUmVG2ZqIc8LR4X2sUkjbr1BgyhN/+1iFU8M4QrV/eMtu33bTu7l5YxRuhTJsKMS7zFTQmtEJBcF+HTEUaPMObvUz2gQQkPNyKQtuCdGhpWzHuFQOKnWub2UkfdY8Ozm0YFbcQbN0YE844iuMZGlcw/SqfXGNmU6zXF5xsCLY7kmm5tnhoIuydqhveThZFXvOvb0mu+/n9Iz37o76XT//bnWo0g5hn7lJOXXJ1e+V9+MJ362e9I6VP/YZ6G1WohTLtrQTNC7qvJuqgmtkQ0fTdtpLFWuikCCuBNke2oVwFcCHwmswrgTKA1s10EEJKEyOHdQgAzUef/IuQxsm/GfjCESTUcFFHQFNpMnUxM2OsYYmQO8Qvd2IPI3/Ey5krEsl09m7e1iKX6QgThooT74gmsFkQuXvw+53iuE4qx1zeuYczFmJjr6asLOYxznufBBx9cW33sweWdaUogxM2bEsBz739mcYtFMp4F8d9368GUiJK5+m4T9VQX8fjcqe5bjwSDCLQlfSZVPdfP3vqedMHXfnenAEaQ0u+EsQ89kmwod8YY4rfP2OI7JL5PGJNEkbBHmYWWMQs1Q9vq9RsloADeKN72yhXAhcBnMqsAzgRaM9tHgAkS3jsmuPX9nEzw8CBQEABxvAnCGJFcP1ap+vQIIETwkCRX6+gxyUE0MQFi0oPnkTYgvGjHrmSc3b4RlFZ7IxEA1fHDv/FIEn5JAh4muHiW8Awz4e1zlNA6QVcXwF3ez4gc6MuXcOK6mGHCjQhu8wRHNEWXSMabBR/erzky2CJWaFtbyHjfZ153HXzpvy6vMG1g0WOXvL/BpXqcHIs4VXEY4jj2Ay8iMuXwzenQt/90Ov83fjSdd+vXpjO/81Pp2Tfc3BoCPcc4GVIHn+GMkz6s4hQBruczBeEci69GAw2hvthrFcCFukYBXAh8JrMK4EygNbOdBJi8RdITJtMxMeH/qhNZvGxM8vFgIWq6vLpjPF0xIYq9v/Fv/sReXaQzMcfjhKDyPMhy46++R5LFDzxJiKHqOKgmWOpqbdUDW782BHD9z7Y6QwDXhVlbEq822/EsvCMIf8Q8Y5B6GbMkfutbaHtkSu8KAV1X55j3rG8b69eFJ5z/5/kJra3u5a4Lw7F2lnof/RX9XhXGhLt//i/+w5Sufns6/ak/TM9+8BuLP8Il/+D2dP6Fr1gJzNXn6ZPH08kf/JqtW5zgc5/vpfAA8yz1pItEJk09zaB4h+13AxTAhfpfAVwIfCazCuBMoDWz/QSY4OFhC5byawAAIABJREFUxdNVz+TK5PervuqrVns96/sam558iNhBQLCSHyKXSVt1v3Jb/Yj0mPjs0tEr2ziSYoEEcYi4I6yegkCjb5nE0l99vZ5DBHCXBzg80LSnK3N1k/c3+oO9kQigCHNlIt7Hm72uP8dklq7Wxx5PPGKEgUbExqbHDwzgFNmisbdLZ/8O4vdd/yJddvSWF29BaJ45k5587w2DqtjExYc+cOe5QvHMmfTs976tc7/tJtoypU4+N/hsJ8qBqB++n/ipFsa9C6BTKBe/VwFcqAsUwIXAZzKrAM4EWjO7QQCBgBBt2suGp6PvcSjsEeanur+rbaUesR1HcPSliOcaj3QIkF1MvtOXxRKuQwyySEKiq6ooZCwR9kv/doUHx3Mgfrm2LZS27vntEsBnvWANoPBeM8nGe8k4XCe8hyzq9O0TvOX8VMVk33ur1yFIEQKbFsG8c/AnCqRaCBnvs7d7zLMt+Z6zQvPMmbMC+NR339grtHejz/W+D589dmvlMX3uM+m8v//Vq8VLIg5KhqnHIlSfNvDZURW3RJawhYBCGDr/ruam2ChTK98UAQXwpsh21KsALgQ+k1kFcCbQmtl9ApE4qyv8uY0EAgJhUxXF/B2xXV/VX0cTkcKkpzrZpx5+EDJzZQLe/R6d/wkZI0QQ1Pvz+uuvT8eOHetlsOuooMgyHUI4vJFM9JvGZpsAZkGHsYJXmjGFZxMh2jR+aBMhzn0m7b0esnIR7ebdGHrGcN1OJLTbRBuxBVs41duJZ25fExHVBTBjjc+m9L53Dh0Gm7l+tRf4p9Klv/nD6Y3nPbOKwmChh8/JEmHDvGMsjFLGZPKvbrcg9wPvzpQtBJuBbq0DCSiABwKb63IF8Fwkl1mPAniZ/WKrtpRAhLqObT7iJbzLsbc3wmX71rnOS8cElMn4Pnqj+vLLcV19XzAhjIwdxEFXuOK6/qXtRCEgxpjAx1nTVdGHLbyqVU9TPfSZtjAOEQJdBQ8xorir3V31rPs9oeKw6bO9oKuevp72Ie2FOQL73nvvPec2uCOq+mTyHWJva6595zekQ9/4Q4mJ5BdXUdAHVt7fUwsIg64zJISfhRzGB6HzbGfZdKbnehuq3x9jogbgi1cYzy8LL4h5y9YTUAAX6kIFcCHwmcwqgDOB1sx+EGASVU9CMuXJxxzfgojGG9c26aZ9COAQPVXxg5jhiKYlHVcyhd+S72WSije4Ol66xC3PQ38R1lzfX4sIRSAifLu8nFXbTVmghySPInyb+jbt5YzjdEguNbZ0ec/H1BsZvOvil7rcf5lSeIEjo3FOD3BsWeGzsCujMuOLrSMISD4fWbioZvsfMzb63kM7WRTjJwptjvOTx3ijCcf3c7xvDyz6OgVwoe5RABcCn8msAjgTaM3sBwGEyVyFMFnERd/ESFW7N91008oDgBejXtrCbRE9HGPDRJGwOSZ/TqDm6s3mevAcMvGO0OTqfl0mvxG6Hn9yHV4iQiXx7iBe6SuE71CvPnYRrvVC6DNidkjf9xHMjGU8z21HJUU7GPd44Hg27qkKehZoPvnJT472qGKfRQfqrJ/DPKanES7XXHNN43tGn9WzxY+xsfX3vLTfNqfwrTKL3Ax8rtEn8dlW54rwrQrQ+D2fv5sOI67u3a22izbjheb97hLwWz9OfIA2AgrgQmNDAVwIfCazCuBMoDWz+wQQM0y25ip9REWTLfaQMdFnwlQP9yQJE3tNmyZTTfZyTP7m4rWt9SCiEHqEJsdeVzyH6wRtHDkTInnMs+MhQmhSELv8m3GBAGXCjcjuW7rGKmOSJGCEdRIlUc/KjPBA2IdAWSdOYwGH9439zbS9S8zC9sYbb1wtCvFcPOddd921+veQUn234h1qE/QePzOE7OaurW83wBKish7ez7hngaVeNrWHm3YRPXDLLbesPL1NW13qpw1sjpI1L5iAArhQ5yiAC4HPZFYBnAm0ZnafABNyBEXfTNBdRBBFTMqaPMAIJSZmMfEndC8EC5MmvLf8mxDqalKedSHViIn6fsXwPCJcLJsnQH8PFWVTWoXoDQFcrYf/RwwwQe86Gon7uBYvWdueyXpoN9cjSqtlyJFJCG5Ew6OPProS1tTXds4wdtgWgOe4WvCAcy8CuimsmneZe/FEsxiBl473qs++6CaBNaWfvHcaAd4rFgWrZ6gzdqpbBfgdC5j18c4443NxzoINPrNvuOGG1d523nk+f+P9wSaLRIzLoZEdc7bTuhZBQAFcqBsUwIXAZzKrAM4EWjP7QYAJMyGWc5W2PaEIACZlTKIoCNWmSTzXISxiotd1rmrdk4dgxo4Tsbl6dHvqiUl63wUdQoFZbGGshcCN/cUIzbvvvntjD89iD2MbEY54wG6M+WgDCcGawrp5ZxE+/A7POwtI7BUOL3X8vn60UdvDeOb2xrp5UsX17QaME/q6Gt4ckQpVQ5s4wxkxzgJnFD7Hoy2x4IIwNux5Upfvys0K4EI9qQAuBD6TWQVwJtCa2X0CQwVDHyLXXntt4/5CJvxMoro8E3VBy32EmjZ52yIkNoQ0ogXPCd7fXMlg+jDxmnwE6hP1LsuMV0Qwe5mr4fddIdJd9fb5fVfCuD4JsAhL5Zmrbe9aNKq2bVPhsn2e32u6CfD5F2H/cTUCOMQmXuBD3/8HKbGIeepUOvgz71ktjLB4MkfhOyKysFfrqy90EmWQOwP1HM9nHRshoADeCNbuShXA3Yy2+QoF8Db3nm1fDIE4fqK6qj+1cXgj+GkL7ewSFW3ZgsNDzaSPvzMhi7OGq7biPFmeg0nimGRcUxl4f1kCQwUwrX3b296WPvGJT5z1tsa5pps+kqVLALPwE1my+1LtqrNaj+9IX6plr2sa03z+sdCH97V6dvHB979rFV3Ttce864kioSECvB5iTXuwW30/+DcRCJtOvtXVbn+/CAIK4ELdoAAuBD6TWQVwJtCa2V0C7A0k5LPPXsmhFNgXxmSI/Wr1goc2vFpt56O2TeD5f7wahN7h7WKChuAlfJPQQEQxnrw4Sina0HW8ztDn8/plEyBxVCzqECFQPT4rzhpl3PPDeGLCzj0I4CiM4Xp4foQoR4gn9/cNtW4jxp5exvG6vcRDBC12+l7vnt9lj+Nq6xhrcaRWfQEDz+uZ7/t36RKOJ3vJA8w1Q5NRYaP6WcnnbGR6r5NqW8jcRCh9vV3b02t73VIFcKHuVwAXAp/JrAI4E2jN7CaBpvMbxz4pIpoJfAjpCCMlZO/2229/WbUIizhqpc0mdUT4MgKDCT2igx8EDcK5b4IrbFHXmDMpxzLxvoEEXjpyJu6acvRMPVy0Lgbr/8bDGscqVUOIq0c7IaAjyReCABuMS8QCY5K/j11IQtRQ1kUqdEVN1Gl3CWDeCUSTnrqB43QBlzNW8b5WF14iq3qMIaIXLvgrP5vS1W9PZ1JKp377f0vpX/6Dta1n/L71rW9Nd9xxx2oxkX/zWU1hzPN/Ufi8J+y+KcSaMYUXeK5Fxwi/NrR6AYNvWBMUwMN4zXa1Ang2lIusSAG8yG6xUdtCABF56623rkLkhpyb2vR89YRVcc3NN9+8SiAU2UCxycQcr3CfjMEIAyZZTLyGZNptaiOCedOhrNvS90ts59nwzZcah6g89d4bRjW1fnxMXQxyHBGTaSbW9eO2qgaZ/BNaz3VdWaXDIxzZevs2HPHNvevEL9ESeKKHvKfrBDDv3spjeAZpZNlGAm0h/tW93NV3qs/7xLYSflj44Rx2PndZ6OE9aBr/696dyAZdFc2IaIQz/8dYjiid4E/EBt8V1Xv4HRFDiH3udzvLVo1WBXCh7lIAFwKfyawCOBNozewWAUQoE4rwYCFeEQOs2I8tCFXCR3//93//ZVVQP4UJG169qUJ2TBuZaDFxqk+sxtTlPTMTeMfXpUPf+qOrSqvisc+Eva0leMhuuummsx7ZtjDftkzlUS/iFOHZFgJatx8er76e4Oqe9rZn4b2h9DnCqFpHkwCO/Zp60mYewwWqW5e1n0gX+vhVP/jv0/mvec2qdX3eJ865DqHLuxECle+GIdEziFvs83kbi594pPnuqZ7RzXjkGn4QuHiOyWZeL7wDPG8cu1QAtybHEVAAj+M2+S4F8GSEi67g4kMfuPOpKWFyi346GyeBDRFgBZ6Q5WphIo4ojmNX+F31CKKuppBB98SJE4sUmEzGEL96u7p6sczv657faEWfCXtbixGsjPEu4bouuzLil+ODeF+m7vFta2c1xDqu4R0M7xhihEzODz/88ODOaRLAfF8OETKDjXpDNgKMSfbnti22rI4kuuya9MI3fyCd95pD6dSH3p/S//Nza9vHmI/ztcc+CHbZmlKNVmAcI2IZe+sWh9rGJp5h2kWdXDNXaPXYZ/S+3gQUwL1RzXuhAnhenkur7eLLPnj3U1MmSUt7INsjgU0TaEuiUrfL2aKs2LMa33RGb/36Lk9a7E/jvkg8xKRoU8Ii2hf7jLHFJIp/85nBn0PCSTfdL/tcf1PoMzymLG6uS9wTrBmz7F9sC8Wnjk9+8pOrpEN9PbpD+7FJANfP/CVcG0GBEB9S6nXzLrMP3rI7BLqSGPJZxxaXvoKR+mI/+lhKTUcvXXTRRavF0bGLLxE63eQdHttO78tCQAGcBfPLjSiAC4HPZFYPcCbQmtkdAkwkWOXvmtAjGNn3hQhhP2XXURoI5rYkU5E1t04x/h9bQ/dN9u2RSKIVSbriPkLt2A8ceztNBNSX6PzXzbn3l9Y1RTi0tfrIkSOrsd0kgnlP+N2mxiZtqotUBEs9a/qb3/zm1Vgd2o6qB9gzfucft0upkZBiPt+axgf9zmd4XwGMlxUBPHSsVVlgq75oymIqbYlw6Aizju+hPltTaBvXNT0L32tEMBEmzeImdvgZK7iX0rc70A4FcKFOVAAXAp/JrHuAM4HWzG4QwNvKpL7v5CbCQ5kQrSItTp1aC2JdOOm6G5mwMKmZkkW3qf7Y94uA55kjyy/XsqeN/49wPyZWTNDYt1ZPzLIbvb/gp/je30yHrrzmbANPnXw4pR981+gG422ib/sUQv9Z/KhOlBmL7Fsn+VXfd6WPraZr2KfM3l7azJ9M2ut78WkLCdyGJv+BAeIIQR3nxI5tp/ctm8C6/cCMZz7X+iYAJFIGMdm1faBOBDt8hjJWq9E1fMbyvcNnLNEN1MvpAIhf/i/G/VjCvKMswNYXdZuE+Fgb3jeagAJ4NLppNyqAp/Fb+t0K4KX3kO1bFIGYiPRpFJ4x9gDHyjyeqq79g13HrnTZDeE5NSw69lDSdkQvEyNEDmIAIY/YiWNs6pM8fs8Ejj9NFNTVY8v8Pf3NhLhNvFbPE2XMsr+26lVqy2g+99NihwgFhEkccVQPU2Z8sgDF/km8fIjgtnOzm9rHvYRxu6gzd+8tr751Yf8Rlsxna5yBvS4nAh5bRPCQgpBt+sysRmQwDmMLDON4jpB86uM94dnquS1Y/Gl6Tq5F6HMv3wduhxnS04OuVQAPwjXfxQrg+VgusSYF8BJ7xTYtlgCTBCZJ1URXTY3Fk0voZz0s+OjRo+n+++9fTciZRFFPVTxMFcC0hQkUdXeJYCYs/DCBweuABy0mVvEn9TG5QQjHZCf2/1a9wU0MmvaxLbZjbdjLCNSPQeICxj8eX7xPd955Z2I8MzaqR7kQ9o+Xqk9I5lTsiN5IbsWC03333XfOIhPjmPZW9/7y7iFM+hyJxLuEoFb8Tu2p7bh/Xeg/n9UIVP5k/ISntu3J+LyM7ON9n57P2qYoobqYjmiiTWw7QQDz3RFjnvFfF7d89sOg+h0Q+5N5Bt+Xvj3e6zoFcC9M81+kAJ6f6ZJqVAAvqTdsy1YQiEzPTSFuhNHhOcPz27YizqT98ccfX4VqMkHHMxxnMyI6qnu/qivv/J1JF5OOdVlGY6LGpKnuwWNiwv9xDfVhN0KXaXvs5612RP1YGu7rE9Yax+YwEeQHb4X7ybZiiK8aWU/2xviI/b70IyIYbyrjsTppZ/x37XefgwKT9K/4iq9YecB4LxADvDuMu8h2y1hte19476iDMO2mQj3UbebzOXpre+pgXFePGap/FvJ5yGcZn2mIw7YoF+qgriGlaZ95vR6+N/jcph0sNG1ifMZ+YD4D+M6gXfF91nZ2cvU5aSPv1qbaN4TpDlyrAC7UiQrgQuAzmVUAZwKtmd0iUN8zhTjAs4Qw6NpnGIKSP0NIxnmRTODj90xsqp7YIMikHE/FOhHMpIxJCIKaeqiTSU0kTqn2RoQ2dyX1GtqDtBObsIlCW+K54v+YYBHCagjdUMKbv54xgUcoPD0RZrzOclNW5k21NDzAvAscdYRXOqIzIlR03WINz3fdddelY8eOndNE3omu93hTz2S9ZQn0Ea58hsX4aPvcYmxRV2TNZ2GIvyOe+f/q5yJP3HT0Ef/PQiuLmZRISEhdCMyhUTbcQ6RPRB/RHr4rqguTCHuuiYVS7Mbe43X7pOu9RjQTkSGRSAsR3TeRWNkRsDjrCuBCXaIALgQ+k1kFcCbQmtkdAohPvtxj0sBkgUlDPfNs2xNX93AxIWBSwQSHSTzeKP6Pa5isNIUxM/liUsEkKgRy1Usbk6yqSI76mkRuX4/u0B7sChOv1wdPJniGzw0lvfnrEcEITBZU6mKxaj2SpfVNFjS15WxHIJoCTxuidcwRLyQXQkgQ3hqlfozS1HZ6/3YR4LOT0Pl1i4KMuyFh/tQZmdJDHPP5jkeVzzyiKCLDc5UWi4iRVIvPRwR07M8fulATe335MzzHCOx6pAP2+E6iPbH4NfR7or6dJxJ8waDt2LTtGiXZWqsAzob6XEMK4ELgM5lVAGcCrZndIIDoZNLNBCRClft4xdqenknBFVdcsZoEMcG4++67e4UXR31MgJg8MUlhxX6d0N2GHgjPit7g5fUW+9q7Jvyct7tOIM/9VEzeaRMCvc9Z2232id5AoLB1YaiomPuZrG8ZBNYdjUQLY+/5lNaGuEWAdnlHiayIbSV81rPog4DGE90knKe0i8VYbFAvf0cQ1z3WXfWvy2exTvB31RtRTHu0UKoA7hoUG/q9AnhDYBdSrQJ4IR1hM7aDAN4iBGtMvDkaBU/BmD2PsQeROpgIxRmPQ0KRmfxv8gzgEr2iCC5BvZ9NJt28A4xdkrkR/cDiDR5TPEbHjx/vnMj3s9TvKt49hMqY969ugXpYzPrIRz5iOH4//Dt/VWQQr0fiIL7mGHN9AfKdQHK56h7hOFovl0eV7zwWidr2R9efpSuhI4KfSJGhJwXw3JQ92qKgAO77osx8nQJ4ZqALq04BvLAOsTnLJlDNjMtEAK/YPffcM6jRCGhW1fFYdXnUBlW8QxfHUUqxfw6P8CaSvewQsiyPgsi9+eabV+H/MYFlAt412d1U45iUI0SGHjfT1p6heyo39VzWuxwCLDAiuqo5F3JHCdAGvjeq4xNxHue/56RFWxDCIYbbFmz7fiawANzlwY5j1yIxH9sV9ui7UwGcc4BXbCmAC4HPZFYBnAm0ZnaDwJBzgNueuO/EYDeIzfMUTPTMKDoPyym1MAF9xzvesQoVXkJhEs5CEp7gOUokGZqjLuvYHQIhvCIxVH3P+KafNER4JMratL2+9TcdMxb39v2ei33N1RDw2CMdpxLw/CyAxkIX3l9DoPv2kteNJaAAHktuO+5TAG9HP9nKhRAYc7Zjtenso+Jnj8K3Zus5ksR4jNJsOEdXdOONN672BFaTnNUTnuGdyREiGseCDdk2sO7B+3ijRoPzxq0mUB1j/B0xhghDDDPeu/bwbvXDr2l8hGfXL+krgLmvKUEWeS2IOIkjn+DcdfbyjjLWA1yoYxXAhcBnMqsAzgRaM7tDoHpMxNCn4rijtnNHh9a1b9cjfqvnze7b8y/lefschTIlMdyQ52RSzL5kxkWfs6m76s4d2trVHn+/TAJN70BbYieuDZG8zKeZ3ipCxCNbdNQ29ji0+kJnnFU/vZVbW4MCuFDXKYALgc9kVgGcCbRmdosASVFIBjRk0s3+YUI2CeW1DCdACFzfo6aG1+4dQwiQCZ2JaVtp8/6wj5F3h8Q3cy1m4CXivFEWppqODRvyXFxrGPRQYvt1PZ/5iD3GP17gqmcYTyZjO847r573i0COo952jRgsWIiqfh8OFcB40YnAMNfDy0aHArjQC6MALgQ+k1kFcCbQmtk9AkPDoTlH9b777ts9EBmfaI6jRzI2d2dNdXmBmQgjguvRDiGMeXfYt8ueeia9Y87vrcJFBLMvmUWmqYXMtJy1apFAF4HIQj5k4eXkyZM7dQ5unEJANvhqhugmAcwCQZwnzN/rC8hDz1bu6p8d+b0CuFBHKoALgc9kVgGcCbRmdpNAU+hX25MqgKeNgdxHj0xr7e7f3eUFRpQiSGPCCxFCQZnkRiEiAjE8dU88k2lEyBzeI9pCKLRFAn0JRDREnMMeWZKrmaOjLsYoWY8Zs4yzbc9rEJ8DsR8/hG2TAMbLS+RHvK9wwpseCwj8fyTF6st+D65TABfqZAVwIfCZzCqAM4HWzG4S4IubCUCfJDysjnMt4ZqW4QTcnzmc2SbvIPwTkbuuMMFlst8mTJn8Us8YAcwZxPxQmFjjRcYLNTVKgC0KCHWLBKYS4PsBccyfTT+rc33/m99cRUOstgS8750rryjCmfv4ziAaIY4JIvKCehDNkQWZ/6OepiRcfC9xPX9uYkzTPn6wT6FN2GJ7EPku4l3ks5vn4HOgqR2XXnrpantQFBMenjPyFMBTX8SR9yuAR4LbktsUwFvSUTZzuQTqX97rWorHmMk6YZaW/gSY3OE53KOjL/rDKXQlk9wmD9eQ5jB5RsQSBj2kMKl/wxvecM5iUojsI0eOrATD2CzUnDHKPk6LBDZJAKG42kf8I7etRC/j99R7b1hFTbAoxBjmB8HIn3H+LmM/rkd41gUlv2chiHcTGwhPFodyZqnmuW655ZZzcjasO8qOdrKHOD5P+I6sRopssh+2oG4FcKFOUgAXAp/JrAI4E2jN7C4BvFgI2z7l8ssvX01u+HKPfY9xpMbUfZB97G/rNYqS5fUcCa2GJIFregImyrw7Q8VqVzZ13kmiLZhQE3GBB6pPlAZtNNv48sbaTrfofR8+xwPc9p6Ep3VdmH/1hALEMQutpRYNeQcR3din7Yj4dW2pJ5Y0HPrsSFAAF/oAUAAXAp/JrAI4E2jN7DaBrv2Q9afHS0UyFCYJTFpY8SaJT1vhWBkmBIjnu+++e7dh1p5uFSZ48uRePfM2PCyeqghBntLeq666Kt1///2DqmB/4SOPPNLp1eI9Q9Ay+UYER6jmOmPuNR/UFV6cgQDhwbxvLDixXYDvi3qJ9zEijLZxHzvfh0RUVReriMZgAXSPiwK4UOcrgAuBz2RWAZwJtGZ2m8DQjNARzsZkm4lK25ER1Iu4Pn78+Arg0KMltp06fPCW5wzf23ZmudrPGCaioa9nta1d7H+MPbx9245NQqC79vviYaaNt99+e7rmmmt6H73EpBvvmeOub494XQ4C7PflXWnaRxsJ57Y9fJ93lsiN2DPNv3kPCeXeRlE/w7hQAM8AcUwVCuAx1LbnHgXw9vSVLV0wASbkTLTHhoTWhS1f+kzw8VhVzw3eNwHM3tBIALPg7t/bprHXkEWaKYV3hkUezngmVJK9gF3CFntERXQllItEWXfdddfKRp+MuyT24gdP2z333DPl0bxXAhJIaRXpNEW8Tr1/yztBAVyoAxXAhcBnMqsAzgRaM7tPgEkzIcpjCpN59nZVQzubRAC/RyjMcdzLmHbmvIc90RyJYVkugaGRD01PQh0sHuHVYpKMAOZPFj7wdjXtD+Z6wuK7MtvGucPUxztD3dzDglV1YSW2GPD/jDtCLhl77stf7tizZfMTiHN851x0jLBm3uN9+N6av1eSAngDUPtUqQDuQ2l7r1EAb2/f2fKFEWDyjJcpzjQc0zzEAPu91h0L05UAaIzdpd2DSKl6vpfWPtvzIoEpiz5tDEO08nvGemR0Rrxi7957710JZgRql0c3zhlm0Yh7yVwd9zAxpxBuyR7keoIeBbCjfB8J8P3FQi7RRwjWpv3GQ7jwDrKv1/dpCLVzrlUAj0Y37UYF8DR+S79bAbz0HrJ9W0VgSEbotgfrSgrEnkn2gvUJE90qeC81lv1eiN+pE69tfPZtazOiNLxGc7W9KoBDoLKwFMe58H7cd999qz29TNIJoV4XCo1394477liJ5iFhmHMlX0MA9Em+NRc/65HAUAKIXr67WATifUIAx5FEvHdsyeFPrmFBiu+fvokJ4/sKMY0X2H31Q3tHD/BgYjPdoACeCeRCq1EAL7RjbNb2Epi6L5IJAx5QJhxthYkI3iwmE3ieEYxDJvdLpMsEKY6+6PLsLbH9+9Ym9teSeGfuUhfA9fqZmLMV4MSJE6vJOnvl2zKoX3fddemBBx5YvR+8L0PekTmyQbM4wDFPiIUhtudman0SaCPA+xTnBvehNOR4It5Pxn+IaY+z60P4ZdfoAR6FbfpNCuDpDJdcgwJ4yb1j27aSAIKUyTar5mNKn+Q+9XoRBEyw14VOj2lLznvwkjFBcp9YTurjbOEhIqR4jnL06NGXJTrrim5AWCJ8EcFtgpmJNwspcQwSXisWlvomqmMBikzQUwqfAXivjWqYQtF7N0kgPLR9bDCOw5Pb5/p6csht/47q88wbuEYBvAGofapUAPehtL3XKIC3t+9s+YIJTPECjxHAoECQ4JVjktKWPGjByJLnPS65d77UthB1c7W2y+PbZocoCEKw28Y6ZwAjkCm8E4R58mffEExCPclCPrbEXspIjKf3ayxJ79sUAb6nCNHvuyjEuzMkmRWLTgjsKLyrHGtnGURAATwI13wXK4DnY7nEmhTAS+wV27T1BEj6wcRiTBkrgKu28H7hUd2mMyHn8LiN4e09wwgQ9sxCy1xlrADuss97hEDdH1mpAAAgAElEQVT+2Mc+troUby77hxlnffaXM9l/5JFHusy0/p4okAj95CI80adOnRpdnzdKYCgBxh8LViyOxh76qGNMvoqhyQnrSfLinUJwG+nTuzcVwL1RzXuhAnhenkurTQG8tB6xPTtBAO8PoY+EgA0pTFaYtJO1dkrZxvOCzRI6pcfz3VsXdlMtzymAGffxziFemWiTSZrQyzhT+/rrr19lku5T2DvcddRSUz1NIeJ6v/oQ95o5CPAOsAjLn9UkddWj5Ybu/aVd7GUfktCt6Zg03gO+H4mIMNdDr95WAPfCNP9Fw2Zv89u3xs0SUABvlq+17zGBSIAzRATP4f0F+ZyiIlcX4qFbl/grVzu0006AiSvZlOcsc4zVOMcX0dvmWYp3i/cRodyWOKv6bGMEMJN+vORNx6HRvr4h2HMytq79IsBCKlFA9TGI+OSHd4RFIcZqHAfWRYhxS/hy/biwdfd1eZnJWWFyuC7yZoHuJLShCxTAGwK7kGoVwAvpCJuxmwQQweyz6psQay4BzN4rfrYpKRbhoXoElv0ezJn8Kp50DgHMkUjrvLq8g0zc8YBR+kRIjAlZJuQT0VHf/oDoQHjjQRvjUV72qLB1SyLA9gTGX9N3TuyB77vnt/5cvEN8TrM/vk/pyhTvvvg+FBXAvSht4CIF8AagLqhKBfCCOsOm7C6BdV6h6lMTtkbpe8biOmJzCIucPTI0vC5n27T1IoEpe9vbGI4dp4jaSDDF+8XEHI9tU8FrTYQB4Ztci0BYtziEhwxvV9c+RUQtXq5IJERYaZO4QEyzH7+vcHC8SWAsgU28o9W2xDFIjOeuaIYrrrhibYIto3569bIh0L0wzX+RAnh+pkuqUQG8pN6wLTtNgMkyE3Z+1q3Ad3mz+kKqHm+BbWxyJupSi+FwS+2ZL7VrE5NrxCkCkT3gQwoJre6///6ztxBtwb77KGRwru5X5L1ikQWxui65FZN6JuZdCeQQvrHPsqvdQ5MHddXn7yUQBGKLDWH3jDPeAxZ5hmy9GUOTxSHeEX6awqJ5z2JBt63+6p7kMW3Yk3sUwIU6WgFcCHwmswrgTKA1I4HqhIXJCkK4PknhbNM+k+8xNMd62sbYGnPPmP2WY+x4z3gCsagSyW26zuvta4mQ5L7eIJJwIT4ZL+v2D5JMDo9sdd/tV37lV6Y/+IM/aGwWwhchzqS8zz7HIeenmgCr70jwuqEE2OtLZAHvAoKzae/50DqHXM97g20WsKrZ1WlXn/3FhkF30lYAdyLazAUK4M1wXUqtCuCl9ITt2DsCTFQI44xJApN1Jg1TM0C3gbz66qvTfffdt1jOhJz2OZ5msQ+wBw279tprV5NsvD4IRbywTSK0HhrZx9PDAg1hzNzbtn+d9wNxyqS5T2FBqert5V2rt3eo8MUuC1eEd/b1srm406e3vGYogThnF28sizf8e+z+3qG269fzXhJ1wZ9DkuVNPW97aruXcn9EaTUsvimAC3WSArgQ+ExmFcCZQGtGAm0EYqUcAcCX4IMPPrgRWITGIQhOnDixkfqnVMqXPgK4a0/ZFBveO50ACzZ4f9vK6ev/eEpf/wMpfegH08Fjv3f2svDaRhIe/kTEMlmnzhCSCFREMAL7xhtvXHmVIjEa1zDR75O9OQzXk13F+Ap7IeT7eHyrz0y7EcB9i4s7fUl53RAC1SzLjO2+CzJDbAy5ljbwLtGOvp5orn/ssceGmNm5a1n8JqIEfg15DBTAhXpcAVwIfCazCuBMoDUjgXUE8M4iEtjDtclMsezLQnBsyss8ppf50mf/rwmCxtDLe0/Xvr7T3/N/p3TBK1L6wufTwR/56tbGMUHmPF4myYQ+R2GRJrIkMyYIY56yb31d2P9UzxN7l/tO8nnfuhJq5e1Jre0Cga5jhrblGff9eDAW1FhYJAKq4ZxlBXChgawALgQ+k1kFcCbQmpHAOgJM/JnsU+Y6CqnJHkmC8LwtSWzqAdiud4NxynhtKm0e4KZrm44iQmATohyeWvbK4x0ZkxWdBSXOlW47Wmtq0rW+e4BNgLVd43tbWssxR7yLpUKe5+TkFoG1NBXAcw62AXUpgAfA2sJLFcBb2Gk2efcIVPdMEVrJpL3qGRvzxHh6WVFGBETZpLge00buUSCMJVfmPgQp4nJq6XMWLzYOHz48ygvcNtbxxLII1OBpGfRIfc9EJow7zh8eZMCLJdBCANGL+O17vvzSQbIIWt3usPT2Zm6fAjgz8DCnAC4EPpNZBXAm0JqRQBcBBEGUsZN+Qj6jMMlHrMRZqZEkZVN7jLuer+33CJExHr6x9rxvOoG+3s91lvoKYMI82bt+/PjxQQ2njUQ6xHFGCF8WhNhjPHTPb5PhatTGuoa5/3dQt3nxGgII3jjmiM/2XSu8ryz8xjaIXXu+kc+jAB4JbuptCuCpBJd9vwJ42f1j6/aIAJ5fVvbZH4k4GJLsB0xMjvDMtZ1xyp5Lko1M9SzP2SWR9MPsz3NS3XxdRCwQTj8l6U5fARxPc+TIkZXntm8GaMKpL7744rMJdoiEmHPs9/GEz3b80eXXpIu+9cfSM7/w3Sk9du/mO1gLiyFAZmd+EL5T3rfFPFCPhrBohUe45L55WC8kKaMCuMeY2cQlCuBNUF1OnQrg5fSFLdlzAggKRCyhy3iohoZN9glv7nNNzm4wPDQn7XltIS4jumBMzUMFMDY4gol3BK9qV6kf+0UEBN7fuQqLTV1euD7HP/Vpz0V/50PpwiO3pOdP3Jae+dGv73OL12wpgTiqiwWciy66aGfCnId2x9zv61D79APfwwtYnFUAD+28ma5XAM8EcqHVKIAX2jE2a/8IVCfUY4Rqn3vWZcXNTXw271juhmtvRQAPCZmQxybhGSOAsdtnnHNdfazjzSECYg6vEsKEBF1dZbbkPnqAu1Bv/e9ZTGHxkz+XcKRRaaC8p7yvJb2wLLghxKfmC5jIUgE8EeDY2xXAY8ltx30K4O3oJ1u5BwSmCOC+YdNLEsBTs/DuwZBY/CP2TQTV9CBjBTBjGA/wun2CnK2N96YeLj1HGHTf8G+zmy9++C6mgSyo8C7tS4hzX/DkhigpPlncI5cAnzVEKxUqCuBC4BXAhcBnMqsAzgRaMxJYR4CJD3uAYwLEly4e0giDZiWaCVKcmcuqNCFyTPQp7BG75557OiFfddVVqz3C2OFLveTq+mzesc6n9oJNEojQ/b42uB4vF6HMJ06c6HvbOdfheaYOBG59q0C8Eyyw1MscXmDeNd7PrjKH2O6y4e+3lwCfwfzsUjbnuXujdBh0PA/7r/m8YW9yge9MBfDcA6tnfQrgnqC29DIF8JZ2nM3eLQJNGWUj1PPVr371KplPJLdCDBMqh4CNjM7XXnttevjhh1vPPA1acW4kX+IIEL7U77///qwwEfYkI+K8V8v2E2AhhjFZLaff/p6U/uR7U/qtD6SDH/21c3431vNbJ4XXjOzQCOBYOKIt7PNtEr9xP2OvejTY0B7ou/f5iSeeWNR520Of0+s3S4BxRJmyj36zLSxfO99TRHvw/dh2nnfOVhL9MUcG+YFtVgAPBDbX5QrguUgusx4F8DL7xVbtGYGmPYUI4Cj1o4vwcpGkA9EbhaOT2DPFURJ9C2HXCIIh9/Stu3od9TOJoej5HUNw2ffceOON5ySYOv29v5vSgQMpnTmTDr7/Xec0fo4wfCIZELF1oct4JlRx3eIKXuM4GmkMVRakWIBaV9i/+Oijj46p3nv2hMDQyIk9wfKyxwyPK99TU97bLeanAC7UeQrgQuAzmVUAZwKtGQmsI4BnNgRi/brrrruuV3gz9x09ejQ98MADg1ap5xAkTc/GZAWxgacZr2946QqEkDn4NkgAr8itt956zmLMOg/wlPFG6DGeM6IW2sZRV5KsqQKY5yUEe11ZSujmBrvdqicQ4LOQ6AVLfwJ7vKVAAdx/mMx6pQJ4VpyLq0wBvLgusUH7SCDOAK4/O+IRcTzEm0Q4NHsr+x7fMEWQNPUVwgTxO+eZq/s4Jrbpmdct4NSfY+x4I+oBz2rXUUZdApj7p4ZTdnnvSifv2aaxs49tbdryso8chjwz32csXm06WmlImzJdqwDOBLpuRgFcCHwmswrgTKA1I4F1BJom1GSgRMweO3ZsMDy8C7FfKfYOt1US4oW9aGNsRb2s0LMfc45jZgY/sDcUJYBnluRQfcpYAUzYc5/96ox9wuybFoAiidzUSfS6fcDYYMHKSIc+o2E/r+EILba9WIYT2MPoCgXw8GEyyx0K4FkwLrYSBfBiu8aG7ROBugcNbxd7DQlnnlKog3BNPFL1I2Hq9Y4VJuGVM6nVlJ7a7nv7novLUw4dZ4xhRO29997bW1Q2eYFJGof3t29kxLoeYf99ZJuuX8exLbxvFgm0ERjyvkjx5QSmbmPYMqYK4EIdpgAuBD6TWQVwJtCakcA6AmTRJYNtFBJaTRW/VXuxd5I62zy0Q4UJ9ePlItvtujNZ7fndJzDkPOC3v/3t6Y477ugdytjX81ulXB3LjFGiE+Y8x5M9nCTcYotCtfAe5Egqt/sjarefkHFD1I9lHAG+w4jyKJCReVyDp92lAJ7Gb/TdCuDR6LbiRgXwVnSTjdx1AvUQ0q59jGN5XH/99en48eONgvXNb35zuvvuu3tXzeSDkOc9zczZm9M+XNh0FFLbcyNOOX6LcVgt11xzzUoUI1Zjny/eX/ZLdu37rdsKAYy3l3s3tUCD8GerAe0mAsKw530Y7fM845B98/NY3K1aeN/WHXe2Q0+rAC7UmQrgQuAzmVUAZwKtGQmsI0CYMhPpKGO8sX0IrxPWCBCEyV133dWnqtWEf08mIL147PNFjBs8on0KYxuhWD3Ci//jvE+EKqKSBSGuYZFlSAK4ENeIaBLB4Y1VlPbpFa/JTaDPcVq527Rt9vYkFFoBXGhgKoALgc9kVgGcCbRmJNBGoOlYlU0IYJIUMekiZLle+B0/9bOF1/Ua9UxNJuSo2A0CJGwjk3mfwtimPPTQQ6s/GXckBUIATyl4ijmfl3qwcd999zWO9Sk2vFcCcxBgrJJIjYUjy3gCbGsgCmnHiwK4UAcrgAuBz2RWAZwJtGYk0EagyROwCQHctq+Y/WjYw2N25ZVXnuOZW9dreNfwtFkkAIG+maDDSxvJqPDQhhieQvLIkSOrMUx529vetqrz4x//+JQqvVcCGyHAgg8/cTb6RozsQaV6gPegkws+ogK4IPwMphXAGSBrQgLrCNQTYJEchT2VfY586UsWLxvJhBAFHCOB+GDyhfeZ/8dbRiEMmmy7fQre3yZvcp97vWb3CDCWGLtdk/pNLO5AE7tHjx49u9+X/exdmc93rxd8om0gwFhl2wuRE5bxBNiCswenD+gBHj9EJt2pAJ6Eb/E3K4AX30U2cNcJEDoaEyFC4hARc3jEqtwQq2TORAhzBAcheNhEtLAXM7JEP/jgg70za+7heYy7PhQnP9+683Gj8k0J4Hrj98Q7NLnPrKAMAc8Cns7dEOjpDK2hnYACeLdHhwJ4t/vXp1s4gXryoDFHvnQ9Yhx71ORtiPBnxMJQb5kh0F3k9+/3jDE8W4xrFlU4qqRecglgbG8q+/P+9axPPDcBvcDTiZIk77HHHpte0bJr0ANcqH8UwIXAZzKrAM4EWjMSaCJQ9QKwF5jQ57kzKyOAm8QvE7Crr766d8hzvf0mwXJMNxEgwuDmm29eRRLUz7ImwzNjfkhm5zbKjOm2M63ZV/zII4/YQRJYNIHXXHZ5es1/9A3pwG2/lc77/GcX3dalNm4PwqAVwIUGnwK4EPhMZhXAmUBrRgJNBC677LJV5mXKps7+bRPAQ/b71tuu99fx3EYgPFuE28fRSAhSBCv79aZmeyZKgjGNlxkhTSh+tfC7kydPmqHcIbp4Aq/8438pvfrP/0A6cMe/SRf8xt9XBI/osT3Y6qAAHjEu5rhFATwHxeXWoQBebt/Ysj0gwLEteH43JYDxwiFI6h5gxDZCZMwxRorfPRiYEx8RTy/7gecuRCyQ6Tk8v/VFHP4fj5Chz3OTt76NEHjla9Ohb3l/OvDW/zRd8Fv/Szr/Dz+0ETO7XOkebHVQABcawArgQuAzmVUAZwKtGQk0EagegbSJvZFke2afb7WQZAuv2ZjzExEWTfs67V0J1AmwF5gka3MVsqUznkl8E6UeNbEH4ZBz4bSehRAwDHp8R+zJVgcF8PghMulOBfAkfIu/WQG8+C6ygbtMgP2S7ImkbCIEGsFKqGgU7HFeKuL3+PHjvdHiKSZztAKjN7K9v7DpfOupUOIsa0Kr2TrAeIwQaMKr594/P7W93i+BLgLkfWBxxzKcwJ4syCqAhw+NWe5QAM+CcbGVKIAX2zU2bNcJRMbcCFHGA1xPGjSVQQjXqCe8zNhGcPc5a5hVdry+7Omset+mts37d58A0Qb1CIQpT01oNWKBUOfqUWGMUUL6Cfm3SGCbCPB+8J5YhhP47Gc/O/j0guFWit+hAC7UBQrgQuAzmVUAZwKtGQnUCVQnPnhl2ds4Z0EURPKhugDm3xxTQxIuxATeYK5tKiQZ4Xdch5fNIoG+BHJ5t4hocHGmb6943dIIVJMhLq1tS27PqVOnXpYEb8ntHdk2BfBIcFNvUwBPJbjs+xXAy+4fW7fDBKoCeBPhz+yXZA8mHuYmAVxFiwDnWKNnn332HOKf+9znVsKCcNP673a4a3y0GQkQrlwNw5+x6lVVRDkQ+ty2gDO3PeuTwNwE2F7yute9bu5qe9fXliyxdwUFLqTNRH3swXuvAC4wvjCpAC4EPpNZBXAm0JqRQJ0A4pREQZS5E2CxN4r6q9mfEdx4Gh577LHGziAML8KduSDCSqlnTLZoe1wCECB0nmiDTRQiEjjyaA8mwZvAZ51LIfC+D6fLeEdOnUoHf+Y9G2sV7wkLo3w/xJ/8Pd4f3lV+4mi+jTVkhor34PijoKQAnmG8jKlCATyG2vbcowDenr6ypTtGAHF6xRVXbEQAszpez8DbR2STkIvkRYhkRC9eYYsEphDA+xvnAU+pp37vnuz/mxOZdS2UwKEP3PniYuWZM+ng+981Wyv5DEfgxg+it09hsRQhzHdB/Qi9Pvdv+hoEO2eA78nClwJ40wOqpX4FcCHwmcwqgDOB1owE6gQ2KYDZr1ufuPQRwLQRLzEC+sEHH3RfpcN2MoFNZIN2z+/kbrGCJRF434fTpYcOpfNm8gCzdYV3ZI7zsNdtYUCAVrfY5ELKc7E4qwDORXw/7SiAd7vfFcC73b8+3YIJMHF4wxvesGohIWd4X+fyuMbEoM/+3zoi9iOzt+qRRx4x6dWCx8+2NI0oh7m8SIzrp59+OuH9tUhglwjMsVceYUho8BzCN9jyvUT29aZ3GKFNorucZQ/3/OsBzjnAKrYUwIXAZzKrAM4EWjMSaCJw5ZVXnv3vvh7asSQR24jaroIAPnbsWCLDpkUCUwlwbNEck2TEL2OSSbdFArtCgEVKBCZCc4o3NfI39A1zHsKvaR9/7L8nd0R9u82Quodcu4fiFzwK4CGDZMZrFcAzwlxgVQrgBXaKTdoPAvXzHzctgPEwcJ5vVyEjKcciWSQwB4E+SbDWhVIy6X3uuedWP3sS8jgHduvYEgJ9M0CzrYWftsL7Qdjzpkr1TO8Qv7SdLTM5yum//kvpzGVvSqcevz+lH/rqHCaXYkMBXKgnFMCFwGcyqwDOBFozEqgTuOiii1ar/lEOHz6cHnjggY2AQjgwaSHUep2XgQkWIpk9wBYJzEGA8cY+YCbLdU8woZqEMzN5p+BJih/uw9vrWJyjF6xjqQSqpwG0tZFFIKIfSr4LsZe/mnmdUwz4ztjkMWfB5PT3/m5KBw6s7J167w1L7c5NtEsBvAmqPepUAPeAtMWXKIC3uPNs+nYTqK6oI0wJFe0Tojz2qTnPF0ERR11Uw9aYVBA6xzUeeTSWsPd1EYhQT8YhwnfOvYpdtv29BJZKgMWhiy++uFFILiXhGxFLnFXMCQERicH+fhZM+f9Nh0E/81d/MV3w+sNb5wGmX8lbMKEogCfAm3KrAngKveXfqwBefh/Zwh0kUD8a5siRI+nEiRMbf1KEB54EJjC0AS8bwnddaN3GG6UBCUhAAhJYLU4SJUHCKT6bWZDE47qUglCPBVIWbCmcwz1HAq+uZ4TFJkO8u+yP/T2L23jPSU428ntWATwW/sT7FMATAS78dgXwwjvI5u0mAVaF8YZFIfEUxw7lKIhgJi0WCUhAAhKQwBgCiHQEHQLvEEc4nXfepCRe69pA6DcnJJQMAR/DKO7BQw4vRPCICCsF8BT4E+5VAE+AtwW3KoC3oJNs4u4RqB8Ns+kEWFWCSwmp271e9YkkIAEJ7CcBvMN4hec68qxKkQXbbc7+HhFfI59DAVzolVIAFwKfyawCOBNozUggCLBiXs2cyd4qBHEuDzCr6IRBj1iJthMlIAEJSEACjQSIaiK6ae7CPuNtzxfA9z4/LEAPDIVWAM89oHrWpwDuCWpLL1MAb2nH2eztJUC4GHu9olxzzTXp3nvvzfpATCb6HImUtVEak4AEJCCBrSXQ58izMQ9H4q1tDX+uPu8l73xPOu8v/FB66uffm7748d/ui0IB3JfUzNcpgGcGurDqFMAL6xCbs9sE2CeFtzeOIuIYiRLeWBKrPPnkk7sN26eTgAQkIIFsBPqeaTy0QZyOsAtngB/6sdvTgQtekc584fPpme95W19RrwAeOmBmul4BPBPIhVajAF5ox9is3SPAMRH14yIQwm94wxvSww8/nPWBdyGkLCswjUlAAhKQwFoCfJ+REZptPXMVhO8mjwecq5296nnr16TLvu0DKf3qD6TPf/Q3+2a1VgD3gjv/RQrg+ZkuqUYF8JJ6w7bsNAGSg+D9rZecCbCw/dxzz628zhYJSEACEpDAnATivOCuZFinv+UfpfTG61N66Fg6+I//2tkmkJuCLToczxd/7oL3Nx7woosuSq997WtX3l+yQhONxcIBW6MoDSc0KIDnHKAD6lIAD4C1hZcqgLew02zy9hKoZ3/mSXIKYCYUhD4PTMKxvcBtuQQkIAEJZCVA1mMSYnGmcWz3+fzVfyx98c/9z+m8f/F96Yt3/l468/1/8GLGaI5Sev+7Vu2rnlOftcEZjV1yySWrc4GjxN5mIsRajihUAGfsn6opBXAh8JnMKoAzgdaMBCBA9mcyQVbLps8AZvUc4Yvn97Of/awdIQEJSEACEtg4AcQvIniV9PHv/m5Kr3hV+uLnn0sn/87bUvpvfz0duuot6cBDx9L5P/+X09NPP73yhu56Ie8HYrepEJnF93StKIALDQoFcCHwmcwqgDOB1owEIBDhT0GDlWBWyh9//PHJgJ599tlVHSQiYVU5QskQv7sUQjYZlBVIQAISkEBWAgdu/BPp4r/64+nUP/rOlD75e2dtI5AJd+Zn1wvf93iAmwrf0WS7bojOUgAXGhgK4ELgM5lVAGcCrRkJQKB+TMTRo0fT8ePHZ4HD+YLPPPPMLHVZiQQkIAEJSEAC8xCoL35HrQh/vrdZtG4561gBPE8XDK5FATwY2VbdoADequ6ysdtOoLoCzH5gEl7gqZ2j4EXeh1X0OVhZhwQkIAEJSCAnAUQwEV+xLxrbhH5H9FZLWxTAOTupYksBXAh8JrMK4EygNSMBCHBEBAlCKHMnvyJ8KhJqSFsCEpCABCQggWURIOT70ksvXTWKsOdHH320a4uSArhQFyqAC4HPZFYBnAm0ZiTA8RBf9mVfdhaEAtgxIQEJSEACEtgfAiTBJBkmpeeRhArgQsNDAVwIfCazCuBMoDUjgfoeoLECmDBnQqjqmSRZSfZ4I8eZBCQgAQksnQDbgRCA9QSNfK/tQiQT39GEOxPeXH3Gah6QJ554os8WKAVwocGsAC4EPpNZBXAm0JqRAGFPhD9FGSuAOcroqaeeWp0lSMZn9hDz4/5fx5gEJCABCWwDAXJgIBLjeL4QjHhIyWex7SI4FrxJTslPfFeTBRoRzPd1z9MfFMCFBrQCuBD4TGYVwJlAa0YCrGwT+sQXH1+Ghw8fTg899NBgMCGAB9/oDRKQgAQkIIEFEHjd6163+h5sKk8++eRWnwmMmOe83wMHDpz1/vJ/n/70pxMCnzwgLGL3jNhSABcarwrgQuAzmVUAZwKtGQnwBUgoVCTBGOsBJqSKzJEWCUhAAhKQwDYSICcGSSGrGZHjOXpkRl70IxP6fPHFTK+/VPBo4/Gth3z3eBAFcA9Im7hEAbwJqsupUwG8nL6wJXtE4Morr0xHjhxJJ06cGPzUeJDZO8QEgi/TlrMDB9frDRKQgAQkIIFcBA4dOrQKB66XnntjczVzsB28v9UcHXxP49UeeeShAnhwD8xzgwJ4Ho5LrUUBvNSesV07S4AQKMQvX4od5/81Moj72APM/ewvskhAAhKQgAS2iUD1WMBqu7c5oWM1yVU8E+HObF0aWRTAI8FNvU0BPJXgsu9XAC+7f2zdDhJgHzAieGwINEj4MuVH7+8ODhAfSQISkMAeEGAPMN+HEQaNUCR5FAJ4W0td1M+Qs0MBXGgwKIALgc9kVgGcCbRmJBAEyH5JcoyxAhgP8KlTp9LnPvc5oUpAAhKQgAS2lgD7ZRG9bO0hSRRbe7b1RAP2/fI8UfiuRsyP2Pdb7U8FcKHRrQAuBD6TWQVwJtCakUAQiBXiMQJ44l4iO0ECEpCABCSwKAKIXn62dVGXBW2OOaxntSbx1WOPPTaVtQJ4KsGR9yuAR4LbktsUwFvSUTZzdwjEecAkwnr44YcHPdhMX6iDbHqxBCQgAQlIQAJfIkDYNoKXZFevfe1rz0l6xdnGbE/imCMSX00801gBXGjgKYALgc9kVgGcCbRmJAABviwRwJwDePXVV6dHHnlk0Ko3X6ZkyLRIQAISkIAEJFCGQCxk161vYJFaAVymi5MCuBD4THG8XAQAABFASURBVGYVwJlAa0YCQYAEWCT+OHz48EoM33bbbb33CBEidvLkSWFKQAISkIAEJFCAwCWXXJI4haGp4P0lR8eMRQE8I8whVSmAh9DavmsVwNvXZ7Z4iwkgfhG9hE8dPXp05QE+ffp05xOx9xfvL8ce9bm+s0IvkIAEJCABCUhgEAHCnUna1VaefvrpUccbrmmEAnhQD813sQJ4PpZLrEkBvMResU07S4BV4+p+oeuvvz4dO3Zs7fNyjMIzzzyz2k9kkYAEJCABCUggP4HqFqY262xRYrF6xqIAnhHmkKoUwENobd+1CuDt6zNbvMUE4gxgHoEs0GSIXHfkA/uJHn/88d4h0luMxqZLQAISkIAEFkvgVa96VTp06FBr+2Y69qhevwK40IhQABcCn8msAjgTaM3sLwG8vghZQpcJncIDHAL4oYceWgvmqaeeSniALRKQgAQkIAEJlCPAGb+c9dtUEL+EP2/g+1oBXKjLFcCFwGcyqwDOBFoz+0sgEmawf/eVr3zl2eMS+hyDhGh+8skn9xeeTy4BCUhAAhJYAAFyd/BDYSGbxW3+zZFHJL5aF801ofkK4AnwptyqAJ5Cb/n3KoCX30e2cEsJ8MV44MCBldeX8wL5e3x58khvetOb0oMPPtj5dGR9JvuzRQISkIAEJCCBZRBgTzDf7WR+3mBRAG8Q7rqqFcCFwGcyqwDOBFoz+00A8Xv55ZefI4CvuuqqdP/993eCIXyaUGgSaxBmZZGABCQgAQlIYC8IKIALdbMCuBD4TGYVwJlAa2a/CZA8g71DCGEK3l/2/w4RtFxL0iyzQe/3WPLpJSABCUhgbwgogAt1tQK4EPhMZhXAmUBrZr8JECbFGcBDEmA1EXv00UcVwPs9lHx6CUhAAhLYHwIK4EJ9rQAuBD6TWQVwJtCa2W8CeH7x4L7uda9LF1544eoIpK4M0HViJNr49Kc/vd8gfXoJSEACEpDA/hBQABfqawVwIfCZzCqAM4HWzH4TQPTi/Y1kWEMFMPt/yQY9JGR6v4n79BKQgAQkIIGtJ6AALtSFCuBC4DOZVQBnAq2Z/SbA8UeXXnrpWQhDBDBHIZEJWvG732PIp5eABCQggb0joAAu1OUK4ELgM5lVAGcCrZn9JoDnl/BnypEjR1bHH5Hduatw/BHi1yIBCUhAAhKQwN4RUAAX6nIFcCHwmcwqgDOB1sx+E8ADTBZoxC+hzH3PDXz88cfTF77whf2G59NLQAISkIAE9pOAArhQvyuAC4HPZFYBnAm0ZvabQJwDjBcYAdyncNwRWZ8tEpCABCQgAQnsJQEFcKFuVwAXAp/JrAI4E2jN7DeBCIHm/F/Cn7sK+30j8VXXtf5eAhKQgAQkIIGdJKAALtStCuBC4DOZVQBnAq2Z/SZwwQUXrJJgEQLd5/gjQqSfeuopE1/t97Dx6SUgAQlIYL8JKIAL9b8CuBD4TGYVwJlAa0YChw4dSkePHk2PPfZYIiSaEOemwp5fzvs167NjRgISkIAEJLDXBBTAhbpfAVwIfCazCuBMoDWz3wTOO++8dNFFF6XLL788XXLJJaszge+6666XQUH0PvHEE+n555/fb2A+vQQkIAEJSEACCuBCY0ABXAh8JrMK4EygNSMBRC8iGJHLOcAPP/zwy6B47JHjRAISkIAEJCCBlwgogAsNBQVwIfCZzCqAM4HWjAQQv4Q+v+pVr2oVwIhjwp89+sjxIgEJSEACEth7AgrgQkNAAVwIfCazCuBMoDUjAQQw+375ueWWW1bZoOv7fPkdCbCefvppgUlAAhKQgAQksN8EFMCF+l8BXAh8JrMK4EygNSMBMkEfPHgwnT59Or3+9a9fJcQKTy+C9zOf+cwKkl5gx4oEJCABCUhAAiklBXChYaAALgQ+k1kFcCbQmpEAoc9kguZ8X84FrpaLL754FRqNCEYA8+ejjz4qNAlIQAISkIAE9peAArhQ3yuAC4HPZFYBnAm0ZiTwyle+ciVyEcBxBBKJsdgXjOAlAdaFF164EscvvPBCevbZZ4UmAQlIQAISkMD+ElAAF+p7BXAh8JnMKoAzgdaMBCCAACbsOY454kgkBDHh0YjhKPz97rvvFpoEJCABCUhAAvtLQAFcqO8VwIXAZzKrAM4EWjMSqBPA80voM95fhHFVAF9xxRXpox/9qNAkIAEJSEACEthfAgrgQn2vAC4EPpNZBXAm0JqRQJUA4dCvfvWrV+HOjzzyyCoMGi8w/xfHJN1xxx2rjNAWCUhAAhKQgAT2koACuFC3K4ALgc9kVgGcCbRmJFAngOB9zWtek5566qnVr84///xVdmjE8Bvf+MZ0/Pjx9MQTT7zsqCRJSkACEpCABCSwFwQUwIW6WQFcCHwmswrgTKA1I4EuApwTHGHQCOCHHnrobDIsE2J10fP3EpCABCQggZ0joAAu1KUK4ELgM5lVAGcCrRkJNBE477zzVmHQHH1EMiwEMF7hw4cPrwRwFLJCs1f4s5/9rCAlIAEJSEACEtgPAgrgQv2sAC4EPpNZBXAm0JqRQJ0AIc94fRHBZIY+ePDgyuNLaPSRI0fOEcBxLyL55MmTZ49RkqoEJCABCUhAAjtLQAFcqGsVwIXAZzKrAM4EWjMS6CKAEEYUI4AvvfTS1TnA/Jsfzg0mIZZJsboo+nsJSEACEpDAzhBQABfqSgVwIfCZzCqAM4HWjAQkIAEJSEACEpCABAYQUAAPgDXnpQrgOWkury4F8PL6xBZJQAISkIAEJCABCUhAAVxoDCiAC4HPZFYBnAm0ZiQgAQlIQAISkIAEJDCAgAJ4AKw5L1UAz0lzeXUpgJfXJ7ZIAhKQgAQkIAEJSEACCuBCY0ABXAh8JrMK4EygNSMBCUhAAhKQgAQkIIEBBBTAA2DNeakCeE6ay6tLAby8PrFFEpCABCQgAQlIQAISUAAXGgMK4ELgM5lVAGcCrRkJSEACEpCABCQgAQkMIKAAHgBrzksVwHPSXF5dCuDl9YktkoAEJCABCUhAAhKQgAK40BhQABcCn8msAjgTaM1IQAISkIAEJCABCUhgAAEF8ABYc16qAJ6T5vLqUgAvr09skQQkIAEJSEACEpCABBTAhcaAArgQ+ExmFcCZQGtGAhKQgAQkIAEJSEACAwgogAfAmvNSBfCcNJdXlwJ4eX1iiyQgAQlIQAISkIAEJKAALjQGFMCFwGcyqwDOBFozEpCABCQgAQlIQAISGEBAATwA1pyXKoDnpLm8uhTAy+sTWyQBCUhAAhKQgAQkIAEFcKExoAAuBD6TWQVwJtCakYAEJCABCUhAAhKQwAACCuABsOa8VAE8J83l1aUAXl6f2CIJSEACEpCABCQgAQkogAuNAQVwIfCZzCqAM4HWjAQkIAEJSEACEpCABAYQUAAPgDXnpQrgOWkury4F8PL6xBZJQAISkIAEJCABCUhAAVxoDCiAC4HPZFYBnAm0ZiQgAQlIQAISkIAEJDCAgAJ4AKw5L1UAz0lzeXW9KaX0wPKaZYskIAEJSEACEpCABCSw1wQOp5Qe3GsChR5eAVwIfCaz9O8bU0rPZLKnGQlIQAISkIAEJCABCUhgPYGLUkoPpZS+KKj8BBTA+ZlrUQISkIAEJCABCUhAAhKQgAQKEFAAF4CuSQlIQAISkIAEJCABCUhAAhLIT0ABnJ+5FiUgAQlIQAISkIAEJCABCUigAAEFcAHompSABCQgAQlIQAISkIAEJCCB/AQUwPmZa1ECEpCABCQgAQlIQAISkIAEChBQABeArkkJSEACEpCABCQgAQlIQAISyE9AAZyfuRYlIAEJSEACEpCABCQgAQlIoAABBXAB6JqUgAQkIAEJSEACEpCABCQggfwEFMD5mWtRAhKQgAQkIAEJSEACEpCABAoQUAAXgK5JCUhAAhKQgAQkIAEJSEACEshPQAGcn7kWJSABCUhAAhKQgAQkIAEJSKAAAQVwAeialIAEJCABCUhAAhKQgAQkIIH8BBTA+ZlrUQISkIAEJCABCUhAAhKQgAQKEFAAF4CuSQlIQAISkIAEJCABCUhAAhLIT0ABnJ+5FiUgAQlIQAISkIAEJCABCUigAAEFcAHompSABCQgAQlIQAISkIAEJCCB/AQUwPmZa1ECEpCABCQgAQlIQAISkIAEChBQABeArkkJSEACEpCABCQgAQlIQAISyE9AAZyfuRYlIAEJSEACEpCABCQgAQlIoAABBXAB6JqUgAQkIAEJSEACEpCABCQggfwEFMD5mWtRAhKQgAQkIAEJSEACEpCABAoQUAAXgK5JCUhAAhKQgAQkIAEJSEACEshPQAGcn7kWJSABCUhAAhKQgAQkIAEJSKAAAQVwAeialIAEJCABCUhAAhKQgAQkIIH8BBTA+ZlrUQISkIAEJCABCUhAAhKQgAQKEFAAF4CuSQlIQAISkIAEJCABCUhAAhLIT0ABnJ+5FiUgAQlIQAISkIAEJCABCUigAAEFcAHompSABCQgAQlIQAISkIAEJCCB/AQUwPmZa1ECEpCABCQgAQlIQAISkIAEChBQABeArkkJSEACEpCABCQgAQlIQAISyE9AAZyfuRYlIAEJSEACEpCABCQgAQlIoAABBXAB6JqUgAQkIAEJSEACEpCABCQggfwEFMD5mWtRAhKQgAQkIAEJSEACEpCABAoQUAAXgK5JCUhAAhKQgAQkIAEJSEACEshPQAGcn7kWJSABCUhAAhKQgAQkIAEJSKAAAQVwAeialIAEJCABCUhAAhKQgAQkIIH8BBTA+ZlrUQISkIAEJCABCUhAAhKQgAQKEFAAF4CuSQlIQAISkIAEJCABCUhAAhLIT0ABnJ+5FiUgAQlIQAISkIAEJCABCUigAAEFcAHompSABCQgAQlIQAISkIAEJCCB/AQUwPmZa1ECEpCABCQgAQlIQAISkIAEChBQABeArkkJSEACEpCABCQgAQlIQAISyE9AAZyfuRYlIAEJSEACEpCABCQgAQlIoAABBXAB6JqUgAQkIAEJSEACEpCABCQggfwEFMD5mWtRAhKQgAQkIAEJSEACEpCABAoQUAAXgK5JCUhAAhKQgAQkIAEJSEACEshPQAGcn7kWJSABCUhAAhKQgAQkIAEJSKAAAQVwAeialIAEJCABCUhAAhKQgAQkIIH8BBTA+ZlrUQISkIAEJCABCUhAAhKQgAQKEFAAF4CuSQlIQAISkIAEJCABCUhAAhLIT0ABnJ+5FiUgAQlIQAISkIAEJCABCUigAAEFcAHompSABCQgAQlIQAISkIAEJCCB/AQUwPmZa1ECEpCABCQgAQlIQAISkIAEChBQABeArkkJSEACEpCABCQgAQlIQAISyE9AAZyfuRYlIAEJSEACEpCABCQgAQlIoAABBXAB6JqUgAQkIAEJSEACEpCABCQggfwEFMD5mWtRAhKQgAQkIAEJSEACEpCABAoQUAAXgK5JCUhAAhKQgAQkIAEJSEACEshPQAGcn7kWJSABCUhAAhKQgAQkIAEJSKAAAQVwAeialIAEJCABCUhAAhKQgAQkIIH8BBTA+ZlrUQISkIAEJCABCUhAAhKQgAQKEFAAF4CuSQlIQAISkIAEJCABCUhAAhLIT0ABnJ+5FiUgAQlIQAISkIAEJCABCUigAAEFcAHompSABCQgAQlIQAISkIAEJCCB/AQUwPmZa1ECEpCABCQgAQlIQAISkIAEChBQABeArkkJSEACEpCABCQgAQlIQAISyE9AAZyfuRYlIAEJSEACEpCABCQgAQlIoAABBXAB6JqUgAQkIAEJSEACEpCABCQggfwEFMD5mWtRAhKQgAQkIAEJSEACEpCABAoQUAAXgK5JCUhAAhKQgAQkIAEJSEACEshPQAGcn7kWJSABCUhAAhKQgAQkIAEJSKAAgf8fExrFy8CFNPYAAAAASUVORK5CYII=\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Mercator of World\n",
"plt.figure(1, figsize=(12,6))\n",
"m1 = Basemap(projection='merc',\n",
" llcrnrlat=-60,\n",
" urcrnrlat=65,\n",
" llcrnrlon=-180,\n",
" urcrnrlon=180,\n",
" lat_ts=0,\n",
" resolution='c')\n",
"\n",
"m1.fillcontinents(color='#191919',lake_color='#000000') # dark grey land, black lakes\n",
"m1.drawmapboundary(fill_color='#000000') # black background\n",
"m1.drawcountries(linewidth=0.1, color=\"w\") # thin white line for country borders\n",
"\n",
"# Plot the data\n",
"mxy = m1(events[\"longitude\"].tolist(), events[\"latitude\"].tolist())\n",
"m1.scatter(mxy[0], mxy[1], s=3, c=\"#1292db\", lw=0, alpha=1, zorder=5)\n",
"\n",
"#plt.title(\"Global view of events\")\n",
"plt.show()\n",
"\n",
"events_train = train.merge(events, how='left', on='device_id')\n",
"\n",
"female_events = events_train[events_train['gender'] == 'F']\n",
"male_events = events_train[events_train['gender'] == 'M']"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\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>"
]
},
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"output_type": "display_data"
},
{
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\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x16d9fa20>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.figure(2, figsize=(12,6))\n",
"m2 = Basemap(projection='merc',\n",
" llcrnrlat=-60,\n",
" urcrnrlat=65,\n",
" llcrnrlon=-180,\n",
" urcrnrlon=180,\n",
" lat_ts=0,\n",
" resolution='c')\n",
"\n",
"m2.fillcontinents(color='#191919',lake_color='#000000') # dark grey land, black lakes\n",
"m2.drawmapboundary(fill_color='#000000') # black background\n",
"m2.drawcountries(linewidth=0.1, color=\"w\") # thin white line for country borders\n",
"\n",
"# Plot the data\n",
"mxy = m2(female_events[\"longitude\"].tolist(), female_events[\"latitude\"].tolist())\n",
"m2.scatter(mxy[0], mxy[1], s=1, c=\"#f60087\", lw=0, alpha=0.5, zorder=5)\n",
"\n",
"mxy = m2(male_events[\"longitude\"].tolist(), male_events[\"latitude\"].tolist())\n",
"m2.scatter(mxy[0], mxy[1], s=1, c=\"#0087f6\", lw=0, alpha=0.5, zorder=5)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": 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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\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>"
]
},
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"output_type": "display_data"
},
{
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\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x4ade5710>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.figure(3, figsize=(12,12))\n",
"m3 = Basemap(projection='merc',\n",
" llcrnrlat=0,\n",
" urcrnrlat=55,\n",
" llcrnrlon=75,\n",
" urcrnrlon=145,\n",
" lat_ts=0,\n",
" resolution='c')\n",
"\n",
"m3.fillcontinents(color='#191919',lake_color='#000000') # dark grey land, black lakes\n",
"m3.drawmapboundary(fill_color='#000000') # black background\n",
"m3.drawcountries(linewidth=0.1, color=\"w\") # thin white line for country borders\n",
"\n",
"# Plot the data\n",
"mxy = m3(female_events[\"longitude\"].tolist(), female_events[\"latitude\"].tolist())\n",
"m3.scatter(mxy[0], mxy[1], s=3, c=\"#ff69b4\", lw=0, alpha=0.5, zorder=5)\n",
"\n",
"mxy = m3(male_events[\"longitude\"].tolist(), male_events[\"latitude\"].tolist())\n",
"m3.scatter(mxy[0], mxy[1], s=3, c=\"#1292db\", lw=0, alpha=0.5, zorder=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"The tables above show that the majority of the events happen around (0,0) which\n",
"is located in the middle of the Atlantic Ocean. It is safe to assume that these\n",
"logs on position are a product of users not wanting to share their position and\n",
"therefore are useless. The majority of the other coordinates are located in China,\n",
"only a few pin users in other parts of the world. Although a third of the data\n",
"bears no information I still think it is worth to use the information on position\n",
"as the two figures show there is a cerain difference of distribution between females and males\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Make a pivot table showing average age per area of a grid, also store the counts\n",
"\n",
"age_train = train.merge(events, how='left', on='device_id')\n",
"age_train = age_train.fillna(-1)\n",
"age_train['lon_round2'] = age_train['longitude'].round(decimals=2)\n",
"age_train['lat_round2'] = age_train['latitude'].round(decimals=2)\n",
"age_train['lon_round1'] = age_train['longitude'].round(decimals=1)\n",
"age_train['lat_round1'] = age_train['latitude'].round(decimals=1)\n",
"age_train['lon_round'] = age_train['longitude'].round()\n",
"age_train['lat_round'] = age_train['latitude'].round()\n",
"# remove those events taking place in the middle of the ocean"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(388, 314)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Sample it down to only the China region\n",
"lon_min, lon_max = 75, 135\n",
"lat_min, lat_max = 15, 55\n",
"\n",
"idx_china = (age_train[\"longitude\"]>lon_min) &\\\n",
" (age_train[\"longitude\"]<lon_max) &\\\n",
" (age_train[\"latitude\"]>lat_min) &\\\n",
" (age_train[\"latitude\"]<lat_max)\n",
"\n",
"age_train_china = (age_train[idx_china])\n",
"age_train_china.shape\n",
"df_age_china = pd.pivot_table(age_train_china,\n",
" values='age',\n",
" index='lon_round1',\n",
" columns='lat_round1',\n",
" aggfunc=np.mean)\n",
"df_age_china.shape\n",
"df_cnt_china = pd.pivot_table(age_train_china,\n",
" values='age',\n",
" index='lon_round1',\n",
" columns='lat_round1',\n",
" aggfunc='count')\n",
"df_cnt_china.shape\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\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"
],
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\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib import gridspec\n",
"\n",
"plt.figure(6, figsize=(12,6))\n",
"gs = gridspec.GridSpec(1, 3, width_ratios=[0.935, 1, 1])\n",
"plt.subplot(gs[0])\n",
"m5c = Basemap(projection='merc',\n",
" llcrnrlat=18,\n",
" urcrnrlat=45,\n",
" llcrnrlon=100,\n",
" urcrnrlon=125,\n",
" lat_ts=35,\n",
" resolution='c')\n",
"# m5c.fillcontinents(color='#191919',lake_color='#000000') # dark grey land, black lakes\n",
"m5c.drawmapboundary(fill_color='#000000') # black background\n",
"# m5c.drawcountries(linewidth=0.1, color=\"w\") # thin white line for country borders\n",
"\n",
"# Plot the data\n",
"mxy = m5c(female_events[\"longitude\"].tolist(), female_events[\"latitude\"].tolist())\n",
"m5c.scatter(mxy[0], mxy[1], s=1, c=\"#f60087\", lw=0, alpha=0.5, zorder=5)\n",
"\n",
"mxy = m5c(male_events[\"longitude\"].tolist(), male_events[\"latitude\"].tolist())\n",
"m5c.scatter(mxy[0], mxy[1], s=1, c=\"#005296\", lw=0, alpha=0.5, zorder=5)\n",
"\n",
"plt.title('Events distribution divided by gender')\n",
"\n",
"plt.subplot(gs[1])\n",
"m5a = Basemap(projection='merc',\n",
" llcrnrlat=18,\n",
" urcrnrlat=45,\n",
" llcrnrlon=100,\n",
" urcrnrlon=125,\n",
" lat_ts=35,\n",
" resolution='c')\n",
"\n",
"# Construct a heatmap\n",
"lons = df_age_china.index.values\n",
"lats = df_age_china.columns.values\n",
"x, y = np.meshgrid(lons, lats)\n",
"px, py = m5a(x, y)\n",
"data_values = df_age_china.values\n",
"masked_data = np.ma.masked_invalid(data_values.T)\n",
"cmap= plt.cm.jet_r\n",
"cmap.set_bad(color=\"#000000\")\n",
"\n",
"# Plot the heatmap\n",
"m5a.pcolormesh(px, py, masked_data, cmap=cmap, zorder=5)\n",
"m5a.colorbar().set_label('average age')\n",
"plt.title('Average age per grid area in China')\n",
"\n",
"plt.subplot(gs[2])\n",
"m5b = Basemap(projection='merc',\n",
" llcrnrlat=18,\n",
" urcrnrlat=45,\n",
" llcrnrlon=100,\n",
" urcrnrlon=125,\n",
" lat_ts=35,\n",
" resolution='c')\n",
"# Construct a heatmap\n",
"data_values = df_cnt_china.values\n",
"masked_data = np.ma.masked_invalid(data_values.T)\n",
"cmap = plt.cm.jet_r\n",
"cmap.set_bad(color=\"#000000\")\n",
"# Plot the heatmap\n",
"m5b.pcolormesh(px, py, masked_data, cmap=cmap, zorder=5)\n",
"m5b.colorbar().set_label(\"count\")\n",
"plt.title(\"Event count per grid area in China\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"# Sample it down to only the Beijing region\n",
"lon_min, lon_max = 116, 117\n",
"lat_min, lat_max = 39.75, 40.25\n",
"\n",
"idx_beijing = (age_train[\"longitude\"]>lon_min) &\\\n",
" (age_train[\"longitude\"]<lon_max) &\\\n",
" (age_train[\"latitude\"]>lat_min) &\\\n",
" (age_train[\"latitude\"]<lat_max)\n",
"df_plot = age_train[idx_beijing]\n",
"\n",
"df_age_beij = pd.pivot_table(df_plot,\n",
" values='age',\n",
" index='longitude',\n",
" columns='latitude',\n",
" aggfunc=np.mean)\n",
"\n",
"df_cnt_beij = pd.pivot_table(df_plot,\n",
" values='age',\n",
" index='longitude',\n",
" columns='latitude',\n",
" aggfunc='count')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\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"
],
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\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"# Age plot\n",
"plt.figure(7, figsize=(12,6))\n",
"# Plot avg age per grid\n",
"plt.subplot(121)\n",
"m5a = Basemap(projection='merc',\n",
" llcrnrlat=lat_min,\n",
" urcrnrlat=lat_max,\n",
" llcrnrlon=lon_min,\n",
" urcrnrlon=lon_max,\n",
" lat_ts=35,\n",
" resolution='c')\n",
"# Construct a heatmap\n",
"lons = df_age_beij.index.values\n",
"lats = df_age_beij.columns.values\n",
"x, y = np.meshgrid(lons, lats)\n",
"px, py = m5a(x, y)\n",
"data_values = df_age_beij.values\n",
"masked_data = np.ma.masked_invalid(data_values.T)\n",
"cmap = plt.cm.viridis\n",
"cmap.set_bad(color=\"#191919\")\n",
"# Plot the heatmap\n",
"m5a.pcolormesh(px, py, masked_data, cmap=cmap, zorder=5)\n",
"m5a.colorbar().set_label(\"average age\")\n",
"plt.title(\"Average age per grid area in Beijing\")\n",
"\n",
"# Plot count per grid\n",
"plt.subplot(122)\n",
"m5b = Basemap(projection='merc',\n",
" llcrnrlat=lat_min,\n",
" urcrnrlat=lat_max,\n",
" llcrnrlon=lon_min,\n",
" urcrnrlon=lon_max,\n",
" lat_ts=35,\n",
" resolution='c')\n",
"# Construct a heatmap\n",
"data_values = df_cnt_beij.values\n",
"masked_data = np.ma.masked_invalid(data_values.T)\n",
"cmap = plt.cm.viridis\n",
"cmap.set_bad(color=\"#191919\")\n",
"# Plot the heatmap\n",
"m5b.pcolormesh(px, py, masked_data, cmap=cmap, zorder=5)\n",
"m5b.colorbar().set_label(\"count\")\n",
"plt.title(\"Event count per grid area in Beijing\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\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": {
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\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#middle of the oocean\n",
"lon_min, lon_max = -5, 5\n",
"lat_min, lat_max = -5, 5\n",
"\n",
"plt.figure(8, figsize=(12,6))\n",
"m2 = Basemap(projection='merc',\n",
" llcrnrlat=lat_min,\n",
" urcrnrlat=lat_max,\n",
" llcrnrlon=lon_min,\n",
" urcrnrlon=lon_max,\n",
" lat_ts=0,\n",
" resolution='c')\n",
"\n",
"m2.fillcontinents(color='#191919',lake_color='#000000') # dark grey land, black lakes\n",
"m2.drawmapboundary(fill_color='#000000') # black background\n",
"m2.drawcountries(linewidth=0.1, color=\"w\") # thin white line for country borders\n",
"\n",
"# Plot the data\n",
"mxy = m2(female_events[\"longitude\"].tolist(), female_events[\"latitude\"].tolist())\n",
"m2.scatter(mxy[0], mxy[1], s=3, c=\"#ff69b4\", lw=0, alpha=0.5, zorder=5)\n",
"\n",
"mxy = m2(male_events[\"longitude\"].tolist(), male_events[\"latitude\"].tolist())\n",
"m2.scatter(mxy[0], mxy[1], s=3, c=\"#1292db\", lw=0, alpha=0.5, zorder=5)\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": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
astro4dev/OAD-Data-Science-Toolkit | Teaching Materials/Programming/Python/PythonISYA2018/04.Astropy/03_FITS.ipynb | 1 | 1443123 | null | gpl-3.0 |
RedHatInsights/insights-core | docs/notebooks/Diagnostic Walkthrough.ipynb | 1 | 8327 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Diagnostic Walkthrough\n",
"\n",
"A simple use-case for troubleshooting is the identification of installed software on the system. In this example, we will examine checking a system for the usage of `bash` based on data from the `rpm` command. This \"walkthrough\" will avoid going into details. Instead, it will simply lay out how the use-case could be handled using `insights-core`. More detailed tutorials can be found [in the docs](http://insights-core.readthedocs.io/en/latest/).\n",
"\n",
"We'll assume we have `insights-core` already installed following the [instructions on the README.rst](https://github.com/RedHatInsights/insights-core/blob/master/README.rst). Next we need to import the necessary modules."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0, \"../..\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from insights import rule, make_fail, make_pass, run\n",
"from insights.parsers.installed_rpms import InstalledRpms"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"The first import line has the most common components used when creating rules. The `rule` decorator marks a function that encodes logic to be applied by the framework and the required or optional components it needs to execute. `@rule` decorated functions use `make_fail` or `make_pass` to return results. The `run` method executes the system, simplifying usage of `insights-core` for small, standalone scripts and from the python interpreter.\n",
"\n",
"We also import the [InstalledRpms parser](http://insights-core.readthedocs.io/en/latest/shared_parsers_catalog/installed_rpms.html#installedrpms-command-rpm-qa). This is a class that structures the results of the `rpm -qa` command.\n",
"\n",
"Next, we create our \"rule\" function."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"@rule(InstalledRpms)\n",
"def report(rpms):\n",
" rpm = rpms.get_max(\"bash\")\n",
" if rpm:\n",
" return make_pass(\"BASH_INSTALLED\", version=rpm.nvr)\n",
" return make_fail(\"BASH_INSTALLED\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Here, the `report` method will let us know if the `bash` package is installed, and if so, the latest version encountered. The name of the function isn't important. The `@rule` decorator defines the `report` function as a \"rule\" component type and indicates that it depends on the `InstalledRpms` parser. This parser will be passed into the function as the first argument.\n",
"\n",
"The rest of the `report` function is fairly easy to understand, noting that the `get_max` function returns the maximum version encountered of the package specified, or `None` if the package is not found.\n",
"\n",
"Let's try running this function using the `run` method."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"{'pass_key': 'BASH_INSTALLED',\n",
" 'type': 'pass',\n",
" 'version': u'bash-4.4.23-1.fc28'}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results = run(report)\n",
"results[report]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"The `run` command executed the framework collecting `rpm` information from my system, parsing it using the `InstalledRpms` class, and then running the `report` function. It found that `bash` was installed. \n",
"\n",
"The results are keyed by function (`report` in this case). Multiple functions can be executed, each with its own response.\n",
"\n",
"The InstalledRpms class has structured the results of the `rpm -qa` command, parsing the rows from the command output. That is, each package NVR is separated into its own fields. One consequence of this is that the package name is distinct. When we look for `bash`, the parser doesn't match, for example, `bash-completion` (also on my system.) It also means the version information is understood. So, we can do things like check a range of versions.\n",
"\n",
"First, let's define our range using the `bash` NVRs we care about. We'll imagine there's a particular bug that affects `bash` starting in 4.4.16-1 and is fixed in 4.4.22-1."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from insights.parsers.installed_rpms import InstalledRpm\n",
"\n",
"lower = InstalledRpm.from_package(\"bash-4.4.16-1.fc27\")\n",
"upper = InstalledRpm.from_package(\"bash-4.4.22-1.fc27\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Now, we'll modify the `report` function to check ranges."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"@rule(InstalledRpms)\n",
"def report(rpms):\n",
" rpm = rpms.get_max(\"bash\")\n",
" if rpm and rpm >= lower and rpm < upper:\n",
" return make_fail(\"BASH_AFFECTED\", version=rpm.nvr)\n",
" elif rpm:\n",
" return make_pass(\"BASH_AFFECTED\", version=rpm.nvr)\n",
" else:\n",
" return make_pass(\"NO_BASH\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Now we can run this as before."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"{'pass_key': 'BASH_AFFECTED', 'type': 'pass', 'version': u'bash-4.4.23-1.fc28'}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results = run(report)\n",
"results[report]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"A few notes about this example:\n",
"\n",
"- The code here could be packaged up in a script, along with other rules, to be easily reused.\n",
"- The rule can be executed against a live host, sosreport, Red Hat Insights archive, or a directory formed from an expanded archive.\n",
"- While we defined only a rule, we could also define other components like the command to be run and a parser to structure the content. The [stand_alone.py](https://github.com/RedHatInsights/insights-core/blob/master/stand_alone.py) is a simple example containing these three components. \n",
"\n",
"The code above, (and this notebook) can be executed if `insights-core` (and jupyter-notebook) is installed. So feel free to run and experiment with the example."
]
}
],
"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
}
| apache-2.0 |
wujinjun/TFbook | chapter6/VGGNet-16.ipynb | 1 | 23921 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from datetime import datetime\n",
"import math\n",
"import time\n",
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def conv_op(input_op, name, kh, kw, n_out, dh, dw, p):\n",
" n_in = input_op.get_shape()[-1].value\n",
" \n",
"\n",
" with tf.name_scope(name) as scope:\n",
" kernel = tf.get_variable(scope+\"w\",shape=[kh, kw, n_in, n_out],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer_conv2d())\n",
" conv = tf.nn.conv2d(input_op, kernel, (1,dh,dw,1),padding='SAME')\n",
" bias_init_val = tf.constant(0.0,shape=[n_out],dtype=tf.float32)\n",
" biases = tf.Variable(bias_init_val,trainable=True,name='b')\n",
" z = tf.nn.bias_add(conv,biases)\n",
" activation = tf.nn.relu(z,name=scope)\n",
" p += [kernel, biases]\n",
" return activation"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def fc_op(input_op, name, n_out, p):\n",
" n_in = input_op.get_shape()[-1].value\n",
" \n",
" with tf.name_scope(name) as scope:\n",
" kernel = tf.get_variable(scope+\"w\",shape=[n_in,n_out],dtype= tf.float32,initializer=tf.contrib.layers.xavier_initializer())\n",
" biases = tf.Variable(tf.constant(0.1,shape=[n_out],dtype=tf.float32),name='b')\n",
" avtivation = tf.nn.relu_layer(input_op,kernel,biases,name=scope)\n",
" p += [kernel,biases]\n",
" return avtivation"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def mpool_op(input_op,name, kh,kw,dh,dw):\n",
" return tf.nn.max_pool(input_op,ksize=[1,kh,kw,1],strides=[1,dh,dw,1],padding='SAME',name=name)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def inference_op(input_op,keep_prob):\n",
" p = []\n",
" conv1_1 = conv_op(input_op,name=\"conv1_1\",kh=3,kw=3,n_out=64,dh=1,dw=1,p=p)\n",
" conv1_2 = conv_op(conv1_1,name=\"conv1_2\",kh=3,kw=3,n_out=64,dh=1,dw=1,p=p)\n",
" pool1 = mpool_op(conv1_2,name=\"pool1\",kh=2,kw=2,dw=2,dh=2)\n",
" \n",
" conv2_1 = conv_op(pool1,name=\"conv2_1\",kh=3,kw=3,n_out=128,dh=1,dw=1,p=p)\n",
" conv2_2 = conv_op(conv2_1,name=\"conv2_2\",kh=3,kw=3,n_out=128,dh=1,dw=1,p=p)\n",
" pool2 = mpool_op(conv2_2,name=\"pool2\",kh=2,kw=2,dh=2,dw=2)\n",
" \n",
" conv3_1 = conv_op(pool2,name=\"conv3_1\",kh=3,kw=3,n_out=256,dh=1,dw=1,p=p)\n",
" conv3_2 = conv_op(conv3_1,name=\"conv3_2\",kh=3,kw=3,n_out=256,dh=1,dw=1,p=p)\n",
" conv3_3 = conv_op(conv3_2,name=\"conv3_3\",kh=3,kw=3,n_out=256,dh=1,dw=1,p=p)\n",
" pool3 = mpool_op(conv3_3,name=\"pool3\",kh=2,kw=2,dh=2,dw=2)\n",
" \n",
" conv4_1 = conv_op(pool3,name=\"conv4_1\",kh=3,kw=3,n_out=512,dh=1,dw=1,p=p)\n",
" conv4_2 = conv_op(conv4_1,name=\"conv4_2\",kh=3,kw=3,n_out=512,dh=1,dw=1,p=p)\n",
" conv4_3 = conv_op(conv4_2,name=\"conv4_3\",kh=3,kw=3,n_out=512,dh=1,dw=1,p=p)\n",
" pool4 = mpool_op(conv4_3,name=\"pool4\",kh=2,kw=2,dh=2,dw=2)\n",
" \n",
" conv5_1 = conv_op(pool4,name=\"conv5_1\",kh=3,kw=3,n_out=512,dh=1,dw=1,p=p)\n",
" conv5_2 = conv_op(conv5_1,name=\"conv5_2\",kh=3,kw=3,n_out=512,dh=1,dw=1,p=p)\n",
" conv5_3 = conv_op(conv5_2,name=\"conv5_3\",kh=3,kw=3,n_out=512,dh=1,dw=1,p=p)\n",
" pool5 = mpool_op(conv5_3,name=\"pool5\",kh=2,kw=2,dh=2,dw=2)\n",
" \n",
" shp = pool5.get_shape()\n",
" flattened_shape = shp[1].value * shp[2].value * shp[3].value\n",
" resh1 = tf.reshape(pool5,[-1,flattened_shape],name=\"resh1\")\n",
" \n",
" fc6 = fc_op(resh1,name=\"fc6\",n_out=4096,p=p)\n",
" fc6_drop = tf.nn.dropout(fc6,keep_prob,name=\"fc6_drop\")\n",
" \n",
" fc7 = fc_op(fc6_drop,name=\"fc7\",n_out=4096,p=p)\n",
" fc7_drop = tf.nn.dropout(fc7,keep_prob,name=\"fc7_drop\")\n",
" \n",
" fc8 = fc_op(fc7_drop,name=\"fc8\",n_out=1000,p=p)\n",
" softmax=tf.nn.softmax(fc8)\n",
" predictions = tf.argmax(softmax,1)\n",
" return predictions,softmax,fc8,p"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def time_tensprflow_run(session,target,feed,info_string):\n",
" num_step_burn_in =10\n",
" total_duration = 0.0\n",
" total_duration_squared = 0.0\n",
" for i in range(num_batches+num_step_burn_in):\n",
" start_time =time.time()\n",
" _ = session.run(target,feed_dict=feed)\n",
" duration = time.time()-start_time\n",
" if i >= num_step_burn_in:\n",
" if not i%10:\n",
" print('%s: step %d, duration = %.3f' % (datetime.now(),i-num_step_burn_in,duration))\n",
" total_duration += duration\n",
" total_duration_squared += duration * duration\n",
" mn = total_duration / num_batches\n",
" vr = total_duration_squared / num_batches - mn * mn\n",
" sd = math.sqrt(vr)\n",
" print('%s: %s across %d step, %.3f +/- %.3f sec/batch' % (datetime.now(),info_string,num_batches,mn,sd))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2017-03-27 20:46:11.774594: step 0, duration = 0.151\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-5752525f060f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m32\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mnum_batches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mrun_benchmark\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-7-5752525f060f>\u001b[0m in \u001b[0;36mrun_benchmark\u001b[0;34m()\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0msess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mtime_tensprflow_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mkeep_prob\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"Forward\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0mobjective\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0ml2_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfc8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mgrad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgradients\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjective\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-6-c0d6244983f2>\u001b[0m in \u001b[0;36mtime_tensprflow_run\u001b[0;34m(session, target, feed, info_string)\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_batches\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mnum_step_burn_in\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mstart_time\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[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0mduration\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[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mnum_step_burn_in\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/wjj/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 765\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 766\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 767\u001b[0;31m run_metadata_ptr)\n\u001b[0m\u001b[1;32m 768\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 769\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/wjj/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 963\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 964\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 965\u001b[0;31m feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m 966\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 967\u001b[0m \u001b[0mresults\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/home/wjj/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1013\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1014\u001b[0m return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1015\u001b[0;31m target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m 1016\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1017\u001b[0m return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
"\u001b[0;32m/home/wjj/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1020\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1021\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\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 1023\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1024\u001b[0m \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/wjj/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m 1002\u001b[0m return tf_session.TF_Run(session, options,\n\u001b[1;32m 1003\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m status, run_metadata)\n\u001b[0m\u001b[1;32m 1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1006\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"def run_benchmark():\n",
" with tf.Graph().as_default():\n",
" image_size = 224\n",
" images = tf.Variable(tf.random_normal([batch_size,\n",
" image_size,\n",
" image_size,3],\n",
" dtype=tf.float32,\n",
" stddev=1e-1))\n",
" \n",
" keep_prob = tf.placeholder(tf.float32)\n",
" predictions,softmax,fc8,p = inference_op(images,keep_prob)\n",
" \n",
" init = tf.global_variables_initializer()\n",
" sess = tf.Session()\n",
" sess.run(init)\n",
" time_tensprflow_run(sess,predictions,{keep_prob:1.0},\"Forward\")\n",
" objective = tf.nn.l2_loss(fc8)\n",
" grad = tf.gradients(objective,p)\n",
" time_tensprflow_run(sess,grad,{keep_prob:0.5},\"Forward-backward\")\n",
"\n",
"batch_size = 32\n",
"num_batches = 100\n",
"run_benchmark()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def conv_op(input_op, name, kh, kw, n_out, dh, dw, p):\n",
" n_in = input_op.get_shape()[-1].value\n",
" \n",
" with tf.name_scope(name) as scope:\n",
" kernel = tf.get_variable(scope+\"w\",shape=[kh, kw, n_in, n_out],dtype=tf.float32,initializer=tf.contrib.layers.xavier_initializer_conv2d())\n",
" conv = tf.nn.conv2d(input_op, kernel, (1,dh,dw,1),padding='SAME')\n",
" bias_init_val = tf.constant(0.0,shape=[n_out],dtype=tf.float32)\n",
" biases = tf.Variable(bias_init_val,trainable=True,name='b')\n",
" z = tf.nn.bias_add(conv,biases)\n",
" activation = tf.nn.relu(z,name=scope)\n",
" p += [kernel, biases]\n",
" return activation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def fc_op(input_op, name, n_out, p):\n",
" n_in = input_op.get_shape()[-1].value\n",
" \n",
" with tf.name_scope(name) as scope:\n",
" kernel = tf.get_variable(scope+\"w\",shape=[n_in,n_out],dtype= tf.float32,initializer=tf.contrib.layers.xavier_initializer())\n",
" biases = tf.Variable(tf.constant(0.1,shape=[n_out],dtype=tf.float32),name='b')\n",
" avtivation = tf.nn.relu_layer(input_op,kernel,biases,name=scope)\n",
" p += [kernel,biases]\n",
" return avtivation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def mpool_op(input_op,name, kh,kw,dh,dw):\n",
" return tf.nn.max_pool(input_op,ksize=[1,kh,kw,1],strides=[1,dh,dw,1],padding='SAME',name=name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def inference_op(input_op,keep_prob):\n",
" p = []\n",
" conv1_1 = conv_op(input_op,name=\"conv1_1\",kh=3,kw=3,n_out=64,dh=1,dw=1,p=p)\n",
" conv1_2 = conv_op(conv1_1,name=\"conv1_2\",kh=3,kw=3,n_out=64,dh=1,dw=1,p=p)\n",
" pool1 = mpool_op(conv1_2,name=\"pool1\",kh=2,kw=2,dw=2,dh=2)\n",
" \n",
" conv2_1 = conv_op(pool1,name=\"conv2_1\",kh=3,kw=3,n_out=128,dh=1,dw=1,p=p)\n",
" conv2_2 = conv_op(conv2_1,name=\"conv2_2\",kh=3,kw=3,n_out=128,dh=1,dw=1,p=p)\n",
" pool2 = mpool_op(conv2_2,name=\"pool2\",kh=2,kw=2,dh=2,dw=2)\n",
" \n",
" conv3_1 = conv_op(pool2,name=\"conv3_1\",kh=3,kw=3,n_out=256,dh=1,dw=1,p=p)\n",
" conv3_2 = conv_op(conv3_1,name=\"conv3_2\",kh=3,kw=3,n_out=256,dh=1,dw=1,p=p)\n",
" conv3_3 = conv_op(conv3_2,name=\"conv3_3\",kh=3,kw=3,n_out=256,dh=1,dw=1,p=p)\n",
" pool3 = mpool_op(conv3_3,name=\"pool3\",kh=2,kw=2,dh=2,dw=2)\n",
" \n",
" conv4_1 = conv_op(pool3,name=\"conv4_1\",kh=3,kw=3,n_out=512,dh=1,dw=1,p=p)\n",
" conv4_2 = conv_op(conv4_1,name=\"conv4_2\",kh=3,kw=3,n_out=512,dh=1,dw=1,p=p)\n",
" conv4_3 = conv_op(conv4_2,name=\"conv4_3\",kh=3,kw=3,n_out=512,dh=1,dw=1,p=p)\n",
" pool4 = mpool_op(conv4_3,name=\"pool4\",kh=2,kw=2,dh=2,dw=2)\n",
" \n",
" conv5_1 = conv_op(pool4,name=\"conv5_1\",kh=3,kw=3,n_out=512,dh=1,dw=1,p=p)\n",
" conv5_2 = conv_op(conv5_1,name=\"conv5_2\",kh=3,kw=3,n_out=512,dh=1,dw=1,p=p)\n",
" conv5_3 = conv_op(conv5_2,name=\"conv5_3\",kh=3,kw=3,n_out=512,dh=1,dw=1,p=p)\n",
" pool5 = mpool_op(conv5_3,name=\"pool5\",kh=2,kw=2,dh=2,dw=2)\n",
" \n",
" shp = pool5.get_shape()\n",
" flattened_shape = shp[1].value * shp[2].value * shp[3].value\n",
" resh1 = tf.reshape(pool5,[-1,flattened_shape],name=\"resh1\")\n",
" \n",
" fc6 = fc_op(resh1,name=\"fc6\",n_out=4096,p=p)\n",
" fc6_drop = tf.nn.dropout(fc6,keep_prob,name=\"fc6_drop\")\n",
" \n",
" fc7 = fc_op(fc6_drop,name=\"fc7\",n_out=4096,p=p)\n",
" fc7_drop = tf.nn.dropout(fc7,keep_prob,name=\"fc7_drop\")\n",
" \n",
" fc8 = fc_op(fc7_drop,name=\"fc8\",n_out=1000,p=p)\n",
" softmax=tf.nn.softmax(fc8)\n",
" predictions = tf.argmax(softmax,1)\n",
" return predictions,softmax,fc8,p"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def time_tensprflow_run(session,target,feed,info_string):\n",
" num_step_burn_in =10\n",
" total_duration = 0.0\n",
" total_duration_squared = 0.0\n",
" for i in range(num_batches+num_step_burn_in):\n",
" start_time =time.time()\n",
" _ = session.run(target,feed_dict=feed)\n",
" duration = time.time()-start_time\n",
" if i >= num_step_burn_in:\n",
" if not i%10:\n",
" print('%s: step %d, duration = %.3f' % (datetime.now(),i-num_step_burn_in,duration))\n",
" total_duration += duration\n",
" total_duration_squared += duration * duration\n",
" mn = total_duration / num_batches\n",
" vr = total_duration_squared / num_batches - mn * mn\n",
" sd = math.sqrt(vr)\n",
" print('%s: %s across %d step, %.3f +/- %.3f sec/batch' % (datetime.now(),info_string,num_batches,mn,sd))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def run_benchmark():\n",
" with tf.Graph().as_default():\n",
" image_size = 224\n",
" images = tf.Variable(tf.random_normal([batch_size,\n",
" image_size,\n",
" image_size,3],\n",
" dtype=tf.float32,\n",
" stddev=1e-1))\n",
" \n",
" keep_prob = tf.placeholder(tf.float32)\n",
" predictions,softmax,fc8,p = inference_op(images,keep_prob)\n",
" \n",
" init = tf.global_variables_initializer()\n",
" sess = tf.Session()\n",
" sess.run(init)\n",
" time_tensprflow_run(sess,predictions,{keep_prob:1.0},\"Forward\")\n",
" objective = tf.nn.l2_loss(fc8)\n",
" grad = tf.gradients(objective,p)\n",
" time_tensprflow_run(sess,grad,{keep_prob:0.5},\"Forward-backward\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"batch_size = 32\n",
"num_batches = 100\n",
"run_benchmark()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
ipython/ipywidgets | docs/source/examples/Lorenz Differential Equations.ipynb | 1 | 211331 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploring the Lorenz System of Differential Equations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this Notebook we explore the Lorenz system of differential equations:\n",
"\n",
"$$\n",
"\\begin{aligned}\n",
"\\dot{x} & = \\sigma(y-x) \\\\\n",
"\\dot{y} & = \\rho x - y - xz \\\\\n",
"\\dot{z} & = -\\beta z + xy\n",
"\\end{aligned}\n",
"$$\n",
"\n",
"This is one of the classic systems in non-linear differential equations. It exhibits a range of different behaviors as the parameters (\\\\(\\sigma\\\\), \\\\(\\beta\\\\), \\\\(\\rho\\\\)) are varied."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, we import the needed things from IPython, NumPy, Matplotlib and SciPy."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from ipywidgets import interact, interactive\n",
"from IPython.display import clear_output, display, HTML"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy import integrate\n",
"\n",
"from matplotlib import pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"from matplotlib.colors import cnames\n",
"from matplotlib import animation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Computing the trajectories and plotting the result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We define a function that can integrate the differential equations numerically and then plot the solutions. This function has arguments that control the parameters of the differential equation (\\\\(\\sigma\\\\), \\\\(\\beta\\\\), \\\\(\\rho\\\\)), the numerical integration (`N`, `max_time`) and the visualization (`angle`)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def solve_lorenz(N=10, angle=0.0, max_time=4.0, sigma=10.0, beta=8./3, rho=28.0):\n",
"\n",
" fig = plt.figure()\n",
" ax = fig.add_axes([0, 0, 1, 1], projection='3d')\n",
" ax.axis('off')\n",
"\n",
" # prepare the axes limits\n",
" ax.set_xlim((-25, 25))\n",
" ax.set_ylim((-35, 35))\n",
" ax.set_zlim((5, 55))\n",
" \n",
" def lorenz_deriv(x_y_z, t0, sigma=sigma, beta=beta, rho=rho):\n",
" \"\"\"Compute the time-derivative of a Lorenz system.\"\"\"\n",
" x, y, z = x_y_z\n",
" return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]\n",
"\n",
" # Choose random starting points, uniformly distributed from -15 to 15\n",
" np.random.seed(1)\n",
" x0 = -15 + 30 * np.random.random((N, 3))\n",
"\n",
" # Solve for the trajectories\n",
" t = np.linspace(0, max_time, int(250*max_time))\n",
" x_t = np.asarray([integrate.odeint(lorenz_deriv, x0i, t)\n",
" for x0i in x0])\n",
" \n",
" # choose a different color for each trajectory\n",
" colors = plt.cm.viridis(np.linspace(0, 1, N))\n",
"\n",
" for i in range(N):\n",
" x, y, z = x_t[i,:,:].T\n",
" lines = ax.plot(x, y, z, '-', c=colors[i])\n",
" plt.setp(lines, linewidth=2)\n",
"\n",
" ax.view_init(30, angle)\n",
" plt.show()\n",
"\n",
" return t, x_t"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's call the function once to view the solutions. For this set of parameters, we see the trajectories swirling around two points, called attractors. "
]
},
{
"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": [
"t, x_t = solve_lorenz(angle=0, N=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using IPython's `interactive` function, we can explore how the trajectories behave as we change the various parameters."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4217a3a589674870ad61a74a938ca1e5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(IntSlider(value=10, description='N', max=50), FloatSlider(value=0.0, description='angle'…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"w = interactive(solve_lorenz, angle=(0.,360.), max_time=(0.1, 4.0), \n",
" N=(0,50), sigma=(0.0,50.0), rho=(0.0,50.0))\n",
"display(w)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The object returned by `interactive` is a `Widget` object and it has attributes that contain the current result and arguments:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"t, x_t = w.result"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'N': 10,\n",
" 'angle': 0.0,\n",
" 'beta': 2.6666666666666665,\n",
" 'max_time': 4.0,\n",
" 'rho': 28.0,\n",
" 'sigma': 10.0}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w.kwargs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After interacting with the system, we can take the result and perform further computations. In this case, we compute the average positions in \\\\(x\\\\), \\\\(y\\\\) and \\\\(z\\\\)."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"xyz_avg = x_t.mean(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10, 3)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xyz_avg.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Creating histograms of the average positions (across different trajectories) show that on average the trajectories swirl about the attractors."
]
},
{
"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(xyz_avg[:,0])\n",
"plt.title('Average $x(t)$');"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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(xyz_avg[:,1])\n",
"plt.title('Average $y(t)$');"
]
},
{
"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"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
"04e7f25477ca43ad85634c9042d8cbe8": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.2.0",
"model_name": "FloatSliderModel",
"state": {
"description": "angle",
"layout": "IPY_MODEL_dd849319216849a1adeb5b17b636bc4d",
"max": 360,
"step": 0.1,
"style": "IPY_MODEL_f42383cf926248908e94e47c779d850d"
}
},
"2cca762d801e42aaba2056ac1ace3d86": {
"model_module": "@jupyter-widgets/output",
"model_module_version": "1.0.0",
"model_name": "OutputModel",
"state": {
"layout": "IPY_MODEL_e6d69fd543f5409abc456480cf150c8b",
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 432x288 with 1 Axes>"
},
"metadata": {},
"output_type": "display_data"
}
]
}
},
"2e9cde1f1530426f99e67593be69c03f": {
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}
},
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"state": {
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],
"children": [
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"layout": "IPY_MODEL_4eab92b28d9a4bffaee27a87809f11e7"
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},
"4eab92b28d9a4bffaee27a87809f11e7": {
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},
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"model_name": "FloatSliderModel",
"state": {
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"layout": "IPY_MODEL_b5c51ae8954f4f419fd58592983f0e94",
"max": 4,
"min": 0.1,
"step": 0.1,
"style": "IPY_MODEL_71578a518db745029ba93f4f52d7929f",
"value": 4
}
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| bsd-3-clause |
evanmiltenburg/python-for-text-analysis | Chapters/Chapter 04 - Boolean Expressions and Conditions.ipynb | 1 | 33105 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Chapter 4 - Boolean Expressions and Conditions\n",
"\n",
"*This notebook uses code snippets and explanations from [this course](https://github.com/kadarakos/python-course/blob/master/Chapter%202%20-%20Conditions.ipynb).*\n",
"\n",
"So far, we have learned how to use Python as a basic calculator and how to store information in variables. Now we will set the first steps to an actual useful program. A lot of programming has to do with executing code if a particular condition holds. This enables a program to act upon its inputs. For example: an app on your phone might give a warning if the battery level is lower than 5%. This means that the program needs to check if the variable `battery_level` is lower than the value of 5. We can do these checks using so called Boolean expressions. These Boolean expressions are the main element in probably one of the most used things in Python: *if statements*.\n",
"\n",
"#### At the end of this topic, you will be able to:\n",
"* work with and understand *boolean expressions*\n",
"* work with and understand *if statements*\n",
"* understand what *indentation* is\n",
"* understand what *nesting* is\n",
"\n",
"\n",
"#### If you want to learn more about these topics, you might find the following links useful:\n",
"* Documentation: [Built-in Types (boolean expressions)](https://docs.python.org/3.5/library/stdtypes.html#)\n",
"* Video: [Python Booleans](https://www.youtube.com/watch?v=9OK32jb_TdI)\n",
"* Video: [Conditionals](https://www.youtube.com/watch?v=mQrci1kAwh4)\n",
"* Explanation: [if elif else](http://www.programiz.com/python-programming/if-elif-else)\n",
"\n",
"If you have **questions** about this chapter, please contact us([email protected])**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Boolean expressions\n",
"A **Boolean expression** (or simply: boolean) is an expression that results in the type `bool` in Python. Possible values are either **`True`** or **`False`**. Boolean expressions are the building blocks of programming. Any expression that results in `True` or `False` can be considered a Boolean expression. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So far you've mainly seen:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(type('this is a string'))\n",
"print(type(101))\n",
"print(type(0.8))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we're introducing:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(type(False))\n",
"print(type(True))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.1 Comparison operators\n",
"\n",
"Here is a list of **[comparison operators](https://docs.python.org/3/library/stdtypes.html#comparisons)** used in Boolean expressions:\n",
"\n",
"(You have already used these operators in the previous chapters, but we are treating them in more detail here.) \n",
"\n",
"\n",
"| Operator | Meaning | `True` | `False` |\n",
"|-----------|--------|--------|--------|\n",
"| `==` |\tequal\t | `2 == 2` | `2 == 3` | \n",
"| `!=` |\tnot equal\t| `3 != 2` | `2 != 2` |\n",
"| `<` | less than | `2 < 13` | `2 < 2` |\n",
"| `<=` |\tless than or equal to \t| `2 <= 2` | `3 <= 2` |\n",
"| `>` |\tgreater than \t | `13 > 2` | `2 > 13` |\n",
"| `>=` |\tgreather than or equal to | `3 >= 3` | `2 >= 3` |\n",
"\n",
"Remember that the single = is reserved for assignment! Boolean expressions look at variables but never change them."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(2 < 5)\n",
"print(2 <= 5)\n",
"print(3 > 7)\n",
"print(3 >= 7)\n",
"print(3 == 3)\n",
"print(\"school\" == \"school\")\n",
"print(\"Python\" != \"SPSS\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The relevant 'logical operators' that we used here are: <, <=, >,>=,==,!=. In Python-speak, we say that such a logical expression gets 'evaluated' when you run the code. The outcome of such an evaluation is a 'binary value' or a so-called 'boolean' that can take only two possible values: `True` or `False`. You can assign such a boolean to a variable:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"greater = 5 > 2\n",
"print(greater, type(greater))\n",
"greater = 5 < 2\n",
"print(greater, type(greater))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at some examples. Try to guess the output based on the information about the operators in the table above. Hence, will the expression result in `True` or `False` in the following examples?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(5 == 5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(5 == 4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(10 < 20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(10 < 8)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(10 < 10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(10 <= 10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(20 >= 21)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(20 == 20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(1 == '1')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(1 != 2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"boolean_expression = 5 == 4\n",
"print(boolean_expression)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2 Membership operators\n",
"Python also has so-called **[membership operators](https://docs.python.org/3.5/reference/expressions.html#not-in)**:\n",
"\n",
"| Operator | function | `True` | `False` |\n",
"|-----------|--------|--------|--------|\n",
"| `in` | left object is a member of right object | `\"c\" in \"cat\"` | `\"f\" in \"cat\"` |\n",
"| `not in` |left object is NOT a member of right object\t| `\"f\" not in \"cat\"` | `\"c\" not in \"cat\"` |\n",
"\n",
"We have already seen the operator **`in`** being used for checking whether a string (single or multiple characters) is a substring of another one:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"fun\" in \"function\")\n",
"print(\"pie\" in \"python\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can only use membership operators with *iterables* (i.e. python objects that can be split up into smaller components - e.g. characters of a string). The following will therefore not work, because an integer is not iterable:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(5 in 10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, we can use membership operators with other types of 'containers', such as *lists*. We will discuss lists in much more detail later on, but they represent ordered sequences of objects like strings, integers or a combination. We can use *in* and *not in* to check whether an object is a member of a list:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"letters = ['a','b','c','d']\n",
"numbers = [1,2,3,4,5]\n",
"mixed = [1,2,3,'a','b','c']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('a' in letters)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('g' not in letters)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('d' in mixed)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(1 in numbers)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(3 not in mixed)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('a' not in 'hello world')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.3 And, or, and not\n",
"\n",
"Finally, boolean operations are often performed using the [**boolean operators `and`, `or` and `not`**](https://docs.python.org/3.5/library/stdtypes.html#boolean-operations-and-or-not). Given two boolean expressions, **bool1** and **bool2**, this is how they work:\n",
"\n",
"| operation | function | `True` | `False` | \n",
"|-----------|--------|----------|---------|\n",
"| **bool1** `and` **bool2** | `True` if both **bool1** and **bool2** are `True`, otherwise `False` | (`5 == 5 and 3 < 5`) | (`5 == 5 and 3 > 5`) |\n",
"| **bool1** `or` **bool2** |\t`True` when at least one of the boolean expressions is `True`, otherwise `False`\t| (`5 == 5 or 3 > 5`) | (`5 != 5 or 3 > 5`) |\n",
"| `not` **bool1** | `True` if **bool1** is `False`, otherwise `False` | (`not 5 != 5`) | (`not 5 == 5`) |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are some examples of **`and`**:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"letters = ['a','b','c','d']\n",
"numbers = [1,2,3,4,5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('a' in letters and 2 in numbers)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"z\" in letters and 3 in numbers)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"f\" in letters and 0 in numbers)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are some examples of **`or`**:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"letters = ['a','b','c','d']\n",
"numbers = [1,2,3,4,5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('f' in letters or 2 in numbers)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('a' in letters or 2 in numbers)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('f' in letters or 10 in numbers)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are some example of **`not`**:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a_string = \"hello\"\n",
"letters = ['a','b','c','d']\n",
"numbers = [1,2,3,4,5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(not a_string.endswith(\"o\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(not a_string.startswith(\"o\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(not 'x' in letters)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(not 4 == 4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(not (4 == 4 and \"4\" == 4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that for some of these, there are alternative ways of writing them. For example, `'x not in y'` and `'not x in y'` are identical, and so are `'not x == y'` and `'x != y'`. For now, it does not really matter which one you use. If you want to read more about it, have a look [here](https://stackoverflow.com/questions/8738388/order-of-syntax-for-using-not-and-in-keywords) and [here](https://stackoverflow.com/questions/31026754/python-if-not-vs-if/31026976)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(not 'x' in letters)\n",
"print('x' not in letters)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(not 4 == 4)\n",
"print(4 != 4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.4 EXTRA: `all()` and `any()`\n",
"Take a look at the following example. Do you think it is clear?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"test\" != \"testing\" and 1 == 1 and 2 == 2 or 20 in [1, 20, 3, 4,5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Not really, right? Luckily, Python has another trick to deal with this type of examples: [**`all` and `any`**]((https://docs.python.org/3/library/functions.html#all). Given a list of boolean expressions, this is how they work:\n",
"\n",
"| operation | function |\n",
"|-----------|--------|\n",
"| `all` | True if all boolean expressions are True, otherwise False |\t\n",
"| `any` | True if at least one boolean expression is True, otherwise False |\n",
"\n",
"If you don't completely understand `all()` and `any()`, don't worry, you will not necessarily need them right now. They are just a nice alternative to make your code more readable and you may appreciate that in the future."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are some examples of **`all()`**:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"letters = ['a','b','c','d']\n",
"numbers = [1,2,3,4,5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"list_bools1 = ['a' in letters, \n",
" 2 in numbers]\n",
"print(list_bools1)\n",
"boolean_expression1 = all(list_bools1)\n",
"print(boolean_expression1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"list_bools2 = ['a' in letters, \n",
" 20 in numbers]\n",
"print(list_bools2)\n",
"boolean_expression2 = all(list_bools2)\n",
"print(boolean_expression2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are some examples of **`any()`**:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"list_bools3 = ['f' in letters, \n",
" 200 in numbers]\n",
"print(list_bools3)\n",
"boolean_expression3 = any(list_bools3)\n",
"print(boolean_expression3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"list_bools4 = ['a' in letters, \n",
" 20 in numbers,\n",
" 2 in numbers]\n",
"print(list_bools4)\n",
"boolean_expression4 = any(list_bools4)\n",
"print(boolean_expression4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Conditions: `if` statements\n",
"You might wonder why we took quite some time explaining boolean expresisons. One of the reasons is that they are the main element in probably one of the most used things in Python: **`if` statements**. The following picture explains what happens in an `if` statement in Python.\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at an example (modify the value of *number* to understand what is happening here):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"number = 2 # try changing this value to 6\n",
"if number <= 5:\n",
" print(number)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can use as many `if` statements as you like:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"number = 5\n",
"if number == 5: \n",
" print(\"number equals 5\")\n",
"if number > 4: \n",
" print(\"number is greater than 4\")\n",
"if number >= 5:\n",
" print(\"number is greater than or equals 5\")\n",
"if number < 6: \n",
" print(\"number is less than 6\") \n",
"if number <= 5:\n",
" print(\"number is less than or equals 5\")\n",
"if number != 6 :\n",
" print(\"number does not equal 6\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.1 Two-way decisions\n",
"\n",
"But what if we want to have options for two different scenarios? We could just use a bunch of `if` statements. However, Python has a more efficient way. Apart from `if` we also have the **`else` statement** for two-way decisions (modify the value of `number` to understand what is happening here):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"number = 10 # try changing this value to 2\n",
"if number <= 5:\n",
" print(number)\n",
"else:\n",
" print('number is higher than 5')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now Python always runs one of the two pieces of code. It's like arriving at a fork in the road and choosing one path to follow."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2 Multi-way decisions\n",
"\n",
"But of course we don't have to stop there. If you have multiple options, you can use the **`elif` statement**. For every `if` block, you can have one `if` statement, multiple `elif` statements and one `else` statement. So now we know the entire **`if-elif-else` construct**:\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"age = 21\n",
"if age < 12:\n",
" print(\"You're still a child!\")\n",
"elif age < 18:\n",
" print(\"You are a teenager!\")\n",
"elif age < 30:\n",
" print(\"You're pretty young!\")\n",
"else:\n",
" print(\"Wow, you're old!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First the `if` statement will be evaluated. Only if that statement turns out to be `False` the computer will proceed to evaluate the `elif` statement. If the `elif` statements in turn would prove to be `False`, the machine will proceed and execute the lines of code associated with the `else` statement. You can think of this coding structure as a decision tree! Remember: if somewhere along the tree, your machine comes across a logical expression which is `True`, it won't bother anymore to evaluate the remaining options! Note that the statements are evaluated in order of occurence."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Can you identify the difference between the code above and the code below? (Try changing `age`)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"age = 21\n",
"if age < 12:\n",
" print(\"You're still a child!\")\n",
"if age < 18:\n",
" print(\"You are a teenager!\")\n",
"if age < 30:\n",
" print(\"You're pretty young!\")\n",
"else:\n",
" print(\"Wow, you're old!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Remember:** \n",
"- `if-if`: your code wil check all the `if` statements\n",
"- `if-elif`: if one condition results to `True`, it will not check the other conditions\n",
"\n",
"Unless you *need* to check all conditions, using `if-elif` is usually preferred because it's *more efficient*."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Indentation\n",
"\n",
"Let's take another look at the example from above (we've added line numbers):\n",
"```python\n",
"1. if number <= 5:\n",
"2. print(number)\n",
"3. else:\n",
"4. print('number is higher than 5')\n",
"```\n",
"You might have noticed that line 2 starts with 4 spaces. This is on purpose! The indentation lets Python know when it needs to execute the piece of code. When the boolean expression in line 1 is `True`, Python executes the code from the next line that starts four spaces or one tab (an indent) to the right. This is called **indentation**. All statements with the same distance to the right belong to the same 'block' of code.\n",
"\n",
"Unlike other languages, Python does not make use of curly braces to mark the start and end of pieces of code, like `if` statements. The only delimiter is a colon (:) and the indentation of the code. Both four spaces and tabs can be used for indentation. This indentation must be used consistently throughout your code. The most popular way to indent is four spaces (see [stackoverflow](http://stackoverflow.com/questions/120926/why-does-python-pep-8-strongly-recommend-spaces-over-tabs-for-indentation)). For now, you do not have to worry about this, since a tab is automatically converted to four spaces in notebooks.\n",
"\n",
"Take a look at the code below. We see that the indented block is not executed, but the unindented lines of code are. Now go ahead and change the value of the `person` variable. The conversation should be a bit longer now! "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"person = \"John\"\n",
"print(\"hello!\")\n",
"if person == \"Alice\":\n",
" print(\"how are you today?\") #this is indented\n",
" print(\"do you want to join me for lunch?\") #this is indented\n",
"elif person == \"Lisa\":\n",
" print(\"let's talk some other time!\") #this is indented\n",
"print(\"goodbye!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.1 Nesting\n",
"\n",
"We have seen that all statements with the same distance to the right belong to the same block of code, i.e. the statements within a block line up vertically. The block ends at a line less indented or the end of the file. \n",
"Blocks can contain blocks as welll; this way, we get a nested block structure. The block that has to be more deeply **nested** is simply indented further to the right:\n",
"\n",
"\n",
"\n",
"There may be a situation when you want to check for another condition after a condition resolves to `True`. In such a situation, you can use the nested `if` construct. As you can see if you run the code below, the second `if` statement is only executed if the first `if` statement returns `True`. Try changing the value of x to see what the code does."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x = float(input(\"Enter a number: \"))\n",
"if x >= 0:\n",
" if x == 0:\n",
" print(\"Zero\")\n",
" else:\n",
" print(\"Positive number\")\n",
"else:\n",
" print(\"Negative number\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 1: \n",
"It's important to practice a lot with boolean expressions. Here is a list of them, which orginate from [learnpythonthehardway](http://learnpythonthehardway.org/book/ex28.html). Try to guess the output."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(True and True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(False and True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(1 == 1 and 2 == 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"test\" == \"test\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(1 == 1 or 2 != 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(True and 1 == 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(False and 0 != 0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(True or 1 == 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"test\" == \"testing\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(1 != 0 and 2 == 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"test\" != \"testing\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"test\" == 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(not (True and False))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(not (1 == 1 and 0 != 1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(not (10 == 1 or 1000 == 1000))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(not (1 != 10 or 3 == 4))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(not (\"testing\" == \"testing\" and \"Zed\" == \"Cool Guy\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(1 == 1 and (not (\"testing\" == 1 or 1 == 0)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"chunky\" == \"bacon\" and (not (3 == 4 or 3 == 3)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(3 == 3 and (not (\"testing\" == \"testing\" or \"Python\" == \"Fun\")))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"test\" != \"testing\" and 1 == 1 and 2 == 2 and 20 in [1, 20, 3, 4,5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 2: \n",
"Write a small program that defines a variable `weight`. If the weight is > 50 pounds, print \"There is a $25 charge for luggage that heavy.\" If it is not, print: \"Thank you for your business.\" If the weight is exactly 50, print: \"Pfiew! The weight is just right!\". Change the value of weight a couple of times to check whether your code works. Make use of the logical operators and the if-elif-else construct!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# insert your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 3: \n",
"What's wrong in the following code? Correct the mistake."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"my_string = \"hello\"\n",
"if my_string == \"hello\":\n",
"print(\"world\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Why is the last line in the following code red? Correct the mistake."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"my_string = \"hello\"\n",
"if my_string == \"hello\":\n",
" print(\"world\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What's wrong in the following code? Correct the mistake."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"my_string = \"hello\"\n",
"if my_string == \"hello\"\n",
" print(\"world\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What's wrong in the following code? Correct the mistake."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"my_string = \"hello\"\n",
"if my_string = \"hello\":\n",
" print(\"world\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 4: \n",
"Can you rewrite the code below without nesting? Hint: use the `if-elif-else` construct."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x = float(input(\"Enter a number: \"))\n",
"if x >= 0:\n",
" if x == 0:\n",
" print(\"Zero\")\n",
" else:\n",
" print(\"Positive number\")\n",
"else:\n",
" print(\"Negative number\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 5: \n",
"A friend wants your advice on how much oranges he should buy. Write a program that will give the advice to buy 24 oranges if the price is lower than 1.50 EUR per kg, 12 oranges if the price is between 1.50 EUR and 3 EUR, and only 1 orange if the price is higher than 3 EUR. But also tell him that he should only buy them if the oranges are fresh; otherwise, he should not get any. Use nesting and the if-elif-else construct."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"orange_quality = \"fresh\"\n",
"orange_price = 1.75\n",
"# your code here"
]
}
],
"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": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| apache-2.0 |
SATHVIKRAJU/Stock-Market-Prediction-using-Natural-Language-Processing | Final26_4_7pm - Naive Bayes.ipynb | 1 | 10647 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"from sklearn.naive_bayes import GaussianNB"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Label</th>\n",
" <th>Top1</th>\n",
" <th>Top2</th>\n",
" <th>Top3</th>\n",
" <th>Top4</th>\n",
" <th>Top5</th>\n",
" <th>Top6</th>\n",
" <th>Top7</th>\n",
" <th>Top8</th>\n",
" <th>...</th>\n",
" <th>Top16</th>\n",
" <th>Top17</th>\n",
" <th>Top18</th>\n",
" <th>Top19</th>\n",
" <th>Top20</th>\n",
" <th>Top21</th>\n",
" <th>Top22</th>\n",
" <th>Top23</th>\n",
" <th>Top24</th>\n",
" <th>Top25</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2000-01-03</td>\n",
" <td>0</td>\n",
" <td>A 'hindrance to operations': extracts from the...</td>\n",
" <td>Scorecard</td>\n",
" <td>Hughes' instant hit buoys Blues</td>\n",
" <td>Jack gets his skates on at ice-cold Alex</td>\n",
" <td>Chaos as Maracana builds up for United</td>\n",
" <td>Depleted Leicester prevail as Elliott spoils E...</td>\n",
" <td>Hungry Spurs sense rich pickings</td>\n",
" <td>Gunners so wide of an easy target</td>\n",
" <td>...</td>\n",
" <td>Flintoff injury piles on woe for England</td>\n",
" <td>Hunters threaten Jospin with new battle of the...</td>\n",
" <td>Kohl's successor drawn into scandal</td>\n",
" <td>The difference between men and women</td>\n",
" <td>Sara Denver, nurse turned solicitor</td>\n",
" <td>Diana's landmine crusade put Tories in a panic</td>\n",
" <td>Yeltsin's resignation caught opposition flat-f...</td>\n",
" <td>Russian roulette</td>\n",
" <td>Sold out</td>\n",
" <td>Recovering a title</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" Date Label Top1 \\\n",
"0 2000-01-03 0 A 'hindrance to operations': extracts from the... \n",
"\n",
" Top2 Top3 \\\n",
"0 Scorecard Hughes' instant hit buoys Blues \n",
"\n",
" Top4 \\\n",
"0 Jack gets his skates on at ice-cold Alex \n",
"\n",
" Top5 \\\n",
"0 Chaos as Maracana builds up for United \n",
"\n",
" Top6 \\\n",
"0 Depleted Leicester prevail as Elliott spoils E... \n",
"\n",
" Top7 Top8 \\\n",
"0 Hungry Spurs sense rich pickings Gunners so wide of an easy target \n",
"\n",
" ... Top16 \\\n",
"0 ... Flintoff injury piles on woe for England \n",
"\n",
" Top17 \\\n",
"0 Hunters threaten Jospin with new battle of the... \n",
"\n",
" Top18 Top19 \\\n",
"0 Kohl's successor drawn into scandal The difference between men and women \n",
"\n",
" Top20 \\\n",
"0 Sara Denver, nurse turned solicitor \n",
"\n",
" Top21 \\\n",
"0 Diana's landmine crusade put Tories in a panic \n",
"\n",
" Top22 Top23 \\\n",
"0 Yeltsin's resignation caught opposition flat-f... Russian roulette \n",
"\n",
" Top24 Top25 \n",
"0 Sold out Recovering a title \n",
"\n",
"[1 rows x 27 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Read in the data\n",
"#Data = pd.read_csv('Full_Data.csv', encoding = \"ISO-8859-1\")\n",
"#Data.head(1)\n",
"data = pd.read_csv('Full_Data.csv', encoding = \"ISO-8859-1\")\n",
"data.head(1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"train = data[data['Date'] < '20150101']\n",
"test = data[data['Date'] > '20141231']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Removing punctuations\n",
"slicedData= train.iloc[:,2:27]\n",
"slicedData.replace(to_replace=\"[^a-zA-Z]\", value=\" \", regex=True, inplace=True)\n",
"\n",
"# Renaming column names for ease of access\n",
"list1= [i for i in range(25)]\n",
"new_Index=[str(i) for i in list1]\n",
"slicedData.columns= new_Index\n",
"slicedData.head(5)\n",
"\n",
"# Convertng headlines to lower case\n",
"for index in new_Index:\n",
" slicedData[index]=slicedData[index].str.lower()\n",
"slicedData.head(1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"headlines = []\n",
"for row in range(0,len(slicedData.index)):\n",
" headlines.append(' '.join(str(x) for x in slicedData.iloc[row,0:25]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"headlines[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"basicvectorizer = CountVectorizer(ngram_range=(1,1))\n",
"basictrain = basicvectorizer.fit_transform(headlines)\n",
"print(basictrain.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"basicmodel = GaussianNB()\n",
"basicmodel = basicmodel.fit(basictrain.toarray(), train[\"Label\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"testheadlines = []\n",
"for row in range(0,len(test.index)):\n",
" testheadlines.append(' '.join(str(x) for x in test.iloc[row,2:27]))\n",
"basictest = basicvectorizer.transform(testheadlines)\n",
"predictions = basicmodel.predict(basictest.toarray())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"predictions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pd.crosstab(test[\"Label\"], predictions, rownames=[\"Actual\"], colnames=[\"Predicted\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.metrics import classification_report\n",
"from sklearn.metrics import f1_score\n",
"from sklearn.metrics import accuracy_score \n",
"from sklearn.metrics import confusion_matrix\n",
"\n",
"print (classification_report(test[\"Label\"], predictions))\n",
"print (accuracy_score(test[\"Label\"], predictions))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"basicvectorizer2 = CountVectorizer(ngram_range=(1,2))\n",
"basictrain2 = basicvectorizer2.fit_transform(headlines)\n",
"print(basictrain2.shape)\n",
"\n",
"basicmodel2 = GaussianNB()\n",
"basicmodel2 = basicmodel2.fit(basictrain2, train[\"Label\"])\n",
"\n",
"basictest2 = basicvectorizer2.transform(testheadlines)\n",
"predictions2 = basicmodel2.predict(basictest2)\n",
"\n",
"pd.crosstab(test[\"Label\"], predictions2, rownames=[\"Actual\"], colnames=[\"Predicted\"])\n",
"\n",
"print (classification_report(test[\"Label\"], predictions2))\n",
"print (accuracy_score(test[\"Label\"], predictions2))\n",
"print (classification_report(test[\"Label\"], predictions2))\n",
"print (accuracy_score(test[\"Label\"], predictions2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"basicvectorizer3 = CountVectorizer(ngram_range=(2,3))\n",
"basictrain3 = basicvectorizer3.fit_transform(headlines)\n",
"print(basictrain3.shape)\n",
"\n",
"basicmodel3 = GaussianNB()\n",
"basicmodel3 = basicmodel3.fit(basictrain3, train[\"Label\"])\n",
"\n",
"basictest3 = basicvectorizer3.transform(testheadlines)\n",
"predictions3 = basicmodel3.predict(basictest3)\n",
"\n",
"pd.crosstab(test[\"Label\"], predictions3, rownames=[\"Actual\"], colnames=[\"Predicted\"])\n",
"\n",
"print (classification_report(test[\"Label\"], predictions3))\n",
"print (accuracy_score(test[\"Label\"], predictions3))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
PaulSoderlind/FinancialTheoryMSc | Ch01_Basics.ipynb | 1 | 8396 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Basics\n",
"\n",
"of return calculations: returns, average returns and volatilities of portfolios."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Packages and Extra Functions\n",
"\n",
"The notebook uses the functions `printmat()` and `printlnPs()` for formatted printing of matrices and numbers. These functions call on the `Printf` package."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"printyellow (generic function with 1 method)"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using Printf\n",
"\n",
"include(\"jlFiles/printmat.jl\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Return Calculations\n",
"\n",
"The return of holding the asset between $t-1$ and $t$ is\n",
"\n",
"$\n",
"R_t = (P_t+D_t)/P_{t-1} - 1,\n",
"$\n",
"\n",
"where $P_t$ is the price (measured after dividends) and $D_t$ is the dividend.\n",
"\n",
"We can calculate the returns by a loop or by a more compact notation, see below."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"period return, %\n",
"2 10.000\n",
"3 0.926\n",
"\n"
]
}
],
"source": [
"P = [100,108,109] #prices (after dividends) for t=1,2,3\n",
"D = [0,2,0] #dividends, could also use [0;2;0]\n",
"\n",
"R = zeros(length(P)) #where to store the results\n",
"for t = 2:length(P) #P[2] is the 2nd element of P \n",
" R[t] = (P[t] + D[t])/P[t-1] - 1\n",
"end\n",
"R = R[2:end] #get rid of R[1] since we have no return there\n",
"\n",
"printmat(R*100,colNames=[\"return, %\"],rowNames=2:3,cell00=\"period\",width=15)\n",
"\n",
"#R_alt = (P[2:end] + D[2:end])./P[1:end-1] .- 1 #vectorized alternative, notice the ./ and .-"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cumulating Returns\n",
"\n",
"Net returns can be cumulated into a portfolio value as \n",
"\n",
"$\n",
"V_t = V_{t-1}(1+R_t)\n",
"$\n",
"\n",
"where we need a starting value (initial investment) for the portfolio (a common choice is to normalise to $V_0=1$).\n",
"\n",
"With log returns, $r_t=\\log(1+R_t)$, we instead do \n",
"\n",
"$\n",
"\\ln V_t = \\ln V_{t-1} + r_t\n",
"$\n",
"\n",
"If the return series is an excess return, add the riskfree rate to convert it to get net returns - and then cumulate as described above.\n",
"\n",
"### A Remark on the Code\n",
"Use `cumprod([a,b]` to calculate `[a,a*b]` and `cumsum([a,b]` to calculate `[a,a+b]`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"period R V lnV\n",
"1 0.200 1.200 0.182\n",
"2 -0.350 0.780 -0.248\n",
"3 0.250 0.975 -0.025\n",
"\n",
"\u001b[31m\u001b[1mCheck that lnV really equals log.(V). Also, try a loop instead\u001b[22m\u001b[39m\n"
]
}
],
"source": [
"R = [20,-35,25]/100 #returns for t=1,2,3\n",
"V = cumprod(1.0 .+ R) #V(t) = V(t-1)*(1+R(t)), starting at 1 in t=0\n",
"r = log.(1.0 .+ R) #log returns\n",
"lnV = cumsum(r) #lnV(t) = lnV(t-1) + r(t) \n",
"\n",
"printmat(R,V,lnV,colNames=[\"R\",\"V\",\"lnV\"],rowNames=1:3,cell00=\"period\")\n",
"\n",
"printred(\"Check that lnV really equals log.(V). Also, try a loop instead\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Portfolio Return: Definition, Expected Value and Variance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We form a portfolio by combining $n$ assets: $w$ is the vector of $n$ portfolio weights, $R$ is a vector of returns, $\\mu$ a vector of expected expected (average) returns and $\\Sigma$ the $n \\times n$ covariance matrix.\n",
"\n",
"The portfolio return, the expected portfolio return and the portfolio variance can be computed as:\n",
"\n",
"$R_p = w'R,$\n",
"\n",
"$\\text{E}R_p = w'\\mu$ and\n",
"\n",
"$\\text{Var}(R_p) = w'\\Sigma w$\n",
"\n",
"The covariance of two portfolios (with weights $v$ and $w$, respectively) can be computed as \n",
"\n",
"$\\text{Cov}(R_q,R_p) = v'\\Sigma w$."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mPortfolio weights:\u001b[22m\u001b[39m\n",
"asset 1 0.800\n",
"asset 2 0.200\n",
"\n",
"\u001b[34m\u001b[1mReturns:\u001b[22m\u001b[39m\n",
"asset 1 0.100\n",
"asset 2 0.050\n",
"\n",
"\u001b[34m\u001b[1mPortfolio return: \u001b[22m\u001b[39m\n",
" 0.090\n"
]
}
],
"source": [
"w = [0.8,0.2]\n",
"R = [10,5]/100 #returns of asset 1 and 2\n",
"Rₚ = w'R #R\\_p[TAB] to get Rₚ\n",
"\n",
"printblue(\"Portfolio weights:\")\n",
"printmat(w,rowNames=[\"asset 1\",\"asset 2\"])\n",
"\n",
"printblue(\"Returns:\")\n",
"printmat(R,rowNames=[\"asset 1\",\"asset 2\"])\n",
"\n",
"printblue(\"Portfolio return: \")\n",
"printlnPs(Rₚ)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mexpected returns*100: \u001b[22m\u001b[39m\n",
"asset 1 9.000\n",
"asset 2 6.000\n",
"\n",
"\u001b[34m\u001b[1mcovariance matrix*100^2:\u001b[22m\u001b[39m\n",
" asset 1 asset 2\n",
"asset 1 256.000 96.000\n",
"asset 2 96.000 144.000\n",
"\n"
]
}
],
"source": [
"μ = [9,6]/100 #\\mu[TAB] to get μ\n",
"Σ = [256 96; #\\Sigma[TAB]\n",
" 96 144]/100^2\n",
"\n",
"printblue(\"expected returns*100: \")\n",
"printmat(μ*100,rowNames=[\"asset 1\",\"asset 2\"])\n",
"\n",
"printblue(\"covariance matrix*100^2:\")\n",
"printmat(Σ*100^2,rowNames=[\"asset 1\",\"asset 2\"],colNames=[\"asset 1\",\"asset 2\"])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Expected portfolio return: 0.084\n",
"Portfolio variance and std: 0.020 0.142\n"
]
}
],
"source": [
"ERₚ = w'μ\n",
"VarRₚ = w'Σ*w\n",
"\n",
"printlnPs(\"Expected portfolio return: \",ERₚ)\n",
"printlnPs(\"Portfolio variance and std:\",VarRₚ,sqrt(VarRₚ))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Covariance of q and p: 0.014\n"
]
}
],
"source": [
"v = [0.3,0.7] #weights in portfolio q\n",
"\n",
"printlnPs(\"Covariance of q and p: \",v'Σ*w)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"@webio": {
"lastCommId": null,
"lastKernelId": null
},
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Julia 1.7.2",
"language": "julia",
"name": "julia-1.7"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.7.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
benhoyle/udacity-tensorflow | 3_regularization.ipynb | 1 | 44800 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"deletable": true,
"editable": true,
"id": "kR-4eNdK6lYS"
},
"source": [
"Deep Learning\n",
"=============\n",
"\n",
"Assignment 3\n",
"------------\n",
"\n",
"Previously in `2_fullyconnected.ipynb`, you trained a logistic regression and a neural network model.\n",
"\n",
"The goal of this assignment is to explore regularization techniques."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"collapsed": true,
"deletable": true,
"editable": true,
"id": "JLpLa8Jt7Vu4"
},
"outputs": [],
"source": [
"# These are all the modules we'll be using later. Make sure you can import them\n",
"# before proceeding further.\n",
"from __future__ import print_function\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"from six.moves import cPickle as pickle"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"deletable": true,
"editable": true,
"id": "1HrCK6e17WzV"
},
"source": [
"First reload the data we generated in `1_notmnist.ipynb`."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
}
]
},
"colab_type": "code",
"collapsed": false,
"deletable": true,
"editable": true,
"executionInfo": {
"elapsed": 11777,
"status": "ok",
"timestamp": 1449849322348,
"user": {
"color": "",
"displayName": "",
"isAnonymous": false,
"isMe": true,
"permissionId": "",
"photoUrl": "",
"sessionId": "0",
"userId": ""
},
"user_tz": 480
},
"id": "y3-cj1bpmuxc",
"outputId": "e03576f1-ebbe-4838-c388-f1777bcc9873"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training set (200000, 28, 28) (200000,)\n",
"Validation set (10000, 28, 28) (10000,)\n",
"Test set (10000, 28, 28) (10000,)\n"
]
}
],
"source": [
"pickle_file = 'notMNIST.pickle'\n",
"\n",
"with open(pickle_file, 'rb') as f:\n",
" save = pickle.load(f)\n",
" train_dataset = save['train_dataset']\n",
" train_labels = save['train_labels']\n",
" valid_dataset = save['valid_dataset']\n",
" valid_labels = save['valid_labels']\n",
" test_dataset = save['test_dataset']\n",
" test_labels = save['test_labels']\n",
" del save # hint to help gc free up memory\n",
" print('Training set', train_dataset.shape, train_labels.shape)\n",
" print('Validation set', valid_dataset.shape, valid_labels.shape)\n",
" print('Test set', test_dataset.shape, test_labels.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"deletable": true,
"editable": true,
"id": "L7aHrm6nGDMB"
},
"source": [
"Reformat into a shape that's more adapted to the models we're going to train:\n",
"- data as a flat matrix,\n",
"- labels as float 1-hot encodings."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
}
]
},
"colab_type": "code",
"collapsed": false,
"deletable": true,
"editable": true,
"executionInfo": {
"elapsed": 11728,
"status": "ok",
"timestamp": 1449849322356,
"user": {
"color": "",
"displayName": "",
"isAnonymous": false,
"isMe": true,
"permissionId": "",
"photoUrl": "",
"sessionId": "0",
"userId": ""
},
"user_tz": 480
},
"id": "IRSyYiIIGIzS",
"outputId": "3f8996ee-3574-4f44-c953-5c8a04636582"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training set (200000, 784) (200000, 10)\n",
"Validation set (10000, 784) (10000, 10)\n",
"Test set (10000, 784) (10000, 10)\n"
]
}
],
"source": [
"image_size = 28\n",
"num_labels = 10\n",
"\n",
"def reformat(dataset, labels):\n",
" dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)\n",
" # Map 1 to [0.0, 1.0, 0.0 ...], 2 to [0.0, 0.0, 1.0 ...]\n",
" labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)\n",
" return dataset, labels\n",
"train_dataset, train_labels = reformat(train_dataset, train_labels)\n",
"valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)\n",
"test_dataset, test_labels = reformat(test_dataset, test_labels)\n",
"print('Training set', train_dataset.shape, train_labels.shape)\n",
"print('Validation set', valid_dataset.shape, valid_labels.shape)\n",
"print('Test set', test_dataset.shape, test_labels.shape)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"collapsed": true,
"deletable": true,
"editable": true,
"id": "RajPLaL_ZW6w"
},
"outputs": [],
"source": [
"def accuracy(predictions, labels):\n",
" return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))\n",
" / predictions.shape[0])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"deletable": true,
"editable": true,
"id": "sgLbUAQ1CW-1"
},
"source": [
"---\n",
"Problem 1\n",
"---------\n",
"\n",
"Introduce and tune L2 regularization for both logistic and neural network models. Remember that L2 amounts to adding a penalty on the norm of the weights to the loss. In TensorFlow, you can compute the L2 loss for a tensor `t` using `nn.l2_loss(t)`. The right amount of regularization should improve your validation / test accuracy.\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"---\n",
"Logistic Model\n",
"----"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"batch_size = 128\n",
"beta = 0.005\n",
"\n",
"graph = tf.Graph()\n",
"with graph.as_default():\n",
"\n",
" # Input data. For the training data, we use a placeholder that will be fed\n",
" # at run time with a training minibatch.\n",
" tf_train_dataset = tf.placeholder(tf.float32,\n",
" shape=(batch_size, image_size * image_size))\n",
" tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n",
" tf_valid_dataset = tf.constant(valid_dataset)\n",
" tf_test_dataset = tf.constant(test_dataset)\n",
" \n",
" # Variables.\n",
" weights = tf.Variable(\n",
" tf.truncated_normal([image_size * image_size, num_labels]))\n",
" biases = tf.Variable(tf.zeros([num_labels]))\n",
" \n",
" # Training computation.\n",
" logits = tf.matmul(tf_train_dataset, weights) + biases\n",
" loss = tf.reduce_mean(\n",
" tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits)\n",
" + beta*tf.nn.l2_loss(weights)\n",
" )\n",
" \n",
" # Optimizer.\n",
" optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\n",
" \n",
" # Predictions for the training, validation, and test data.\n",
" train_prediction = tf.nn.softmax(logits)\n",
" valid_prediction = tf.nn.softmax(\n",
" tf.matmul(tf_valid_dataset, weights) + biases)\n",
" test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initialized\n",
"Minibatch loss at step 0: 32.632149\n",
"Minibatch accuracy: 9.4%\n",
"Validation accuracy: 14.4%\n",
"Minibatch loss at step 500: 1.791600\n",
"Minibatch accuracy: 77.3%\n",
"Validation accuracy: 79.7%\n",
"Minibatch loss at step 1000: 0.927333\n",
"Minibatch accuracy: 78.9%\n",
"Validation accuracy: 81.8%\n",
"Minibatch loss at step 1500: 0.755874\n",
"Minibatch accuracy: 83.6%\n",
"Validation accuracy: 82.0%\n",
"Minibatch loss at step 2000: 0.919513\n",
"Minibatch accuracy: 74.2%\n",
"Validation accuracy: 81.8%\n",
"Minibatch loss at step 2500: 0.689223\n",
"Minibatch accuracy: 82.8%\n",
"Validation accuracy: 82.2%\n",
"Minibatch loss at step 3000: 0.855758\n",
"Minibatch accuracy: 79.7%\n",
"Validation accuracy: 82.6%\n",
"Test accuracy: 88.7%\n"
]
}
],
"source": [
"num_steps = 3001\n",
"\n",
"with tf.Session(graph=graph) as session:\n",
" tf.global_variables_initializer().run()\n",
" print(\"Initialized\")\n",
" for step in range(num_steps):\n",
" # Pick an offset within the training data, which has been randomized.\n",
" # Note: we could use better randomization across epochs.\n",
" offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
" # Generate a minibatch.\n",
" batch_data = train_dataset[offset:(offset + batch_size), :]\n",
" batch_labels = train_labels[offset:(offset + batch_size), :]\n",
" # Prepare a dictionary telling the session where to feed the minibatch.\n",
" # The key of the dictionary is the placeholder node of the graph to be fed,\n",
" # and the value is the numpy array to feed to it.\n",
" feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n",
" _, l, predictions = session.run(\n",
" [optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
" if (step % 500 == 0):\n",
" print(\"Minibatch loss at step %d: %f\" % (step, l))\n",
" print(\"Minibatch accuracy: %.1f%%\" % accuracy(predictions, batch_labels))\n",
" print(\"Validation accuracy: %.1f%%\" % accuracy(\n",
" valid_prediction.eval(), valid_labels))\n",
" print(\"Test accuracy: %.1f%%\" % accuracy(test_prediction.eval(), test_labels))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"* beta = 0.01 - Test accuracy: 88.5%\n",
"* beta = 0.5 - Test accuracy: 55.5%\n",
"* beta = 0.005 - Test accuracy: 88.7%\n",
"* beta = 0.001 - Test accuracy: 88.7%\n",
"* beta = 0.0001 - Test accuracy: 86.2%\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"---\n",
"Hidden Layer Model\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"batch_size = 128\n",
"beta=0.005\n",
"\n",
"graph = tf.Graph()\n",
"with graph.as_default():\n",
"\n",
" # Input data. For the training data, we use a placeholder that will be fed\n",
" # at run time with a training minibatch.\n",
" tf_train_dataset = tf.placeholder(tf.float32,\n",
" shape=(batch_size, image_size * image_size))\n",
" tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n",
" tf_valid_dataset = tf.constant(valid_dataset)\n",
" tf_test_dataset = tf.constant(test_dataset)\n",
" \n",
" # Variables.\n",
" weights_layer_1 = tf.Variable(\n",
" tf.truncated_normal([image_size * image_size, num_labels]))\n",
" biases_layer_1 = tf.Variable(tf.zeros([num_labels]))\n",
" # Layer 2 weights have an input dimension = output of first layer\n",
" weights_layer_2 = tf.Variable(\n",
" tf.truncated_normal([num_labels, num_labels]))\n",
" biases_layer_2 = tf.Variable(tf.zeros([num_labels]))\n",
" \n",
" # Training computation.\n",
" logits_layer_1 = tf.matmul(tf_train_dataset, weights_layer_1) + biases_layer_1\n",
" relu_output = tf.nn.relu(logits_layer_1)\n",
" logits_layer_2 = tf.matmul(relu_output, weights_layer_2) + biases_layer_2\n",
" loss = tf.reduce_mean(\n",
" tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits_layer_2)\n",
" + beta*tf.nn.l2_loss(weights_layer_1)\n",
" + beta*tf.nn.l2_loss(weights_layer_2)\n",
" )\n",
" \n",
" # Optimizer.\n",
" optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\n",
" \n",
" # Predictions for the training, validation, and test data.\n",
" train_prediction = tf.nn.softmax(logits_layer_2)\n",
"\n",
" logits_l_1_valid = tf.matmul(tf_valid_dataset, weights_layer_1) + biases_layer_1\n",
" relu_valid = tf.nn.relu(logits_l_1_valid) \n",
" logits_l_2_valid = tf.matmul(relu_valid, weights_layer_2) + biases_layer_2 \n",
" valid_prediction = tf.nn.softmax(logits_l_2_valid)\n",
"\n",
" logits_l_1_test = tf.matmul(tf_test_dataset, weights_layer_1) + biases_layer_1\n",
" relu_test = tf.nn.relu(logits_l_1_test) \n",
" logits_l_2_test = tf.matmul(relu_test, weights_layer_2) + biases_layer_2 \n",
" test_prediction = tf.nn.softmax(logits_l_2_test)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initialized\n",
"Minibatch loss at step 0: 40.259315\n",
"Minibatch accuracy: 16.4%\n",
"Validation accuracy: 25.9%\n",
"Minibatch loss at step 500: 1.924523\n",
"Minibatch accuracy: 77.3%\n",
"Validation accuracy: 78.6%\n",
"Minibatch loss at step 1000: 0.966337\n",
"Minibatch accuracy: 78.9%\n",
"Validation accuracy: 81.9%\n",
"Minibatch loss at step 1500: 0.662468\n",
"Minibatch accuracy: 82.8%\n",
"Validation accuracy: 82.0%\n",
"Minibatch loss at step 2000: 0.787361\n",
"Minibatch accuracy: 78.9%\n",
"Validation accuracy: 82.3%\n",
"Minibatch loss at step 2500: 0.658163\n",
"Minibatch accuracy: 81.2%\n",
"Validation accuracy: 82.3%\n",
"Minibatch loss at step 3000: 0.867185\n",
"Minibatch accuracy: 81.2%\n",
"Validation accuracy: 82.8%\n",
"Test accuracy: 88.7%\n"
]
}
],
"source": [
"num_steps = 3001\n",
"\n",
"with tf.Session(graph=graph) as session:\n",
" tf.global_variables_initializer().run()\n",
" print(\"Initialized\")\n",
" for step in range(num_steps):\n",
" # Pick an offset within the training data, which has been randomized.\n",
" # Note: we could use better randomization across epochs.\n",
" offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
" # Generate a minibatch.\n",
" batch_data = train_dataset[offset:(offset + batch_size), :]\n",
" batch_labels = train_labels[offset:(offset + batch_size), :]\n",
" # Prepare a dictionary telling the session where to feed the minibatch.\n",
" # The key of the dictionary is the placeholder node of the graph to be fed,\n",
" # and the value is the numpy array to feed to it.\n",
" feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n",
" _, l, predictions = session.run(\n",
" [optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
" if (step % 500 == 0):\n",
" print(\"Minibatch loss at step %d: %f\" % (step, l))\n",
" print(\"Minibatch accuracy: %.1f%%\" % accuracy(predictions, batch_labels))\n",
" print(\"Validation accuracy: %.1f%%\" % accuracy(\n",
" valid_prediction.eval(), valid_labels))\n",
" print(\"Test accuracy: %.1f%%\" % accuracy(test_prediction.eval(), test_labels))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"beta = 0.01 - Test accuracy: 88.7%\n",
"beta = 0.5 - Test accuracy: 45.9% (and slow)\n",
"beta = 0.005 - Test accuracy: 89.2%\n",
"beta = 0.001 - Test accuracy: 89.2%\n",
"beta = 0.0001 - Test accuracy: 85.7%"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"deletable": true,
"editable": true,
"id": "na8xX2yHZzNF"
},
"source": [
"---\n",
"Problem 2\n",
"---------\n",
"Let's demonstrate an extreme case of overfitting. Restrict your training data to just a few batches. What happens?\n",
"\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initialized\n",
"Minibatch loss at step 0: 37.813343\n",
"Minibatch accuracy: 11.7%\n",
"Validation accuracy: 22.2%\n",
"Minibatch loss at step 500: 1.518300\n",
"Minibatch accuracy: 94.5%\n",
"Validation accuracy: 69.3%\n",
"Minibatch loss at step 1000: 0.338952\n",
"Minibatch accuracy: 99.2%\n",
"Validation accuracy: 74.0%\n",
"Minibatch loss at step 1500: 0.231990\n",
"Minibatch accuracy: 99.2%\n",
"Validation accuracy: 74.7%\n",
"Minibatch loss at step 2000: 0.216898\n",
"Minibatch accuracy: 99.2%\n",
"Validation accuracy: 75.3%\n",
"Minibatch loss at step 2500: 0.204409\n",
"Minibatch accuracy: 100.0%\n",
"Validation accuracy: 75.8%\n",
"Minibatch loss at step 3000: 0.192178\n",
"Minibatch accuracy: 100.0%\n",
"Validation accuracy: 76.0%\n",
"Test accuracy: 82.9%\n"
]
}
],
"source": [
"num_steps = 3001\n",
"\n",
"#Restrict training data\n",
"reduced_train_dataset = train_dataset[:640, :]\n",
"reduced_train_labels = train_labels[:640, :]\n",
"\n",
"with tf.Session(graph=graph) as session:\n",
" tf.global_variables_initializer().run()\n",
" print(\"Initialized\")\n",
" for step in range(num_steps):\n",
" # Pick an offset within the training data, which has been randomized.\n",
" # Note: we could use better randomization across epochs.\n",
" offset = (step * batch_size) % (reduced_train_labels.shape[0] - batch_size)\n",
" # Generate a minibatch.\n",
" batch_data = reduced_train_dataset[offset:(offset + batch_size), :]\n",
" batch_labels = reduced_train_labels[offset:(offset + batch_size), :]\n",
" # Prepare a dictionary telling the session where to feed the minibatch.\n",
" # The key of the dictionary is the placeholder node of the graph to be fed,\n",
" # and the value is the numpy array to feed to it.\n",
" feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n",
" _, l, predictions = session.run(\n",
" [optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
" if (step % 500 == 0):\n",
" print(\"Minibatch loss at step %d: %f\" % (step, l))\n",
" print(\"Minibatch accuracy: %.1f%%\" % accuracy(predictions, batch_labels))\n",
" print(\"Validation accuracy: %.1f%%\" % accuracy(\n",
" valid_prediction.eval(), valid_labels))\n",
" print(\"Test accuracy: %.1f%%\" % accuracy(test_prediction.eval(), test_labels))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Restricted to 1000 samples in each batch - we get quick convergence and 100% accuracy on the mini-batch but poor performance on the validation dataset and poorer performance on the unseen test dataset.\n",
"* Minibatch loss at step 3000: 0.268746\n",
"* Minibatch accuracy: 100.0%\n",
"* Validation accuracy: 78.4%\n",
"* Test accuracy: 85.1% \n",
"On 640 samples: \n",
"* Minibatch loss at step 3000: 0.196770\n",
"* Minibatch accuracy: 100.0%\n",
"* Validation accuracy: 76.6%\n",
"* Test accuracy: 83.6%"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"deletable": true,
"editable": true,
"id": "ww3SCBUdlkRc"
},
"source": [
"---\n",
"Problem 3\n",
"---------\n",
"Introduce Dropout on the hidden layer of the neural network. Remember: Dropout should only be introduced during training, not evaluation, otherwise your evaluation results would be stochastic as well. TensorFlow provides `nn.dropout()` for that, but you have to make sure it's only inserted during training.\n",
"\n",
"What happens to our extreme overfitting case?\n",
"\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"batch_size = 128\n",
"beta=0.005\n",
"\n",
"graph = tf.Graph()\n",
"with graph.as_default():\n",
"\n",
" # Input data. For the training data, we use a placeholder that will be fed\n",
" # at run time with a training minibatch.\n",
" tf_train_dataset = tf.placeholder(tf.float32,\n",
" shape=(batch_size, image_size * image_size))\n",
" tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n",
" tf_valid_dataset = tf.constant(valid_dataset)\n",
" tf_test_dataset = tf.constant(test_dataset)\n",
" \n",
" # Variables.\n",
" weights_layer_1 = tf.Variable(\n",
" tf.truncated_normal([image_size * image_size, num_labels]))\n",
" biases_layer_1 = tf.Variable(tf.zeros([num_labels]))\n",
" # Layer 2 weights have an input dimension = output of first layer\n",
" weights_layer_2 = tf.Variable(\n",
" tf.truncated_normal([num_labels, num_labels]))\n",
" biases_layer_2 = tf.Variable(tf.zeros([num_labels]))\n",
" \n",
" # Training computation.\n",
" logits_layer_1 = tf.matmul(tf_train_dataset, weights_layer_1) + biases_layer_1\n",
" relu_output = tf.nn.relu(logits_layer_1)\n",
" # Introduce dropout - probability feature is kept is passed as a variable\n",
" keep_probability = tf.placeholder(tf.float32)\n",
" dropout_output = tf.nn.dropout(relu_output, keep_probability)\n",
"\n",
" logits_layer_2 = tf.matmul(dropout_output, weights_layer_2) + biases_layer_2\n",
" loss = tf.reduce_mean(\n",
" tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits_layer_2)\n",
" + beta*tf.nn.l2_loss(weights_layer_1)\n",
" + beta*tf.nn.l2_loss(weights_layer_2)\n",
" )\n",
" \n",
" # Optimizer.\n",
" optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\n",
" \n",
" # Predictions for the training, validation, and test data.\n",
" train_prediction = tf.nn.softmax(logits_layer_2)\n",
"\n",
" logits_l_1_valid = tf.matmul(tf_valid_dataset, weights_layer_1) + biases_layer_1\n",
" relu_valid = tf.nn.relu(logits_l_1_valid) \n",
" logits_l_2_valid = tf.matmul(relu_valid, weights_layer_2) + biases_layer_2 \n",
" valid_prediction = tf.nn.softmax(logits_l_2_valid)\n",
"\n",
" logits_l_1_test = tf.matmul(tf_test_dataset, weights_layer_1) + biases_layer_1\n",
" relu_test = tf.nn.relu(logits_l_1_test) \n",
" logits_l_2_test = tf.matmul(relu_test, weights_layer_2) + biases_layer_2 \n",
" test_prediction = tf.nn.softmax(logits_l_2_test)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initialized\n",
"Minibatch loss at step 0: 59.214775\n",
"Minibatch accuracy: 12.5%\n",
"Validation accuracy: 14.9%\n",
"Minibatch loss at step 500: 2.834952\n",
"Minibatch accuracy: 39.1%\n",
"Validation accuracy: 64.0%\n",
"Minibatch loss at step 1000: 1.622742\n",
"Minibatch accuracy: 46.1%\n",
"Validation accuracy: 75.2%\n",
"Minibatch loss at step 1500: 1.387890\n",
"Minibatch accuracy: 53.1%\n",
"Validation accuracy: 77.3%\n",
"Minibatch loss at step 2000: 1.412264\n",
"Minibatch accuracy: 56.2%\n",
"Validation accuracy: 77.8%\n",
"Minibatch loss at step 2500: 1.323213\n",
"Minibatch accuracy: 57.0%\n",
"Validation accuracy: 79.0%\n",
"Minibatch loss at step 3000: 1.544850\n",
"Minibatch accuracy: 45.3%\n",
"Validation accuracy: 77.6%\n",
"Test accuracy: 83.8%\n"
]
}
],
"source": [
"num_steps = 3001\n",
"\n",
"with tf.Session(graph=graph) as session:\n",
" tf.global_variables_initializer().run()\n",
" print(\"Initialized\")\n",
" for step in range(num_steps):\n",
" # Pick an offset within the training data, which has been randomized.\n",
" # Note: we could use better randomization across epochs.\n",
" offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
" # Generate a minibatch.\n",
" batch_data = train_dataset[offset:(offset + batch_size), :]\n",
" batch_labels = train_labels[offset:(offset + batch_size), :]\n",
" # Prepare a dictionary telling the session where to feed the minibatch.\n",
" # The key of the dictionary is the placeholder node of the graph to be fed,\n",
" # and the value is the numpy array to feed to it.\n",
" feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels, keep_probability: 0.5}\n",
" _, l, predictions = session.run(\n",
" [optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
" if (step % 500 == 0):\n",
" print(\"Minibatch loss at step %d: %f\" % (step, l))\n",
" print(\"Minibatch accuracy: %.1f%%\" % accuracy(predictions, batch_labels))\n",
" print(\"Validation accuracy: %.1f%%\" % accuracy(\n",
" valid_prediction.eval(), valid_labels))\n",
" print(\"Test accuracy: %.1f%%\" % accuracy(test_prediction.eval(), test_labels))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Dropout doesn't improve performance for me - maybe I'm applying it wrong - getting test accuracy of 80%. \n",
" \n",
"Try on reduced batch size data below:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initialized\n",
"Minibatch loss at step 0: 52.094055\n",
"Minibatch accuracy: 14.8%\n",
"Validation accuracy: 15.7%\n",
"Minibatch loss at step 500: 2.330360\n",
"Minibatch accuracy: 57.8%\n",
"Validation accuracy: 70.8%\n",
"Minibatch loss at step 1000: 1.143688\n",
"Minibatch accuracy: 67.2%\n",
"Validation accuracy: 72.2%\n",
"Minibatch loss at step 1500: 1.009757\n",
"Minibatch accuracy: 69.5%\n",
"Validation accuracy: 71.8%\n",
"Minibatch loss at step 2000: 1.038862\n",
"Minibatch accuracy: 68.0%\n",
"Validation accuracy: 73.6%\n",
"Minibatch loss at step 2500: 0.943375\n",
"Minibatch accuracy: 71.1%\n",
"Validation accuracy: 73.3%\n",
"Minibatch loss at step 3000: 1.094494\n",
"Minibatch accuracy: 66.4%\n",
"Validation accuracy: 72.0%\n",
"Test accuracy: 79.1%\n"
]
}
],
"source": [
"num_steps = 3001\n",
"\n",
"#Restrict training data\n",
"reduced_train_dataset = train_dataset[:640, :]\n",
"reduced_train_labels = train_labels[:640, :]\n",
"\n",
"with tf.Session(graph=graph) as session:\n",
" tf.global_variables_initializer().run()\n",
" print(\"Initialized\")\n",
" for step in range(num_steps):\n",
" # Pick an offset within the training data, which has been randomized.\n",
" # Note: we could use better randomization across epochs.\n",
" offset = (step * batch_size) % (reduced_train_labels.shape[0] - batch_size)\n",
" # Generate a minibatch.\n",
" batch_data = reduced_train_dataset[offset:(offset + batch_size), :]\n",
" batch_labels = reduced_train_labels[offset:(offset + batch_size), :]\n",
" # Prepare a dictionary telling the session where to feed the minibatch.\n",
" # The key of the dictionary is the placeholder node of the graph to be fed,\n",
" # and the value is the numpy array to feed to it.\n",
" feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels, keep_probability: 0.5}\n",
" _, l, predictions = session.run(\n",
" [optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
" if (step % 500 == 0):\n",
" print(\"Minibatch loss at step %d: %f\" % (step, l))\n",
" print(\"Minibatch accuracy: %.1f%%\" % accuracy(predictions, batch_labels))\n",
" print(\"Validation accuracy: %.1f%%\" % accuracy(\n",
" valid_prediction.eval(), valid_labels))\n",
" print(\"Test accuracy: %.1f%%\" % accuracy(test_prediction.eval(), test_labels))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Does reduce overfitting but does not increase accuracy."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"deletable": true,
"editable": true,
"id": "-b1hTz3VWZjw"
},
"source": [
"---\n",
"Problem 4\n",
"---------\n",
"\n",
"Try to get the best performance you can using a multi-layer model! The best reported test accuracy using a deep network is [97.1%](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html?showComment=1391023266211#c8758720086795711595).\n",
"\n",
"One avenue you can explore is to add multiple layers.\n",
"\n",
"Another one is to use learning rate decay:\n",
"\n",
" global_step = tf.Variable(0) # count the number of steps taken.\n",
" learning_rate = tf.train.exponential_decay(0.5, global_step, ...)\n",
" optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)\n",
" \n",
" ---\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Try adding an additional layer:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"batch_size = 128\n",
"beta=0.005\n",
"\n",
"graph = tf.Graph()\n",
"with graph.as_default():\n",
"\n",
" # Input data. For the training data, we use a placeholder that will be fed\n",
" # at run time with a training minibatch.\n",
" tf_train_dataset = tf.placeholder(tf.float32,\n",
" shape=(None, image_size * image_size))\n",
" tf_train_labels = tf.placeholder(tf.float32, shape=(None, num_labels))\n",
" \n",
" # Variables.\n",
" weights_layer_1 = tf.Variable(\n",
" tf.truncated_normal([image_size * image_size, num_labels]))\n",
" biases_layer_1 = tf.Variable(tf.zeros([num_labels]))\n",
" # Layer 2 weights have an input dimension = output of first layer\n",
" weights_layer_2 = tf.Variable(\n",
" tf.truncated_normal([num_labels, num_labels]))\n",
" biases_layer_2 = tf.Variable(tf.zeros([num_labels]))\n",
" # Layer 3\n",
" weights_layer_3 = tf.Variable(\n",
" tf.truncated_normal([num_labels, num_labels]))\n",
" biases_layer_3 = tf.Variable(tf.zeros([num_labels]))\n",
" \n",
" # Training computation.\n",
" # Compute layer 1\n",
" logits_layer_1 = tf.matmul(tf_train_dataset, weights_layer_1) + biases_layer_1\n",
" relu_output_1 = tf.nn.relu(logits_layer_1)\n",
" # Introduce dropout - probability feature is kept is passed as a variable\n",
" keep_probability = tf.placeholder(tf.float32)\n",
" dropout_output_1 = tf.nn.dropout(relu_output_1, keep_probability)\n",
" # Compute layer 2\n",
" logits_layer_2 = tf.matmul(dropout_output_1, weights_layer_2) + biases_layer_2\n",
" relu_output_2 = tf.nn.relu(logits_layer_2)\n",
" dropout_output_2 = tf.nn.dropout(relu_output_2, keep_probability)\n",
" # Computer layer 3\n",
" logits_layer_3 = tf.matmul(dropout_output_2, weights_layer_3) + biases_layer_3\n",
" \n",
" loss = tf.reduce_mean(\n",
" tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits_layer_3)\n",
" + beta*tf.nn.l2_loss(weights_layer_1)\n",
" + beta*tf.nn.l2_loss(weights_layer_2)\n",
" + beta*tf.nn.l2_loss(weights_layer_3)\n",
" )\n",
" \n",
" # Optimizer.\n",
" optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\n",
" \n",
" # Predictions for the data.\n",
" train_prediction = tf.nn.softmax(logits_layer_3)\n",
"\n",
" # Determine accuracy\n",
" correct_prediction = tf.equal(tf.argmax(train_prediction,1), tf.argmax(tf_train_labels,1))\n",
" accuracy_calc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))*100"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initialized\n",
"Minibatch loss at step 0: 43.138603\n",
"Minibatch accuracy: 9.4%\n",
"Validation accuracy: 15.4%\n",
"Minibatch loss at step 500: 2.713095\n",
"Minibatch accuracy: 40.6%\n",
"Validation accuracy: 50.3%\n",
"Minibatch loss at step 1000: 1.376885\n",
"Minibatch accuracy: 60.9%\n",
"Validation accuracy: 74.8%\n",
"Minibatch loss at step 1500: 1.298263\n",
"Minibatch accuracy: 73.4%\n",
"Validation accuracy: 75.8%\n",
"Minibatch loss at step 2000: 1.260409\n",
"Minibatch accuracy: 71.1%\n",
"Validation accuracy: 74.8%\n",
"Minibatch loss at step 2500: 1.015574\n",
"Minibatch accuracy: 77.3%\n",
"Validation accuracy: 75.4%\n",
"Minibatch loss at step 3000: 1.433252\n",
"Minibatch accuracy: 68.0%\n",
"Validation accuracy: 73.7%\n",
"Test accuracy: 80.2%\n"
]
}
],
"source": [
"num_steps = 3001\n",
"\n",
"with tf.Session(graph=graph) as session:\n",
" tf.global_variables_initializer().run()\n",
" print(\"Initialized\")\n",
" for step in range(num_steps):\n",
" # Pick an offset within the training data, which has been randomized.\n",
" # Note: we could use better randomization across epochs.\n",
" offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
" # Generate a minibatch.\n",
" batch_data = train_dataset[offset:(offset + batch_size), :]\n",
" batch_labels = train_labels[offset:(offset + batch_size), :]\n",
" # Prepare a dictionary telling the session where to feed the minibatch.\n",
" # The key of the dictionary is the placeholder node of the graph to be fed,\n",
" # and the value is the numpy array to feed to it.\n",
" feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels, keep_probability: 1.0}\n",
" \n",
" _, l, accuracy = session.run(\n",
" [optimizer, loss, accuracy_calc], feed_dict=feed_dict\n",
" )\n",
" \n",
" if (step % 500 == 0):\n",
" print(\"Minibatch loss at step %d: %f\" % (step, l))\n",
" print(\"Minibatch accuracy: %.1f%%\" % accuracy)\n",
" valid_feed_dict = {tf_train_dataset : valid_dataset, tf_train_labels : valid_labels, keep_probability: 1.0}\n",
" print(\"Validation accuracy: %.1f%%\" % accuracy_calc.eval(feed_dict=valid_feed_dict))\n",
"\n",
" test_feed_dict = {tf_train_dataset : test_dataset, tf_train_labels : test_labels, keep_probability: 1.0}\n",
" print(\"Test accuracy: %.1f%%\" % accuracy_calc.eval(feed_dict=test_feed_dict))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"87.2 with 3 layers and no dropout \n",
"Dies at 10% accuracy with 0.5 dropout - is it basically destroying all the information? \n",
"Yes even with 0.9 keep probability - only get 25% \n",
"\n",
"My code may be wrong somehow. "
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"---\n",
"Learning rate decay\n",
"---"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"batch_size = 128\n",
"beta=0.005\n",
"\n",
"graph = tf.Graph()\n",
"with graph.as_default():\n",
"\n",
" # Input data. For the training data, we use a placeholder that will be fed\n",
" # at run time with a training minibatch.\n",
" tf_train_dataset = tf.placeholder(tf.float32,\n",
" shape=(batch_size, image_size * image_size))\n",
" tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n",
" tf_valid_dataset = tf.constant(valid_dataset)\n",
" tf_test_dataset = tf.constant(test_dataset)\n",
" \n",
" # Variables.\n",
" weights_layer_1 = tf.Variable(\n",
" tf.truncated_normal([image_size * image_size, num_labels]))\n",
" biases_layer_1 = tf.Variable(tf.zeros([num_labels]))\n",
" # Layer 2 weights have an input dimension = output of first layer\n",
" weights_layer_2 = tf.Variable(\n",
" tf.truncated_normal([num_labels, num_labels]))\n",
" biases_layer_2 = tf.Variable(tf.zeros([num_labels]))\n",
" \n",
" # Training computation.\n",
" logits_layer_1 = tf.matmul(tf_train_dataset, weights_layer_1) + biases_layer_1\n",
" relu_output = tf.nn.relu(logits_layer_1)\n",
" logits_layer_2 = tf.matmul(relu_output, weights_layer_2) + biases_layer_2\n",
" loss = tf.reduce_mean(\n",
" tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits_layer_2)\n",
" + beta*tf.nn.l2_loss(weights_layer_1)\n",
" + beta*tf.nn.l2_loss(weights_layer_2)\n",
" )\n",
"\n",
" # Optimizer.\n",
" global_step = tf.Variable(0) # count the number of steps taken.\n",
" learning_rate = tf.train.exponential_decay(0.5, global_step, 100, 0.96)\n",
" optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step) \n",
" \n",
" # Predictions for the training, validation, and test data.\n",
" train_prediction = tf.nn.softmax(logits_layer_2)\n",
"\n",
" logits_l_1_valid = tf.matmul(tf_valid_dataset, weights_layer_1) + biases_layer_1\n",
" relu_valid = tf.nn.relu(logits_l_1_valid) \n",
" logits_l_2_valid = tf.matmul(relu_valid, weights_layer_2) + biases_layer_2 \n",
" valid_prediction = tf.nn.softmax(logits_l_2_valid)\n",
"\n",
" logits_l_1_test = tf.matmul(tf_test_dataset, weights_layer_1) + biases_layer_1\n",
" relu_test = tf.nn.relu(logits_l_1_test) \n",
" logits_l_2_test = tf.matmul(relu_test, weights_layer_2) + biases_layer_2 \n",
" test_prediction = tf.nn.softmax(logits_l_2_test)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initialized\n",
"Minibatch loss at step 0: 43.019760\n",
"Minibatch accuracy: 4.7%\n",
"Validation accuracy: 14.4%\n",
"Minibatch loss at step 500: 2.011266\n",
"Minibatch accuracy: 75.8%\n",
"Validation accuracy: 79.5%\n",
"Minibatch loss at step 1000: 0.933745\n",
"Minibatch accuracy: 75.0%\n",
"Validation accuracy: 82.5%\n",
"Minibatch loss at step 1500: 0.643496\n",
"Minibatch accuracy: 85.9%\n",
"Validation accuracy: 82.6%\n",
"Minibatch loss at step 2000: 0.855891\n",
"Minibatch accuracy: 80.5%\n",
"Validation accuracy: 82.4%\n",
"Minibatch loss at step 2500: 0.665540\n",
"Minibatch accuracy: 79.7%\n",
"Validation accuracy: 83.1%\n",
"Minibatch loss at step 3000: 0.827709\n",
"Minibatch accuracy: 82.8%\n",
"Validation accuracy: 83.5%\n",
"Test accuracy: 89.3%\n"
]
}
],
"source": [
"num_steps = 3001\n",
"\n",
"with tf.Session(graph=graph) as session:\n",
" tf.global_variables_initializer().run()\n",
" print(\"Initialized\")\n",
" for step in range(num_steps):\n",
" # Pick an offset within the training data, which has been randomized.\n",
" # Note: we could use better randomization across epochs.\n",
" offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
" # Generate a minibatch.\n",
" batch_data = train_dataset[offset:(offset + batch_size), :]\n",
" batch_labels = train_labels[offset:(offset + batch_size), :]\n",
" # Prepare a dictionary telling the session where to feed the minibatch.\n",
" # The key of the dictionary is the placeholder node of the graph to be fed,\n",
" # and the value is the numpy array to feed to it.\n",
" feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n",
" _, l, predictions = session.run(\n",
" [optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
" if (step % 500 == 0):\n",
" print(\"Minibatch loss at step %d: %f\" % (step, l))\n",
" print(\"Minibatch accuracy: %.1f%%\" % accuracy(predictions, batch_labels))\n",
" print(\"Validation accuracy: %.1f%%\" % accuracy(\n",
" valid_prediction.eval(), valid_labels))\n",
" print(\"Test accuracy: %.1f%%\" % accuracy(test_prediction.eval(), test_labels))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Got 89.9% - with rate decay every 500 steps"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"default_view": {},
"name": "3_regularization.ipynb",
"provenance": [],
"version": "0.3.2",
"views": {}
},
"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 |
paix120/DataScienceLearningClubActivities | Activity03/Hummingbird Migration Interactive Jupyter Notebook.ipynb | 1 | 1964721 | null | gpl-2.0 |
chapman-phys220-2016f/cw-13-aareston | cw13-juliasets.ipynb | 1 | 32469 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Most of this is a giant mess."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What we learned from it, however, is that using numba for a jit in certain circumstances is not feasable with completely general code. ie: The numeric_ODE module as it stands, is supposed to take any ODE as an input and integrate it. However, because of scoping issues and the necessity to pass an anonymous function through a function call, numba was not able"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 384 ms, sys: 288 ms, total: 672 ms\n",
"Wall time: 1.91 s\n",
"CPU times: user 1.77 s, sys: 28 ms, total: 1.8 s\n",
"Wall time: 3.6 s\n"
]
}
],
"source": [
"import numpy as np\n",
"import numba as nb #uncomment for numba\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"def julia(c):\n",
" #@np.vectorize #comment for numba\n",
" @nb.vectorize #uncomment for numba\n",
" def j(z):\n",
" for n in range(100):\n",
" z = z**2 + c\n",
" if abs(z) > 2:\n",
" return n\n",
" return 0\n",
" return j\n",
"\n",
"j = julia(0.345 + 0.45j)\n",
"\n",
"@nb.jit #uncomment for numba\n",
"def cplane(min=-1.5, max=1.5, points=3000):\n",
" r = np.linspace(-1.5, 1.5, points)\n",
" x, y = np.meshgrid(r,r)\n",
" z = x + y * 1j\n",
" return z\n",
"\n",
"%time z = cplane()\n",
"%time jset = j(z)\n",
"\n",
"plt.figure(1, (20,15))\n",
"plt.imshow(jset, cmap=plt.cm.bone)\n",
"plt.xticks([])\n",
"plt.yticks([])\n",
"plt.title(\"Julia Set : c = 0.345 + 0.45j\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import numeric_ode"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"u_0 = (0,1)\n",
"a = 0\n",
"b = 10\n",
"delta_t = 0.0001\n",
"mass = 1\n",
"delta = 0.25\n",
"force = 0.4\n",
"omega = 1"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypingError",
"evalue": "Failed at nopython (nopython frontend)\nUntyped global name 'euler_step': cannot determine Numba type of <class 'function'>\nFile \"numeric_ode.py\", line 189\n\nThis error may have been caused by the following argument(s):\n- argument 4: cannot determine Numba type of <class 'function'>\n",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypingError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-ed406cc83f72>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mtvals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\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[1;32m 3\u001b[0m \u001b[0mu_prime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mmass\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mdelta\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m3\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mforce\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0momega\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"time u_p = numeric_ode.integrate(u_0, a, b, delta_t, u_prime, 'rk4') #With Numba\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/projects/anaconda3/lib/python3.5/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m 2156\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\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 2157\u001b[0m \u001b[0mmagic_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2158\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2159\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2160\u001b[0m \u001b[0;31m#-------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/projects/anaconda3/lib/python3.5/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line)\u001b[0m\n\u001b[1;32m 2077\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2078\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2079\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\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 2080\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2081\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<decorator-gen-59>\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
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"\u001b[0;32m/projects/anaconda3/lib/python3.5/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m 1178\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1179\u001b[0m \u001b[0mst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1180\u001b[0;31m \u001b[0mexec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_ns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1181\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1182\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m/projects/anaconda3/lib/python3.5/site-packages/numba/dispatcher.py\u001b[0m in \u001b[0;36m_compile_for_args\u001b[0;34m(self, *args, **kws)\u001b[0m\n\u001b[1;32m 307\u001b[0m for i, err in failed_args))\n\u001b[1;32m 308\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 309\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 310\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 311\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minspect_llvm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/projects/anaconda3/lib/python3.5/site-packages/numba/dispatcher.py\u001b[0m in \u001b[0;36m_compile_for_args\u001b[0;34m(self, *args, **kws)\u001b[0m\n\u001b[1;32m 284\u001b[0m \u001b[0margtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtypeof_pyval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 285\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 286\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margtypes\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 287\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTypingError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 288\u001b[0m \u001b[0;31m# Intercept typing error that may be due to an argument\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/projects/anaconda3/lib/python3.5/site-packages/numba/dispatcher.py\u001b[0m in \u001b[0;36mcompile\u001b[0;34m(self, sig)\u001b[0m\n\u001b[1;32m 530\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 531\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache_misses\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 532\u001b[0;31m \u001b[0mcres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 533\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_overload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 534\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_overload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/projects/anaconda3/lib/python3.5/site-packages/numba/dispatcher.py\u001b[0m in \u001b[0;36mcompile\u001b[0;34m(self, args, return_type)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0mimpl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m flags=flags, locals=self.locals)\n\u001b[0m\u001b[1;32m 82\u001b[0m \u001b[0;31m# Check typing error if object mode is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtyping_error\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_pyobject\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/projects/anaconda3/lib/python3.5/site-packages/numba/compiler.py\u001b[0m in \u001b[0;36mcompile_extra\u001b[0;34m(typingctx, targetctx, func, args, return_type, flags, locals, library)\u001b[0m\n\u001b[1;32m 682\u001b[0m pipeline = Pipeline(typingctx, targetctx, library,\n\u001b[1;32m 683\u001b[0m args, return_type, flags, locals)\n\u001b[0;32m--> 684\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile_extra\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 686\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/projects/anaconda3/lib/python3.5/site-packages/numba/compiler.py\u001b[0m in \u001b[0;36mcompile_extra\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m 346\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlifted\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 347\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlifted_from\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 348\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compile_bytecode\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 349\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 350\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcompile_ir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc_ir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlifted\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlifted_from\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/projects/anaconda3/lib/python3.5/site-packages/numba/compiler.py\u001b[0m in \u001b[0;36m_compile_bytecode\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 647\u001b[0m \"\"\"\n\u001b[1;32m 648\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc_ir\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 649\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compile_core\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 650\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 651\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_compile_ir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/projects/anaconda3/lib/python3.5/site-packages/numba/compiler.py\u001b[0m in \u001b[0;36m_compile_core\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 634\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 635\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 636\u001b[0;31m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 637\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mres\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 638\u001b[0m \u001b[0;31m# Early pipeline completion\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/projects/anaconda3/lib/python3.5/site-packages/numba/compiler.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, status)\u001b[0m\n\u001b[1;32m 233\u001b[0m \u001b[0;31m# No more fallback pipelines?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_final_pipeline\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 235\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mpatched_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 236\u001b[0m \u001b[0;31m# Go to next fallback pipeline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/projects/anaconda3/lib/python3.5/site-packages/numba/compiler.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, status)\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 226\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstage_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m \u001b[0mstage\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 228\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0m_EarlyPipelineCompletion\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 229\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/projects/anaconda3/lib/python3.5/site-packages/numba/compiler.py\u001b[0m in \u001b[0;36mstage_nopython_frontend\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 434\u001b[0m \u001b[0mself\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[1;32m 435\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 436\u001b[0;31m self.locals)\n\u001b[0m\u001b[1;32m 437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 438\u001b[0m with self.fallback_context('Function \"%s\" has invalid return type'\n",
"\u001b[0;32m/projects/anaconda3/lib/python3.5/site-packages/numba/compiler.py\u001b[0m in \u001b[0;36mtype_inference_stage\u001b[0;34m(typingctx, interp, args, return_type, locals)\u001b[0m\n\u001b[1;32m 782\u001b[0m \u001b[0minfer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseed_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 783\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 784\u001b[0;31m \u001b[0minfer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_constraint\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 785\u001b[0m \u001b[0minfer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpropagate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 786\u001b[0m \u001b[0mtypemap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrestype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcalltypes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minfer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/projects/anaconda3/lib/python3.5/site-packages/numba/typeinfer.py\u001b[0m in \u001b[0;36mbuild_constraint\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 715\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mblk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitervalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 716\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0minst\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 717\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstrain_statement\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 718\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 719\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mreturn_types_from_partial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/projects/anaconda3/lib/python3.5/site-packages/numba/typeinfer.py\u001b[0m in \u001b[0;36mconstrain_statement\u001b[0;34m(self, inst)\u001b[0m\n\u001b[1;32m 874\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconstrain_statement\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 875\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mir\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAssign\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 876\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtypeof_assign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 877\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mir\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSetItem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 878\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtypeof_setitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/projects/anaconda3/lib/python3.5/site-packages/numba/typeinfer.py\u001b[0m in \u001b[0;36mtypeof_assign\u001b[0;34m(self, inst)\u001b[0m\n\u001b[1;32m 932\u001b[0m src=value.name, loc=inst.loc))\n\u001b[1;32m 933\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mir\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGlobal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mir\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFreeVar\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--> 934\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtypeof_global\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 935\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mir\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mArg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 936\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtypeof_arg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/projects/anaconda3/lib/python3.5/site-packages/numba/typeinfer.py\u001b[0m in \u001b[0;36mtypeof_global\u001b[0;34m(self, inst, target, gvar)\u001b[0m\n\u001b[1;32m 1023\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtypeof_global\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgvar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1024\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1025\u001b[0;31m \u001b[0mtyp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresolve_value_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgvar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1026\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypingError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1027\u001b[0m if (gvar.name == self.func_id.func_name\n",
"\u001b[0;32m/projects/anaconda3/lib/python3.5/site-packages/numba/typeinfer.py\u001b[0m in \u001b[0;36mresolve_value_type\u001b[0;34m(self, inst, val)\u001b[0m\n\u001b[1;32m 951\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 952\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 953\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mTypingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 954\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 955\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtypeof_arg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypingError\u001b[0m: Failed at nopython (nopython frontend)\nUntyped global name 'euler_step': cannot determine Numba type of <class 'function'>\nFile \"numeric_ode.py\", line 189\n\nThis error may have been caused by the following argument(s):\n- argument 4: cannot determine Numba type of <class 'function'>\n"
]
}
],
"source": [
"n = int((b - a) / float(delta_t)) #Number of points in t-mesh\n",
"tvals = np.linspace(a, b, n)\n",
"u_prime = lambda u, t: np.array((u[1], 1 / mass * (-delta * u[1] + u[0] - (u[0]) ** 3 + force * np.cos(omega * t))))\n",
"%time u_p = numeric_ode.integrate(u_0, a, b, delta_t, u_prime, 'rk4') #With Numba"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.82 s, sys: 0 ns, total: 1.82 s\n",
"Wall time: 1.88 s\n"
]
}
],
"source": [
"n = int((b - a) / float(delta_t)) #Number of points in t-mesh\n",
"tvals = np.linspace(a, b, n)\n",
"u_prime = lambda u, t: np.array((u[1], 1 / mass * (-delta * u[1] + u[0] - (u[0]) ** 3 + force * np.cos(omega * t))))\n",
"%time u_p = numeric_ode.integrate(u_0, a, b, delta_t, u_prime, 'rk4') #Without Numba"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Anaconda (Python 3)",
"language": "python",
"name": "anaconda3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
sophie63/FlyLFM | Notebooks/.ipynb_checkpoints/100148_for_turning_components-checkpoint.ipynb | 1 | 279689 | {
"cells": [
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[H\u001b[2J"
]
}
],
"source": [
"clear all"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy import stats\n",
"from scipy import io\n",
"import scipy.io as sio\n",
"%matplotlib inline \n",
"import pylab\n",
"import csv\n",
"from Tkinter import Tk\n",
"from tkFileDialog import askopenfilename\n",
"from tkFileDialog import askdirectory\n",
"import nibabel as nb\n",
"from scipy import io\n",
"import nibabel as nb\n",
"from scipy.interpolate import interp1d\n",
"from scipy import ndimage"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn import linear_model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Open data"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/media/sophie/db554c18-e3eb-41e2-afad-7de1c92bf4a5/panNeuronalGCaMP62/FreeBehavior/100133/old/100133Final/100133ss2on250cregcdFF30sMpsfkfint599Smith0_4_60TS.mat\n"
]
}
],
"source": [
"# from http://stackoverflow.com/questions/3579568/choosing-a-file-in-python-with-simple-dialog\n",
"Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing\n",
"filename = askopenfilename() # show an \"Open\" dialog box and return the path to the selected file\n",
"print(filename)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(20651, 599)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Ua=sio.loadmat(filename)\n",
"DT=Ua['TSo']\n",
"DT.shape"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/media/sophie/db554c18-e3eb-41e2-afad-7de1c92bf4a5/panNeuronalGCaMP62/FreeBehavior/100133/old/100133Final/100133ss2on250cregcdFF30sMpsfkfint599Smith0_4_60IC.nii\n"
]
}
],
"source": [
"# from http://stackoverflow.com/questions/3579568/choosing-a-file-in-python-with-simple-dialog\n",
"Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing\n",
"filename2 = askopenfilename() # show an \"Open\" dialog box and return the path to the selected file\n",
"print(filename2)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(180, 97, 10, 599)"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img1 = nb.load(filename2)\n",
"data = img1.get_data()\n",
"S=data.shape\n",
"S"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Z-score"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Demean=np.zeros(S)\n",
"Dmaps=np.zeros(S)\n",
"Dvar=np.zeros(S)\n",
"Var=np.zeros(S[3])\n",
"D2=np.zeros([S[0],S[1],5,S[3]])\n",
"Tvar=np.zeros(S[3])"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for i in range(S[3]):\n",
" Demean[:,:,:,i]=data[:,:,:,i]-np.mean(np.mean(np.mean(data[:,:,:,i],0),0),0)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for i in range(S[3]):\n",
" Dsq=np.reshape(Demean[:,:,:,i],S[0]*S[1]*S[2])\n",
" Var[i]=np.sqrt(np.var(Dsq))\n",
" Dvar=Demean[:,:,:,i]/Var[i]\n",
" Dmaps[:,:,:,i]=Dvar-2\n",
" Tvar[i]=np.var(DT[i,:])\n",
"Dmaps[Dmaps<0]=0"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/media/sophie/db554c18-e3eb-41e2-afad-7de1c92bf4a5/panNeuronalGCaMP62/FreeBehavior/100133/old/100133Final/100133Xk.mat\n"
]
}
],
"source": [
"# from http://stackoverflow.com/questions/3579568/choosing-a-file-in-python-with-simple-dialog\n",
"Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing\n",
"filename = askopenfilename() # show an \"Open\" dialog box and return the path to the selected file\n",
"print(filename)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Ua=sio.loadmat(filename)\n",
"Xk=Ua['Xk']"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/media/sophie/db554c18-e3eb-41e2-afad-7de1c92bf4a5/panNeuronalGCaMP62/FreeBehavior/100133/old/100133Final/AVG_100133ss2on250cregcpsf.nii\n"
]
},
{
"data": {
"text/plain": [
"(180, 97, 10)"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# from http://stackoverflow.com/questions/3579568/choosing-a-file-in-python-with-simple-dialog\n",
"from Tkinter import Tk\n",
"from tkFileDialog import askopenfilename\n",
"\n",
"Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing\n",
"filenamet = askopenfilename() # show an \"Open\" dialog box and return the path to the selected file\n",
"print(filenamet)\n",
"nimt=nb.load(filenamet)\n",
"Dtemp=np.squeeze(nimt.get_data())\n",
"Dtemp.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fit turns"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/javascript": [
"IPython.OutputArea.auto_scroll_threshold =4000;"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%javascript\n",
"IPython.OutputArea.auto_scroll_threshold =4000;"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"if S[2]>5:\n",
" Nstack=5\n",
" Int100=[(i+1)*100/Nstack for i in range(Nstack)]\n",
" Percs=np.percentile(range(S[2]),Int100)\n",
" Indices=np.split(range(S[2]),Percs)\n",
" D1=np.zeros([S[0],S[1],Nstack])\n",
" Dmean=np.squeeze(data[:,:,range(Nstack),2])\n",
" for i in range(Nstack):\n",
" Vmean=np.mean(Dtemp[:,:,Indices[i]],2)\n",
" Dmean[:,:,i]=Vmean\n",
"else:\n",
" Nstack=S[2]\n",
" D1=np.zeros([S[0],S[1],S[2]])\n",
" Dmean=data[:,:,range(S[2])] \n",
" Dmean=np.squeeze(Dtemp[:,:,:])"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for j in range(S[3]):\n",
"\n",
" a=''\n",
" if S[2]>5:\n",
" for i in range(Nstack):\n",
" V=Dmaps[:,:,Indices[i],j]\n",
" D1[:,:,i]=np.max(V,2)\n",
" D2[:,:,:,j]=D1\n",
" D1[D1==0]=np.nan"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fa34e274890>"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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vU6WqE3NrCEb7UdMlFd5jwwTMaiIluXPQpUGnpA5y0PE7vVV6cfyXaWU5BV9S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\n3pzRSIAlAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa34e326ed0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(Dmean[:,:,1],cmap=plt.cm.gray)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_cmap=plt.cm.jet\n",
"my_cmap.set_bad(alpha=0)\n",
"Good_ICs=np.zeros(S[3])\n",
"Label_ICs=[]\n",
"pylab.rcParams['figure.figsize'] = (15, 2.5)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"algorithm = linear_model.LinearRegression()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Sxk=Xk.shape"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(20651, 8)"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Sxk"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X=np.zeros((Sxk[0],2))"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X[:,0]=(Xk[:,0]-np.mean(Xk[:,0]))/np.std(Xk[:,0])\n",
"X[:,1]=(Xk[:,1]-np.mean(Xk[:,1]))/np.std(Xk[:,1])\n",
"#X[:,2]=(Xk[:,3]-np.mean(Xk[:,3]))/np.std(Xk[:,3])\n",
"#X[:,3]=(Xk[:,4]-np.mean(Xk[:,4]))/np.std(Xk[:,4])\n",
"#X[:,4]=(Xk[:,6]-np.mean(Xk[:,6]))/np.std(Xk[:,6])\n",
"#X[:,5]=(Xk[:,7]-np.mean(Xk[:,7]))/np.std(Xk[:,7])"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fa34e284550>]"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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BQVZBr891uYQ3DBDFeQyGwPEGQ4quysr4LCpKz1dWlvixWEQ12FTB5XFhcPFg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\njcCe7QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa34e284090>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(X[:,0])\n",
"plt.plot(X[:,1])\n",
"#plt.plot(X[:,2])\n",
"#plt.plot(X[:,3])\n",
"#plt.plot(X[:,4])\n",
"#plt.plot(X[:,5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot all components for turning left, right, walking, and grooming"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Rsq=np.zeros((1,S[3]))\n",
"Betas=np.zeros((2,S[3]))"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(20651, 2)"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(20651, 599)"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DT.shape"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for j in range(S[3]):\n",
" model = algorithm.fit(X, DT[:,j])\n",
" Betas[:,j] = model.coef_\n",
" Rsq[:,j] = model.score(X,DT[:,j])"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"RsqUni=np.zeros((6,S[3]))\n",
"BetaUni=np.zeros((6,S[3]))"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Sx=X.shape"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for k in range(2):\n",
" for j in range(S[3]):\n",
" model = algorithm.fit(np.reshape(X[:,k],(Sx[0],1)), DT[:,j])\n",
" BetaUni[k,j] = model.coef_\n",
" RsqUni[k,j] = model.score(np.reshape(X[:,k],(Sx[0],1)),DT[:,j])\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fa417bbbe50>]"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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81NMUuCwEjlWXjRtpe/39FDQy9u6ltR4lrAWeeCK/WjM3RyR57Vrg8ceBP/5j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\nFK+RsDbKbXTzCCUpS1PgXALHx+/G5D/9KfC7v1v7u7ZT4AB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WwLn32tAQ9Ss5UcUK3Kmn\nRg/6vPjmNyPFkSs0SixdSsGkXCuL4ebo8rkaG4sslFmr2x47Bjz/+cA73hER56xVKLn8/5ln0ky4\nBFsbmcADkQLX359OWHxuCZfsA7UKHO/HDYQmJ8MWSh+B43PHBM6X68JjIz/70iyUIQWOi8CkIY+F\ncsMGmhRzbZJ83K4Cx5MCUoHjHDi+fyV8Aa0k3ZLArVhB1+uJJ0iB+NGP/ARubs5/jXjfv/VbteXH\nGT4Ct38/vR/KU5qcBD7wgShOuftu+ss5SocPUzDLExr33ENVh0MK3PbtfgUOoDGHxym3AiVA14KX\nJWAFzlp6LmZV4NxnK49zMufq8GG61oODcdcQt0EqcF1d9HsOzjdsoPuan9EhAgfUun/SkNdCKZ9N\n3d3UR3p6au13WXPgtm+n41q1ivqtrEJ56JC/v2dR4M47L67Abd1KferLX6b3+NrydxphoXze84D7\n70/ejgQTzIEBOl9SxRwboz7ASyls2lT7bDh6lNo/NkaxirRQpilw1kZFTBgnbBETIEzg3Pw3ICI+\nISQROJkjkiUHrreXBqCZGfrNxo3h2Qk3N0bikUdIumcymmShXLWKbrLDh6OBiIt7fO5zVGlJnrNQ\n0r5PgXNz4LgtfN6qVZq1cXPgGJ2d1MYQgWNCyd9NgkvEfARu7dq4Ddb3u6EhInryBvzwh6PBJLSM\nQFYLZT0K3NNPU/v54XD4MD0MX/SiWgWOCTJXU2OsWBEF9i5cVUKqrK6FkicHmMixwnn4cLIC57NQ\n8mcuWP1etYr29fDDtQSOF/LevDl6j+9Fnx2RsXRpbTCZpsBxO2W/TCNwixbRzNljj9FrHpj5vEkF\nP4sCF/oMiGae+T13xpYXRc5C4AB6EO3bFx3jxz5GD3F3rHOr5a1eTcc5OUkP4yIWSglup0+B27s3\nrMD5LJRApMDJ90JYvpxUvk2bgL/8y2jMTbNQssVlcLDWOinBhSikAsdKeBYC56tCyc4GGURnIXAT\nE9mKmPC+efudnZFa7kJaeHl7vuOQFXplDtzcHNmdJPFKQpoCJ4+RFbgQgfMpcIcO0bEsX07XiZ/l\nWRQ4PlYfgQNo4vClL6W8qh/+MD7W8W+B2mspJ57GxoD/+I/a47Y2TuB4vDl0KJpI9ZE4jk/47113\n0Tj/zDPRIswbN9IY/fTTdJ2eftqfAwdEairgj9P4vnKti0DUd6QCx0tc8Bpn3FZ57mQRk71744WQ\nAD+BO++8ZAulVOY4D47jmzVrory4JAIXSo8JIW8VSnlu+RowgXOLmPDnSQrc9u103VmBm52lY3nB\nC6gtDz9cvwLHBG7zZrLVLlkSETg+l2mKcZKFko/19NOjysZZ0N9P57Ovj36/cmWcwG3dGhXrcif3\nZmcprmICt3VruoWSrf7Dw9Q3e3risfNJR+A6O/15OWkKnLRQbtgQL1OdZRkBSeD4odfVRTM/SZ1b\nKlvudqV9Eki2UK5cGeWhSAVuYoI6HHfgUBK8VOBkgOnmwPkUOIDW4kgjcG4VysHBuOrD301CmoWS\nb+o1a2rzClw1wVXgnnmG1AAgfvyswE5PR4NyUQulu4xASIF78YsjAnfrrZQ3sHSpf4mA5cvjxQeA\nqA+EFDh3UdupqYgY8Hlju8gZZ8QVuLPOivcZFz7SxOcqpMBNT0cE7vHHk3Pg+L2QhdLddhqB86ls\nrGhl7ZcAzahLAnfKKWELpU+Bk/ZK38QU919e0Jrfk8rw4sV0TOPj2SyUQK0C95rXRMqlS+DkdVm9\nmgJjLlpQVIFjcDvdwItVcJ8Ct2JFlFgvLZRAnMBlsVA++WTtvl0L5eOPR9dv+fLI4iKDZh9WrIjn\nwEkLZRphSapC6VoosxI4n7pz5EjtOCItlEDYRiktlID/mOTYLRU4/svEPQuBS1Pg+vqiHFUfgXOL\nmLg5cPv3R793LbBZCRw/o10Cd8YZpNJu3w784Ad+BU7ul8H75mf5vffGP7/jDlLmZF/k5YE2bKB/\ng4PAtdf+3/a+PUzLqlz/XsPMN98Mc57hNMMAchBkQA6C0CYDRUDE0p0ZcVkeamvuq6xt7czce9eu\n7NL0aluW7WpnSWVJmaW/dgdr26h4ygREcAiIjY6Ag6AjBxGUWfuP+3tc613ver/v/WbmN6as+7q4\nmHnnO7yHZ6313M9zP88CTjvN7MUFmOxJVxcJ3v/8D/CBD/A1nZ2cbzIZ3vd160hmpk+PBk3se2iT\nJV+wyiZwrs3aBE4c2G3bmN0ePdqUGPgklBJsveceYPHi6Oc2N0evWbZG2L+f98UnobQzc9KJUshe\nQwN/b2yME7j2duO7pSVwl10G3Hcfv9fO2KapgRNIk7LeZuCEwAGmBk7uR1MTbW7VKvoANtJk4MaO\n5Tm8+KJpwz9iBO/9smXRDJzUGOZDGgmlUsXJKLu6mAF7+ml+ni1hFFImRMslcNKQRAICI0dGu1CO\nGuXPwI0fz2tx69+AtziB87Uxz2aTCVyhDNzevX2TUMpiJ3p1WYiGD/cbt9b5a+BcAlcoAycETiYc\ncfZ9BM4nywF4/OBBk9lJqoFzCdyUKTTmnp64HKGiggPCdnDcGjg7ypsPPgnl0KH8HOkM1dDAgW07\nyL4aOJvASdv9hx/mZ7oZOPlckeLlI3Bukwppq9/UlE5Cefgws22dnbwme7NSNwMH8H/XeZT7n0Tg\ngGgG7sgRTkwjRkSPdXfzPtk1cDJ5JwVEfBm4QhJKwEgoX3stv4RSjiU1MbHhk3PK+1580WRG7Psh\n52QTuEIZHCBK4F54wUzMQFRC2dcMnBA4CUq4C5nUqfQ2A2d/Zz4CV1fHcV1by3+ydUhvId/vk1CW\nlPibmIwZw3NwJZSAaWICpAsMbd0a/257kT50iBuh33YbX6+U6W63Z09+AtfaStt2CZzdTCjfuSXV\nwLkSSvcZJ0kofU1Menr8GTj73iUROLuJCVB8F8qSEjP3pJFQ5nNsteazFEdUmpi4GTjZWNonobQJ\nnDixxRC4JAklANx0E2W648bFs9qAsZGkDNyuXXxObgZu0ybgd7/jPG4/x8GDSXwaG+njdHTwHi1d\nal4j/slzzzFwWFJCW9+xg/dS5vy6OhK4444DLr7YzAeunbS0GLKUlIGT+lnXZu1OuJJF+t//5XeO\nGcOfgWQJ5b59PMd3vCP6uS0t8QxcSwvtdvv2eAbO3pYJoHO9das55/p6ZuCmTo2OicOH2Yzoox/l\n767d+zKgPT0kRt/6Fj9bFADFSijl/knwKonA+bpyyvYI8qwlAyefCXBsvPe9cR87yccVSJO1pibK\nb6XMpbmZ53n22VECd9xx/SOhBCijTCJwGzdyD2PB7t3MFD79dDyLKaTM3VZBIEmaJAmlbLNibw+1\nZ4/xE+wGYYK3NIHzTQw1Nf62w2kklD09+QmcRJnyNTGxI1L5CNzatcDb354sodSak5B0oASS94Gz\nJZQ7d0YzcDt3cuDKxJXUxETuYyZj9r+Q3+0aE5kA7CYmACfWwYNpsL4M3KOPslOSwLeNgNy3fPBl\n4KTT54ED+TNw+SSUO3ZwkZszh9FHH4GzJ4skAifOyMsvG3srLTVykDQSSoDRqqFDOcHcfz8zcEB6\nAic24LN5O6omrzlyxNQ52PeruzuegRs+3NRg+ZDUxEQ+1/d6wGTggOjCLO13tTYytbQZOF82UBwh\ncfLSZOCKJXCSqbS3EejPDNyIEdEMnG2bsrCkJXDNzX4CJ/IrgS8DZxM44P+fhHLMGP8zlrbsPgml\nLMD2ZychXwZO5ohbbuHivGaN+TzJCBSSUP72t8xYZDKcH0RCaW8jkAQ7m+N2oeythNKX3QHyNzEB\nOCf5OlG6Gbhiu1BWVxsb7quEcs8erpWSTRwyJN6J1q6N9Ukot2/vewYuicA1N/NchGC6BE7GflIG\nbudOkqvdu6Mys2ef5d9/+9s4gRszxtjCs88CX/gCx7w4mHYGbssWZpuEhP31r1Gnfu1argmXXw5c\ney2PuzVwkoHTuu8SygMHSNrGjjVdPF95hWPeXZOPHGH93tveFn8mvgzciBEct9u2xYPOEnQTstLa\nyqCqLaHs6KCPZmfgbr+diqTJk829EbtfvpwBWRcdHVwT7rorTkp6Q+Ck/vfAAY61Q4eiBK66mu+9\n8Ubgy1/msUOHot2V7QxcoTE5fHj03rqQ+WHoUODWW03wYNQoyonHjzfE/PnnWY5QiMC56574RpKF\nlfll1qzkOrhrrwW++lVDqnfv5jouGTibQLkZOLuO88ABjh2ppd6/P0rg5PrdxoJ795K0vfgiv1P6\nXQiOOQKXzQK//338tWkklABvsHT7A9J1obQbStiLqQwWXw3cY49xIjx4ML4w9PQAn/wkH+qcOdFr\nSJJQSgbObWIizUskA+drVLF/f9SZ378/KiFNI6EcOdIsEG5Hp4oKpt6nTTPHamo4aLq6iq+BczNw\nsuHpvn1mUDc0JG8jIM/T1iiL5GbZMk6wroTSlqnJfUmKFGez0c3Oy8pMVCxNBg7gYjNuHPeFOvdc\nE9n2SSiz2b5n4A4fNpkduV+HD9O23AycZDjzSSiLqYErRODE7m19eFVVugxcPgnlli2cPJNq4Poq\nobQJnC19821sD5h7ni8Dd/Cg2RJAjtmbuAMmum1/J5AsoRwxwt8BNo2EsrOT59JfBE6p6PYMAJ+t\nr/4NMAQuSUJp16HkgzSWcMmj3MtXXwVuuIEdJh9/PErgtm8vnIETKEV5VU2NObdiJJT5ulBWVMSf\nsSgdbCQ1MZHXu9+dVkJpZ+B899tec9wulNXV5r19lVDKuLbPGYhn4OxMrSuhfO65OIFLWv/t/eXs\na02SUAqSCFyaDFxLCx1TOwvX2cksUXd3/gzcs8/ScZ4yBXjiCb5GWsV3dRlHUkiYnc2sq+P7JYNi\nX699DwcP5jHZLzRJQpnUxET+92Xgtm83DWbscxBfxSefBPw1cELgdu2K+iwlJXwO9jEhcLaE8uWX\neR9tAnfHHdwnVGATuPXr/RtUP/QQ8Pd/z2tKyiq56OmJBkIEQuDE+f/mN5kNtAmcqAeuvJKyTcAQ\nUxkvdgau0Lwutpi0XYL4DUOGsCTkne/k8XPP5d6ZQswB3svjj+9dBm7fvqh8EuC67G60DtAWfv1r\nnnNnJ/1RXwbOJXBuBu4jHyER7Orid9kSynwE7tAh1s2NHMlreeYZjksbxxyBS0IaCSVAIzjrLOB7\n3+PvdhMTITD59oGzCVy+DNyGDXyAmzbFCdx999GwHnggOonLNcg5aW3S3jU1plW7LaGUNsWuhLKi\nwpCJdeuMVLO8PJqBc9t0y0IlKX6bwEla3peB27IlSuCU4qDdtKk4R9mVQh44YDYu37/fDOp8EkrJ\nWNbVRQncyJHcOPbOO/m5dgbOR+CSHI2KCn6uS+CEOAjyZeCEwG3YYFriA8VLKJOamADRGjipnbRr\nIUVC6WbgGhvzE7i+SiiBeGG/OOMyxuwmJsVm4CorOWEfPRqVtbkEzg4spMnAjR9PO5Ks2Pjx/pb+\nfcnACcm224qLcyNw9+tJk4HzXaPU0tnRSZfA9fRwrIvz3VcJ5fDh8fuSyRQmcEkSyqoqk/XKh/Jy\n003ThizSTz3Fz7/oIjq9tvIgTQbOximn8H8Zh73pQil1gYcO9a4GztfEBMjfxASgw+uLtIvEXDr2\nuXvZ2ddx8KA/Ayc2lEZCKY6Nz2G0CQdg6lxdAie/n3kmcOqp0b8BfcvA5ZNQClpaeE+KrYHbuZNj\n9uSTo5mFzk7ggx/kz74MXGMjx3BXF5/jjBkMIgOcV0480RC4UaMMCfvzn6MZOCAuZ3bvr1zfjh3+\n+ayYDNz+/XEJpRtMAsya9fDDZoy555NE4AD/edjHpDGL+DdCHtwM3JYt0b0LJXgs25QIabbx0EOs\nez/rrGg36ZoaPjPZ7Fk2RweMD+CONZn3Kiv5vffcQ+Joz5EA7eG880ytvZ1ZlE6hroQyCc3NXEt9\n19bTY3wu2Zbj7W/n30pL+T1SC/fKKyTnxx/POSXf/nlJTUzc8Th6tFnvbXz729xncu5cBuW6u023\n0RdeiGYxtTayyCNHTDB5717gZz+jn9bVRT/a18REztUm5LYvJQQuTQbu8cdNg6D+woATuNGjGelI\n41gBpi14EuRvoj8WQ29uNk6ZLwKntckAFEvgKiqMHMeO0mzYwCJj2zgB08RENog+epTnJgu6fJ+b\ngZsxIy5naG2lIXV1MTs2dy7f48vA2V3eKipM/YD8vbzcZOCkrbhL4MrKGNmwMXYsMxZ9bWJSVWUG\nRhoJpZCxykoee/VVLjTS1vbUU+lklJXxM4qRUALJGbhslrYiE0m+DNyIEXQsvvY1I2sE+q+JCRDN\nuu7YEf0eV0LZ3U2n68ABs3gljafeNDEBTAbO54jaDSkke9GXGjhpSy9ECIjOJTJfFJOBKyujTW/e\n7M/AyTgXmbG9UMv3F6qBs7OkEpRwHdbeSCjd6wd4fmVlRjLnq4GT7+uPDNzEicxyuaiuNnIkF42N\nvE5RSNjXIaQ/zbOzA1E2pJvrX/7C8zv+eNqb3XBl1y4+lzQZOBtpM3CuhFKctmzWbLwLUK3h3qdi\nJJTSzdbG8uVRGf/xx/Ne2BB1SV2dIXA+iL3Om0eZul0DV12d3lkEzF6Xvui0Ox6U4rzi1rjK70uW\nGIdSzgdIJnBuQ4pCEsqXXvITuJIS3l+37kXmHXdOa2oy9ewjRvC5bNhg/t7ZCbznPfybPU6HD6dd\niOSvsZHX7xK4adOiGTi5B488Es3AAdGAkZyzT7K4c2dU4SNI28Rk8mQSkNWroxk4H4GTYPPWrdG9\ntOzz8UkoZdz6CJzbETCbNXJLWXMnTDCqntde4/nZTT5k7t62jZ+5bl383B58kKU/F1wAnH66OT5+\nPK/zO9/hs73kEvM3n3wSMPOeNLD54x8ZKHcJ3J13ksQ8/TR9EiFZIj8uRkIJ0Gf94x/jx196ifeu\ntJTjcOnSuG0PGkSf9JlnSIBEZrx/P7te+pCWwGWzHDtu4Om++5j1POkkkiKpR5XnaksoDx3iOQvZ\nF3J39Cif/6ZNfP+kSUYJNmyY2etQa6OQEFK3Zw8/zyZwaTJwn/wksHKl/570FgNO4CZNAn76UxY/\npkHaDJw7mUyaxEkvSUIpkURp+OEjcCKhtItXN2ygXG/nzngTk46OONmRaxDDz2T4uvnzgX/7Nx6z\nHVy5FlnMyso4kOz28wsWsOh5zRpG84BoN075HjcDZw8+pbigV1UZZ8FH4CZPjt//444zBM7nRPsg\ndXaSfXQllEkZOB+BU8oMertr2Yc/bK69txJKNwNXVcXvs7NwSRk4mTjOPTcqxQCKy8CVlvoj4T4J\nZU9PlMBJBLm7m5PKwYOccGprTQ1PMRm4tBLK447jQpUUVQR4DsXsA+ergbO7Gspmqz4JZVq7FEyd\nygVa2gYfOWLGj8gZn3ySf0vKsqXJwAHGprdsiTqsSU1M8kkofdeoFMetyEJ9Ekqg/whcZSVw/vnx\n49deC1x6qf89StGpy2ZNls2uIaqqSkfg5D0ugSspoa2tWUN7yWbpnMm9Kivj81i/vvcErtiNvG2b\ntLdn+cxn4vXfxewDV18fz1T+4z9GnYqJEw2Be/hhftajj5I8lpTQ9pKup7yc8+LGjXQe3Qyc/J/G\nWQSMjLKjgwRMHDSXwAFxAucL7Nh/A6IErqTE35kVyN+Fcu1ajpukAMTKlf4MnG9tmTmTdigZuLY2\n3kuA89nu3XxWnZ3RteqXv+T9aWxkhkRsPC2Be+WVeAbOJXCXXx5tiiLv3bGD52yrbwCjEsiXgauo\n4PueeYb2Mn682VB769b4fctk+NpsNh4cA6J1eUA8A+cGL1wCB5BkbNxoMlVlZfy/qYlBu85Os8WL\nQAIXf/kLG6scOBDN2D3/PO97WxvH0VVXmb8NGsTa23/6J57v+vXmb/kInNhPVRVtoqqKgUV7bJ5w\nAsfbkCHRzCLAeaS1Nb2EEmDg20fgbN/pkkuMv+pCsqsij21o4P2cPj2ecXrtNa79tu2IhNKuh7Y/\nWySaAtmm66STmGUWhYmcqy2hFDu1g0ySpbz2Wo6Zzk7aU10dx5NkMJ9+Ou5vAtEM3N69tE13/XE3\n8j54kNlaX6azLxhwArd8OSMVvoHqQ5qNvIH4ZDJpEtl1UhdK2wl3HT57gc5kTNH17t1k7qedxt/d\nz+3o4ODyXYOdGfv0pynp+fSneUwGrZ2Be+UVOhjNzZwAbMf/9NNZwNnaau5jJkNjFkmlrwbO3WdP\nJntbQukWBLsTOMBshewnljbT0dREo9+wgdeWyZiFX7paVlfn30bAnlAkEigZOICb+H72syYK6iNw\n1dXJDltFBQejXYdmR9tFupq0jYBkRHyQxgduBs7XxCSJ1FRVmYY4cn5AlMBJi/NBg/h8amoYQZTv\nyUfgzj+fjp+NfBJKeU5SQ/PDH8Zfky8D15ttBIBo5FuyY/b7pDERkJ7AzZ/P7IJIymSza7sebe1a\n//hOm4GT5yRO5aZN+TNwlZWU4fr2xwR4jnatnA3Z+wiIE7iKCtNavD8IXBIqK5NtGeDibH+vK6FM\n8+ykjtR3/rW1nBPFXk44IS7R2bAhvYRS0FsJpS2ZdPfXdJFWQllRkY6ATpxIR1Brtpn/0Y+iCo5C\nBG7DBrPPmlsDJ+9PI6EEOA889BADkS++aJpD9JXA+TJwUlNTrITyX/8VuPrq4saFT4EAmHboQjwm\nTuScLA2ohgzhdbkdAiUY1tBAAiDr3NSpfJZS/+wjcCKNk7Wsro527j6juXP9+66tW0cbddujp+1C\nCfBeLFvG66ipYQb3+9/3Syg3bvRvNwLwnCWQ/cwztEe5Hlt1JXAllAB9pS1bTBfKYcP43qYmkjJf\nJ0GZuzdv5jM78cSoA/7ww7x/vu7pAH2xu+9mbwcJpALJJUQugZs/n3NW0j6d48ZRRmkTuLvvpv0X\nk4FbsIAN1+wui0DUd5oyxb/2AZzHt22LErj77mNw+fHHo6/t7o526wSSM3AAAw7SJAXgd7z2Gn3j\nWbP8GTi7C6XYqdwPub+/+Q0DF6NHM4s6bBjPXWtT2rN2rQmC2QTOzsB1dPB73efp7uW3erUJxPQn\nBpzASRFkWhTqQmk3MbHR2kpjEamKO4HbjnRSBg6IyiiffDJqyK6EctMmv5GLhFK+q73dtKoFomlz\n+1qGD+eEv349jUHqjBYtonHJ4iv34dFHGZ2T78mXgbPR0ECnX+RpgqoqRlFcSBTPdpTTRMsXL+Zk\nJvJJgINryxZOyEpxkrSvy66B6+gwtS4StZEaOID38POfNw6yT0J5ww3+bIF81733mu93CZxk4HyZ\nloqK+EJow5eBmzYtLhmpqUm2d9shAaKyTcGMGbzHQuzr6zlpSNQzn4Ry1Kj4Iibjz/ceaeedLxhj\nL0qnn04HrS8SSiB6jm4Nh5yrnFva2q6FC4Ff/cqQDpFH2BLKjRv943vwYM4zaTNwcryyMv82AiUl\nzM4kQSnT1dLFCSeQwEmBt+00KcXvspuY9KUGrrcYMyb6vW4Tk7QZuKRxV1cXJXCTJ8cJ3KuvDoyE\n0s3A2RJKHxoa6Kxcey0zAPv3G4mSjbQErr6e9vbEE3T6fv7zKIGbNo1yLx+yWa5BS5ZEax0lAAdQ\ndeDWgSShtha44grgX/6Fe5v96lc8nkTgfF0ofZBgr+3IyT121/+jR/0BpPJyOlsbN0Zlb2lQXu4n\nsTNm0Hfo7OTzy2Z5rzZv5jG3ftNFYyPJu9h5Nsv7tG4dndepU5kJeP55Q15bWqJywNGG8yrsAAAU\nXElEQVSj/fVlPrS0MPs3e3Y8sytBpt2746UilZXRAKOLc8+l/fkI3MGDcbJoQ2SUDz5IIijb+9TU\nxFUfSRk46Vbe2GjGkTT3cdUQQJzATZ8edcBFPpkPixdz7Z061UgK16zxE327edPgwSRWst4kEbit\nW/2y/poa0wugEIYO5f1ZsyZ63A1+J2HhQqrq9uwxBE4yei6Bc/0xoDgCJ9k32cqgvJxz5NChJlFh\nSyiFwFVW0pZlfJ58Mn+fNIlBgWHDaE+S6KipYUBXFG5JNXAHD8blk0BcQvmHP3A+6eyMEru+YsAJ\nnJCQtCimiYmNkhIOup4e02nJjmjmI3D2YLEJnOzvZhM4WRi6u/lg3E5s7jVkMty00x4YUgsmE5EY\nmRC4W29lmls+Y8IEDjib6GQyNA4hcEJg7G0EkiL5DQ3Uq48ZE5U3fPGL/kVs7Fj+X15uyFKaaPmi\nRSQX0sAE4P9f/jKjwgCjKldfbd5jR7F/+lPeO8DIcGwJpQ1pFuBOQvmyAtksG9DIQmcTONlfBvBn\nWhYuBH7wg+Rr99XAfeMbcYJcKANn26bdOEUwbx7w3/8dJXA330zNuPyeNitlf28S6SuGwF13HRdp\naSjUmyYmQHShT8rAAXF5ZT7IeYmtuBm4igqer4/AzZ1Lp+/QoXQ1cHJursPgZuDSQBxCF5KBe+kl\nP5Gtr49v+DrQKJSBS0vgkhxguZ9C4KZOjTpO0tChNwTOrsHMd24PPMAGFQcOGLucOJGkpVAGbsMG\nOiff/S4/5+ST49/Z0JA/829j4kRK/+bNI3n705+Mg1JWluzgyxw8ZQrre30E7sYb06/tsjn6ZZeR\nFD7wAM/H3m5EUKyEsqUluo7Ka10CJ2PMdf7Ly9n97/LL0zdaEyRl4KqqTLBAnNe2No7PtAQOiK5z\ny5bRLqTjY20t5xdZ48eMiZZzTJhAOWMaNDfzvMQ2bNTWMiO0enV8vzbxM3zyf4BlMyUlfgmlnGMS\nZsygnaxebeoem5riRE3Ow5eBk/OfP9/UItkZuCQCJxs1T59upKuAaWCSBkLgfv1r2v03vxl/zeLF\n3EYB4Fp51lmFCdxf/0rf070PjY1RP6oQfDLK+++PbiGVhPe8hwT45ZeNRLW9nZ8p+7g98QTVd6tX\nxwmcBFx9HZXtLpcAx4zImpXiOX7wg1S0Abxuu4mJEDjJArtBN7m/koETuxECN3s2f6+upp287W3c\nT1QycICfwEnfBMlq/uEPzPhNmhStf+0rUhE4pdQZSqlNSqnNSqlPe/6eUUrdrpTaopR6WCnluaTe\nobcSSoAPR6QJixZxkRAjLSYDJ8Xe99zDiWToUBPRlIVB6t98k5ddA1dVFSdFUgsmsDNwzc2sd7N1\n6koB//mfximXawAMIfBJKPNl4B56KFqEC3DB8EUUxfGRe+YrhPbhtNMYtdq713xuQwMH5HXXxV/f\n3t7+uoRy2zYOZJGvSpenffv8jkOShDIfpMZKJtEkCaUv01JWlt+R8mXgfEiTgbO/E4hm4ObN40Ih\nE3p9PSOIF1zA3xct4uJfDGbOTL6HxRA4gezJWOw2AnLt9kIvY7C9vf3199l2mZYIKUUSbktN9+xh\nAKi01HyOj8DJZs9btxaXgbMj5EA8A5cGY8f6n43UwPmaBsj11dZybhSHeqDRXxLKfASuosKMy/PO\n47wJ0F4kY1QsgZNASpKjKmhr41qxZg07nsn1XH89HcF819fSwkYFv/kN23ffe2+046JgyZL8gSMb\nEyfS+Vi4kPOANLAqBLHptjbgYx+jwwZEJZTF4KSTgK98xch4Z8/mOX396/F7+uEPA+9/v/m9ri75\neZWWRkmOPV9OmEDCKnVUvmg/YLJB4hACZm4phPHjeR0+zJrFeVquT+rg0hA4eUZ2pvn88ylZlzll\n2LBoBvTCC7nxeG8g99DeCklQW0vp4OzZ8efQ0JCfhA0ZQttxVSeyBuR774oVwI9/bDJwAMeWb+1J\nysABJjMl5DafhFJ8j82bSeAWLuR4PHyYa5fdg8CGz15OPJEZ0yuu4Bj0BUuWLzfXtnQp7ddOFrjw\nSSgFpaXAv/97/D1JcBuZ9PRQZi2B9XwoK6OirKmJ9i17BX/oQ8zAPfAAfUutgU99Kk7gAM4ju3fH\n13yprxNIBk4wdiy/W2y1ocFfAweYju825P4OHRoncDt2RAncL3/Jz8xmub6Kf+gjcEoZGeUtt3Bd\nnz07LsPtKxJyMvaJqBIA3wCwEMBOAI8ppe7SWm+yXvYhAC9orScopZYDuB7A+/rjBKur8y+UduG7\ni0mTjOEPHcqoy4oVwDnn0FDkva7TZxO4YcPofN11Fwf5Bz7A87nqKk7YIlNMamACRCWUq1fHo4yD\nB0cJqCuh1DpeaOw64eXldFQkupXJkLzZ+9UlOcsyoFwCl4TKSp6bXdORxgGsq+Pg+6//Ms/rE59g\ndx4faWlvb8eyZQtw+DCwahUlGBJhrKlhlKe52d9qvKyMA3D79qjjnA/ZLAm63H97b5XKSk7e3/pW\ntNtnWvgycEmvS3pObgTJJ6EcNy66sNXX097lOnyLciH49mcUXH55fumoj8BlMqaVehL582XgMhlu\ntmo7nXfcwcX1C19ox4IFC2IZuGKIyaJFpk6hvt7UedrZFl+XNICOdEdH/NlWVJBE7doVJ3C+DNy2\nbWZvpzS45RZ/DUZLC8f+mjV+Amd3vPvJT9JncfoTM2dGuwhms0atMG8e63wL4ayzontr2qir4z2W\n+aGszIyD9vZ2/N3fLUB1dfFj2ZY65cPixfz3i19QOSB2OXUqpbH5sp6DBrEBTE8PnZDbbqOtu/DV\nACVh4kTKxebO5TzwyCPp3ie239YWjcjbGbhiYG+vAnAOefe7o0TNPmcbS5dynPowYgTJoaCtjd2A\nAcrR9u2jQzlrVjKBKy/nljQ2OWlv59xSCBMnUrXiw6xZ9B/sc7vjDs6dPgfQhpyLPc9OmcLvk7lQ\nfAVBJlO8XQtkLkgicFpzLXZRXx+XzLlYtSp+LE0GbskS4OKLSahmzuSx2bPpVLvwZeDkHrtEp6mJ\nJDpJQrl+PW2luZnrwLRpDKiMHMnz9SUOfPYydSr9nLa29H4WYHzKfBm4ESPyy0/TYP58kn7xT+6/\nn/fK1wPBh8suM3OB2OSZZ9K/u+QSZuilM66PwNXUcJ0sJKF86in6M0mQPduyWRPAt0mZLwNXV8dx\nL5JceW1NjbmvNTUMHtx4IxvTCOrrk8dvVRU5x/XXkxyXlvJ+DiiBA3AygC1a66cBQCl1O4CzAdgE\n7mwAn8v9fAdI+PoFl18e7QLpQjJmPidm0qRolHPRIhKxxx5jMfYVV/D40qVmYaqoiDqcw4dT7vLU\nU1xEZRG+8kr+391NovTgg8lFnraE0lcwn5SBGzqUE8fkyYUneWkvbP8ukR6JsOeTUGYy6TXyAAdW\nbxzlm29mHaREUXwSCBvZLGWSN91ER0hQW8to+o9+5H+fbNZ5/fX+Or6k77JlIZdeamxv8GDgS1+i\n87N5c+/kNUePRvfq82H48LjERHDiiZSRCnwETik6v/K3FSvincf6E4WifCLxsSG1jrfeyoicD/bm\n3/b7li+PHnODJnZdVzEZOID3askS/iwETuw6myUpSmrpv2QJCYf7bJctA665Jh5I8BE42Y/ns59N\nv3AmkX3pRHnJJczkuLDnsjPPTPdd/Y1x4ygjFlRVUQkgTSck+pkPs2Yl/622Nr9TOGlS/r8nIS2B\nE7zrXYwU27bxxS/mX9cEJSUkqbfdlu5+5IOQoTlzOD+mfe4ilXfJVG8JnIt8DpkLpZJtXgizIJMx\ngc+SEmYEbriB2YGk+tiPfjR5/u0LzjknSgrb2riRcE0N16h8EIfXDZRdcIFxBt0MXF/Q3Ex5pu8+\nSK16Mc+sENIQuEyGpHHTJvN66WTrYtw4U+YhkAyca6/nnMNxsHt3XBGRzXJf4W9/2yQRLrqIAejR\nowvXv9mYOpVE8J//uXDm3kZzM9cVn83LfrObNzMI1xc0NPDz7ryTge9Vq4xqJw3q6mjP8lmtrbTb\nWbNIkFesoI/+3e9G9/QTNDUxQO76Kq2tJHZHjvD9Gzcmd4YF+LfWVpMB27UrfwZu+nRm/gGuv3Zp\nz6xZJvgnx0V9IChE4D7zGZJhse1p01h/3F9IQ+BaAFjbEOJZkNR5X6O1PqqU6lZKNWitnT5axaNQ\nFKm2NjkDMGVKfMDOmROPLA0fbpyrK6+MLs4nnkjd8ne+wyieC9mTrKPDdJV0Ye+V48OQIdHJUrok\nZTKcXJKi/jaqqqLRx9paOpy3326MOYk4TJ5MvXQxTskppxjDlY0702DmTOqifYPYh8GDmRVZuTJa\n8/fe93LyPeMM//vGjWO0uRjYmnMgGhRobaVjfdVVrEspJHtxIY0jVq5ktCoJo0ZR6uNDSUlUPlBa\nyn82gQMoh5BNPm2Z7RuBpia/TOuBB0j+bJu1cc01vYsgf/3r5n1f+UpxkclBg0yGprGRsl6ZK6qr\nTYdXH97xDiPZsyEL06WXRgmcFLbbmDmTi13ajHEhXHklP8vnaLwRksk06CtJsdHUlF/eO2pU4YyB\nD9J1NS0GDeI87M4ZaR25972P2dTeZlQEM2YwoJBWUi6oquI4ctePCROSN2r/W8RFF9FB/MQnmFnx\nqXbyBQT6glGjos2z2toYgDx6tDCRLi3lubv28/GPm7q+d76z/7LoQnZ9GD6c9TzumtMXVFQkl2vY\n+NSn0m2EfM018WMjRzLD6waxp09nQH/VqvicOGcO8B//Ed0K5d3vNvWRN99c+FwEtbWcA3yZy3xQ\nis/ZB8l4ClnqKxYtomR5xQoG/nwZ8TRoajJB8yuuMHvUAVHFhY0f/IDf58pyRRZ98snMxE2blt/O\n7WdSXU3F1IUX8ndp3GUjkzHXOWxYVLlkl+bU1XEddbnGhRcmq5qGDaOt2D6O1ND1F5QuEAZUSp0L\nYInW+tLc7+8HcLLW+mPWa57MvWZn7vetude84HxWiphjQEBAQEBAQEBAQEDAWxda6yJyslGkycDt\nAGAnCUfmjtl4FkArgJ1KqUEAanzZt76caEBAQEBAQEBAQEBAwLGONF0oHwMwXik1WimVAZuT3O28\n5v8ByCUqcR6Ae/vvFAMCAgICAgICAgICAgKAFBm4XE3bRwHcAxK+W7TWHUqpzwN4TGv9KwC3APih\nUmoLgL3opw6UAQEBAQEBAQEBAQEBAQYFa+ACAgICAgICAgICAgIC/jaQaiPv/kChzcADji0opW5R\nSnUppdZbx+qVUvcopf6ilPqdUqrW+ttNuY3i1ymlUm4MEPBWgFJqpFLqXqXURqXUk0qpj+WOB3sJ\niEEpVa6UelQptTZnL5/LHR+jlHoktwb9RClVmjueUUrdnrOXh5VSBTZtCXirQSlVopRao5S6O/d7\nsJUAL5RS25VST+Tmlz/ljoW1KCAGpVStUupnSqmOnP8ypz9tZUAInLUZ+BIAbQBWKKUStr0OOEbw\nfdAebFwF4A9a64lgHeVnAEAptRTAOK31BAAfBvCtgTzRgDccrwH4hNa6DcDbAHwkN38EewmIQWt9\nGMCpWusZAKYDWKqUmgPgywC+orU+HkA3AGmW/iEAL+Ts5asACuzMFfAWxMcBPGX9HmwlIAk9ABZo\nrWdorWVLrbAWBfjwNQC/1lqfAGAauH92v9nKQGXgXt8MXGv9KgDZDDzgGIXWejWAF53DZwNYmft5\nJYyNnA3gB7n3PQqgVik1bCDOM+CNh9b6Oa31utzPBwB0gN1wg70EeKG1fjn3YzlY660BnApAtlFd\nCUC2I7bt6A4ACwfoNAP+BqCUGgngTADftQ6fhmArAX4oxH3nsBYFRKCUqgFwitb6+wCgtX5Na/0S\n+tFWBorA+TYDbxmg7w5482Co1roLoNMOQIzXtZ8dCPZzTEIpNQbMqjwCYFiwlwAfcpK4tQCeA/B7\nAH8F0K217sm9xF6DXrcXrfVRAN1KqSK3uw54E+NGAJ8CST6UUo0AXgy2EpAADeB3SqnHlFL/kDsW\n1qIAF8cB2KOU+n5Onv0dpVQl+tFWBqwGLiCgFwgddgJeh1KqCox6fzyXiXPtI9hLAABAa92Tk1CO\nBBUgxUj2w36lxwiUUssAdOUy/PZzT2sDwVaOPczTWs8Cs7YfUUqdgrAWBcRRCmAmgJu11jMBHATl\nk/1mKwNF4NJsBh4Q0CUpY6XUcAC7c8d3gBvFC4L9HGPINRG4A8APtdZ35Q4HewnIC631PgDtYO1k\nXa4eG4jaxOv2opQaBKBGa/3CAJ9qwBuDeQDepZTaBuAnoHTya6B8KdhKQAxa6125/58H8EswQBTW\nogAXzwLo1Fr/Off7z0FC12+2MlAELs1m4AHHHhSiEcy7AVyU+/kiAHdZxy8AAKXUXFAK1TUwpxjw\nN4LvAXhKa/0161iwl4AYlFJN0tlLKVUBYBHYoOKPAM7LvexCRO3lwtzP54GF5QHHALTWV2utR2mt\nx4J+yb1a6/cj2EqAB0qpypwSBEqpwQAWA3gSYS0KcJB7zp1KqeNzhxYC2Ih+tJUB2wdOKXUGGNmS\nzcCvG5AvDvibhFLqxwAWAGgE0AXgc2A062dgFOJpAO/VWnfnXv8NAGeAaeiLtdZr3oDTDngDoJSa\nB+B+cKHUuX9XA/gTgJ8i2EuABaXUVLA4vCT3b5XW+ktKqePABlr1ANYCeL/W+lWlVDmAHwKYAWAv\ngPdprbe/IScf8IZBKTUfwCe11u8KthLgQ84ufgGuQaUAbtNaX5ergwxrUUAESqlpYHOkMgDbAFwM\nYBD6yVbCRt4BAQEBAQEBAQEBAQFvEoQmJgEBAQEBAQEBAQEBAW8SBAIXEBAQEBAQEBAQEBDwJkEg\ncAEBAQEBAQEBAQEBAW8SBAIXEBAQEBAQEBAQEBDwJkEgcAEBAQEBAQEBAQEBAW8SBAIXEBAQEBAQ\nEBAQEBDwJkEgcAEBAQEBAQEBAQEBAW8S/B8rhju/1xwPlgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa34e28e390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(RsqUni[0,:])"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import random"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'Final_map' 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-49-afdbaf60a348>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mdel\u001b[0m \u001b[0mFinal_map\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mFmaps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'Final_map' is not defined"
]
}
],
"source": [
"del Final_map\n",
"del Fmaps"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"if S[2]>5:\n",
" Final_map=np.zeros([S[0],S[1],5,3])\n",
" Fmaps=np.zeros([S[0],S[1],5,3])\n",
"else:\n",
" Final_map=np.zeros([S[0],S[1],3]) \n",
" Fmaps=np.zeros([S[0],S[1],3]) \n",
"C=np.zeros([S[3],3])\n",
"C1=np.zeros([6,3])\n",
"C1[0][:]=(1,0,0)\n",
"C1[1][:]=(0,1,0)\n",
"C1[2][:]=(0,0,1)\n",
"C1[3][:]=(0.8,0.8,0)\n",
"C1[4][:]=(0,1,1)\n",
"C1[5][:]=(1,0,1)\n",
"S1=DT.shape"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"29.7914333303\n",
"29.7914333303\n",
"29.7914333303\n",
"17.9295539152\n",
"17.9295539152\n",
"17.9295539152\n",
"25.2155076678\n",
"25.2155076678\n",
"25.2155076678\n",
"23.1147937288\n",
"23.1147937288\n",
"23.1147937288\n",
"16.7671774329\n",
"16.7671774329\n",
"16.7671774329\n",
"13.1082267339\n",
"13.1082267339\n",
"13.1082267339\n",
"20.1183414754\n",
"20.1183414754\n",
"20.1183414754\n"
]
}
],
"source": [
"C=np.zeros((S[3],3))\n",
"i=0\n",
"l=0\n",
"Betas2=Betas\n",
"for j in range(S[3]): \n",
" if Betas2[0,j]>0.7*np.max(Betas2[0,:]):\n",
" #if 1>0.1:\n",
" #C[j,:]=C1[i%6][:]\n",
" C[j,2]=1\n",
" C[j,1]=random.uniform(0,1)\n",
" #C[j,2]=1\n",
" for k in range(3): \n",
" M=np.max(np.squeeze(np.reshape(D2[:,:,:,j],S[0]*S[1]*5)))\n",
" print(M)\n",
" Fmaps[:,:,:,k]=0.8*D2[:,:,:,j]*C[j,k]/M\n",
" Final_map=Final_map+Fmaps\n",
" #Betas[0,j]=0\n",
" #print(Indexo[j])\n",
" i=i+1\n",
" l=l+1\n",
" #if l==2:\n",
" #break\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"C=np.zeros((S[3],3))\n",
"i=0\n",
"l=0\n",
"Betas2=Betas\n",
"for j in range(S[3]): \n",
" if Betas2[1,j]>0.7*np.max(Betas2[1,:]):\n",
" #if 1>0.1:\n",
" #C[j,:]=C1[i%6][:]\n",
" C[j,0]=1\n",
" C[j,1]=random.uniform(0,1)\n",
" #C[j,2]=1\n",
" for k in range(3): \n",
" M=np.max(np.squeeze(np.reshape(D2[:,:,:,j],S[0]*S[1]*5)))\n",
" Fmaps[:,:,:,k]=0.8*D2[:,:,:,j]*C[j,k]/M\n",
" Final_map=Final_map+Fmaps\n",
" #Betas2[1,j]=0\n",
" #print(Indexo[j])\n",
" i=i+1\n",
" l=l+1\n",
" #if l==2:\n",
" # break"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Dzk/3QGk4e4PqmLRmWdtzebaeM0W9D5mGW6BlWun7qfmFC20cniX8HPNW70n7KtLU4KNY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FT8zk8nkmjqVdTD6zpXlxYx0IquRsyHIWYT40rjxC4prAq6cYLnasRcKQoZPg8bcfVwaKlzgK5x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i2GhlmQnBudx61ojzLXU6jCXZC9aqzLHahGw1h9AS3WrRagqYHRPac8giaBODDQUrbjF3Ftmc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\ng45Glta44ziw44EaaDwzuPma8Xd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"text/plain": [
"<matplotlib.figure.Figure at 0x7fa34e284750>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pylab.rcParams['figure.figsize'] = (15, 6)\n",
"C2=np.zeros(3)\n",
"\n",
"Df=np.zeros([S[0],S[1],5,3]) \n",
" \n",
"for i in range(3):\n",
" Df[:,:,:,i]=Final_map[:,:,:,i]+Dmean/30\n",
" #Df=Df/(np.max(np.max(np.max(Df),3)))\n",
"if S[2]>5:\n",
" N=Nstack\n",
"else:\n",
" N=S[2]\n",
"for i in range(N):\n",
" #if Good_ICs[j]:\n",
" plt.subplot(1,N,i+1)\n",
" plt.imshow(Df[:,:,i],cmap=plt.cm.gray)\n",
" plt.imshow(Df[:,:,i,:],cmap=my_cmap,interpolation='none')\n",
" frame1 = plt.gca()\n",
" frame1.axes.get_xaxis().set_visible(False)\n",
" frame1.axes.get_yaxis().set_visible(False)\n",
"plt.tight_layout(pad=0,w_pad=0,h_pad=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-2-clause |
ashleysmart/mlgym | stock_price/GuassianMixture-SynthTrade.ipynb | 1 | 567393 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import math\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"from keras import backend as K\n",
"from keras.engine.topology import Layer"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from keras.models import Sequential, Model\n",
"from keras.layers import Input, Lambda, Concatenate, Flatten, Reshape\n",
"from keras.layers import Dense, Activation, Conv1D\n",
"from keras.optimizers import Adam\n",
"from keras import objectives"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"class ScaleNormalizer(Layer):\n",
" def __init__(self, **kwargs):\n",
" super(ScaleNormalizer, self).__init__(**kwargs)\n",
"\n",
" def build(self, inputShape):\n",
" self.size = inputShape[1]\n",
" \n",
" def call(self, out_scale, mask=None):\n",
" # softmax to normalize the scaling so this is a probability distribution \n",
" # these need to sum to 1, as they scale a guassian normal functions which have area 1\n",
" max_scale = K.max(out_scale, axis=1, keepdims=True)\n",
" out_scale = out_scale - max_scale\n",
" out_scale = K.exp(out_scale)\n",
" sum_scale = K.sum(out_scale, axis=1, keepdims=True)\n",
" out_scale = out_scale / sum_scale\n",
" return out_scale\n",
" \n",
" def compute_output_shape(self, inputShape):\n",
" return inputShape \n",
" \n",
"class MuNormalizer(Layer):\n",
" def __init__(self, numComponents=None, outputDim=None, **kwargs):\n",
" self.numComponents=numComponents\n",
" self.outputDim=outputDim\n",
" super(MuNormalizer, self).__init__(**kwargs)\n",
"\n",
" def build(self, inputShape):\n",
" self.size = inputShape[1]\n",
" \n",
" def call(self, mu, mask=None):\n",
" # means so whatever is ok\n",
" return mu\n",
" \n",
" def compute_output_shape(self, inputShape):\n",
" return inputShape\n",
"\n",
"class SigmaNormalizer(Layer):\n",
" def __init__(self, **kwargs):\n",
" super(SigmaNormalizer, self).__init__(**kwargs)\n",
"\n",
" def build(self, inputShape):\n",
" self.size = inputShape[1]\n",
" \n",
" def call(self, out_sigma, mask=None):\n",
" # sigma *must* be positive, hit it with an expodential which is always >0\n",
" # this will give it prior bias as well\n",
" out_sigma = K.exp(out_sigma)\n",
" return out_sigma\n",
" \n",
" def compute_output_shape(self, inputShape):\n",
" return inputShape"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"class ProbabilityLayer(Layer):\n",
" def calc_normal(self, x, scale, mu, sigma):\n",
" const = 1 / math.sqrt(2*math.pi)\n",
" var = (1 / (sigma + 1e-8))\n",
" \n",
" result = x - mu\n",
" result = result * var\n",
" result = -K.square(result)/2\n",
" result = K.exp(result) \n",
" result = result * scale * var * const\n",
"\n",
" return result\n",
" \n",
" def __init__(self, **kwargs):\n",
" super(ProbabilityLayer, self).__init__(**kwargs)\n",
"\n",
" def build(self, inputShapes):\n",
" pass\n",
"\n",
" def call(self, params, mask=None):\n",
" target = params[0] \n",
" scale = params[1] \n",
" mu = params[2]\n",
" sigma = params[3]\n",
" \n",
" result = self.calc_normal(target, scale, mu, sigma)\n",
" result = K.sum(result, axis=1, keepdims=True)\n",
" \n",
" return result\n",
" \n",
" def compute_output_shape(self, inputShapes):\n",
" return (inputShapes[0][0], 1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"class TradeGenerator:\n",
" def __init__(self, count=10000):\n",
" self.count = count\n",
"\n",
" self.spike_rate = 0.80 + 0.2*np.random.uniform()\n",
" self.cycle_rate = 0.80 + 0.2*np.random.uniform()\n",
" self.trend_rate = 0.80 + 0.2*np.random.uniform()\n",
" self.trend_scale = 4.0\n",
" \n",
" self.value = 100\n",
" self.ema = self.value\n",
" self.trend = self.trend_scale * np.random.uniform()\n",
" \n",
" def __iter__(self):\n",
" while self.count > 0:\n",
" self.count -= 1\n",
" \n",
" self.value += np.random.normal() + self.trend\n",
"\n",
" # spike\n",
" if np.random.uniform() > self.spike_rate:\n",
" self.value += (np.random.uniform() - 0.5)*0.3 * self.value\n",
"\n",
" # cycling\n",
" ratio = 0.95\n",
" self.ema = ratio * self.ema + (1.0 - ratio) * self.value \n",
" if np.random.uniform() > self.cycle_rate:\n",
" self.trend = (self.ema - self.value) / (np.random.uniform() * 20 + 10)\n",
" \n",
" # re-trending\n",
" if np.random.uniform() > self.trend_rate:\n",
" # trend change\n",
" self.trend = self.trend_scale * np.random.normal() \n",
" \n",
" if self.value <= 0.0:\n",
" self.value = 1.0\n",
"\n",
" yield(self.value) "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def generate(sampleSize =600,\n",
" samplePast =20,\n",
" samplesFuture=5):\n",
"\n",
" sampleLength = samplePast + samplesFuture\n",
" \n",
" trades_past = []\n",
" x = []\n",
" y = []\n",
" trades_future= []\n",
" \n",
" for i in range(sampleSize):\n",
" seq = np.array([i for i in TradeGenerator(count=sampleLength)])\n",
"\n",
" # normalize data to \"now\" point\n",
" seq = seq / seq[samplePast]\n",
"\n",
" for i in range(sampleLength):\n",
" trades_past.append(seq[:samplePast])\n",
" trades_future.append(seq[samplePast:])\n",
" x .append(i)\n",
" y .append(seq[i]) \n",
"\n",
"\n",
" \n",
" return np.array(trades_past), np.array(x), np.array(y), np.array(trades_future)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"trades_past,x_data,y_data,trades_future = generate()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((15000, 20), (15000,), (15000,), (15000, 5))"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trades_past.shape, x_data.shape, y_data.shape,trades_future.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"target = np.ones((y_data.shape[0], 1))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"def build_model(inputSize=20, # prior trades\n",
" convWindow=5,\n",
" filterSize=3,\n",
" embeddingSize=6,\n",
" numComponents=24,\n",
" outputDim=1):\n",
"\n",
" # using and auto encoder structure as i dont want memorizeation of inputs\n",
" # TODO consider a UNET design \n",
"\n",
" # network to \"encode\" trade info \n",
" i_trades = Input(shape=(inputSize,))\n",
"\n",
" x = Reshape((-1,1))(i_trades)\n",
" x = Conv1D(3, 5)(x)\n",
" x = Activation('relu')(x)\n",
" x = Conv1D(3, 5)(x)\n",
" x = Activation('relu')(x)\n",
" x = Conv1D(3, 5)(x)\n",
" x = Flatten()(x)\n",
" x = Dense(embeddingSize)(x)\n",
"\n",
" # network to learn the mixture params \n",
" i_x = Input(shape=(1,))\n",
"\n",
" x = Concatenate()([x,i_x])\n",
" x = Dense(128)(x)\n",
" x = Activation('relu')(x)\n",
" x = Dense(128)(x)\n",
" x = Activation('relu')(x)\n",
" x = Dense(128)(x)\n",
" x = Activation('relu')(x)\n",
" x = Dense(numComponents*3)(x)\n",
"\n",
" # now splice it up into the mixture params\n",
" scale = Lambda(lambda x: x[:,:numComponents], output_shape=(numComponents,))(x)\n",
" mu = Lambda(lambda x: x[:,numComponents:2*numComponents], output_shape=(numComponents,))(x)\n",
" sigma = Lambda(lambda x: x[:,2*numComponents:], output_shape=(numComponents,))(x)\n",
"\n",
" # correct the params into the right ranges/priors\n",
" scale = ScaleNormalizer()(scale)\n",
" mu = MuNormalizer(numComponents=numComponents, outputDim=outputDim)(mu)\n",
" sigma = SigmaNormalizer()(sigma)\n",
"\n",
" # give us an output tap on the params so we can get an idea of the sanity if needed\n",
" # model_mix_settings = Model(inputs=[i_trades,i_x], outputs=[scale,mu,sigma])\n",
"\n",
" # now add the layers that computes probility out from the the mixture functions at the (x,y) point\n",
" o_y = Input(shape=(1,))\n",
" x = ProbabilityLayer()([o_y,scale,mu,sigma])\n",
"\n",
" model_train = Model(inputs=[i_trades,i_x,o_y], outputs=x)\n",
" model_train.summary()\n",
"\n",
" opt = Adam(lr=0.001)\n",
" model_train.compile(loss='binary_crossentropy',optimizer=opt)\n",
" \n",
" return model_train"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"__________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
"input_4 (InputLayer) (None, 20) 0 \n",
"__________________________________________________________________________________________________\n",
"reshape_2 (Reshape) (None, 20, 1) 0 input_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv1d_4 (Conv1D) (None, 16, 3) 18 reshape_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_6 (Activation) (None, 16, 3) 0 conv1d_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv1d_5 (Conv1D) (None, 12, 3) 48 activation_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_7 (Activation) (None, 12, 3) 0 conv1d_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv1d_6 (Conv1D) (None, 8, 3) 48 activation_7[0][0] \n",
"__________________________________________________________________________________________________\n",
"flatten_2 (Flatten) (None, 24) 0 conv1d_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_6 (Dense) (None, 6) 150 flatten_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"input_5 (InputLayer) (None, 1) 0 \n",
"__________________________________________________________________________________________________\n",
"concatenate_2 (Concatenate) (None, 7) 0 dense_6[0][0] \n",
" input_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_7 (Dense) (None, 128) 1024 concatenate_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_8 (Activation) (None, 128) 0 dense_7[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_8 (Dense) (None, 128) 16512 activation_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_9 (Activation) (None, 128) 0 dense_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_9 (Dense) (None, 128) 16512 activation_9[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_10 (Activation) (None, 128) 0 dense_9[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense_10 (Dense) (None, 72) 9288 activation_10[0][0] \n",
"__________________________________________________________________________________________________\n",
"lambda_4 (Lambda) (None, 24) 0 dense_10[0][0] \n",
"__________________________________________________________________________________________________\n",
"lambda_5 (Lambda) (None, 24) 0 dense_10[0][0] \n",
"__________________________________________________________________________________________________\n",
"lambda_6 (Lambda) (None, 24) 0 dense_10[0][0] \n",
"__________________________________________________________________________________________________\n",
"input_6 (InputLayer) (None, 1) 0 \n",
"__________________________________________________________________________________________________\n",
"scale_normalizer_2 (ScaleNormal (None, 24) 0 lambda_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"mu_normalizer_2 (MuNormalizer) (None, 24) 0 lambda_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"sigma_normalizer_2 (SigmaNormal (None, 24) 0 lambda_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"probability_layer_2 (Probabilit (None, 1) 0 input_6[0][0] \n",
" scale_normalizer_2[0][0] \n",
" mu_normalizer_2[0][0] \n",
" sigma_normalizer_2[0][0] \n",
"==================================================================================================\n",
"Total params: 43,600\n",
"Trainable params: 43,600\n",
"Non-trainable params: 0\n",
"__________________________________________________________________________________________________\n"
]
}
],
"source": [
"model_train = build_model()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/1000\n",
"15000/15000 [==============================] - 1s 62us/step - loss: 1.9751\n",
"Epoch 2/1000\n",
"15000/15000 [==============================] - 0s 14us/step - loss: 1.6493\n",
"Epoch 3/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 1.4664\n",
"Epoch 4/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 1.2284\n",
"Epoch 5/1000\n",
"15000/15000 [==============================] - 0s 13us/step - loss: 1.0702\n",
"Epoch 6/1000\n",
"15000/15000 [==============================] - 0s 14us/step - loss: 0.9223\n",
"Epoch 7/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.8564\n",
"Epoch 8/1000\n",
"15000/15000 [==============================] - 0s 13us/step - loss: 0.8225\n",
"Epoch 9/1000\n",
"15000/15000 [==============================] - 0s 15us/step - loss: 0.7632\n",
"Epoch 10/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.8374\n",
"Epoch 11/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.8621\n",
"Epoch 12/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.7033\n",
"Epoch 13/1000\n",
"15000/15000 [==============================] - 0s 13us/step - loss: 0.6676\n",
"Epoch 14/1000\n",
"15000/15000 [==============================] - 0s 15us/step - loss: 0.7074\n",
"Epoch 15/1000\n",
"15000/15000 [==============================] - 0s 17us/step - loss: 0.6968\n",
"Epoch 16/1000\n",
"15000/15000 [==============================] - 0s 15us/step - loss: 0.6639\n",
"Epoch 17/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.5928\n",
"Epoch 18/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.5519\n",
"Epoch 19/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.5649\n",
"Epoch 20/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.5102\n",
"Epoch 21/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.4908\n",
"Epoch 22/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.4880\n",
"Epoch 23/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.4402\n",
"Epoch 24/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.4338\n",
"Epoch 25/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.4207\n",
"Epoch 26/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.3848\n",
"Epoch 27/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.3694\n",
"Epoch 28/1000\n",
"15000/15000 [==============================] - 0s 9us/step - loss: 0.3454\n",
"Epoch 29/1000\n",
"15000/15000 [==============================] - 0s 9us/step - loss: 0.3191\n",
"Epoch 30/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.3021\n",
"Epoch 31/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.2885\n",
"Epoch 32/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.2750\n",
"Epoch 33/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.2658\n",
"Epoch 34/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.2589\n",
"Epoch 35/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.2472\n",
"Epoch 36/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.2236\n",
"Epoch 37/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.2160\n",
"Epoch 38/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.2019\n",
"Epoch 39/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.1942\n",
"Epoch 40/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.1797\n",
"Epoch 41/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.1695\n",
"Epoch 42/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.1561\n",
"Epoch 43/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.1440\n",
"Epoch 44/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.1366\n",
"Epoch 45/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.1238\n",
"Epoch 46/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.1169\n",
"Epoch 47/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.1130\n",
"Epoch 48/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.1029\n",
"Epoch 49/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0996\n",
"Epoch 50/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0927\n",
"Epoch 51/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0932\n",
"Epoch 52/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0872\n",
"Epoch 53/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0873\n",
"Epoch 54/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0865\n",
"Epoch 55/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0829\n",
"Epoch 56/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0828\n",
"Epoch 57/1000\n",
"15000/15000 [==============================] - 0s 9us/step - loss: 0.0806\n",
"Epoch 58/1000\n",
"15000/15000 [==============================] - 0s 9us/step - loss: 0.0800\n",
"Epoch 59/1000\n",
"15000/15000 [==============================] - 0s 9us/step - loss: 0.0780\n",
"Epoch 60/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0761\n",
"Epoch 61/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0787\n",
"Epoch 62/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0786\n",
"Epoch 63/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0793\n",
"Epoch 64/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0795\n",
"Epoch 65/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0781\n",
"Epoch 66/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0758\n",
"Epoch 67/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0775\n",
"Epoch 68/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0790\n",
"Epoch 69/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0778\n",
"Epoch 70/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0786\n",
"Epoch 71/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0796\n",
"Epoch 72/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0769\n",
"Epoch 73/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0742\n",
"Epoch 74/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0735\n",
"Epoch 75/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0732\n",
"Epoch 76/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0703\n",
"Epoch 77/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0699\n",
"Epoch 78/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0694\n",
"Epoch 79/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0687\n",
"Epoch 80/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0698\n",
"Epoch 81/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0673\n",
"Epoch 82/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0660\n",
"Epoch 83/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0661\n",
"Epoch 84/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0654\n",
"Epoch 85/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0645\n",
"Epoch 86/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0638\n",
"Epoch 87/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0635\n",
"Epoch 88/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0622\n",
"Epoch 89/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0613\n",
"Epoch 90/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0614\n",
"Epoch 91/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0613\n",
"Epoch 92/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0613\n",
"Epoch 93/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0603\n"
]
},
{
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"output_type": "stream",
"text": [
"Epoch 94/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0599\n",
"Epoch 95/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0619\n",
"Epoch 96/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0620\n",
"Epoch 97/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0629\n",
"Epoch 98/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0622\n",
"Epoch 99/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0608\n",
"Epoch 100/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0599\n",
"Epoch 101/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0593\n",
"Epoch 102/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0584\n",
"Epoch 103/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0583\n",
"Epoch 104/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0580\n",
"Epoch 105/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0580\n",
"Epoch 106/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0573\n",
"Epoch 107/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0564\n",
"Epoch 108/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0562\n",
"Epoch 109/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0555\n",
"Epoch 110/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0560\n",
"Epoch 111/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0554\n",
"Epoch 112/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0546\n",
"Epoch 113/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0551\n",
"Epoch 114/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0547\n",
"Epoch 115/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0545\n",
"Epoch 116/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0547\n",
"Epoch 117/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0535\n",
"Epoch 118/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0528\n",
"Epoch 119/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0532\n",
"Epoch 120/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0529\n",
"Epoch 121/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0531\n",
"Epoch 122/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0536\n",
"Epoch 123/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0539\n",
"Epoch 124/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0530\n",
"Epoch 125/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0524\n",
"Epoch 126/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0520\n",
"Epoch 127/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0517\n",
"Epoch 128/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0516\n",
"Epoch 129/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0517\n",
"Epoch 130/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0513\n",
"Epoch 131/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0507\n",
"Epoch 132/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0502\n",
"Epoch 133/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0498\n",
"Epoch 134/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0500\n",
"Epoch 135/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0498\n",
"Epoch 136/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0497\n",
"Epoch 137/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0497\n",
"Epoch 138/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0496\n",
"Epoch 139/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0492\n",
"Epoch 140/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0488\n",
"Epoch 141/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0482\n",
"Epoch 142/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0480\n",
"Epoch 143/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0477\n",
"Epoch 144/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0475\n",
"Epoch 145/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0476\n",
"Epoch 146/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0473\n",
"Epoch 147/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0471\n",
"Epoch 148/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0471\n",
"Epoch 149/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0468\n",
"Epoch 150/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0468\n",
"Epoch 151/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0468\n",
"Epoch 152/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0466\n",
"Epoch 153/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0463\n",
"Epoch 154/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0459\n",
"Epoch 155/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0457\n",
"Epoch 156/1000\n",
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"Epoch 157/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0453\n",
"Epoch 158/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0451\n",
"Epoch 159/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0447\n",
"Epoch 160/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0445\n",
"Epoch 161/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0442\n",
"Epoch 162/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0445\n",
"Epoch 163/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0444\n",
"Epoch 164/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0445\n",
"Epoch 165/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0450\n",
"Epoch 166/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0444\n",
"Epoch 167/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0441\n",
"Epoch 168/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0441\n",
"Epoch 169/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0439\n",
"Epoch 170/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0441\n",
"Epoch 171/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0434\n",
"Epoch 172/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0433\n",
"Epoch 173/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0437\n",
"Epoch 174/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0438\n",
"Epoch 175/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0433\n",
"Epoch 176/1000\n",
"15000/15000 [==============================] - 0s 9us/step - loss: 0.0429\n",
"Epoch 177/1000\n",
"15000/15000 [==============================] - 0s 9us/step - loss: 0.0424\n",
"Epoch 178/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0425\n",
"Epoch 179/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0425\n",
"Epoch 180/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0422\n",
"Epoch 181/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0423\n",
"Epoch 182/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0424\n",
"Epoch 183/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0421\n",
"Epoch 184/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0419\n",
"Epoch 185/1000\n"
]
},
{
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"output_type": "stream",
"text": [
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0415\n",
"Epoch 186/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0414\n",
"Epoch 187/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0414\n",
"Epoch 188/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0414\n",
"Epoch 189/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0414\n",
"Epoch 190/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0412\n",
"Epoch 191/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0412\n",
"Epoch 192/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0410\n",
"Epoch 193/1000\n",
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"Epoch 194/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0405\n",
"Epoch 195/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0403\n",
"Epoch 196/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0403\n",
"Epoch 197/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0399\n",
"Epoch 198/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0398\n",
"Epoch 199/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0395\n",
"Epoch 200/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0395\n",
"Epoch 201/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0393\n",
"Epoch 202/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0392\n",
"Epoch 203/1000\n",
"15000/15000 [==============================] - 0s 15us/step - loss: 0.0390\n",
"Epoch 204/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0389\n",
"Epoch 205/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0388\n",
"Epoch 206/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0388\n",
"Epoch 207/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0386\n",
"Epoch 208/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0382\n",
"Epoch 209/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0382\n",
"Epoch 210/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0380\n",
"Epoch 211/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0378\n",
"Epoch 212/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0378\n",
"Epoch 213/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0377\n",
"Epoch 214/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0375\n",
"Epoch 215/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0374\n",
"Epoch 216/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0374\n",
"Epoch 217/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0376\n",
"Epoch 218/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0373\n",
"Epoch 219/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0459\n",
"Epoch 220/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0441\n",
"Epoch 221/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0442\n",
"Epoch 222/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0450\n",
"Epoch 223/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0443\n",
"Epoch 224/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0442\n",
"Epoch 225/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0442\n",
"Epoch 226/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0436\n",
"Epoch 227/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0438\n",
"Epoch 228/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0428\n",
"Epoch 229/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0426\n",
"Epoch 230/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0424\n",
"Epoch 231/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0416\n",
"Epoch 232/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0412\n",
"Epoch 233/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0405\n",
"Epoch 234/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0396\n",
"Epoch 235/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0396\n",
"Epoch 236/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0396\n",
"Epoch 237/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0400\n",
"Epoch 238/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0395\n",
"Epoch 239/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0386\n",
"Epoch 240/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0379\n",
"Epoch 241/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0379\n",
"Epoch 242/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0378\n",
"Epoch 243/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0379\n",
"Epoch 244/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0378\n",
"Epoch 245/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0370\n",
"Epoch 246/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0366\n",
"Epoch 247/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0367\n",
"Epoch 248/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0366\n",
"Epoch 249/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0370\n",
"Epoch 250/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0361\n",
"Epoch 251/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0353\n",
"Epoch 252/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0351\n",
"Epoch 253/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0353\n",
"Epoch 254/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0351\n",
"Epoch 255/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0349\n",
"Epoch 256/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0345\n",
"Epoch 257/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0343\n",
"Epoch 258/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0340\n",
"Epoch 259/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0336\n",
"Epoch 260/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0335\n",
"Epoch 261/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0331\n",
"Epoch 262/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0332\n",
"Epoch 263/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0331\n",
"Epoch 264/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0328\n",
"Epoch 265/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0327\n",
"Epoch 266/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0325\n",
"Epoch 267/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0323\n",
"Epoch 268/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0334\n",
"Epoch 269/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0329\n",
"Epoch 270/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0328\n",
"Epoch 271/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0329\n",
"Epoch 272/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0324\n",
"Epoch 273/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0322\n",
"Epoch 274/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0320\n",
"Epoch 275/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0323\n",
"Epoch 276/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0321\n"
]
},
{
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"output_type": "stream",
"text": [
"Epoch 277/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0322\n",
"Epoch 278/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0320\n",
"Epoch 279/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0317\n",
"Epoch 280/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0318\n",
"Epoch 281/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0319\n",
"Epoch 282/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0321\n",
"Epoch 283/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0315\n",
"Epoch 284/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0322\n",
"Epoch 285/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0318\n",
"Epoch 286/1000\n",
"15000/15000 [==============================] - 0s 14us/step - loss: 0.0313\n",
"Epoch 287/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0330\n",
"Epoch 288/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0317\n",
"Epoch 289/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0311\n",
"Epoch 290/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0310\n",
"Epoch 291/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0310\n",
"Epoch 292/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0310\n",
"Epoch 293/1000\n",
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"15000/15000 [==============================] - 0s 11us/step - loss: 0.0293\n",
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"15000/15000 [==============================] - 0s 11us/step - loss: 0.0291\n",
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"15000/15000 [==============================] - 0s 10us/step - loss: 0.0288\n",
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"15000/15000 [==============================] - 0s 11us/step - loss: 0.0284\n",
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"15000/15000 [==============================] - 0s 11us/step - loss: 0.0297\n",
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"15000/15000 [==============================] - 0s 11us/step - loss: 0.0301\n",
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"15000/15000 [==============================] - 0s 11us/step - loss: 0.0295\n",
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"Epoch 321/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0301\n",
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"15000/15000 [==============================] - 0s 12us/step - loss: 0.0293\n",
"Epoch 323/1000\n",
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"Epoch 324/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0307\n",
"Epoch 325/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0311\n",
"Epoch 326/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0300\n",
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"15000/15000 [==============================] - 0s 11us/step - loss: 0.0284\n",
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"15000/15000 [==============================] - 0s 11us/step - loss: 0.0603\n",
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"15000/15000 [==============================] - 0s 11us/step - loss: 0.0524\n",
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"15000/15000 [==============================] - 0s 12us/step - loss: 0.1236\n",
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"15000/15000 [==============================] - 0s 12us/step - loss: 0.4543\n",
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"15000/15000 [==============================] - 0s 11us/step - loss: 0.4344\n",
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"15000/15000 [==============================] - 0s 12us/step - loss: 0.8049\n",
"Epoch 335/1000\n",
"15000/15000 [==============================] - 0s 13us/step - loss: 0.9412\n",
"Epoch 336/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.9430\n",
"Epoch 337/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.8013\n",
"Epoch 338/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.6001\n",
"Epoch 339/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.2219\n",
"Epoch 340/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.3992\n",
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"15000/15000 [==============================] - 0s 11us/step - loss: 0.3418\n",
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"15000/15000 [==============================] - 0s 11us/step - loss: 0.2168\n",
"Epoch 344/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.1959\n",
"Epoch 345/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.1823\n",
"Epoch 346/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.1796\n",
"Epoch 347/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.1159\n",
"Epoch 348/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0944\n",
"Epoch 349/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.1109\n",
"Epoch 350/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.1196\n",
"Epoch 351/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.1036\n",
"Epoch 352/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0887\n",
"Epoch 353/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0848\n",
"Epoch 354/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0926\n",
"Epoch 355/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0897\n",
"Epoch 356/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0850\n",
"Epoch 357/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0753\n",
"Epoch 358/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0706\n",
"Epoch 359/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0636\n",
"Epoch 360/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0603\n",
"Epoch 361/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0585\n",
"Epoch 362/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0548\n",
"Epoch 363/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0510\n",
"Epoch 364/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0509\n",
"Epoch 365/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0489\n",
"Epoch 366/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0454\n",
"Epoch 367/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0434\n",
"Epoch 368/1000\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0481\n",
"Epoch 369/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0476\n",
"Epoch 370/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0529\n",
"Epoch 371/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0554\n",
"Epoch 372/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0552\n",
"Epoch 373/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0524\n",
"Epoch 374/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0477\n",
"Epoch 375/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0417\n",
"Epoch 376/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0422\n",
"Epoch 377/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0423\n",
"Epoch 378/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0408\n",
"Epoch 379/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0440\n",
"Epoch 380/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0440\n",
"Epoch 381/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0425\n",
"Epoch 382/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0399\n",
"Epoch 383/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0370\n",
"Epoch 384/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0468\n",
"Epoch 385/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0364\n",
"Epoch 386/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0376\n",
"Epoch 387/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0391\n",
"Epoch 388/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0392\n",
"Epoch 389/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0375\n",
"Epoch 390/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0349\n",
"Epoch 391/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0402\n",
"Epoch 392/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0343\n",
"Epoch 393/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0357\n",
"Epoch 394/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0362\n",
"Epoch 395/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0354\n",
"Epoch 396/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0337\n",
"Epoch 397/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0363\n",
"Epoch 398/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0345\n",
"Epoch 399/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0367\n",
"Epoch 400/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0371\n",
"Epoch 401/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0360\n",
"Epoch 402/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0340\n",
"Epoch 403/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0338\n",
"Epoch 404/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0350\n",
"Epoch 405/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0340\n",
"Epoch 406/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0370\n",
"Epoch 407/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0380\n",
"Epoch 408/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0365\n",
"Epoch 409/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0341\n",
"Epoch 410/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0343\n",
"Epoch 411/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0398\n",
"Epoch 412/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0370\n",
"Epoch 413/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0413\n",
"Epoch 414/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0440\n",
"Epoch 415/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0452\n",
"Epoch 416/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0453\n",
"Epoch 417/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0433\n",
"Epoch 418/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0398\n",
"Epoch 419/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0356\n",
"Epoch 420/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0332\n",
"Epoch 421/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0481\n",
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"15000/15000 [==============================] - 0s 12us/step - loss: 0.0327\n",
"Epoch 423/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0347\n",
"Epoch 424/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0369\n",
"Epoch 425/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0376\n",
"Epoch 426/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0367\n",
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"15000/15000 [==============================] - 0s 11us/step - loss: 0.0326\n",
"Epoch 429/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0336\n",
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"15000/15000 [==============================] - 0s 11us/step - loss: 0.0321\n",
"Epoch 432/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0332\n",
"Epoch 433/1000\n",
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"Epoch 434/1000\n",
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"Epoch 435/1000\n",
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"Epoch 436/1000\n",
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"Epoch 437/1000\n",
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"Epoch 438/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0312\n",
"Epoch 439/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0322\n",
"Epoch 440/1000\n",
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"Epoch 441/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0317\n",
"Epoch 442/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0308\n",
"Epoch 443/1000\n",
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"Epoch 444/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0308\n",
"Epoch 445/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0305\n",
"Epoch 446/1000\n",
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"Epoch 447/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0309\n",
"Epoch 448/1000\n",
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"Epoch 449/1000\n",
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"Epoch 450/1000\n",
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"Epoch 451/1000\n",
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"Epoch 452/1000\n",
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"Epoch 453/1000\n",
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"Epoch 455/1000\n",
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"Epoch 456/1000\n",
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"Epoch 458/1000\n",
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"Epoch 459/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0306\n"
]
},
{
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"output_type": "stream",
"text": [
"Epoch 460/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0303\n",
"Epoch 461/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0302\n",
"Epoch 462/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0302\n",
"Epoch 463/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0298\n",
"Epoch 464/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0297\n",
"Epoch 465/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0297\n",
"Epoch 466/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0297\n",
"Epoch 467/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0297\n",
"Epoch 468/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0296\n",
"Epoch 469/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0294\n",
"Epoch 470/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0294\n",
"Epoch 471/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0293\n",
"Epoch 472/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0292\n",
"Epoch 473/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0292\n",
"Epoch 474/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0291\n",
"Epoch 475/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0290\n",
"Epoch 476/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0290\n",
"Epoch 477/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0289\n",
"Epoch 478/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0289\n",
"Epoch 479/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0288\n",
"Epoch 480/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0288\n",
"Epoch 481/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0287\n",
"Epoch 482/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0286\n",
"Epoch 483/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0285\n",
"Epoch 484/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0285\n",
"Epoch 485/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0284\n",
"Epoch 486/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0284\n",
"Epoch 487/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0283\n",
"Epoch 488/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0283\n",
"Epoch 489/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0282\n",
"Epoch 490/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0281\n",
"Epoch 491/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0288\n",
"Epoch 492/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0290\n",
"Epoch 493/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0290\n",
"Epoch 494/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0288\n",
"Epoch 495/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0288\n",
"Epoch 496/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0289\n",
"Epoch 497/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0289\n",
"Epoch 498/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0286\n",
"Epoch 499/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0287\n",
"Epoch 500/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0285\n",
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"15000/15000 [==============================] - 0s 11us/step - loss: 0.0282\n",
"Epoch 506/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0281\n",
"Epoch 507/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0280\n",
"Epoch 508/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0279\n",
"Epoch 509/1000\n",
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"Epoch 510/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0277\n",
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"Epoch 512/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0277\n",
"Epoch 513/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0276\n",
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"15000/15000 [==============================] - 0s 12us/step - loss: 0.0276\n",
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"15000/15000 [==============================] - 0s 11us/step - loss: 0.0276\n",
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"15000/15000 [==============================] - 0s 11us/step - loss: 0.0275\n",
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"15000/15000 [==============================] - 0s 11us/step - loss: 0.0274\n",
"Epoch 520/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0273\n",
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"15000/15000 [==============================] - 0s 10us/step - loss: 0.0273\n",
"Epoch 522/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0272\n",
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"15000/15000 [==============================] - 0s 11us/step - loss: 0.0272\n",
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"15000/15000 [==============================] - 0s 11us/step - loss: 0.0272\n",
"Epoch 525/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0271\n",
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"15000/15000 [==============================] - 0s 10us/step - loss: 0.0271\n",
"Epoch 527/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0270\n",
"Epoch 528/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0270\n",
"Epoch 529/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0270\n",
"Epoch 530/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0270\n",
"Epoch 531/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0269\n",
"Epoch 532/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0270\n",
"Epoch 533/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0269\n",
"Epoch 534/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0269\n",
"Epoch 535/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0269\n",
"Epoch 536/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0270\n",
"Epoch 537/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0270\n",
"Epoch 538/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0269\n",
"Epoch 539/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0270\n",
"Epoch 540/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0269\n",
"Epoch 541/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0269\n",
"Epoch 542/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0268\n",
"Epoch 543/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0267\n",
"Epoch 544/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0267\n",
"Epoch 545/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0267\n",
"Epoch 546/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0267\n",
"Epoch 547/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0267\n",
"Epoch 548/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0266\n",
"Epoch 549/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0265\n",
"Epoch 550/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0265\n",
"Epoch 551/1000\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0264\n",
"Epoch 552/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0265\n",
"Epoch 553/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0265\n",
"Epoch 554/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0264\n",
"Epoch 555/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0264\n",
"Epoch 556/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0263\n",
"Epoch 557/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0263\n",
"Epoch 558/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0263\n",
"Epoch 559/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0262\n",
"Epoch 560/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0262\n",
"Epoch 561/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0261\n",
"Epoch 562/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0261\n",
"Epoch 563/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0261\n",
"Epoch 564/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0260\n",
"Epoch 565/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0260\n",
"Epoch 566/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0260\n",
"Epoch 567/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0260\n",
"Epoch 568/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0260\n",
"Epoch 569/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0260\n",
"Epoch 570/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0259\n",
"Epoch 571/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0259\n",
"Epoch 572/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0259\n",
"Epoch 573/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0258\n",
"Epoch 574/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0258\n",
"Epoch 575/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0258\n",
"Epoch 576/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0257\n",
"Epoch 577/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0257\n",
"Epoch 578/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0257\n",
"Epoch 579/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0257\n",
"Epoch 580/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0256\n",
"Epoch 581/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0255\n",
"Epoch 582/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0255\n",
"Epoch 583/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0255\n",
"Epoch 584/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0255\n",
"Epoch 585/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0254\n",
"Epoch 586/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0254\n",
"Epoch 587/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0254\n",
"Epoch 588/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0255\n",
"Epoch 589/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0254\n",
"Epoch 590/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0254\n",
"Epoch 591/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0255\n",
"Epoch 592/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0255\n",
"Epoch 593/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0254\n",
"Epoch 594/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0254\n",
"Epoch 595/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0253\n",
"Epoch 596/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0253\n",
"Epoch 597/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0253\n",
"Epoch 598/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0254\n",
"Epoch 599/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0253\n",
"Epoch 600/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0253\n",
"Epoch 601/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0253\n",
"Epoch 602/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0254\n",
"Epoch 603/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0253\n",
"Epoch 604/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0253\n",
"Epoch 605/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0252\n",
"Epoch 606/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0252\n",
"Epoch 607/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0252\n",
"Epoch 608/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0252\n",
"Epoch 609/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0251\n",
"Epoch 610/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0251\n",
"Epoch 611/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0250\n",
"Epoch 612/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0250\n",
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"15000/15000 [==============================] - 0s 12us/step - loss: 0.0250\n",
"Epoch 614/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0249\n",
"Epoch 615/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0250\n",
"Epoch 616/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0249\n",
"Epoch 617/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0249\n",
"Epoch 618/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0249\n",
"Epoch 619/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0248\n",
"Epoch 620/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0248\n",
"Epoch 621/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0248\n",
"Epoch 622/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0247\n",
"Epoch 623/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0248\n",
"Epoch 624/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0248\n",
"Epoch 625/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0248\n",
"Epoch 626/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0247\n",
"Epoch 627/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0248\n",
"Epoch 628/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0248\n",
"Epoch 629/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0248\n",
"Epoch 630/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0247\n",
"Epoch 631/1000\n",
"15000/15000 [==============================] - 0s 13us/step - loss: 0.0247\n",
"Epoch 632/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0247\n",
"Epoch 633/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0246\n",
"Epoch 634/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0246\n",
"Epoch 635/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0246\n",
"Epoch 636/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0246\n",
"Epoch 637/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0246\n",
"Epoch 638/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0246\n",
"Epoch 639/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0246\n",
"Epoch 640/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0246\n",
"Epoch 641/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0246\n",
"Epoch 642/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0246\n"
]
},
{
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"output_type": "stream",
"text": [
"Epoch 643/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0246\n",
"Epoch 644/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0245\n",
"Epoch 645/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0245\n",
"Epoch 646/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0245\n",
"Epoch 647/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0245\n",
"Epoch 648/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0244\n",
"Epoch 649/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0245\n",
"Epoch 650/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0244\n",
"Epoch 651/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0245\n",
"Epoch 652/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0244\n",
"Epoch 653/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0244\n",
"Epoch 654/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0244\n",
"Epoch 655/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0245\n",
"Epoch 656/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0246\n",
"Epoch 657/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0247\n",
"Epoch 658/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0247\n",
"Epoch 659/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0246\n",
"Epoch 660/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0246\n",
"Epoch 661/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0246\n",
"Epoch 662/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0246\n",
"Epoch 663/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0245\n",
"Epoch 664/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0245\n",
"Epoch 665/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0244\n",
"Epoch 666/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0245\n",
"Epoch 667/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0244\n",
"Epoch 668/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0244\n",
"Epoch 669/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0243\n",
"Epoch 670/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0243\n",
"Epoch 671/1000\n",
"15000/15000 [==============================] - 0s 15us/step - loss: 0.0243\n",
"Epoch 672/1000\n",
"15000/15000 [==============================] - 0s 15us/step - loss: 0.0242\n",
"Epoch 673/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0242\n",
"Epoch 674/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0242\n",
"Epoch 675/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0242\n",
"Epoch 676/1000\n",
"15000/15000 [==============================] - 0s 14us/step - loss: 0.0241\n",
"Epoch 677/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0241\n",
"Epoch 678/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0241\n",
"Epoch 679/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0240\n",
"Epoch 680/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0240\n",
"Epoch 681/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0240\n",
"Epoch 682/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0239\n",
"Epoch 683/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0239\n",
"Epoch 684/1000\n",
"15000/15000 [==============================] - 0s 13us/step - loss: 0.0239\n",
"Epoch 685/1000\n",
"15000/15000 [==============================] - 0s 14us/step - loss: 0.0239\n",
"Epoch 686/1000\n",
"15000/15000 [==============================] - 0s 13us/step - loss: 0.0239\n",
"Epoch 687/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0238\n",
"Epoch 688/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0238\n",
"Epoch 689/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0238\n",
"Epoch 690/1000\n",
"15000/15000 [==============================] - 0s 14us/step - loss: 0.0238\n",
"Epoch 691/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0238\n",
"Epoch 692/1000\n",
"15000/15000 [==============================] - 0s 13us/step - loss: 0.0238\n",
"Epoch 693/1000\n",
"15000/15000 [==============================] - 0s 15us/step - loss: 0.0238\n",
"Epoch 694/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0237\n",
"Epoch 695/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0237\n",
"Epoch 696/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0237\n",
"Epoch 697/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0237\n",
"Epoch 698/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0236\n",
"Epoch 699/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0236\n",
"Epoch 700/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0236\n",
"Epoch 701/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0236\n",
"Epoch 702/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0236\n",
"Epoch 703/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0235\n",
"Epoch 704/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0235\n",
"Epoch 705/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0235\n",
"Epoch 706/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0234\n",
"Epoch 707/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0235\n",
"Epoch 708/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0235\n",
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"15000/15000 [==============================] - 0s 12us/step - loss: 0.0234\n",
"Epoch 710/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0234\n",
"Epoch 711/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0234\n",
"Epoch 712/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0234\n",
"Epoch 713/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0234\n",
"Epoch 714/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0233\n",
"Epoch 715/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0233\n",
"Epoch 716/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0233\n",
"Epoch 717/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0233\n",
"Epoch 718/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0233\n",
"Epoch 719/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0233\n",
"Epoch 720/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0232\n",
"Epoch 721/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0232\n",
"Epoch 722/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0232\n",
"Epoch 723/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0232\n",
"Epoch 724/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0231\n",
"Epoch 725/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0231\n",
"Epoch 726/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0231\n",
"Epoch 727/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0233\n",
"Epoch 728/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0233\n",
"Epoch 729/1000\n",
"15000/15000 [==============================] - 0s 15us/step - loss: 0.0233\n",
"Epoch 730/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0235\n",
"Epoch 731/1000\n",
"15000/15000 [==============================] - 0s 14us/step - loss: 0.0233\n",
"Epoch 732/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0234\n",
"Epoch 733/1000\n",
"15000/15000 [==============================] - 0s 16us/step - loss: 0.0233\n",
"Epoch 734/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0233\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 735/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0234\n",
"Epoch 736/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0233\n",
"Epoch 737/1000\n",
"15000/15000 [==============================] - 0s 13us/step - loss: 0.0234\n",
"Epoch 738/1000\n",
"15000/15000 [==============================] - 0s 14us/step - loss: 0.0233\n",
"Epoch 739/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0232\n",
"Epoch 740/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0233\n",
"Epoch 741/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0232\n",
"Epoch 742/1000\n",
"15000/15000 [==============================] - 0s 13us/step - loss: 0.0233\n",
"Epoch 743/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0234\n",
"Epoch 744/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0235\n",
"Epoch 745/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0236\n",
"Epoch 746/1000\n",
"15000/15000 [==============================] - 0s 13us/step - loss: 0.0234\n",
"Epoch 747/1000\n",
"15000/15000 [==============================] - 0s 17us/step - loss: 0.0234\n",
"Epoch 748/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0235\n",
"Epoch 749/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0234\n",
"Epoch 750/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0235\n",
"Epoch 751/1000\n",
"15000/15000 [==============================] - 0s 13us/step - loss: 0.0232\n",
"Epoch 752/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0236\n",
"Epoch 753/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0234\n",
"Epoch 754/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0236\n",
"Epoch 755/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0232\n",
"Epoch 756/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0235\n",
"Epoch 757/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0233\n",
"Epoch 758/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0234\n",
"Epoch 759/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0232\n",
"Epoch 760/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0233\n",
"Epoch 761/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0232\n",
"Epoch 762/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0232\n",
"Epoch 763/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0230\n",
"Epoch 764/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0234\n",
"Epoch 765/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0231\n",
"Epoch 766/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0232\n",
"Epoch 767/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0230\n",
"Epoch 768/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0231\n",
"Epoch 769/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0230\n",
"Epoch 770/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0230\n",
"Epoch 771/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0230\n",
"Epoch 772/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0230\n",
"Epoch 773/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0230\n",
"Epoch 774/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0229\n",
"Epoch 775/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0230\n",
"Epoch 776/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0229\n",
"Epoch 777/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0229\n",
"Epoch 778/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0228\n",
"Epoch 779/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0227\n",
"Epoch 780/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0228\n",
"Epoch 781/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0228\n",
"Epoch 782/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0227\n",
"Epoch 783/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0227\n",
"Epoch 784/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0227\n",
"Epoch 785/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0227\n",
"Epoch 786/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0227\n",
"Epoch 787/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0226\n",
"Epoch 788/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0226\n",
"Epoch 789/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0225\n",
"Epoch 790/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0226\n",
"Epoch 791/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0226\n",
"Epoch 792/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0226\n",
"Epoch 793/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0226\n",
"Epoch 794/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0226\n",
"Epoch 795/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0226\n",
"Epoch 796/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0225\n",
"Epoch 797/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0225\n",
"Epoch 798/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0225\n",
"Epoch 799/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0225\n",
"Epoch 800/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0224\n",
"Epoch 801/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0225\n",
"Epoch 802/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0224\n",
"Epoch 803/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0224\n",
"Epoch 804/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0223\n",
"Epoch 805/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0225\n",
"Epoch 806/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0224\n",
"Epoch 807/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0223\n",
"Epoch 808/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0224\n",
"Epoch 809/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0224\n",
"Epoch 810/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0224\n",
"Epoch 811/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0224\n",
"Epoch 812/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0225\n",
"Epoch 813/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0224\n",
"Epoch 814/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0224\n",
"Epoch 815/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0224\n",
"Epoch 816/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0223\n",
"Epoch 817/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0224\n",
"Epoch 818/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0223\n",
"Epoch 819/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0223\n",
"Epoch 820/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0223\n",
"Epoch 821/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0223\n",
"Epoch 822/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0223\n",
"Epoch 823/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0224\n",
"Epoch 824/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0224\n",
"Epoch 825/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0224\n",
"Epoch 826/1000\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0223\n",
"Epoch 827/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0222\n",
"Epoch 828/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0223\n",
"Epoch 829/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0223\n",
"Epoch 830/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0222\n",
"Epoch 831/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0222\n",
"Epoch 832/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0223\n",
"Epoch 833/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0222\n",
"Epoch 834/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0221\n",
"Epoch 835/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0221\n",
"Epoch 836/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0221\n",
"Epoch 837/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0221\n",
"Epoch 838/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0221\n",
"Epoch 839/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0220\n",
"Epoch 840/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0220\n",
"Epoch 841/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0220\n",
"Epoch 842/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0221\n",
"Epoch 843/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0221\n",
"Epoch 844/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0221\n",
"Epoch 845/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0220\n",
"Epoch 846/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0219\n",
"Epoch 847/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0220\n",
"Epoch 848/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0220\n",
"Epoch 849/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0220\n",
"Epoch 850/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0219\n",
"Epoch 851/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0220\n",
"Epoch 852/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0220\n",
"Epoch 853/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0218\n",
"Epoch 854/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0218\n",
"Epoch 855/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0218\n",
"Epoch 856/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0218\n",
"Epoch 857/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0218\n",
"Epoch 858/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0220\n",
"Epoch 859/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0221\n",
"Epoch 860/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0218\n",
"Epoch 861/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0221\n",
"Epoch 862/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0221\n",
"Epoch 863/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0220\n",
"Epoch 864/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0224\n",
"Epoch 865/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0220\n",
"Epoch 866/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0223\n",
"Epoch 867/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0220\n",
"Epoch 868/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0222\n",
"Epoch 869/1000\n",
"15000/15000 [==============================] - 0s 12us/step - loss: 0.0223\n",
"Epoch 870/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0221\n",
"Epoch 871/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0221\n",
"Epoch 872/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0220\n",
"Epoch 873/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0220\n",
"Epoch 874/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0218\n",
"Epoch 875/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0221\n",
"Epoch 876/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0219\n",
"Epoch 877/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0218\n",
"Epoch 878/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0219\n",
"Epoch 879/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0218\n",
"Epoch 880/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0219\n",
"Epoch 881/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0217\n",
"Epoch 882/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0219\n",
"Epoch 883/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0218\n",
"Epoch 884/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0218\n",
"Epoch 885/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0218\n",
"Epoch 886/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0218\n",
"Epoch 887/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0216\n",
"Epoch 888/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0218\n",
"Epoch 889/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0216\n",
"Epoch 890/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0216\n",
"Epoch 891/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0217\n",
"Epoch 892/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0216\n",
"Epoch 893/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0216\n",
"Epoch 894/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0215\n",
"Epoch 895/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0215\n",
"Epoch 896/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0215\n",
"Epoch 897/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0215\n",
"Epoch 898/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0214\n",
"Epoch 899/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0215\n",
"Epoch 900/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0214\n",
"Epoch 901/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0215\n",
"Epoch 902/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0214\n",
"Epoch 903/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0214\n",
"Epoch 904/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0214\n",
"Epoch 905/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0214\n",
"Epoch 906/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0214\n",
"Epoch 907/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0213\n",
"Epoch 908/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0213\n",
"Epoch 909/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0214\n",
"Epoch 910/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0212\n",
"Epoch 911/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0214\n",
"Epoch 912/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0213\n",
"Epoch 913/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0213\n",
"Epoch 914/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0217\n",
"Epoch 915/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0214\n",
"Epoch 916/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0213\n",
"Epoch 917/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0215\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 918/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0212\n",
"Epoch 919/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0214\n",
"Epoch 920/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0213\n",
"Epoch 921/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0212\n",
"Epoch 922/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0212\n",
"Epoch 923/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0212\n",
"Epoch 924/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0212\n",
"Epoch 925/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0211\n",
"Epoch 926/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0211\n",
"Epoch 927/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0210\n",
"Epoch 928/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0211\n",
"Epoch 929/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0210\n",
"Epoch 930/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0210\n",
"Epoch 931/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0210\n",
"Epoch 932/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0210\n",
"Epoch 933/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0210\n",
"Epoch 934/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0209\n",
"Epoch 935/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0209\n",
"Epoch 936/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0209\n",
"Epoch 937/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0209\n",
"Epoch 938/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0209\n",
"Epoch 939/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0209\n",
"Epoch 940/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0209\n",
"Epoch 941/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0209\n",
"Epoch 942/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0209\n",
"Epoch 943/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0209\n",
"Epoch 944/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0208\n",
"Epoch 945/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0209\n",
"Epoch 946/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0209\n",
"Epoch 947/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0209\n",
"Epoch 948/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0208\n",
"Epoch 949/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0208\n",
"Epoch 950/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0208\n",
"Epoch 951/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0207\n",
"Epoch 952/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0208\n",
"Epoch 953/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0208\n",
"Epoch 954/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0207\n",
"Epoch 955/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0208\n",
"Epoch 956/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0207\n",
"Epoch 957/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0207\n",
"Epoch 958/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0207\n",
"Epoch 959/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0207\n",
"Epoch 960/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0207\n",
"Epoch 961/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0208\n",
"Epoch 962/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0208\n",
"Epoch 963/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0207\n",
"Epoch 964/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0206\n",
"Epoch 965/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0207\n",
"Epoch 966/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0208\n",
"Epoch 967/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0213\n",
"Epoch 968/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0212\n",
"Epoch 969/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0206\n",
"Epoch 970/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0208\n",
"Epoch 971/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0209\n",
"Epoch 972/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0208\n",
"Epoch 973/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0206\n",
"Epoch 974/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0208\n",
"Epoch 975/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0207\n",
"Epoch 976/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0207\n",
"Epoch 977/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0208\n",
"Epoch 978/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0207\n",
"Epoch 979/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0207\n",
"Epoch 980/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0208\n",
"Epoch 981/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0207\n",
"Epoch 982/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0207\n",
"Epoch 983/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0206\n",
"Epoch 984/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0209\n",
"Epoch 985/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0206\n",
"Epoch 986/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0205\n",
"Epoch 987/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0206\n",
"Epoch 988/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0207\n",
"Epoch 989/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0205\n",
"Epoch 990/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0205\n",
"Epoch 991/1000\n",
"15000/15000 [==============================] - 0s 10us/step - loss: 0.0207\n",
"Epoch 992/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0205\n",
"Epoch 993/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0206\n",
"Epoch 994/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0207\n",
"Epoch 995/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0205\n",
"Epoch 996/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0206\n",
"Epoch 997/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0205\n",
"Epoch 998/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0204\n",
"Epoch 999/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0204\n",
"Epoch 1000/1000\n",
"15000/15000 [==============================] - 0s 11us/step - loss: 0.0205\n"
]
}
],
"source": [
"hist = model_train.fit([trades_past,x_data, y_data], target, batch_size=x_data.shape[0], epochs=1000, verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x117f395c0>]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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t/mZxd06z1pPpwN/TWb5NYdgt/qYpucVv1nIyHvjLLX4H/uYpuTunWcvJdOCvPKrBOf7mCV/cNWs5mQ78bvE3X7qR7xy/WWvIdODv7Srn+A+Njje5Ju0rnerxM9rMWkPmA38hJ/YNO/A3S7qRH478Zi0h04FfKj+T/+Z7f+xBQJokpjyWuYkVMbNJmQ78ab57tzn8PH6z1pP5wL9oQZLnH3Pgb4b0k5gd+M1aQ+YD/4f+/WsBOOQWf1OEL+6atZy6Ar+kjZKelLRN0vUzLP8tSUOSHkpev51adq2kp5PXtY2sfD16F7hnTzM51WPWemoOvSgpD3wCeDuwA9gqacsMY+f+Q0RcN23bZcCNwAAQwIPJtvM2LFalS6dz/M1RKkEhGfjeF3fNWkM9Lf5LgG0RsT0ixoDbgavq3P87gLsiYm8S7O8CNs6tqnOztKcDgL2H/Uz+ZihFkE9GxXGL36w11BP4VwEvpOZ3JGXT/ZKkhyV9UdKa49wWSZskDUoaHBoaqqNa9Vnd100+J7btOtSwfVr9Isot/vK0A79ZK2jUxd3/B6yLiNdSbtX/3+PdQUTcGhEDETHQ39/foGpBVyHPhpW9fO2hFxu2T6vflBa/n9Fm1hLqCfw7gTWp+dVJ2aSI2BMRo8nsp4DX1bvtfPi581by4oER38TVBKUICvnc5LSZNV89gX8rsEHSekmdwNXAlvQKks5IzV4JPJ5M3wlcIalPUh9wRVI2r/p6OomAg6O+wDvfSkEqx9/kypgZUEevnogoSrqOcsDOA5sj4lFJNwGDEbEF+D1JVwJFYC/wW8m2eyV9kPKXB8BNEbH3JBxHVYsXlC/wvjIyzpLujvl++7Y1UQoeemH/5Lxb/GatoWbgB4iIO4A7ppV9IDV9A3DDLNtuBjafQB1P2OLu8mG+csR9+efTM7sPT5l34DdrDZm/cxdgRW8XAE++fLDJNWkvXYWp/7yc6jFrDW0R+C9e28fpixdw9xO7ml2VtlJp4b9qyQLA3TnNWkVbBP5cTrzuzD4e3Xmg2VVpK5WhFn/19eWOXU71mLWGtgj8AKctXsDuQ757dz5VUjsdSXfO0XF35DdrBW0T+Jf3dnJotMiRcY+/O18qqZ1Kd87rv/xIM6tjZon2CfwLOwE/s2c+TSSBv/LIBjNrDW0T+Ff1dQPw7LQuhnbyVB7R4MBv1lraJvCff8ZiAB598ZUm16R9VC7m5vNH/5m5Z49Z87VN4F/e28WS7g6e2+sW/3wpzZDqGS36Aq9Zs7VN4Ac4c3kPz+0ZbnY12kalV0868B/285LMmq6tAv+aZT28sNeBf75Mtvjz6cDvXlVmzdZWgf/MZT3s2DdCccLphvlQeQx2Pnf0n9lo0YHfrNnaKvCvXdZDsRS8dOBIs6vSFiZv4HKO36yltFfgX94DwPNO98yL0rQbuMAtfrNW0FaBf/2KhQA8/RM/pXM+VFI96Ry/H9tg1nxtFfjPWNLNqqXd/ODZeR8Lpi0d7dWTzvE78Js1W12BX9JGSU9K2ibp+hmW/5GkxyQ9LOluSWemlk1Ieih5bZm+7Xy79KxlPLB9r28kmgcz9+N3qses2WoGfkl54BPAO4HzgWsknT9ttR8CAxHxWuCLwP9KLRuJiAuT15UNqvecXXbWcvYcHuPpXYeaXZXMm5gxx+8Wv1mz1dPivwTYFhHbI2IMuB24Kr1CRNwTEZUrpvcDqxtbzcb5mQ395ARfe2hns6uSeTHZjz+V6nGO36zp6gn8q4AXUvM7krLZvBf4Rmp+gaRBSfdL+sU51LGhTl+ygNevW8Y/PjXU7Kpk3kwPaXOqx6z5GnpxV9JvAAPAX6aKz4yIAeA/AB+R9OpZtt2UfEEMDg2d3KB8wZqlPPWTQ5O9TuzkmCnVs/ewB7w3a7Z6Av9OYE1qfnVSNoWktwF/ClwZEaOV8ojYmfzdDtwLXDTTm0TErRExEBED/f39dR/AXKzp62asWGLo0GjtlW3Opg/EAvDi/pFmVcfMEvUE/q3ABknrJXUCVwNTeudIugj4JOWgvytV3iepK5leAVwOPNaoys/Vq/t7Afjh8/uaXJNsq/ygyulo4N/pwG/WdDUDf0QUgeuAO4HHgc9HxKOSbpJU6aXzl0Av8IVp3TbPAwYl/StwD/ChiGh64L9k/TKWdHdw75PO859MlcHWc4J/+uO38NZzVzrwm7WAQj0rRcQdwB3Tyj6Qmn7bLNt9H3jNiVTwZCjkc1y4Zin3b99DRCB5hKiTodKPP5cTa5b1cPbKXv55225/5mZN1lZ37qa986dP59k9wzzxsh/fcLLEtFTPuuULGSuW/KwksyZr28D/pg0rAHhg+54m1yS70qkegNev6wPgC4M7mlUlM6ONA//qvh5WLe3mgWf83J6TZTLVk7T4N5y2iDOX9/CUH5Jn1lRtG/ihfJF367P7/Nyek2Qy1ZPqzrmit4vhMd/EZdZMbR34B9b1sfvQqMfhPUkmYmqqB6CnM88hj7tr1lRtHfgvXb8cgLuf2FVjTZuLyYFYUj14ersKDI858Js1U1sH/rNX9nLu6Yv49mM/aXZVMqlyA1e662ZPZ8EDrps1WVsHfoDLz17Bg8/vY48f39BwE8mg9ulHNizsyrNz/wh/9tVHmlUts7bX9oH/mkvWUCoFH79nW7OrkjkjySOYuzvyk2VLujsA+Lv7n+fIuFv+Zs3Q9oH/7JWLeMu5K/nsfc+xfciDszTSyFgRCRZ0HP1n1tN59GbxR3YeaEa1zNpe2wd+gPdfcQ4TpeCKD3+Xe5/0hd5GGRmfoLsjPyXHf9HapZPTv3LLfXxv2+5mVM2srdX1rJ6sO/f0xfzJu87lr771FL/16a2s6O3kvDMWMz5RQog1y7r53bduYM2ynmZX9ZQyPDYxJc0D5aEvB//sbfzqLfexffdh/vqup7j87BVNqqFZe3LgT2z6mVdzzSVr+ex9z/Hp7z3Dc3uGeXH/CIu7O7hv+x6+8OAO3vjq5bzuzGVcsHoJl561nN6uAqPFCSJgwbQAZzAyNkF357Gfy4reLu76o5/lY3c/zUfvfpofPr+Pi9b2NaGGZu1JrXjX6sDAQAwODjbt/StPjyxOlCjkc2zbdYi/u/85tj67l8deeoUIkKAjn2OsWKKQExev7eOs/oW8ur+X05csoLerwOLuDpakXp2F9smslUrBWX9SfqDrsx/6tzOuc3i0yBs/9B1W93Vz26bL6O7Ic88Tu7j4zD5W9HbNZ3XNTnmSHkxGO6zJLf4ZVHLSlUHCz17Zy59f+VMAHDwyziM7DvCDZ/eyf3icQk6UAh7esZ+7HvsJtx9+Ydb99nTmp3wRLOnuYGlPar6nkyXdHSxeUKC7I8/i5MtiYWeB3qQs3TVyNrsPjfLgc/tYtrCTRQsKrFu+cN5/keyuo3vswq4CH/m1C9n0t4Nc+j/vZiTp5bN+xUI+958u5fBokR88s49fGVhNR372L83HX3qF4bEiP3x+Pz9zTj/nnLaoYcdhlkVu8TfY3sNj7Dp4hOGxCV4ZGedA5TVc/rt/hrIDI+OTQa+WznyOBR05FnYVJn9V9HYV6OnMs6Cj/PrqD3dO2Z8Er1rSzeq+bvoXdbGit4vF3R0s7e6Y/HXSv6iLvp5OOgs5hseK9PV00lXITX7RjE+U/53U+6vl4R37ufLj3+P9bz+H3/25DVXX/d623fzXL/wrLx04wrmnL5rxUdmdhRy/dPEqujsKLO/tpKczzzO7D/O5B56nOG3s5Bt/4Xzeeu5K8jlxxpJuShFVvzjMsuB4Wvx1BX5JG4GPAnngUxHxoWnLu4DPAq8D9gC/FhHPJstuAN4LTAC/FxF31nq/Uznwz9VocYIDI+O8MjLOK0eKDI9OcPDIOGMTJQ6NFjk8WuTIeImR8QlGxiY4NFrk0JEiB0fHOXQktWx8guHRIq9dvZT/8pZXs394nGd2H2b70CFe3H+EXQePsOfwGAeP1PfYBAkKOTE+ESzoyLGsp5Puzjw9nYXkb/n18oEjTJSCXE4UJ2Kyq+aW6y7ntauX1ngXODRa5Jmhw7xm9RI+P/gCT718kGd2H67rcRrnnNZLVyFftXvo4gUFli3sPOYLoJDP0ZkXHfkcnYUcHflcMi068znyuRz7h8eQREdeFPI5CjmVX5XpZPt8Ui6JnMpPJRXlh9QpPa/yfHq9nMofdq7GesfMc+zyWbcjmc9N3W7G9XLp/QNo8t9D5TenkuVHy4+uM1EKJiKICAq58mejyWOt1K+8TWWfSr2fKBfOtKzytNeq27fhQD8NTfVIygOfAN4O7AC2StoybQjF9wL7IuJsSVcDfwH8mqTzKY/R+1PAq4BvSzonInznzjRdhTwrF+VZuWjBvLzfRCk4MDLOyweOcHisyE9eOcIrI0WGx4p0d+Y5eKTIWLHEWLHEeKl8I9bw6ATDYxOMjBcZHitP7zk0xgtjRXo6y4G1lFwfufzs5Szt7uT8MxbXVZ/ergKvWb0EgF8dWDNZHhGUAp58+SAj4xPsOTTKeWcsZtvQIc49fRGHR4ucvXIRY8USnx98gQ0re8nlxMM7DlDIiRcPjNCVz7F/ZJz9w+MUk2Mp7xuKpWB8IjnOiRKHxyYYL5YYmyjPjxdLLOnpTD6zEsWJYDz5WywFxYnUdKlEsRS04I/otjX9SyGXFKS/YKavQ3o+NZ1LFmqW7eHYL5zK7OTf1Jdj8laT2wnoW9jJl37njY3/IKapJ8d/CbAtIrYDSLoduIqpg6ZfBfx5Mv1F4OMqfwJXAbdHxCjwjKRtyf7ua0z1ba7yObFsYSfLFnY2uypVSSIvOP9VU79Apnet7Szk+I3Lzpycf/26ZfNSv9lUvrBKUf4imPKX5G8JgqPrlSIgOLod5Yvk5eL0/ir7qrH/aevNuP8Z9zdtPnVMR4+vXKfJ6WRRJOvlc0pa+WKiVCqnCo85jvK6MbmP1HTq/SrvdbT86AMAK3WNGdZjyv6Ollc+h6iy/eS+p5WXs4oxecxT1k99NslaUxZM/xzT61emFy2Yn8uu9bzLKiB9xXIHcOls60REUdIBYHlSfv+0bVfNubZmp4jKF1ae9ks5WOtrmStekjZJGpQ0ODQ01OzqmJllVj2BfyewJjW/OimbcR1JBWAJ5Yu89WwLQETcGhEDETHQ399fX+3NzOy41RP4twIbJK2X1En5Yu2WaetsAa5Npn8Z+E6UE1lbgKsldUlaD2wAftCYqpuZ2VzUzPEnOfvrgDspd+fcHBGPSroJGIyILcDfAH+bXLzdS/nLgWS9z1O+EFwE3ucePWZmzeUbuMzMMuB4+vG3zMVdMzObHw78ZmZtxoHfzKzNtGSOX9IQ8NwcN18BtNuwTj7m9uBjzr4TOd4zI6KuvvAtGfhPhKTBei9wZIWPuT34mLNvvo7XqR4zszbjwG9m1mayGPhvbXYFmsDH3B58zNk3L8ebuRy/mZlVl8UWv5mZVZGZwC9po6QnJW2TdH2z69MoktZIukfSY5IelfT7SfkySXdJejr525eUS9LHks/hYUkXN/cI5k5SXtIPJX09mV8v6YHk2P4heWggyUMA/yEpf0DSumbWe64kLZX0RUlPSHpc0huyfp4l/WHy7/pHkm6TtCBr51nSZkm7JP0oVXbc51XStcn6T0u6dqb3qlcmAr+ODg/5TuB84BqVh33MgiLw/og4H7gMeF9ybNcDd0fEBuDuZB7Kn8GG5LUJuHn+q9wwvw88npr/C+DDEXE2sI/ykJ+QGvoT+HCy3qnoo8A3I+Jc4ALKx57Z8yxpFfB7wEBE/DTlh0BWhm7N0nn+DLBxWtlxnVdJy4AbKQ+CdQlwY+XLYk5icqi1U/cFvAG4MzV/A3BDs+t1ko71a5THP34SOCMpOwN4Mpn+JHBNav3J9U6lF+WxG+4G3gp8nfLwpLuBwvRzTvnJsW9IpgvJemr2MRzn8S4Bnple7yyfZ46O3LcsOW9fB96RxfMMrAN+NNfzClwDfDJVPmW9431losXPzMNDZm6Ix+Sn7UXAA8BpEfFSsuhl4LRkOiufxUeAPwYqo6MvB/ZHRDGZTx/XlKE/gcrQn6eS9cAQ8OkkvfUpSQvJ8HmOiJ3AXwHPAy9RPm8Pku3zXHG857Wh5zsrgT/zJPUCXwL+ICKFdsyGAAAB1UlEQVReSS+LchMgM92zJP08sCsiHmx2XeZRAbgYuDkiLgIOc/TnP5DJ89wHXEX5S+9VwEKOTYlkXjPOa1YCf91DPJ6KJHVQDvp/HxFfTop/IumMZPkZwK6kPAufxeXAlZKeBW6nnO75KLA0GdoTph7XbEN/nkp2ADsi4oFk/ouUvwiyfJ7fBjwTEUMRMQ58mfK5z/J5rjje89rQ852VwF/P8JCnJEmiPMLZ4xHx16lF6eEur6Wc+6+UvzvpHXAZcCD1k/KUEBE3RMTqiFhH+Vx+JyJ+HbiH8tCecOwxzzT05ykjIl4GXpD0b5Kin6M8cl1mzzPlFM9lknqSf+eVY87seU453vN6J3CFpL7kl9IVSdncNPuiRwMvnrwLeAr4MfCnza5PA4/rTZR/Bj4MPJS83kU5t3k38DTwbWBZsr4o93D6MfAI5R4TTT+OEzj+NwNfT6bPojxm8zbgC0BXUr4gmd+WLD+r2fWe47FeCAwm5/qrQF/WzzPwP4AngB8Bfwt0Ze08A7dRvoYxTvmX3Xvncl6B/5gc+zbgPSdSJ9+5a2bWZrKS6jEzszo58JuZtRkHfjOzNuPAb2bWZhz4zczajAO/mVmbceA3M2szDvxmZm3m/wO1kJ7EDYCIXQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(hist.history[\"loss\"])"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"# TODO correct function params.. its using things not handed to it\n",
"\n",
"def render(idx=0):# now select a trade sequance and rennder its predictionn space\n",
" x = np.arange(0, 25.0, 0.5)\n",
" y = np.arange(0.5, 2, 0.05)\n",
" X, Y = np.meshgrid(x, y)\n",
"\n",
" past_selection = trades_past[idx:idx+1,:].flatten()\n",
" past_x = np.arange(past_selection.shape[0])\n",
"\n",
" trade_input = np.repeat(past_selection.reshape((1,-1)), np.prod(X.shape), axis=0)\n",
" future_selection = trades_future[idx:idx+1,:].flatten()\n",
" future_x = np.arange(future_selection.shape[0]) + past_selection.shape[0]\n",
"\n",
" Z = model_train.predict([trade_input, X.flatten().reshape((-1,1)), Y.flatten().reshape((-1,1))])\n",
" Z = Z.reshape(X.shape)\n",
" \n",
" fig, (ax1,ax2) = plt.subplots(1,2, sharey=True, figsize=(20,7))\n",
" \n",
" im = ax1.pcolormesh(X,Y,Z, cmap='YlGn')\n",
" fig.colorbar(im, ax=ax1)\n",
" ax1.contour(X, Y, Z)\n",
" ax1.plot(past_x , past_selection, \"r\")\n",
" ax1.plot(future_x , future_selection, \"r\")\n",
" \n",
" logZ = np.log(Z)\n",
" im = ax2.pcolormesh(X,Y,logZ, cmap='YlGn') \n",
" fig.colorbar(im, ax=ax2)\n",
" ax2.contour(X, Y, logZ)\n",
" ax2.plot(past_x , past_selection, \"r\")\n",
" ax2.plot(future_x , future_selection, \"r\")\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x504 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"render(0)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x504 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"render(500)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x504 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"render(1000)"
]
},
{
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
ocefpaf/folium | examples/CustomPanes.ipynb | 2 | 404858 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load GeoJSON as in [GeoJSON_and_choropleth.ipynb](https://github.com/python-visualization/folium/blob/main/examples/GeoJSON_and_choropleth.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2019-04-05T22:38:12.549432Z",
"start_time": "2019-04-05T22:38:12.533446Z"
}
},
"outputs": [],
"source": [
"import json\n",
"\n",
"import folium\n",
"import requests\n",
"\n",
"\n",
"url = (\n",
" \"https://raw.githubusercontent.com/python-visualization/folium/main/examples/data\"\n",
")\n",
"us_states = f\"{url}/us-states.json\"\n",
"geo_json_data = json.loads(requests.get(us_states).text)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using CustomPane to place labels above choropleth"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Map without custom pane"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2019-04-05T22:38:12.758314Z",
"start_time": "2019-04-05T22:38:12.555438Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    
        <script>
            L_NO_TOUCH = false;
            L_DISABLE_3D = false;
        </script>
    
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.js"></script>
    <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css"/>
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css"/>
    
            <meta name="viewport" content="width=device-width,
                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
            <style>
                #map_5525b1217194499f8b53beeddc1f0edd {
                    position: relative;
                    width: 100.0%;
                    height: 100.0%;
                    left: 0.0%;
                    top: 0.0%;
                }
            </style>
        
</head>
<body>    
    
            <div class="folium-map" id="map_5525b1217194499f8b53beeddc1f0edd" ></div>
        
</body>
<script>    
    
            var map_5525b1217194499f8b53beeddc1f0edd = L.map(
                "map_5525b1217194499f8b53beeddc1f0edd",
                {
                    center: [43.0, -100.0],
                    crs: L.CRS.EPSG3857,
                    zoom: 4,
                    zoomControl: true,
                    preferCanvas: false,
                }
            );

            

        
    
            var tile_layer_c6a0c70fc8ae449abda1759c6b71e146 = L.tileLayer(
                "https://stamen-tiles-{s}.a.ssl.fastly.net/toner/{z}/{x}/{y}.png",
                {"attribution": "Map tiles by \u003ca href=\"http://stamen.com\"\u003eStamen Design\u003c/a\u003e, under \u003ca href=\"http://creativecommons.org/licenses/by/3.0\"\u003eCC BY 3.0\u003c/a\u003e. Data by \u0026copy; \u003ca href=\"http://openstreetmap.org\"\u003eOpenStreetMap\u003c/a\u003e, under \u003ca href=\"http://www.openstreetmap.org/copyright\"\u003eODbL\u003c/a\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
            ).addTo(map_5525b1217194499f8b53beeddc1f0edd);
        
    
        function geo_json_38e15e05dcd04ef59e86a4594a978f1d_onEachFeature(feature, layer) {
            layer.on({
            });
        };
        var geo_json_38e15e05dcd04ef59e86a4594a978f1d = L.geoJson(null, {
                onEachFeature: geo_json_38e15e05dcd04ef59e86a4594a978f1d_onEachFeature,
            
        });

        function geo_json_38e15e05dcd04ef59e86a4594a978f1d_add (data) {
            geo_json_38e15e05dcd04ef59e86a4594a978f1d
                .addData(data)
                .addTo(map_5525b1217194499f8b53beeddc1f0edd);
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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f62740baa90>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map([43, -100], zoom_start=4, tiles=\"stamentoner\")\n",
"\n",
"folium.GeoJson(geo_json_data).add_to(m)\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Map with custom pane"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2019-04-05T22:38:13.237039Z",
"start_time": "2019-04-05T22:38:12.789303Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    
        <script>
            L_NO_TOUCH = false;
            L_DISABLE_3D = false;
        </script>
    
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.js"></script>
    <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css"/>
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css"/>
    
            <meta name="viewport" content="width=device-width,
                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
            <style>
                #map_d7f3689e2d594c999e055c81a69e97b8 {
                    position: relative;
                    width: 100.0%;
                    height: 100.0%;
                    left: 0.0%;
                    top: 0.0%;
                }
            </style>
        
</head>
<body>    
    
            <div class="folium-map" id="map_d7f3689e2d594c999e055c81a69e97b8" ></div>
        
</body>
<script>    
    
            var map_d7f3689e2d594c999e055c81a69e97b8 = L.map(
                "map_d7f3689e2d594c999e055c81a69e97b8",
                {
                    center: [43.0, -100.0],
                    crs: L.CRS.EPSG3857,
                    zoom: 4,
                    zoomControl: true,
                    preferCanvas: false,
                }
            );

            

        
    
            var tile_layer_56e93c5c45864e0ab6e8c933342f2fa3 = L.tileLayer(
                "https://stamen-tiles-{s}.a.ssl.fastly.net/toner-background/{z}/{x}/{y}{r}.png",
                {"attribution": "Map tiles by \u003ca href=\"http://stamen.com\"\u003eStamen Design\u003c/a\u003e, under \u003ca href=\"http://creativecommons.org/licenses/by/3.0\"\u003eCC BY 3.0\u003c/a\u003e. Data by \u003ca href=\"http://openstreetmap.org\"\u003eOpenStreetMap\u003c/a\u003e, under \u003ca href=\"http://www.openstreetmap.org/copyright\"\u003eODbL\u003c/a\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
            ).addTo(map_d7f3689e2d594c999e055c81a69e97b8);
        
    
        function geo_json_4c49e3809b8b4b698baa5152b03ce5de_onEachFeature(feature, layer) {
            layer.on({
            });
        };
        var geo_json_4c49e3809b8b4b698baa5152b03ce5de = L.geoJson(null, {
                onEachFeature: geo_json_4c49e3809b8b4b698baa5152b03ce5de_onEachFeature,
            
        });

        function geo_json_4c49e3809b8b4b698baa5152b03ce5de_add (data) {
            geo_json_4c49e3809b8b4b698baa5152b03ce5de
                .addData(data)
                .addTo(map_d7f3689e2d594c999e055c81a69e97b8);
        }
            geo_json_4c49e3809b8b4b698baa5152b03ce5de_add({"features": [{"geometry": {"coordinates": [[[-87.359296, 35.00118], [-85.606675, 34.984749], [-85.431413, 34.124869], [-85.184951, 32.859696], [-85.069935, 32.580372], [-84.960397, 32.421541], [-85.004212, 32.322956], [-84.889196, 32.262709], [-85.058981, 32.13674], [-85.053504, 32.01077], [-85.141136, 31.840985], [-85.042551, 31.539753], [-85.113751, 31.27686], [-85.004212, 31.003013], [-85.497137, 30.997536], [-87.600282, 30.997536], [-87.633143, 30.86609], [-87.408589, 30.674397], [-87.446927, 30.510088], [-87.37025, 30.427934], [-87.518128, 30.280057], [-87.655051, 30.247195], [-87.90699, 30.411504], [-87.934375, 30.657966], [-88.011052, 30.685351], [-88.10416, 30.499135], [-88.137022, 30.318396], [-88.394438, 30.367688], [-88.471115, 31.895754], [-88.241084, 33.796253], [-88.098683, 34.891641], [-88.202745, 34.995703], [-87.359296, 35.00118]]], "type": "Polygon"}, "id": "AL", "properties": {"name": "Alabama"}, "type": "Feature"}, {"geometry": {"coordinates": [[[[-131.602021, 55.117982], [-131.569159, 55.28229], [-131.355558, 55.183705], [-131.38842, 55.01392], [-131.645836, 55.035827], [-131.602021, 55.117982]]], [[[-131.832052, 55.42469], [-131.645836, 55.304197], [-131.749898, 55.128935], [-131.832052, 55.189182], [-131.832052, 55.42469]]], [[[-132.976733, 56.437924], [-132.735747, 56.459832], [-132.631685, 56.421493], [-132.664547, 56.273616], [-132.878148, 56.240754], [-133.069841, 56.333862], [-132.976733, 56.437924]]], [[[-133.595627, 56.350293], [-133.162949, 56.317431], [-133.05341, 56.125739], [-132.620732, 55.912138], [-132.472854, 55.780691], [-132.4619, 55.671152], [-132.357838, 55.649245], [-132.341408, 55.506844], [-132.166146, 55.364444], [-132.144238, 55.238474], [-132.029222, 55.276813], [-131.97993, 55.178228], [-131.958022, 54.789365], [-132.029222, 54.701734], [-132.308546, 54.718165], [-132.385223, 54.915335], [-132.483808, 54.898904], [-132.686455, 55.046781], [-132.746701, 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            var pane_614e256e8a95456693f459635a79edc2 = map_d7f3689e2d594c999e055c81a69e97b8.createPane(
                "labels");
            pane_614e256e8a95456693f459635a79edc2.style.zIndex = 625;
            
                pane_614e256e8a95456693f459635a79edc2.style.pointerEvents = 'none';
            
        
    
            var tile_layer_bba6f99f82c94414bdb9ea0571b51494 = L.tileLayer(
                "https://stamen-tiles-{s}.a.ssl.fastly.net/toner-labels/{z}/{x}/{y}{r}.png",
                {"attribution": "Map tiles by \u003ca href=\"http://stamen.com\"\u003eStamen Design\u003c/a\u003e, under \u003ca href=\"http://creativecommons.org/licenses/by/3.0\"\u003eCC BY 3.0\u003c/a\u003e. Data by \u003ca href=\"http://openstreetmap.org\"\u003eOpenStreetMap\u003c/a\u003e, under \u003ca href=\"http://www.openstreetmap.org/copyright\"\u003eODbL\u003c/a\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "pane": "labels", "subdomains": "abc", "tms": false}
            ).addTo(map_d7f3689e2d594c999e055c81a69e97b8);
        
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f62740ba130>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map([43, -100], zoom_start=4, tiles=\"stamentonerbackground\")\n",
"\n",
"folium.GeoJson(geo_json_data).add_to(m)\n",
"\n",
"folium.map.CustomPane(\"labels\").add_to(m)\n",
"\n",
"# Final layer associated to custom pane via the appropriate kwarg\n",
"folium.TileLayer(\"stamentonerlabels\", pane=\"labels\").add_to(m)\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Same, but with a different tileset"
]
},
{
"cell_type": "code",
"execution_count": 4,
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[-80.967707, 37.290541], [-80.513121, 37.482234], [-80.474782, 37.421987], [-80.29952, 37.509618], [-80.294043, 37.690357], [-80.184505, 37.849189], [-79.998289, 37.997066], [-79.921611, 38.177805], [-79.724442, 38.364021], [-79.647764, 38.594052], [-79.477979, 38.457129], [-79.313671, 38.413313], [-79.209609, 38.495467], [-78.996008, 38.851469], [-78.870039, 38.763838], [-78.404499, 39.169131], [-78.349729, 39.464886]]]], "type": "MultiPolygon"}, "id": "VA", "properties": {"name": "Virginia"}, "type": "Feature"}, {"geometry": {"coordinates": [[[[-117.033359, 49.000239], [-117.044313, 47.762451], [-117.038836, 46.426077], [-117.055267, 46.343923], [-116.92382, 46.168661], [-116.918344, 45.993399], [-118.988627, 45.998876], [-119.125551, 45.933153], [-119.525367, 45.911245], [-119.963522, 45.823614], [-120.209985, 45.725029], [-120.505739, 45.697644], [-120.637186, 45.746937], [-121.18488, 45.604536], [-121.217742, 45.670259], [-121.535404, 45.725029], [-121.809251, 45.708598], [-122.247407, 45.549767], [-122.762239, 45.659305], [-122.811531, 45.960537], [-122.904639, 46.08103], [-123.11824, 46.185092], [-123.211348, 46.174138], [-123.370179, 46.146753], [-123.545441, 46.261769], [-123.72618, 46.300108], [-123.874058, 46.239861], [-124.065751, 46.327492], [-124.027412, 46.464416], [-123.895966, 46.535616], [-124.098612, 46.74374], [-124.235536, 47.285957], [-124.31769, 47.357157], [-124.427229, 47.740543], [-124.624399, 47.88842], [-124.706553, 48.184175], [-124.597014, 48.381345], [-124.394367, 48.288237], [-123.983597, 48.162267], [-123.704273, 48.167744], [-123.424949, 48.118452], [-123.162056, 48.167744], [-123.036086, 48.080113], [-122.800578, 48.08559], [-122.636269, 47.866512], [-122.515777, 47.882943], [-122.493869, 47.587189], [-122.422669, 47.318818], [-122.324084, 47.346203], [-122.422669, 47.576235], [-122.395284, 47.800789], [-122.230976, 48.030821], [-122.362422, 48.123929], [-122.373376, 48.288237], [-122.471961, 48.468976], [-122.422669, 48.600422], [-122.488392, 48.753777], [-122.647223, 48.775685], [-122.795101, 48.8907], [-122.756762, 49.000239], [-117.033359, 49.000239]]], [[[-122.718423, 48.310145], [-122.586977, 48.35396], [-122.608885, 48.151313], [-122.767716, 48.227991], [-122.718423, 48.310145]]], [[[-123.025132, 48.583992], [-122.915593, 48.715438], [-122.767716, 48.556607], [-122.811531, 48.419683], [-123.041563, 48.458022], [-123.025132, 48.583992]]]], "type": "MultiPolygon"}, "id": "WA", "properties": {"name": "Washington"}, "type": "Feature"}, {"geometry": {"coordinates": [[[-80.518598, 40.636951], [-80.518598, 39.722302], [-79.477979, 39.722302], [-79.488933, 39.20747], [-79.291763, 39.300578], [-79.094593, 39.470363], [-78.963147, 39.437501], [-78.765977, 39.585379], [-78.470222, 39.514178], [-78.431884, 39.623717], [-78.267575, 39.61824], [-78.174467, 39.694917], [-78.004682, 39.601809], [-77.834897, 39.601809], [-77.719881, 39.322485], [-77.82942, 39.130793], [-78.349729, 39.464886], [-78.404499, 39.169131], [-78.870039, 38.763838], [-78.996008, 38.851469], [-79.209609, 38.495467], [-79.313671, 38.413313], [-79.477979, 38.457129], [-79.647764, 38.594052], [-79.724442, 38.364021], [-79.921611, 38.177805], [-79.998289, 37.997066], [-80.184505, 37.849189], [-80.294043, 37.690357], [-80.29952, 37.509618], [-80.474782, 37.421987], [-80.513121, 37.482234], [-80.967707, 37.290541], [-81.225123, 37.235771], [-81.362047, 37.339833], [-81.55374, 37.208387], [-81.679709, 37.20291], [-81.849494, 37.285064], [-81.986418, 37.454849], [-81.969987, 37.537003], [-82.101434, 37.553434], [-82.293127, 37.668449], [-82.342419, 37.783465], [-82.50125, 37.931343], [-82.621743, 38.123036], [-82.594358, 38.424267], [-82.331465, 38.446175], [-82.293127, 38.577622], [-82.172634, 38.632391], [-82.221926, 38.785745], [-82.03571, 39.026731], [-81.887833, 38.873376], [-81.783771, 38.966484], [-81.811156, 39.0815], [-81.685186, 39.273193], [-81.57017, 39.267716], [-81.455155, 39.410117], [-81.345616, 39.344393], [-81.219646, 39.388209], [-80.830783, 39.711348], [-80.737675, 40.078303], [-80.600752, 40.319289], [-80.595275, 40.472643], [-80.666475, 40.582182], [-80.518598, 40.636951]]], "type": "Polygon"}, "id": "WV", "properties": {"name": "West Virginia"}, "type": "Feature"}, {"geometry": {"coordinates": [[[-90.415429, 46.568478], [-90.229213, 46.508231], [-90.119674, 46.338446], [-89.09001, 46.135799], [-88.662808, 45.987922], [-88.531362, 46.020784], [-88.10416, 45.922199], [-87.989145, 45.796229], [-87.781021, 45.675736], [-87.791975, 45.500474], [-87.885083, 45.363551], [-87.649574, 45.341643], [-87.742682, 45.199243], [-87.589328, 45.095181], [-87.627666, 44.974688], [-87.819359, 44.95278], [-87.983668, 44.722749], [-88.043914, 44.563917], [-87.928898, 44.536533], [-87.775544, 44.640595], [-87.611236, 44.837764], [-87.403112, 44.914442], [-87.238804, 45.166381], [-87.03068, 45.22115], [-87.047111, 45.089704], [-87.189511, 44.969211], [-87.468835, 44.552964], [-87.545512, 44.322932], [-87.540035, 44.158624], [-87.644097, 44.103854], [-87.737205, 43.8793], [-87.704344, 43.687607], [-87.791975, 43.561637], [-87.912467, 43.249452], [-87.885083, 43.002989], [-87.76459, 42.783912], [-87.802929, 42.493634], [-88.788778, 42.493634], [-90.639984, 42.510065], [-90.711184, 42.636034], [-91.067185, 42.75105], [-91.143862, 42.909881], [-91.176724, 43.134436], [-91.056231, 43.254929], [-91.204109, 43.353514], [-91.215062, 43.501391], [-91.269832, 43.616407], [-91.242447, 43.775238], [-91.43414, 43.994316], [-91.592971, 44.032654], [-91.877772, 44.202439], [-91.927065, 44.333886], [-92.233773, 44.443425], [-92.337835, 44.552964], [-92.545959, 44.569394], [-92.808852, 44.750133], [-92.737652, 45.117088], [-92.75956, 45.286874], [-92.644544, 45.440228], [-92.770513, 45.566198], [-92.885529, 45.577151], [-92.869098, 45.719552], [-92.639067, 45.933153], [-92.354266, 46.015307], [-92.29402, 46.075553], [-92.29402, 46.667063], [-92.091373, 46.749217], [-92.014696, 46.705401], [-91.790141, 46.694447], [-91.09457, 46.864232], [-90.837154, 46.95734], [-90.749522, 46.88614], [-90.886446, 46.754694], [-90.55783, 46.584908], [-90.415429, 46.568478]]], "type": "Polygon"}, "id": "WI", "properties": {"name": "Wisconsin"}, "type": "Feature"}, {"geometry": {"coordinates": [[[-109.080842, 45.002073], [-105.91517, 45.002073], [-104.058488, 44.996596], [-104.053011, 43.002989], [-104.053011, 41.003906], [-105.728954, 40.998429], [-107.919731, 41.003906], [-109.04798, 40.998429], [-111.047063, 40.998429], [-111.047063, 42.000709], [-111.047063, 44.476286], [-111.05254, 45.002073], [-109.080842, 45.002073]]], "type": "Polygon"}, "id": "WY", "properties": {"name": "Wyoming"}, "type": "Feature"}], "type": "FeatureCollection"});

        
    
            var pane_fce96276682140fdb0a6d1636bb98f0c = map_6f3506c38dee4fdda0409578b1a0d773.createPane(
                "labels");
            pane_fce96276682140fdb0a6d1636bb98f0c.style.zIndex = 625;
            
                pane_fce96276682140fdb0a6d1636bb98f0c.style.pointerEvents = 'none';
            
        
    
            var tile_layer_3f045844d3ee45f6b794c8ce525c407b = L.tileLayer(
                "https://{s}.basemaps.cartocdn.com/light_only_labels/{z}/{x}/{y}{r}.png",
                {"attribution": "(c) \u003ca href=\"http://www.openstreetmap.org/copyright\"\u003eOpenStreetMap\u003c/a\u003e contributors (c) \u003ca href=\"http://cartodb.com/attributions\"\u003eCartoDB\u003c/a\u003e, CartoDB \u003ca href =\"http://cartodb.com/attributions\"\u003eattributions\u003c/a\u003e", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "pane": "labels", "subdomains": "abc", "tms": false}
            ).addTo(map_6f3506c38dee4fdda0409578b1a0d773);
        
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f62198a3190>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map([43, -100], zoom_start=4, tiles=\"CartoDBPositronNoLabels\")\n",
"\n",
"folium.GeoJson(geo_json_data).add_to(m)\n",
"\n",
"folium.map.CustomPane(\"labels\").add_to(m)\n",
"\n",
"# Final layer associated to custom pane via the appropriate kwarg\n",
"folium.TileLayer(\"CartoDBPositronOnlyLabels\", pane=\"labels\").add_to(m)\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"More tile providers can be found at https://leaflet-extras.github.io/leaflet-providers/preview/."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
prabhamatta/Analyzing-Open-Data | notebooks/.ipynb_checkpoints/Day_07_C_Google_Map_API-checkpoint.ipynb | 1 | 19205 | {
"metadata": {
"name": "",
"signature": "sha256:a735158b97c33375bf5f7bc5407fd6a758d16dbb1670dd8a0676f671f5250696"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://developers.google.com/maps/ specifically [Google Maps JavaScript API v3 \u2014 Google Developers](https://developers.google.com/maps/documentation/javascript/)\n",
"\n",
"Go to [Getting Started - Google Maps JavaScript API v3 \u2014 Google Developers](https://developers.google.com/maps/documentation/javascript/tutorial)\n",
"\n",
"How to read in from a local file -- use script?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.core.display import HTML, Javascript"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# load the Google Maps API library\n",
"# TO DO: make it easy to add API key\n",
"\n",
"def gmap_init():\n",
" js = \"\"\"\n",
"window.gmap_initialize = function() {};\n",
"$.getScript('https://maps.googleapis.com/maps/api/js?v=3&sensor=false&callback=gmap_initialize');\n",
"\"\"\"\n",
" return Javascript(data=js)\n",
"\n",
"gmap_init()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"\n",
"window.gmap_initialize = function() {};\n",
"$.getScript('https://maps.googleapis.com/maps/api/js?v=3&sensor=false&callback=gmap_initialize');\n"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"<IPython.core.display.Javascript at 0x5843b90>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%html\n",
"<style type=\"text/css\">\n",
" .map-canvas { height: 300px; }\n",
"</style"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<style type=\"text/css\">\n",
" .map-canvas { height: 300px; }\n",
"</style"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x58567b0>"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# generate a random id\n",
"\n",
"import uuid\n",
"div_id = 'i' + str(uuid.uuid4())\n",
"\n",
"html = \"\"\"<div id=\"%s\" class=\"map-canvas\"/>\"\"\" % (div_id)\n",
"\n",
"js = \"\"\"\n",
"<script type=\"text/Javascript\">\n",
" (function(){\n",
" var mapOptions = {\n",
" zoom: 8,\n",
" center: new google.maps.LatLng(-34.397, 150.644)\n",
" };\n",
"\n",
" var map = new google.maps.Map(document.getElementById('%s'),\n",
" mapOptions);\n",
" })(); \n",
"</script>\n",
"\"\"\" % (div_id)\n",
"\n",
"HTML(html+js)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div id=\"ia9773171-63c3-48e8-b156-1781c8f868db\" class=\"map-canvas\"/>\n",
"<script type=\"text/Javascript\">\n",
" (function(){\n",
" var mapOptions = {\n",
" zoom: 8,\n",
" center: new google.maps.LatLng(-34.397, 150.644)\n",
" };\n",
"\n",
" var map = new google.maps.Map(document.getElementById('ia9773171-63c3-48e8-b156-1781c8f868db'),\n",
" mapOptions);\n",
" })(); \n",
"</script>\n"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"<IPython.core.display.HTML at 0x5861510>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import uuid\n",
"\n",
"def gmap(lat=37.8717,long=-122.2728,zoom=8):\n",
"\n",
" div_id = 'i' + str(uuid.uuid4())\n",
"\n",
" html = \"\"\"<div id=\"%s\" class=\"map-canvas\"/>\"\"\" % (div_id)\n",
"\n",
" js = \"\"\"\n",
" <script type=\"text/Javascript\">\n",
" (function(){\n",
" var mapOptions = {\n",
" zoom: %s,\n",
" center: new google.maps.LatLng(%s, %s)\n",
" };\n",
"\n",
" var map = new google.maps.Map(document.getElementById('%s'),\n",
" mapOptions);\n",
" })(); \n",
" </script>\n",
" \"\"\" % (zoom, lat,long, div_id)\n",
"\n",
" return HTML(html+js)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import jinja2\n",
"\n",
"TEMPLATE = \"\"\"{{greeting}}, {{name}}\"\"\"\n",
"\n",
"my_template = jinja2.Template(TEMPLATE)\n",
"my_template.render(greeting=\"hello\", name=\"RY\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"u'hello, RY'"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Can we ask google for list of instantiated maps?"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Plotting markers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://developers.google.com/maps/documentation/javascript/markers\n",
"\n",
" var myLatlng = new google.maps.LatLng(-25.363882,131.044922);\n",
" var mapOptions = {\n",
" zoom: 4,\n",
" center: myLatlng\n",
" }\n",
" var map = new google.maps.Map(document.getElementById(\"map-canvas\"), mapOptions);\n",
"\n",
" // To add the marker to the map, use the 'map' property\n",
" var marker = new google.maps.Marker({\n",
" position: myLatlng,\n",
" map: map,\n",
" title:\"Hello World!\"\n",
" });\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%html\n",
"<div id=\"markers\" class=\"map-canvas\"/>"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div id=\"markers\" class=\"map-canvas\"/>"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x5b519b0>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%javascript\n",
"\n",
"var myLatlng = new google.maps.LatLng(37.8717,-122.2728);\n",
"\n",
"var mapOptions = {\n",
" zoom: 8,\n",
" center: myLatlng\n",
"};\n",
"\n",
"var map = new google.maps.Map(document.getElementById('markers'),\n",
" mapOptions);\n",
"\n",
"// To add the marker to the map, use the 'map' property\n",
"var marker = new google.maps.Marker({\n",
" position: myLatlng,\n",
" map: map,\n",
" title:\"Berkeley\"\n",
"});"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"\n",
"var myLatlng = new google.maps.LatLng(37.8717,-122.2728);\n",
"\n",
"var mapOptions = {\n",
" zoom: 8,\n",
" center: myLatlng\n",
"};\n",
"\n",
"var map = new google.maps.Map(document.getElementById('markers'),\n",
" mapOptions);\n",
"\n",
"// To add the marker to the map, use the 'map' property\n",
"var marker = new google.maps.Marker({\n",
" position: myLatlng,\n",
" map: map,\n",
" title:\"Berkeley\"\n",
"});"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x5b4e3d0>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Drawing Circles on Map"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"%%javascript\n",
"//https://developers.google.com/maps/documentation/javascript/examples/circle-simple\n",
"\n",
" // This example creates circles on the map, representing\n",
" // populations in the United States.\n",
"\n",
" // First, create an object containing LatLng and population for each city.\n",
" var citymap = {};\n",
" citymap['chicago'] = {\n",
"\n",
" center: new google.maps.LatLng(41.878113, -87.629798),\n",
" population: 2842518\n",
" };\n",
" citymap['newyork'] = {\n",
" center: new google.maps.LatLng(40.714352, -74.005973),\n",
" population: 8143197\n",
" };\n",
" citymap['losangeles'] = {\n",
" center: new google.maps.LatLng(34.052234, -118.243684),\n",
" population: 3844829\n",
" };\n",
" var cityCircle;\n",
"\n",
" function initialize() {\n",
" // Create the map.\n",
" var mapOptions = {\n",
" zoom: 4,\n",
" center: new google.maps.LatLng(37.09024, -95.712891),\n",
" mapTypeId: google.maps.MapTypeId.TERRAIN\n",
" };\n",
"\n",
" var map = new google.maps.Map(document.getElementById('map-canvas'),\n",
" mapOptions);\n",
"\n",
" // Construct the circle for each value in citymap.\n",
" // Note: We scale the population by a factor of 20.\n",
" for (var city in citymap) {\n",
" var populationOptions = {\n",
" strokeColor: '#FF0000',\n",
" strokeOpacity: 0.8,\n",
" strokeWeight: 2,\n",
" fillColor: '#FF0000',\n",
" fillOpacity: 0.35,\n",
" map: map,\n",
" center: citymap[city].center,\n",
" radius: citymap[city].population / 20\n",
" };\n",
" // Add the circle for this city to the map.\n",
" cityCircle = new google.maps.Circle(populationOptions);\n",
" }\n",
" }\n",
"\n",
" google.maps.event.addDomListener(window, 'load', initialize);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"//https://developers.google.com/maps/documentation/javascript/examples/circle-simple\n",
"\n",
" // This example creates circles on the map, representing\n",
" // populations in the United States.\n",
"\n",
" // First, create an object containing LatLng and population for each city.\n",
" var citymap = {};\n",
" citymap['chicago'] = {\n",
"\n",
" center: new google.maps.LatLng(41.878113, -87.629798),\n",
" population: 2842518\n",
" };\n",
" citymap['newyork'] = {\n",
" center: new google.maps.LatLng(40.714352, -74.005973),\n",
" population: 8143197\n",
" };\n",
" citymap['losangeles'] = {\n",
" center: new google.maps.LatLng(34.052234, -118.243684),\n",
" population: 3844829\n",
" };\n",
" var cityCircle;\n",
"\n",
" function initialize() {\n",
" // Create the map.\n",
" var mapOptions = {\n",
" zoom: 4,\n",
" center: new google.maps.LatLng(37.09024, -95.712891),\n",
" mapTypeId: google.maps.MapTypeId.TERRAIN\n",
" };\n",
"\n",
" var map = new google.maps.Map(document.getElementById('map-canvas'),\n",
" mapOptions);\n",
"\n",
" // Construct the circle for each value in citymap.\n",
" // Note: We scale the population by a factor of 20.\n",
" for (var city in citymap) {\n",
" var populationOptions = {\n",
" strokeColor: '#FF0000',\n",
" strokeOpacity: 0.8,\n",
" strokeWeight: 2,\n",
" fillColor: '#FF0000',\n",
" fillOpacity: 0.35,\n",
" map: map,\n",
" center: citymap[city].center,\n",
" radius: citymap[city].population / 20\n",
" };\n",
" // Add the circle for this city to the map.\n",
" cityCircle = new google.maps.Circle(populationOptions);\n",
" }\n",
" }\n",
"\n",
" google.maps.event.addDomListener(window, 'load', initialize);"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x5b60230>"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%html\n",
"<div id=\"circles\" class=\"map-canvas\"/>"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div id=\"circles\" class=\"map-canvas\"/>"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x5b60bd0>"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%javascript\n",
"\n",
"// This example creates circles on the map, representing\n",
"// populations in the United States.\n",
"\n",
"// First, create an object containing LatLng and population for each city.\n",
"var citymap = {};\n",
"citymap['chicago'] = {\n",
" center: new google.maps.LatLng(41.878113, -87.629798),\n",
" population: 2842518\n",
"};\n",
"citymap['newyork'] = {\n",
" center: new google.maps.LatLng(40.714352, -74.005973),\n",
" population: 8143197\n",
"};\n",
"citymap['losangeles'] = {\n",
" center: new google.maps.LatLng(34.052234, -118.243684),\n",
" population: 3844829\n",
"};\n",
"var cityCircle;\n",
"\n",
"\n",
"var mapOptions = {\n",
" zoom: 4,\n",
" center: new google.maps.LatLng(37.09024, -95.712891),\n",
" mapTypeId: google.maps.MapTypeId.TERRAIN\n",
"};\n",
"\n",
"\n",
" var map = new google.maps.Map(document.getElementById('circles'),\n",
" mapOptions);\n",
"\n",
" // Construct the circle for each value in citymap.\n",
" // Note: We scale the population by a factor of 20.\n",
" for (var city in citymap) {\n",
" var populationOptions = {\n",
" strokeColor: '#FF0000',\n",
" strokeOpacity: 0.8,\n",
" strokeWeight: 2,\n",
" fillColor: '#FF0000',\n",
" fillOpacity: 0.35,\n",
" map: map,\n",
" center: citymap[city].center,\n",
" radius: citymap[city].population / 20\n",
" };\n",
" // Add the circle for this city to the map.\n",
" cityCircle = new google.maps.Circle(populationOptions);\n",
" }\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"\n",
"// This example creates circles on the map, representing\n",
"// populations in the United States.\n",
"\n",
"// First, create an object containing LatLng and population for each city.\n",
"var citymap = {};\n",
"citymap['chicago'] = {\n",
" center: new google.maps.LatLng(41.878113, -87.629798),\n",
" population: 2842518\n",
"};\n",
"citymap['newyork'] = {\n",
" center: new google.maps.LatLng(40.714352, -74.005973),\n",
" population: 8143197\n",
"};\n",
"citymap['losangeles'] = {\n",
" center: new google.maps.LatLng(34.052234, -118.243684),\n",
" population: 3844829\n",
"};\n",
"var cityCircle;\n",
"\n",
"\n",
"var mapOptions = {\n",
" zoom: 4,\n",
" center: new google.maps.LatLng(37.09024, -95.712891),\n",
" mapTypeId: google.maps.MapTypeId.TERRAIN\n",
"};\n",
"\n",
"\n",
" var map = new google.maps.Map(document.getElementById('circles'),\n",
" mapOptions);\n",
"\n",
" // Construct the circle for each value in citymap.\n",
" // Note: We scale the population by a factor of 20.\n",
" for (var city in citymap) {\n",
" var populationOptions = {\n",
" strokeColor: '#FF0000',\n",
" strokeOpacity: 0.8,\n",
" strokeWeight: 2,\n",
" fillColor: '#FF0000',\n",
" fillOpacity: 0.35,\n",
" map: map,\n",
" center: citymap[city].center,\n",
" radius: citymap[city].population / 20\n",
" };\n",
" // Add the circle for this city to the map.\n",
" cityCircle = new google.maps.Circle(populationOptions);\n",
" }\n"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x5b5f370>"
]
}
],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[IPython-User] using Google Charts in IPython](http://lists.ipython.scipy.org/pipermail/ipython-user/2013-May/012694.html):\n",
"\n",
"> google.load blanks the page unless you give it a\n",
"callback <http://stackoverflow.com/questions/9519673/why-does-google-load-cause-my-page-to-go-blank>\n",
".\n",
"\n",
"Also: [javascript - Google Maps API v3 - TypeError: Result of expression 'google.maps.LatLng' undefined] is not a constructor - Stack Overflow](http://stackoverflow.com/questions/6577404/google-maps-api-v3-typeerror-result-of-expression-google-maps-latlng-undef/8361021#8361021)\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | apache-2.0 |
ilanfri/StatsML | lead_generation.ipynb | 1 | 541270 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import os,sys\n",
"#import csv\n",
"import pandas as pan\n",
"import cPickle as pickle\n",
"import pprint\n",
"#import glob\n",
"#import tables #PyTables used to generate HDF5 file instead of pickle\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Companies House data set used as a list of source companies which could be of interest for B2B lead generation.\n",
"Obtain the data set here:\n",
"http://download.companieshouse.gov.uk/en_output.html\n",
"\n",
"First we read the data set into a Pandas DataFrame and serialise it into a pickle file."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pickle file containing data found. Loading it...\n"
]
},
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CompanyName</th>\n",
" <th>CompanyNumber</th>\n",
" <th>RegAddress_CareOf</th>\n",
" <th>RegAddress_POBox</th>\n",
" <th>RegAddress_AddressLine1</th>\n",
" <th>RegAddress_AddressLine2</th>\n",
" <th>RegAddress_PostTown</th>\n",
" <th>RegAddress_County</th>\n",
" <th>RegAddress_Country</th>\n",
" <th>RegAddress_PostCode</th>\n",
" <th>...</th>\n",
" <th>PreviousName_6_CONDATE</th>\n",
" <th>PreviousName_6_CompanyName</th>\n",
" <th>PreviousName_7_CONDATE</th>\n",
" <th>PreviousName_7_CompanyName</th>\n",
" <th>PreviousName_8_CONDATE</th>\n",
" <th>PreviousName_8_CompanyName</th>\n",
" <th>PreviousName_9_CONDATE</th>\n",
" <th>PreviousName_9_CompanyName</th>\n",
" <th>PreviousName_10_CONDATE</th>\n",
" <th>PreviousName_10_CompanyName</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> ! LTD</td>\n",
" <td> 08209948</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> METROHOUSE 57 PEPPER ROAD</td>\n",
" <td> HUNSLET</td>\n",
" <td> LEEDS</td>\n",
" <td> YORKSHIRE</td>\n",
" <td> NaN</td>\n",
" <td> LS10 2RU</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> !BIG IMPACT GRAPHICS LIMITED</td>\n",
" <td> 07382019</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 335 ROSDEN HOUSE</td>\n",
" <td> 372 OLD STREET</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> EC1V 9AV</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> !K7 TOURING LIMITED</td>\n",
" <td> 08937297</td>\n",
" <td> C/O THE GREENE PARTNERSHIP LLP</td>\n",
" <td> NaN</td>\n",
" <td> 10TH FLOOR MAPLE HOUSE</td>\n",
" <td> HIGH STREET</td>\n",
" <td> POTTERS BAR</td>\n",
" <td> HERTFORDSHIRE</td>\n",
" <td> NaN</td>\n",
" <td> EN6 5BA</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> !NFERNO LTD.</td>\n",
" <td> 04753368</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> FIRST FLOOR THAVIES INN HOUSE 3-4</td>\n",
" <td> HOLBORN CIRCUS</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> EC1N 2HA</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> !NSPIRED LTD</td>\n",
" <td> SC421617</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 12 BON ACCORD SQUARE</td>\n",
" <td> NaN</td>\n",
" <td> ABERDEEN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> AB11 6DJ</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> !NVERTD DESIGNS LIMITED</td>\n",
" <td> 09152972</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 32 RECTORY ROAD</td>\n",
" <td> NaN</td>\n",
" <td> STEPPINGLEY</td>\n",
" <td> NaN</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> MK45 5AT</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> !OBAC LIMITED</td>\n",
" <td> FC031362</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 1ST AND 2ND FLOORS ELIZABETH HOUSE</td>\n",
" <td> LES RUETIES BRAYES</td>\n",
" <td> ST PETER PORT</td>\n",
" <td> GY1 1EW</td>\n",
" <td> GUERNSEY</td>\n",
" <td> NaN</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> !OBAC UK LIMITED</td>\n",
" <td> 07687209</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> ENDEAVOUR HOUSE</td>\n",
" <td> COOPERS END ROAD</td>\n",
" <td> STANSTED AIRPORT</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> CM24 1SJ</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> !YOZO FASS LIMITED</td>\n",
" <td> 02714021</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 1 VERONICA HOUSE</td>\n",
" <td> WICKHAM ROAD</td>\n",
" <td> BROCKLEY</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> SE4 1NQ</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> \"1 C O LIMITED\"</td>\n",
" <td> 03811958</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> FANE COURT</td>\n",
" <td> GREEN ROAD SHIPBOURNE</td>\n",
" <td> TONBRIDGE</td>\n",
" <td> KENT</td>\n",
" <td> NaN</td>\n",
" <td> TN11 9PL</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10 </th>\n",
" <td> \"2 ECOUTE\" LIMITED</td>\n",
" <td> 06439541</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 38 PAXTON GARDENS</td>\n",
" <td> WOKING</td>\n",
" <td> SURREY</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> GU21 5TS</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11 </th>\n",
" <td> \"243 RUGBY ROAD MANAGEMENT COMPANY LIMITED\"</td>\n",
" <td> 05914136</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 45 SUMMER ROW</td>\n",
" <td> NaN</td>\n",
" <td> BIRMINGHAM</td>\n",
" <td> WEST MIDLANDS</td>\n",
" <td> NaN</td>\n",
" <td> B3 1JJ</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12 </th>\n",
" <td> \"309\" WEST END LANE MANAGEMENT LIMITED</td>\n",
" <td> 02943302</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 7 GRANARD BUSINESS CENTRE</td>\n",
" <td> BUNNS LANE</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> ENGLAND</td>\n",
" <td> NW7 2DQ</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13 </th>\n",
" <td> \"A LITTLE BIT DIFFERENT\" LIMITED</td>\n",
" <td> 08878402</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 1 LOCKE STREET</td>\n",
" <td> NaN</td>\n",
" <td> BARNSLEY</td>\n",
" <td> SOUTH YORKSHIRE</td>\n",
" <td> NaN</td>\n",
" <td> S70 6ND</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14 </th>\n",
" <td> \"A TASTE OF TUSCANY\" LTD</td>\n",
" <td> 06473722</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 5 ELSTREE GATE, ELSTREE WAY</td>\n",
" <td> BOREHAMWOOD</td>\n",
" <td> HERTFORDSHIRE</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> WD6 1JD</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15 </th>\n",
" <td> \"A\" ADVISORY LLP</td>\n",
" <td> OC355684</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 3RD FLOOR</td>\n",
" <td> 5 LLOYD'S AVENUE</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> EC3N 3AE</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16 </th>\n",
" <td> \"A\" CERAMICS LIMITED</td>\n",
" <td> 04494986</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 132 MANCHESTER ROAD</td>\n",
" <td> SHAW</td>\n",
" <td> OLDHAM</td>\n",
" <td> LANCASHIRE</td>\n",
" <td> NaN</td>\n",
" <td> OL2 7DD</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17 </th>\n",
" <td> \"A\" CONCEPT LIMITED</td>\n",
" <td> 02537158</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 31 OVAL ROAD</td>\n",
" <td> CAMDEN TOWN</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NW1 7EA</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18 </th>\n",
" <td> \"A\" TRAFFIC SOLUTION LTD</td>\n",
" <td> 05852396</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> THE OLD EXCHANGE</td>\n",
" <td> 234 SOUTHCHURCH ROAD</td>\n",
" <td> SOUTHEND-ON-SEA</td>\n",
" <td> ESSEX</td>\n",
" <td> NaN</td>\n",
" <td> SS1 2EG</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19 </th>\n",
" <td> \"A-Z-ENGINEERS LONDON\" LTD</td>\n",
" <td> 09025577</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 203 LONDON ROAD 203 LONDON ROAD</td>\n",
" <td> FLAT 4</td>\n",
" <td> LONDON,MITCHAM</td>\n",
" <td> NaN</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> CR4 2JD</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20 </th>\n",
" <td> \"A.B.J.Z DRIVER HIRE \" LTD</td>\n",
" <td> 09151627</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 32A WOLSDON STREET</td>\n",
" <td> WOLSDON STREET</td>\n",
" <td> PLYMOUTH</td>\n",
" <td> NaN</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> PL1 5EH</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21 </th>\n",
" <td> \"A.K.WELDING SERVICE\" LTD</td>\n",
" <td> 08981806</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 15 TEAGUES CRESCENT</td>\n",
" <td> TRENCH</td>\n",
" <td> TELFORD</td>\n",
" <td> SHROPSHIRE</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> TF2 6RQ</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22 </th>\n",
" <td> \"AA LOGISTIKS\" LIMITED</td>\n",
" <td> 09478701</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 25 CULVERT ROAD</td>\n",
" <td> NaN</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> N15 5HF</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23 </th>\n",
" <td> \"AGAD\" ADVERTISING CORPORATION LTD.</td>\n",
" <td> 09465805</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 131 OATLANDS DRIVE</td>\n",
" <td> NaN</td>\n",
" <td> SLOUGH</td>\n",
" <td> NaN</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> SL1 3HN</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 </th>\n",
" <td> \"AJA PROPERTY DEVELOPMENT LTD\"</td>\n",
" <td> 05651002</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 10 WOODBERRY AVENUE</td>\n",
" <td> HARROW</td>\n",
" <td> MIDDLESEX</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> HA2 6AU</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25 </th>\n",
" <td> \"ALL WRAPPED UP\" EVENTS MANAGEMENT LTD</td>\n",
" <td> SC313991</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 5 MELVILLE CRESCENT</td>\n",
" <td> NaN</td>\n",
" <td> EDINBURGH</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> EH3 7JA</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26 </th>\n",
" <td> \"ALTAI CASHMERE\" LLC</td>\n",
" <td> FC027187</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> BUILDING LEFT TO THE \"TUUL\"</td>\n",
" <td> DRY CLEANING</td>\n",
" <td> KHAN-UUL DISTRICT</td>\n",
" <td> ULAANBAATAR, MONGOLIA</td>\n",
" <td> MONGOLIA</td>\n",
" <td> NaN</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27 </th>\n",
" <td> \"AND BREATHE\" LTD</td>\n",
" <td> 09008930</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 20 HORNCASTLE ROAD</td>\n",
" <td> NaN</td>\n",
" <td> BOSTON</td>\n",
" <td> NaN</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> PE21 9BU</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28 </th>\n",
" <td> \"APOLLO'S BAR\" LIMITED</td>\n",
" <td> 09044937</td>\n",
" <td> C/O</td>\n",
" <td> NaN</td>\n",
" <td> 4 TAKELY RIDE</td>\n",
" <td> NaN</td>\n",
" <td> BASILDON</td>\n",
" <td> ESSEX</td>\n",
" <td> ENGLAND</td>\n",
" <td> SS16 5BE</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29 </th>\n",
" <td> \"ARTHUR BALFOUR\",CONSERVATIVE WORKING MEN'S CL...</td>\n",
" <td> IP10067R</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211048</th>\n",
" <td> \\ COMPANY LTD</td>\n",
" <td> 05060411</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> WWW.BUY-THIS-COMPANY-NAME.COM</td>\n",
" <td> SUITE B, 29 HARLEY STREET</td>\n",
" <td> LONDON</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> W1G 9QR</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211049</th>\n",
" <td> \\TOOLTRAC\\ LTD</td>\n",
" <td> 06465593</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> DEPT 302 43 OWSTON ROAD</td>\n",
" <td> CARCROFT</td>\n",
" <td> DONCASTER</td>\n",
" <td> SOUTH YORKSHIRE</td>\n",
" <td> NaN</td>\n",
" <td> DN6 8DA</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211050</th>\n",
" <td> ]PERFORMANCE S P A C E [ STUDIOS C.I.C.</td>\n",
" <td> 09138062</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> SWAN WHARF</td>\n",
" <td> 60A DACE ROAD</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> E3 2NQ</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211051</th>\n",
" <td> ]PS[ STUDIOS LTD</td>\n",
" <td> 08046314</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> UNIT 7 ENCLAVE</td>\n",
" <td> 50 RESOLUTION WAY</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> SE8 4AL</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211052</th>\n",
" <td> _XURBIA_XENDLESS LIMITED</td>\n",
" <td> 06312240</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 41 GREAT PORTLAND STREET</td>\n",
" <td> NaN</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> W1W 7LA</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211053</th>\n",
" <td> `AT YOUR SERVICE` (WALES) LIMITED</td>\n",
" <td> 05658675</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> UNIT H/I LONLAS VILLAGE WORKSHOPS</td>\n",
" <td> LONLAS BUSINESS PARK</td>\n",
" <td> SKEWEN</td>\n",
" <td> NEATH</td>\n",
" <td> NaN</td>\n",
" <td> SA10 6RR</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211054</th>\n",
" <td> `DESIGNBLU´ LIMITED</td>\n",
" <td> 06904076</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> CORNERSTONE HOUSE MIDLAND WAY</td>\n",
" <td> THORNBURY</td>\n",
" <td> BRISTOL</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> BS35 2BS</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211055</th>\n",
" <td> `THE JERUSALEM ARTS TRUST FOR WALES` - CANOLFA...</td>\n",
" <td> 06585960</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> SALEM BAPTIST CHAPEL</td>\n",
" <td> BELL BANK</td>\n",
" <td> HAY-ON-WYE</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> HR3 5AE</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211056</th>\n",
" <td> {BOBA HEADS} LTD</td>\n",
" <td> 09470943</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 10 BOWBROOK GRANGE</td>\n",
" <td> NaN</td>\n",
" <td> SHREWSBURY</td>\n",
" <td> SHROPSHIRE</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> SY3 8XT</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211057</th>\n",
" <td> £ LIMITED</td>\n",
" <td> 09471545</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> DEPT 2 43 OWSTON ROAD</td>\n",
" <td> CARCROFT</td>\n",
" <td> DONCASTER</td>\n",
" <td> SOUTH YORKSHIRE</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> DN6 8DA</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211058</th>\n",
" <td> £1 BAGUETTES & PIES LIMITED</td>\n",
" <td> 08458719</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> UNIT 62 LONGTON EXCHANGE</td>\n",
" <td> LONGTON</td>\n",
" <td> STOKE-ON-TRENT</td>\n",
" <td> STAFFS</td>\n",
" <td> NaN</td>\n",
" <td> ST3 2JA</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211059</th>\n",
" <td> £1 SANDWICH COMPANY LIMITED</td>\n",
" <td> 09254601</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 4 PEMBROKE COURT</td>\n",
" <td> NaN</td>\n",
" <td> NEWCASTLE UPON TYNE</td>\n",
" <td> NaN</td>\n",
" <td> ENGLAND</td>\n",
" <td> NE3 2YT</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211060</th>\n",
" <td> £1 STORE LTD</td>\n",
" <td> 07489976</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 26 PARVILLS</td>\n",
" <td> PARVILLS</td>\n",
" <td> WALTHAM ABBEY</td>\n",
" <td> ESSEX</td>\n",
" <td> ENGLAND</td>\n",
" <td> EN9 1QG</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211061</th>\n",
" <td> £10 RECORDS LIMITED</td>\n",
" <td> SC422612</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 4 SAUCHENHALL PATH</td>\n",
" <td> MOODIESBURN</td>\n",
" <td> GLASGOW</td>\n",
" <td> NORTH LANARKSHIRE</td>\n",
" <td> NaN</td>\n",
" <td> G69 0NS</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211062</th>\n",
" <td> £1K CARS LTD</td>\n",
" <td> 07607359</td>\n",
" <td> PRUDHOE AUTOCARE</td>\n",
" <td> NaN</td>\n",
" <td> THE OLD CO-OP BUILDINGS</td>\n",
" <td> TYNE VIEW TERRACE</td>\n",
" <td> PRUDHOE</td>\n",
" <td> NORTHUMBERLAND</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> NE42 5PX</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211063</th>\n",
" <td> £ASY AS 123 LIMITED</td>\n",
" <td> 09197224</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 5 DESPARD ROAD</td>\n",
" <td> EASTERN GREEN</td>\n",
" <td> COVENTRY</td>\n",
" <td> WEST MIDLANDS</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> CV5 7DG</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211064</th>\n",
" <td> £CHESHAM ESTATES LIMITED</td>\n",
" <td> 04968129</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> DEVONSHIRE HOUSE</td>\n",
" <td> 60 GOSWELL ROAD</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> EC1M 7AD</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211065</th>\n",
" <td> £DUCASHION LTD</td>\n",
" <td> 07625283</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 3 MAYHILL ROAD</td>\n",
" <td> NaN</td>\n",
" <td> GREENWICH</td>\n",
" <td> GREATER LONDON</td>\n",
" <td> NaN</td>\n",
" <td> SE7 7JG</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211066</th>\n",
" <td> £££ LTD</td>\n",
" <td> 08344093</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> METRO HOUSE 57 PEPPER ROAD</td>\n",
" <td> HUNSLET</td>\n",
" <td> LEEDS</td>\n",
" <td> YORKSHIRE</td>\n",
" <td> NaN</td>\n",
" <td> LS10 2RU</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211067</th>\n",
" <td> £££ SAVE TYRES LTD</td>\n",
" <td> 09475149</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> UNIT 1</td>\n",
" <td> BALME ROAD</td>\n",
" <td> CLECKHEATON</td>\n",
" <td> WEST YORKSHIRE</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> BD19 4EW</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211068</th>\n",
" <td> ¥IVA LTD</td>\n",
" <td> 09460559</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 6 SHIREHALL PARK</td>\n",
" <td> NaN</td>\n",
" <td> HENDON</td>\n",
" <td> NaN</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> NW4 2QL</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211069</th>\n",
" <td> ÁLPHA MASCHIO LTD</td>\n",
" <td> 09468796</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 28 LEONARD ROAD</td>\n",
" <td> FOREST GATE</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> ENGLAND</td>\n",
" <td> E7 0DB</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211070</th>\n",
" <td> ÉLAN INTERNATIONAL LTD</td>\n",
" <td> 09554305</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 71-75 SHELTON STREET</td>\n",
" <td> NaN</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> WC2H 9JQ</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211071</th>\n",
" <td> ÉLAN PROJECTS CONSULTANCY LTD.</td>\n",
" <td> 09481566</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 31 BARDOLPH STREET</td>\n",
" <td> NaN</td>\n",
" <td> LEICESTER</td>\n",
" <td> NaN</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> LE4 6EH</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211072</th>\n",
" <td> ÉTOILE CONSULTANCY LTD</td>\n",
" <td> 09498296</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 414-416 BLACKPOOL ROAD</td>\n",
" <td> ASHTON-ON-RIBBLE</td>\n",
" <td> PRESTON</td>\n",
" <td> LANCS</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> PR2 2DX</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211073</th>\n",
" <td> Ó MUIRIGH SOLICITORS LIMITED</td>\n",
" <td> NI629669</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 24-26 SPRINGFIELD ROAD</td>\n",
" <td> NaN</td>\n",
" <td> BELFAST</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> BT12 7AG</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211074</th>\n",
" <td> ‘OW ‘BOUT ME? LIMITED</td>\n",
" <td> 09495226</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 5 DUCKETTS WHARF</td>\n",
" <td> SOUTH STREET</td>\n",
" <td> BISHOP'S STORTFORD</td>\n",
" <td> HERTFORDSHIRE</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> CM23 3AR</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211075</th>\n",
" <td> “SAIL IN GREECE ADVENTURES” LTD</td>\n",
" <td> 09511422</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> INTERNATIONAL HOUSE</td>\n",
" <td> 24 HOLBORN VIADUCT</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> ENGLAND</td>\n",
" <td> EC1A 2BN</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211076</th>\n",
" <td> €URO IMPORTS LIMITED</td>\n",
" <td> 08182582</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> PONDSIDE MILL LANE</td>\n",
" <td> INSKIP</td>\n",
" <td> PRESTON</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> PR4 0TP</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211077</th>\n",
" <td> €UROTECH LTD</td>\n",
" <td> 06642625</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> REED HOUSE 16 HIGH STREET</td>\n",
" <td> WEST WRATTING</td>\n",
" <td> CAMBRIDGE</td>\n",
" <td> CAMBRIDGESHIRE</td>\n",
" <td> NaN</td>\n",
" <td> CB21 5LU</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3611077 rows × 53 columns</p>\n",
"</div>"
],
"text/plain": [
" CompanyName CompanyNumber \\\n",
"0 ! LTD 08209948 \n",
"1 !BIG IMPACT GRAPHICS LIMITED 07382019 \n",
"2 !K7 TOURING LIMITED 08937297 \n",
"3 !NFERNO LTD. 04753368 \n",
"4 !NSPIRED LTD SC421617 \n",
"5 !NVERTD DESIGNS LIMITED 09152972 \n",
"6 !OBAC LIMITED FC031362 \n",
"7 !OBAC UK LIMITED 07687209 \n",
"8 !YOZO FASS LIMITED 02714021 \n",
"9 \"1 C O LIMITED\" 03811958 \n",
"10 \"2 ECOUTE\" LIMITED 06439541 \n",
"11 \"243 RUGBY ROAD MANAGEMENT COMPANY LIMITED\" 05914136 \n",
"12 \"309\" WEST END LANE MANAGEMENT LIMITED 02943302 \n",
"13 \"A LITTLE BIT DIFFERENT\" LIMITED 08878402 \n",
"14 \"A TASTE OF TUSCANY\" LTD 06473722 \n",
"15 \"A\" ADVISORY LLP OC355684 \n",
"16 \"A\" CERAMICS LIMITED 04494986 \n",
"17 \"A\" CONCEPT LIMITED 02537158 \n",
"18 \"A\" TRAFFIC SOLUTION LTD 05852396 \n",
"19 \"A-Z-ENGINEERS LONDON\" LTD 09025577 \n",
"20 \"A.B.J.Z DRIVER HIRE \" LTD 09151627 \n",
"21 \"A.K.WELDING SERVICE\" LTD 08981806 \n",
"22 \"AA LOGISTIKS\" LIMITED 09478701 \n",
"23 \"AGAD\" ADVERTISING CORPORATION LTD. 09465805 \n",
"24 \"AJA PROPERTY DEVELOPMENT LTD\" 05651002 \n",
"25 \"ALL WRAPPED UP\" EVENTS MANAGEMENT LTD SC313991 \n",
"26 \"ALTAI CASHMERE\" LLC FC027187 \n",
"27 \"AND BREATHE\" LTD 09008930 \n",
"28 \"APOLLO'S BAR\" LIMITED 09044937 \n",
"29 \"ARTHUR BALFOUR\",CONSERVATIVE WORKING MEN'S CL... IP10067R \n",
"... ... ... \n",
"211048 \\ COMPANY LTD 05060411 \n",
"211049 \\TOOLTRAC\\ LTD 06465593 \n",
"211050 ]PERFORMANCE S P A C E [ STUDIOS C.I.C. 09138062 \n",
"211051 ]PS[ STUDIOS LTD 08046314 \n",
"211052 _XURBIA_XENDLESS LIMITED 06312240 \n",
"211053 `AT YOUR SERVICE` (WALES) LIMITED 05658675 \n",
"211054 `DESIGNBLU´ LIMITED 06904076 \n",
"211055 `THE JERUSALEM ARTS TRUST FOR WALES` - CANOLFA... 06585960 \n",
"211056 {BOBA HEADS} LTD 09470943 \n",
"211057 £ LIMITED 09471545 \n",
"211058 £1 BAGUETTES & PIES LIMITED 08458719 \n",
"211059 £1 SANDWICH COMPANY LIMITED 09254601 \n",
"211060 £1 STORE LTD 07489976 \n",
"211061 £10 RECORDS LIMITED SC422612 \n",
"211062 £1K CARS LTD 07607359 \n",
"211063 £ASY AS 123 LIMITED 09197224 \n",
"211064 £CHESHAM ESTATES LIMITED 04968129 \n",
"211065 £DUCASHION LTD 07625283 \n",
"211066 £££ LTD 08344093 \n",
"211067 £££ SAVE TYRES LTD 09475149 \n",
"211068 ¥IVA LTD 09460559 \n",
"211069 ÁLPHA MASCHIO LTD 09468796 \n",
"211070 ÉLAN INTERNATIONAL LTD 09554305 \n",
"211071 ÉLAN PROJECTS CONSULTANCY LTD. 09481566 \n",
"211072 ÉTOILE CONSULTANCY LTD 09498296 \n",
"211073 Ó MUIRIGH SOLICITORS LIMITED NI629669 \n",
"211074 ‘OW ‘BOUT ME? LIMITED 09495226 \n",
"211075 “SAIL IN GREECE ADVENTURES” LTD 09511422 \n",
"211076 €URO IMPORTS LIMITED 08182582 \n",
"211077 €UROTECH LTD 06642625 \n",
"\n",
" RegAddress_CareOf RegAddress_POBox \\\n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 C/O THE GREENE PARTNERSHIP LLP NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"5 NaN NaN \n",
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"16 NaN NaN \n",
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"25 NaN NaN \n",
"26 NaN NaN \n",
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"28 C/O NaN \n",
"29 NaN NaN \n",
"... ... ... \n",
"211048 NaN NaN \n",
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"\n",
" RegAddress_AddressLine1 RegAddress_AddressLine2 \\\n",
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"1 335 ROSDEN HOUSE 372 OLD STREET \n",
"2 10TH FLOOR MAPLE HOUSE HIGH STREET \n",
"3 FIRST FLOOR THAVIES INN HOUSE 3-4 HOLBORN CIRCUS \n",
"4 12 BON ACCORD SQUARE NaN \n",
"5 32 RECTORY ROAD NaN \n",
"6 1ST AND 2ND FLOORS ELIZABETH HOUSE LES RUETIES BRAYES \n",
"7 ENDEAVOUR HOUSE COOPERS END ROAD \n",
"8 1 VERONICA HOUSE WICKHAM ROAD \n",
"9 FANE COURT GREEN ROAD SHIPBOURNE \n",
"10 38 PAXTON GARDENS WOKING \n",
"11 45 SUMMER ROW NaN \n",
"12 7 GRANARD BUSINESS CENTRE BUNNS LANE \n",
"13 1 LOCKE STREET NaN \n",
"14 5 ELSTREE GATE, ELSTREE WAY BOREHAMWOOD \n",
"15 3RD FLOOR 5 LLOYD'S AVENUE \n",
"16 132 MANCHESTER ROAD SHAW \n",
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"18 THE OLD EXCHANGE 234 SOUTHCHURCH ROAD \n",
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"20 32A WOLSDON STREET WOLSDON STREET \n",
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"29 NaN NaN \n",
"... ... ... \n",
"211048 WWW.BUY-THIS-COMPANY-NAME.COM SUITE B, 29 HARLEY STREET \n",
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"211050 SWAN WHARF 60A DACE ROAD \n",
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"211059 4 PEMBROKE COURT NaN \n",
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"211074 5 DUCKETTS WHARF SOUTH STREET \n",
"211075 INTERNATIONAL HOUSE 24 HOLBORN VIADUCT \n",
"211076 PONDSIDE MILL LANE INSKIP \n",
"211077 REED HOUSE 16 HIGH STREET WEST WRATTING \n",
"\n",
" RegAddress_PostTown RegAddress_County RegAddress_Country \\\n",
"0 LEEDS YORKSHIRE NaN \n",
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"12 LONDON NaN ENGLAND \n",
"13 BARNSLEY SOUTH YORKSHIRE NaN \n",
"14 HERTFORDSHIRE NaN NaN \n",
"15 LONDON NaN NaN \n",
"16 OLDHAM LANCASHIRE NaN \n",
"17 LONDON NaN NaN \n",
"18 SOUTHEND-ON-SEA ESSEX NaN \n",
"19 LONDON,MITCHAM NaN UNITED KINGDOM \n",
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"21 TELFORD SHROPSHIRE UNITED KINGDOM \n",
"22 LONDON NaN UNITED KINGDOM \n",
"23 SLOUGH NaN UNITED KINGDOM \n",
"24 MIDDLESEX NaN NaN \n",
"25 EDINBURGH NaN NaN \n",
"26 KHAN-UUL DISTRICT ULAANBAATAR, MONGOLIA MONGOLIA \n",
"27 BOSTON NaN UNITED KINGDOM \n",
"28 BASILDON ESSEX ENGLAND \n",
"29 NaN NaN NaN \n",
"... ... ... ... \n",
"211048 LONDON LONDON NaN \n",
"211049 DONCASTER SOUTH YORKSHIRE NaN \n",
"211050 LONDON NaN NaN \n",
"211051 LONDON NaN UNITED KINGDOM \n",
"211052 LONDON NaN NaN \n",
"211053 SKEWEN NEATH NaN \n",
"211054 BRISTOL NaN NaN \n",
"211055 HAY-ON-WYE NaN NaN \n",
"211056 SHREWSBURY SHROPSHIRE UNITED KINGDOM \n",
"211057 DONCASTER SOUTH YORKSHIRE UNITED KINGDOM \n",
"211058 STOKE-ON-TRENT STAFFS NaN \n",
"211059 NEWCASTLE UPON TYNE NaN ENGLAND \n",
"211060 WALTHAM ABBEY ESSEX ENGLAND \n",
"211061 GLASGOW NORTH LANARKSHIRE NaN \n",
"211062 PRUDHOE NORTHUMBERLAND UNITED KINGDOM \n",
"211063 COVENTRY WEST MIDLANDS UNITED KINGDOM \n",
"211064 LONDON NaN NaN \n",
"211065 GREENWICH GREATER LONDON NaN \n",
"211066 LEEDS YORKSHIRE NaN \n",
"211067 CLECKHEATON WEST YORKSHIRE UNITED KINGDOM \n",
"211068 HENDON NaN UNITED KINGDOM \n",
"211069 LONDON NaN ENGLAND \n",
"211070 LONDON NaN UNITED KINGDOM \n",
"211071 LEICESTER NaN UNITED KINGDOM \n",
"211072 PRESTON LANCS UNITED KINGDOM \n",
"211073 BELFAST NaN NaN \n",
"211074 BISHOP'S STORTFORD HERTFORDSHIRE UNITED KINGDOM \n",
"211075 LONDON NaN ENGLAND \n",
"211076 PRESTON NaN NaN \n",
"211077 CAMBRIDGE CAMBRIDGESHIRE NaN \n",
"\n",
" RegAddress_PostCode ... PreviousName_6_CONDATE \\\n",
"0 LS10 2RU ... NaN \n",
"1 EC1V 9AV ... NaN \n",
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"11 B3 1JJ ... NaN \n",
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"14 WD6 1JD ... NaN \n",
"15 EC3N 3AE ... NaN \n",
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"20 PL1 5EH ... NaN \n",
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"... ... ... ... \n",
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"211076 PR4 0TP ... NaN \n",
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"\n",
" PreviousName_6_CompanyName PreviousName_7_CONDATE \\\n",
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"... ... ... \n",
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" PreviousName_7_CompanyName PreviousName_8_CONDATE \\\n",
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"29 NaN NaN \n",
"... ... ... \n",
"211048 NaN NaN \n",
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"\n",
" PreviousName_8_CompanyName PreviousName_9_CONDATE \\\n",
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"27 NaN NaN \n",
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"29 NaN NaN \n",
"... ... ... \n",
"211048 NaN NaN \n",
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"211050 NaN NaN \n",
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"211071 NaN NaN \n",
"211072 NaN NaN \n",
"211073 NaN NaN \n",
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"211075 NaN NaN \n",
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"211077 NaN NaN \n",
"\n",
" PreviousName_9_CompanyName PreviousName_10_CONDATE \\\n",
"0 NaN NaN \n",
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"22 NaN NaN \n",
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"24 NaN NaN \n",
"25 NaN NaN \n",
"26 NaN NaN \n",
"27 NaN NaN \n",
"28 NaN NaN \n",
"29 NaN NaN \n",
"... ... ... \n",
"211048 NaN NaN \n",
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"211050 NaN NaN \n",
"211051 NaN NaN \n",
"211052 NaN NaN \n",
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"211059 NaN NaN \n",
"211060 NaN NaN \n",
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"211062 NaN NaN \n",
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"211065 NaN NaN \n",
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"\n",
" PreviousName_10_CompanyName \n",
"0 NaN \n",
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"24 NaN \n",
"25 NaN \n",
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"27 NaN \n",
"28 NaN \n",
"29 NaN \n",
"... ... \n",
"211048 NaN \n",
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"211050 NaN \n",
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"211053 NaN \n",
"211054 NaN \n",
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"211063 NaN \n",
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"211065 NaN \n",
"211066 NaN \n",
"211067 NaN \n",
"211068 NaN \n",
"211069 NaN \n",
"211070 NaN \n",
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"211075 NaN \n",
"211076 NaN \n",
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"\n",
"[3611077 rows x 53 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rootdir=\"/home/ilan/Desktop/GI_interview_project\"\n",
"datadir=\"/home/ilan/Desktop/GI_interview_project/company_data\"\n",
"\n",
"os.chdir(datadir)\n",
"\n",
"pklfile=\"data.pkl\"\n",
"#hffile=\"data.h5\"\n",
"folderpath=os.path.join(datadir,pklfile)\n",
"#folderpath=os.path.join(rootdir,hffile)\n",
"if (os.path.exists(folderpath)==True):\n",
" print(\"Pickle file containing data found. Loading it...\")\n",
" data=pickle.load(open(folderpath,'r'))\n",
" #data = tables.open_file(folderpath, driver=\"H5FD_CORE\")\n",
"else:\n",
" print(\"Reading in csv file and creating pickle...\")\n",
" filenames =['BasicCompanyData-2015-05-01-part1_5.csv', 'BasicCompanyData-2015-05-01-part2_5.csv',\\\n",
" 'BasicCompanyData-2015-05-01-part3_5.csv', 'BasicCompanyData-2015-05-01-part4_5.csv',\\\n",
" 'BasicCompanyData-2015-05-01-part5_5.csv']\n",
" list_ = []\n",
"# for i,j in enumerate(filenames):\n",
"# if (i == 0):\n",
"# data = pan.read_csv(j, delimiter=',',index_col=False)\n",
"# list_.append(data)\n",
"# print data.head(1)\n",
"# elif (i > 0):\n",
"# data = pan.read_csv(j, delimiter=',',skiprows=1,index_col=False)\n",
"# list_.append(data)\n",
"# print data.head(1)\n",
"# data = pan.concat(list_)\n",
" for i in filenames:\n",
" data = pan.read_csv(i, delimiter=',',index_col=False)\n",
" list_.append(data)\n",
" #print data.head(1)\n",
" data = pan.concat(list_)\n",
" # Remove dots and whitespaces from column titles\n",
" colnames = [str(i).replace('.','_').strip() for i in list(data.columns.values)]\n",
" data.columns=colnames\n",
" # Remove period in the label column\n",
" #data['Label']=data['Label'].apply(lambda x: x.strip('.'))\n",
" with open(pklfile,'wb') as output:\n",
" pickle.dump(data, output, pickle.HIGHEST_PROTOCOL)\n",
"\n",
"os.chdir(rootdir)\n",
" \n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To get a feel for the data set we do some basic data set exploration."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index([u'CompanyName', u'CompanyNumber', u'RegAddress_CareOf', u'RegAddress_POBox', u'RegAddress_AddressLine1', u'RegAddress_AddressLine2', u'RegAddress_PostTown', u'RegAddress_County', u'RegAddress_Country', u'RegAddress_PostCode', u'CompanyCategory', u'CompanyStatus', u'CountryOfOrigin', u'DissolutionDate', u'IncorporationDate', u'Accounts_AccountRefDay', u'Accounts_AccountRefMonth', u'Accounts_NextDueDate', u'Accounts_LastMadeUpDate', u'Accounts_AccountCategory', u'Returns_NextDueDate', u'Returns_LastMadeUpDate', u'Mortgages_NumMortCharges', u'Mortgages_NumMortOutstanding', u'Mortgages_NumMortPartSatisfied', u'Mortgages_NumMortSatisfied', u'SICCode_SicText_1', u'SICCode_SicText_2', u'SICCode_SicText_3', u'SICCode_SicText_4', u'LimitedPartnerships_NumGenPartners', u'LimitedPartnerships_NumLimPartners', u'URI', u'PreviousName_1_CONDATE', u'PreviousName_1_CompanyName', u'PreviousName_2_CONDATE', u'PreviousName_2_CompanyName', u'PreviousName_3_CONDATE', u'PreviousName_3_CompanyName', u'PreviousName_4_CONDATE', u'PreviousName_4_CompanyName', u'PreviousName_5_CONDATE', u'PreviousName_5_CompanyName', u'PreviousName_6_CONDATE', u'PreviousName_6_CompanyName', u'PreviousName_7_CONDATE', u'PreviousName_7_CompanyName', u'PreviousName_8_CONDATE', u'PreviousName_8_CompanyName', u'PreviousName_9_CONDATE', u'PreviousName_9_CompanyName', u'PreviousName_10_CONDATE', u'PreviousName_10_CompanyName'], dtype='object')\n",
"191387081\n"
]
},
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DissolutionDate</th>\n",
" <th>Accounts_AccountRefDay</th>\n",
" <th>Accounts_AccountRefMonth</th>\n",
" <th>Mortgages_NumMortCharges</th>\n",
" <th>Mortgages_NumMortOutstanding</th>\n",
" <th>Mortgages_NumMortPartSatisfied</th>\n",
" <th>Mortgages_NumMortSatisfied</th>\n",
" <th>LimitedPartnerships_NumGenPartners</th>\n",
" <th>LimitedPartnerships_NumLimPartners</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td> 0</td>\n",
" <td> 3565670.000000</td>\n",
" <td> 3565670.000000</td>\n",
" <td> 3611077.000000</td>\n",
" <td> 3611077.000000</td>\n",
" <td> 3611077.000000</td>\n",
" <td> 3611077.000000</td>\n",
" <td> 3611077.000000</td>\n",
" <td> 3611077.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>NaN</td>\n",
" <td> 30.261290</td>\n",
" <td> 6.351142</td>\n",
" <td> 0.715750</td>\n",
" <td> 0.446035</td>\n",
" <td> 0.000648</td>\n",
" <td> 0.268702</td>\n",
" <td> 0.009763</td>\n",
" <td> 0.032917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>NaN</td>\n",
" <td> 2.639808</td>\n",
" <td> 3.621669</td>\n",
" <td> 9.078691</td>\n",
" <td> 7.347536</td>\n",
" <td> 0.072631</td>\n",
" <td> 4.631352</td>\n",
" <td> 0.142520</td>\n",
" <td> 1.336482</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>NaN</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> -5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>NaN</td>\n",
" <td> 30.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</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>NaN</td>\n",
" <td> 31.000000</td>\n",
" <td> 6.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</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>NaN</td>\n",
" <td> 31.000000</td>\n",
" <td> 10.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>NaN</td>\n",
" <td> 31.000000</td>\n",
" <td> 12.000000</td>\n",
" <td> 6121.000000</td>\n",
" <td> 6121.000000</td>\n",
" <td> 81.000000</td>\n",
" <td> 5720.000000</td>\n",
" <td> 110.000000</td>\n",
" <td> 823.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DissolutionDate Accounts_AccountRefDay Accounts_AccountRefMonth \\\n",
"count 0 3565670.000000 3565670.000000 \n",
"mean NaN 30.261290 6.351142 \n",
"std NaN 2.639808 3.621669 \n",
"min NaN 1.000000 1.000000 \n",
"25% NaN 30.000000 3.000000 \n",
"50% NaN 31.000000 6.000000 \n",
"75% NaN 31.000000 10.000000 \n",
"max NaN 31.000000 12.000000 \n",
"\n",
" Mortgages_NumMortCharges Mortgages_NumMortOutstanding \\\n",
"count 3611077.000000 3611077.000000 \n",
"mean 0.715750 0.446035 \n",
"std 9.078691 7.347536 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 0.000000 \n",
"50% 0.000000 0.000000 \n",
"75% 0.000000 0.000000 \n",
"max 6121.000000 6121.000000 \n",
"\n",
" Mortgages_NumMortPartSatisfied Mortgages_NumMortSatisfied \\\n",
"count 3611077.000000 3611077.000000 \n",
"mean 0.000648 0.268702 \n",
"std 0.072631 4.631352 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 0.000000 \n",
"50% 0.000000 0.000000 \n",
"75% 0.000000 0.000000 \n",
"max 81.000000 5720.000000 \n",
"\n",
" LimitedPartnerships_NumGenPartners LimitedPartnerships_NumLimPartners \n",
"count 3611077.000000 3611077.000000 \n",
"mean 0.009763 0.032917 \n",
"std 0.142520 1.336482 \n",
"min 0.000000 -5.000000 \n",
"25% 0.000000 0.000000 \n",
"50% 0.000000 0.000000 \n",
"75% 0.000000 0.000000 \n",
"max 110.000000 823.000000 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print data.columns\n",
"print data.size\n",
"data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Private Limited Company 3340497\n",
"PRI/LTD BY GUAR/NSC (Private, limited by guarantee, no share capital) 88475\n",
"Limited Liability Partnership 60148\n",
"PRI/LBG/NSC (Private, Limited by guarantee, no share capital, use of 'Limited' exemption) 42215\n",
"Limited Partnership 32777\n",
"Other company type 11724\n",
"Community Interest Company 10724\n",
"Industrial and Provident Society 10108\n",
"Public Limited Company 7507\n",
"Private Unlimited Company 4914\n",
"Royal Charter Company 850\n",
"Investment Company with Variable Capital 547\n",
"Private Unlimited 234\n",
"Registered Society 157\n",
"Investment Company with Variable Capital(Umbrella) 87\n",
"European Public Limited-Liability Company (SE) 43\n",
"Old Public Company 28\n",
"PRIV LTD SECT. 30 (Private limited company, section 30 of the Companies Act) 19\n",
"Other Company Type 12\n",
"Investment Company with Variable Capital (Securities) 11\n",
"dtype: int64\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f29f5f03650>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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RNETwIIqGCB5E0RDBg1oNWQnj119/xfjx47Fs2TK0bt0aycnJMJvNyMvLQ+fOnfHcc885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\n0bywYBAR0bywYBAR0bz8F7USNNO/EgMUAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2a2cf1d2d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# All the labels in the data, and their counts\n",
"categorycounts=data['CompanyCategory'].value_counts()\n",
"print categorycounts\n",
"categorycounts.plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Active 3366544\n",
"Active - Proposal to Strike off 154723\n",
"Liquidation 80033\n",
"In Administration 3467\n",
"Live but Receiver Manager on at least one charge 2390\n",
"Voluntary Arrangement 1649\n",
"ADMINISTRATIVE RECEIVER 1335\n",
"In Administration/Administrative Receiver 356\n",
"RECEIVERSHIP 268\n",
"ADMINISTRATION ORDER 156\n",
"RECEIVER MANAGER / ADMINISTRATIVE RECEIVER 80\n",
"In Administration/Receiver Manager 66\n",
"VOLUNTARY ARRANGEMENT / RECEIVER MANAGER 8\n",
"In Administration/Receivership 1\n",
"VOLUNTARY ARRANGEMENT / ADMINISTRATIVE RECEIVER 1\n",
"dtype: int64\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f2a2cf1d690>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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NUqQyt6N2SCzDMAzzW2B/uxdUV1cjKioKTz/9NCIjIwEAPXr0kNY/88wzGDt2LIDaX/tn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FFQUMCQIUPg4ODA1LDXdSrjelbdFomPj4eRkZG0hyFVunbtiokTJ4IQgtjYWIwfP575LDY2lnXl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\nc96K4FmROwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f29f464a3d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# All the labels in the data, and their counts\n",
"statuscounts=data['CompanyStatus'].value_counts()\n",
"print statuscounts\n",
"statuscounts.plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" CompanyName CompanyNumber RegAddress_CareOf RegAddress_POBox \\\n",
"0 ! LTD 08209948 NaN NaN \n",
"\n",
" RegAddress_AddressLine1 RegAddress_AddressLine2 RegAddress_PostTown \\\n",
"0 METROHOUSE 57 PEPPER ROAD HUNSLET LEEDS \n",
"\n",
" RegAddress_County RegAddress_Country RegAddress_PostCode \\\n",
"0 YORKSHIRE NaN LS10 2RU \n",
"\n",
" ... PreviousName_6_CONDATE \\\n",
"0 ... NaN \n",
"\n",
" PreviousName_6_CompanyName PreviousName_7_CONDATE \\\n",
"0 NaN NaN \n",
"\n",
" PreviousName_7_CompanyName PreviousName_8_CONDATE \\\n",
"0 NaN NaN \n",
"\n",
" PreviousName_8_CompanyName PreviousName_9_CONDATE \\\n",
"0 NaN NaN \n",
"\n",
" PreviousName_9_CompanyName PreviousName_10_CONDATE \\\n",
"0 NaN NaN \n",
"\n",
" PreviousName_10_CompanyName \n",
"0 NaN \n",
"\n",
"[1 rows x 53 columns]\n"
]
}
],
"source": [
"class Mask(object):\n",
" def __init__(self,df,field,match):\n",
" self.df = df\n",
" self.field = field\n",
" self.match = match\n",
" self.function = lambda x, y, z: x.loc[x[y] == z]\n",
" def __call__(self):\n",
" return self.function(self.df,self.field,self.match)\n",
" #return self.df.loc[self.df[self.field] == self.match]\n",
" \n",
"\n",
"#data[data.CompanyName == \"! LTD\"]\n",
"#data.loc[data[\"CompanyName\"] == \"! LTD\"]\n",
"result = Mask(data, \"CompanyName\", \"! LTD\")\n",
"print result()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" CompanyName CompanyNumber RegAddress_CareOf RegAddress_POBox \\\n",
"0 ! LTD 08209948 NaN NaN \n",
"\n",
" RegAddress_AddressLine1 RegAddress_AddressLine2 RegAddress_PostTown \\\n",
"0 METROHOUSE 57 PEPPER ROAD HUNSLET LEEDS \n",
"\n",
" RegAddress_County RegAddress_Country RegAddress_PostCode \\\n",
"0 YORKSHIRE NaN LS10 2RU \n",
"\n",
" ... PreviousName_6_CONDATE \\\n",
"0 ... NaN \n",
"\n",
" PreviousName_6_CompanyName PreviousName_7_CONDATE \\\n",
"0 NaN NaN \n",
"\n",
" PreviousName_7_CompanyName PreviousName_8_CONDATE \\\n",
"0 NaN NaN \n",
"\n",
" PreviousName_8_CompanyName PreviousName_9_CONDATE \\\n",
"0 NaN NaN \n",
"\n",
" PreviousName_9_CompanyName PreviousName_10_CONDATE \\\n",
"0 NaN NaN \n",
"\n",
" PreviousName_10_CompanyName \n",
"0 NaN \n",
"\n",
"[1 rows x 53 columns]\n"
]
}
],
"source": [
"class booleanMask(object):\n",
" def __init__(self,function):\n",
" self.function = function\n",
" #def __and__(self,other):\n",
" # self.function = self.function & other.function\n",
" def __call__(self,df):\n",
" self.df = df\n",
" return map(self.function, [self.df])[0]\n",
" \n",
"company_mask = booleanMask(lambda x: x.CompanyName == \"! LTD\")\n",
"##print company_mask(data)\n",
"print data[company_mask(data)]\n",
"\n",
"\n",
"# MASKS CAN NOW BE COMBINED\n",
"#uk_mask = booleanMask(lambda x: x.RegAddress_Country == \"UNITED KINGDOM\")\n",
"#active_mask = booleanMask(lambda x: x.CompanyStatus == \"Active\")\n",
"#print data[uk_mask(data) & active_mask(data)]\n",
"\n",
"# FOR VALIDATION TO MAKE SURE BOOLEANMASK IS GIVING WHAT WE EXPECT\n",
"#data.loc[(data[\"RegAddress_Country\"] == \"UNITED KINGDOM\") & (data[\"CompanyStatus\"] == \"Active\")]\n",
"#print len(data.loc[(data[\"RegAddress_Country\"] == \"UNITED KINGDOM\") & (data[\"CompanyStatus\"] == \"Active\")])\n",
"#print len(data[uk_mask(data) & active_mask(data)])\n",
"#print map(lambda x: x.CompanyName == \"! LTD\", [data])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CompanyName</th>\n",
" <th>CompanyNumber</th>\n",
" <th>RegAddress_CareOf</th>\n",
" <th>RegAddress_POBox</th>\n",
" <th>RegAddress_AddressLine1</th>\n",
" <th>RegAddress_AddressLine2</th>\n",
" <th>RegAddress_PostTown</th>\n",
" <th>RegAddress_County</th>\n",
" <th>RegAddress_Country</th>\n",
" <th>RegAddress_PostCode</th>\n",
" <th>...</th>\n",
" <th>PreviousName_6_CONDATE</th>\n",
" <th>PreviousName_6_CompanyName</th>\n",
" <th>PreviousName_7_CONDATE</th>\n",
" <th>PreviousName_7_CompanyName</th>\n",
" <th>PreviousName_8_CONDATE</th>\n",
" <th>PreviousName_8_CompanyName</th>\n",
" <th>PreviousName_9_CONDATE</th>\n",
" <th>PreviousName_9_CompanyName</th>\n",
" <th>PreviousName_10_CONDATE</th>\n",
" <th>PreviousName_10_CompanyName</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>175175</th>\n",
" <td> EARO ESTATES LTD</td>\n",
" <td> 09480942</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 4 MELTON ROAD</td>\n",
" <td> NaN</td>\n",
" <td> MANCHESTER</td>\n",
" <td> NaN</td>\n",
" <td> ENGLAND</td>\n",
" <td> M8 4HG</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>207052</th>\n",
" <td> AMERICAN EXPRESSO LIMITED</td>\n",
" <td> 04255922</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> SUITE 100</td>\n",
" <td> THE STUDIO ST NICHOLAS CLOSE</td>\n",
" <td> ELSTREE</td>\n",
" <td> HERTFORDSHIRE</td>\n",
" <td> NaN</td>\n",
" <td> WD6 3EW</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>390136</th>\n",
" <td> SIXTEEN25</td>\n",
" <td> 08230187</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 10 DENMARK STREET</td>\n",
" <td> NaN</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> WC2H 8LS</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38519 </th>\n",
" <td> DATASAFE SERVICES LIMITED</td>\n",
" <td> 01999773</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> UNITED HOUSE</td>\n",
" <td> NORTH ROAD</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> N7 9DP</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>354624</th>\n",
" <td> BALLYNARIS LTD</td>\n",
" <td> NI621115</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 55 MILEBUSH ROAD</td>\n",
" <td> NaN</td>\n",
" <td> DROMORE</td>\n",
" <td> CO. DOWN</td>\n",
" <td> NaN</td>\n",
" <td> BT25 1RU</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>533389</th>\n",
" <td> SUBSEA INSPECTION SW LTD</td>\n",
" <td> 09156040</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> TIMBERLY</td>\n",
" <td> SOUTH STREET</td>\n",
" <td> AXMINSTER</td>\n",
" <td> DEVON</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> EX13 5AD</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>446674</th>\n",
" <td> SOUTHWOOD BUILDING CONTRACTORS LIMITED</td>\n",
" <td> 06389143</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> UNIT 16 ENDEAVOUR BUSINESS PARK</td>\n",
" <td> CROW ARCH LANE</td>\n",
" <td> RINGWOOD</td>\n",
" <td> HAMPSHIRE</td>\n",
" <td> NaN</td>\n",
" <td> BH24 1PN</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>471345</th>\n",
" <td> MISS B CREATIVE LIMITED</td>\n",
" <td> 07248291</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 470 HUCKNALL ROAD</td>\n",
" <td> NaN</td>\n",
" <td> NOTTINGHAM</td>\n",
" <td> NOTTINGHAMSHIRE</td>\n",
" <td> NaN</td>\n",
" <td> NG5 1FX</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>249166</th>\n",
" <td> S D C CONSULTANCY SERVICES LIMITED</td>\n",
" <td> 06767060</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 39 WHITE CLOVER SQUARE</td>\n",
" <td> NaN</td>\n",
" <td> LYMM</td>\n",
" <td> CHESHIRE</td>\n",
" <td> NaN</td>\n",
" <td> WA13 0RX</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>245418</th>\n",
" <td> APPLECORE DEVELOPMENTS LIMITED</td>\n",
" <td> 04183144</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> PORTWAY HILL HOUSE</td>\n",
" <td> PORTWAY HILL</td>\n",
" <td> BATCOMBE</td>\n",
" <td> SOMERSET</td>\n",
" <td> NaN</td>\n",
" <td> BA4 6BR</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>137301</th>\n",
" <td> KMJ PROPERTY (TUNBRIDGE WELLS) LIMITED</td>\n",
" <td> 06915745</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> BUCKINGHAM HOUSE</td>\n",
" <td> MYRTLE LANE</td>\n",
" <td> BILLINGSHURST</td>\n",
" <td> WEST SUSSEX</td>\n",
" <td> NaN</td>\n",
" <td> RH14 9SG</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8488 </th>\n",
" <td> PRC PLUMBING AND HEATING LIMITED</td>\n",
" <td> 08310381</td>\n",
" <td> CERTAX ACCOUNTING</td>\n",
" <td> NaN</td>\n",
" <td> ALCESTER BUSINESS CENTRE</td>\n",
" <td> KINWARTON FARM ROAD</td>\n",
" <td> ALCESTER</td>\n",
" <td> WARWICKSHIRE</td>\n",
" <td> NaN</td>\n",
" <td> B49 6EL</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>834199</th>\n",
" <td> UMSI LIMITED</td>\n",
" <td> FC031028</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> BRANCH REGISTRATION</td>\n",
" <td> REFER TO PARENT REGISTRY</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>332724</th>\n",
" <td> AZA-MAB LTD</td>\n",
" <td> 09439314</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 88 COMMERCIAL WAY</td>\n",
" <td> NaN</td>\n",
" <td> PECKHAM</td>\n",
" <td> NaN</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> SE15 5GG</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>647771</th>\n",
" <td> HEXAGON ASSOCIATES LIMITED</td>\n",
" <td> NI627785</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> UNIT 1</td>\n",
" <td> 22-218 UPPER NEWTOWNARDS ROAD</td>\n",
" <td> BELFAST</td>\n",
" <td> NaN</td>\n",
" <td> NORTHERN IRELAND</td>\n",
" <td> BT4 3ET</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>270759</th>\n",
" <td> ARROW GLOBAL MASSEY LIMITED</td>\n",
" <td> 08612076</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 20-22 BEDFORD ROW</td>\n",
" <td> NaN</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> WC1R 4JS</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>479413</th>\n",
" <td> GILESWOODFORD LIMITED</td>\n",
" <td> 05243089</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> UNITY CHAMBERS</td>\n",
" <td> 34 HIGH EAST STREET</td>\n",
" <td> DORCHESTER</td>\n",
" <td> DOREST</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>658126</th>\n",
" <td> HIGHWOOD HOUSE PUBLISHING LTD</td>\n",
" <td> 05112696</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> HIGHWOOD HOUSE</td>\n",
" <td> WINTERS LANE REDHILL</td>\n",
" <td> BRISTOL</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> BS40 5SH</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51107 </th>\n",
" <td> DAZZITTO PHOTOGRAPHY LIMITED</td>\n",
" <td> 08425973</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 50 ERNEST ROAD</td>\n",
" <td> NaN</td>\n",
" <td> CHATHAM</td>\n",
" <td> KENT</td>\n",
" <td> NaN</td>\n",
" <td> ME4 5PT</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>181148</th>\n",
" <td> EASTHOLME COURT MANAGEMENT LIMITED</td>\n",
" <td> 02198033</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 7 COTTONS MEADOW</td>\n",
" <td> KINGSTONE</td>\n",
" <td> HEREFORDSHIRE</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> HR2 9EW</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>221642</th>\n",
" <td> LIBERATION MEDIA LTD</td>\n",
" <td> 07955916</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 32 DAM STREET</td>\n",
" <td> NaN</td>\n",
" <td> LICHFIELD</td>\n",
" <td> STAFFORDSHIRE</td>\n",
" <td> NaN</td>\n",
" <td> WS13 6AA</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61971 </th>\n",
" <td> A M AGENCIES LTD</td>\n",
" <td> NI071258</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> UNIT B6 CLARA HOUSE</td>\n",
" <td> DUNMURRY OFFICE PARKUPPER DUNMURRY LANE DUNMURRY</td>\n",
" <td> BELFAST</td>\n",
" <td> ANTRIM</td>\n",
" <td> NaN</td>\n",
" <td> BT17 0AJ</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>308502</th>\n",
" <td> FAA INSTALLATIONS LIMITED</td>\n",
" <td> 08070586</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 8 HARVEST CLOSE</td>\n",
" <td> NaN</td>\n",
" <td> PONTEFRACT</td>\n",
" <td> WEST YORKSHIRE</td>\n",
" <td> NaN</td>\n",
" <td> WF8 2UR</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>743155</th>\n",
" <td> COFFEE CULTURE CAFE & EATERY LIMITED</td>\n",
" <td> 09346166</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> EMERSON HOUSE</td>\n",
" <td> HEYES LANE</td>\n",
" <td> ALDERLEY EDGE</td>\n",
" <td> NaN</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> SK9 7LF</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122930</th>\n",
" <td> ADERYN BUILDING CONSULTANCY LIMITED</td>\n",
" <td> 09127060</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> RADNOR HOUSE GREENWOOD CLOSE</td>\n",
" <td> CARDIFF GATE BUSINESS PARK</td>\n",
" <td> CARDIFF</td>\n",
" <td> NaN</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> CF23 8AA</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>555312</th>\n",
" <td> BUZZWORKS HOTELS LIMITED</td>\n",
" <td> SC171299</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 132 MAIN STREET</td>\n",
" <td> NaN</td>\n",
" <td> PRESTWICK</td>\n",
" <td> AYRSHIRE</td>\n",
" <td> NaN</td>\n",
" <td> KA9 1PB</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48011 </th>\n",
" <td> JORDAN-HOWELL LTD</td>\n",
" <td> 08942110</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 3 SOWOOD GARDENS</td>\n",
" <td> NaN</td>\n",
" <td> OSSETT</td>\n",
" <td> WEST YORKSHIRE</td>\n",
" <td> NaN</td>\n",
" <td> WF5 0SP</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>527074</th>\n",
" <td> BROOMCO (3113) LIMITED</td>\n",
" <td> NF003379</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> UNIT 9</td>\n",
" <td> GORTRUSH INDUSTRIAL ESTATE</td>\n",
" <td> OMAGH</td>\n",
" <td> CO TYRONE</td>\n",
" <td> NaN</td>\n",
" <td> BT78 5EJ</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127133</th>\n",
" <td> DOREL (EUROPE) LTD.</td>\n",
" <td> 03278346</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 1168/1170 MELTON ROAD</td>\n",
" <td> SYSTON</td>\n",
" <td> LEICESTER</td>\n",
" <td> LEICESTERSHIRE</td>\n",
" <td> NaN</td>\n",
" <td> LE7 2HB</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>665016</th>\n",
" <td> OLD SPOT BREWERY LIMITED</td>\n",
" <td> 05384602</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> MANOR FARM STATION ROAD</td>\n",
" <td> CULLINGWORTH</td>\n",
" <td> BRADFORD</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> BD13 5HN</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>623591</th>\n",
" <td> TERRAHOUSE MANANGEMENT LIMITED</td>\n",
" <td> 09201714</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 48 BALDRY GARDENS</td>\n",
" <td> STREATHAM COMMON</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> SW16 3DJ</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>177950</th>\n",
" <td> EAST LINCS SMART REPAIR LTD</td>\n",
" <td> 08016004</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 15 LINDEN WAY</td>\n",
" <td> NaN</td>\n",
" <td> BOSTON</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> PE21 9DY</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21255 </th>\n",
" <td> 25 SPENCER ROAD RTM COMPANY LTD</td>\n",
" <td> 07165614</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 104 ALEXANDRIA ROAD</td>\n",
" <td> NaN</td>\n",
" <td> SIDMOUTH</td>\n",
" <td> DEVON</td>\n",
" <td> NaN</td>\n",
" <td> EX10 9HG</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>624559</th>\n",
" <td> CASTLEFORD WATER TREATMENT LLP</td>\n",
" <td> OC352830</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> RUSSELL HOUSE</td>\n",
" <td> 140 HIGH STREET</td>\n",
" <td> EDGWARE</td>\n",
" <td> MIDDLESEX</td>\n",
" <td> NaN</td>\n",
" <td> HA8 7LW</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>657174</th>\n",
" <td> ODDC LIMITED</td>\n",
" <td> 09355223</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> SUITE 34, NEW HOUSE</td>\n",
" <td> 67-68 HATTON GARDEN</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> EC1N 8JY</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>313028</th>\n",
" <td> FAIRFIELD PROPERTY MANAGEMENT SERVICES (NE) LTD</td>\n",
" <td> 08444232</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> FREDERICK HOUSE DEAN GROUP BUSINESS PARK</td>\n",
" <td> BRENDA ROAD</td>\n",
" <td> HARTLEPOOL</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> TS25 2BW</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21130 </th>\n",
" <td> PRIDE SEA EDUCATION & CONSULTING LIMITED</td>\n",
" <td> 07783359</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 48 WALTHALL STREET</td>\n",
" <td> NaN</td>\n",
" <td> CREWE</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> CW2 7LA</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>167182</th>\n",
" <td> WRITTLE ROAD (CHELMSFORD) RESIDENTS ASSOCIATIO...</td>\n",
" <td> 04046956</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> UNIT 1B</td>\n",
" <td> LITTLE HYDE FARM LITTLE HYDE</td>\n",
" <td> LANE, INGATESTONE</td>\n",
" <td> ESSEX</td>\n",
" <td> NaN</td>\n",
" <td> CM4 0DU</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>454276</th>\n",
" <td> SPECIALIST KITCHEN FITTERS LIMITED</td>\n",
" <td> 09487433</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 73 ADDISON ROAD</td>\n",
" <td> NaN</td>\n",
" <td> TUNBRIDGE WELLS</td>\n",
" <td> KENT</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> TN2 3GG</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47226 </th>\n",
" <td> JONNY DECKER LIMITED</td>\n",
" <td> 07941935</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 29B MONTAGUE ROAD</td>\n",
" <td> NaN</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> E8 2HN</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>366302</th>\n",
" <td> FLEET MATTERS LTD</td>\n",
" <td> 05583300</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 3 WOOD END CLOSE, FARNHAM COMMON</td>\n",
" <td> SLOUGH</td>\n",
" <td> BUCKINGHAMSHIRE</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> SL2 3RF</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134560</th>\n",
" <td> RED EYE CONSULTING LIMITED</td>\n",
" <td> 07983909</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 2 CHALFONT SQUARE</td>\n",
" <td> OLD FOUNDRY ROAD</td>\n",
" <td> IPSWICH</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> IP4 2AJ</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>496464</th>\n",
" <td> MONTES LP</td>\n",
" <td> SL007667</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 50 LOTHIAN ROAD</td>\n",
" <td> FESTIVAL SQUARE</td>\n",
" <td> EDINBURGH</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> EH3 9WJ</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>314358</th>\n",
" <td> SEACONTRACTORS MARITIME PERSONNEL (UK) LTD</td>\n",
" <td> 05366317</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> WELLINGTON HOUSE FALCON COURT</td>\n",
" <td> PRESTON FARM INDUSTRIAL ESTATE</td>\n",
" <td> STOCKTON-ON-TEES</td>\n",
" <td> CLEVELAND</td>\n",
" <td> NaN</td>\n",
" <td> TS18 3TS</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>727389</th>\n",
" <td> ICENIUM LTD</td>\n",
" <td> 09037652</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 5 WESTFIELD ROAD</td>\n",
" <td> REGENTS PARK</td>\n",
" <td> SOUTHAMPTON</td>\n",
" <td> HAMPSHIRE</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> SO15 4HQ</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>729811</th>\n",
" <td> THROUGH LIFE SUPPORT LIMITED</td>\n",
" <td> 07845099</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> CENTRE GATE</td>\n",
" <td> COLSTON AVENUE</td>\n",
" <td> BRISTOL</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> BS1 4TR</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>649371</th>\n",
" <td> THE COOLER WATER COMPANY LIMITED</td>\n",
" <td> 05777329</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> THE BLUE FARMHOUSE</td>\n",
" <td> 86-90 CUMBERLAND STREET</td>\n",
" <td> WOODBRIDGE</td>\n",
" <td> SUFFOLK</td>\n",
" <td> NaN</td>\n",
" <td> IP12 4AE</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49902 </th>\n",
" <td> VOB ENTERPRISE LTD</td>\n",
" <td> 09131667</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 9 BROMLEY RD</td>\n",
" <td> CATFORD</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> SE6 2TS</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>541127</th>\n",
" <td> BULAU GENERAL CONSTRUCTION LIMITED</td>\n",
" <td> 09293723</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 25 FRONTFIELD CRESCENT</td>\n",
" <td> NaN</td>\n",
" <td> PLYMOUTH</td>\n",
" <td> NaN</td>\n",
" <td> UNITED KINGDOM</td>\n",
" <td> PL6 6RY</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>261446</th>\n",
" <td> ARETHA INTERNATIONAL LTD</td>\n",
" <td> 05800646</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 2 HARLESTON CLOSE</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> E5 9NH</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>783723</th>\n",
" <td> INTERNATIONAL CORRESPONDENCE SCHOOLS LIMITED</td>\n",
" <td> SC434382</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> BRECKENRIDGE HOUSE</td>\n",
" <td> 274 SAUCHIEHALL STREET</td>\n",
" <td> GLASGOW</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> G2 3EH</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>386823</th>\n",
" <td> SIR JOHN SOANE'S MUSEUM TRUST</td>\n",
" <td> 07965957</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 13 LINCOLN'S INN FIELDS</td>\n",
" <td> NaN</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> WC2A 3BP</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>226889</th>\n",
" <td> ELMWOOD RESIDENTIAL HOME LIMITED</td>\n",
" <td> 01665156</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> ELMWOOD</td>\n",
" <td> COLYFORD</td>\n",
" <td> COLYTON</td>\n",
" <td> DEVON</td>\n",
" <td> NaN</td>\n",
" <td> EX24 6QJ</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>725314</th>\n",
" <td> CLIMATE ACTION WEST COMMUNITY INTEREST COMPANY</td>\n",
" <td> 06568552</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> GREAT BOW WHARF</td>\n",
" <td> BOW STREET</td>\n",
" <td> LANGPORT</td>\n",
" <td> SOMERSET</td>\n",
" <td> NaN</td>\n",
" <td> TA10 9PN</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86855 </th>\n",
" <td> R C & T LIMITED</td>\n",
" <td> 09517358</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 32 LAWTON HALL ESTATE</td>\n",
" <td> BULWELL HALL ESTATE</td>\n",
" <td> BULWELL</td>\n",
" <td> NOTTINGHAMSHIRE</td>\n",
" <td> ENGLAND</td>\n",
" <td> NG6 8BL</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132853</th>\n",
" <td> ADVICE WISE SOLICITORS LTD</td>\n",
" <td> 08058295</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> OLYMPIC HOUSE, 3RD FLOOR</td>\n",
" <td> 28-42 CLEMENTS ROAD</td>\n",
" <td> ILFORD</td>\n",
" <td> ESSEX</td>\n",
" <td> ENGLAND</td>\n",
" <td> IG1 1BA</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>847039</th>\n",
" <td> UPPER BIRNIE FARMS (1987)</td>\n",
" <td> SL001632</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> UPPER BIRNIE</td>\n",
" <td> BENHOLM</td>\n",
" <td> KINCARDINESHIRE</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>703525</th>\n",
" <td> THE STAFFORD OUTDOOR COMPANY LIMITED</td>\n",
" <td> 04761613</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 45 MILL STREET</td>\n",
" <td> NaN</td>\n",
" <td> STAFFORD</td>\n",
" <td> STAFFS</td>\n",
" <td> ENGLAND</td>\n",
" <td> NaN</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>633482</th>\n",
" <td> CC INTERNATIONAL LTD</td>\n",
" <td> 09566910</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 20-22 WENLOCK ROAD</td>\n",
" <td> NaN</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> ENGLAND</td>\n",
" <td> N1 7GU</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565516</th>\n",
" <td> SYCAMORE WOODS LTD</td>\n",
" <td> 09456012</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> CRAVEN HOUSE , GROUND FLOOR 40-44 UXBRIDGE ROAD</td>\n",
" <td> EALING</td>\n",
" <td> LONDON</td>\n",
" <td> NaN</td>\n",
" <td> ENGLAND</td>\n",
" <td> W5 2BS</td>\n",
" <td>...</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>100 rows × 53 columns</p>\n",
"</div>"
],
"text/plain": [
" CompanyName CompanyNumber \\\n",
"175175 EARO ESTATES LTD 09480942 \n",
"207052 AMERICAN EXPRESSO LIMITED 04255922 \n",
"390136 SIXTEEN25 08230187 \n",
"38519 DATASAFE SERVICES LIMITED 01999773 \n",
"354624 BALLYNARIS LTD NI621115 \n",
"533389 SUBSEA INSPECTION SW LTD 09156040 \n",
"446674 SOUTHWOOD BUILDING CONTRACTORS LIMITED 06389143 \n",
"471345 MISS B CREATIVE LIMITED 07248291 \n",
"249166 S D C CONSULTANCY SERVICES LIMITED 06767060 \n",
"245418 APPLECORE DEVELOPMENTS LIMITED 04183144 \n",
"137301 KMJ PROPERTY (TUNBRIDGE WELLS) LIMITED 06915745 \n",
"8488 PRC PLUMBING AND HEATING LIMITED 08310381 \n",
"834199 UMSI LIMITED FC031028 \n",
"332724 AZA-MAB LTD 09439314 \n",
"647771 HEXAGON ASSOCIATES LIMITED NI627785 \n",
"270759 ARROW GLOBAL MASSEY LIMITED 08612076 \n",
"479413 GILESWOODFORD LIMITED 05243089 \n",
"658126 HIGHWOOD HOUSE PUBLISHING LTD 05112696 \n",
"51107 DAZZITTO PHOTOGRAPHY LIMITED 08425973 \n",
"181148 EASTHOLME COURT MANAGEMENT LIMITED 02198033 \n",
"221642 LIBERATION MEDIA LTD 07955916 \n",
"61971 A M AGENCIES LTD NI071258 \n",
"308502 FAA INSTALLATIONS LIMITED 08070586 \n",
"743155 COFFEE CULTURE CAFE & EATERY LIMITED 09346166 \n",
"122930 ADERYN BUILDING CONSULTANCY LIMITED 09127060 \n",
"555312 BUZZWORKS HOTELS LIMITED SC171299 \n",
"48011 JORDAN-HOWELL LTD 08942110 \n",
"527074 BROOMCO (3113) LIMITED NF003379 \n",
"127133 DOREL (EUROPE) LTD. 03278346 \n",
"665016 OLD SPOT BREWERY LIMITED 05384602 \n",
"... ... ... \n",
"623591 TERRAHOUSE MANANGEMENT LIMITED 09201714 \n",
"177950 EAST LINCS SMART REPAIR LTD 08016004 \n",
"21255 25 SPENCER ROAD RTM COMPANY LTD 07165614 \n",
"624559 CASTLEFORD WATER TREATMENT LLP OC352830 \n",
"657174 ODDC LIMITED 09355223 \n",
"313028 FAIRFIELD PROPERTY MANAGEMENT SERVICES (NE) LTD 08444232 \n",
"21130 PRIDE SEA EDUCATION & CONSULTING LIMITED 07783359 \n",
"167182 WRITTLE ROAD (CHELMSFORD) RESIDENTS ASSOCIATIO... 04046956 \n",
"454276 SPECIALIST KITCHEN FITTERS LIMITED 09487433 \n",
"47226 JONNY DECKER LIMITED 07941935 \n",
"366302 FLEET MATTERS LTD 05583300 \n",
"134560 RED EYE CONSULTING LIMITED 07983909 \n",
"496464 MONTES LP SL007667 \n",
"314358 SEACONTRACTORS MARITIME PERSONNEL (UK) LTD 05366317 \n",
"727389 ICENIUM LTD 09037652 \n",
"729811 THROUGH LIFE SUPPORT LIMITED 07845099 \n",
"649371 THE COOLER WATER COMPANY LIMITED 05777329 \n",
"49902 VOB ENTERPRISE LTD 09131667 \n",
"541127 BULAU GENERAL CONSTRUCTION LIMITED 09293723 \n",
"261446 ARETHA INTERNATIONAL LTD 05800646 \n",
"783723 INTERNATIONAL CORRESPONDENCE SCHOOLS LIMITED SC434382 \n",
"386823 SIR JOHN SOANE'S MUSEUM TRUST 07965957 \n",
"226889 ELMWOOD RESIDENTIAL HOME LIMITED 01665156 \n",
"725314 CLIMATE ACTION WEST COMMUNITY INTEREST COMPANY 06568552 \n",
"86855 R C & T LIMITED 09517358 \n",
"132853 ADVICE WISE SOLICITORS LTD 08058295 \n",
"847039 UPPER BIRNIE FARMS (1987) SL001632 \n",
"703525 THE STAFFORD OUTDOOR COMPANY LIMITED 04761613 \n",
"633482 CC INTERNATIONAL LTD 09566910 \n",
"565516 SYCAMORE WOODS LTD 09456012 \n",
"\n",
" RegAddress_CareOf RegAddress_POBox \\\n",
"175175 NaN NaN \n",
"207052 NaN NaN \n",
"390136 NaN NaN \n",
"38519 NaN NaN \n",
"354624 NaN NaN \n",
"533389 NaN NaN \n",
"446674 NaN NaN \n",
"471345 NaN NaN \n",
"249166 NaN NaN \n",
"245418 NaN NaN \n",
"137301 NaN NaN \n",
"8488 CERTAX ACCOUNTING NaN \n",
"834199 NaN NaN \n",
"332724 NaN NaN \n",
"647771 NaN NaN \n",
"270759 NaN NaN \n",
"479413 NaN NaN \n",
"658126 NaN NaN \n",
"51107 NaN NaN \n",
"181148 NaN NaN \n",
"221642 NaN NaN \n",
"61971 NaN NaN \n",
"308502 NaN NaN \n",
"743155 NaN NaN \n",
"122930 NaN NaN \n",
"555312 NaN NaN \n",
"48011 NaN NaN \n",
"527074 NaN NaN \n",
"127133 NaN NaN \n",
"665016 NaN NaN \n",
"... ... ... \n",
"623591 NaN NaN \n",
"177950 NaN NaN \n",
"21255 NaN NaN \n",
"624559 NaN NaN \n",
"657174 NaN NaN \n",
"313028 NaN NaN \n",
"21130 NaN NaN \n",
"167182 NaN NaN \n",
"454276 NaN NaN \n",
"47226 NaN NaN \n",
"366302 NaN NaN \n",
"134560 NaN NaN \n",
"496464 NaN NaN \n",
"314358 NaN NaN \n",
"727389 NaN NaN \n",
"729811 NaN NaN \n",
"649371 NaN NaN \n",
"49902 NaN NaN \n",
"541127 NaN NaN \n",
"261446 NaN NaN \n",
"783723 NaN NaN \n",
"386823 NaN NaN \n",
"226889 NaN NaN \n",
"725314 NaN NaN \n",
"86855 NaN NaN \n",
"132853 NaN NaN \n",
"847039 NaN NaN \n",
"703525 NaN NaN \n",
"633482 NaN NaN \n",
"565516 NaN NaN \n",
"\n",
" RegAddress_AddressLine1 \\\n",
"175175 4 MELTON ROAD \n",
"207052 SUITE 100 \n",
"390136 10 DENMARK STREET \n",
"38519 UNITED HOUSE \n",
"354624 55 MILEBUSH ROAD \n",
"533389 TIMBERLY \n",
"446674 UNIT 16 ENDEAVOUR BUSINESS PARK \n",
"471345 470 HUCKNALL ROAD \n",
"249166 39 WHITE CLOVER SQUARE \n",
"245418 PORTWAY HILL HOUSE \n",
"137301 BUCKINGHAM HOUSE \n",
"8488 ALCESTER BUSINESS CENTRE \n",
"834199 BRANCH REGISTRATION \n",
"332724 88 COMMERCIAL WAY \n",
"647771 UNIT 1 \n",
"270759 20-22 BEDFORD ROW \n",
"479413 UNITY CHAMBERS \n",
"658126 HIGHWOOD HOUSE \n",
"51107 50 ERNEST ROAD \n",
"181148 7 COTTONS MEADOW \n",
"221642 32 DAM STREET \n",
"61971 UNIT B6 CLARA HOUSE \n",
"308502 8 HARVEST CLOSE \n",
"743155 EMERSON HOUSE \n",
"122930 RADNOR HOUSE GREENWOOD CLOSE \n",
"555312 132 MAIN STREET \n",
"48011 3 SOWOOD GARDENS \n",
"527074 UNIT 9 \n",
"127133 1168/1170 MELTON ROAD \n",
"665016 MANOR FARM STATION ROAD \n",
"... ... \n",
"623591 48 BALDRY GARDENS \n",
"177950 15 LINDEN WAY \n",
"21255 104 ALEXANDRIA ROAD \n",
"624559 RUSSELL HOUSE \n",
"657174 SUITE 34, NEW HOUSE \n",
"313028 FREDERICK HOUSE DEAN GROUP BUSINESS PARK \n",
"21130 48 WALTHALL STREET \n",
"167182 UNIT 1B \n",
"454276 73 ADDISON ROAD \n",
"47226 29B MONTAGUE ROAD \n",
"366302 3 WOOD END CLOSE, FARNHAM COMMON \n",
"134560 2 CHALFONT SQUARE \n",
"496464 50 LOTHIAN ROAD \n",
"314358 WELLINGTON HOUSE FALCON COURT \n",
"727389 5 WESTFIELD ROAD \n",
"729811 CENTRE GATE \n",
"649371 THE BLUE FARMHOUSE \n",
"49902 9 BROMLEY RD \n",
"541127 25 FRONTFIELD CRESCENT \n",
"261446 2 HARLESTON CLOSE \n",
"783723 BRECKENRIDGE HOUSE \n",
"386823 13 LINCOLN'S INN FIELDS \n",
"226889 ELMWOOD \n",
"725314 GREAT BOW WHARF \n",
"86855 32 LAWTON HALL ESTATE \n",
"132853 OLYMPIC HOUSE, 3RD FLOOR \n",
"847039 UPPER BIRNIE \n",
"703525 45 MILL STREET \n",
"633482 20-22 WENLOCK ROAD \n",
"565516 CRAVEN HOUSE , GROUND FLOOR 40-44 UXBRIDGE ROAD \n",
"\n",
" RegAddress_AddressLine2 RegAddress_PostTown \\\n",
"175175 NaN MANCHESTER \n",
"207052 THE STUDIO ST NICHOLAS CLOSE ELSTREE \n",
"390136 NaN LONDON \n",
"38519 NORTH ROAD LONDON \n",
"354624 NaN DROMORE \n",
"533389 SOUTH STREET AXMINSTER \n",
"446674 CROW ARCH LANE RINGWOOD \n",
"471345 NaN NOTTINGHAM \n",
"249166 NaN LYMM \n",
"245418 PORTWAY HILL BATCOMBE \n",
"137301 MYRTLE LANE BILLINGSHURST \n",
"8488 KINWARTON FARM ROAD ALCESTER \n",
"834199 REFER TO PARENT REGISTRY NaN \n",
"332724 NaN PECKHAM \n",
"647771 22-218 UPPER NEWTOWNARDS ROAD BELFAST \n",
"270759 NaN LONDON \n",
"479413 34 HIGH EAST STREET DORCHESTER \n",
"658126 WINTERS LANE REDHILL BRISTOL \n",
"51107 NaN CHATHAM \n",
"181148 KINGSTONE HEREFORDSHIRE \n",
"221642 NaN LICHFIELD \n",
"61971 DUNMURRY OFFICE PARKUPPER DUNMURRY LANE DUNMURRY BELFAST \n",
"308502 NaN PONTEFRACT \n",
"743155 HEYES LANE ALDERLEY EDGE \n",
"122930 CARDIFF GATE BUSINESS PARK CARDIFF \n",
"555312 NaN PRESTWICK \n",
"48011 NaN OSSETT \n",
"527074 GORTRUSH INDUSTRIAL ESTATE OMAGH \n",
"127133 SYSTON LEICESTER \n",
"665016 CULLINGWORTH BRADFORD \n",
"... ... ... \n",
"623591 STREATHAM COMMON LONDON \n",
"177950 NaN BOSTON \n",
"21255 NaN SIDMOUTH \n",
"624559 140 HIGH STREET EDGWARE \n",
"657174 67-68 HATTON GARDEN LONDON \n",
"313028 BRENDA ROAD HARTLEPOOL \n",
"21130 NaN CREWE \n",
"167182 LITTLE HYDE FARM LITTLE HYDE LANE, INGATESTONE \n",
"454276 NaN TUNBRIDGE WELLS \n",
"47226 NaN LONDON \n",
"366302 SLOUGH BUCKINGHAMSHIRE \n",
"134560 OLD FOUNDRY ROAD IPSWICH \n",
"496464 FESTIVAL SQUARE EDINBURGH \n",
"314358 PRESTON FARM INDUSTRIAL ESTATE STOCKTON-ON-TEES \n",
"727389 REGENTS PARK SOUTHAMPTON \n",
"729811 COLSTON AVENUE BRISTOL \n",
"649371 86-90 CUMBERLAND STREET WOODBRIDGE \n",
"49902 CATFORD LONDON \n",
"541127 NaN PLYMOUTH \n",
"261446 LONDON NaN \n",
"783723 274 SAUCHIEHALL STREET GLASGOW \n",
"386823 NaN LONDON \n",
"226889 COLYFORD COLYTON \n",
"725314 BOW STREET LANGPORT \n",
"86855 BULWELL HALL ESTATE BULWELL \n",
"132853 28-42 CLEMENTS ROAD ILFORD \n",
"847039 BENHOLM KINCARDINESHIRE \n",
"703525 NaN STAFFORD \n",
"633482 NaN LONDON \n",
"565516 EALING LONDON \n",
"\n",
" RegAddress_County RegAddress_Country RegAddress_PostCode \\\n",
"175175 NaN ENGLAND M8 4HG \n",
"207052 HERTFORDSHIRE NaN WD6 3EW \n",
"390136 NaN NaN WC2H 8LS \n",
"38519 NaN NaN N7 9DP \n",
"354624 CO. DOWN NaN BT25 1RU \n",
"533389 DEVON UNITED KINGDOM EX13 5AD \n",
"446674 HAMPSHIRE NaN BH24 1PN \n",
"471345 NOTTINGHAMSHIRE NaN NG5 1FX \n",
"249166 CHESHIRE NaN WA13 0RX \n",
"245418 SOMERSET NaN BA4 6BR \n",
"137301 WEST SUSSEX NaN RH14 9SG \n",
"8488 WARWICKSHIRE NaN B49 6EL \n",
"834199 NaN NaN NaN \n",
"332724 NaN UNITED KINGDOM SE15 5GG \n",
"647771 NaN NORTHERN IRELAND BT4 3ET \n",
"270759 NaN NaN WC1R 4JS \n",
"479413 DOREST NaN NaN \n",
"658126 NaN NaN BS40 5SH \n",
"51107 KENT NaN ME4 5PT \n",
"181148 NaN NaN HR2 9EW \n",
"221642 STAFFORDSHIRE NaN WS13 6AA \n",
"61971 ANTRIM NaN BT17 0AJ \n",
"308502 WEST YORKSHIRE NaN WF8 2UR \n",
"743155 NaN UNITED KINGDOM SK9 7LF \n",
"122930 NaN UNITED KINGDOM CF23 8AA \n",
"555312 AYRSHIRE NaN KA9 1PB \n",
"48011 WEST YORKSHIRE NaN WF5 0SP \n",
"527074 CO TYRONE NaN BT78 5EJ \n",
"127133 LEICESTERSHIRE NaN LE7 2HB \n",
"665016 NaN NaN BD13 5HN \n",
"... ... ... ... \n",
"623591 NaN UNITED KINGDOM SW16 3DJ \n",
"177950 NaN NaN PE21 9DY \n",
"21255 DEVON NaN EX10 9HG \n",
"624559 MIDDLESEX NaN HA8 7LW \n",
"657174 NaN UNITED KINGDOM EC1N 8JY \n",
"313028 NaN NaN TS25 2BW \n",
"21130 NaN NaN CW2 7LA \n",
"167182 ESSEX NaN CM4 0DU \n",
"454276 KENT UNITED KINGDOM TN2 3GG \n",
"47226 NaN NaN E8 2HN \n",
"366302 NaN NaN SL2 3RF \n",
"134560 NaN NaN IP4 2AJ \n",
"496464 NaN NaN EH3 9WJ \n",
"314358 CLEVELAND NaN TS18 3TS \n",
"727389 HAMPSHIRE UNITED KINGDOM SO15 4HQ \n",
"729811 NaN NaN BS1 4TR \n",
"649371 SUFFOLK NaN IP12 4AE \n",
"49902 NaN UNITED KINGDOM SE6 2TS \n",
"541127 NaN UNITED KINGDOM PL6 6RY \n",
"261446 NaN NaN E5 9NH \n",
"783723 NaN NaN G2 3EH \n",
"386823 NaN NaN WC2A 3BP \n",
"226889 DEVON NaN EX24 6QJ \n",
"725314 SOMERSET NaN TA10 9PN \n",
"86855 NOTTINGHAMSHIRE ENGLAND NG6 8BL \n",
"132853 ESSEX ENGLAND IG1 1BA \n",
"847039 NaN NaN NaN \n",
"703525 STAFFS ENGLAND NaN \n",
"633482 NaN ENGLAND N1 7GU \n",
"565516 NaN ENGLAND W5 2BS \n",
"\n",
" ... PreviousName_6_CONDATE \\\n",
"175175 ... NaN \n",
"207052 ... NaN \n",
"390136 ... NaN \n",
"38519 ... NaN \n",
"354624 ... NaN \n",
"533389 ... NaN \n",
"446674 ... NaN \n",
"471345 ... NaN \n",
"249166 ... NaN \n",
"245418 ... NaN \n",
"137301 ... NaN \n",
"8488 ... NaN \n",
"834199 ... NaN \n",
"332724 ... NaN \n",
"647771 ... NaN \n",
"270759 ... NaN \n",
"479413 ... NaN \n",
"658126 ... NaN \n",
"51107 ... NaN \n",
"181148 ... NaN \n",
"221642 ... NaN \n",
"61971 ... NaN \n",
"308502 ... NaN \n",
"743155 ... NaN \n",
"122930 ... NaN \n",
"555312 ... NaN \n",
"48011 ... NaN \n",
"527074 ... NaN \n",
"127133 ... NaN \n",
"665016 ... NaN \n",
"... ... ... \n",
"623591 ... NaN \n",
"177950 ... NaN \n",
"21255 ... NaN \n",
"624559 ... NaN \n",
"657174 ... NaN \n",
"313028 ... NaN \n",
"21130 ... NaN \n",
"167182 ... NaN \n",
"454276 ... NaN \n",
"47226 ... NaN \n",
"366302 ... NaN \n",
"134560 ... NaN \n",
"496464 ... NaN \n",
"314358 ... NaN \n",
"727389 ... NaN \n",
"729811 ... NaN \n",
"649371 ... NaN \n",
"49902 ... NaN \n",
"541127 ... NaN \n",
"261446 ... NaN \n",
"783723 ... NaN \n",
"386823 ... NaN \n",
"226889 ... NaN \n",
"725314 ... NaN \n",
"86855 ... NaN \n",
"132853 ... NaN \n",
"847039 ... NaN \n",
"703525 ... NaN \n",
"633482 ... NaN \n",
"565516 ... NaN \n",
"\n",
" PreviousName_6_CompanyName PreviousName_7_CONDATE \\\n",
"175175 NaN NaN \n",
"207052 NaN NaN \n",
"390136 NaN NaN \n",
"38519 NaN NaN \n",
"354624 NaN NaN \n",
"533389 NaN NaN \n",
"446674 NaN NaN \n",
"471345 NaN NaN \n",
"249166 NaN NaN \n",
"245418 NaN NaN \n",
"137301 NaN NaN \n",
"8488 NaN NaN \n",
"834199 NaN NaN \n",
"332724 NaN NaN \n",
"647771 NaN NaN \n",
"270759 NaN NaN \n",
"479413 NaN NaN \n",
"658126 NaN NaN \n",
"51107 NaN NaN \n",
"181148 NaN NaN \n",
"221642 NaN NaN \n",
"61971 NaN NaN \n",
"308502 NaN NaN \n",
"743155 NaN NaN \n",
"122930 NaN NaN \n",
"555312 NaN NaN \n",
"48011 NaN NaN \n",
"527074 NaN NaN \n",
"127133 NaN NaN \n",
"665016 NaN NaN \n",
"... ... ... \n",
"623591 NaN NaN \n",
"177950 NaN NaN \n",
"21255 NaN NaN \n",
"624559 NaN NaN \n",
"657174 NaN NaN \n",
"313028 NaN NaN \n",
"21130 NaN NaN \n",
"167182 NaN NaN \n",
"454276 NaN NaN \n",
"47226 NaN NaN \n",
"366302 NaN NaN \n",
"134560 NaN NaN \n",
"496464 NaN NaN \n",
"314358 NaN NaN \n",
"727389 NaN NaN \n",
"729811 NaN NaN \n",
"649371 NaN NaN \n",
"49902 NaN NaN \n",
"541127 NaN NaN \n",
"261446 NaN NaN \n",
"783723 NaN NaN \n",
"386823 NaN NaN \n",
"226889 NaN NaN \n",
"725314 NaN NaN \n",
"86855 NaN NaN \n",
"132853 NaN NaN \n",
"847039 NaN NaN \n",
"703525 NaN NaN \n",
"633482 NaN NaN \n",
"565516 NaN NaN \n",
"\n",
" PreviousName_7_CompanyName PreviousName_8_CONDATE \\\n",
"175175 NaN NaN \n",
"207052 NaN NaN \n",
"390136 NaN NaN \n",
"38519 NaN NaN \n",
"354624 NaN NaN \n",
"533389 NaN NaN \n",
"446674 NaN NaN \n",
"471345 NaN NaN \n",
"249166 NaN NaN \n",
"245418 NaN NaN \n",
"137301 NaN NaN \n",
"8488 NaN NaN \n",
"834199 NaN NaN \n",
"332724 NaN NaN \n",
"647771 NaN NaN \n",
"270759 NaN NaN \n",
"479413 NaN NaN \n",
"658126 NaN NaN \n",
"51107 NaN NaN \n",
"181148 NaN NaN \n",
"221642 NaN NaN \n",
"61971 NaN NaN \n",
"308502 NaN NaN \n",
"743155 NaN NaN \n",
"122930 NaN NaN \n",
"555312 NaN NaN \n",
"48011 NaN NaN \n",
"527074 NaN NaN \n",
"127133 NaN NaN \n",
"665016 NaN NaN \n",
"... ... ... \n",
"623591 NaN NaN \n",
"177950 NaN NaN \n",
"21255 NaN NaN \n",
"624559 NaN NaN \n",
"657174 NaN NaN \n",
"313028 NaN NaN \n",
"21130 NaN NaN \n",
"167182 NaN NaN \n",
"454276 NaN NaN \n",
"47226 NaN NaN \n",
"366302 NaN NaN \n",
"134560 NaN NaN \n",
"496464 NaN NaN \n",
"314358 NaN NaN \n",
"727389 NaN NaN \n",
"729811 NaN NaN \n",
"649371 NaN NaN \n",
"49902 NaN NaN \n",
"541127 NaN NaN \n",
"261446 NaN NaN \n",
"783723 NaN NaN \n",
"386823 NaN NaN \n",
"226889 NaN NaN \n",
"725314 NaN NaN \n",
"86855 NaN NaN \n",
"132853 NaN NaN \n",
"847039 NaN NaN \n",
"703525 NaN NaN \n",
"633482 NaN NaN \n",
"565516 NaN NaN \n",
"\n",
" PreviousName_8_CompanyName PreviousName_9_CONDATE \\\n",
"175175 NaN NaN \n",
"207052 NaN NaN \n",
"390136 NaN NaN \n",
"38519 NaN NaN \n",
"354624 NaN NaN \n",
"533389 NaN NaN \n",
"446674 NaN NaN \n",
"471345 NaN NaN \n",
"249166 NaN NaN \n",
"245418 NaN NaN \n",
"137301 NaN NaN \n",
"8488 NaN NaN \n",
"834199 NaN NaN \n",
"332724 NaN NaN \n",
"647771 NaN NaN \n",
"270759 NaN NaN \n",
"479413 NaN NaN \n",
"658126 NaN NaN \n",
"51107 NaN NaN \n",
"181148 NaN NaN \n",
"221642 NaN NaN \n",
"61971 NaN NaN \n",
"308502 NaN NaN \n",
"743155 NaN NaN \n",
"122930 NaN NaN \n",
"555312 NaN NaN \n",
"48011 NaN NaN \n",
"527074 NaN NaN \n",
"127133 NaN NaN \n",
"665016 NaN NaN \n",
"... ... ... \n",
"623591 NaN NaN \n",
"177950 NaN NaN \n",
"21255 NaN NaN \n",
"624559 NaN NaN \n",
"657174 NaN NaN \n",
"313028 NaN NaN \n",
"21130 NaN NaN \n",
"167182 NaN NaN \n",
"454276 NaN NaN \n",
"47226 NaN NaN \n",
"366302 NaN NaN \n",
"134560 NaN NaN \n",
"496464 NaN NaN \n",
"314358 NaN NaN \n",
"727389 NaN NaN \n",
"729811 NaN NaN \n",
"649371 NaN NaN \n",
"49902 NaN NaN \n",
"541127 NaN NaN \n",
"261446 NaN NaN \n",
"783723 NaN NaN \n",
"386823 NaN NaN \n",
"226889 NaN NaN \n",
"725314 NaN NaN \n",
"86855 NaN NaN \n",
"132853 NaN NaN \n",
"847039 NaN NaN \n",
"703525 NaN NaN \n",
"633482 NaN NaN \n",
"565516 NaN NaN \n",
"\n",
" PreviousName_9_CompanyName PreviousName_10_CONDATE \\\n",
"175175 NaN NaN \n",
"207052 NaN NaN \n",
"390136 NaN NaN \n",
"38519 NaN NaN \n",
"354624 NaN NaN \n",
"533389 NaN NaN \n",
"446674 NaN NaN \n",
"471345 NaN NaN \n",
"249166 NaN NaN \n",
"245418 NaN NaN \n",
"137301 NaN NaN \n",
"8488 NaN NaN \n",
"834199 NaN NaN \n",
"332724 NaN NaN \n",
"647771 NaN NaN \n",
"270759 NaN NaN \n",
"479413 NaN NaN \n",
"658126 NaN NaN \n",
"51107 NaN NaN \n",
"181148 NaN NaN \n",
"221642 NaN NaN \n",
"61971 NaN NaN \n",
"308502 NaN NaN \n",
"743155 NaN NaN \n",
"122930 NaN NaN \n",
"555312 NaN NaN \n",
"48011 NaN NaN \n",
"527074 NaN NaN \n",
"127133 NaN NaN \n",
"665016 NaN NaN \n",
"... ... ... \n",
"623591 NaN NaN \n",
"177950 NaN NaN \n",
"21255 NaN NaN \n",
"624559 NaN NaN \n",
"657174 NaN NaN \n",
"313028 NaN NaN \n",
"21130 NaN NaN \n",
"167182 NaN NaN \n",
"454276 NaN NaN \n",
"47226 NaN NaN \n",
"366302 NaN NaN \n",
"134560 NaN NaN \n",
"496464 NaN NaN \n",
"314358 NaN NaN \n",
"727389 NaN NaN \n",
"729811 NaN NaN \n",
"649371 NaN NaN \n",
"49902 NaN NaN \n",
"541127 NaN NaN \n",
"261446 NaN NaN \n",
"783723 NaN NaN \n",
"386823 NaN NaN \n",
"226889 NaN NaN \n",
"725314 NaN NaN \n",
"86855 NaN NaN \n",
"132853 NaN NaN \n",
"847039 NaN NaN \n",
"703525 NaN NaN \n",
"633482 NaN NaN \n",
"565516 NaN NaN \n",
"\n",
" PreviousName_10_CompanyName \n",
"175175 NaN \n",
"207052 NaN \n",
"390136 NaN \n",
"38519 NaN \n",
"354624 NaN \n",
"533389 NaN \n",
"446674 NaN \n",
"471345 NaN \n",
"249166 NaN \n",
"245418 NaN \n",
"137301 NaN \n",
"8488 NaN \n",
"834199 NaN \n",
"332724 NaN \n",
"647771 NaN \n",
"270759 NaN \n",
"479413 NaN \n",
"658126 NaN \n",
"51107 NaN \n",
"181148 NaN \n",
"221642 NaN \n",
"61971 NaN \n",
"308502 NaN \n",
"743155 NaN \n",
"122930 NaN \n",
"555312 NaN \n",
"48011 NaN \n",
"527074 NaN \n",
"127133 NaN \n",
"665016 NaN \n",
"... ... \n",
"623591 NaN \n",
"177950 NaN \n",
"21255 NaN \n",
"624559 NaN \n",
"657174 NaN \n",
"313028 NaN \n",
"21130 NaN \n",
"167182 NaN \n",
"454276 NaN \n",
"47226 NaN \n",
"366302 NaN \n",
"134560 NaN \n",
"496464 NaN \n",
"314358 NaN \n",
"727389 NaN \n",
"729811 NaN \n",
"649371 NaN \n",
"49902 NaN \n",
"541127 NaN \n",
"261446 NaN \n",
"783723 NaN \n",
"386823 NaN \n",
"226889 NaN \n",
"725314 NaN \n",
"86855 NaN \n",
"132853 NaN \n",
"847039 NaN \n",
"703525 NaN \n",
"633482 NaN \n",
"565516 NaN \n",
"\n",
"[100 rows x 53 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# DEFINE A REDUCED DATASET FOR PROTOTYPING\n",
"from random import sample\n",
"# number/fraction of entries to use\n",
"#ents = int(len(X)*0.1)\n",
"ents = 100\n",
"# Take a random sample from the data\n",
"smalldataind = sample(range(0,len(data)-1),ents)\n",
"#print smalldataind\n",
"\n",
"# HERE\n",
"#smalldataind = [784400, 333248, 3037529, 333413, 1851904, 1569996, 2958604, 769824, 2848095, 896580]\n",
"\n",
"smalldata = data.iloc[smalldataind]\n",
"smalldata"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pickle file containing URL data found. Loading it...\n"
]
},
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CompanyAddress1</th>\n",
" <th>CompanyName</th>\n",
" <th>URLs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> M8 4HG</td>\n",
" <td> EARO ESTATES LTD</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> WD6 3EW</td>\n",
" <td> AMERICAN EXPRESSO LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> WC2H 8LS</td>\n",
" <td> SIXTEEN25</td>\n",
" <td> [https://www.sc.com/uk/, https://www.sc.com/en...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> N7 9DP</td>\n",
" <td> DATASAFE SERVICES LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> BT25 1RU</td>\n",
" <td> BALLYNARIS LTD</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> EX13 5AD</td>\n",
" <td> SUBSEA INSPECTION SW LTD</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> BH24 1PN</td>\n",
" <td> SOUTHWOOD BUILDING CONTRACTORS LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> NG5 1FX</td>\n",
" <td> MISS B CREATIVE LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> WA13 0RX</td>\n",
" <td> S D C CONSULTANCY SERVICES LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> BA4 6BR</td>\n",
" <td> APPLECORE DEVELOPMENTS LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> RH14 9SG</td>\n",
" <td> KMJ PROPERTY (TUNBRIDGE WELLS) LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> B49 6EL</td>\n",
" <td> PRC PLUMBING AND HEATING LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> NaN</td>\n",
" <td> UMSI LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> SE15 5GG</td>\n",
" <td> AZA-MAB LTD</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> BT4 3ET</td>\n",
" <td> HEXAGON ASSOCIATES LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> WC1R 4JS</td>\n",
" <td> ARROW GLOBAL MASSEY LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td> NaN</td>\n",
" <td> GILESWOODFORD LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> BS40 5SH</td>\n",
" <td> HIGHWOOD HOUSE PUBLISHING LTD</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td> ME4 5PT</td>\n",
" <td> DAZZITTO PHOTOGRAPHY LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td> HR2 9EW</td>\n",
" <td> EASTHOLME COURT MANAGEMENT LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td> WS13 6AA</td>\n",
" <td> LIBERATION MEDIA LTD</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td> BT17 0AJ</td>\n",
" <td> A M AGENCIES LTD</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td> WF8 2UR</td>\n",
" <td> FAA INSTALLATIONS LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td> SK9 7LF</td>\n",
" <td> COFFEE CULTURE CAFE & EATERY LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td> CF23 8AA</td>\n",
" <td> ADERYN BUILDING CONSULTANCY LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td> KA9 1PB</td>\n",
" <td> BUZZWORKS HOTELS LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td> WF5 0SP</td>\n",
" <td> JORDAN-HOWELL LTD</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td> BT78 5EJ</td>\n",
" <td> BROOMCO (3113) LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td> LE7 2HB</td>\n",
" <td> DOREL (EUROPE) LTD.</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td> BD13 5HN</td>\n",
" <td> OLD SPOT BREWERY LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td> SW16 3DJ</td>\n",
" <td> TERRAHOUSE MANANGEMENT LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td> PE21 9DY</td>\n",
" <td> EAST LINCS SMART REPAIR LTD</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td> EX10 9HG</td>\n",
" <td> 25 SPENCER ROAD RTM COMPANY LTD</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td> HA8 7LW</td>\n",
" <td> CASTLEFORD WATER TREATMENT LLP</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td> EC1N 8JY</td>\n",
" <td> ODDC LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td> TS25 2BW</td>\n",
" <td> FAIRFIELD PROPERTY MANAGEMENT SERVICES (NE) LTD</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td> CW2 7LA</td>\n",
" <td> PRIDE SEA EDUCATION & CONSULTING LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td> CM4 0DU</td>\n",
" <td> WRITTLE ROAD (CHELMSFORD) RESIDENTS ASSOCIATIO...</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td> TN2 3GG</td>\n",
" <td> SPECIALIST KITCHEN FITTERS LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td> E8 2HN</td>\n",
" <td> JONNY DECKER LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td> SL2 3RF</td>\n",
" <td> FLEET MATTERS LTD</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td> IP4 2AJ</td>\n",
" <td> RED EYE CONSULTING LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td> EH3 9WJ</td>\n",
" <td> MONTES LP</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td> TS18 3TS</td>\n",
" <td> SEACONTRACTORS MARITIME PERSONNEL (UK) LTD</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td> SO15 4HQ</td>\n",
" <td> ICENIUM LTD</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td> BS1 4TR</td>\n",
" <td> THROUGH LIFE SUPPORT LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td> IP12 4AE</td>\n",
" <td> THE COOLER WATER COMPANY LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td> SE6 2TS</td>\n",
" <td> VOB ENTERPRISE LTD</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td> PL6 6RY</td>\n",
" <td> BULAU GENERAL CONSTRUCTION LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td> E5 9NH</td>\n",
" <td> ARETHA INTERNATIONAL LTD</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td> G2 3EH</td>\n",
" <td> INTERNATIONAL CORRESPONDENCE SCHOOLS LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td> WC2A 3BP</td>\n",
" <td> SIR JOHN SOANE'S MUSEUM TRUST</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td> EX24 6QJ</td>\n",
" <td> ELMWOOD RESIDENTIAL HOME LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td> TA10 9PN</td>\n",
" <td> CLIMATE ACTION WEST COMMUNITY INTEREST COMPANY</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td> NG6 8BL</td>\n",
" <td> R C & T LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td> IG1 1BA</td>\n",
" <td> ADVICE WISE SOLICITORS LTD</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96</th>\n",
" <td> NaN</td>\n",
" <td> UPPER BIRNIE FARMS (1987)</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td> NaN</td>\n",
" <td> THE STAFFORD OUTDOOR COMPANY LIMITED</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td> N1 7GU</td>\n",
" <td> CC INTERNATIONAL LTD</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td> W5 2BS</td>\n",
" <td> SYCAMORE WOODS LTD</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>100 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" CompanyAddress1 CompanyName \\\n",
"0 M8 4HG EARO ESTATES LTD \n",
"1 WD6 3EW AMERICAN EXPRESSO LIMITED \n",
"2 WC2H 8LS SIXTEEN25 \n",
"3 N7 9DP DATASAFE SERVICES LIMITED \n",
"4 BT25 1RU BALLYNARIS LTD \n",
"5 EX13 5AD SUBSEA INSPECTION SW LTD \n",
"6 BH24 1PN SOUTHWOOD BUILDING CONTRACTORS LIMITED \n",
"7 NG5 1FX MISS B CREATIVE LIMITED \n",
"8 WA13 0RX S D C CONSULTANCY SERVICES LIMITED \n",
"9 BA4 6BR APPLECORE DEVELOPMENTS LIMITED \n",
"10 RH14 9SG KMJ PROPERTY (TUNBRIDGE WELLS) LIMITED \n",
"11 B49 6EL PRC PLUMBING AND HEATING LIMITED \n",
"12 NaN UMSI LIMITED \n",
"13 SE15 5GG AZA-MAB LTD \n",
"14 BT4 3ET HEXAGON ASSOCIATES LIMITED \n",
"15 WC1R 4JS ARROW GLOBAL MASSEY LIMITED \n",
"16 NaN GILESWOODFORD LIMITED \n",
"17 BS40 5SH HIGHWOOD HOUSE PUBLISHING LTD \n",
"18 ME4 5PT DAZZITTO PHOTOGRAPHY LIMITED \n",
"19 HR2 9EW EASTHOLME COURT MANAGEMENT LIMITED \n",
"20 WS13 6AA LIBERATION MEDIA LTD \n",
"21 BT17 0AJ A M AGENCIES LTD \n",
"22 WF8 2UR FAA INSTALLATIONS LIMITED \n",
"23 SK9 7LF COFFEE CULTURE CAFE & EATERY LIMITED \n",
"24 CF23 8AA ADERYN BUILDING CONSULTANCY LIMITED \n",
"25 KA9 1PB BUZZWORKS HOTELS LIMITED \n",
"26 WF5 0SP JORDAN-HOWELL LTD \n",
"27 BT78 5EJ BROOMCO (3113) LIMITED \n",
"28 LE7 2HB DOREL (EUROPE) LTD. \n",
"29 BD13 5HN OLD SPOT BREWERY LIMITED \n",
".. ... ... \n",
"70 SW16 3DJ TERRAHOUSE MANANGEMENT LIMITED \n",
"71 PE21 9DY EAST LINCS SMART REPAIR LTD \n",
"72 EX10 9HG 25 SPENCER ROAD RTM COMPANY LTD \n",
"73 HA8 7LW CASTLEFORD WATER TREATMENT LLP \n",
"74 EC1N 8JY ODDC LIMITED \n",
"75 TS25 2BW FAIRFIELD PROPERTY MANAGEMENT SERVICES (NE) LTD \n",
"76 CW2 7LA PRIDE SEA EDUCATION & CONSULTING LIMITED \n",
"77 CM4 0DU WRITTLE ROAD (CHELMSFORD) RESIDENTS ASSOCIATIO... \n",
"78 TN2 3GG SPECIALIST KITCHEN FITTERS LIMITED \n",
"79 E8 2HN JONNY DECKER LIMITED \n",
"80 SL2 3RF FLEET MATTERS LTD \n",
"81 IP4 2AJ RED EYE CONSULTING LIMITED \n",
"82 EH3 9WJ MONTES LP \n",
"83 TS18 3TS SEACONTRACTORS MARITIME PERSONNEL (UK) LTD \n",
"84 SO15 4HQ ICENIUM LTD \n",
"85 BS1 4TR THROUGH LIFE SUPPORT LIMITED \n",
"86 IP12 4AE THE COOLER WATER COMPANY LIMITED \n",
"87 SE6 2TS VOB ENTERPRISE LTD \n",
"88 PL6 6RY BULAU GENERAL CONSTRUCTION LIMITED \n",
"89 E5 9NH ARETHA INTERNATIONAL LTD \n",
"90 G2 3EH INTERNATIONAL CORRESPONDENCE SCHOOLS LIMITED \n",
"91 WC2A 3BP SIR JOHN SOANE'S MUSEUM TRUST \n",
"92 EX24 6QJ ELMWOOD RESIDENTIAL HOME LIMITED \n",
"93 TA10 9PN CLIMATE ACTION WEST COMMUNITY INTEREST COMPANY \n",
"94 NG6 8BL R C & T LIMITED \n",
"95 IG1 1BA ADVICE WISE SOLICITORS LTD \n",
"96 NaN UPPER BIRNIE FARMS (1987) \n",
"97 NaN THE STAFFORD OUTDOOR COMPANY LIMITED \n",
"98 N1 7GU CC INTERNATIONAL LTD \n",
"99 W5 2BS SYCAMORE WOODS LTD \n",
"\n",
" URLs \n",
"0 NaN \n",
"1 NaN \n",
"2 [https://www.sc.com/uk/, https://www.sc.com/en... \n",
"3 NaN \n",
"4 NaN \n",
"5 NaN \n",
"6 NaN \n",
"7 NaN \n",
"8 NaN \n",
"9 NaN \n",
"10 NaN \n",
"11 NaN \n",
"12 NaN \n",
"13 NaN \n",
"14 NaN \n",
"15 NaN \n",
"16 NaN \n",
"17 NaN \n",
"18 NaN \n",
"19 NaN \n",
"20 NaN \n",
"21 NaN \n",
"22 NaN \n",
"23 NaN \n",
"24 NaN \n",
"25 NaN \n",
"26 NaN \n",
"27 NaN \n",
"28 NaN \n",
"29 NaN \n",
".. ... \n",
"70 NaN \n",
"71 NaN \n",
"72 NaN \n",
"73 NaN \n",
"74 NaN \n",
"75 NaN \n",
"76 NaN \n",
"77 NaN \n",
"78 NaN \n",
"79 NaN \n",
"80 NaN \n",
"81 NaN \n",
"82 NaN \n",
"83 NaN \n",
"84 NaN \n",
"85 NaN \n",
"86 NaN \n",
"87 NaN \n",
"88 NaN \n",
"89 NaN \n",
"90 NaN \n",
"91 NaN \n",
"92 NaN \n",
"93 NaN \n",
"94 NaN \n",
"95 NaN \n",
"96 NaN \n",
"97 NaN \n",
"98 NaN \n",
"99 NaN \n",
"\n",
"[100 rows x 3 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# svn checkout http://pygoogle.googlecode.com/svn/trunk pygoogle-read-only\n",
"# python setup.py build\n",
"# sudo python setup.py install\n",
"\n",
"from pygoogle import pygoogle\n",
"from time import sleep\n",
"from pprint import pprint\n",
"#g = pygoogle('! LTD company')\n",
"#g.pages = 1\n",
"#print '*Found %s results*'%(g.get_result_count())\n",
"#g.get_urls()\n",
"\n",
"#print list(smalldata['CompanyName'].values)\n",
"compnames = list(smalldata['CompanyName'].values)\n",
"#compadds = list(smalldata['RegAddress_AddressLine1'].values)\n",
"compadds = list(smalldata['RegAddress_PostCode'].values)\n",
"#compadds = [i.split(' ')[0] for i in list(smalldata['RegAddress_PostCode'].values)]\n",
"\n",
"\n",
"#urls = []\n",
"#counter = 0\n",
"#for i,j in zip(compnames,compadds):\n",
"# g = pygoogle(i+' contact '+j)\n",
"# g.pages = 1\n",
"# urls.append(g.get_urls())\n",
"# counter += 1\n",
"# sleep(np.random.uniform(5,10))\n",
"#print urls\n",
"\n",
"os.chdir(datadir)\n",
"\n",
"urlpklfile=\"URLs.pkl\"\n",
"urlfolderpath=os.path.join(datadir,urlpklfile)\n",
"if (os.path.exists(urlfolderpath)==True):\n",
" print(\"Pickle file containing URL data found. Loading it...\")\n",
" urls=pickle.load(open(urlfolderpath,'r'))\n",
"else:\n",
" print(\"Fetching company URLs from Google...\")\n",
" urls = []\n",
" counter = 0\n",
" for i,j in zip(compnames,compadds):\n",
" g = pygoogle(i+' contact '+j)\n",
" g.pages = 1\n",
" urls.append(g.get_urls())\n",
" if (counter % 10 == 0):\n",
" with open(urlpklfile,'wb') as output:\n",
" pickle.dump(urls, output, pickle.HIGHEST_PROTOCOL)\n",
" counter += 1\n",
" sleep(np.random.uniform(5,10))\n",
" with open(urlpklfile,'wb') as output:\n",
" pickle.dump(urls, output, pickle.HIGHEST_PROTOCOL)\n",
"\n",
"os.chdir(rootdir)\n",
"\n",
" \n",
"#urls = [[u'https://www.facebook.com/andrea.shaw.564', u'https://www.facebook.com/dianne.schultz1', u'http://www.192.com/atoz/business/brentwood/financial--advisers--(independent)/', u'https://classictvhistory.wordpress.com/tag/have-gun-will-travel/', u'http://i.dujour.com/december-print/', u'http://www.greenvillecountybar.org/Gbar_News_PDF/2014/122014.pdf', u'http://dartmouthalumnimagazine.com/class-notes/1970/all', u'http://www.dls.org/pdf/magazine/october_2007_magazine.pdf'], [u'http://www.city-data.com/clackamas-county/D/Delenka-Lane-2.html', u'http://law.justia.com/cases/alaska/supreme-court/2011/', u'https://www.facebook.com/htmody', u'https://www.facebook.com/terry.meyers.5', u'http://www.ciwf.com/media/1141326/outofsight-full-report.pdf', u'http://www.losfoundation.org/wp-content/uploads/2013/06/Donors-2011_2012.pdf', u'http://svcf.org/help/recognition/', u'https://www.ipo.gov.uk/t-tmj/tm-journals/2015-007/owner.html'], [u'https://www.sc.com/uk/contact-us/', u'https://www.sc.com/en/contact-us/', u'https://www.sc.com/je/contact-us/index.html', u'https://www.sc.com/hk/investor-relations/_documents/en/news/20130905d.pdf', u'http://www.aim25.ac.uk/cgi-bin/vcdf/detail?coll_id=18442&inst_id=118&nv1=search&nv2=', u'http://www.bloomberg.com/research/stocks/people/person.asp?personId=8307423&ticker=STAN:LN', u'http://www.hkexnews.hk/listedco/listconews/sehk/2015/0519/LTN20150519338.pdf', u'http://www.sebi.gov.in/dp/stdchtdrhp.pdf'], [u'https://www.facebook.com/theoldglovefactorymarketplace', u'https://www.grinnell.edu/about/visit/spaces/old-glove-factory', u'http://en.wikipedia.org/wiki/GlaxoSmithKline', u'http://www.dailykos.com/story/2013/01/06/1163848/-KosAbility-Trying-to-Clean-Out-an-Old-House-with-Arthritis-and-Asthma', u'http://www.cdc.gov/NCEH/publications/books/housing/cha05.htm', u'http://www.slideshare.net/MedlineIndustriesInc/surgical-gloves-a-comprehensive-guide', u'http://www.cpsc.gov/pagefiles/112284/5015.pdf', u'http://ftp.asahq.org/publicationsAndServices/latexallergy.pdf'], [u'http://www.thegsa.co.za/index.php?nav=destination_country&view=28', u'https://www.facebook.com/anna.brass1'], [], [u'http://books.openedition.org/obp/326', u'http://www.hrblock.com/tax-offices/local-offices/#!/en/office-profile/12546', u'http://www.caicv.org/dev/data/fckeditor/cms/file/Quorum_July2010WEB.pdf', u'https://play.google.com/store/apps/details?id=com.mhriley.spendingtracker&hl=en', u'https://www.facebook.com/walter.kajer.1', u'http://duchyofcornwall.org/assets/images/documents/Poundbury_Factsheet_2013.pdf', u'http://www.lihp.org/Content/2011 annual report.pdf', u'http://www.kildare.ie/business/directory/list-companies.asp?Category=Business Services'], [u'http://cera.govt.nz/sites/default/files/common/tc3-residential-rebuild-booklet-A4-20121204.pdf', u'http://www.thomsonlocal.com/Funeral-Directors/in/Surrey/', u'http://www.britishculinaryfederation.co.uk/bcf/wp-content/uploads/2011/06/091124_Culinary_News_December_v6.pdf', u'http://www.hackney.gov.uk/Assets/Documents/ht276.pdf', u'http://www.insightpublications.com.au/pdf_preview/isp-julius-caesar-10-pages.pdf', u'http://www.tripadvisor.co.uk/Hotel_Review-g191252-d491974-Reviews-Trimstone_Manor_Country_House_Hotel-Ilfracombe_Devon_England.html', u'http://www.lincoln.ac.nz/Documents/LEaP/WMK ICRF Final May 2013.pdf', u'http://delvinvillage.com/directory/'], [u'http://www.deloitte.com/', u'http://www.schencksc.com/2015rpctour/', u'http://www.schencksc.com/2013recforum/', u'https://www.linkedin.com/in/jeffreyshlefstein', u'http://www.aicpa.org/BecomeACPA/Pages/InternshipsandCooperativePrograms.aspx', u'http://www.freshbooks.com/accountants/map', u'http://www.mncpa.org/find-a-cpa/cpa-yellow-pages/list.aspx?l=c', u'http://cdn.colorado.gov/cs/Satellite?blobcol=urldata&blobheadername1=Content-Disposition&blobheadername2=Content-Type&blobheadervalue1=inline;+filename=\"March+28,+2007+Board+Meeting+Minutes.pdf\"&blobheadervalue2=application/pdf&blobkey=id&blobtable=MungoBlobs&blobwhere=1251832310203&ssbinary=true'], []]\n",
"#urls =[[u'http://www.192.com/atoz/business/brentwood/financial--advisers--(independent)/', u'http://www.ucl.ac.uk/consultants/homepage'], [u'http://www.contactps.ca/', u'https://411.ca/business/profile/7759616'], [u'https://www.sc.com/en/contact-us/', u'https://www.sc.com/', u'https://www.sc.com/je/contact-us/index.html', u'https://www.sc.com/hk/investor-relations/_documents/en/news/20090902a.pdf', u'http://www.sebi.gov.in/dp/stdchtdrhp.pdf', u'http://www.bloomberg.com/research/stocks/people/person.asp?personId=8307423&ticker=STAN:LN', u'http://vpr.hkma.gov.hk/pdf/100269/fd_int/fd_int_0613_pt01.pdf', u'http://www.fogl.com/fogl/uploads/companypresentations/annual_report_2012.pdf'], [], [u'https://openaccess.adb.org/bitstream/handle/11540/1651/Volume 28_No 2_2011_06.pdf?sequence=1', u'http://yourtireshopsupply.com/manufacturer/27/grey-pneumatic-corp', u'https://www.facebook.com/people/\\xe0\\xb8\\xa8\\xe0\\xb8\\xb4\\xe0\\xb8\\xa3\\xe0\\xb8\\xb4\\xe0\\xb8\\xa3\\xe0\\xb8\\xb1\\xe0\\xb8\\x95\\xe0\\xb8\\x99\\xe0\\xb9\\x8c-\\xe0\\xb8\\x97\\xe0\\xb8\\xa7\\xe0\\xb8\\xb4\\xe0\\xb8\\xa7\\xe0\\xb8\\xb1\\xe0\\xb8\\x92\\xe0\\xb8\\x99\\xe0\\xb9\\x8c/100004117395751', u'https://th-th.facebook.com/donnapa.apple', u'https://www.facebook.com/sasesopit.muttamara', u'https://th-th.facebook.com/KLShopbymarie', u'https://th-th.facebook.com/soraya.lomsungnoen.1', u'https://th-th.facebook.com/namthip.bunthong.7'], [u'http://agra-alliance.org/download/53396d7f2a934/', u'https://www.africare.org/wp-content/uploads/2014/08/AFSRNo4_BrysonEley_SuccessStoryGuide_Final_Jan7_2008_updated_June08.pdf'], [u'https://www.clearbooks.co.uk/directory/business', u'https://www.tapa.co.uk/the-tapa-opt-out-ledger.php', u'http://www.dailymail.co.uk/health/article-1330839/Blundering-doctors-leave-mother-terrified-falsely-diagnosing-brain-haemorrhage.html'], [u'http://www.priorygroup.com/location-results/item/the-priory-hospital-glasgow', u'http://www.yell.com/biz/1st-choice-plumbing-and-heating-glasgow-901468909/', u'https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Consumer-Business/gx-cb-global-powers-of-retailing.pdf', u'http://www.rightmove.co.uk/property-for-sale/property-30497721.html', u'http://www.hazelwood.glasgow.sch.uk/', u'https://plus.google.com/+Paranetuklimited', u'http://www.kinningparkcomplex.org/projects-overview/bike-project/', u'https://www.glasgow.gov.uk/CHttpHandler.ashx?id=14911&p=0'], [u'http://www.scleeaccountant.com/', u'http://www.192.com/places/sk/sk8-1/sk8-1nq/', u'https://www.icpas.org/hc-career-center.aspx?id=21550', u'https://www.linkedin.com/pub/leona-crouch/26/b42/b17', u'http://www.burkertvaluation.com/wp-content/uploads/2014/04/Rpb-Vitae_General.pdf', u'http://www.alec.co.uk/cvtips/examgrcv.htm', u'http://www.chaos.umd.edu/misc/origplates.html', u'http://www.atiner.gr/bio/Syrrakos.doc'], [u'https://uk.linkedin.com/pub/david-wasilewski/27/143/368']]\n",
"\n",
"\n",
"# TO USE A HAND-PICKED SET OF URLS TO AVOID REPEAT REQUESTS TO GOOGLE, WHICH GET YOU BLOCKED\n",
"urls = [[u'http://www.192.com/atoz/business/brentwood/financial--advisers--(independent)/'], [u'http://www.plantmethods.com/content/10/October/2014', u'http://www.plantmethods.com/content?page=2&itemsPerPage=25'], [u'https://www.sc.com/uk/contact-us/', u'https://www.sc.com/en/contact-us/', u'https://www.sc.com/je/contact-us/index.html', u'https://www.sc.com/hk/investor-relations/_documents/en/news/20130905d.pdf', u'https://www.sc.com/hk/investor-relations/_documents/en/news/20140520b.pdf', u'http://www.bloomberg.com/research/stocks/people/person.asp?personId=8307423&ticker=STAN:LN', u'http://www.sebi.gov.in/dp/stdchtdrhp.pdf', u'http://www.hkexnews.hk/listedco/listconews/sehk/2015/0519/LTN20150519338.pdf'], [u'http://www.nhs.uk/Services/Trusts/Pharmacies/DefaultView.aspx?id=89768', u'http://www.boots.com/'], [], [], [u'https://www.xero.com/', u'http://www.sage.com/'], [u'http://www.mastercard.us/', u'http://www.baxterstorey.co.uk/'], [u'http://www.192.com/places/sk/sk8-1/sk8-1nq/', u'http://www.ey.com/', u'http://www.grantthornton.com/'], []]\n",
"\n",
"\n",
"#print len(urls)\n",
"#pprint(urls)\n",
"\n",
"#filteredurls = urls[:]\n",
"#for count,i in enumerate(filteredurls[:]):\n",
"# for j in i:\n",
"# print j\n",
"# if ('contact' not in j):\n",
"# filteredurls[count].remove(j)\n",
"# print \"NOT FOUND\"\n",
" #print j\n",
" #print filteredurls[count]\n",
"#print filteredurls\n",
"\n",
"# This one exceeds maximum recursion\n",
"#def empty(seq):\n",
"# try:\n",
"# return all(map(empty, seq))\n",
"# except TypeError:\n",
"# return False\n",
"\n",
"def empty(seq):\n",
" \"\"\"Check if a nested list (list of lists) is completely empty, if so return 'True'\"\"\"\n",
" containslist = []\n",
" for i in range(0,len(seq)-1):\n",
" if seq[i]:\n",
" containslist.append(False)\n",
" else:\n",
" containslist.append(True)\n",
" if (False in containslist):\n",
" return False\n",
" else:\n",
" return True\n",
"\n",
"def filtering(initem):\n",
" \"\"\" Check if string 'contact' is in URL, if so split by it and keep first part, else return empty list\"\"\"\n",
" if ('contact' in initem):\n",
" return initem.split('contact')[0]\n",
" else:\n",
" return []\n",
" \n",
"filteredurls = [np.nan]*len(urls)\n",
"for i in range(0,len(urls)-1):\n",
" filteredurls[i] = [filtering(j) for j in urls[i]]\n",
" if empty(filteredurls[i]):\n",
" #if not filteredurls[i]:\n",
" filteredurls[i] = np.nan\n",
"#pprint(filteredurls)\n",
"\n",
"#filteredurls = urls[:]\n",
"#for i,j in enumerate(urls):\n",
"# toremove = [k for k in urls[i] if 'contact' not in urls[i]]\n",
"# for l in j:\n",
"# if(j in toremove):\n",
"# filteredurls[i].remove(j)\n",
"#print filteredurls\n",
"\n",
"d = {'CompanyName' : pan.Series(compnames), 'CompanyAddress1' : pan.Series(compadds), 'URLs' : pan.Series(filteredurls)}\n",
"\n",
"dfurls = pan.DataFrame(d)\n",
"dfurls\n",
"\n",
"#urls = [pygoogle(i).get_urls()[0] for i in list(smalldata['CompanyName'].values)]\n",
"#print urls\n",
"#smalldata['WebURL'] = Series([pygoogle(i).get_urls()[0] for i in data['CompanyName']], index=smalldata.index)\n",
"#compnames = smalldata.iterrows()[1]\n",
"#print compnames\n",
"\n",
"\n",
"#for i in range(0,len(smalldata)-1):\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.growthintel.com/about-us/\n"
]
}
],
"source": [
"import re\n",
"from mechanize import Browser\n",
"\n",
"# http://stackoverflow.com/questions/1011975/how-to-get-links-on-a-webpage-using-mechanize-and-open-those-links\n",
"def findAboutUs(inputlink):\n",
" \"\"\"Given an initial (hopefully, homepage) URL, look for an 'About Us' link, if not found just return initial URL.\"\"\"\n",
" \n",
" if (inputlink == np.nan):\n",
" return np.nan\n",
" \n",
" #print inputlink\n",
" br = Browser()\n",
" br.open(inputlink)\n",
"\n",
" aboutuslinks = []\n",
" # br.links(url_regex=\"about\")\n",
" # br.links(text_regex=\"About( us)?\")\n",
" for link in br.links(text_regex=\"About\"):\n",
" #print inputlink, link.url\n",
" aboutuslinks.append(link)\n",
" #br.follow_link(link) # takes EITHER Link instance OR keyword args\n",
" #br.back()\n",
"\n",
" #print aboutuslinks\n",
"\n",
" # http://stackoverflow.com/questions/10994251/mechanize-urllib-beautifulsoup-relative-paths\n",
" for i,j in enumerate(aboutuslinks):\n",
" \"\"\"Mechanize often returns relative links, split into .base_url and .url We join them -if necessary- here.\"\"\"\n",
" domain = re.search('(http:\\/\\/.*\\.\\D+?|https:\\/\\/.*\\.\\D+?)\\/',j.base_url.strip())\n",
" if domain:\n",
" domain = domain.group(1)\n",
" if re.search('mailto',j.url.strip()) != None:\n",
" pass\n",
" elif re.search('(http:\\/\\/.*\\.\\D+?|https:\\/\\/.*\\.\\D+?)\\/',j.url.strip()) != None:\n",
" u = j.url.strip()#.encode('utf8')\n",
" elif re.search('^/',j.url.strip()) != None:\n",
" u = domain+j.url.strip()#.encode('utf8')\n",
" else:\n",
" u = domain+'/'+j.url.strip()#.encode('utf8')\n",
" aboutuslinks[i] = u\n",
" \n",
" # Some non-About Us links somehow still make it here, filter them out by requiring an 'about' in the URL\n",
" #print aboutuslinks\n",
" aboutuslinks = [i for i in aboutuslinks if 'about' in i]\n",
" #print aboutuslinks\n",
"\n",
" # If multiple 'About Us' links found (sometimes duplicates), take the first one only\n",
" if (aboutuslinks and isinstance(aboutuslinks, list)):\n",
" aboutuslink = aboutuslinks[0]\n",
" else:\n",
" aboutuslink = aboutuslinks\n",
"\n",
" # If no 'About us' link is found return initial (input) link\n",
" if aboutuslink:\n",
" return aboutuslink\n",
" else:\n",
" return inputlink\n",
"\n",
"\n",
"#print findAboutUs(\"https://www.sc.com/uk/\")\n",
"print findAboutUs(\"http://www.growthintel.com\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"Boost B2B marketing conversion rates with smart data.\n",
"\n",
"\n",
"Home\n",
"Free Demo\n",
"Case Studies\n",
"Resources\n",
"We’re hiring!\n",
" \n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"About us\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"Growth Intelligence is a fast-growing business based in Canary Wharf. Founded three years ago by an ex journalist and a military computer scientist, the company uses web-scale ‘open source’ data and machine learning to deliver Predictive Marketing to a number of major blue-chip customers and some newer entrants.\n",
"The software tracks the performance and activity of all the companies in the economy in real-time using their data ‘footprint’. By learning the common patterns of behaviour at companies known to have a ‘need’ for a product at a certain time (because they bought a product at that time) it is able to predict which companies are more and less likely to have a need for a product today.\n",
"The system works like a ‘recommendation engine’ to predicatively segment businesses into ‘needs categories’ using open big data. This system is more flexible, sophisticated and much more likely to create useful groupings of customers than a simply dividing by revenue, credit score or SIC code. It could be used in conjunction of simpler revenue-split methods to assign businesses to account management teams with expertise in meeting a specific need.\n",
" Our clients have seen up to 14x performance boosts in marketing ROI. Try Growth Intelligence today and see what we can do for your business.\n",
"\n",
"Tom Gatten\n",
"CEO\n",
"Tom has worked at the BBC World Service, the Times of India and business intelligence firm Screen Digest. Tom graduated with the top first in his year from Oxford. He defined the original algorithms and the theoretical principles behind the core software of Growth Intelligence and now leads company strategy.\n",
"\n",
"\n",
"\n",
"Prashant Majmudar\n",
"CTO\n",
"Prashant draws on six years delivering major defence and security software projects to government and corporations at BAE Systems Detica. Prior to Detica, Prashant worked for three years at DSTL, the government defence research agency. Prashant holds a first-class degree in Physics from Warwick.\n",
"\n",
"\n",
"\n",
"Alex Mitchell\n",
"Data Team Lead\n",
"Alex has worked at both BAE Systems and most recently for five years at BAE Systems Detica as a Principal Consultant before joining Growth Intelligence to lead the data science team. Alex holds a First-class degree in Computer Science from Nottingham University and a Masters in Machine Learning and Data Mining from Bristol University, where he was awarded the top distinction.\n",
"\n",
"\n",
"\n",
"Hemel Popat\n",
"Advisor\n",
"Hemel oversees Growth Intelligence’s technical strategy. Hemel has a background as a Technical Architect at the Ministry of Justice, Technical Authority for Central Government and Principal Consultant at PA Consulting Group and NatWest Bank.\n",
"\n",
"\n",
"\n",
"Sam Stephens\n",
"Software Developer\n",
"\n",
"\n",
"\n",
"Chiara Mureddu\n",
"Operations Manager\n",
"\n",
"\n",
"\n",
"Dylan Barth\n",
"Software Developer\n",
"Prior to joining the Growth Intelligence team, Dylan co-founded the Foundation for Learning Equality, a startup nonprofit with the ambitious goal of bridging the digital divide by bringing free online learning materials to the offline world. He holds a B. S. in Cognitive Science with a specialization in Human-Computer Interaction from UC San Diego.\n",
"\n",
"\n",
"\n",
"Hal Varian\n",
"Advisory Board\n",
"Hal R. Varian is the chief economist at Google. Since 2002 he has been involved in many aspects of the company, including auction design, econometric analysis, finance, corporate strategy and public policy. Hal had a major role in analysing and refining Google’s ad-auction system which contributed so much to Google’s success. Hal has been at the forefront of innovation at the crossroads between finance and web technology for many years.\n",
"\n",
"\n",
"\n",
"JP Rangaswami\n",
"Advisory Board\n",
"JP Rangaswami is the Chief Scientist at Salesforce.com. He reports directly to Marc Benioff (CEO) and is responsible for Salesforce’s product strategy, in particular innovation through Salesforce Apps and Force.com.\n",
"\n",
"\n",
"\n",
"Paul Johnson\n",
"Advisory Board\n",
"Paul is a Fellow of the Institute of Chartered Accountants in England and Wales. He spent 38 years with KPMG Europe LLP, becoming a Partner in 1988 and has extensive experience of working with companies in a variety of different industries in both the listed and private sectors. For the last 12 years he was Chairman of KPMG London and Eastern Counties and a member of KPMG’s UK Markets Executive.\n",
"\n",
"\n",
"\n",
"Jens Lapinski\n",
"Advisory Board\n",
"Co-founder at Forward Labs, a London-based startup studio. Director at Techstars London from January 2014 onwards.\n",
"\n",
"\n",
"\n",
"Jeremy Silver\n",
"Advisory Board\n",
"Jeremy Silver is an entrepreneur, digital media adviser and thought-leader. He is Executive Chairman of Semetric (real-time analytics for the entertainment industry – check out musicmetric) and Chair of MusicGlue (online ticketing and services for artists).\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"Recent Tweets \n",
"\n",
"Follow @growthintel\n",
"\n",
"\n",
" \n",
" \n",
"\n",
"PagesHome\n",
"About us\n",
"Free Demo\n",
"How does Gi make marketing smarter? Meet the Buyer Matrix™\n",
"Case Studies\n",
"Careers\n",
"Resources\n",
"Press Kit\n",
"Blog\n",
"Contact us\n",
"Privacy Policy\n",
" \n",
"\n",
"Contact us [email protected] 42, 1 Canada Square, E14 5AA, [email protected]: +44 (0)20 3725 7575\r\n",
"Office: +44 (0)20 3668 3664\n",
" \n",
"\n",
"Follow us \n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"©2014 Growth Intelligence® (Pelucid Ltd.) - Smart B2B Lead Generation We're hiring!\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n"
]
}
],
"source": [
"#from lxml import html\n",
"#import requests\n",
"#page = requests.get('https://www.sc.com/uk/')\n",
"#tree = html.fromstring(page.text)\n",
"#print tree\n",
"\n",
"#from BeautifulSoup import BeautifulSoup\n",
"#import bs4\n",
"\n",
"from bs4 import BeautifulSoup\n",
"import urllib\n",
"\n",
"def retrieveText(inputlink):\n",
" \"\"\"Fetch the text from a link to an HTML file\"\"\"\n",
" \n",
" if (inputlink == np.nan):\n",
" return np.nan\n",
" \n",
" html = urllib.urlopen(inputlink).read()\n",
" soup = BeautifulSoup(html)\n",
" texts = soup.findAll(text=True)\n",
"\n",
" # http://stackoverflow.com/questions/1936466/beautifulsoup-grab-visible-webpage-text\n",
" #def visible(element):\n",
" # if element.parent.name in ['style', 'script', '[document]', 'head', 'title']:\n",
" # return False\n",
" # elif element.parent.name isinstance(element, Comment):\n",
" # #elif re.match('<!--.*-->', str(element)):\n",
" # return False\n",
" # return True\n",
" \n",
" #visible_texts = filter(visible, texts)\n",
" \n",
" [s.extract() for s in soup(['style', 'script', '[document]', 'head', 'title'])]\n",
" visible_text = soup.getText()\n",
" \n",
" return visible_text\n",
"\n",
"\n",
"#print retrieveText('https://www.sc.com/uk/about-us/index.html')\n",
"print retrieveText('http://www.growthintel.com/about-us/')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"Boost B2B marketing conversion rates with smart data.\n",
"\n",
"\n",
"Home\n",
"Free Demo\n",
"Case Studies\n",
"Resources\n",
"We’re hiring!\n",
" \n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"About us\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"Growth Intelligence is a fast-growing business based in Canary Wharf. Founded three years ago by an ex journalist and a military computer scientist, the company uses web-scale ‘open source’ data and machine learning to deliver Predictive Marketing to a number of major blue-chip customers and some newer entrants.\n",
"The software tracks the performance and activity of all the companies in the economy in real-time using their data ‘footprint’. By learning the common patterns of behaviour at companies known to have a ‘need’ for a product at a certain time (because they bought a product at that time) it is able to predict which companies are more and less likely to have a need for a product today.\n",
"The system works like a ‘recommendation engine’ to predicatively segment businesses into ‘needs categories’ using open big data. This system is more flexible, sophisticated and much more likely to create useful groupings of customers than a simply dividing by revenue, credit score or SIC code. It could be used in conjunction of simpler revenue-split methods to assign businesses to account management teams with expertise in meeting a specific need.\n",
" Our clients have seen up to 14x performance boosts in marketing ROI. Try Growth Intelligence today and see what we can do for your business.\n",
"\n",
"Tom Gatten\n",
"CEO\n",
"Tom has worked at the BBC World Service, the Times of India and business intelligence firm Screen Digest. Tom graduated with the top first in his year from Oxford. He defined the original algorithms and the theoretical principles behind the core software of Growth Intelligence and now leads company strategy.\n",
"\n",
"\n",
"\n",
"Prashant Majmudar\n",
"CTO\n",
"Prashant draws on six years delivering major defence and security software projects to government and corporations at BAE Systems Detica. Prior to Detica, Prashant worked for three years at DSTL, the government defence research agency. Prashant holds a first-class degree in Physics from Warwick.\n",
"\n",
"\n",
"\n",
"Alex Mitchell\n",
"Data Team Lead\n",
"Alex has worked at both BAE Systems and most recently for five years at BAE Systems Detica as a Principal Consultant before joining Growth Intelligence to lead the data science team. Alex holds a First-class degree in Computer Science from Nottingham University and a Masters in Machine Learning and Data Mining from Bristol University, where he was awarded the top distinction.\n",
"\n",
"\n",
"\n",
"Hemel Popat\n",
"Advisor\n",
"Hemel oversees Growth Intelligence’s technical strategy. Hemel has a background as a Technical Architect at the Ministry of Justice, Technical Authority for Central Government and Principal Consultant at PA Consulting Group and NatWest Bank.\n",
"\n",
"\n",
"\n",
"Sam Stephens\n",
"Software Developer\n",
"\n",
"\n",
"\n",
"Chiara Mureddu\n",
"Operations Manager\n",
"\n",
"\n",
"\n",
"Dylan Barth\n",
"Software Developer\n",
"Prior to joining the Growth Intelligence team, Dylan co-founded the Foundation for Learning Equality, a startup nonprofit with the ambitious goal of bridging the digital divide by bringing free online learning materials to the offline world. He holds a B. S. in Cognitive Science with a specialization in Human-Computer Interaction from UC San Diego.\n",
"\n",
"\n",
"\n",
"Hal Varian\n",
"Advisory Board\n",
"Hal R. Varian is the chief economist at Google. Since 2002 he has been involved in many aspects of the company, including auction design, econometric analysis, finance, corporate strategy and public policy. Hal had a major role in analysing and refining Google’s ad-auction system which contributed so much to Google’s success. Hal has been at the forefront of innovation at the crossroads between finance and web technology for many years.\n",
"\n",
"\n",
"\n",
"JP Rangaswami\n",
"Advisory Board\n",
"JP Rangaswami is the Chief Scientist at Salesforce.com. He reports directly to Marc Benioff (CEO) and is responsible for Salesforce’s product strategy, in particular innovation through Salesforce Apps and Force.com.\n",
"\n",
"\n",
"\n",
"Paul Johnson\n",
"Advisory Board\n",
"Paul is a Fellow of the Institute of Chartered Accountants in England and Wales. He spent 38 years with KPMG Europe LLP, becoming a Partner in 1988 and has extensive experience of working with companies in a variety of different industries in both the listed and private sectors. For the last 12 years he was Chairman of KPMG London and Eastern Counties and a member of KPMG’s UK Markets Executive.\n",
"\n",
"\n",
"\n",
"Jens Lapinski\n",
"Advisory Board\n",
"Co-founder at Forward Labs, a London-based startup studio. Director at Techstars London from January 2014 onwards.\n",
"\n",
"\n",
"\n",
"Jeremy Silver\n",
"Advisory Board\n",
"Jeremy Silver is an entrepreneur, digital media adviser and thought-leader. He is Executive Chairman of Semetric (real-time analytics for the entertainment industry – check out musicmetric) and Chair of MusicGlue (online ticketing and services for artists).\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"Recent Tweets \n",
"\n",
"Follow @growthintel\n",
"\n",
"\n",
" \n",
" \n",
"\n",
"PagesHome\n",
"About us\n",
"Free Demo\n",
"How does Gi make marketing smarter? Meet the Buyer Matrix™\n",
"Case Studies\n",
"Careers\n",
"Resources\n",
"Press Kit\n",
"Blog\n",
"Contact us\n",
"Privacy Policy\n",
" \n",
"\n",
"Contact us [email protected] 42, 1 Canada Square, E14 5AA, [email protected]: +44 (0)20 3725 7575\r\n",
"Office: +44 (0)20 3668 3664\n",
" \n",
"\n",
"Follow us \n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"©2014 Growth Intelligence® (Pelucid Ltd.) - Smart B2B Lead Generation We're hiring!\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n"
]
}
],
"source": [
"def createDescription(inputlink):\n",
" \"\"\"Link the findAboutUs() and retrieveText() functions to obtain company description from input link\"\"\"\n",
" if (isinstance(inputlink,list)):\n",
" inputlink = inputlink[0]\n",
" \n",
" if (inputlink == np.nan):\n",
" return np.nan\n",
" else:\n",
" link = findAboutUs(inputlink)\n",
" text = retrieveText(link)\n",
" return text\n",
" #if (isinstance(inputlinks,list)):\n",
" # link = findAboutUs(\"http://portent.io\")\n",
" # link = findAboutUs(inputlinks[0])\n",
" #else:\n",
" # link = findAboutUs(inputlinks)\n",
" #text = retrieveText(link)\n",
" #return text\n",
"\n",
"#print createDescription(np.nan)\n",
"#print createDescription(\"https://www.sc.com/uk/\")\n",
"testlink = \"http://www.growthintel.com\"\n",
"print createDescription(testlink)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"##dfurls = dfurls.drop('CompanyDescription', 1)\n",
"#print dfurls[ pan.notnull(dfurls['URLs']) ]\n",
"#dfurls['AboutUsURL'] = dfurls['URLs'].apply(lambda x: findAboutUs(x))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#dfurls = dfurls.drop('AboutUsURL', 1)\n",
"#dfurls['CompanyDescription'] = dfurls['URLs'].apply(lambda x: createDescription(x))\n",
"#dfurls\n",
"#print dfurls.ix[dfurls['CompanyName'] == 'STANDARD CHARTERED NOMINEES LIMITED', 'CompanyDescription'].values\n",
"\n",
"\n",
"#os.chdir(datadir)\n",
"#descpklfile=\"descriptions.pkl\"\n",
"#descfolderpath=os.path.join(datadir,descpklfile)\n",
"#if (os.path.exists(descfolderpath)==True):\n",
"# print(\"Pickle file containing company descriptions data found. Loading it...\")\n",
"# dfurls=pickle.load(open(descfolderpath,'r'))\n",
"#else:\n",
"# print(\"Fetching company descriptions...\")\n",
"# dfurls['CompanyDescription'] = dfurls['URLs'].apply(lambda x: createDescription(x))\n",
"# with open(descpklfile,'wb') as output:\n",
"# pickle.dump(dfurls, output, pickle.HIGHEST_PROTOCOL)\n",
"#os.chdir(rootdir) \n",
"#dfurls"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"45\n"
]
},
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AboutUsURL</th>\n",
" <th>CompanyName</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> http://www.mckinsey.com/about_us</td>\n",
" <td> McKinsey & Company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> http://www.thewhitecompany.com/help/our-story/</td>\n",
" <td> The White Company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> http://corporate.marksandspencer.com/aboutus</td>\n",
" <td> Marks & Spencer</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> http://www.kidsco.org.uk/about-us</td>\n",
" <td> Kids Company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> http://www.thunderhead.com/what-we-do/about-us/</td>\n",
" <td> Thunderhead</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> https://www.astonmartin.com/en/company/about-us</td>\n",
" <td> Aston Martin</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> http://www.bicestervillage.com/en/company/abou...</td>\n",
" <td> Bicester Village</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> http://www.solarcentury.com/uk/about-solarcent...</td>\n",
" <td> Solarcentury</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> http://www.slc.co.uk/about-us.aspx</td>\n",
" <td> Student Loans Company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> https://stationers.org/about.html</td>\n",
" <td> The Stationers' Company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> http://www.rsc.org.uk/about-us/</td>\n",
" <td> Royal Shakespeare Company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> http://www.snellgroup.com/company/about-us/</td>\n",
" <td> Snell</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> http://www.waxchandlers.org.uk/about-us/index.php</td>\n",
" <td> The Wax Chandlers Company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> http://www.expeditors.com/our-company/about-us...</td>\n",
" <td> Expeditors</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> http://www.carbonneutral.com/about-us</td>\n",
" <td> The Carbon Neutral Company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> http://www.pewterers.org.uk/the_company/aboutu...</td>\n",
" <td> The Pewterers' Company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td> http://www.vauxhall.co.uk/about-vauxhall/about...</td>\n",
" <td> Vauxhall</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> http://ee.co.uk/our-company/about-ee</td>\n",
" <td> EE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td> http://www.candoco.co.uk/about-us/</td>\n",
" <td> Candoco Dance Company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td> http://www.victrex.com/en/company/about-us</td>\n",
" <td> Victrex</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td> http://www.ensus.co.uk/Company/About_us/</td>\n",
" <td> Ensus</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td> http://www.anglianwater.co.uk/about-us/</td>\n",
" <td> Anglian Water</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td> http://www.chequeandcredit.co.uk/about_us/</td>\n",
" <td> The Cheque and Credit Clearing Company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td> http://www.vodafone.co.uk/about-us/company-his...</td>\n",
" <td> Vodafone</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td> http://www.people1sttraining.co.uk/about-us</td>\n",
" <td> People 1st</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td> http://www.starbucks.co.uk/about-us</td>\n",
" <td> Starbucks</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td> http://www.merlinentertainments.biz/about-us</td>\n",
" <td> Merlin Entertainments</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td> http://www.bloomsbury.com/uk/company/about-us/</td>\n",
" <td> Bloomsbury Publishing</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td> http://www.alcatelonetouch.com/global-en/compa...</td>\n",
" <td> Alcatel One Touch</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td> http://masonkings.jd-dealer.co.uk/About-us/Our...</td>\n",
" <td> Masons Kings</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td> http://www.oxfordbus.co.uk/about-us/</td>\n",
" <td> Oxford Bus Company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td> http://www.patient.co.uk/about-us</td>\n",
" <td> Patient.co.uk</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td> http://www.bootstrapcompany.co.uk/about-us/</td>\n",
" <td> Bootstrap Company</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td> http://www.fusionfurniturecompany.co.uk/about.php</td>\n",
" <td> Fusion Furniture</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td> http://www.siemens.co.uk/en/about_us/</td>\n",
" <td> Siemens</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td> http://www.bosch.co.uk/en/uk/about_bosch_home_...</td>\n",
" <td> Bosch UK</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td> https://www.qualcomm.com/company/about</td>\n",
" <td> Qualcomm</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td> https://www.apple.com/about/</td>\n",
" <td> Apple</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td> http://www2.mercedes-benz.co.uk/content/united...</td>\n",
" <td> Mercedes-Benz UK</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td> http://www.ibm.com/ibm/uk/en/</td>\n",
" <td> IBM UK</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td> https://www.google.co.uk/about/</td>\n",
" <td> Google</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td> http://www.intel.com/content/www/us/en/company...</td>\n",
" <td> Intel</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td> http://pages.ebay.co.uk/aboutebay.html</td>\n",
" <td> ebay</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td> http://www.webmd.com/about-webmd-policies/abou...</td>\n",
" <td> WebMD</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td> http://www.growthintel.com/about-us/</td>\n",
" <td> Growth Intelligence</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AboutUsURL \\\n",
"0 http://www.mckinsey.com/about_us \n",
"1 http://www.thewhitecompany.com/help/our-story/ \n",
"2 http://corporate.marksandspencer.com/aboutus \n",
"3 http://www.kidsco.org.uk/about-us \n",
"4 http://www.thunderhead.com/what-we-do/about-us/ \n",
"5 https://www.astonmartin.com/en/company/about-us \n",
"6 http://www.bicestervillage.com/en/company/abou... \n",
"7 http://www.solarcentury.com/uk/about-solarcent... \n",
"8 http://www.slc.co.uk/about-us.aspx \n",
"9 https://stationers.org/about.html \n",
"10 http://www.rsc.org.uk/about-us/ \n",
"11 http://www.snellgroup.com/company/about-us/ \n",
"12 http://www.waxchandlers.org.uk/about-us/index.php \n",
"13 http://www.expeditors.com/our-company/about-us... \n",
"14 http://www.carbonneutral.com/about-us \n",
"15 http://www.pewterers.org.uk/the_company/aboutu... \n",
"16 http://www.vauxhall.co.uk/about-vauxhall/about... \n",
"17 http://ee.co.uk/our-company/about-ee \n",
"18 http://www.candoco.co.uk/about-us/ \n",
"19 http://www.victrex.com/en/company/about-us \n",
"20 http://www.ensus.co.uk/Company/About_us/ \n",
"21 http://www.anglianwater.co.uk/about-us/ \n",
"22 http://www.chequeandcredit.co.uk/about_us/ \n",
"23 http://www.vodafone.co.uk/about-us/company-his... \n",
"24 http://www.people1sttraining.co.uk/about-us \n",
"25 http://www.starbucks.co.uk/about-us \n",
"26 http://www.merlinentertainments.biz/about-us \n",
"27 http://www.bloomsbury.com/uk/company/about-us/ \n",
"28 http://www.alcatelonetouch.com/global-en/compa... \n",
"29 http://masonkings.jd-dealer.co.uk/About-us/Our... \n",
"30 http://www.oxfordbus.co.uk/about-us/ \n",
"31 http://www.patient.co.uk/about-us \n",
"32 http://www.bootstrapcompany.co.uk/about-us/ \n",
"33 http://www.fusionfurniturecompany.co.uk/about.php \n",
"34 http://www.siemens.co.uk/en/about_us/ \n",
"35 http://www.bosch.co.uk/en/uk/about_bosch_home_... \n",
"36 https://www.qualcomm.com/company/about \n",
"37 https://www.apple.com/about/ \n",
"38 http://www2.mercedes-benz.co.uk/content/united... \n",
"39 http://www.ibm.com/ibm/uk/en/ \n",
"40 https://www.google.co.uk/about/ \n",
"41 http://www.intel.com/content/www/us/en/company... \n",
"42 http://pages.ebay.co.uk/aboutebay.html \n",
"43 http://www.webmd.com/about-webmd-policies/abou... \n",
"44 http://www.growthintel.com/about-us/ \n",
"\n",
" CompanyName \n",
"0 McKinsey & Company \n",
"1 The White Company \n",
"2 Marks & Spencer \n",
"3 Kids Company \n",
"4 Thunderhead \n",
"5 Aston Martin \n",
"6 Bicester Village \n",
"7 Solarcentury \n",
"8 Student Loans Company \n",
"9 The Stationers' Company \n",
"10 Royal Shakespeare Company \n",
"11 Snell \n",
"12 The Wax Chandlers Company \n",
"13 Expeditors \n",
"14 The Carbon Neutral Company \n",
"15 The Pewterers' Company \n",
"16 Vauxhall \n",
"17 EE \n",
"18 Candoco Dance Company \n",
"19 Victrex \n",
"20 Ensus \n",
"21 Anglian Water \n",
"22 The Cheque and Credit Clearing Company \n",
"23 Vodafone \n",
"24 People 1st \n",
"25 Starbucks \n",
"26 Merlin Entertainments \n",
"27 Bloomsbury Publishing \n",
"28 Alcatel One Touch \n",
"29 Masons Kings \n",
"30 Oxford Bus Company \n",
"31 Patient.co.uk \n",
"32 Bootstrap Company \n",
"33 Fusion Furniture \n",
"34 Siemens \n",
"35 Bosch UK \n",
"36 Qualcomm \n",
"37 Apple \n",
"38 Mercedes-Benz UK \n",
"39 IBM UK \n",
"40 Google \n",
"41 Intel \n",
"42 ebay \n",
"43 WebMD \n",
"44 Growth Intelligence "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"AboutUsURLs = [[\"McKinsey & Company\", \"http://www.mckinsey.com/about_us\"], [\"The White Company\", \"http://www.thewhitecompany.com/help/our-story/\"], [\"Marks & Spencer\", \"http://corporate.marksandspencer.com/aboutus\"], [\"Kids Company\", \"http://www.kidsco.org.uk/about-us\"], [\"Thunderhead\", \"http://www.thunderhead.com/what-we-do/about-us/\"], [\"Aston Martin\", \"https://www.astonmartin.com/en/company/about-us\"], [\"Bicester Village\", \"http://www.bicestervillage.com/en/company/about-us\"], [\"Solarcentury\", \"http://www.solarcentury.com/uk/about-solarcentury/\"], [\"Student Loans Company\", \"http://www.slc.co.uk/about-us.aspx\"], [\"The Stationers' Company\", \"https://stationers.org/about.html\"], [\"Royal Shakespeare Company\", \"http://www.rsc.org.uk/about-us/\"], [\"Snell\", \"http://www.snellgroup.com/company/about-us/\"], [\"The Wax Chandlers Company\", \"http://www.waxchandlers.org.uk/about-us/index.php\"], [\"Expeditors\", \"http://www.expeditors.com/our-company/about-us.asp\"], [\"The Carbon Neutral Company\", \"http://www.carbonneutral.com/about-us\"], [\"The Pewterers' Company\", \"http://www.pewterers.org.uk/the_company/aboutus.html\"], [\"Vauxhall\", \"http://www.vauxhall.co.uk/about-vauxhall/about-us/company.html\"], [\"EE\", \"http://ee.co.uk/our-company/about-ee\"], [\"Candoco Dance Company\", \"http://www.candoco.co.uk/about-us/\"], [\"Victrex\", \"http://www.victrex.com/en/company/about-us\"], [\"Ensus\", \"http://www.ensus.co.uk/Company/About_us/\"], [\"Anglian Water\", \"http://www.anglianwater.co.uk/about-us/\"], [\"The Cheque and Credit Clearing Company\", \"http://www.chequeandcredit.co.uk/about_us/\"], [\"Vodafone\", \"http://www.vodafone.co.uk/about-us/company-history/\"], [\"People 1st\",\"http://www.people1sttraining.co.uk/about-us\"], [\"Starbucks\",\"http://www.starbucks.co.uk/about-us\"], [\"Merlin Entertainments\",\"http://www.merlinentertainments.biz/about-us\"], [\"Bloomsbury Publishing\",\"http://www.bloomsbury.com/uk/company/about-us/\"], [\"Alcatel One Touch\",\"http://www.alcatelonetouch.com/global-en/company/aboutus.html\"], [\"Masons Kings\",\"http://masonkings.jd-dealer.co.uk/About-us/Our-Company\"], [\"Oxford Bus Company\",\"http://www.oxfordbus.co.uk/about-us/\"], [\"Patient.co.uk\",\"http://www.patient.co.uk/about-us\"], [\"Bootstrap Company\",\"http://www.bootstrapcompany.co.uk/about-us/\"], [\"Fusion Furniture\",\"http://www.fusionfurniturecompany.co.uk/about.php\"], [\"Siemens\",\"http://www.siemens.co.uk/en/about_us/\"], [\"Bosch UK\",\"http://www.bosch.co.uk/en/uk/about_bosch_home_2/about-bosch-in-great-britain.php#\"], [\"Qualcomm\",\"https://www.qualcomm.com/company/about\"], [\"Apple\",\"https://www.apple.com/about/\"], [\"Mercedes-Benz UK\",\"http://www2.mercedes-benz.co.uk/content/unitedkingdom/mpc/mpc_unitedkingdom_website/en/home_mpc/passengercars/home/passenger_cars_world/about_us.html\"], [\"IBM UK\",\"http://www.ibm.com/ibm/uk/en/\"], [\"Google\",\"https://www.google.co.uk/about/\"], [\"Intel\",\"http://www.intel.com/content/www/us/en/company-overview/company-overview.html\"], [\"ebay\",\"http://pages.ebay.co.uk/aboutebay.html\"], [\"WebMD\",\"http://www.webmd.com/about-webmd-policies/about-who-we-are\"], [\"Growth Intelligence\",\"http://www.growthintel.com/about-us/\"] ]\n",
"\n",
"#pprint(AboutUsURLs)\n",
"print len(AboutUsURLs)\n",
"\n",
"cnames = [i for i,j in AboutUsURLs]\n",
"caboutusurls = [j for i,j in AboutUsURLs]\n",
"#print cnames\n",
"\n",
"descdict = {'CompanyName' : pan.Series(cnames), 'AboutUsURL' : pan.Series(caboutusurls)}\n",
"descdf = pan.DataFrame(descdict)\n",
"descdf"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pickle file containing company descriptions data found. Loading it...\n"
]
},
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AboutUsURL</th>\n",
" <th>CompanyName</th>\n",
" <th>CompanyDescription</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> http://www.mckinsey.com/about_us</td>\n",
" <td> McKinsey & Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nSkip main navi...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> http://www.thewhitecompany.com/help/our-story/</td>\n",
" <td> The White Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nBedroom\\n\\n\\n\\n\\nShop Be...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> http://corporate.marksandspencer.com/aboutus</td>\n",
" <td> Marks & Spencer</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\nmenu\\nback\\n\\nsearch\\nstore find...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> http://www.kidsco.org.uk/about-us</td>\n",
" <td> Kids Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\nHome\\nAbout Us\\nOur Work\\n...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> http://www.thunderhead.com/what-we-do/about-us/</td>\n",
" <td> Thunderhead</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> https://www.astonmartin.com/en/company/about-us</td>\n",
" <td> Aston Martin</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nWe use cookies o...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> http://www.bicestervillage.com/en/company/abou...</td>\n",
" <td> Bicester Village</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nBicester Village • Chic ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> http://www.solarcentury.com/uk/about-solarcent...</td>\n",
" <td> Solarcentury</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\nUnited KingdomUK\\nBeneluxNL\\...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> http://www.slc.co.uk/about-us.aspx</td>\n",
" <td> Student Loans Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\nJump to Content [Accesskey...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> https://stationers.org/about.html</td>\n",
" <td> The Stationers' Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\nHomeHiring Stationers' HallHire...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> http://www.rsc.org.uk/about-us/</td>\n",
" <td> Royal Shakespeare Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\nAccept and Close\\n\\r\\n We...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> http://www.snellgroup.com/company/about-us/</td>\n",
" <td> Snell</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nSolutions\\n\\n\\n\\nLive ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> http://www.waxchandlers.org.uk/about-us/index.php</td>\n",
" <td> The Wax Chandlers Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nAbout us\\nThe Wax ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> http://www.expeditors.com/our-company/about-us...</td>\n",
" <td> Expeditors</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nOur Company\\n\\nAbout U...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> http://www.carbonneutral.com/about-us</td>\n",
" <td> The Carbon Neutral Company</td>\n",
" <td> \\n\\n\\n\\n\\nBusiness carbon offsetting and carbo...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> http://www.pewterers.org.uk/the_company/aboutu...</td>\n",
" <td> The Pewterers' Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n \\n\\n\\nHome\\n The Compan...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td> http://www.vauxhall.co.uk/about-vauxhall/about...</td>\n",
" <td> Vauxhall</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nHomeConfigurator\\n\\n\\n\\n...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> http://ee.co.uk/our-company/about-ee</td>\n",
" <td> EE</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nSkip to main cont...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td> http://www.candoco.co.uk/about-us/</td>\n",
" <td> Candoco Dance Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nMenu\\n\\n\\nFollow us on ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td> http://www.victrex.com/en/company/about-us</td>\n",
" <td> Victrex</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nContact Us\\n\\n\\nMy...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td> http://www.ensus.co.uk/Company/About_us/</td>\n",
" <td> Ensus</td>\n",
" <td> HomeSitemapImprintLinksContactFull text search...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td> http://www.anglianwater.co.uk/about-us/</td>\n",
" <td> Anglian Water</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nWe use Google Analytic...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td> http://www.chequeandcredit.co.uk/about_us/</td>\n",
" <td> The Cheque and Credit Clearing Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\nSkip to content\\n\\n\\n\\n\\n\\n\\n\\n\\n\\...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td> http://www.vodafone.co.uk/about-us/company-his...</td>\n",
" <td> Vodafone</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nSkip to navigation\\n...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td> http://www.people1sttraining.co.uk/about-us</td>\n",
" <td> People 1st</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n0203 074 1212\\n\\n\\...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td> http://www.starbucks.co.uk/about-us</td>\n",
" <td> Starbucks</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nskip to Main N...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td> http://www.merlinentertainments.biz/about-us</td>\n",
" <td> Merlin Entertainments</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td> http://www.bloomsbury.com/uk/company/about-us/</td>\n",
" <td> Bloomsbury Publishing</td>\n",
" <td> </td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td> http://www.alcatelonetouch.com/global-en/compa...</td>\n",
" <td> Alcatel One Touch</td>\n",
" <td> Home Products Smart phones Feature ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td> http://masonkings.jd-dealer.co.uk/About-us/Our...</td>\n",
" <td> Masons Kings</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n \\n\\n\\n \\n\\n\\n\\n\\n\\n\\n ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td> http://www.oxfordbus.co.uk/about-us/</td>\n",
" <td> Oxford Bus Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\nOxford Bus Company\\n\\n\\nOur servic...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td> http://www.patient.co.uk/about-us</td>\n",
" <td> Patient.co.uk</td>\n",
" <td> Skip to contentPatient.co.uk - Trusted medical...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td> http://www.bootstrapcompany.co.uk/about-us/</td>\n",
" <td> Bootstrap Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\n\\n...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td> http://www.fusionfurniturecompany.co.uk/about.php</td>\n",
" <td> Fusion Furniture</td>\n",
" <td> \\n\\n\\n\\n\\[email protected]\\n...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td> http://www.siemens.co.uk/en/about_us/</td>\n",
" <td> Siemens</td>\n",
" <td> \\n\\n\\n\\nSkip to Content\\n\\n\\n\\n\\nSIEMENS\\n\\n\\n...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td> http://www.bosch.co.uk/en/uk/about_bosch_home_...</td>\n",
" <td> Bosch UK</td>\n",
" <td> \\nERROR\\nThe requested URL could not be retrie...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td> https://www.qualcomm.com/company/about</td>\n",
" <td> Qualcomm</td>\n",
" <td> \\n\\n\\n\\nSite Map\\nProducts\\nInvention\\nNews\\n...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td> https://www.apple.com/about/</td>\n",
" <td> Apple</td>\n",
" <td> \\n\\n\\n\\n\\n\\nMenu\\nApple\\n\\n\\n\\n\\nApple\\nStore\\...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td> http://www2.mercedes-benz.co.uk/content/united...</td>\n",
" <td> Mercedes-Benz UK</td>\n",
" <td> Latest offersNew Car offersUsed Car offersQui...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td> http://www.ibm.com/ibm/uk/en/</td>\n",
" <td> IBM UK</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nSelect a country/region:...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td> https://www.google.co.uk/about/</td>\n",
" <td> Google</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\nSkip to content\\n\\n\\nFollow us o...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td> http://www.intel.com/content/www/us/en/company...</td>\n",
" <td> Intel</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nToggle...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td> http://pages.ebay.co.uk/aboutebay.html</td>\n",
" <td> ebay</td>\n",
" <td> \\n\\n\\n\\nSkip to main contenteBayShop by catego...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td> http://www.webmd.com/about-webmd-policies/abou...</td>\n",
" <td> WebMD</td>\n",
" <td> \\nAccess Denied\\n \\nYou don't have permission ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td> http://www.growthintel.com/about-us/</td>\n",
" <td> Growth Intelligence</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nBoost B2B marketing ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AboutUsURL \\\n",
"0 http://www.mckinsey.com/about_us \n",
"1 http://www.thewhitecompany.com/help/our-story/ \n",
"2 http://corporate.marksandspencer.com/aboutus \n",
"3 http://www.kidsco.org.uk/about-us \n",
"4 http://www.thunderhead.com/what-we-do/about-us/ \n",
"5 https://www.astonmartin.com/en/company/about-us \n",
"6 http://www.bicestervillage.com/en/company/abou... \n",
"7 http://www.solarcentury.com/uk/about-solarcent... \n",
"8 http://www.slc.co.uk/about-us.aspx \n",
"9 https://stationers.org/about.html \n",
"10 http://www.rsc.org.uk/about-us/ \n",
"11 http://www.snellgroup.com/company/about-us/ \n",
"12 http://www.waxchandlers.org.uk/about-us/index.php \n",
"13 http://www.expeditors.com/our-company/about-us... \n",
"14 http://www.carbonneutral.com/about-us \n",
"15 http://www.pewterers.org.uk/the_company/aboutu... \n",
"16 http://www.vauxhall.co.uk/about-vauxhall/about... \n",
"17 http://ee.co.uk/our-company/about-ee \n",
"18 http://www.candoco.co.uk/about-us/ \n",
"19 http://www.victrex.com/en/company/about-us \n",
"20 http://www.ensus.co.uk/Company/About_us/ \n",
"21 http://www.anglianwater.co.uk/about-us/ \n",
"22 http://www.chequeandcredit.co.uk/about_us/ \n",
"23 http://www.vodafone.co.uk/about-us/company-his... \n",
"24 http://www.people1sttraining.co.uk/about-us \n",
"25 http://www.starbucks.co.uk/about-us \n",
"26 http://www.merlinentertainments.biz/about-us \n",
"27 http://www.bloomsbury.com/uk/company/about-us/ \n",
"28 http://www.alcatelonetouch.com/global-en/compa... \n",
"29 http://masonkings.jd-dealer.co.uk/About-us/Our... \n",
"30 http://www.oxfordbus.co.uk/about-us/ \n",
"31 http://www.patient.co.uk/about-us \n",
"32 http://www.bootstrapcompany.co.uk/about-us/ \n",
"33 http://www.fusionfurniturecompany.co.uk/about.php \n",
"34 http://www.siemens.co.uk/en/about_us/ \n",
"35 http://www.bosch.co.uk/en/uk/about_bosch_home_... \n",
"36 https://www.qualcomm.com/company/about \n",
"37 https://www.apple.com/about/ \n",
"38 http://www2.mercedes-benz.co.uk/content/united... \n",
"39 http://www.ibm.com/ibm/uk/en/ \n",
"40 https://www.google.co.uk/about/ \n",
"41 http://www.intel.com/content/www/us/en/company... \n",
"42 http://pages.ebay.co.uk/aboutebay.html \n",
"43 http://www.webmd.com/about-webmd-policies/abou... \n",
"44 http://www.growthintel.com/about-us/ \n",
"\n",
" CompanyName \\\n",
"0 McKinsey & Company \n",
"1 The White Company \n",
"2 Marks & Spencer \n",
"3 Kids Company \n",
"4 Thunderhead \n",
"5 Aston Martin \n",
"6 Bicester Village \n",
"7 Solarcentury \n",
"8 Student Loans Company \n",
"9 The Stationers' Company \n",
"10 Royal Shakespeare Company \n",
"11 Snell \n",
"12 The Wax Chandlers Company \n",
"13 Expeditors \n",
"14 The Carbon Neutral Company \n",
"15 The Pewterers' Company \n",
"16 Vauxhall \n",
"17 EE \n",
"18 Candoco Dance Company \n",
"19 Victrex \n",
"20 Ensus \n",
"21 Anglian Water \n",
"22 The Cheque and Credit Clearing Company \n",
"23 Vodafone \n",
"24 People 1st \n",
"25 Starbucks \n",
"26 Merlin Entertainments \n",
"27 Bloomsbury Publishing \n",
"28 Alcatel One Touch \n",
"29 Masons Kings \n",
"30 Oxford Bus Company \n",
"31 Patient.co.uk \n",
"32 Bootstrap Company \n",
"33 Fusion Furniture \n",
"34 Siemens \n",
"35 Bosch UK \n",
"36 Qualcomm \n",
"37 Apple \n",
"38 Mercedes-Benz UK \n",
"39 IBM UK \n",
"40 Google \n",
"41 Intel \n",
"42 ebay \n",
"43 WebMD \n",
"44 Growth Intelligence \n",
"\n",
" CompanyDescription \n",
"0 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nSkip main navi... \n",
"1 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nBedroom\\n\\n\\n\\n\\nShop Be... \n",
"2 \\n\\n\\n\\n\\n\\n\\nmenu\\nback\\n\\nsearch\\nstore find... \n",
"3 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\nHome\\nAbout Us\\nOur Work\\n... \n",
"4 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n... \n",
"5 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nWe use cookies o... \n",
"6 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nBicester Village • Chic ... \n",
"7 \\n\\n\\n\\n\\n\\n\\n\\n\\nUnited KingdomUK\\nBeneluxNL\\... \n",
"8 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\nJump to Content [Accesskey... \n",
"9 \\n\\n\\n\\n\\n\\n\\nHomeHiring Stationers' HallHire... \n",
"10 \\n\\n\\n\\n\\n\\n\\n\\n\\nAccept and Close\\n\\r\\n We... \n",
"11 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nSolutions\\n\\n\\n\\nLive ... \n",
"12 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nAbout us\\nThe Wax ... \n",
"13 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nOur Company\\n\\nAbout U... \n",
"14 \\n\\n\\n\\n\\nBusiness carbon offsetting and carbo... \n",
"15 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n \\n\\n\\nHome\\n The Compan... \n",
"16 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nHomeConfigurator\\n\\n\\n\\n... \n",
"17 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nSkip to main cont... \n",
"18 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nMenu\\n\\n\\nFollow us on ... \n",
"19 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nContact Us\\n\\n\\nMy... \n",
"20 HomeSitemapImprintLinksContactFull text search... \n",
"21 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nWe use Google Analytic... \n",
"22 \\n\\n\\n\\n\\n\\nSkip to content\\n\\n\\n\\n\\n\\n\\n\\n\\n\\... \n",
"23 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nSkip to navigation\\n... \n",
"24 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n0203 074 1212\\n\\n\\... \n",
"25 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nskip to Main N... \n",
"26 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n... \n",
"27 \n",
"28 Home Products Smart phones Feature ... \n",
"29 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n \\n\\n\\n \\n\\n\\n\\n\\n\\n\\n ... \n",
"30 \\n\\n\\n\\n\\n\\nOxford Bus Company\\n\\n\\nOur servic... \n",
"31 Skip to contentPatient.co.uk - Trusted medical... \n",
"32 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\n\\n... \n",
"33 \\n\\n\\n\\n\\[email protected]\\n... \n",
"34 \\n\\n\\n\\nSkip to Content\\n\\n\\n\\n\\nSIEMENS\\n\\n\\n... \n",
"35 \\nERROR\\nThe requested URL could not be retrie... \n",
"36 \\n\\n\\n\\nSite Map\\nProducts\\nInvention\\nNews\\n... \n",
"37 \\n\\n\\n\\n\\n\\nMenu\\nApple\\n\\n\\n\\n\\nApple\\nStore\\... \n",
"38 Latest offersNew Car offersUsed Car offersQui... \n",
"39 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nSelect a country/region:... \n",
"40 \\n\\n\\n\\n\\n\\n\\nSkip to content\\n\\n\\nFollow us o... \n",
"41 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nToggle... \n",
"42 \\n\\n\\n\\nSkip to main contenteBayShop by catego... \n",
"43 \\nAccess Denied\\n \\nYou don't have permission ... \n",
"44 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nBoost B2B marketing ... "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.chdir(datadir)\n",
"descpklfile=\"descriptions.pkl\"\n",
"descfolderpath=os.path.join(datadir,descpklfile)\n",
"if (os.path.exists(descfolderpath)==True):\n",
" print(\"Pickle file containing company descriptions data found. Loading it...\")\n",
" descdf=pickle.load(open(descfolderpath,'r'))\n",
"else:\n",
" print(\"Fetching company descriptions...\")\n",
" descdf['CompanyDescription'] = descdf['AboutUsURL'].apply(lambda x: retrieveText(x))\n",
" with open(descpklfile,'wb') as output:\n",
" pickle.dump(descdf, output, pickle.HIGHEST_PROTOCOL)\n",
"os.chdir(rootdir)\n",
"\n",
"descdf"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"skip to Main Navigation\n",
"skip to Main Content\n",
"skip to Footer\n",
"\n",
"Starbucks Coffee Company\n",
"\n",
"\n",
"\n",
"Search this site\n",
"\n",
"\n",
"Find a Store\n",
"\n",
"Sign In\n",
"\n",
"\n",
"\n",
"\n",
"Navigation\n",
"\n",
"\n",
"\n",
"\n",
"Coffee\n",
"\n",
"\n",
"Menu\n",
"\n",
"\n",
"Coffeehouse\n",
"\n",
"\n",
"Responsibility\n",
"\n",
"\n",
"Shop\n",
"\n",
"\n",
"Card\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"Search\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"Our Coffees\n",
"Whole Bean CoffeeGround CoffeeStarbucks VIA®View All Coffees\n",
"Find Your Perfect CoffeeStarbucks Reserve Coffee\n",
"Espresso\n",
"Origin EspressoThe Best EquipmentThe IngredientsWho makes it\n",
"How to Brew Great Coffee\n",
"Coffee PressPour-OverIced Pour-OverCoffee Brewer\n",
"Ethical Sourcing\n",
"CoffeeCoffee QualityFarmer Support\n",
"Learn More\n",
"New Starbucks DiscoveriesStarbucks High Arabica StandardsLearn About the Starbucks RoastCoffee PassionGreen Coffee ExtractLearn About Coffee FormsThe Clover® Brewing SystemCoffee FAQs\n",
"\n",
"\n",
"Looking for Coffee Beverages?\n",
"Filter CoffeeEspresso BeveragesFrappuccino® Blended Beverages\n",
"\n",
"\n",
"Kenya Whole bean CoffeeElegant and Sweet\n",
"\n",
"\n",
"\n",
"\n",
"Drinks\n",
"Bottled DrinksBrewed TeaChocolate BeveragesEspresso BeveragesFilter CoffeeFrappuccino Blended BeveragesStarbucks RefreshaCold Brew\n",
"Food\n",
"BreakfastMuffins, Pastries & DoughnutsLunchCakes & CookiesFresh FruitGrab and Go\n",
"Nutrition\n",
"Beverage and Food Information\n",
"Learn More\n",
"Menu FAQs\n",
"\n",
"\n",
"Looking for Coffee at Home?\n",
"Whole Bean CoffeesStarbucks VIA® Instant and Microground CoffeeStarbucks Reserve™ Coffee \n",
"\n",
"\n",
"Maple MacchiatoA new twist on a classic with delicious Canadian maple syrup.\n",
"\n",
"\n",
"\n",
"\n",
"EntertainmentStarbucks Stores\n",
"Starbucks Evenings\n",
"Wireless Internet\n",
"Powermat Wireless Charging\n",
"Coffee Master ProgrammeMobile Applications\n",
"Starbucks Android AppStarbucks iPhone App\n",
"Online CommunityStore Design\n",
"Apprenticeship ProgrammeBarista Championship\n",
"\n",
"Looking for Something Else?\n",
"Whole Bean CoffeesDrinksAbout Us\n",
"\n",
"\n",
"Starbucks Mobile AppsiPhone update now available.\n",
"\n",
"\n",
"\n",
"\n",
"Community\n",
"Backing YouthGood NeighboursYouth Action Case StudiesYouth Action 2014\n",
"Ethical Sourcing\n",
"CoffeeFarmer SupportTeaCocoaStore Products\n",
"Environment\n",
"Recycling & Reducing WasteEnergyWaterGreen BuildingClimate Change\n",
"DiversityLearn More\n",
"Starbucks™ Shared Planet™PoliciesCommunity Investments FAQs\n",
"\n",
"\n",
"Looking for Starbucks Information?\n",
"About UsCoffeeNutrition\n",
"\n",
"\n",
"Community Involvement For Starbucks – community means two things – being involved in the communities where we are, and backing young people – nationwide \n",
"\n",
"\n",
"\n",
"\n",
"Coffee\n",
"Blonde Roast CoffeeMedium Roast CoffeeDark Roast CoffeeStarbucks Reserve® CoffeeStarbucks VIA®SubscriptionsCoffee Tours\n",
"Tea\n",
"Black TeasGreen TeasHerbal InfusionsTea Tours\n",
"Verismo™\n",
"Verismo™ MachinesVerismo™ PodsRegistrationSubscriptions\n",
"Merchandise\n",
"Mugs & TumblersCold BeveragesCoffee PressesSyrups & Sauces\n",
"\n",
"\n",
"\n",
"Drinkware to welcome back spring.Pastel Palettes and Bright Surprises\n",
"\n",
"\n",
"\n",
"\n",
"Buy a Card\n",
"Starbucks CardStarbucks Card eGiftCorporate Sales\n",
"Manage Your Card\n",
"Check BalanceTop-Up Your CardView Transactions\n",
"My Starbucks Rewards\n",
"Register Your CardView Your StarsRewards Program Ts and Cs\n",
"Learn More\n",
"Card FAQsMy Starbucks Rewards FAQsManage Your Account\n",
"Card Terms and Conditions\n",
"\n",
"Looking for Starbucks Mobile Applications?\n",
"Get the Starbucks® app for iPhone® and Android™\n",
"\n",
"\n",
"My Starbucks RewardsStart earning rewards today.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"About Us\n",
"\n",
"\n",
"It happens millions of times each week – a customer receives a drink from a Starbucks barista – but each interaction is unique. \n",
"It’s just a moment in time – just one hand reaching over the counter to present a cup to another outstretched hand.\n",
"But it’s a connection.\n",
"We make sure everything we do honors that connection – from our commitment to the highest quality coffee in the world, to the way we engage with our customers and communities to do business responsibly.\n",
"From our beginnings as a single store nearly forty years ago, in every place that we’ve been, and every place that we touch, we've tried to make it a little better than we found it.\n",
"\n",
"\n",
"\n",
"About Us\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"About Us\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"Facebook\n",
"\n",
"\n",
"\n",
"\n",
"Twitter\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"About Us\n",
"\n",
"\n",
" \n",
"\n",
"Our Contribution \n",
"\n",
"Our Heritage \n",
"\n",
"Our Company \n",
"\n",
"\n",
"\n",
"\n",
"For Business\n",
"\n",
"\n",
" \n",
"\n",
"Franchised Opportunities \n",
"\n",
"Real Estate \n",
"\n",
"Starbucks on the go \n",
"\n",
"\n",
"\n",
"\n",
"Career Centre\n",
"\n",
"\n",
" \n",
"\n",
"Working at Starbucks \n",
"\n",
"Support Centre Careers \n",
"\n",
"Retail Careers \n",
"\n",
"International Careers \n",
"\n",
"\n",
"\n",
"\n",
"Online Community\n",
"\n",
"\n",
" \n",
"\n",
"Facebook \n",
"\n",
"Twitter \n",
"\n",
"YouTube \n",
"\n",
"\n",
"\n",
"\n",
"Quick Links \n",
"\n",
"\n",
"My Account \n",
"\n",
"Store Locator \n",
"\n",
"Nutrition Info \n",
"\n",
"Customer Service \n",
"\n",
"What’s New \n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"Change Region\n",
"English \n",
"\n",
"\n",
"\n",
"\n",
"Web AccessibilityPrivacy StatementTerms of UseMy Starbucks Rewards FAQsPartnersSite Map\n",
"\n",
"\n",
"\n",
"© 2014 Starbucks Corporation. All rights reserved.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n"
]
}
],
"source": [
"descdf\n",
"#print descdf.ix[descdf['CompanyName'] == 'Starbucks', 'CompanyDescription'].values\n",
"print descdf.ix[descdf['CompanyName'] == 'Starbucks', 'CompanyDescription'].values[0].encode('utf-8')\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pickle file containing preprocessed company data found. Loading it...\n"
]
},
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AboutUsURL</th>\n",
" <th>CompanyName</th>\n",
" <th>CompanyDescription</th>\n",
" <th>Tokens</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> http://www.mckinsey.com/about_us</td>\n",
" <td> McKinsey & Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nSkip main navi...</td>\n",
" <td> [skip, main, navigation, client, service, clie...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> http://www.thewhitecompany.com/help/our-story/</td>\n",
" <td> The White Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nBedroom\\n\\n\\n\\n\\nShop Be...</td>\n",
" <td> [bedroom, shop, bedding, view, bed, linen, col...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> http://corporate.marksandspencer.com/aboutus</td>\n",
" <td> Marks & Spencer</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\nmenu\\nback\\n\\nsearch\\nstore find...</td>\n",
" <td> [menu, back, search, store, finder, com, store...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> http://www.kidsco.org.uk/about-us</td>\n",
" <td> Kids Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\nHome\\nAbout Us\\nOur Work\\n...</td>\n",
" <td> [home, u, work, contact, search, usour, philos...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> http://www.thunderhead.com/what-we-do/about-us/</td>\n",
" <td> Thunderhead</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n...</td>\n",
" <td> [thunderhead, u, management, team, career, new...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> https://www.astonmartin.com/en/company/about-us</td>\n",
" <td> Aston Martin</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nWe use cookies o...</td>\n",
" <td> [use, cooky, website, continuing, use, website...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> http://www.bicestervillage.com/en/company/abou...</td>\n",
" <td> Bicester Village</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nBicester Village • Chic ...</td>\n",
" <td> [bicester, village, chic, outlet, shopping, ho...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> http://www.solarcentury.com/uk/about-solarcent...</td>\n",
" <td> Solarcentury</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\nUnited KingdomUK\\nBeneluxNL\\...</td>\n",
" <td> [united, kingdomuk, beneluxnl, south, africaza...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> http://www.slc.co.uk/about-us.aspx</td>\n",
" <td> Student Loans Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\nJump to Content [Accesskey...</td>\n",
" <td> [jump, content, accesskey, jump, navigation, a...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> https://stationers.org/about.html</td>\n",
" <td> The Stationers' Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\nHomeHiring Stationers' HallHire...</td>\n",
" <td> [homehiring, stationer, hallhire, informationt...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> http://www.rsc.org.uk/about-us/</td>\n",
" <td> Royal Shakespeare Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\nAccept and Close\\n\\r\\n We...</td>\n",
" <td> [accept, close, use, cooky, website, using, si...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> http://www.snellgroup.com/company/about-us/</td>\n",
" <td> Snell</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nSolutions\\n\\n\\n\\nLive ...</td>\n",
" <td> [solution, live, television, sport, production...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> http://www.waxchandlers.org.uk/about-us/index.php</td>\n",
" <td> The Wax Chandlers Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nAbout us\\nThe Wax ...</td>\n",
" <td> [u, wax, chandler, company, one, city, london,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> http://www.expeditors.com/our-company/about-us...</td>\n",
" <td> Expeditors</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nOur Company\\n\\nAbout U...</td>\n",
" <td> [company, u, philosophy, key, fact, history, l...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> http://www.carbonneutral.com/about-us</td>\n",
" <td> The Carbon Neutral Company</td>\n",
" <td> \\n\\n\\n\\n\\nBusiness carbon offsetting and carbo...</td>\n",
" <td> [business, carbon, offsetting, carbon, managem...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> http://www.pewterers.org.uk/the_company/aboutu...</td>\n",
" <td> The Pewterers' Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n \\n\\n\\nHome\\n The Compan...</td>\n",
" <td> [home, company, u, master, warden, company, hi...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td> http://www.vauxhall.co.uk/about-vauxhall/about...</td>\n",
" <td> Vauxhall</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nHomeConfigurator\\n\\n\\n\\n...</td>\n",
" <td> [homeconfigurator, find, retailer, please, ent...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> http://ee.co.uk/our-company/about-ee</td>\n",
" <td> EE</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nSkip to main cont...</td>\n",
" <td> [skip, main, content, skip, search, looking, o...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td> http://www.candoco.co.uk/about-us/</td>\n",
" <td> Candoco Dance Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nMenu\\n\\n\\nFollow us on ...</td>\n",
" <td> [menu, follow, u, twitter, join, u, facebook, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td> http://www.victrex.com/en/company/about-us</td>\n",
" <td> Victrex</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nContact Us\\n\\n\\nMy...</td>\n",
" <td> [contact, u, victrex, news, event, datasheets,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td> http://www.ensus.co.uk/Company/About_us/</td>\n",
" <td> Ensus</td>\n",
" <td> HomeSitemapImprintLinksContactFull text search...</td>\n",
" <td> [homesitemapimprintlinkscontactfull, text, sea...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td> http://www.anglianwater.co.uk/about-us/</td>\n",
" <td> Anglian Water</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nWe use Google Analytic...</td>\n",
" <td> [use, google, analytics, cooky, aid, improving...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td> http://www.chequeandcredit.co.uk/about_us/</td>\n",
" <td> The Cheque and Credit Clearing Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\nSkip to content\\n\\n\\n\\n\\n\\n\\n\\n\\n\\...</td>\n",
" <td> [skip, content, print, page, home, u, objectiv...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td> http://www.vodafone.co.uk/about-us/company-his...</td>\n",
" <td> Vodafone</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nSkip to navigation\\n...</td>\n",
" <td> [skip, navigation, skip, secondary, navigation...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td> http://www.people1sttraining.co.uk/about-us</td>\n",
" <td> People 1st</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n0203 074 1212\\n\\n\\...</td>\n",
" <td> [0203, 074, 1212, enquiry, people1sttraining, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td> http://www.starbucks.co.uk/about-us</td>\n",
" <td> Starbucks</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nskip to Main N...</td>\n",
" <td> [skip, main, navigation, skip, main, content, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td> http://www.merlinentertainments.biz/about-us</td>\n",
" <td> Merlin Entertainments</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n...</td>\n",
" <td> [menu, home, company, u, people, location, par...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td> http://www.bloomsbury.com/uk/company/about-us/</td>\n",
" <td> Bloomsbury Publishing</td>\n",
" <td> </td>\n",
" <td> []</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td> http://www.alcatelonetouch.com/global-en/compa...</td>\n",
" <td> Alcatel One Touch</td>\n",
" <td> Home Products Smart phones Feature ...</td>\n",
" <td> [home, product, smart, phone, feature, phone, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td> http://masonkings.jd-dealer.co.uk/About-us/Our...</td>\n",
" <td> Masons Kings</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n \\n\\n\\n \\n\\n\\n\\n\\n\\n\\n ...</td>\n",
" <td> [44, 1626, 852140, 44, 1626, 852140, product, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td> http://www.oxfordbus.co.uk/about-us/</td>\n",
" <td> Oxford Bus Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\nOxford Bus Company\\n\\n\\nOur servic...</td>\n",
" <td> [oxford, bus, company, service, city, park, ri...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td> http://www.patient.co.uk/about-us</td>\n",
" <td> Patient.co.uk</td>\n",
" <td> Skip to contentPatient.co.uk - Trusted medical...</td>\n",
" <td> [skip, contentpatient, co, uk, trusted, medica...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td> http://www.bootstrapcompany.co.uk/about-us/</td>\n",
" <td> Bootstrap Company</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\n\\n...</td>\n",
" <td> [open, close, menu, community, work, skip, con...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td> http://www.fusionfurniturecompany.co.uk/about.php</td>\n",
" <td> Fusion Furniture</td>\n",
" <td> \\n\\n\\n\\n\\[email protected]\\n...</td>\n",
" <td> [sale, fusionfurniturecompany, co, uk, 0800, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td> http://www.siemens.co.uk/en/about_us/</td>\n",
" <td> Siemens</td>\n",
" <td> \\n\\n\\n\\nSkip to Content\\n\\n\\n\\n\\nSIEMENS\\n\\n\\n...</td>\n",
" <td> [skip, content, siemens, u, skip, site, identi...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td> http://www.bosch.co.uk/en/uk/about_bosch_home_...</td>\n",
" <td> Bosch UK</td>\n",
" <td> \\nERROR\\nThe requested URL could not be retrie...</td>\n",
" <td> [error, requested, url, could, retrieved, foll...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td> https://www.qualcomm.com/company/about</td>\n",
" <td> Qualcomm</td>\n",
" <td> \\n\\n\\n\\nSite Map\\nProducts\\nInvention\\nNews\\n...</td>\n",
" <td> [site, map, product, invention, news, companyc...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td> https://www.apple.com/about/</td>\n",
" <td> Apple</td>\n",
" <td> \\n\\n\\n\\n\\n\\nMenu\\nApple\\n\\n\\n\\n\\nApple\\nStore\\...</td>\n",
" <td> [menu, apple, apple, store, mac, iphone, watch...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td> http://www2.mercedes-benz.co.uk/content/united...</td>\n",
" <td> Mercedes-Benz UK</td>\n",
" <td> Latest offersNew Car offersUsed Car offersQui...</td>\n",
" <td> [latest, offersnew, car, offersused, car, offe...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td> http://www.ibm.com/ibm/uk/en/</td>\n",
" <td> IBM UK</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nSelect a country/region:...</td>\n",
" <td> [select, country, region, united, kingdom, ibm...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td> https://www.google.co.uk/about/</td>\n",
" <td> Google</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\nSkip to content\\n\\n\\nFollow us o...</td>\n",
" <td> [skip, content, follow, u, google, google, sea...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td> http://www.intel.com/content/www/us/en/company...</td>\n",
" <td> Intel</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nToggle...</td>\n",
" <td> [toggle, navigation, toggle, search, menu, usa...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td> http://pages.ebay.co.uk/aboutebay.html</td>\n",
" <td> ebay</td>\n",
" <td> \\n\\n\\n\\nSkip to main contenteBayShop by catego...</td>\n",
" <td> [skip, main, contentebayshop, categoryenter, s...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td> http://www.webmd.com/about-webmd-policies/abou...</td>\n",
" <td> WebMD</td>\n",
" <td> \\nAccess Denied\\n \\nYou don't have permission ...</td>\n",
" <td> [access, denied, permission, access, http, www...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td> http://www.growthintel.com/about-us/</td>\n",
" <td> Growth Intelligence</td>\n",
" <td> \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nBoost B2B marketing ...</td>\n",
" <td> [boost, b2b, marketing, conversion, rate, smar...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AboutUsURL \\\n",
"0 http://www.mckinsey.com/about_us \n",
"1 http://www.thewhitecompany.com/help/our-story/ \n",
"2 http://corporate.marksandspencer.com/aboutus \n",
"3 http://www.kidsco.org.uk/about-us \n",
"4 http://www.thunderhead.com/what-we-do/about-us/ \n",
"5 https://www.astonmartin.com/en/company/about-us \n",
"6 http://www.bicestervillage.com/en/company/abou... \n",
"7 http://www.solarcentury.com/uk/about-solarcent... \n",
"8 http://www.slc.co.uk/about-us.aspx \n",
"9 https://stationers.org/about.html \n",
"10 http://www.rsc.org.uk/about-us/ \n",
"11 http://www.snellgroup.com/company/about-us/ \n",
"12 http://www.waxchandlers.org.uk/about-us/index.php \n",
"13 http://www.expeditors.com/our-company/about-us... \n",
"14 http://www.carbonneutral.com/about-us \n",
"15 http://www.pewterers.org.uk/the_company/aboutu... \n",
"16 http://www.vauxhall.co.uk/about-vauxhall/about... \n",
"17 http://ee.co.uk/our-company/about-ee \n",
"18 http://www.candoco.co.uk/about-us/ \n",
"19 http://www.victrex.com/en/company/about-us \n",
"20 http://www.ensus.co.uk/Company/About_us/ \n",
"21 http://www.anglianwater.co.uk/about-us/ \n",
"22 http://www.chequeandcredit.co.uk/about_us/ \n",
"23 http://www.vodafone.co.uk/about-us/company-his... \n",
"24 http://www.people1sttraining.co.uk/about-us \n",
"25 http://www.starbucks.co.uk/about-us \n",
"26 http://www.merlinentertainments.biz/about-us \n",
"27 http://www.bloomsbury.com/uk/company/about-us/ \n",
"28 http://www.alcatelonetouch.com/global-en/compa... \n",
"29 http://masonkings.jd-dealer.co.uk/About-us/Our... \n",
"30 http://www.oxfordbus.co.uk/about-us/ \n",
"31 http://www.patient.co.uk/about-us \n",
"32 http://www.bootstrapcompany.co.uk/about-us/ \n",
"33 http://www.fusionfurniturecompany.co.uk/about.php \n",
"34 http://www.siemens.co.uk/en/about_us/ \n",
"35 http://www.bosch.co.uk/en/uk/about_bosch_home_... \n",
"36 https://www.qualcomm.com/company/about \n",
"37 https://www.apple.com/about/ \n",
"38 http://www2.mercedes-benz.co.uk/content/united... \n",
"39 http://www.ibm.com/ibm/uk/en/ \n",
"40 https://www.google.co.uk/about/ \n",
"41 http://www.intel.com/content/www/us/en/company... \n",
"42 http://pages.ebay.co.uk/aboutebay.html \n",
"43 http://www.webmd.com/about-webmd-policies/abou... \n",
"44 http://www.growthintel.com/about-us/ \n",
"\n",
" CompanyName \\\n",
"0 McKinsey & Company \n",
"1 The White Company \n",
"2 Marks & Spencer \n",
"3 Kids Company \n",
"4 Thunderhead \n",
"5 Aston Martin \n",
"6 Bicester Village \n",
"7 Solarcentury \n",
"8 Student Loans Company \n",
"9 The Stationers' Company \n",
"10 Royal Shakespeare Company \n",
"11 Snell \n",
"12 The Wax Chandlers Company \n",
"13 Expeditors \n",
"14 The Carbon Neutral Company \n",
"15 The Pewterers' Company \n",
"16 Vauxhall \n",
"17 EE \n",
"18 Candoco Dance Company \n",
"19 Victrex \n",
"20 Ensus \n",
"21 Anglian Water \n",
"22 The Cheque and Credit Clearing Company \n",
"23 Vodafone \n",
"24 People 1st \n",
"25 Starbucks \n",
"26 Merlin Entertainments \n",
"27 Bloomsbury Publishing \n",
"28 Alcatel One Touch \n",
"29 Masons Kings \n",
"30 Oxford Bus Company \n",
"31 Patient.co.uk \n",
"32 Bootstrap Company \n",
"33 Fusion Furniture \n",
"34 Siemens \n",
"35 Bosch UK \n",
"36 Qualcomm \n",
"37 Apple \n",
"38 Mercedes-Benz UK \n",
"39 IBM UK \n",
"40 Google \n",
"41 Intel \n",
"42 ebay \n",
"43 WebMD \n",
"44 Growth Intelligence \n",
"\n",
" CompanyDescription \\\n",
"0 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nSkip main navi... \n",
"1 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nBedroom\\n\\n\\n\\n\\nShop Be... \n",
"2 \\n\\n\\n\\n\\n\\n\\nmenu\\nback\\n\\nsearch\\nstore find... \n",
"3 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\nHome\\nAbout Us\\nOur Work\\n... \n",
"4 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n... \n",
"5 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nWe use cookies o... \n",
"6 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nBicester Village • Chic ... \n",
"7 \\n\\n\\n\\n\\n\\n\\n\\n\\nUnited KingdomUK\\nBeneluxNL\\... \n",
"8 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\nJump to Content [Accesskey... \n",
"9 \\n\\n\\n\\n\\n\\n\\nHomeHiring Stationers' HallHire... \n",
"10 \\n\\n\\n\\n\\n\\n\\n\\n\\nAccept and Close\\n\\r\\n We... \n",
"11 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nSolutions\\n\\n\\n\\nLive ... \n",
"12 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nAbout us\\nThe Wax ... \n",
"13 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nOur Company\\n\\nAbout U... \n",
"14 \\n\\n\\n\\n\\nBusiness carbon offsetting and carbo... \n",
"15 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n \\n\\n\\nHome\\n The Compan... \n",
"16 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nHomeConfigurator\\n\\n\\n\\n... \n",
"17 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nSkip to main cont... \n",
"18 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nMenu\\n\\n\\nFollow us on ... \n",
"19 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nContact Us\\n\\n\\nMy... \n",
"20 HomeSitemapImprintLinksContactFull text search... \n",
"21 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nWe use Google Analytic... \n",
"22 \\n\\n\\n\\n\\n\\nSkip to content\\n\\n\\n\\n\\n\\n\\n\\n\\n\\... \n",
"23 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nSkip to navigation\\n... \n",
"24 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n0203 074 1212\\n\\n\\... \n",
"25 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nskip to Main N... \n",
"26 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n... \n",
"27 \n",
"28 Home Products Smart phones Feature ... \n",
"29 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n \\n\\n\\n \\n\\n\\n\\n\\n\\n\\n ... \n",
"30 \\n\\n\\n\\n\\n\\nOxford Bus Company\\n\\n\\nOur servic... \n",
"31 Skip to contentPatient.co.uk - Trusted medical... \n",
"32 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\n\\n... \n",
"33 \\n\\n\\n\\n\\[email protected]\\n... \n",
"34 \\n\\n\\n\\nSkip to Content\\n\\n\\n\\n\\nSIEMENS\\n\\n\\n... \n",
"35 \\nERROR\\nThe requested URL could not be retrie... \n",
"36 \\n\\n\\n\\nSite Map\\nProducts\\nInvention\\nNews\\n... \n",
"37 \\n\\n\\n\\n\\n\\nMenu\\nApple\\n\\n\\n\\n\\nApple\\nStore\\... \n",
"38 Latest offersNew Car offersUsed Car offersQui... \n",
"39 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nSelect a country/region:... \n",
"40 \\n\\n\\n\\n\\n\\n\\nSkip to content\\n\\n\\nFollow us o... \n",
"41 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nToggle... \n",
"42 \\n\\n\\n\\nSkip to main contenteBayShop by catego... \n",
"43 \\nAccess Denied\\n \\nYou don't have permission ... \n",
"44 \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nBoost B2B marketing ... \n",
"\n",
" Tokens \n",
"0 [skip, main, navigation, client, service, clie... \n",
"1 [bedroom, shop, bedding, view, bed, linen, col... \n",
"2 [menu, back, search, store, finder, com, store... \n",
"3 [home, u, work, contact, search, usour, philos... \n",
"4 [thunderhead, u, management, team, career, new... \n",
"5 [use, cooky, website, continuing, use, website... \n",
"6 [bicester, village, chic, outlet, shopping, ho... \n",
"7 [united, kingdomuk, beneluxnl, south, africaza... \n",
"8 [jump, content, accesskey, jump, navigation, a... \n",
"9 [homehiring, stationer, hallhire, informationt... \n",
"10 [accept, close, use, cooky, website, using, si... \n",
"11 [solution, live, television, sport, production... \n",
"12 [u, wax, chandler, company, one, city, london,... \n",
"13 [company, u, philosophy, key, fact, history, l... \n",
"14 [business, carbon, offsetting, carbon, managem... \n",
"15 [home, company, u, master, warden, company, hi... \n",
"16 [homeconfigurator, find, retailer, please, ent... \n",
"17 [skip, main, content, skip, search, looking, o... \n",
"18 [menu, follow, u, twitter, join, u, facebook, ... \n",
"19 [contact, u, victrex, news, event, datasheets,... \n",
"20 [homesitemapimprintlinkscontactfull, text, sea... \n",
"21 [use, google, analytics, cooky, aid, improving... \n",
"22 [skip, content, print, page, home, u, objectiv... \n",
"23 [skip, navigation, skip, secondary, navigation... \n",
"24 [0203, 074, 1212, enquiry, people1sttraining, ... \n",
"25 [skip, main, navigation, skip, main, content, ... \n",
"26 [menu, home, company, u, people, location, par... \n",
"27 [] \n",
"28 [home, product, smart, phone, feature, phone, ... \n",
"29 [44, 1626, 852140, 44, 1626, 852140, product, ... \n",
"30 [oxford, bus, company, service, city, park, ri... \n",
"31 [skip, contentpatient, co, uk, trusted, medica... \n",
"32 [open, close, menu, community, work, skip, con... \n",
"33 [sale, fusionfurniturecompany, co, uk, 0800, 0... \n",
"34 [skip, content, siemens, u, skip, site, identi... \n",
"35 [error, requested, url, could, retrieved, foll... \n",
"36 [site, map, product, invention, news, companyc... \n",
"37 [menu, apple, apple, store, mac, iphone, watch... \n",
"38 [latest, offersnew, car, offersused, car, offe... \n",
"39 [select, country, region, united, kingdom, ibm... \n",
"40 [skip, content, follow, u, google, google, sea... \n",
"41 [toggle, navigation, toggle, search, menu, usa... \n",
"42 [skip, main, contentebayshop, categoryenter, s... \n",
"43 [access, denied, permission, access, http, www... \n",
"44 [boost, b2b, marketing, conversion, rate, smar... "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from nltk.corpus import stopwords\n",
"from nltk.tokenize import WordPunctTokenizer\n",
"from nltk.tokenize import PunktWordTokenizer\n",
"#from nltk.tokenize import RegexpTokenizer\n",
"from nltk.stem.snowball import EnglishStemmer\n",
"from nltk.stem.snowball import PorterStemmer\n",
"from nltk.stem.lancaster import LancasterStemmer\n",
"from nltk.stem import WordNetLemmatizer\n",
"\n",
"\n",
"english_stops = set(stopwords.words('english'))\n",
"\n",
"\n",
"def tokenizeString(string,lower=True,tokenizer=\"wordpunct\"):\n",
" if tokenizer==\"wordpunct\":\n",
" tokenized=WordPunctTokenizer().tokenize(string)\n",
" if lower==True:\n",
" tokenized=[w.lower() for w in tokenized]\n",
" if tokenizer==\"punktword\":\n",
" tokenized=PunktWordTokenizer().tokenize(string)\n",
" if lower==True:\n",
" tokenized=[w.lower() for w in tokenized]\n",
" return tokenized\n",
"\n",
"def cleanVector(tokens,clean=True,stopremove=True,minlen=2):\n",
" output=[]\n",
" disallowedchar=set([\"!\",\"?\",'\"',\"'\",\",\",\".\",\":\",\";\"])\n",
" english_stops = set(stopwords.words('english'))\n",
" for i in tokens:\n",
" found=False\n",
" if len(set(i).intersection(disallowedchar))>0:\n",
" found=True\n",
" if found==False and stopremove==False:\n",
" output.append(i)\n",
" if found==False and stopremove==True and minlen==0:\n",
" if i not in english_stops:\n",
" output.append(i)\n",
" if found==False and stopremove==True and minlen>0:\n",
" if i not in english_stops and len(i)>=minlen:\n",
" output.append(i)\n",
" return output\n",
"\n",
"def stemVector(vector,method=\"lemmatize\"):\n",
" output=[]\n",
" if method=='lemmatize':\n",
" wnl = WordNetLemmatizer()\n",
" for i in vector:\n",
" i=wnl.lemmatize(i)\n",
" output.append(i)\n",
" if method=='snowball':\n",
" st=EnglishStemmer()\n",
" for i in vector:\n",
" i=st.stem(i)\n",
" output.append(i)\n",
" if method=='porter':\n",
" st=PorterStemmer()\n",
" for i in vector:\n",
" i=st.stem(i)\n",
" output.append(i)\n",
" if method=='lancaster':\n",
" st=LancasterStemmer()\n",
" for i in vector:\n",
" i=st.stem(i)\n",
" output.append(i)\n",
" return output\n",
"\n",
"\n",
"def tokeniseCleanStem(inputstring):\n",
" return stemVector(cleanVector(tokenizeString(inputstring)))\n",
"\n",
"\n",
"os.chdir(datadir)\n",
"descpklfile=\"processeddescriptions.pkl\"\n",
"descfolderpath=os.path.join(datadir,descpklfile)\n",
"if (os.path.exists(descfolderpath)==True):\n",
" print(\"Pickle file containing preprocessed company data found. Loading it...\")\n",
" descdf=pickle.load(open(descfolderpath,'r'))\n",
"else:\n",
" print(\"Cleaning, tokenising and lemmatising company data text...\")\n",
" descdf['Tokens'] = descdf['CompanyDescription'].apply(lambda x: tokeniseCleanStem(x))\n",
" with open(descpklfile,'wb') as output:\n",
" pickle.dump(descdf, output, pickle.HIGHEST_PROTOCOL)\n",
"os.chdir(rootdir)\n",
"\n",
"descdf\n",
"\n",
"#print descdf['Tokens']\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dictionary(4862 unique tokens: [u'limited', u'programmemobile', u'magnetic', u'dynamic', u'four']...)\n"
]
}
],
"source": [
"from gensim import corpora,models\n",
"\n",
"dictionary = corpora.Dictionary(descdf['Tokens'])\n",
"print dictionary\n",
"#print(dictionary.token2id)\n",
"\n",
"corpus = [dictionary.doc2bow(text) for text in descdf['Tokens']]\n",
"#print(corpus)\n",
"\n",
"tfidfmodel = models.TfidfModel(corpus)\n",
"# Apply it to the input corpus\n",
"tfidfcorpus = tfidfmodel[corpus]\n",
"#print(tfidfcorpus)\n",
"dictpath = os.path.join(datadir,'companies.dict')\n",
"dictionary.save(dictpath)\n",
"\n",
"corpuspath = os.path.join(datadir,'corpus.mm')\n",
"corpora.MmCorpus.serialize(corpuspath, corpus)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[u'0.001*vodafone + 0.001*intel + 0.001*tcl + 0.000*cheque + 0.000*ebay + 0.000*patient + 0.000*furniture + 0.000*siemens + 0.000*solar + 0.000*google',\n",
" u'0.001*apple + 0.001*water + 0.000*ibm + 0.000*mckinsey + 0.000*ee + 0.000*merlin + 0.000*starbucks + 0.000*kid + 0.000*victrex + 0.000*qualcomm']"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import logging\n",
"\n",
"logging.basicConfig(filename='companies.log', format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n",
"\n",
"#id2word = corpora.Dictionary.load_from_text(dictpath)\n",
"id2word = dictionary\n",
"#mm = corpora.MmCorpus(corpuspath)\n",
"mm = tfidfcorpus\n",
"\n",
"lda = models.ldamodel.LdaModel(corpus=mm, id2word=id2word, num_topics=2, update_every=1, chunksize=10000, passes=10)\n",
"ldapath = os.path.join(datadir,'companies_lda.model')\n",
"lda.save(ldapath)\n",
"\n",
"lda.print_topics(10)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:gensim.similarities.docsim:scanning corpus to determine the number of features (consider setting `num_features` explicitly)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"The similarity of the query with each one of the computed topics is:\n",
"\n",
"[(0, 0.49411810859580702), (1, 0.50588189140419304)]\n",
"\n",
"\n",
"The similarity of the query to the documents in the corpus is:\n",
"\n",
"[(0, 0.77713782), (1, 0.74038559), (2, 0.76099867), (3, 0.77396727), (4, 0.76290452), (5, 0.74658364), (6, 0.777596), (7, 0.74598134), (8, 0.78152198), (9, 0.75475007), (10, 0.76012248), (11, 0.76894623), (12, 0.76687193), (13, 0.75019664), (14, 0.76371258), (15, 0.76297742), (16, 0.7477091), (17, 0.77451569), (18, 0.77830619), (19, 0.77035463), (20, 0.76058578), (21, 0.79852659), (22, 0.77956843), (23, 0.77771318), (24, 0.76265919), (25, 0.76829535), (26, 0.77843988), (27, 0.99993086), (28, 0.801669), (29, 0.76004219), (30, 0.77450323), (31, 0.7515223), (32, 0.75440323), (33, 0.75473392), (34, 0.75557601), (35, 0.81794977), (36, 0.76628429), (37, 0.80737174), (38, 0.76270026), (39, 0.78740346), (40, 0.75772244), (41, 0.77305758), (42, 0.7703163), (43, 0.81210667), (44, 0.73889565)]\n",
"\n",
"\n",
"The company which best fits the query by LDA-deduced topics is:\n",
"\n",
"AboutUsURL http://www.bloomsbury.com/uk/company/about-us/\n",
"CompanyName Bloomsbury Publishing\n",
"CompanyDescription \n",
"Tokens []\n",
"Name: 27, dtype: object\n"
]
}
],
"source": [
"from gensim.similarities import Similarity\n",
"from gensim import similarities\n",
"\n",
"query = \"Electronics appliances\"\n",
"query = dictionary.doc2bow(tokeniseCleanStem(query))\n",
"\n",
"# Apply the LDA model trained on the corpus to the query\n",
"query_lda = lda[query]\n",
"print \"\\nThe similarity of the query with each one of the computed topics is:\\n\"\n",
"print(query_lda)\n",
"\n",
"index = similarities.MatrixSimilarity(lda[tfidfcorpus])\n",
"\n",
"print \"\\n\\nThe similarity of the query to the documents in the corpus is:\\n\"\n",
"\n",
"sims = index[query_lda] # perform a similarity query against the corpus\n",
"resultlist = list(enumerate(sims))\n",
"print(resultlist)\n",
"\n",
"print \"\\n\\nThe company which best fits the query by LDA-deduced topics is:\\n\"\n",
"resultlist.sort(key=lambda x: x[1], reverse=True)\n",
"result = resultlist[0][0]\n",
"print descdf.iloc[result]"
]
}
],
"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-3-clause |
tensorflow/docs | site/en/r1/tutorials/non-ml/mandelbrot.ipynb | 1 | 9104 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "HhR5048dZ3e1"
},
"source": [
"##### Copyright 2019 The TensorFlow Authors."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "f0A2utIXbPc5"
},
"outputs": [],
"source": [
"#@title Licensed under the Apache License, Version 2.0 (the \"License\");\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": "1qF0JETfbfIR"
},
"source": [
"# Mandelbrot set"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "p8Z8Pb5nbtZ3"
},
"source": [
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/r1/tutorials/non-ml/mandelbrot.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/r1/tutorials/non-ml/mandelbrot.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
" </td>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "p8Z8Pb5nbtZ3"
},
"source": [
"> Note: This is an archived TF1 notebook. These are configured\n",
"to run in TF2's \n",
"[compatibility mode](https://www.tensorflow.org/guide/migrate)\n",
"but will run in TF1 as well. To use TF1 in Colab, use the\n",
"[%tensorflow_version 1.x](https://colab.research.google.com/notebooks/tensorflow_version.ipynb)\n",
"magic."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lqPLlJWqcSFZ"
},
"source": [
"Visualizing the [Mandelbrot set](https://en.wikipedia.org/wiki/Mandelbrot_set) doesn't have anything to do with machine learning, but it makes for a fun example of how one can use TensorFlow for general mathematics. This is actually a pretty naive implementation of the visualization, but it makes the point. (We may end up providing a more elaborate implementation down the line to produce more truly beautiful images.)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "80RrFh7EcnLT"
},
"source": [
"## Basic setup\n",
"\n",
"You'll need a few imports to get started."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xc-QSV_SdEG4"
},
"outputs": [],
"source": [
"# Import libraries for simulation\n",
"import tensorflow.compat.v1 as tf\n",
"\n",
"import numpy as np\n",
"\n",
"# Imports for visualization\n",
"import PIL.Image\n",
"from io import BytesIO\n",
"from IPython.display import clear_output, Image, display\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mP5YEOuTieH0"
},
"source": [
"Now you'll define a function to actually display the image once you have iteration counts."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_q_HC5cGhX4h"
},
"outputs": [],
"source": [
"def DisplayFractal(a, fmt='jpeg'):\n",
" \"\"\"Display an array of iteration counts as a\n",
" colorful picture of a fractal.\"\"\"\n",
" a_cyclic = (6.28*a/20.0).reshape(list(a.shape)+[1])\n",
" img = np.concatenate([10+20*np.cos(a_cyclic),\n",
" 30+50*np.sin(a_cyclic),\n",
" 155-80*np.cos(a_cyclic)], 2)\n",
" img[a==a.max()] = 0\n",
" a = img\n",
" a = np.uint8(np.clip(a, 0, 255))\n",
" f = BytesIO()\n",
" PIL.Image.fromarray(a).save(f, fmt)\n",
" display(Image(data=f.getvalue()))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xEptO88QikEM"
},
"source": [
"# Session and variable initialization\n",
"\n",
"For playing around like this, an interactive session is often used, but a regular session would work as well."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8_yDY6Uih7bD"
},
"outputs": [],
"source": [
"sess = tf.InteractiveSession()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_NFwmNL5iqBd"
},
"source": [
"It's handy that you can freely mix NumPy and TensorFlow."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fHu_sT7chbg_"
},
"outputs": [],
"source": [
"# Use NumPy to create a 2D array of complex numbers\n",
"\n",
"Y, X = np.mgrid[-1.3:1.3:0.005, -2:1:0.005]\n",
"Z = X+1j*Y"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "u7SsqtHqivVW"
},
"source": [
"Now you define and initialize TensorFlow tensors."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UpGYdAWQhhCN"
},
"outputs": [],
"source": [
"xs = tf.constant(Z.astype(np.complex64))\n",
"zs = tf.Variable(xs)\n",
"ns = tf.Variable(tf.zeros_like(xs, tf.float32))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gqvhBLXbi4al"
},
"source": [
"TensorFlow requires that you explicitly initialize variables before using them."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RmjN39LHhob2"
},
"outputs": [],
"source": [
"tf.global_variables_initializer().run()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ao_esnw4jAJp"
},
"source": [
"# Defining and running the computation\n",
"\n",
"Now you specify more of the computation..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZMup0FHjiGEx"
},
"outputs": [],
"source": [
"# Compute the new values of z: z^2 + x\n",
"zs_ = zs*zs + xs\n",
"\n",
"# Have we diverged with this new value?\n",
"not_diverged = tf.abs(zs_) < 4\n",
"\n",
"# Operation to update the zs and the iteration count.\n",
"#\n",
"# Note: We keep computing zs after they diverge! This\n",
"# is very wasteful! There are better, if a little\n",
"# less simple, ways to do this.\n",
"#\n",
"step = tf.group(\n",
" zs.assign(zs_),\n",
" ns.assign_add(tf.cast(not_diverged, tf.float32))\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9qqqbNu7jCrj"
},
"source": [
"... and run it for a couple hundred steps"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "twC_FiUSiN8s"
},
"outputs": [],
"source": [
"for i in range(200): step.run()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vfoDAWtijLKd"
},
"source": [
"Let's see what you've got."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8qqfdbuOiV90"
},
"outputs": [],
"source": [
"DisplayFractal(ns.eval())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vB-3S5cFjVYQ"
},
"source": [
"Not bad!"
]
}
],
"metadata": {
"colab": {
"name": "mandelbrot.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
dreadrel/UWF_2014_spring_COP3990C-2507 | notebooks/.ipynb_checkpoints/Lecture07-checkpoint.ipynb | 1 | 30613 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"files/images/python-logo.png\">"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Formatting & Functions"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# this block is just for the style sheet for the notebook\n",
"from IPython.core.display import HTML\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",
"div.warn { \n",
" background-color: #ff8c00;\n",
" border-color: #00008b;\n",
" border-left: 5px solid #00008b;\n",
" padding: 0.5em;\n",
"}\n",
"\n",
" @font-face {\n",
" font-family: \"Computer Modern\";\n",
" src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.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",
" h2 {\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: 135%;\n",
" font-size: 120%;\n",
" width:600px;\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: 16pt;\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",
"\n"
],
"output_type": "pyout",
"prompt_number": 1,
"text": [
"<IPython.core.display.HTML at 0x413bd90>"
]
}
],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Formatting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following was obtained from ebeab.com"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# define variables\n",
"x = 3.1415926\n",
"y = 1"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# 2 decimal places \n",
"print \"{:.2f}\".format(x)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"3.14\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# 2 decimal palces with sign\n",
"print \"{:+.2f}\".format(x)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"+3.14\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# 2 decimal palces with sign\n",
"print \"{:.2f}\".format(-y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"-1.00\n"
]
}
],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# print with no decimal palces\n",
"print \"{:.0f}\".format(x)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"3\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# left padded with 0's - width 2\n",
"print \"{:0>2d}\".format(y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"01\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# right padd with x's - total width 4\n",
"print \"{:x<4d}\".format(y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1xxx\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# right padd with x's - total width 4\n",
"print \"{:x<4d}\".format(10*y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"10xx\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# insert a comma separator\n",
"print \"{:,}\".format(10000000000000)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"10,000,000,000,000\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# % format\n",
"print \"{:.4%}\".format(0.1235678)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"12.3568%\n"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# exponent notation\n",
"print \"{:.3e}\".format(10000000000000)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1.000e+13\n"
]
}
],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# right justified, with 10\n",
"print \"{:10d}\".format(100)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 100\n"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# left justified, with 10\n",
"print \"{:<10d}\".format(100)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"100 \n"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# center justified, with 10\n",
"print \"{:^10d}\".format(100)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 100 \n"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# string substitution\n",
"s1 = 'so much depends upon {}'.format('a red wheel barrow')\n",
"s2 = 'glazed with {} water beside the {} chickens'.format('rain', 'white')\n",
"print s1\n",
"print s2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"so much depends upon a red wheel barrow\n",
"glazed with rain water beside the white chickens\n"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# another substitution\n",
"s1 = \" {0} is better than {1} \".format(\"emacs\", \"vim\")\n",
"s2 = \" {1} is better than {0} \".format(\"emacs\", \"vim\")\n",
"print s1\n",
"print s2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" emacs is better than vim \n",
" vim is better than emacs \n"
]
}
],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"## defining formats\n",
"email_f = \"Your email address was {email}\".format\n",
"\n",
"## use elsewhere\n",
"print(email_f(email=\"[email protected]\"))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Your email address was [email protected]\n"
]
}
],
"prompt_number": 40
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Functions"
]
},
{
"cell_type": "heading",
"level": 5,
"metadata": {},
"source": [
"Why Use Functions? (from your textbook)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before we get into the details, let\u2019s establish a clear picture of what functions are all\n",
"about. Functions are a nearly universal program-structuring device. You may have\n",
"come across them before in other languages, where they may have been called subroutines\n",
"or procedures. As a brief introduction, functions serve two primary development\n",
"roles:<br><br><br>\n",
"<i>Maximizing code reuse and minimizing redundancy</i><br>\n",
"<p>As in most programming languages, Python functions are the simplest way to\n",
"package logic you may wish to use in more than one place and more than one time.\n",
"Up until now, all the code we\u2019ve been writing has run immediately. Functions allow\n",
"us to group and generalize code to be used arbitrarily many times later. Because\n",
"they allow us to code an operation in a single place and use it in many places,\n",
"Python functions are the most basic factoring tool in the language: they allow us\n",
"to reduce code redundancy in our programs, and thereby reduce maintenance effort.</p>\n",
"<br><i>Procedural decomposition</i><br>\n",
"<p>Functions also provide a tool for splitting systems into pieces that have well-defined\n",
"roles. For instance, to make a pizza from scratch, you would start by mixing the\n",
"dough, rolling it out, adding toppings, baking it, and so on. If you were programming\n",
"a pizza-making robot, functions would help you divide the overall \u201cmake\n",
"pizza\u201d task into chunks\u2014one function for each subtask in the process. It\u2019s easier\n",
"to implement the smaller tasks in isolation than it is to implement the entire process\n",
"at once. In general, functions are about procedure\u2014how to do something, rather\n",
"than what you\u2019re doing it to. We\u2019ll see why this distinction matters in Part VI, when\n",
"we start making new objects with classes.\n",
"In this part of the book, we\u2019ll explore the tools used to code functions in Python: function\n",
"basics, scope rules, and argument passing, along with a few related concepts such\n",
"as generators and functional tools. Because its importance begins to become more apparent\n",
"at this level of coding, we\u2019ll also revisit the notion of polymorphism, which was introduced earlier in the book. As you\u2019ll see, functions don\u2019t imply much new syntax,\n",
" but they do lead us to some bigger programming ideas.</p>"
]
},
{
"cell_type": "heading",
"level": 5,
"metadata": {},
"source": [
"Examples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fibonacci series has the reccursive relation <br>\n",
"$$\n",
"F_n = F_{n-1} + F_{n-2}\n",
"$$\n",
"with the seed values\n",
"$$\n",
"F_1 = 1,\\; F_2 = 1\n",
"$$\n",
"or \n",
"$$\n",
"F_0 = 0,\\; F_1 = 1\n",
"$$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# code that computes Fibonacci series up to n\n",
"def fibSeries(n):\n",
" f0, f1 = 0, 1\n",
" while f0 < n:\n",
" print f0,\n",
" f0, f1 = f1, f0 + f1"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# evaluate the series up to 100\n",
"fib(100)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0 1 1 2 3 5 8 13 21 34 55 89\n"
]
}
],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# code that computes Fibonacci series up to n\n",
"def fibSeries(n):\n",
" \"\"\"compute Fibonacci series up to n\"\"\"\n",
" f0, f1 = 0, 1\n",
" while f0 < n:\n",
" print f0,\n",
" f0, f1 = f1, f0 + f1"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# what does the function do????\n",
"fibSeries.__doc__"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 51,
"text": [
"'compute Fibonacci series up to n'"
]
}
],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# sometimes we don't want to put the print statement inside the function\n",
"# create a function that returns a list\n",
"def fibSeries(n):\n",
" \"\"\"compute Fibonacci series up to n and returns the series as a list\"\"\"\n",
" \n",
" series = [] # store numbers here\n",
" f0, f1 = 0, 1 # seed values\n",
" \n",
" while f0 < n:\n",
" series.append(f0)\n",
" f0, f1 = f1, f0 + f1\n",
" \n",
" # return the list\n",
" return series"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# now call the function\n",
"result = fibSeries(1000)\n",
"print result"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987]\n"
]
}
],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# define a function inside a function\n",
"def f1():\n",
" x = 99\n",
" print x + 2\n",
" def f2():\n",
" print x + 1\n",
" def f3():\n",
" print x + 3 # Found in f1's local scope!\n",
" f3()\n",
" f2()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# you can only call the top-level function\n",
"f1()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"101\n",
"100\n",
"102\n"
]
}
],
"prompt_number": 59
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def changer(a, b): \n",
" a = 2 \n",
" b[0] = 'spam' "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X = 1\n",
"L = [1, 2]\n",
"changer(X, L)\n",
"X, L"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 35,
"text": [
"(1, ['spam', 2])"
]
}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X = 1\n",
"L = [1, 2]\n",
"changer(X, L[:])\n",
"X, L"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 34,
"text": [
"(1, [1, 2])"
]
}
],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X = 1\n",
"a = X\n",
"a = 2\n",
"print (X)\n",
"print (a)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"3\n",
"2\n"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"L = [1, 2]\n",
"b = L\n",
"b[0] = 'spam'\n",
"print L"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['spam', 2]\n"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"L[1] = 'jam'\n",
"print b"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['spam', 'jam']\n"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Scope"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# x is a global variable\n",
"x = 5\n",
"def func():\n",
" print x # x has the same value as the global value\n",
"\n",
"func()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"5\n"
]
}
],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# x is a global variable\n",
"x = 5\n",
"def func():\n",
" x = 9 # redefine x here\n",
" print x\n",
" \n",
"\n",
"func()\n",
"print x"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"9\n",
"5\n"
]
}
],
"prompt_number": 72
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# x gets overridden inside the function only\n",
"x = 5\n",
"def func(x):\n",
" print x\n",
"\n",
"func(10)\n",
"print x"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"10\n",
"5\n"
]
}
],
"prompt_number": 70
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# x is a global variable but gets changed inside a function\n",
"x = 5\n",
"def func():\n",
" global x\n",
" x = 9 # change here\n",
" print x\n",
" \n",
"\n",
"func()\n",
"print x"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"9\n",
"9\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def maker(N):\n",
" def action(X):\n",
" return X ** N\n",
" return action\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f = maker(2)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f(3)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"9"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f(4)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"16"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"g = maker(3)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"g(3)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"27"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"g(4)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
"64"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# another way to define it with lambda\n",
"def maker2(N):\n",
" return lambda X: X ** N\n",
"\n",
"\n",
"h = maker2(3)\n",
"h(4)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
"64"
]
}
],
"prompt_number": 21
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Return values"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# return a value from the function\n",
"def times(a, b):\n",
" return a * b\n",
"\n",
"print times(10, 2)\n",
"print times(3.1415, 4)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"20\n",
"12.566\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# functions are typeless\n",
"times('No!', 4)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"'No!No!No!No!'"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# complex objects can be passed to the function\n",
"def intersect(seq1, seq2):\n",
" res = [] # Start empty\n",
" \n",
" for x in seq1: # Scan seq1\n",
" if x in seq2: # Common item?\n",
" res.append(x) # Add to end\n",
" \n",
" return res\n",
"\n",
"\n",
"s1 = \"SPAM\"\n",
"s2 = \"SCAM\"\n",
"intersect(s1, s2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"['S', 'A', 'M']"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# this could have been done in an easy and compact way\n",
"[x for x in s1 if x in s2]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"['S', 'A', 'M']"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# passing on 2 types of objects\n",
"x = intersect([1, 2, 3], (1, 4))\n",
"print x"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1]\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Default Arguments"
]
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Packing and Unpacking"
]
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Anonymous Functions: lambda"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"L = [lambda x: x ** 2, # Inline function definition\n",
" lambda x: x ** 3,\n",
" lambda x: x ** 4] # A list of three callable functions\n",
"\n",
"\n",
"for f in L:\n",
" print(f(2)) # Prints 4, 8, 16"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"4\n",
"8\n",
"16\n"
]
}
],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(L[0](3)) # Prints 9"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"9\n"
]
}
],
"prompt_number": 65
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def f1(x): return x ** 2\n",
"def f2(x): return x ** 3 # Define named functions\n",
"def f3(x): return x ** 4\n",
"\n",
"L = [f1, f2, f3] # Reference by name\n",
"\n",
"for f in L:\n",
" print(f(2)) # Prints 4, 8, 16\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"4\n",
"8\n",
"16\n"
]
}
],
"prompt_number": 66
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(L[0](3)) # Prints 9"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"9\n"
]
}
],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | apache-2.0 |
Shekharrajak/daru-view | spec/dummy_iruby/Daru DataFrame and DataTables.ipynb | 2 | 6848 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Note\n",
"\n",
"Please visit https://github.com/Shekharrajak/daru-data_tables for updated API and examples. It works in Ruby web application framework but there is already an issue open for IRuby notebook: https://github.com/Shekharrajak/daru-data_tables/issues/2\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/shekharrajak/.rbenv/versions/2.5.3/lib/ruby/gems/2.5.0/bundler/gems/daru-data_tables-af3460a049da/lib/daru/data_tables/version.rb:3: warning: already initialized constant Daru::View::VERSION\n",
"/home/shekharrajak/Documents/githubRepos/sciruby/windows-daru-view/daru-view/lib/daru/view/version.rb:3: warning: previous definition of VERSION was here\n"
]
},
{
"data": {
"text/plain": [
"true"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"require 'daru/data_tables'\n",
"require 'daru'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<b> Daru::DataFrame(5x3) </b>\n",
"<table>\n",
" <thead>\n",
" \n",
" <tr>\n",
" <th></th>\n",
" \n",
" <th>a</th>\n",
" \n",
" <th>b</th>\n",
" \n",
" <th>c</th>\n",
" \n",
" </tr>\n",
" \n",
"</thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <td>one</td>\n",
" \n",
" <td>1</td>\n",
" \n",
" <td>11</td>\n",
" \n",
" <td>11</td>\n",
" \n",
" </tr>\n",
" \n",
" <tr>\n",
" <td>two</td>\n",
" \n",
" <td>2</td>\n",
" \n",
" <td>12</td>\n",
" \n",
" <td>22</td>\n",
" \n",
" </tr>\n",
" \n",
" <tr>\n",
" <td>three</td>\n",
" \n",
" <td>3</td>\n",
" \n",
" <td>13</td>\n",
" \n",
" <td>33</td>\n",
" \n",
" </tr>\n",
" \n",
" <tr>\n",
" <td>four</td>\n",
" \n",
" <td>4</td>\n",
" \n",
" <td>14</td>\n",
" \n",
" <td>44</td>\n",
" \n",
" </tr>\n",
" \n",
" <tr>\n",
" <td>five</td>\n",
" \n",
" <td>5</td>\n",
" \n",
" <td>15</td>\n",
" \n",
" <td>55</td>\n",
" \n",
" </tr>\n",
" \n",
"\n",
" \n",
"</tbody>\n",
"</table>"
],
"text/plain": [
"#<Daru::DataFrame(5x3)>\n",
" a b c\n",
" one 1 11 11\n",
" two 2 12 22\n",
" three 3 13 33\n",
" four 4 14 44\n",
" five 5 15 55"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = Daru::DataFrame.new({b: [11,12,13,14,15], a: [1,2,3,4,5],\n",
" c: [11,22,33,44,55]},\n",
" order: [:a, :b, :c],\n",
" index: [:one, :two, :three, :four, :five])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Please refer: https://github.com/Shekharrajak/daru-data_tables/issues/2\n",
"# Daru::View::DataTables.init_iruby\n",
"# t = DataTables::DataTable.new(pageLength: 3)\n",
"# t.to_html\n",
"# t.to_html(id='table_id1')\n",
"# table_opts = {\n",
"# class: \"display\", \n",
"# cellspacing: \"0\",\n",
"# width: \"100%\" \n",
"# }\n",
"# options ={\n",
"# table_options: table_opts\n",
"# } \n",
"# t.to_html(id='table_id1', options)\n",
"\n",
"# id is automatically added into table_options\n",
"# options\n",
"# options[:table_options][:table_html] = df.to_html_thead + df.to_html_tbody\n",
"# html_code = t.to_html(id='table_id1', options)\n",
"# Fix me: It is showing normal html code. That means DataTables js and css is not working or loading.\n",
"# IRuby.html html_code\n",
"# html_code.html_safe\n",
"# Fix me: It is showing normal html code. That means DataTables js and css is not working or loading.\n",
"# IRuby.html html_code\n",
"\n",
"# t_opts = {\n",
"# data: [[1,1,1],\n",
"# [1,2,3],\n",
"# [11,12,13],\n",
"# [1,2,3],\n",
"# [11,12,13],\n",
"# [1,2,3],\n",
"# [11,12,13]\n",
"# ],\n",
"# pageLength: 4\n",
"# }\n",
"# table_from_array = DataTables::DataTable.new(t_opts)\n",
"# table_opts = {\n",
"# class: \"display\",\n",
"# cellspacing: \"0\",\n",
"# width: \"50%\",\n",
"# table_html: \"\n",
"# <thead>\n",
"# <tr>\n",
"# <th>Num1 </th>\n",
"# <th>Num2 </th>\n",
"# <th>Num3 </th>\n",
"# </tr>\n",
"# </thead>\"\n",
"# }\n",
"# options = {\n",
"# table_options: table_opts\n",
"# }\n",
"# html_code_array_sorted = table_from_array.to_html(id='table_id4', options)\n",
"\n",
"# IRuby.html html_code_array_sorted\n",
"\n",
"# IRuby.display(IRuby.javascript(table_from_array.to_html('id1')))\n",
"# IRuby.html(table_from_array.to_html(id='table_id4', options))\n",
"# table_html = \"\n",
"# <table id='id1'>\n",
"# <thead>\n",
"# <tr>\n",
"# <th>Num1 </th>\n",
"# <th>Num2 </th>\n",
"# <th>Num3 </th>\n",
"# </tr>\n",
"# </thead>\n",
"# </table>\"\n",
"# IRuby.html table_html\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Ruby 2.5.3",
"language": "ruby",
"name": "ruby"
},
"language_info": {
"file_extension": ".rb",
"mimetype": "application/x-ruby",
"name": "ruby",
"version": "2.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
nmsutton/MemoryModule | python_version/examples/FFSSN.ipynb | 1 | 35484 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:2.3em;font-weight:bold'><center>Reinforcement Learning Using Spiking Neural Networks</center></div><br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:2em;text-decoration:underline;font-weight:bold'>Latest Results</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.5em;'>\n",
"The chart below is based trained neuron responses from each letter of the alphabet being presented 3 times in repetition then having the next letter presented. Neurons, as represented as y-axis values, having points on the graph specific to only 1 letter (3 points in a row) shows the model having learned well and otherwise shows an area learning can improve. Points represent neurons responding (being activated) through firing spikes. Highest performance after parameter adjustment has been measured as 97.6% overall accuracy (TP+TN/TP+FP+TN+FN) and 71.8% precision (TP/TP+FP) to input (while the result has been repeated more repeatability testing is needed). Work is undergoing now to auto-optimize parameters to increase performance even further. <a target=\"_blank\" href='/github/tartavull/snn-rl/blob/master/optSim.ipynb'>Parameter Optimization</a>\n",
"<center><br>$\\small\\hspace{3pt}Realistic\\hspace{1pt}Neuron\\hspace{1pt}Property\\hspace{1pt}Modeling\\hspace{1pt}Key\\hspace{1pt}Features: Spike\\hspace{1pt}Timing\\hspace{1pt}Dependent\\hspace{1pt}Plasticity*Active$<br>$\\small Dendrites*Direct\\hspace{1pt}to\\hspace{1pt}Soma\\hspace{1pt}Signaling*Lateral\\hspace{1pt}Inhibition * Learning\\hspace{1pt}Rate * Neurosci.\\hspace{1pt}Toolkit$</center>\n",
"<br><br><center>26 Char Test Results with Spike Occurences of Neurons<img src='http://nbviewer.ipython.org/github/tartavull/snn-rl/blob/master/img/26charTest.jpg'><br><br></center>\n",
"\n",
"Letters used as stimuli were represented as images with 15 pixels (3x5) and presented sequentially for 100ms at a time 3 times in a row. Spikes occur when a neuron's voltage reaches 10mv. Once a spike occurs the voltages of the other neurons are inhibited which is visible as their large drops on the plot. The inhibition and excitation is greater than wanted (-65mv at some points) but work is being done to scale that better. An area undergoing active work is improving the discrimination of characters close in pixel presentation to each other, for example 'B' and 'D' have the same neuron in the video below. <a target=\"_blank\" href=\"http://nmsutton.heroku.com\">Nate</a> will work further on the performance after his current work on joining a lab or other position, <a target=\"_blank\" href='https://www.linkedin.com/in/tartavull'>Ignacio</a> as well if he can get the chance but we welcome contributions to this open source project. The video below shows a close up view of voltage response measurement of four neurons trained to respond to example letters (A, B, C, and D). We are trying to improve the simulations eyesight like a human would have trying to read a eye chart! Overall the neurons specialized reasonably well to the input but there is room for greater performance as well. \n",
"\n",
"<br><br>Intro material: <a target=\"_blank\" href='https://github.com/tartavull/snn-rl/blob/master/README.md'>ReadMe</a> and <a target=\"_blank\" href='http://nbviewer.ipython.org/github/tartavull/snn-rl/blob/master/notebooks/introduction.ipynb'>Intro</a>. Authors: <a target=\"_blank\" href=\"http://nmsutton.heroku.com\">Nate</a> and <a target=\"_blank\" href='https://www.linkedin.com/in/tartavull'>Ignacio</a>. Our <a target=\"_blank\" href='https://github.com/tartavull/snn-rl'>code</a>.\n",
"<br>Based on: <a target=\"_blank\" href='http://www.personal.psu.edu/lnl/papers/Gupta_Long_2007.pdf'>Character Recognition using Spiking Neural Networks</a>.\n",
"\n",
"<br><br>You may ask, how did the neurons get trained? Let me explain!<br><br></div>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<center><div style=\"font-size:1.5em\"><iframe width=\"80%\" height=\"700\" src=\"http://www.youtube.com/embed/DUZldskw51A\" loop=1 allowfullscreen></iframe><br><a target=\"_blank\" href=\"/notebooks/furtherFormulas/3dAnimScatterPlotHdf5.ipynb\">Animation</a> <a target=\"_blank\" href=\"/github/tartavull/snn-rl/blob/master/testVoltage.mp4\">Video Download</a></div></center>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import HTML;HTML('<center><div style=\"font-size:1.5em\"><iframe width=\"80%\" height=\"700\" src=\"http://www.youtube.com/embed/DUZldskw51A\" loop=1 allowfullscreen></iframe><br><a target=\"_blank\" href=\"/notebooks/furtherFormulas/3dAnimScatterPlotHdf5.ipynb\">Animation</a> <a target=\"_blank\" href=\"/github/tartavull/snn-rl/blob/master/testVoltage.mp4\">Video Download</a></div></center>')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:2em;text-decoration:underline;font-weight:bold'>Training</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.5em;'>Values are trained for the model by displaying each character after each other for one spike interval (100ms).</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.7em;text-decoration:underline;font-weight:bold'>Time and Refractory Period</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.5em'>Time is initally incremented and refractory period status variables are processed. <a target=\"_blank\" href='/github/tartavull/snn-rl/blob/master/furtherFormulas/timeAndRefracCalcs.ipynb#timePeriodAndRefractoryCalcs'>Time&Refrac</a></div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.7em;text-decoration:underline;font-weight:bold'>First layer and Dirac function</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.5em'>Spikes from the first layer are represented as presynaptic spike times across a 11 epoch (30000ms) timeframe. They are coded into a variable that is has lookups done in it to refer back to when spikes have occured. Spikes found trigger a dirac function which is one of the cofactors in the dendrite and somaDirect equations which build their signal that leads to the soma potential. <a target=\"_blank\" href='/github/tartavull/snn-rl/blob/master/furtherFormulas/cofactorCalculations.ipynb#diracCalc'>Dirac Function</a></div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.7em;text-decoration:underline;font-weight:bold'>Weight</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.5em'>The weights are set at random initially but at a range of values that allows dirac activated dendrite and someDirect signals to \n",
"combine to create postsynaptic spikes (from output neurons) from the beginning or soon enough afterward. After initial spiking has occured the reinforcement learning comes into effect where character input that causes a presynaptic spike and post synapic spike after that causes a weight increase. The increase is defined by the weighting equations. <a href='/github/tartavull/snn-rl/blob/master/furtherFormulas/cofactorCalculations.ipynb'>weight change</a>, \n",
"<a href='/github/tartavull/snn-rl/blob/master/furtherFormulas/cofactorCalculations.ipynb'>returnDeltaW</a>, \n",
"<a target=\"_blank\" href='/github/tartavull/snn-rl/blob/master/furtherFormulas/cofactorCalculations.ipynb#returnNewW'>returnNewW</a></div>"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<center><br><div style=\"font-size:1.5em\">Weights of All Neurons During the Training Process with 4 Characters<br><br><iframe width=\"60%\" height=\"500\" src=\"http://www.youtube.com/embed/-ae2j13XQBU\" loop=1 allowfullscreen></iframe><br><a target=\"_blank\" href=\"/github/tartavull/snn-rl/blob/master/furtherFormulas/3dBarChartAnim.ipynb#returnNewW\">3dBarChartAnim</a></div></center>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"from IPython.display import HTML;HTML('<center><br><div style=\"font-size:1.5em\">Weights of All Neurons During the Training Process with 4 Characters<br><br><iframe width=\"60%\" height=\"500\" src=\"http://www.youtube.com/embed/-ae2j13XQBU\" loop=1 allowfullscreen></iframe><br><a target=\"_blank\" href=\"/github/tartavull/snn-rl/blob/master/furtherFormulas/3dBarChartAnim.ipynb#returnNewW\">3dBarChartAnim</a></div></center>')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.5em'>The below video shows the final weights produced after training 26 neurons with the full alphabet for 30000ms total time. Each neuron is intended to have weights specialized in accordance with only one distinct character.</div>"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<center><br><div style=\"font-size:1.5em\">Weights of All Neurons After Training with 26 Characters<br><br><iframe width=\"60%\" height=\"500\" src=\"http://www.youtube.com/embed/8R_BrMk8VYM\" loop=1 allowfullscreen></iframe><br><a target=\"_blank\" href=\"/github/tartavull/snn-rl/blob/master/furtherFormulas/3dBarChartRotatingAnim.ipynb#returnNewW\">3dBarChartRotatingAnim</a></div></center>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"from IPython.display import HTML;HTML('<center><br><div style=\"font-size:1.5em\">Weights of All Neurons After Training with 26 Characters<br><br><iframe width=\"60%\" height=\"500\" src=\"http://www.youtube.com/embed/8R_BrMk8VYM\" loop=1 allowfullscreen></iframe><br><a target=\"_blank\" href=\"/github/tartavull/snn-rl/blob/master/furtherFormulas/3dBarChartRotatingAnim.ipynb#returnNewW\">3dBarChartRotatingAnim</a></div></center>')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.5em'><center>Weights of the Neurons After Training with 4 Characters:<img src=\"http://nbviewer.ipython.org/github/tartavull/snn-rl/blob/master/img/weights.jpeg\"><a target=\"_blank\" href='/github/tartavull/snn-rl/blob/master/furtherFormulas/3dBarChartGenerator.ipynb#returnNewW'>3dBarChartGenerator</a></center></div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.7em;text-decoration:underline;font-weight:bold'>Tau</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.5em'>The learning rate for the dendrite, tau, is dependednt on the results of the weight calculation. It creates faster learning when a stronger weight is present. <a target=\"_blank\" href='/github/tartavull/snn-rl/blob/master/furtherFormulas/cofactorCalculations.ipynb#tauDCalc'>Tau</a></div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.7em;text-decoration:underline;font-weight:bold'>Resistance</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.5em'>Resistance is dependent on tau and assists with allowing the voltage to reach a spiking level even with lower weights and tau. <a target=\"_blank\" href='/github/tartavull/snn-rl/blob/master/furtherFormulas/cofactorCalculations.ipynb#resistanceCalc'>Resistance</a>\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<center><div style=\"font-size:1.5em\">Relationship Between Weight, Tau, and Resistance:<br><br></div><iframe width=\"60%\" height=\"500\" src=\"http://www.youtube.com/embed/f9QN9Q1FqPY\" loop=1 allowfullscreen></iframe><br><div style=\"font-size:1.5em\"><a target=\"_blank\" href=\"/github/tartavull/snn-rl/blob/master/furtherFormulas/3dBarWTauRAnim.ipynb#returnNewW\">3dBarWTauRAnim</a></div></center>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"from IPython.display import HTML;HTML('<center><div style=\"font-size:1.5em\">Relationship Between Weight, Tau, and Resistance:<br><br></div><iframe width=\"60%\" height=\"500\" src=\"http://www.youtube.com/embed/f9QN9Q1FqPY\" loop=1 allowfullscreen></iframe><br><div style=\"font-size:1.5em\"><a target=\"_blank\" href=\"/github/tartavull/snn-rl/blob/master/furtherFormulas/3dBarWTauRAnim.ipynb#returnNewW\">3dBarWTauRAnim</a></div></center>')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:2.0em;text-decoration:underline;font-weight:bold'>Dendrite Input</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.5em'>This is one receptor of presynaptic input that translates signals into other values that are passed to the soma. \n",
"The equation below specifies how. Cofactors in the equation are devided by their units to normalize the values. <br>Dendrite equ (equ. 1) in the <a target=\"_blank\" href='www.personal.psu.edu/lnl/papers/Gupta_Long_2007.pdf'>paper</a> is $\\tau_d^i*d_(I^i_d(t))/dt=-I_d^i(t)+R_d^i*w^i*\\delta (t-t_f^i)$ <br>Equ in Brian2 (open source prog): dv/dt = (((-v/mV)+((r/mV)*(w/volt)*(dirac/volt)))/(tau))*mV : volt \n",
"<a target=\"_blank\" href='#mainCode'>Dendrite Equations</a></div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:2.0em;text-decoration:underline;font-weight:bold'>Soma Direct Input</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.5em;'>Another receptor of presynaptic input. <br>SomaDirect equ (equ. 4) in the <a target=\"_blank\" href='www.personal.psu.edu/lnl/papers/Gupta_Long_2007.pdf'>paper</a> is $\\tau_s*d_(I_s(t))/dt=-I_s(t)\\sum_{i}w^i*\\delta (t-t_f^i)$ <br>In Brian2: dv/dt = (((-v/mV)+(summedWandDirac/volt))/(tauS))*mV : volt \n",
"<a target=\"_blank\" href='#mainCode'>SomaDirect Equations</a></div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.7em;text-decoration:underline;font-weight:bold'>Lateral Inhibition</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.5em;'>Due to competition amongst neurons inhibition signals are sent from one to another when they receive input. A winner-take-all type of implementation was created where inhibition is auto tuned, the neuron with the greatest soma membrane potential change is the only one allowed to create a post-synaptic spike. The membrane potential is scaled based on the inhibition.\n",
"<a target=\"_blank\" href='/github/tartavull/snn-rl/blob/master/furtherFormulas/lateralInhibition.ipynb#lateralInhibition'>Lateral Inhibition</a></div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:2.0em;text-decoration:underline;font-weight:bold'>Soma Membrane Potential Charge (Um)</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.5em;'>Um (equ. 5) in <a target=\"_blank\" href='www.personal.psu.edu/lnl/papers/Gupta_Long_2007.pdf'>paper</a>: $\\tau_m*d_(U_m(t))/dt=-U_m(t)+R_m(I_d(t)+I_s(t))$ <br>\n",
"Um prior to lat. inh.: dprelimV/dt = (-prelimV+((Rm/mV)*(SynI+DendI*1.0)))/(tauM) : volt (unless refractory)\n",
"<br>Um after lat. inh.: dv/dt = v/(1*second): volt\n",
"<a target=\"_blank\" href='#mainCode'><br>Soma Membrane Potential Equations</a><br><br><center>Spikes of Neurons During Training<img src=\"http://nbviewer.ipython.org/github/tartavull/snn-rl/blob/master/img/trainingSpikes.jpg\">The dots represent a spike fired for an input character stimulus. Notice how over time the neurons specialize in the way they designate themselves for only one character. That is the reinforcement learning causing the neurons to specilize and in this example it takes toward 10000ms for that. In some training simulations they do not all correctly become designated to one character.\n",
"<br><br>\n",
"Below the spikes generated during training with the full alphabet are shown. Greater specialization toward the end shows the degree of effectiveness in the training.</center>\n",
"\n",
"<br><br><center>26 Char Training with Neuron Spikes<a target=\"_blank\" href='http://nbviewer.ipython.org/github/tartavull/snn-rl/blob/master/img/26charTrain.jpg'><img src='http://nbviewer.ipython.org/github/tartavull/snn-rl/blob/master/img/26charTrain1024Width.jpg'>Click to enlarge</a></center>\n",
"<br>\n",
"\n",
"</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.7em;text-decoration:underline;font-weight:bold'>Check for resets</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.5em;'>After each spike interval has passed some logic is included to reset values upon spike occurences. <a target=\"_blank\" href='/github/tartavull/snn-rl/blob/master/furtherFormulas/cofactorCalculations.ipynb#mainCode'>Resets</a></div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:2.0em;text-decoration:underline;font-weight:bold'>Testing</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:1.5em;'>The weights and subsequently tau and resistance generated are used as the trained model values and tests are run to evaluate the performance. Input characters are presented three times in a row for three spike intervals (300ms total). Observed spikes fired and not are compared to expected values and performance is reported.\n",
"<a target=\"_blank\" href='/notebooks/furtherFormulas/testingProcesses.ipynb#intitializeTrainedModelParameters'>intitializeTrainedModelParameters</a>, <a target=\"_blank\" href='/notebooks/furtherFormulas/testingProcesses.ipynb#evaluateClassifierPerf'>evaluateClassifierPerf</a>, <a target=\"_blank\" href='/github/tartavull/snn-rl/blob/master/furtherFormulas/testingProcesses.ipynb#OutputEvaluationResults'>OutputEvaluationResults</a><br><br><center>Further descriptions of the simulation results are <a target=\"_blank\" href='http://nbviewer.ipython.org/github/tartavull/snn-rl/blob/master/analysisFurtherFormulas.ipynb'>here</a></center></div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='font-size:2.0em;text-decoration:underline;font-weight:bold'>Main code for the simulation:<a id='mainCode'></a></div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"'''\n",
"\tCopyright 2015, Nate Sutton and Ignacio Tartavull\n",
"\tThis is the main file for the Spiking Neual Networks\n",
"\tReinforcement Learning simulation. \n",
"\n",
"\tMore info:\n",
"\thttps://github.com/tartavull/snn-rl/blob/master/README.md\n",
"\thttp://nbviewer.ipython.org/github/tartavull/snn-rl/blob/master/notebooks/introduction.ipynb\n",
"\thttp://nbviewer.ipython.org/github/tartavull/snn-rl/blob/master/FFSSN.ipynb\n",
"'''\n",
"\n",
"from furtherFormulas.architecture_further_formulas import *\n",
"from furtherFormulas.cofactorCalculations import *\n",
"from furtherFormulas.timeAndRefracCalcs import *\n",
"from furtherFormulas.outputPrinting import *\n",
"from furtherFormulas.testingProcesses import *\n",
"from furtherFormulas.lateralInhibition import *\n",
"from furtherFormulas.generalUtilities import *\n",
"\n",
"timeAndRefrac = timeAndRefrac\t\n",
"\n",
"class gupta_paper:\n",
"\t'''\n",
"\t\tMain program variables are set the simulation is run. Specific neuron models are\n",
"\t\tdefined for computing processing of electrophysiology in the soma, direct to soma\n",
"\t\tsignals, and active dendrites. Equations with variables\n",
"\t\tare defined in the equations() objects.\n",
"\t'''\n",
"\tneuralnet = Network()\n",
"\tdictionary = dictionary()\n",
"\tspiketimes = dictionary.spikeTimes(dictionaryLongitude, spikeInterval, testingSpikesPerChar, testingEpochs)\n",
"\ttrainingSpikeTimes = dictionary.spikeTimes(dictionaryLongitude, spikeInterval, trainingSpikesPerChar, trainingEpochs)\n",
"\tLIK = SpikeGeneratorGroup(N=15, indices=spiketimes[:,0], times=spiketimes[:,1]*ms)\n",
"\t# W = W and other lines below are to avoid an odd 'reference before assignment' error\n",
"\tW = W\n",
"\ttauM = tauM\n",
"\tneuronIndex = neuronIndex\n",
"\tgeneralClockDt = generalClockDt\n",
"\trunTime = runTime\n",
"\trunTimeScaling = runTimeScaling\n",
"\tevaluateClassifier = evaluateClassifier\n",
"\taccelerateTraining = accelerateTraining\n",
"\tdiracScaling = diracScaling\n",
"\tsomaDirectScaling = somaDirectScaling\n",
"\tnegativeWeightReinforcement = negativeWeightReinforcement\n",
"\tpositiveWeightReinforcement = positiveWeightReinforcement\n",
"\ttimeAndRefrac = timeAndRefrac\t\n",
"\ttestRun = testRun\n",
"\tlatInhibSettings = latInhibSettings\n",
"\tstandardPrint = standardPrint\n",
"\tverbosePrint = verbosePrint\n",
"\ttestingRunTime = testingRunTime\n",
"\toptResultsFile = optResultsFile\n",
"\tminWeightsRand = minWeightsRand\n",
"\tmaxWeightsRand = maxWeightsRand\n",
"\ttotalSpikeIntervals = totalSpikeIntervals\n",
"\n",
"\tdef run_model(self):\n",
"\t\tneuralnet = self.neuralnet\n",
"\t\tdictionary = self.dictionary\n",
"\n",
"\t\teqs = Equations('''\n",
"\t\t\tdv/dt = v/(1*second): volt\n",
"\t\t\tdprelimV/dt = (-prelimV+((Rm/mV)*(SynI+DendI*1.0)))/(tauM) : volt (unless refractory)\n",
"\t\t\tRm = 80*mV : volt\n",
"\t\t\ttauM = 30*ms : second\n",
"\t V : volt\n",
"\t DendI : volt\n",
"\t SynI : volt\n",
"\t v2 : volt\t\n",
"\t\t\tUmSpikeFired : volt\t\n",
"\t\t\tbeginRefrac : volt\n",
"\t\t ''')\t\t\t\n",
"\n",
"\t\tdendriteEqs = Equations('''\n",
"\t\t\tdv/dt = (((-v/mV)+((r/mV)*(w/volt)*(dirac/volt)))/(tau))*mV : volt\n",
"\t\t\tV : volt\n",
"\t r : volt\n",
"\t w : volt\n",
"\t dirac : volt\n",
"\t tau : second\n",
"\t v2: volt\n",
"\t\t\t''')\n",
"\n",
"\t\tdirectToSomaEqs = Equations('''\n",
"\t\t\tdv/dt = (((-v/mV)+(summedWandDirac/volt))/(tauS))*mV : volt\n",
"\t\t\ttauS = 2*ms : second\n",
"\t\t\tV : volt\n",
"\t\t\tsummedWandDirac : volt\n",
"\t\t\tv2 : volt\n",
"\t\t\tspikeFired : boolean\n",
"\t\t\t''')\t\t\n",
"\n",
"\t\tclass ADDSNeuronModel(NeuronGroup, gupta_paper): \n",
"\t\t\t'''\n",
"\t\t\t\tThis is the model used for electrophysiology occuring in the Soma\n",
"\t\t\t'''\n",
"\t\t\tneuronIndex = self.neuronIndex\n",
"\t\t\tgeneralClockDt = self.generalClockDt\n",
"\n",
"\t\t\tdef __init__(self, params):\n",
"\t\t\t\tself = parseArgs(self, sys.argv, dictionaryLongitude)\t\t\n",
"\n",
"\t\t\t\tNeuronGroup.__init__(self, N=dictionaryLongitude, model=eqs,threshold='v>10*mV', reset='v=-0.002 * mV; dv=0; v2=10*mV;UmSpikeFired=1*mV;beginRefrac=1*mV;inhibitionVoltage=prelimV',refractory=8*ms,clock=Clock(dt=self.generalClockDt))\n",
"\t\t\t\t@network_operation(dt=self.generalClockDt)\n",
"\t\t\t\tdef additionToNetwork(): \n",
"\t\t\t\t\tneuronIndex = self.neuronIndex\n",
"\t\t\t\t\ttimeAndRefrac.spikeIntervalCounter = (floor(timeAndRefrac.time/timeAndRefrac.spikeIntervalUnformatted) * timeAndRefrac.spikeIntervalUnformatted)*10\n",
"\n",
"\t\t\t\t\tdef dendCalcs(neuronIndex, ADDSObj, dendObj, spiketimes, evaluationActive):\n",
"\t\t\t\t\t\t'''\n",
"\t\t\t\t\t\t\tBelow sequentially Dirac, Tau, then Resistance are calculated every end of a spike-time interval.\n",
"\t\t\t\t\t\t\tThe resulting Dend I is added to the Um calc for the ADDS soma.\n",
"\t\t\t\t\t\t'''\n",
"\t\t\t\t\t\ttimeAndRefrac = self.timeAndRefrac\n",
"\n",
"\t\t\t\t\t\t# Dirac\n",
"\t\t\t\t\t\tdendObj[neuronIndex].dirac = diracCalc(dendObj, neuronIndex, spiketimes, timeAndRefrac.time, timeAndRefrac.lastSpikeInterval)\n",
"\n",
"\t\t\t\t\t\t# Initialize weights\n",
"\t\t\t\t\t\tif (evaluationActive==False and timeAndRefrac.time == 0.000):\n",
"\t\t\t\t\t\t\tdend[neuronIndex].w = W[neuronIndex]*volt\n",
"\n",
"\t\t\t\t\t\tif (evaluationActive==False and timeAndRefrac.refractoryPointSwitch==True):\n",
"\t\t\t\t\t\t\t# Only change weights of neuron fired\n",
"\t\t\t\t\t\t\tif ADDSObj.UmSpikeFired[neuronIndex] == 1*mV:\n",
"\t\t\t\t\t\t\t\t# Weights\n",
"\t\t\t\t\t\t\t\tWeightChangeCalculation(neuronIndex, spiketimes, timeAndRefrac.time, self.negativeWeightReinforcement, self.positiveWeightReinforcement, M, dendObj)\t\n",
"\t\t\t\t\t\t\t# Tau\n",
"\t\t\t\t\t\t\ttauDCalc(neuronIndex, dendObj)\n",
"\t\t\t\t\t\t\t# Resistance\n",
"\t\t\t\t\t\t\tresistanceCalc(neuronIndex, dendObj, self.tauM)\n",
"\n",
"\t\t\t\t\t\t# TODO: check do I need additional loop below?\n",
"\t\t\t\t\t\tfor indexOfDend in range(dictionaryLongitude):\n",
"\t\t\t\t\t\t\tADDSObj.DendI[indexOfDend] = sum(dendObj[indexOfDend].v[:])*dendCalcScaling\n",
"\n",
"\t\t\t\t\t\t#print 'ADDSObj.t',ADDSObj.t,'ADDSObj.DendI',ADDSObj.DendI,'neuronIndex',neuronIndex,'dendObj[neuronIndex].dirac',dendObj[neuronIndex].dirac,'dendObj[neuronIndex].tau',dendObj[neuronIndex].tau,'dendObj[neuronIndex].w',dendObj[neuronIndex].w,'dendObj[neuronIndex].r',dendObj[neuronIndex].r\n",
"\n",
"\t\t\t\t\tdef somaDirectCalcs(neuronIndex, ADDSObj, somaDirectObj, dendObj):\n",
"\t\t\t\t\t\tdotProductWandDirac = sum(w*d for w,d in zip(dendObj[neuronIndex].w[:], dendObj[neuronIndex].dirac[:]))\n",
"\t\t\t\t\t\t#somaDirectObj.summedWandDirac[neuronIndex] = ((dotProductWandDirac*volt)/(volt**2))*self.somaDirectScaling\n",
"\t\t\t\t\t\tsomaDirect.summedWandDirac[neuronIndex] = ((dotProductWandDirac*volt)/(volt**2))*self.somaDirectScaling\n",
"\n",
"\t\t\t\t\t\tfor neuronNumber in range(dictionaryLongitude):\n",
"\t\t\t\t\t\t\t#ADDSObj.SynI[neuronNumber] = somaDirectObj.v[neuronNumber]\t\t\n",
"\t\t\t\t\t\t\tADDS.SynI[neuronNumber] = somaDirect.v[neuronNumber]\t\t\n",
"\n",
"\t\t\t\t\t\t#print 'ADDSObj.t',ADDSObj.t,'ADDSObj.SynI',ADDSObj.SynI,'neuronIndex',neuronIndex,'somaDirectObj.summedWandDirac',somaDirectObj.summedWandDirac[neuronIndex],'dendObj[neuronIndex].w',dendObj[neuronIndex].w,'dendObj[neuronIndex].dirac',dendObj[neuronIndex].dirac\n",
"\n",
"\t\t\t\t\tdef mainSimulationCalcs(ADDSObj, dendObj, somaDirectObj, spiketimes, evaluationActive):\n",
"\t\t\t\t\t\t'''\n",
"\t\t\t\t\t\t\tdend then somaDirect calcs are done which are then used to set lat inhib.\n",
"\t\t\t\t\t\t\tSoma Um calcs are done automatically using equations entered for brian\n",
"\t\t\t\t\t\t\tonce dend and somaDirect are updated\n",
"\t\t\t\t\t\t'''\n",
"\t\t\t\t\t\tpreTNorm = self.timeAndRefrac.time\n",
"\t\t\t\t\t\ttNorm = preTNorm - (floor((preTNorm/.001)*.01) * .1)\n",
"\t\t\t\t\t\t\n",
"\t\t\t\t\t\tself.timeAndRefrac = timePeriodAndRefractoryCalcs(self.timeAndRefrac)\n",
"\n",
"\t\t\t\t\t\tif (evaluationActive==True) and (timeAndRefrac.time == 0.000 or timeAndRefrac.time == 0.001):\n",
"\t\t\t\t\t\t\tinitializeTrainedModelParameters(dendObj)\n",
"\n",
"\t\t\t\t\t\t# Option to accelerate computations for training\n",
"\t\t\t\t\t\tif self.accelerateTraining == False or (evaluationActive == False and (tNorm <= .005 or tNorm >= .096)):\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"\t\t\t\t\t\t\tif self.accelerateTraining == True and (tNorm >= .096 and tNorm < .099):\n",
"\t\t\t\t\t\t\t\tfor i in range(dictionaryLongitude):\n",
"\t\t\t\t\t\t\t\t\tADDSObj.DendI[i]=0*mV\n",
"\t\t\t\t\t\t\t\t\tADDSObj.SynI[i]=0*mV\n",
"\t\t\t\t\t\t\t\t\tADDSObj.prelimV[i]=0*mV\n",
"\t\t\t\t\t\t\t\t\tADDSObj.v[i]=0*mV\t\t\t\t\t\t\t\t\n",
"\t\t\t\t\t\t\t\t\tfor i2 in range(len(dend[0].v)):\n",
"\t\t\t\t\t\t\t\t\t\tdendObj[i].v[i2] = 0*mV\n",
"\t\t\t\t\t\t\t\t\tsomaDirectObj.v[i] = 0*mV\t\t\n",
"\n",
"\t\t\t\t\t\t\tfor neuronIndex in range(dictionaryLongitude):\n",
"\t\t\t\t\t\t\t\tdendCalcs(neuronIndex, ADDSObj, dendObj, spiketimes, evaluationActive)\n",
"\n",
"\t\t\t\t\t\t\t\tsomaDirectCalcs(neuronIndex, ADDSObj, somaDirectObj, dendObj)\t\t\t\t\t\t\t\t\n",
"\n",
"\t\t\t\t\t\t\tADDSObj.v, self.latInhibSettings = lateralInhibition(ADDSObj, self.timeAndRefrac, self.latInhibSettings)\n",
"\n",
"\t\t\t\t\t\t\t#if ADDSObj.t > 100*ms:\n",
"\t\t\t\t\t\t\t\t#for i in range(dictionaryLongitude):\n",
"\t\t\t\t\t\t\t\t#\tsomaDirectObj.summedWandDirac[i] += 20*mV\n",
"\t\t\t\t\t\t\t\t#print 'ADDS.t',ADDS.t,'ADDS.SynI',ADDS.SynI,'ADDS.DendI',ADDS.DendI,'ADDSObj.v',ADDSObj.v,'somaDirectObj.summedWandDirac',somaDirectObj.summedWandDirac,'somaDirectObj.v',somaDirectObj.v\n",
"\n",
"\t\t\t\t\t\t\tfor neuronIndex in range(dictionaryLongitude): \n",
"\t\t\t\t\t\t\t\tADDSObj.v2, somaDirectObj.v2, self.timeAndRefrac = checkForResets(neuronIndex, ADDSObj, dendObj, somaDirectObj, self.timeAndRefrac)\n",
"\n",
"\t\t\t\t\t\t\tADDSObj.UmSpikeFired, self.testRun = evaluateClassifierPerf2(ADDSObj, self.testRun)\n",
"\n",
"\t\t\t\t\t\t#roundedSecondsTime = math.floor(Decimal(format((ADDSObj.t), '.1f'))/Decimal(format((1.0*second), '.1f')))\n",
"\t\t\t\t\t\t#if printWeights < roundedSecondsTime:\n",
"\t\t\t\t\t\t#\tself.printWeights = roundedSecondsTime; printWeights(dendObj);\n",
"\t\t\t\t\tif self.evaluateClassifier == False:\n",
"\t\t\t\t\t\tmainSimulationCalcs(ADDS, dend, somaDirect, self.trainingSpikeTimes, False)\n",
"\t\t\t\t\telse:\n",
"\t\t\t\t\t\tmainSimulationCalcs(ADDS, dend, somaDirect, self.spiketimes, True)\t\t\t\t\t\n",
"\n",
"\t\t\t\tself.contained_objects.append(additionToNetwork)\t\t\t\t\n",
"\n",
"\t\tclass DendriteNeuronModel(NeuronGroup):\n",
"\t\t\tgeneralClockDt = self.generalClockDt\n",
"\t\t\tdef __init__(self, params): \n",
"\t\t\t\tNeuronGroup.__init__(self, N=15, model=dendriteEqs,clock=Clock(dt=self.generalClockDt))\n",
"\t\t\t\t@network_operation(dt=self.generalClockDt)\n",
"\t\t\t\tdef additionToNetwork(): \n",
"\t\t\t\t\tplaceHolderForLaterContent = True\n",
"\t\t\t\tself.contained_objects.append(additionToNetwork)\n",
"\n",
"\t\tclass SomaDirectNeuronModel(NeuronGroup): \n",
"\t\t\tgeneralClockDt = self.generalClockDt\n",
"\t\t\tdef __init__(self, params): \n",
"\t\t\t\tNeuronGroup.__init__(self, N=dictionaryLongitude, model=directToSomaEqs,clock=Clock(dt=self.generalClockDt))\n",
"\t\t\t\t@network_operation(dt=self.generalClockDt)\n",
"\t\t\t\tdef additionToNetwork(): \n",
"\t\t\t\t\tplaceHolderForLaterContent = True\n",
"\t\t\t\tself.contained_objects.append(additionToNetwork)\t\t\n",
"\n",
"\t\tdend = [None]*dictionaryLongitude\n",
"\t\tweightMonitors = [None]*dictionaryLongitude\n",
"\t\tfor firstLayerIndex in range(dictionaryLongitude):\n",
"\t\t\tdend[firstLayerIndex] = DendriteNeuronModel(15)\t\n",
"\t\t\tweightMonitors[firstLayerIndex] = StateMonitor(dend[firstLayerIndex], 'w', record=True)\n",
"\t\t\tneuralnet.add(dend[firstLayerIndex])\n",
"\t\t\tneuralnet.add(weightMonitors[firstLayerIndex])\n",
"\t\tsomaDirect = SomaDirectNeuronModel(dictionaryLongitude)\n",
"\t\tneuralnet.add(somaDirect)\n",
"\t\tADDS = ADDSNeuronModel(self)\t\t\t\n",
"\t\tM = SpikeMonitor(ADDS)\n",
"\t\tMv = StateMonitor(ADDS, 'V', record=True)\n",
"\t\tUmM = StateMonitor(ADDS, 'v2', record=True)\n",
"\t\tself.M = M # for ipython compatibility\n",
"\t\tself.UmM = UmM \n",
"\t\tself.weightMonitors = weightMonitors\n",
"\n",
"\t\tneuralnet.add(ADDS)\n",
"\t\tneuralnet.add(M)\n",
"\t\tneuralnet.add(UmM)\n",
"\t\tif (ADDS.evaluateClassifier==True): ADDS.runTime = ADDS.testingRunTime\n",
"\t\tADDS.runTime *= ADDS.runTimeScaling # scaling factor\n",
"\t\tif ADDS.standardPrint: neuralnet.run(ADDS.runTime,report='text')\n",
"\t\telse: neuralnet.run(ADDS.runTime,report='stderr')\n",
"\n",
"\t\tOutputEvaluationResults(dend, self.testRun, ADDS.verbosePrint, ADDS.evaluateClassifier)\n",
"\t\taccuracyPerc = totalCorrectPercentage()\n",
"\t\tprecisionPerc = precisionPercentage()\n",
"\t\twriteOptimizationResults(ADDS, self.testRun, accuracyPerc)\n",
"\n",
"\t\tneuronToPlot = 1\n",
"\t\tcolors = ['r']*1+['g']*1+['b']*1+['y']*1\n",
"\t\tcolors = ['blue', 'green', 'magenta', 'cyan']\n",
"\t\tsubplot(211)\n",
"\t\tplot(M.t/ms, M.i, '.')\n",
"\t\tlegend(['A','B','C','D'], loc='upper left')\t\t\t\n",
"\t\tsubplot(212)\n",
"\t\tplot(UmM.t, UmM.v2.T/mV)\t\n",
"\t\txlabel('Time (ms)')\n",
"\t\tylabel('Membrane Potential (mV)')\n",
"\t\tif (showPlot==True):\n",
"\t\t\tshow()\t\n",
"\n",
"\t\treturn evaluateClassifier, precisionPerc\n",
"\n",
"def main():\n",
"\trun_gupta_paper = gupta_paper()\n",
"\tevaluateClassifier, precisionPerc = run_gupta_paper.run_model()\n",
"\tif evaluateClassifier: print precisionPerc\n",
"\treturn precisionPerc\n",
"\n",
"if __name__ =='__main__':main()"
]
}
],
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| mit |
munichpavel/risklearning | risklearning_demo.ipynb | 1 | 371121 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# risklearning demo\n",
"\n",
"Most, if not all, operational risk capital models assume the existence of stationary frequency and severity distributions (typically Poisson for frequencies, and a subexponential distribution such as lognormal for severities). Yet every quarter (or whenever the model is recalibrated) risk capital goes up almost without fail, either because frequencies increase, severities increase or both.\n",
"\n",
"The assumption of stationary distributions is just one limitation of current approaches to operational risk modeling, but it offers a good inroad for modeling approaches beyond the usual actuarial model typical in operational capital models.\n",
"\n",
"In this notebook, we give a first example of how neural networks can overcome the stationarity assumptions of traditional approaches. The hope is that this is but one of many examples showing a better way to model operational risk.\n",
"\n",
"Note: What follows if very much a work in progress . . .\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<module 'risklearning.learning_frequency' from 'risklearning/learning_frequency.pyc'>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import risklearning.learning_frequency as rlf\n",
"\n",
"reload(rlf)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import scipy.stats as stats\n",
"import math\n",
"import matplotlib.style\n",
"matplotlib.style.use('ggplot')\n",
"import ggplot as gg\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set up frequency distribution to generate samples"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Lambda start value: 1.0, lambda end value: 4.0\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>t</th>\n",
" <th>L1_cat</th>\n",
" <th>L2_cat</th>\n",
" <th>counts</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-1825</td>\n",
" <td>EDPM</td>\n",
" <td>TCEM</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-1824</td>\n",
" <td>EDPM</td>\n",
" <td>TCEM</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-1823</td>\n",
" <td>EDPM</td>\n",
" <td>TCEM</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-1822</td>\n",
" <td>EDPM</td>\n",
" <td>TCEM</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1821</td>\n",
" <td>EDPM</td>\n",
" <td>TCEM</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" t L1_cat L2_cat counts\n",
"0 -1825 EDPM TCEM 1\n",
"1 -1824 EDPM TCEM 3\n",
"2 -1823 EDPM TCEM 1\n",
"3 -1822 EDPM TCEM 1\n",
"4 -1821 EDPM TCEM 0"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Read in Poisson parameters used to simulate loss counts\n",
"lambdas_df = pd.read_csv('data/lambdas_tcem_1d.csv')\n",
"lambda_start = lambdas_df['TCEM'][0]\n",
"lambda_end = lambdas_df['TCEM'].tail(1).iloc[0]\n",
"print('Lambda start value: {}, lambda end value: {}'.format(lambda_start, lambda_end))\n",
"lambda_ts = lambdas_df['TCEM']\n",
"# Read in simulated loss counts\n",
"counts_sim_df = pd.read_csv('data/tcem_1d.csv')\n",
"# EDPM: Execution, Delivery and Process Management\n",
"# TCEM: Transaction Capture, Execution and Maintenance--think fat-finger mistake\n",
"counts_sim_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#%% Do MLE (simple average for Poisson process\n",
"t_start = np.min(counts_sim_df['t'])\n",
"t_end = np.max(counts_sim_df['t'])\n",
"\n",
"n_tenors_train = -t_start\n",
"n_tenors_test = t_end\n",
"\n",
"counts_train = (counts_sim_df[counts_sim_df.t < 0]).groupby('L2_cat').sum()\n",
"counts_test = (counts_sim_df[counts_sim_df.t >= 0]).groupby('L2_cat').sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## MLE for training data\n",
"\n",
"For the Poisson distribution, the MLE of the intensity (here lambda) is just the average of the counts per model horizon. In practice, OpRisk models sometimes take a weighted average, with the weight linearly decreasing over a period of years (see e.g. \"LDA at Work\" by Aue and Kalkbrener)."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lambdas_train = counts_train['counts']/n_tenors_train\n",
"lambdas_test = counts_test['counts']/n_tenors_test\n",
"\n",
"bin_tops = [1,2,3,4,5,6,7,8,9,10,15,101]\n",
"# Recall that digitize (used later) defines bins by lower <= x < upper\n",
"count_tops =[count - 1 for count in bin_tops]\n",
"\n",
"# Calculate bin probabilities from MLE poisson\n",
"poi_mle = stats.poisson(lambdas_train)\n",
"poi_bins = rlf.bin_probs(poi_mle, bin_tops)\n",
"\n",
"mle_probs = pd.DataFrame({'Count Top': count_tops, 'Probs': poi_bins})\n",
"# For later comparison\n",
"mle_probs_vals = list(mle_probs.Probs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prep simulated losses for neural network\n",
"\n",
"For example\n",
"\n",
"* Use one-hot-encoding for L1 and L2 categories (this will make more sense once we look at multiple dependent categories)\n",
"* Bin count data\n",
"* Normalize tenors (i.e. scale so that first tenor maps to -1 with 0 preserved)\n",
"* Export as numpy arrays to feed into keras / tensorflow"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import warnings\n",
"warnings.filterwarnings('ignore') # TODO: improve slicing to avoid warnings\n",
"\n",
"x_train, y_train, x_test, y_test = rlf.prep_count_data(counts_sim_df, bin_tops)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set up the network architecture and train\n",
"\n",
"We use keras with TensorFlow backend. Later we will look at optimizing metaparameters.\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"352/365 [===========================>..] - ETA: 0s"
]
}
],
"source": [
"#from keras.optimizers import SGD\n",
"#sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
"# rl_train_net is a wrapper for standard keras functionality that\n",
"# makes it easier below to optimize hyperparameters\n",
"rl_net = rlf.rl_train_net(x_train, y_train, x_test, y_test, [150], \\\n",
" n_epoch = 300, optimizer = 'adagrad')\n",
"proba = rl_net['probs_nn']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluating the neural network\n",
"Let's see now how the neural network tracks the true distribution over time, and compare with the MLE fitted distribution.\n",
"\n",
"We do this both numerically (Kullback-Leibler divergance) and graphically."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#% Convert proba from wide to long and append to other probs\n",
"mle_probs_vals = list(mle_probs.Probs)\n",
"# TODO: Missing last tenor in nn proba (already in x_test, y_test)\n",
"probs_list = []\n",
"kl_mle_list = []\n",
"kl_nn_list = []\n",
"\n",
"for t in range(proba.shape[0]):\n",
" nn_probs_t = proba[t] \n",
" true_bins_t = rlf.bin_probs(stats.poisson(lambda_ts[-t_start+t]), bin_tops)\n",
" probs_t = pd.DataFrame({'Tenor': t, 'Count Top': count_tops, \\\n",
" 'Probs True': true_bins_t, \\\n",
" 'Probs NN': nn_probs_t, \\\n",
" 'Probs MLE': mle_probs_vals}, \\\n",
" index = range(t*len(count_tops), \\\n",
" t*len(count_tops) + len(count_tops)))\n",
" probs_list.append(probs_t)\n",
" # Calculate KL divergences\n",
" kl_mle_list.append(stats.entropy(true_bins_t, mle_probs_vals))\n",
" kl_nn_list.append(stats.entropy(true_bins_t, nn_probs_t))\n",
"\n",
"probs = pd.concat(probs_list)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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iIiIiMpm71gVolU6n0d/fD0mS0NLSgtbWVtnvz58/j9OnT0MQBIiiiPvvvx+3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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd721725610>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<ggplot: (8785122463729)>"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"probs_tail = probs[probs.Tenor > 360 ]\n",
"\n",
"gg.ggplot(probs_tail, gg.aes(x='Count Top',weight='Probs True')) \\\n",
" + gg.facet_grid('Tenor') \\\n",
" + gg.geom_bar() \\\n",
" + gg.geom_step(gg.aes(y='Probs MLE', color = 'red')) \\\n",
" + gg.geom_step(gg.aes(y='Probs NN', color = 'blue')) \\\n",
" + gg.scale_x_continuous(limits = (0,len(count_tops)))"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" KL MLE KL NN Tenor\n",
"0 0.319970 0.029012 0\n",
"1 0.320601 0.029169 1\n",
"2 0.321232 0.029327 2\n",
"3 0.321864 0.029486 3\n",
"4 0.322496 0.029645 4\n",
" KL MLE KL NN Tenor\n",
"360 0.579818 0.129284 360\n",
"361 0.580628 0.129694 361\n",
"362 0.581437 0.130104 362\n",
"363 0.582247 0.130515 363\n",
"364 0.583058 0.130927 364\n"
]
},
{
"data": {
"image/png": 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AAFAwJRwAAAqmhAMAQMGUcAAAKJgSDgAABVPCAQCgYEo4AAAUTAkHAICCKeEA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\nwgEAoGD/Hw3Vbrql2n6WAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd720a53490>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<ggplot: (8785120745277)>"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# KL divergences\n",
"\n",
"kl_df = pd.DataFrame({'Tenor': range(0, t_end+1), \\\n",
" 'KL MLE': kl_mle_list, \\\n",
" 'KL NN': kl_nn_list})\n",
"\n",
"print kl_df.head()\n",
"print kl_df.tail() \n",
"#% \n",
"# Plot KL divergences\n",
"gg.ggplot(kl_df, gg.aes(x='Tenor')) \\\n",
" + gg.geom_step(gg.aes(y='KL MLE', color = 'red')) \\\n",
" + gg.geom_step(gg.aes(y='KL NN', color = 'blue'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Optimizing network architecture"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training 1 layer(s) of 10 neurons\n",
"352/365 [===========================>..] - ETA: 0s\n",
"Predicting with 1 layer(s) of 10 neurons\n",
"Training 1 layer(s) of 20 neurons\n",
"320/365 [=========================>....] - ETA: 0s\n",
"Predicting with 1 layer(s) of 20 neurons\n",
"Training 1 layer(s) of 50 neurons\n",
"224/365 [=================>............] - ETA: 0s\n",
"Predicting with 1 layer(s) of 50 neurons\n",
"Training 1 layer(s) of 100 neurons\n",
"352/365 [===========================>..] - ETA: 0s\n",
"Predicting with 1 layer(s) of 100 neurons\n",
"Training 1 layer(s) of 150 neurons\n",
"256/365 [====================>.........] - ETA: 0s\n",
"Predicting with 1 layer(s) of 150 neurons\n",
"Training 1 layer(s) of 200 neurons\n",
"224/365 [=================>............] - ETA: 0s\n",
"Predicting with 1 layer(s) of 200 neurons\n",
"Training 2 layer(s) of 10 neurons\n",
"160/365 [============>.................] - ETA: 0s\n",
"Predicting with 2 layer(s) of 10 neurons\n",
"Training 2 layer(s) of 20 neurons\n",
" 32/365 [=>............................] - ETA: 2s\n",
"Predicting with 2 layer(s) of 20 neurons\n",
"Training 2 layer(s) of 50 neurons\n",
"256/365 [====================>.........] - ETA: 0s\n",
"Predicting with 2 layer(s) of 50 neurons\n",
"Training 2 layer(s) of 100 neurons\n",
"288/365 [======================>.......] - ETA: 0s\n",
"Predicting with 2 layer(s) of 100 neurons\n",
"Training 2 layer(s) of 150 neurons\n",
"320/365 [=========================>....] - ETA: 0s\n",
"Predicting with 2 layer(s) of 150 neurons\n",
"Training 2 layer(s) of 200 neurons\n",
"352/365 [===========================>..] - ETA: 0s\n",
"Predicting with 2 layer(s) of 200 neurons\n",
"Training 3 layer(s) of 10 neurons\n",
" 32/365 [=>............................] - ETA: 3s\n",
"Predicting with 3 layer(s) of 10 neurons\n",
"Training 3 layer(s) of 20 neurons\n",
"352/365 [===========================>..] - ETA: 0s\n",
"Predicting with 3 layer(s) of 20 neurons\n",
"Training 3 layer(s) of 50 neurons\n",
"256/365 [====================>.........] - ETA: 0s\n",
"Predicting with 3 layer(s) of 50 neurons\n",
"Training 3 layer(s) of 100 neurons\n",
"365/365 [==============================] - 0s \n",
"\n",
"Predicting with 3 layer(s) of 100 neurons\n",
"Training 3 layer(s) of 150 neurons\n",
"365/365 [==============================] - 0s \n",
"\n",
"Predicting with 3 layer(s) of 150 neurons\n",
"Training 3 layer(s) of 200 neurons\n",
"256/365 [====================>.........] - ETA: 0s\n",
"Predicting with 3 layer(s) of 200 neurons\n"
]
}
],
"source": [
"# More systematically with NN architecture\n",
"# Loop over different architectures, create panel plot\n",
"neurons_list = [10, 20,50,100, 150, 200]\n",
"#neurons_list = [10, 20,50]\n",
"depths_list = [1,2,3]\n",
"optimizer = 'adagrad'\n",
"#%%\n",
"kl_df_list = []\n",
"for depth in depths_list:\n",
" for n_neurons in neurons_list:\n",
" nn_arch = [n_neurons]*depth\n",
" print(\"Training \" + str(depth) + \" layer(s) of \" + str(n_neurons) + \" neurons\")\n",
" rl_net = rlf.rl_train_net(x_train, y_train, x_test, y_test, nn_arch, \\\n",
" n_epoch = 300, optimizer = optimizer)\n",
" proba = rl_net['probs_nn']\n",
" print(\"\\nPredicting with \" + str(depth) + \" layer(s) of \" + str(n_neurons) + \" neurons\")\n",
" probs_kl_dict = rlf.probs_kl(proba, lambda_ts, t_start, t_end+1, bin_tops, mle_probs_vals)\n",
" probs = probs_kl_dict['Probs']\n",
" kl_df_n = probs_kl_dict['KL df']\n",
" \n",
" kl_df_n['Hidden layers'] = depth\n",
" kl_df_n['Neurons per layer'] = n_neurons\n",
" kl_df_n['Architecture'] = str(depth) + '_layers_of_' + str(n_neurons) \\\n",
" + '_neurons'\n",
"\n",
" kl_df_list.append(kl_df_n)\n",
" #%%\n",
"kl_df_hyper = pd.concat(kl_df_list)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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mVNIwYjjJUl+ny7NentLEtdeaGAgECIfDvRBObj3zzDPccccdrFy5skePu3bt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\nD5Tp9WR3orM+k54nr5CXW3TU36pqlMsRPgEAAHA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6neu+9CcijAIAAADADDBhKD08PLeX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\nmAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd721813090>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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t1n3mM5/hwIEDnHvuuZllP/rRj7j77rv5xCc+wcc//vHM87CdPZ6IiIiIiOS3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\n/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Y5mBY7iEodxiqN40a7TPIrrKbOeoWHk6hja9aCfkZwy+6VbXrtec2DrV7JltksIgcVi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\nZZGMAgAAsJNRAAAAdjIKAADALjdSpU1PJuXbAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd7051b7610>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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1FZ/4xCf41Kc+xZQpU1qt+/SnP83evXs555xzMst+9KMfcc899/Dxj3+c0047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\nIiIiIiIiWrn/H6lVNeOiIJy/AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd7050de150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot\n",
"kl_mle = kl_df_n['KL MLE'] # These values are constant over the above loops (KL between MLE and true distribution)\n",
"for depth in depths_list:\n",
" kl_df_depth = kl_df_hyper[kl_df_hyper['Hidden layers'] == depth]\n",
" kl_df_depth = kl_df_hyper[kl_df_hyper['Hidden layers'] == depth]\n",
" kl_depth_vals = kl_df_depth.pivot(index = 'Tenor', columns = 'Neurons per layer', values = 'KL NN')\n",
" kl_depth_vals['KL MLE'] = kl_mle\n",
" kl_depth_vals.plot(title = 'Kullback-Leibler divergences from true distribution \\n for ' \\\n",
" + str(depth) + ' hidden layer(s)', \\\n",
" figsize = (16,10))"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training 2 layer(s) of 10 neurons\n",
" 32/365 [=>............................] - ETA: 8s\n",
"Predicting with 2 layer(s) of 10 neurons\n",
"Training 2 layer(s) of 20 neurons\n",
" 32/365 [=>............................] - ETA: 9s\n",
"Predicting with 2 layer(s) of 20 neurons\n",
"Training 2 layer(s) of 50 neurons\n",
"352/365 [===========================>..] - ETA: 0s\n",
"Predicting with 2 layer(s) of 50 neurons\n",
"Training 3 layer(s) of 10 neurons\n",
"288/365 [======================>.......] - ETA: 0s\n",
"Predicting with 3 layer(s) of 10 neurons\n",
"Training 3 layer(s) of 20 neurons\n",
"320/365 [=========================>....] - ETA: 0s \n",
"Predicting with 3 layer(s) of 20 neurons\n",
"Training 3 layer(s) of 50 neurons\n",
"320/365 [=========================>....] - ETA: 0s \n",
"Predicting with 3 layer(s) of 50 neurons\n"
]
},
{
"data": {
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8UZdccokuvvjimuth4z0eAAAAACSSJfmWJq6u3sSCggIVFxcnoZzEWrp0qZ56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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd6fd227b50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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PQw6lV7QvEUYBAAAAFK1IxGk1vaJZQRgFAAAAULTq3wmq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\nAAAAADvu/wf1+EVuHY8WXgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd6fd227a10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Try again, but now with RMSprop\n",
"neurons_list = [10, 20,50]\n",
"#neurons_list = [50]\n",
"depths_list = [2,3]\n",
"optimizer = 'RMSprop'\n",
"#%%\n",
"kl_df_list = []\n",
"for depth in depths_list:\n",
" for n_neurons in neurons_list:\n",
" nn_arch = [n_neurons]*depth\n",
" print(\"Training \" + str(depth) + \" layer(s) of \" + str(n_neurons) + \" neurons\")\n",
" rl_net = rlf.rl_train_net(x_train, y_train, x_test, y_test, nn_arch, \\\n",
" n_epoch = 300, optimizer = optimizer)\n",
" proba = rl_net['probs_nn']\n",
" print(\"\\nPredicting with \" + str(depth) + \" layer(s) of \" + str(n_neurons) + \" neurons\")\n",
" probs_kl_dict = rlf.probs_kl(proba, lambda_ts, t_start, t_end+1, bin_tops, mle_probs_vals)\n",
" probs = probs_kl_dict['Probs']\n",
" kl_df_n = probs_kl_dict['KL df']\n",
" \n",
" kl_df_n['Hidden layers'] = depth\n",
" kl_df_n['Neurons per layer'] = n_neurons\n",
" kl_df_n['Architecture'] = str(depth) + '_layers_of_' + str(n_neurons) \\\n",
" + '_neurons'\n",
"\n",
" kl_df_list.append(kl_df_n)\n",
" #%%\n",
"kl_df_hyper = pd.concat(kl_df_list)\n",
"\n",
"# Plot\n",
"kl_mle = kl_df_n['KL MLE'] # These values are constant over the above loops (KL between MLE and true distribution)\n",
"for depth in depths_list:\n",
" kl_df_depth = kl_df_hyper[kl_df_hyper['Hidden layers'] == depth]\n",
" kl_df_depth = kl_df_hyper[kl_df_hyper['Hidden layers'] == depth]\n",
" kl_depth_vals = kl_df_depth.pivot(index = 'Tenor', columns = 'Neurons per layer', values = 'KL NN')\n",
" kl_depth_vals['KL MLE'] = kl_mle\n",
" kl_depth_vals.plot(title = 'Kullback-Leibler divergences from true distribution \\n for ' \\\n",
" + str(depth) + ' hidden layer(s)', \\\n",
" figsize = (16,10))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that with 50 nodes per layer, the KL error for RBM Neural Networks is worse than MLE once we are more than 100 tenors (here, days) from the beginning of the test sample. With more nodes per layer, the results are even worse, though we do not show them here."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summary and next steps\n",
"\n",
"We can see by the nn_probs data frame that the probability mass of the neural network shifts to the right, as does the underlying Poisson processes, with its intensity starting at 1 events per tenor / day at - 5 yrs and ending at 4 events per tenor / day at +1 yrs.\n",
"\n",
"Next steps:\n",
"\n",
"* Simulate multiple, correlated Poisson processes\n",
"* Test different optimizers\n",
"* Test non-linear non-stationarities\n",
"* Try recurrent neural network (?)\n",
"* Try convolution network (?)\n",
"\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [env_rl]",
"language": "python",
"name": "Python [env_rl]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
zebogen/pollarity | obama.ipynb | 4 | 5103 | {
"cells": [
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn import metrics\n",
"from sklearn.linear_model import SGDClassifier\n",
"from sklearn import linear_model\n",
"from sklearn import svm\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"linear = svm.SVC()\n",
"df = pd.read_csv(\"./app/Python/FINALOBAMA.csv\", header=0)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"xtrain, xtest, ytrain, ytest = train_test_split(df['Close'], df['Approval'], train_size = 0.8)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/jasminefeldmann1/anaconda/lib/python2.7/site-packages/sklearn/svm/base.py:472: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y_ = column_or_1d(y, warn=True)\n"
]
},
{
"data": {
"text/plain": [
"SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,\n",
" kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
" shrinking=True, tol=0.001, verbose=False)"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"linear.fit(pd.DataFrame(xtrain), pd.DataFrame(ytrain))"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"output = linear.predict(pd.DataFrame(xtest))"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ytest = ytest.reset_index()[\"Approval\"]"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2 46 44\n",
"-1 46 47\n",
"3 46 43\n",
"1 46 45\n",
"0 40 40\n",
"0 46 46\n",
"-3 40 43\n",
"0 46 46\n",
"-3 46 49\n",
"-12 46 58\n",
"3 46 43\n",
"-3 46 49\n",
"-2 46 48\n",
"3 63 60\n",
"2 46 44\n",
"-20 46 66\n",
"-1 46 47\n",
"6 46 40\n",
"3 46 43\n",
"1 46 45\n",
"1 46 45\n",
"-5 46 51\n",
"-6 46 52\n",
"-4 46 50\n",
"3 46 43\n",
"-3 46 49\n",
"1 45 44\n",
"-1 46 47\n",
"5 50 45\n",
"-1 45 46\n",
"0 46 46\n",
"-2 46 48\n",
"0 46 46\n",
"6 46 40\n",
"0 46 46\n",
"4 46 42\n",
"-1 46 47\n",
"4 46 42\n",
"-4 43 47\n",
"0 46 46\n",
"-5 46 51\n",
"0 46 46\n",
"-6 46 52\n",
"-6 46 52\n",
"1 47 46\n",
"-3 43 46\n",
"-4 46 50\n",
"2 43 41\n",
"1 43 42\n",
"-1 46 47\n",
"-6 46 52\n",
"-1 46 47\n",
"-1 45 46\n",
"5 46 41\n",
"-4 46 50\n",
"-11 43 54\n",
"-2 41 43\n",
"0 46 46\n",
"-2 46 48\n",
"5 46 41\n",
"-3 45 48\n",
"4 46 42\n",
"-18 46 64\n",
"-3 45 48\n",
"0 46 46\n",
"3 46 43\n",
"-1 46 47\n",
"-1 46 47\n",
"-6 46 52\n"
]
}
],
"source": [
"for i in range (0, len(output)):\n",
" difference = output[i] - ytest[i]\n",
" print difference, output[i], ytest[i]"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.86594202898550721"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"linear.score(pd.DataFrame(xtrain), pd.DataFrame(ytrain))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
ProfessorKazarinoff/staticsite | content/code/ENGR213/Quiz_Ch5_Ch6_Solutions.ipynb | 1 | 10794 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ENGR213 2019Q3 Ch 5 and Ch 6 Quiz"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pint\n",
"u = pint.UnitRegistry()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"750000.0 millimeter ** 3\n",
"0.00075 meter ** 3\n"
]
}
],
"source": [
"Aa = 50*u.mm*100*u.mm\n",
"ya = (100*u.mm+(100/2)*u.mm)\n",
"Qa = Aa*ya\n",
"print(Qa)\n",
"Qa.ito(u.m**3)\n",
"print(Qa)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2625000.0 millimeter ** 3\n",
"0.002625 meter ** 3\n"
]
}
],
"source": [
"Am = 50*u.mm*300*u.mm\n",
"ym = (200-(50/2))*u.mm\n",
"Qm = Am*ym\n",
"print(Qm)\n",
"Qm.ito(u.m**3)\n",
"print(Qm)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"0.004125 meter<sup>3</sup>"
],
"text/latex": [
"$0.004125\\ \\mathrm{meter}^{3}$"
],
"text/plain": [
"<Quantity(0.004125, 'meter ** 3')>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Qb = 2*Qa + Qm\n",
"Qb"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"5984.042553191488 newton/meter"
],
"text/latex": [
"$5984.042553191488\\ \\frac{\\mathrm{newton}}{\\mathrm{meter}}$"
],
"text/plain": [
"<Quantity(5984.042553191488, 'newton / meter')>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"V = 12.0*1000*u.newtons\n",
"I = (1.504*(10**9))*(u.mm**4)\n",
"I.ito(u.m**4)\n",
"qa = (V*Qa)/I\n",
"qa"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"32912.23404255319 newton/meter"
],
"text/latex": [
"$32912.23404255319\\ \\frac{\\mathrm{newton}}{\\mathrm{meter}}$"
],
"text/plain": [
"<Quantity(32912.23404255319, 'newton / meter')>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qb = (V*Qb)/I\n",
"qb"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"299.2021276595744 newton"
],
"text/latex": [
"$299.2021276595744\\ \\mathrm{newton}$"
],
"text/plain": [
"<Quantity(299.2021276595744, 'newton')>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = 50*u.mm\n",
"s.ito(u.m)\n",
"va = qa*s\n",
"va"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"1645.6117021276596 newton"
],
"text/latex": [
"$1645.6117021276596\\ \\mathrm{newton}$"
],
"text/plain": [
"<Quantity(1645.6117021276596, 'newton')>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vb = qb*s\n",
"vb"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"750000.0"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Qa = 50*100*(200-100/2)\n",
"Qa"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2625000.0"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Qm = (300*50)*(200-(50/2))\n",
"Qm"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4125000.0"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Qb = 2*Qa + Qm\n",
"Qb"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5.98404255319149"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"V = 12000\n",
"I = 1.504e9\n",
"qa = V*Qa/I\n",
"qa"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"299.2021276595745"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = 50\n",
"va = qa*s\n",
"va"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"32.912234042553195"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qb = V*Qb/I\n",
"qb"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1645.6117021276598"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vb = qb*s\n",
"vb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# starting all over again"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"750000.0 millimeter<sup>3</sup>"
],
"text/latex": [
"$750000.0\\ \\mathrm{millimeter}^{3}$"
],
"text/plain": [
"<Quantity(750000.0, 'millimeter ** 3')>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h = 400*u.mm\n",
"c = h/2\n",
"ha = 100*u.mm\n",
"ba = 50*u.mm\n",
"Aa = ha*ba\n",
"ya = c-ha/2\n",
"Qa = Aa*ya\n",
"Qa"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"2625000.0 millimeter<sup>3</sup>"
],
"text/latex": [
"$2625000.0\\ \\mathrm{millimeter}^{3}$"
],
"text/plain": [
"<Quantity(2625000.0, 'millimeter ** 3')>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ht = 50*u.mm\n",
"bt = 300*u.mm\n",
"At = bt*ht\n",
"yt = c-ht/2\n",
"Qt = At*yt\n",
"Qt"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"4125000.0 millimeter<sup>3</sup>"
],
"text/latex": [
"$4125000.0\\ \\mathrm{millimeter}^{3}$"
],
"text/plain": [
"<Quantity(4125000.0, 'millimeter ** 3')>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Qb = 2*Qa + Qt\n",
"Qb"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"5.98404255319149 newton/millimeter"
],
"text/latex": [
"$5.98404255319149\\ \\frac{\\mathrm{newton}}{\\mathrm{millimeter}}$"
],
"text/plain": [
"<Quantity(5.98404255319149, 'newton / millimeter')>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"V = 12.0*1000*u.newtons\n",
"I = 1.504e9*(u.mm**4)\n",
"qa = V*Qa/I\n",
"qa"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"299.2021276595745 newton"
],
"text/latex": [
"$299.2021276595745\\ \\mathrm{newton}$"
],
"text/plain": [
"<Quantity(299.2021276595745, 'newton')>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = 50*u.mm\n",
"va = qa*s\n",
"va"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"32.912234042553195 newton/millimeter"
],
"text/latex": [
"$32.912234042553195\\ \\frac{\\mathrm{newton}}{\\mathrm{millimeter}}$"
],
"text/plain": [
"<Quantity(32.912234042553195, 'newton / millimeter')>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qb = (V*Qb)/I\n",
"qb"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"1645.6117021276598 newton"
],
"text/latex": [
"$1645.6117021276598\\ \\mathrm{newton}$"
],
"text/plain": [
"<Quantity(1645.6117021276598, 'newton')>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vb = qb*s\n",
"vb"
]
},
{
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_source_space_time_frequency.ipynb | 1 | 3711 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n# Compute induced power in the source space with dSPM\n\n\nReturns STC files ie source estimates of induced power\nfor different bands in the source space. The inverse method\nis linear based on dSPM inverse operator.\n\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Authors: Alexandre Gramfort <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport matplotlib.pyplot as plt\n\nimport mne\nfrom mne import io\nfrom mne.datasets import sample\nfrom mne.minimum_norm import read_inverse_operator, source_band_induced_power\n\nprint(__doc__)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Set parameters\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"data_path = sample.data_path()\nraw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'\nfname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'\ntmin, tmax, event_id = -0.2, 0.5, 1\n\n# Setup for reading the raw data\nraw = io.read_raw_fif(raw_fname)\nevents = mne.find_events(raw, stim_channel='STI 014')\ninverse_operator = read_inverse_operator(fname_inv)\n\ninclude = []\nraw.info['bads'] += ['MEG 2443', 'EEG 053'] # bads + 2 more\n\n# picks MEG gradiometers\npicks = mne.pick_types(raw.info, meg=True, eeg=False, eog=True,\n stim=False, include=include, exclude='bads')\n\n# Load condition 1\nevent_id = 1\nevents = events[:10] # take 10 events to keep the computation time low\n# Use linear detrend to reduce any edge artifacts\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,\n baseline=(None, 0), reject=dict(grad=4000e-13, eog=150e-6),\n preload=True, detrend=1)\n\n# Compute a source estimate per frequency band\nbands = dict(alpha=[9, 11], beta=[18, 22])\n\nstcs = source_band_induced_power(epochs, inverse_operator, bands, n_cycles=2,\n use_fft=False, n_jobs=1)\n\nfor b, stc in stcs.items():\n stc.save('induced_power_%s' % b)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"plot mean power\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"plt.plot(stcs['alpha'].times, stcs['alpha'].data.mean(axis=0), label='Alpha')\nplt.plot(stcs['beta'].times, stcs['beta'].data.mean(axis=0), label='Beta')\nplt.xlabel('Time (ms)')\nplt.ylabel('Power')\nplt.legend()\nplt.title('Mean source induced power')\nplt.show()"
],
"outputs": [],
"metadata": {
"collapsed": false
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"file_extension": ".py",
"version": "2.7.13",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} | bsd-3-clause |
lab3000/deeplearngene | demos/lab3000_n1e1p1b1 - deeplearngene demo1.ipynb | 1 | 24190 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**A suggested experimental workflow is to name the clade 'environment' a reference to a location in a notebook, which can be used to keep track of experimental steps**\n",
"\n",
"- The environment paramter is used to name the files generated from clade activities, and also names the folder in which the generated files are stored\n",
"- Evernote or OneNote are useful notebooks for tracking experiment activities\n",
" * Jupyter Notebooks or relevant .py scripts can be stored at each experimental step to record (perhaps redundantly--which is ok in experimental notekeeping) clade functions called or edits to .py scripts (if any) made during a given experimental step "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n",
"WARNING - DLGn1e1p1 - No observers have been added to this run\n",
"INFO - DLGn1e1p1 - Running command 'main'\n",
"INFO - DLGn1e1p1 - Started\n",
"INFO - DLGn1e1p1 - Completed after 0:00:00\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Vectorizing sequence data...\n",
"x_ shape: (8982, 10000)\n",
"46 classes\n",
"Converting class vector to binary class matrix (for use with categorical_crossentropy)\n"
]
}
],
"source": [
"from environment import ex\n",
"import clades\n",
"import pandas as pd\n",
"import os\n",
"\n",
"#limit the architectures that will be generated\n",
"two_layers_max = {'type': 'range', 'bounds': [1, 2]}\n",
"max_ten_units = {'type': 'range', 'bounds': [2, 10]}\n",
"\n",
"#create a new sacred object, which includes the config dictionary\n",
"n1e1p1b1_dict = ex.run(config_updates=\\\n",
" {'population_size':3,\\\n",
" 'environment':'lab3000_n1e1p1b1',\\\n",
" 'max_train_time':5,\\\n",
" 'nb_layers':two_layers_max,\\\n",
" 'nb_units':max_ten_units})\n",
"#create a new clade object, passing in the config dictionary\n",
"n1e1p1b1_clade = clades.GAFC1(n1e1p1b1_dict.config)\n",
"\n",
"#loading the data creates train,test, and validation sets\n",
"#and also creates a folder to store the output of clade activity \n",
"n1e1p1b1_clade.load_data()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Initially the output folder is empty \n",
"* Generations are 0-indexed"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n1e1p1b1_clade.current_generation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- spawn() creates a pandas dataframe of genes which 'encode' the model architectures of a given population\n",
"- the dataframe is saved as a property and also pickled into the experiment folder\n",
" * Note that the pickled dataframe file, and gene and model name includes reference to the generation (Gen0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n1e1p1b1_clade.spawn()"
]
},
{
"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>LR</th>\n",
" <th>activations</th>\n",
" <th>batch_size</th>\n",
" <th>epochs</th>\n",
" <th>gene_name</th>\n",
" <th>layer_units</th>\n",
" <th>loss</th>\n",
" <th>model_name</th>\n",
" <th>nb_layers</th>\n",
" <th>optimizer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.106895</td>\n",
" <td>[relu]</td>\n",
" <td>512</td>\n",
" <td>4</td>\n",
" <td>lab3000_n1e1p1b1+Gen0+gene0</td>\n",
" <td>[4]</td>\n",
" <td>categorical_crossentropy</td>\n",
" <td>lab3000_n1e1p1b1+Gen0+gene0+model.h5</td>\n",
" <td>1</td>\n",
" <td>Adadelta</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.065681</td>\n",
" <td>[relu, softplus]</td>\n",
" <td>512</td>\n",
" <td>16</td>\n",
" <td>lab3000_n1e1p1b1+Gen0+gene1</td>\n",
" <td>[4, 10]</td>\n",
" <td>categorical_crossentropy</td>\n",
" <td>lab3000_n1e1p1b1+Gen0+gene1+model.h5</td>\n",
" <td>2</td>\n",
" <td>RMSProp</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.004449</td>\n",
" <td>[softmax]</td>\n",
" <td>64</td>\n",
" <td>16</td>\n",
" <td>lab3000_n1e1p1b1+Gen0+gene2</td>\n",
" <td>[9]</td>\n",
" <td>categorical_crossentropy</td>\n",
" <td>lab3000_n1e1p1b1+Gen0+gene2+model.h5</td>\n",
" <td>1</td>\n",
" <td>Adam</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" LR activations batch_size epochs \\\n",
"0 0.106895 [relu] 512 4 \n",
"0 0.065681 [relu, softplus] 512 16 \n",
"0 0.004449 [softmax] 64 16 \n",
"\n",
" gene_name layer_units loss \\\n",
"0 lab3000_n1e1p1b1+Gen0+gene0 [4] categorical_crossentropy \n",
"0 lab3000_n1e1p1b1+Gen0+gene1 [4, 10] categorical_crossentropy \n",
"0 lab3000_n1e1p1b1+Gen0+gene2 [9] categorical_crossentropy \n",
"\n",
" model_name nb_layers optimizer \n",
"0 lab3000_n1e1p1b1+Gen0+gene0+model.h5 1 Adadelta \n",
"0 lab3000_n1e1p1b1+Gen0+gene1+model.h5 2 RMSProp \n",
"0 lab3000_n1e1p1b1+Gen0+gene2+model.h5 1 Adam "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n1e1p1b1_clade.genotypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* seed_models() acts as an intermediary between genotypes and model evaluations, which are executed in grow_models()\n",
"* compiled models are saved as .h5 files in the experiment folder"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n1e1p1b1_clade.seed_models()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"this is the index: 0\n",
"and this is the gene: LR 0.106895\n",
"activations [relu]\n",
"batch_size 512\n",
"epochs 4\n",
"gene_name lab3000_n1e1p1b1+Gen0+gene0\n",
"layer_units [4]\n",
"loss categorical_crossentropy\n",
"model_name lab3000_n1e1p1b1+Gen0+gene0+model.h5\n",
"nb_layers 1\n",
"optimizer Adadelta\n",
"Name: 0, dtype: object\n",
"Train on 8083 samples, validate on 899 samples\n",
"Epoch 1/4\n",
"8083/8083 [==============================] - 3s - loss: 3.8003 - acc: 0.1518 - val_loss: 3.7406 - val_acc: 0.2147\n",
"Epoch 2/4\n",
"8083/8083 [==============================] - 1s - loss: 3.6515 - acc: 0.3167 - val_loss: 3.5476 - val_acc: 0.4082\n",
"Epoch 3/4\n",
"7680/8083 [===========================>..] - ETA: 0s - loss: 3.3815 - acc: 0.4320_______Stopping after 5 seconds.\n",
"8083/8083 [==============================] - 1s - loss: 3.3726 - acc: 0.4328 - val_loss: 3.1841 - val_acc: 0.4705\n",
"2080/2246 [==========================>...] - ETA: 0sthis is the index: 1\n",
"and this is the gene: LR 0.0656806\n",
"activations [relu, softplus]\n",
"batch_size 512\n",
"epochs 16\n",
"gene_name lab3000_n1e1p1b1+Gen0+gene1\n",
"layer_units [4, 10]\n",
"loss categorical_crossentropy\n",
"model_name lab3000_n1e1p1b1+Gen0+gene1+model.h5\n",
"nb_layers 2\n",
"optimizer RMSProp\n",
"Name: 0, dtype: object\n",
"Train on 8083 samples, validate on 899 samples\n",
"Epoch 1/16\n",
"8083/8083 [==============================] - 2s - loss: 3.9295 - acc: 0.0040 - val_loss: 3.7433 - val_acc: 0.0044\n",
"Epoch 2/16\n",
"8083/8083 [==============================] - 1s - loss: 3.5759 - acc: 0.0073 - val_loss: 3.4173 - val_acc: 0.0189\n",
"Epoch 3/16\n",
"8083/8083 [==============================] - 1s - loss: 3.2314 - acc: 0.1466 - val_loss: 3.0809 - val_acc: 0.3471\n",
"Epoch 4/16\n",
"7680/8083 [===========================>..] - ETA: 0s - loss: 2.8953 - acc: 0.4474_______Stopping after 5 seconds.\n",
"8083/8083 [==============================] - 1s - loss: 2.8852 - acc: 0.4506 - val_loss: 2.7559 - val_acc: 0.4461\n",
"2246/2246 [==============================] - 0s \n",
"in the else\n",
"this is the index: 2\n",
"and this is the gene: LR 0.00444869\n",
"activations [softmax]\n",
"batch_size 64\n",
"epochs 16\n",
"gene_name lab3000_n1e1p1b1+Gen0+gene2\n",
"layer_units [9]\n",
"loss categorical_crossentropy\n",
"model_name lab3000_n1e1p1b1+Gen0+gene2+model.h5\n",
"nb_layers 1\n",
"optimizer Adam\n",
"Name: 0, dtype: object\n",
"Train on 8083 samples, validate on 899 samples\n",
"Epoch 1/16\n",
"8083/8083 [==============================] - 3s - loss: 3.5798 - acc: 0.1379 - val_loss: 3.3895 - val_acc: 0.2191\n",
"Epoch 2/16\n",
"7936/8083 [============================>.] - ETA: 0s - loss: 3.2423 - acc: 0.2387_______Stopping after 5 seconds.\n",
"8083/8083 [==============================] - 2s - loss: 3.2388 - acc: 0.2380 - val_loss: 3.1041 - val_acc: 0.2570\n",
"2080/2246 [==========================>...] - ETA: 0sin the else\n"
]
}
],
"source": [
"n1e1p1b1_clade.grow_models()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"^^^verbose output of n1e1p1b1_clade.grow_models()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- grow_models() trains the models and generates pickled 'growth analyses' dataframes, one for each model trained, which include train and validation loss and accuracy for each batch and epoch, as well as the time take to run each batch and epoch\n",
"- grow_models() also pickles, and saves as a property, a phenotypes dataframe, which summarizes the performance of each model\n",
" * the misclassed dictionaries store the true and labeled classes for each mislabeled datapoint\n",
"- grow_models() also saves each trained model as a .h5 file"
]
},
{
"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>gene_name</th>\n",
" <th>misclassed</th>\n",
" <th>test_accuracy</th>\n",
" <th>test_loss</th>\n",
" <th>time</th>\n",
" <th>train_accuracy</th>\n",
" <th>train_loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>lab3000_n1e1p1b1+Gen0+gene0</td>\n",
" <td>{'true_class': [3, 10, 1, 4, 3, 3, 3, 5, 1, 1,...</td>\n",
" <td>0.468833</td>\n",
" <td>3.168827</td>\n",
" <td>6.302856</td>\n",
" <td>0.467401</td>\n",
" <td>3.147856</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>lab3000_n1e1p1b1+Gen0+gene1</td>\n",
" <td>{'true_class': [10, 1, 4, 4, 5, 4, 1, 1, 11, 2...</td>\n",
" <td>0.455476</td>\n",
" <td>2.760931</td>\n",
" <td>6.278670</td>\n",
" <td>0.464803</td>\n",
" <td>2.702639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>lab3000_n1e1p1b1+Gen0+gene2</td>\n",
" <td>{'true_class': [3, 10, 1, 3, 3, 3, 3, 3, 5, 1,...</td>\n",
" <td>0.253339</td>\n",
" <td>3.106247</td>\n",
" <td>5.817873</td>\n",
" <td>0.265124</td>\n",
" <td>3.091641</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gene_name \\\n",
"0 lab3000_n1e1p1b1+Gen0+gene0 \n",
"0 lab3000_n1e1p1b1+Gen0+gene1 \n",
"0 lab3000_n1e1p1b1+Gen0+gene2 \n",
"\n",
" misclassed test_accuracy \\\n",
"0 {'true_class': [3, 10, 1, 4, 3, 3, 3, 5, 1, 1,... 0.468833 \n",
"0 {'true_class': [10, 1, 4, 4, 5, 4, 1, 1, 11, 2... 0.455476 \n",
"0 {'true_class': [3, 10, 1, 3, 3, 3, 3, 3, 5, 1,... 0.253339 \n",
"\n",
" test_loss time train_accuracy train_loss \n",
"0 3.168827 6.302856 0.467401 3.147856 \n",
"0 2.760931 6.278670 0.464803 2.702639 \n",
"0 3.106247 5.817873 0.265124 3.091641 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n1e1p1b1_clade.phenotypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* select_parents() selects, by default, the top 20% of models by test accuracy, plut 10% random models; or if the population size is small, such as in this demo case, at least two parent models are selected"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n1e1p1b1_clade.select_parents()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>LR</th>\n",
" <th>activations</th>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>0.106895</td>\n",
" <td>[relu]</td>\n",
" <td>512</td>\n",
" <td>4</td>\n",
" <td>lab3000_n1e1p1b1+Gen0+gene0</td>\n",
" <td>[4]</td>\n",
" <td>categorical_crossentropy</td>\n",
" <td>lab3000_n1e1p1b1+Gen0+gene0+model.h5</td>\n",
" <td>1</td>\n",
" <td>Adadelta</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.004449</td>\n",
" <td>[softmax]</td>\n",
" <td>64</td>\n",
" <td>16</td>\n",
" <td>lab3000_n1e1p1b1+Gen0+gene2</td>\n",
" <td>[9]</td>\n",
" <td>categorical_crossentropy</td>\n",
" <td>lab3000_n1e1p1b1+Gen0+gene2+model.h5</td>\n",
" <td>1</td>\n",
" <td>Adam</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" LR activations batch_size epochs gene_name \\\n",
"0 0.106895 [relu] 512 4 lab3000_n1e1p1b1+Gen0+gene0 \n",
"0 0.004449 [softmax] 64 16 lab3000_n1e1p1b1+Gen0+gene2 \n",
"\n",
" layer_units loss model_name \\\n",
"0 [4] categorical_crossentropy lab3000_n1e1p1b1+Gen0+gene0+model.h5 \n",
"0 [9] categorical_crossentropy lab3000_n1e1p1b1+Gen0+gene2+model.h5 \n",
"\n",
" nb_layers optimizer \n",
"0 1 Adadelta \n",
"0 1 Adam "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n1e1p1b1_clade.parent_genes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* breed() generates a new population of genes, encoding a new generation of models; note that current_generation is incremented when clade.breed() is run"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n1e1p1b1_clade.breed()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n1e1p1b1_clade.current_generation"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>LR</th>\n",
" <th>activations</th>\n",
" <th>batch_size</th>\n",
" <th>epochs</th>\n",
" <th>gene_name</th>\n",
" <th>layer_units</th>\n",
" <th>model_name</th>\n",
" <th>nb_layers</th>\n",
" <th>optimizer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.004449</td>\n",
" <td>[relu]</td>\n",
" <td>512</td>\n",
" <td>16</td>\n",
" <td>lab3000_n1e1p1b1+Gen1+gene0</td>\n",
" <td>[9]</td>\n",
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" <td>1</td>\n",
" <td>Adam</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.004449</td>\n",
" <td>[relu]</td>\n",
" <td>64</td>\n",
" <td>4</td>\n",
" <td>lab3000_n1e1p1b1+Gen1+gene1</td>\n",
" <td>[9]</td>\n",
" <td>lab3000_n1e1p1b1+Gen1+gene1+model.h5</td>\n",
" <td>1</td>\n",
" <td>Adam</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.004449</td>\n",
" <td>[softmax]</td>\n",
" <td>64</td>\n",
" <td>4</td>\n",
" <td>lab3000_n1e1p1b1+Gen1+gene2</td>\n",
" <td>[4]</td>\n",
" <td>lab3000_n1e1p1b1+Gen1+gene2+model.h5</td>\n",
" <td>1</td>\n",
" <td>Adadelta</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" LR activations batch_size epochs gene_name \\\n",
"0 0.004449 [relu] 512 16 lab3000_n1e1p1b1+Gen1+gene0 \n",
"1 0.004449 [relu] 64 4 lab3000_n1e1p1b1+Gen1+gene1 \n",
"2 0.004449 [softmax] 64 4 lab3000_n1e1p1b1+Gen1+gene2 \n",
"\n",
" layer_units model_name nb_layers optimizer \n",
"0 [9] lab3000_n1e1p1b1+Gen1+gene0+model.h5 1 Adam \n",
"1 [9] lab3000_n1e1p1b1+Gen1+gene1+model.h5 1 Adam \n",
"2 [4] lab3000_n1e1p1b1+Gen1+gene2+model.h5 1 Adadelta "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n1e1p1b1_clade.genotypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# after model evolution is run interactively, the commands can be saved to the experiment notebook (here, Evernote)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
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"metadata": {
"collapsed": true
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"source": []
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"collapsed": true
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"cell_type": "code",
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"collapsed": true
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| gpl-3.0 |
InsightLab/data-science-cookbook | 2019/05-linear-regression/Multivariate_Linear_Regression_Tutorial.ipynb | 1 | 11809 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Regressão Linear Múltipla\n",
"\n",
"\n",
"\n",
"## 1. Introdução\n",
"\n",
"Muitos algoritmos de aprendizagem de máquinas utilização métodos de otimização. Esses algoritmos são usados por algoritmos de aprendizado de máquina para encontrar um bom conjunto de parâmetros do modelo, dado um conjunto de dados de treinamento. O algoritmo de otimização mais comum usado na aprendizagem de máquinas é o gradiente descendente estocástico. Neste tutorial, você descobrirá como implementar uma gradiente descendente estocástico para otimizar um algoritmo de regressão linear.\n",
"\n",
"Ao final desta aula voce estará apto a:\n",
"\n",
"1. Estimar os coeficientes de regressão linear usando a descida gradiente estocástica.\n",
"2. Fazer previsões para regressão linear multivariada.\n",
"3. Implementar regressão linear com gradiente descendente estocástico para fazer previsões em novos dados.\n",
"\n",
"### 1.1 Dataset - Seguro de Veículo Sueco\n",
"\n",
"Neste tutorial, usaremos o dataset que trata da Qualidade do Vinhos (Wine Quality Data), o qual pode permitir a previsão da qualidade do vinho branco, ajudando o especialista em vinhos na avaliação de qualidade. O RMSE de linha de base do problema é de aproximadamente 0.148 pontos de qualidade. O arquivo winequality-white.csv está disponível no diretório presente. \n",
"\n",
"### 1.2 Regressão Linear Multivariada\n",
"\n",
"A regressão linear é uma técnica para prever um valor real. Confusamente, esses problemas em que um valor real deve ser previsto são chamados de problemas de regressão. A regressão linear é uma técnica em que uma linha reta é usada para modelar a relação entre valores de entrada e saída. Em mais de duas dimensões, esta linha reta pode ser pensada como um plano ou hiperplano.\n",
"\n",
"As previsões são feitas como uma combinação dos valores de entrada para prever o valor de saída. Cada atributo de entrada (x) é ponderado usando um coeficiente (b), e o objetivo do algoritmo de aprendizagem é descobrir um conjunto de coeficientes que resulte em boas previsões (y).\n",
"\n",
"\n",
"![alt text](images/regressao_linear_multipla_modelo.png \"\")\n",
" \n",
"Os coeficientes podem ser encontrados usando gradiente descendente estocástico.\n",
"\n",
"### 1.3 Gradiente Descendente Estocástico\n",
"\n",
"Gradiente Descendente é o processo de minimização de uma função seguindo a inclinação ou gradiente dessa função. Na aprendizagem em máquina, podemos usar uma técnica que avalie e atualize os coeficientes de cada iteração chamada gradiente descendente estocástico para minimizar o erro de um modelo em nossos dados de treinamento.\n",
"\n",
"A maneira como esse algoritmo de otimização funciona é que cada instância de treinamento é submetida uma de cada vez ao modelo. O modelo faz uma previsão para uma instância de treinamento, o erro é calculado e o modelo é atualizado para reduzir o erro para a próxima previsão. Este processo é repetido para um número fixo de iterações.\n",
"\n",
"Esse procedimento pode ser usado para encontrar o conjunto de coeficientes em um modelo que resulte no menor erro para o modelo nos dados de treinamento. Cada iteração, os coeficientes *(b)* são atualizados usando a equação:\n",
"\n",
"\n",
"![alt text](images/coeficiente_b_gradiente.png \"\")\n",
"\n",
"\n",
"Onde *b* é o coeficiente ou o peso a ser otimizado, a *taxa de aprendizado (learning rate)* é uma taxa que você deve configurar (por exemplo, 0,01), o *erro* é o erro de predição para o modelo nos dados de treinamento atribuídos ao peso e *x* é o valor de entrada.\n"
]
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"source": [
"## 2. Tutorial\n",
"\n",
"Este tutorial é dividido em 3 partes:\n",
"\n",
"1. Fazendo Predições.\n",
"2. Coeficientes de estimativa. \n",
"3. Estudo de caso de qualidade do vinho.\n",
"\n",
"Isso proporcionará a base que você precisa implementar e aplicar uma regressão linear com descida descendente estocástica em seus próprios problemas de modelagem preditiva.\n",
"\n",
"### 2.1 Fazer previsões\n",
"\n",
"O primeiro passo é desenvolver uma função que possa fazer previsões. Isso será necessário tanto na avaliação dos valores dos coeficientes do candidato quanto na aplicação do gradiente descendente estocástico. Após o modelo ser finalizado, começaremos a fazer previsões em dados de teste ou novos dados. Abaixo está uma função chamada *predict ()* que prediz um valor de saída para uma linha, dado um conjunto de coeficientes.\n",
"\n",
"O primeiro coeficiente é sempre a intercepção, também chamado de viés (em ingles, bias) ou b0, pois é independente e não é responsável por um valor de entrada específico."
]
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"# Make a prediction with coefficients\n",
"def predict(row, coefficients):\n",
" yhat = coefficients[0]\n",
" for i in range(len(row)-1):\n",
" yhat += coefficients[i + 1] * row[i]\n",
" return yhat"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exemplo\n",
"\n",
"Dado conjunto de dados abaixo, será apresentado o uso da função predict confome exemplo que segue.\n",
"\n",
"x | y\n",
"--| -\n",
"1 | 1\n",
"2 | 3\n",
"4 | 3\n",
"3 | 2\n",
"5 | 5\n",
"\n",
"Neste exemplo, existe um único valor de entrada (x) e dois valores de coeficiente (b0 e b1). A equação de predição que modelamos para este problema é:\n",
"*y = b0 + b1 × x *\n",
"\n",
"Ou, com os valores de coeficientes específicos, escolhemos à mão como:\n",
"y = 0,4 + 0,8 * x \n",
"\n",
"Ao executar esta função, obtemos previsões razoavelmente próximas da saída esperada (y) valores."
]
},
{
"cell_type": "code",
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{
"name": "stdout",
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"text": [
"Esperado=1.000, Predito=1.200\n",
"Esperado=3.000, Predito=2.000\n",
"Esperado=3.000, Predito=3.600\n",
"Esperado=2.000, Predito=2.800\n",
"Esperado=5.000, Predito=4.400\n"
]
}
],
"source": [
"# Example of making a prediction with coefficients\n",
"# Make a prediction\n",
"def predict(row, coefficients):\n",
" yhat = coefficients[0]\n",
" for i in range(len(row)-1):\n",
" yhat += coefficients[i + 1] * row[i]\n",
" return yhat\n",
"\n",
"dataset = [[1, 1], [2, 3], [4, 3], [3, 2], [5, 5]]\n",
"coef = [0.4, 0.8]\n",
"\n",
"for row in dataset:\n",
" yhat = predict(row, coef)\n",
" print(\"Esperado=%.3f, Predito=%.3f\" % (row[-1], yhat))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2 Estimativa de Coeficientes \n",
"\n",
"Para podermos estimar os valores dos coeficientes para nossos dados de treinamento usando gradiente descendente estocástico precisamos definir dois parâmetros:\n",
"\n",
"1. Taxa de aprendizado: usado para limitar a quantidade que cada coeficiente é corrigido sempre que é atualizado.\n",
"2. Épocas: o número de vezes para percorrer os dados de treinamento ao atualizar os coeficientes.\n",
"\n",
"Estes parametros, juntamente com os dados de treinamento, serão os argumentos para a função. Existem 3 loops que precisamos executar na função:\n",
"\n",
"1. Loop em cada época.\n",
"2. Faça um loop sobre cada linha nos dados de treinamento para uma época.\n",
"3. Faça um loop sobre cada coeficiente e atualize-o para uma linha dos dados em uma época.\n",
"\n",
"Como você pode ver, atualizamos cada coeficiente para cada linha nos dados de treinamento, em cada época. Os coeficientes são atualizados com base no erro que o modelo fez. O erro é calculado como a diferença entre a previsão feita com os coeficientes do candidato e o valor de saída esperado.\n",
"\n",
"*error = prediction − expected*\n",
"\n",
"Existe um coeficiente para ponderar cada atributo de entrada, e estes são atualizados de forma consistente, por exemplo:\n",
" \n",
"b1 (t + 1) = b1 (t) - taxa de aprendizado x erro (t) x x1 (t)\n",
"\n",
"\n",
"O coeficiente especial no início da lista, também chamado de intercepção ou a polarização, é atualizado de forma semelhante, exceto sem uma entrada porque não está associado a um valor de entrada específico:\n",
"\n",
"b0 (t + 1) = b0 (t) - taxa de aprendizagem * erro (t) \n",
"\n",
"Abaixo está uma função denominada coefficients_sgd() que calcula valores de coeficientes para um conjunto de dados de treinamento usando gradiente descendente estocástico.\n",
"\n",
"Usamos uma pequena taxa de aprendizado de 0,001 e treinamos o modelo por 50 épocas, ou 50 exposições dos coeficientes para todo o conjunto de dados de treinamento. Ao executar o exemplo, imprime uma mensagem a cada época com o erro de soma quadrada para aquela época e o conjunto final de coeficientes."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coeficiente Inicial={0}\n",
"epoch=0, lrate=0.001, error=46.236\n",
"epoch=1, lrate=0.001, error=41.305\n",
"epoch=2, lrate=0.001, error=36.930\n",
"epoch=3, lrate=0.001, error=33.047\n",
"epoch=4, lrate=0.001, error=29.601\n",
"epoch=5, lrate=0.001, error=26.543\n",
"epoch=6, lrate=0.001, error=23.830\n",
"epoch=7, lrate=0.001, error=21.422\n",
"epoch=8, lrate=0.001, error=19.285\n",
"epoch=9, lrate=0.001, error=17.389\n",
"[0.10710331074898374, 0.3801081881174074]\n"
]
}
],
"source": [
"# Estimate linear regression coefficients using stochastic gradient descent\n",
"def coefficients_sgd(train, l_rate, n_epoch):\n",
" coef = [0.0 for i in range(len(train[0]))]\n",
" print ('Coeficiente Inicial={0}' % (coef))\n",
" for epoch in range(n_epoch):\n",
" sum_error = 0\n",
" for row in train:\n",
" yhat = predict(row, coef)\n",
" error = yhat - row[-1]\n",
" sum_error += error**2\n",
" coef[0] = coef[0] - l_rate * error\n",
" for i in range(len(row)-1):\n",
" coef[i + 1] = coef[i + 1] - l_rate * error * row[i] \n",
" print(('epoch=%d, lrate=%.3f, error=%.3f' % (epoch, l_rate, sum_error)))\n",
" return coef\n",
"\n",
"# Calculate coefficients\n",
"dataset = [[1, 1], [2, 3], [4, 3], [3, 2], [5, 5]]\n",
"l_rate = 0.001\n",
"n_epoch = 10\n",
"coef = coefficients_sgd(dataset, l_rate, n_epoch)\n",
"print(coef)"
]
}
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| mit |
rubattino/apprecsys | notebooks/baisc_bayesian.ipynb | 1 | 33549 | {
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"execfile(\"../script/context.py\")\n",
"#final.take(1) reach the parsed event data as stated, syntax: (uid, ( list of (appid, c1, c2, c3))"
]
},
{
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{
"ename": "Exception",
"evalue": "It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transforamtion. SparkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063.",
"output_type": "error",
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"\u001b[1;32m/home/mertergun/Desktop/code/spark-1.4.1-bin-hadoop2.6/python/pyspark/rdd.py\u001b[0m in \u001b[0;36mcache\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 223\u001b[0m \"\"\"\n\u001b[0;32m 224\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_cached\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 225\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpersist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mStorageLevel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMEMORY_ONLY_SER\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 226\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 227\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/mertergun/Desktop/code/spark-1.4.1-bin-hadoop2.6/python/pyspark/rdd.py\u001b[0m in \u001b[0;36mpersist\u001b[1;34m(self, storageLevel)\u001b[0m\n\u001b[0;32m 239\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_cached\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 240\u001b[0m \u001b[0mjavaStorageLevel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getJavaStorageLevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstorageLevel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 241\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jrdd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpersist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjavaStorageLevel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 242\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/mertergun/Desktop/code/spark-1.4.1-bin-hadoop2.6/python/pyspark/rdd.py\u001b[0m in \u001b[0;36m_jrdd\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 2361\u001b[0m command = (self.func, profiler, self._prev_jrdd_deserializer,\n\u001b[0;32m 2362\u001b[0m self._jrdd_deserializer)\n\u001b[1;32m-> 2363\u001b[1;33m \u001b[0mpickled_cmd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbvars\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mincludes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_prepare_for_python_RDD\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mctx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcommand\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2364\u001b[0m python_rdd = self.ctx._jvm.PythonRDD(self._prev_jrdd.rdd(),\n\u001b[0;32m 2365\u001b[0m \u001b[0mbytearray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpickled_cmd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/mertergun/Desktop/code/spark-1.4.1-bin-hadoop2.6/python/pyspark/rdd.py\u001b[0m in \u001b[0;36m_prepare_for_python_RDD\u001b[1;34m(sc, command, obj)\u001b[0m\n\u001b[0;32m 2281\u001b[0m \u001b[1;31m# the serialized command will be compressed by broadcast\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2282\u001b[0m \u001b[0mser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCloudPickleSerializer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2283\u001b[1;33m \u001b[0mpickled_command\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2284\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpickled_command\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;33m<<\u001b[0m \u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# 1M\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2285\u001b[0m \u001b[1;31m# The broadcast will have same life cycle as created PythonRDD\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/mertergun/Desktop/code/spark-1.4.1-bin-hadoop2.6/python/pyspark/serializers.py\u001b[0m in \u001b[0;36mdumps\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 425\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 426\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 427\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mcloudpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 428\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 429\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/mertergun/Desktop/code/spark-1.4.1-bin-hadoop2.6/python/pyspark/cloudpickle.py\u001b[0m in \u001b[0;36mdumps\u001b[1;34m(obj, protocol)\u001b[0m\n\u001b[0;32m 620\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 621\u001b[0m \u001b[0mcp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCloudPickler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 622\u001b[1;33m \u001b[0mcp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 623\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 624\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/mertergun/Desktop/code/spark-1.4.1-bin-hadoop2.6/python/pyspark/cloudpickle.py\u001b[0m in \u001b[0;36mdump\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 105\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minject_addons\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 106\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 107\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mPickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 108\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mRuntimeError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 109\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'recursion'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\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[0m\n",
"\u001b[1;32m/usr/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36mdump\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 222\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproto\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 223\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPROTO\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mchr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproto\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 224\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 225\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSTOP\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 226\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 284\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 285\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 286\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Call unbound method with explicit self\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 287\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave_tuple\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 560\u001b[0m \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMARK\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 561\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0melement\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 562\u001b[1;33m \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0melement\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 563\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 564\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmemo\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 284\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 285\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 286\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Call unbound method with explicit self\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 287\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/mertergun/Desktop/code/spark-1.4.1-bin-hadoop2.6/python/pyspark/cloudpickle.py\u001b[0m in \u001b[0;36msave_function\u001b[1;34m(self, obj, name)\u001b[0m\n\u001b[0;32m 197\u001b[0m \u001b[0mklass\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthemodule\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 198\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mklass\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mklass\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 199\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_function_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 200\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 201\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/mertergun/Desktop/code/spark-1.4.1-bin-hadoop2.6/python/pyspark/cloudpickle.py\u001b[0m in \u001b[0;36msave_function_tuple\u001b[1;34m(self, func)\u001b[0m\n\u001b[0;32m 234\u001b[0m \u001b[1;31m# create a skeleton function object and memoize it\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 235\u001b[0m \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_make_skel_func\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 236\u001b[1;33m \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclosure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbase_globals\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 237\u001b[0m \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mREDUCE\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 238\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmemoize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 284\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 285\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 286\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Call unbound method with explicit self\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 287\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave_tuple\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 546\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m3\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mproto\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 547\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0melement\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 548\u001b[1;33m \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0melement\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 549\u001b[0m \u001b[1;31m# Subtle. Same as in the big comment below.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 550\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmemo\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 284\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 285\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 286\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Call unbound method with explicit self\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 287\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave_list\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 598\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 599\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmemoize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 600\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_batch_appends\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\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 601\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 602\u001b[0m \u001b[0mdispatch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mListType\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msave_list\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36m_batch_appends\u001b[1;34m(self, items)\u001b[0m\n\u001b[0;32m 634\u001b[0m \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mAPPENDS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 635\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 636\u001b[1;33m \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtmp\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[0m\n\u001b[0m\u001b[0;32m 637\u001b[0m \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mAPPEND\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 638\u001b[0m \u001b[1;31m# else tmp is empty, and we're done\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 284\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 285\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 286\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Call unbound method with explicit self\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 287\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/mertergun/Desktop/code/spark-1.4.1-bin-hadoop2.6/python/pyspark/cloudpickle.py\u001b[0m in \u001b[0;36msave_function\u001b[1;34m(self, obj, name)\u001b[0m\n\u001b[0;32m 191\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mislambda\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__code__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mco_filename\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'<stdin>'\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mthemodule\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[0;32m 192\u001b[0m \u001b[1;31m#print(\"save global\", islambda(obj), obj.__code__.co_filename, modname, themodule)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 193\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_function_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 194\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 195\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/mertergun/Desktop/code/spark-1.4.1-bin-hadoop2.6/python/pyspark/cloudpickle.py\u001b[0m in \u001b[0;36msave_function_tuple\u001b[1;34m(self, func)\u001b[0m\n\u001b[0;32m 239\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 240\u001b[0m \u001b[1;31m# save the rest of the func data needed by _fill_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 241\u001b[1;33m \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf_globals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 242\u001b[0m \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdct\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 284\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 285\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 286\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Call unbound method with explicit self\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 287\u001b[0m \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave_dict\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 647\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 648\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmemoize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 649\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_batch_setitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miteritems\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 650\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 651\u001b[0m \u001b[0mdispatch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mDictionaryType\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msave_dict\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36m_batch_setitems\u001b[1;34m(self, items)\u001b[0m\n\u001b[0;32m 679\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtmp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 680\u001b[0m \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 681\u001b[1;33m \u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 682\u001b[0m \u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSETITEMS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 683\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 304\u001b[0m \u001b[0mreduce\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"__reduce_ex__\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 305\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mreduce\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 306\u001b[1;33m \u001b[0mrv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreduce\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproto\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 307\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 308\u001b[0m \u001b[0mreduce\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"__reduce__\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/mertergun/Desktop/code/spark-1.4.1-bin-hadoop2.6/python/pyspark/context.py\u001b[0m in \u001b[0;36m__getnewargs__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 250\u001b[0m \u001b[1;31m# This method is called when attempting to pickle SparkContext, which is always an error:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 251\u001b[0m raise Exception(\n\u001b[1;32m--> 252\u001b[1;33m \u001b[1;34m\"It appears that you are attempting to reference SparkContext from a broadcast \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 253\u001b[0m \u001b[1;34m\"variable, action, or transforamtion. SparkContext can only be used on the driver, \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 254\u001b[0m \u001b[1;34m\"not in code that it run on workers. For more information, see SPARK-5063.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mException\u001b[0m: It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transforamtion. SparkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063."
]
}
],
"source": [
"from random import shuffle\n",
"\n",
"\n",
"def remove_duplicates(values):\n",
" output = []\n",
" seen = set()\n",
" for value in values:\n",
" # If value has not been encountered yet,\n",
" # ... add it to both list and set.\n",
" if value not in seen:\n",
" output.append(value)\n",
" seen.add(value)\n",
" return output\n",
"\n",
"def bayesian(line):\n",
" listGroup = line[1]\n",
" shuffle(listGroup) #shuffle the list\n",
" l = len(listGroup) \n",
" numTrain = l * 8 / 10\n",
" numTest = l - numTrain\n",
" trainList = listGroup[:numTrain] #0.8 train set\n",
" testList = listGroup[numTrain:] #0.2 test set\n",
" \n",
" trainRDD = sc.parallelize([1, 2]).cache()\n",
" return trainRDD.count()\n",
" \"\"\"\n",
" newTestList = []\n",
" for t in testList:\n",
" context = [x for x in trainList if x[1]==t[1] and x[2]==t[2] and x[3]==t[3]]\n",
" numContext = float(len(context))\n",
" p_context = numContext/numTrain #P(C1i, C2j, C3k)\n",
" p_app = []\n",
" context_no_duplicate = remove_duplicates(context)\n",
" for c in context_no_duplicate:\n",
" appi = [x for x in trainList if x[0]==c[0]]\n",
" numAppi = float(len(appi))\n",
" p_appi = numAppi/numTrain\n",
" contextAppi = [x for x in trainList if x[0]==c[0] and x[1]==c[1] and x[2]==c[2] and x[3]==c[3]]\n",
" if numAppi != 0: #P(C1i, C2j, C3k | APPid)\n",
" p_contextAppi = len(contextAppi)/numAppi \n",
" else:\n",
" p_contextAppi = 0\n",
" if p_context != 0: #P(APPid | C1i,C2j,C3k = P(C1i, C2j, C3k | APPid) P(APPid) /P(C1i, C2j, C3k)\n",
" p = p_contextAppi * p_appi / p_context\n",
" else:\n",
" p = 0\n",
" p_app.append((c[0],p))\n",
" newTestList.append((t,p_app))\n",
" return newTestList[:10]\n",
" \"\"\"\n",
"result = final.map(bayesian).cache()\n",
"result.collect()[:3]"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(1, 1, 1, 1), (1, 1, 1, 2)]"
]
},
"execution_count": 57,
"output_type": "execute_result",
"metadata": {}
}
],
"source": [
"def remove_duplicates(values):\n",
" output = []\n",
" seen = set()\n",
" for value in values:\n",
" # If value has not been encountered yet,\n",
" # ... add it to both list and set.\n",
" if value not in seen:\n",
" output.append(value)\n",
" seen.add(value)\n",
" return output\n",
"list = [(1,1,1,1),(1,1,1,1),(1,1,1,1),(1,1,1,2)]\n",
"list1 = remove_duplicates(list)\n",
"list1"
]
},
{
"cell_type": "code",
"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": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-2-clause |
araichev/pyclub | homework_solutions/homework_03_solutions.ipynb | 1 | 86601 | {
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"source": [
"from pathlib import Path\n",
"import json\n",
"import sys\n",
"\n",
"import pandas as pd\n",
"import shapely.geometry as sg\n",
"\n",
"import gtfs_tools as gt # Collection of GTFS tools from previous homeworks\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"DATA_DIR = Path('..')/'data'"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# GTFS feed of interest\n",
"\n",
"path = DATA_DIR/'auckland_gtfs_20161017.zip'\n",
"feed = gt.read_gtfs(path)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Problem 2"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def build_geometry_by_shape(feed, shape_ids=None):\n",
" \"\"\"\n",
" Given a GTFS feed object, return a dictionary with structure \n",
" shape ID -> Shapely LineString representation of shape,\n",
" where the dictionary ranges over all shapes in the feed.\n",
" Use WGS84 longitude-latitude coordinates, the native coordinate system of GTFS.\n",
"\n",
" If a list of shape IDs ``shape_ids`` is given, \n",
" then only include the given shape IDs in the dictionary.\n",
" \n",
" NOTES:\n",
" - Raise a ValueError if the feed has no shapes\n",
" \"\"\"\n",
" if feed['shapes'] is None:\n",
" raise ValueError('The feed has no shapes')\n",
"\n",
" d = dict()\n",
" sh = feed['shapes'].copy()\n",
" \n",
" # Restrict shapes if necessary\n",
" if shape_ids is not None:\n",
" sh = sh[sh['shape_id'].isin(shape_ids)]\n",
" \n",
" sh = sh.sort_values(['shape_id', 'shape_pt_sequence'])\n",
"\n",
" for shid, group in sh.groupby('shape_id'):\n",
" lonlats = group[['shape_pt_lon', 'shape_pt_lat']].values\n",
" d[shid] = sg.LineString(lonlats)\n",
"\n",
" return d"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"text/plain": [
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]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Test some\n",
"\n",
"geometry_by_shape = build_geometry_by_shape(feed)\n",
"\n",
"shape_id = feed['trips']['shape_id'].iat[0] # First trip\n",
"geom = geometry_by_shape[shape_id]\n",
"geom"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
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-36.96906, 174.80069 -36.96898, 174.80056 -36.96897, 174.80013 -36.96892, 174.79985 -36.9689, 174.79971 -36.96887, 174.79967 -36.96898)'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"geom.wkt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Problem 4"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def trip_to_geojson(feed, trip_id):\n",
" \"\"\"\n",
" Given a GTFS feed object and a trip ID from that feed,\n",
" return a GeoJSON LineString feature (as a Python dictionary)\n",
" representing the trip's geometry and its metadata\n",
" (trip ID, direction ID, headsign, etc.).\n",
" Use WGS84 coordinates, the native coordinate system of GTFS.\n",
"\n",
" NOTES:\n",
" Raise a ``ValueError`` if the appropriate GTFS data does not exist.\n",
" \"\"\"\n",
" if trip_id not in feed['trips']['trip_id'].values:\n",
" raise ValueError('Trip ID {!s} not present in feed trips'.format(trip_id))\n",
" \n",
" # Get trip data as dictionary, replacing numpy.nan with 'n/a' to ease later\n",
" # conversion to JSON\n",
" t = feed['trips']\n",
" d = t[t['trip_id'] == trip_id].fillna('n/a').to_dict(orient='records')[0]\n",
" \n",
" # Get Shapely LineString for trip shape\n",
" shid = d['shape_id']\n",
" geom = build_geometry_by_shape(feed, shape_ids=[shid])[shid]\n",
" \n",
" # Convert LineString to GeoJSON format\n",
" result = {\n",
" 'type': 'Feature', \n",
" 'properties': d,\n",
" 'geometry': sg.mapping(geom),\n",
" }\n",
" return result\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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[174.80438, -36.969], [174.80418999999998, -36.96905], [174.80393, -36.9691], [174.80374, -36.969120000000004], [174.80353, -36.96915], [174.8032, -36.96917], [174.80293999999998, -36.96917], [174.80221, -36.9691], [174.80213999999998, -36.9691], [174.80199, -36.969120000000004], [174.80188, -36.96914], [174.80178999999998, -36.9691], [174.80166, -36.96907], [174.80153, -36.96906], [174.80069, -36.96898], [174.80056000000002, -36.96897], [174.80013, -36.968920000000004], [174.79985, -36.9689], [174.79971, -36.96887], [174.79967, -36.96898]], \"type\": \"LineString\"}}'"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Test some\n",
"\n",
"trip_id = feed['trips']['trip_id'].iat[0] # First trip\n",
"geoj = trip_to_geojson(feed, trip_id)\n",
"json.dumps(geoj)\n",
"\n",
"# Paste into geojson.io"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Problem 4"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def compute_screen_line_counts(feed, linestring):\n",
" \"\"\"\n",
" Find all trips in the given GTFS feed object that intersect the given Shapely LineString\n",
" (given in WGS84 coordinates), and return a data frame with the columns:\n",
"\n",
" - ``'trip_id'``\n",
" - ``'route_id'``\n",
" - ``'route_short_name'``\n",
" - ``'direction_id'``\n",
" - ``'shape_id'``\n",
" \"\"\"\n",
" # Convert all shapes to linestrings\n",
" geometry_by_shape = build_geometry_by_shape(feed)\n",
" \n",
" # Interate through linestrings to find intersections with screenline\n",
" hits = []\n",
" for shid, geom in geometry_by_shape.items():\n",
" if geom.intersects(linestring):\n",
" hits.append(shid)\n",
" \n",
" # Compile trip info for hits\n",
" t = feed['trips'].copy()\n",
" t = t[t['shape_id'].isin(hits)].copy()\n",
" result = t.merge(feed['routes']) # Add more route info\n",
" \n",
" return result[['trip_id', 'route_id', 'route_short_name', 'direction_id', 'shape_id']]\n"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1209-20161011155909_v46.26\n",
" trip_id route_id route_short_name \\\n",
"0 14306060378-20161011155909_v46.26 route_091 309 \n",
"1 14306060390-20161011155909_v46.26 route_091 309 \n",
"2 14306060351-20161011155909_v46.26 route_091 309 \n",
"3 14306060328-20161011155909_v46.26 route_091 309 \n",
"4 14306060389-20161011155909_v46.26 route_091 309 \n",
"\n",
" direction_id shape_id \n",
"0 1 1209-20161011155909_v46.26 \n",
"1 1 1209-20161011155909_v46.26 \n",
"2 1 1209-20161011155909_v46.26 \n",
"3 1 1209-20161011155909_v46.26 \n",
"4 1 1209-20161011155909_v46.26 \n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Test some\n",
"\n",
"# A trip should intersect itself\n",
"shape_id = feed['trips']['shape_id'].iat[0]\n",
"print(shape_id)\n",
"\n",
"screen_line = build_geometry_by_shape(feed, shape_ids=[shape_id])[shape_id]\n",
"\n",
"counts = compute_screen_line_counts(feed, screen_line)\n",
"print(counts.head())\n",
"\n",
"shape_id in counts['shape_id'].values\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Problem 6"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"174.74128417968748 -36.832532095697715 0.008064651489263497 0.0024522605871553083\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,-73.66261193080828)\"><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.00016129302978526994\" points=\"174.74158287048337,-36.83037852590646 174.74905014038086,-36.832233404901814\" opacity=\"0.8\" /></g></svg>"
],
"text/plain": [
"<shapely.geometry.linestring.LineString at 0x7ff5d39f5278>"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a screen line across the Auckland Harbour Bridge;\n",
"# see http://geojson.io/#id=gist:anonymous/1ef5babe013688950587e853310cda9d&map=14/-36.8301/174.7439\n",
"\n",
"screen_line_json = {\n",
" \"type\": \"Feature\",\n",
" \"properties\": {},\n",
" \"geometry\": {\n",
" \"type\": \"LineString\",\n",
" \"coordinates\": [\n",
" [\n",
" 174.74158287048337,\n",
" -36.83037852590646\n",
" ],\n",
" [\n",
" 174.74905014038086,\n",
" -36.832233404901814\n",
" ]\n",
" ]\n",
" }\n",
"}\n",
"\n",
"screen_line = sg.shape(screen_line_json[\"geometry\"])\n",
"screen_line"
]
},
{
"cell_type": "code",
"execution_count": 89,
"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>trip_id</th>\n",
" <th>route_id</th>\n",
" <th>route_short_name</th>\n",
" <th>direction_id</th>\n",
" <th>shape_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3922046164-20161011151756_v46.25</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>1</td>\n",
" <td>560-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3922053258-20161011151756_v46.25</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>0</td>\n",
" <td>559-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3922046194-20161011151756_v46.25</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>1</td>\n",
" <td>560-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3922053246-20161011151756_v46.25</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>0</td>\n",
" <td>559-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3922046189-20161011155909_v46.26</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>1</td>\n",
" <td>483-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>3922046201-20161011151756_v46.25</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>1</td>\n",
" <td>560-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>3922053270-20161011151756_v46.25</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>0</td>\n",
" <td>559-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>3922046199-20161011151756_v46.25</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>1</td>\n",
" <td>560-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>3922046174-20161011155909_v46.26</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>1</td>\n",
" <td>483-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>3922053247-20161011155909_v46.26</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>0</td>\n",
" <td>482-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>3922046196-20161011155909_v46.26</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>1</td>\n",
" <td>483-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>3922046205-20161011155909_v46.26</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>1</td>\n",
" <td>483-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>3922053259-20161011155909_v46.26</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>0</td>\n",
" <td>482-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>3922046181-20161011155909_v46.26</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>1</td>\n",
" <td>483-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>3922046167-20161011155909_v46.26</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>1</td>\n",
" <td>483-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>3922053237-20161011155909_v46.26</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>0</td>\n",
" <td>482-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>3922053221-20161011151756_v46.25</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>0</td>\n",
" <td>559-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>3922046183-20161011155909_v46.26</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>1</td>\n",
" <td>483-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>3922053277-20161011151756_v46.25</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>0</td>\n",
" <td>559-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>3922053257-20161011155909_v46.26</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>0</td>\n",
" <td>482-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>3922053253-20161011155909_v46.26</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>0</td>\n",
" <td>482-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>3922046176-20161011155909_v46.26</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>1</td>\n",
" <td>483-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>3922046193-20161011155909_v46.26</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>1</td>\n",
" <td>483-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>3922053221-20161011155909_v46.26</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>0</td>\n",
" <td>482-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>3922053263-20161011151756_v46.25</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>0</td>\n",
" <td>559-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>3922053235-20161011155909_v46.26</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>0</td>\n",
" <td>482-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>3922053262-20161011151756_v46.25</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>0</td>\n",
" <td>559-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>3922053251-20161011155909_v46.26</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>0</td>\n",
" <td>482-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>3922046171-20161011151756_v46.25</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>1</td>\n",
" <td>560-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>3922053229-20161011155909_v46.26</td>\n",
" <td>route_274</td>\n",
" <td>922</td>\n",
" <td>0</td>\n",
" <td>482-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4225</th>\n",
" <td>4953028243-20161011151756_v46.25</td>\n",
" <td>route_280</td>\n",
" <td>953</td>\n",
" <td>0</td>\n",
" <td>816-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4226</th>\n",
" <td>4953052954-20161011155909_v46.26</td>\n",
" <td>route_280</td>\n",
" <td>953</td>\n",
" <td>1</td>\n",
" <td>708-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4227</th>\n",
" <td>4953028244-20161011151756_v46.25</td>\n",
" <td>route_280</td>\n",
" <td>953</td>\n",
" <td>0</td>\n",
" <td>816-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4228</th>\n",
" <td>4953052957-20161011151756_v46.25</td>\n",
" <td>route_280</td>\n",
" <td>953</td>\n",
" <td>1</td>\n",
" <td>817-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4229</th>\n",
" <td>4953028241-20161011151756_v46.25</td>\n",
" <td>route_280</td>\n",
" <td>953</td>\n",
" <td>0</td>\n",
" <td>816-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4230</th>\n",
" <td>4953052956-20161011151756_v46.25</td>\n",
" <td>route_280</td>\n",
" <td>953</td>\n",
" <td>1</td>\n",
" <td>817-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4231</th>\n",
" <td>4953028242-20161011155909_v46.26</td>\n",
" <td>route_280</td>\n",
" <td>953</td>\n",
" <td>0</td>\n",
" <td>707-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4232</th>\n",
" <td>4953052956-20161011155909_v46.26</td>\n",
" <td>route_280</td>\n",
" <td>953</td>\n",
" <td>1</td>\n",
" <td>708-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4233</th>\n",
" <td>12528042053-20161011155909_v46.26</td>\n",
" <td>route_325</td>\n",
" <td>N83</td>\n",
" <td>1</td>\n",
" <td>576-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4234</th>\n",
" <td>12528042051-20161011155909_v46.26</td>\n",
" <td>route_325</td>\n",
" <td>N83</td>\n",
" <td>1</td>\n",
" <td>575-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4235</th>\n",
" <td>12528042050-20161011155909_v46.26</td>\n",
" <td>route_325</td>\n",
" <td>N83</td>\n",
" <td>1</td>\n",
" <td>575-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4236</th>\n",
" <td>12528042052-20161011151756_v46.25</td>\n",
" <td>route_325</td>\n",
" <td>N83</td>\n",
" <td>1</td>\n",
" <td>658-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4237</th>\n",
" <td>12528042052-20161011155909_v46.26</td>\n",
" <td>route_325</td>\n",
" <td>N83</td>\n",
" <td>1</td>\n",
" <td>575-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4238</th>\n",
" <td>12528042051-20161011151756_v46.25</td>\n",
" <td>route_325</td>\n",
" <td>N83</td>\n",
" <td>1</td>\n",
" <td>658-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4239</th>\n",
" <td>12528042054-20161011151756_v46.25</td>\n",
" <td>route_325</td>\n",
" <td>N83</td>\n",
" <td>1</td>\n",
" <td>659-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4240</th>\n",
" <td>12528042050-20161011151756_v46.25</td>\n",
" <td>route_325</td>\n",
" <td>N83</td>\n",
" <td>1</td>\n",
" <td>658-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4241</th>\n",
" <td>12528042055-20161011151756_v46.25</td>\n",
" <td>route_325</td>\n",
" <td>N83</td>\n",
" <td>1</td>\n",
" <td>659-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4242</th>\n",
" <td>12528042055-20161011155909_v46.26</td>\n",
" <td>route_325</td>\n",
" <td>N83</td>\n",
" <td>1</td>\n",
" <td>576-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4243</th>\n",
" <td>12528042054-20161011155909_v46.26</td>\n",
" <td>route_325</td>\n",
" <td>N83</td>\n",
" <td>1</td>\n",
" <td>576-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4244</th>\n",
" <td>12528042053-20161011151756_v46.25</td>\n",
" <td>route_325</td>\n",
" <td>N83</td>\n",
" <td>1</td>\n",
" <td>659-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4245</th>\n",
" <td>12866053285-20161011151756_v46.25</td>\n",
" <td>route_251</td>\n",
" <td>866X</td>\n",
" <td>0</td>\n",
" <td>732-20161011151756_v46.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4246</th>\n",
" <td>12866053286-20161011155909_v46.26</td>\n",
" <td>route_251</td>\n",
" <td>866X</td>\n",
" <td>0</td>\n",
" <td>636-20161011155909_v46.26</td>\n",
" </tr>\n",
" <tr>\n",
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],
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" trip_id route_id route_short_name \\\n",
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"... ... ... ... \n",
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"\n",
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"\n",
"[4255 rows x 5 columns]"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"counts = compute_screen_line_counts(feed, screen_line)\n",
"counts"
]
},
{
"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",
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"version": "3.5.2"
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}
| mit |
fifabsas/talleresfifabsas | python/Extras/Pandas_DataFrames/inscripcion_al_curso.ipynb | 1 | 198562 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bienvenides\n",
"\n",
"En esta notebook vamos a levantar un archivo con las respuestas de algun cuatrimestre del cursito de python de la FIFA con Pandas, y vamos también a trabajarlo un poco para ver qué tipo de cosas se pueden hacer y qué información sacar de los datos.\n",
"\n",
"Las ideas que queremos transmitir en este ejemplo son:\n",
" - Abrir un archivo csv y ver su informacion\n",
" - Modificar sus columnas y entradas para hacerlo más ameno\n",
" - Armar histogramas sobre columnas de los datos\n",
" - Hacer plots entre dos columnas\n",
" \n",
" + __Bonus__: Una muestra de como trabajar con fechas en Python\n",
" \n",
" - Extraer informacion dada una cierta condicion\n",
" - Manipular esa informacion para obtener solapamiento de inscriptos"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
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" <th></th>\n",
" <th>Timestamp</th>\n",
" <th>¿Que día/horarios te conviene? [Lunes]</th>\n",
" <th>¿Que día/horarios te conviene? [Martes]</th>\n",
" <th>¿Que día/horarios te conviene? [Miercoles]</th>\n",
" <th>¿Que día/horarios te conviene? [Jueves]</th>\n",
" <th>¿Que día/horarios te conviene? [Viernes]</th>\n",
" <th>¿Cuantos sabes de programación?</th>\n",
" <th>¿Tenes algún tema o librería en particular que te gustaría aprender?(esta pregunta es opcional)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
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" <th>0</th>\n",
" <td>29/03/2020 18:04:09</td>\n",
" <td>Mañana(1030-1330)</td>\n",
" <td>Mañana(1030-1330)</td>\n",
" <td>Mañana(1030-1330)</td>\n",
" <td>Mañana(1030-1330)</td>\n",
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" <td>Tarde(1330-1630)</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>29/03/2020 18:21:25</td>\n",
" <td>Mañana(1030-1330)</td>\n",
" <td>Mañana(1030-1330)</td>\n",
" <td>Mañana(1030-1330)</td>\n",
" <td>Mañana(1030-1330)</td>\n",
" <td>Mañana(1030-1330)</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>29/03/2020 18:22:11</td>\n",
" <td>Mañana(1030-1330), Tarde(1330-1630), Noche(173...</td>\n",
" <td>Mañana(1030-1330), Tarde(1330-1630), Noche(173...</td>\n",
" <td>Mañana(1030-1330), Tarde(1330-1630), Noche(173...</td>\n",
" <td>Mañana(1030-1330), Tarde(1330-1630), Noche(173...</td>\n",
" <td>Mañana(1030-1330), Tarde(1330-1630), Noche(173...</td>\n",
" <td>3</td>\n",
" <td>Programación orientada a objetos</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>29/03/2020 18:34:15</td>\n",
" <td>Noche(1730-2030)</td>\n",
" <td>Noche(1730-2030)</td>\n",
" <td>Noche(1730-2030)</td>\n",
" <td>Noche(1730-2030)</td>\n",
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" <th>166</th>\n",
" <td>04/04/2020 07:55:21</td>\n",
" <td>Mañana(1030-1330)</td>\n",
" <td>Mañana(1030-1330)</td>\n",
" <td>Mañana(1030-1330)</td>\n",
" <td>Mañana(1030-1330)</td>\n",
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" <td>1</td>\n",
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" <tr>\n",
" <th>167</th>\n",
" <td>04/04/2020 09:55:16</td>\n",
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" <tr>\n",
" <th>168</th>\n",
" <td>04/04/2020 13:35:21</td>\n",
" <td>Mañana(1030-1330)</td>\n",
" <td>Mañana(1030-1330)</td>\n",
" <td>Mañana(1030-1330)</td>\n",
" <td>Mañana(1030-1330)</td>\n",
" <td>Mañana(1030-1330)</td>\n",
" <td>2</td>\n",
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" <th>169</th>\n",
" <td>04/04/2020 13:40:15</td>\n",
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" <td>Mañana(1030-1330), Tarde(1330-1630)</td>\n",
" <td>Mañana(1030-1330)</td>\n",
" <td>Mañana(1030-1330), Tarde(1330-1630)</td>\n",
" <td>Mañana(1030-1330)</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>170</th>\n",
" <td>04/04/2020 14:20:55</td>\n",
" <td>Mañana(1030-1330)</td>\n",
" <td>Mañana(1030-1330), Tarde(1330-1630), Noche(173...</td>\n",
" <td>Mañana(1030-1330), Tarde(1330-1630)</td>\n",
" <td>Mañana(1030-1330), Tarde(1330-1630), Noche(173...</td>\n",
" <td>Mañana(1030-1330)</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>171 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" Timestamp ¿Que día/horarios te conviene? [Lunes] \\\n",
"0 29/03/2020 18:04:09 Mañana(1030-1330) \n",
"1 29/03/2020 18:09:10 Tarde(1330-1630) \n",
"2 29/03/2020 18:21:25 Mañana(1030-1330) \n",
"3 29/03/2020 18:22:11 Mañana(1030-1330), Tarde(1330-1630), Noche(173... \n",
"4 29/03/2020 18:34:15 Noche(1730-2030) \n",
".. ... ... \n",
"166 04/04/2020 07:55:21 Mañana(1030-1330) \n",
"167 04/04/2020 09:55:16 Ninguna \n",
"168 04/04/2020 13:35:21 Mañana(1030-1330) \n",
"169 04/04/2020 13:40:15 Mañana(1030-1330) \n",
"170 04/04/2020 14:20:55 Mañana(1030-1330) \n",
"\n",
" ¿Que día/horarios te conviene? [Martes] \\\n",
"0 Mañana(1030-1330) \n",
"1 Tarde(1330-1630) \n",
"2 Mañana(1030-1330) \n",
"3 Mañana(1030-1330), Tarde(1330-1630), Noche(173... \n",
"4 Noche(1730-2030) \n",
".. ... \n",
"166 Mañana(1030-1330) \n",
"167 Tarde(1330-1630) \n",
"168 Mañana(1030-1330) \n",
"169 Mañana(1030-1330), Tarde(1330-1630) \n",
"170 Mañana(1030-1330), Tarde(1330-1630), Noche(173... \n",
"\n",
" ¿Que día/horarios te conviene? [Miercoles] \\\n",
"0 Mañana(1030-1330) \n",
"1 Tarde(1330-1630) \n",
"2 Mañana(1030-1330) \n",
"3 Mañana(1030-1330), Tarde(1330-1630), Noche(173... \n",
"4 Noche(1730-2030) \n",
".. ... \n",
"166 Mañana(1030-1330) \n",
"167 Tarde(1330-1630) \n",
"168 Mañana(1030-1330) \n",
"169 Mañana(1030-1330) \n",
"170 Mañana(1030-1330), Tarde(1330-1630) \n",
"\n",
" ¿Que día/horarios te conviene? [Jueves] \\\n",
"0 Mañana(1030-1330) \n",
"1 Tarde(1330-1630) \n",
"2 Mañana(1030-1330) \n",
"3 Mañana(1030-1330), Tarde(1330-1630), Noche(173... \n",
"4 Noche(1730-2030) \n",
".. ... \n",
"166 Mañana(1030-1330) \n",
"167 Tarde(1330-1630) \n",
"168 Mañana(1030-1330) \n",
"169 Mañana(1030-1330), Tarde(1330-1630) \n",
"170 Mañana(1030-1330), Tarde(1330-1630), Noche(173... \n",
"\n",
" ¿Que día/horarios te conviene? [Viernes] \\\n",
"0 Mañana(1030-1330) \n",
"1 Tarde(1330-1630) \n",
"2 Mañana(1030-1330) \n",
"3 Mañana(1030-1330), Tarde(1330-1630), Noche(173... \n",
"4 Noche(1730-2030) \n",
".. ... \n",
"166 Mañana(1030-1330) \n",
"167 Ninguna \n",
"168 Mañana(1030-1330) \n",
"169 Mañana(1030-1330) \n",
"170 Mañana(1030-1330) \n",
"\n",
" ¿Cuantos sabes de programación? \\\n",
"0 3 \n",
"1 1 \n",
"2 1 \n",
"3 3 \n",
"4 1 \n",
".. ... \n",
"166 1 \n",
"167 1 \n",
"168 2 \n",
"169 1 \n",
"170 1 \n",
"\n",
" ¿Tenes algún tema o librería en particular que te gustaría aprender?(esta pregunta es opcional) \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 Programación orientada a objetos \n",
"4 NaN \n",
".. ... \n",
"166 NaN \n",
"167 NaN \n",
"168 NaN \n",
"169 NaN \n",
"170 NaN \n",
"\n",
"[171 rows x 8 columns]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd # PANDAS: la libreria que nos va a ayudar a manipular estos datos\n",
"import numpy as np # NUMPY: la vieja y confiable\n",
"import matplotlib.pyplot as plt # MATPLOTLIB: para los graficos que hagamos\n",
"import datetime # DATETIME: para manipular los datos de tipo fechas\n",
"\n",
"%matplotlib notebook\n",
"# Aca traigo el archivo subido. Revisando el archivo vemos que el caracter\n",
"# que separa los campos es una coma \",\", entonces lo declaro. Podría ser otra cosa\n",
"# como un punto y coma \";\" o una tabulación \"\\t\", etc...\n",
"respuestas = pd.read_csv(\"inscripcion_data.csv\", sep=',')\n",
"\n",
"# Usando notebooks (IPython en realidad) puedo abusarme de su interactividad y \n",
"# printear cosas de forma fachera solamente dejando escrito su nombre en la celda\n",
"# >>>> OJO con esto <<<<\n",
"respuestas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Es un poco molesto de ver asi la tabla, porque cada horario tiene mucha información que estaba en el form, pero ya no nos sirve, entones podemos reemplazar cada turno por algo mas corto, así como tambien cambiar las preguntas que también son muy largas.\n",
"\n",
"Por ejemplo, el texto `Mañana(1030-1330)` tranquilamente puede ser modificado por algo mas corto como `Man` (asi tambien ahorramos la ñ que da para quilombo).\n",
"Lo mismo podria decirse de `Tarde(1330-1630)` -> `Tar`, idem noche y trasnoche.\n",
"\n",
"Pasa lo mismo con las columnas: `¿Que día/horarios te conviene? [Lunes]` es demasiado largo (e innecesario) para escribir en el código, lo podesmos transformar directamente en `Lun`."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>Tiempo</th>\n",
" <th>Lun</th>\n",
" <th>Mar</th>\n",
" <th>Mie</th>\n",
" <th>Jue</th>\n",
" <th>Vie</th>\n",
" <th>Nivel</th>\n",
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" </thead>\n",
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" <th>0</th>\n",
" <td>29/03/2020 18:04:09</td>\n",
" <td>Man</td>\n",
" <td>Man</td>\n",
" <td>Man</td>\n",
" <td>Man</td>\n",
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" <td>29/03/2020 18:21:25</td>\n",
" <td>Man</td>\n",
" <td>Man</td>\n",
" <td>Man</td>\n",
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" <td>29/03/2020 18:22:11</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
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" <td>29/03/2020 18:34:15</td>\n",
" <td>Noc</td>\n",
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" <td>Noc</td>\n",
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" <td>Noc</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>166</th>\n",
" <td>04/04/2020 07:55:21</td>\n",
" <td>Man</td>\n",
" <td>Man</td>\n",
" <td>Man</td>\n",
" <td>Man</td>\n",
" <td>Man</td>\n",
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" <td>04/04/2020 09:55:16</td>\n",
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" <td>Tar</td>\n",
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" <td>04/04/2020 13:35:21</td>\n",
" <td>Man</td>\n",
" <td>Man</td>\n",
" <td>Man</td>\n",
" <td>Man</td>\n",
" <td>Man</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
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" <th>169</th>\n",
" <td>04/04/2020 13:40:15</td>\n",
" <td>Man</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man</td>\n",
" <td>1</td>\n",
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" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man</td>\n",
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"text/plain": [
" Tiempo Lun Mar Mie \\\n",
"0 29/03/2020 18:04:09 Man Man Man \n",
"1 29/03/2020 18:09:10 Tar Tar Tar \n",
"2 29/03/2020 18:21:25 Man Man Man \n",
"3 29/03/2020 18:22:11 Man, Tar, Noc Man, Tar, Noc Man, Tar, Noc \n",
"4 29/03/2020 18:34:15 Noc Noc Noc \n",
".. ... ... ... ... \n",
"166 04/04/2020 07:55:21 Man Man Man \n",
"167 04/04/2020 09:55:16 None Tar Tar \n",
"168 04/04/2020 13:35:21 Man Man Man \n",
"169 04/04/2020 13:40:15 Man Man, Tar Man \n",
"170 04/04/2020 14:20:55 Man Man, Tar, Noc Man, Tar \n",
"\n",
" Jue Vie Nivel Extras \n",
"0 Man Man 3 NaN \n",
"1 Tar Tar 1 NaN \n",
"2 Man Man 1 NaN \n",
"3 Man, Tar, Noc Man, Tar, Noc 3 Programación orientada a objetos \n",
"4 Noc Noc 1 NaN \n",
".. ... ... ... ... \n",
"166 Man Man 1 NaN \n",
"167 Tar None 1 NaN \n",
"168 Man Man 2 NaN \n",
"169 Man, Tar Man 1 NaN \n",
"170 Man, Tar, Noc Man 1 NaN \n",
"\n",
"[171 rows x 8 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Así cambio todos los nombres de las columnas de un tirón. Pisando los previos\n",
"respuestas.columns = ['Tiempo', 'Lun', 'Mar', 'Mie', 'Jue', 'Vie', 'Nivel', 'Extras']\n",
"\n",
"# Para cambiar los datos del DataFrame, voy a usar la funcion replace.\n",
"# junto a las llamadas 'regex' o Regular Expressions \n",
"# (quien quiera entenderlas, puede googlear, le deseamos mucha suerte)\n",
"#\n",
"# La opcion 'inplace' va a hacer que reemplace los datos donde los encuentra,\n",
"# En vez de generar un nuevo DataFrame con los datos corregidos\n",
"respuestas.replace({'Mañana\\(1030-1330\\)': 'Man',\n",
" 'Tarde\\(1330-1630\\)': 'Tar',\n",
" 'Noche\\(1730-2030\\)': 'Noc',\n",
" 'Ninguna':\"None\"}, regex=True, inplace=True)\n",
" \n",
"respuestas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"---\n",
"Algo trivial de hacer ahora es ver, por ejemplo, la distribución de nivel de la gente que está viniendo al taller. Esto puede servir para enfocar de diferentes maneras los temas que vamos a dar. O bien super básicos, o si podemos apretar más el acelerador y dar mas cosas."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
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" 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",
" /* Instead of a spacer, we start a new button group. */\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
" buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'mpl-button-group';\n",
" continue;\n",
" }\n",
"\n",
" var button = (fig.buttons[name] = document.createElement('button'));\n",
" button.classList = 'mpl-widget';\n",
" button.setAttribute('role', 'button');\n",
" button.setAttribute('aria-disabled', 'false');\n",
" button.addEventListener('click', on_click_closure(method_name));\n",
" button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
"\n",
" var icon_img = document.createElement('img');\n",
" icon_img.src = '_images/' + image + '.png';\n",
" icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
" icon_img.alt = tooltip;\n",
" button.appendChild(icon_img);\n",
"\n",
" buttonGroup.appendChild(button);\n",
" }\n",
"\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" var fmt_picker = document.createElement('select');\n",
" fmt_picker.classList = 'mpl-widget';\n",
" toolbar.appendChild(fmt_picker);\n",
" this.format_dropdown = fmt_picker;\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = document.createElement('option');\n",
" option.selected = fmt === mpl.default_extension;\n",
" option.innerHTML = fmt;\n",
" fmt_picker.appendChild(option);\n",
" }\n",
"\n",
" var status_bar = document.createElement('span');\n",
" status_bar.classList = 'mpl-message';\n",
" toolbar.appendChild(status_bar);\n",
" this.message = status_bar;\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",
"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",
"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], msg['forward']);\n",
" fig.send_message('refresh', {});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
" var x0 = msg['x0'] / fig.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
" var x1 = msg['x1'] / fig.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\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,\n",
" 0,\n",
" fig.canvas.width / fig.ratio,\n",
" fig.canvas.height / fig.ratio\n",
" );\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",
" 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.handle_history_buttons = function (fig, msg) {\n",
" for (var key in msg) {\n",
" if (!(key in fig.buttons)) {\n",
" continue;\n",
" }\n",
" fig.buttons[key].disabled = !msg[key];\n",
" fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
" if (msg['mode'] === 'PAN') {\n",
" fig.buttons['Pan'].classList.add('active');\n",
" fig.buttons['Zoom'].classList.remove('active');\n",
" } else if (msg['mode'] === 'ZOOM') {\n",
" fig.buttons['Pan'].classList.remove('active');\n",
" fig.buttons['Zoom'].classList.add('active');\n",
" } else {\n",
" fig.buttons['Pan'].classList.remove('active');\n",
" fig.buttons['Zoom'].classList.remove('active');\n",
" }\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",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data\n",
" );\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" } else if (\n",
" typeof evt.data === 'string' &&\n",
" evt.data.slice(0, 21) === 'data:image/png;base64'\n",
" ) {\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(\n",
" \"No handler for the '\" + msg_type + \"' message type: \",\n",
" msg\n",
" );\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(\n",
" \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
" e,\n",
" e.stack,\n",
" msg\n",
" );\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",
" }\n",
" if (e.target) {\n",
" targ = e.target;\n",
" } else if (e.srcElement) {\n",
" targ = e.srcElement;\n",
" }\n",
" if (targ.nodeType === 3) {\n",
" // defeat Safari bug\n",
" targ = targ.parentNode;\n",
" }\n",
"\n",
" // pageX,Y are the mouse positions relative to the document\n",
" var boundingRect = targ.getBoundingClientRect();\n",
" var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
" var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
" }\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",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * this.ratio;\n",
" var y = canvas_pos.y * this.ratio;\n",
"\n",
" this.send_message(name, {\n",
" x: x,\n",
" y: y,\n",
" button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event),\n",
" });\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",
" // Prevent repeat events\n",
" if (name === 'key_press') {\n",
" if (event.which === this._key) {\n",
" return;\n",
" } else {\n",
" this._key = event.which;\n",
" }\n",
" }\n",
" if (name === 'key_release') {\n",
" this._key = null;\n",
" }\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which !== 17) {\n",
" value += 'ctrl+';\n",
" }\n",
" if (event.altKey && event.which !== 18) {\n",
" value += 'alt+';\n",
" }\n",
" if (event.shiftKey && event.which !== 16) {\n",
" value += 'shift+';\n",
" }\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, 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",
"\n",
"///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
"// prettier-ignore\n",
"var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";/* global mpl */\n",
"\n",
"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 overridden (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 = document.getElementById(id);\n",
" var ws_proxy = comm_websocket_adapter(comm);\n",
"\n",
" function ondownload(figure, _format) {\n",
" window.open(figure.canvas.toDataURL());\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
" fig.cell_info[0].output_area.element.on(\n",
" 'cleared',\n",
" { fig: fig },\n",
" fig._remove_fig_handler\n",
" );\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function (fig, msg) {\n",
" var width = fig.canvas.width / fig.ratio;\n",
" fig.cell_info[0].output_area.element.off(\n",
" 'cleared',\n",
" fig._remove_fig_handler\n",
" );\n",
" fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
" '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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 width = this.canvas.width / this.ratio;\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] =\n",
" '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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 () {\n",
" fig.push_to_output();\n",
" }, 1000);\n",
"};\n",
"\n",
"mpl.figure.prototype._init_toolbar = function () {\n",
" var fig = this;\n",
"\n",
" var toolbar = document.createElement('div');\n",
" toolbar.classList = 'btn-toolbar';\n",
" this.root.appendChild(toolbar);\n",
"\n",
" function on_click_closure(name) {\n",
" return function (_event) {\n",
" return fig.toolbar_button_onclick(name);\n",
" };\n",
" }\n",
"\n",
" function on_mouseover_closure(tooltip) {\n",
" return function (event) {\n",
" if (!event.currentTarget.disabled) {\n",
" return fig.toolbar_button_onmouseover(tooltip);\n",
" }\n",
" };\n",
" }\n",
"\n",
" fig.buttons = {};\n",
" var buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'btn-group';\n",
" var button;\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",
" /* Instead of a spacer, we start a new button group. */\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
" buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'btn-group';\n",
" continue;\n",
" }\n",
"\n",
" button = fig.buttons[name] = document.createElement('button');\n",
" button.classList = 'btn btn-default';\n",
" button.href = '#';\n",
" button.title = name;\n",
" button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
" button.addEventListener('click', on_click_closure(method_name));\n",
" button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
" buttonGroup.appendChild(button);\n",
" }\n",
"\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = document.createElement('span');\n",
" status_bar.classList = 'mpl-message pull-right';\n",
" toolbar.appendChild(status_bar);\n",
" this.message = status_bar;\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = document.createElement('div');\n",
" buttongrp.classList = 'btn-group inline pull-right';\n",
" button = document.createElement('button');\n",
" button.classList = 'btn btn-mini btn-primary';\n",
" button.href = '#';\n",
" button.title = 'Stop Interaction';\n",
" button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
" button.addEventListener('click', function (_evt) {\n",
" fig.handle_close(fig, {});\n",
" });\n",
" button.addEventListener(\n",
" 'mouseover',\n",
" on_mouseover_closure('Stop Interaction')\n",
" );\n",
" buttongrp.appendChild(button);\n",
" var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
" titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
"};\n",
"\n",
"mpl.figure.prototype._remove_fig_handler = function (event) {\n",
" var fig = event.data.fig;\n",
" if (event.target !== this) {\n",
" // Ignore bubbled events from children.\n",
" return;\n",
" }\n",
" fig.close_ws(fig, {});\n",
"};\n",
"\n",
"mpl.figure.prototype._root_extra_style = function (el) {\n",
" el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
"};\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function (el) {\n",
" // this is important to make the div 'focusable\n",
" el.setAttribute('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",
" } else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\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",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which === 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" fig.ondownload(fig, null);\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(\n",
" 'matplotlib',\n",
" mpl.mpl_figure_comm\n",
" );\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Text(0, 0.5, '# de personas')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# La funcion `hist` devuelve algunos parametros (pueden ver cuales haciendo `plt.hist?`)\n",
"# y por ahora no los necesito asi que le digo que no los guarde.\n",
"# En realidad, le digo que lo guarde en la variable \"_\", que es la forma usual\n",
"# de escribir \"una variable que no me interesa guardar\"\n",
"#\n",
"# Fijense que pasa si le sacan el \"_ = \" !!\n",
"#\n",
"_ = plt.hist(respuestas.Nivel, bins=np.arange(1, 12), \\\n",
" align='left', edgecolor='k', color='lightblue')\n",
"\n",
"plt.xlabel(\"Nivel\")\n",
"plt.ylabel(\"# de personas\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"---\n",
"\n",
"Supongan que nos interesa saber qué dia se anotaron mas personas, o a qué hora. Por ejemplo para saber cuándo hacer algún posteo en una red social, o lo que sea. Podemos hacerlo viendo el campo de `Time` que devuelve el formulario, que tiene las fechas y horas de cada envío de respuesta."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"/* global mpl */\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(\n",
" '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",
"\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 = document.createElement('div');\n",
" this.root.setAttribute('style', 'display: inline-block');\n",
" this._root_extra_style(this.root);\n",
"\n",
" parent_element.appendChild(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",
" if (fig.ratio !== 1) {\n",
" fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
" }\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",
" fig.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 = document.createElement('div');\n",
" titlebar.classList =\n",
" 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
" var titletext = document.createElement('div');\n",
" titletext.classList = 'ui-dialog-title';\n",
" titletext.setAttribute(\n",
" 'style',\n",
" 'width: 100%; text-align: center; padding: 3px;'\n",
" );\n",
" titlebar.appendChild(titletext);\n",
" this.root.appendChild(titlebar);\n",
" this.header = titletext;\n",
"};\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
"\n",
"mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
"\n",
"mpl.figure.prototype._init_canvas = function () {\n",
" var fig = this;\n",
"\n",
" var canvas_div = (this.canvas_div = document.createElement('div'));\n",
" canvas_div.setAttribute(\n",
" 'style',\n",
" 'border: 1px solid #ddd;' +\n",
" 'box-sizing: content-box;' +\n",
" 'clear: both;' +\n",
" 'min-height: 1px;' +\n",
" 'min-width: 1px;' +\n",
" 'outline: 0;' +\n",
" 'overflow: hidden;' +\n",
" 'position: relative;' +\n",
" 'resize: both;'\n",
" );\n",
"\n",
" function on_keyboard_event_closure(name) {\n",
" return function (event) {\n",
" return fig.key_event(event, name);\n",
" };\n",
" }\n",
"\n",
" canvas_div.addEventListener(\n",
" 'keydown',\n",
" on_keyboard_event_closure('key_press')\n",
" );\n",
" canvas_div.addEventListener(\n",
" 'keyup',\n",
" on_keyboard_event_closure('key_release')\n",
" );\n",
"\n",
" this._canvas_extra_style(canvas_div);\n",
" this.root.appendChild(canvas_div);\n",
"\n",
" var canvas = (this.canvas = document.createElement('canvas'));\n",
" canvas.classList.add('mpl-canvas');\n",
" canvas.setAttribute('style', 'box-sizing: content-box;');\n",
"\n",
" this.context = canvas.getContext('2d');\n",
"\n",
" var backingStore =\n",
" this.context.backingStorePixelRatio ||\n",
" this.context.webkitBackingStorePixelRatio ||\n",
" this.context.mozBackingStorePixelRatio ||\n",
" this.context.msBackingStorePixelRatio ||\n",
" this.context.oBackingStorePixelRatio ||\n",
" this.context.backingStorePixelRatio ||\n",
" 1;\n",
"\n",
" this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
" if (this.ratio !== 1) {\n",
" fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
" }\n",
"\n",
" var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
" 'canvas'\n",
" ));\n",
" rubberband_canvas.setAttribute(\n",
" 'style',\n",
" 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
" );\n",
"\n",
" // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
" if (this.ResizeObserver === undefined) {\n",
" if (window.ResizeObserver !== undefined) {\n",
" this.ResizeObserver = window.ResizeObserver;\n",
" } else {\n",
" var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
" this.ResizeObserver = obs.ResizeObserver;\n",
" }\n",
" }\n",
"\n",
" this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
" var nentries = entries.length;\n",
" for (var i = 0; i < nentries; i++) {\n",
" var entry = entries[i];\n",
" var width, height;\n",
" if (entry.contentBoxSize) {\n",
" if (entry.contentBoxSize instanceof Array) {\n",
" // Chrome 84 implements new version of spec.\n",
" width = entry.contentBoxSize[0].inlineSize;\n",
" height = entry.contentBoxSize[0].blockSize;\n",
" } else {\n",
" // Firefox implements old version of spec.\n",
" width = entry.contentBoxSize.inlineSize;\n",
" height = entry.contentBoxSize.blockSize;\n",
" }\n",
" } else {\n",
" // Chrome <84 implements even older version of spec.\n",
" width = entry.contentRect.width;\n",
" height = entry.contentRect.height;\n",
" }\n",
"\n",
" // Keep the size of the canvas and rubber band canvas in sync with\n",
" // the canvas container.\n",
" if (entry.devicePixelContentBoxSize) {\n",
" // Chrome 84 implements new version of spec.\n",
" canvas.setAttribute(\n",
" 'width',\n",
" entry.devicePixelContentBoxSize[0].inlineSize\n",
" );\n",
" canvas.setAttribute(\n",
" 'height',\n",
" entry.devicePixelContentBoxSize[0].blockSize\n",
" );\n",
" } else {\n",
" canvas.setAttribute('width', width * fig.ratio);\n",
" canvas.setAttribute('height', height * fig.ratio);\n",
" }\n",
" canvas.setAttribute(\n",
" 'style',\n",
" 'width: ' + width + 'px; height: ' + height + 'px;'\n",
" );\n",
"\n",
" rubberband_canvas.setAttribute('width', width);\n",
" rubberband_canvas.setAttribute('height', height);\n",
"\n",
" // And update the size in Python. We ignore the initial 0/0 size\n",
" // that occurs as the element is placed into the DOM, which should\n",
" // otherwise not happen due to the minimum size styling.\n",
" if (width != 0 && height != 0) {\n",
" fig.request_resize(width, height);\n",
" }\n",
" }\n",
" });\n",
" this.resizeObserverInstance.observe(canvas_div);\n",
"\n",
" function on_mouse_event_closure(name) {\n",
" return function (event) {\n",
" return fig.mouse_event(event, name);\n",
" };\n",
" }\n",
"\n",
" rubberband_canvas.addEventListener(\n",
" 'mousedown',\n",
" on_mouse_event_closure('button_press')\n",
" );\n",
" rubberband_canvas.addEventListener(\n",
" 'mouseup',\n",
" on_mouse_event_closure('button_release')\n",
" );\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband_canvas.addEventListener(\n",
" 'mousemove',\n",
" on_mouse_event_closure('motion_notify')\n",
" );\n",
"\n",
" rubberband_canvas.addEventListener(\n",
" 'mouseenter',\n",
" on_mouse_event_closure('figure_enter')\n",
" );\n",
" rubberband_canvas.addEventListener(\n",
" 'mouseleave',\n",
" on_mouse_event_closure('figure_leave')\n",
" );\n",
"\n",
" canvas_div.addEventListener('wheel', function (event) {\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" on_mouse_event_closure('scroll')(event);\n",
" });\n",
"\n",
" canvas_div.appendChild(canvas);\n",
" canvas_div.appendChild(rubberband_canvas);\n",
"\n",
" this.rubberband_context = rubberband_canvas.getContext('2d');\n",
" this.rubberband_context.strokeStyle = '#000000';\n",
"\n",
" this._resize_canvas = function (width, height, forward) {\n",
" if (forward) {\n",
" canvas_div.style.width = width + 'px';\n",
" canvas_div.style.height = height + 'px';\n",
" }\n",
" };\n",
"\n",
" // Disable right mouse context menu.\n",
" this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
" event.preventDefault();\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 toolbar = document.createElement('div');\n",
" toolbar.classList = 'mpl-toolbar';\n",
" this.root.appendChild(toolbar);\n",
"\n",
" function on_click_closure(name) {\n",
" return function (_event) {\n",
" return fig.toolbar_button_onclick(name);\n",
" };\n",
" }\n",
"\n",
" function on_mouseover_closure(tooltip) {\n",
" return function (event) {\n",
" if (!event.currentTarget.disabled) {\n",
" return fig.toolbar_button_onmouseover(tooltip);\n",
" }\n",
" };\n",
" }\n",
"\n",
" fig.buttons = {};\n",
" var buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'mpl-button-group';\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",
" /* Instead of a spacer, we start a new button group. */\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
" buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'mpl-button-group';\n",
" continue;\n",
" }\n",
"\n",
" var button = (fig.buttons[name] = document.createElement('button'));\n",
" button.classList = 'mpl-widget';\n",
" button.setAttribute('role', 'button');\n",
" button.setAttribute('aria-disabled', 'false');\n",
" button.addEventListener('click', on_click_closure(method_name));\n",
" button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
"\n",
" var icon_img = document.createElement('img');\n",
" icon_img.src = '_images/' + image + '.png';\n",
" icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
" icon_img.alt = tooltip;\n",
" button.appendChild(icon_img);\n",
"\n",
" buttonGroup.appendChild(button);\n",
" }\n",
"\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" var fmt_picker = document.createElement('select');\n",
" fmt_picker.classList = 'mpl-widget';\n",
" toolbar.appendChild(fmt_picker);\n",
" this.format_dropdown = fmt_picker;\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = document.createElement('option');\n",
" option.selected = fmt === mpl.default_extension;\n",
" option.innerHTML = fmt;\n",
" fmt_picker.appendChild(option);\n",
" }\n",
"\n",
" var status_bar = document.createElement('span');\n",
" status_bar.classList = 'mpl-message';\n",
" toolbar.appendChild(status_bar);\n",
" this.message = status_bar;\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",
"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",
"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], msg['forward']);\n",
" fig.send_message('refresh', {});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
" var x0 = msg['x0'] / fig.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
" var x1 = msg['x1'] / fig.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\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,\n",
" 0,\n",
" fig.canvas.width / fig.ratio,\n",
" fig.canvas.height / fig.ratio\n",
" );\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",
" 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.handle_history_buttons = function (fig, msg) {\n",
" for (var key in msg) {\n",
" if (!(key in fig.buttons)) {\n",
" continue;\n",
" }\n",
" fig.buttons[key].disabled = !msg[key];\n",
" fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
" if (msg['mode'] === 'PAN') {\n",
" fig.buttons['Pan'].classList.add('active');\n",
" fig.buttons['Zoom'].classList.remove('active');\n",
" } else if (msg['mode'] === 'ZOOM') {\n",
" fig.buttons['Pan'].classList.remove('active');\n",
" fig.buttons['Zoom'].classList.add('active');\n",
" } else {\n",
" fig.buttons['Pan'].classList.remove('active');\n",
" fig.buttons['Zoom'].classList.remove('active');\n",
" }\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",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data\n",
" );\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" } else if (\n",
" typeof evt.data === 'string' &&\n",
" evt.data.slice(0, 21) === 'data:image/png;base64'\n",
" ) {\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(\n",
" \"No handler for the '\" + msg_type + \"' message type: \",\n",
" msg\n",
" );\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(\n",
" \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
" e,\n",
" e.stack,\n",
" msg\n",
" );\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",
" }\n",
" if (e.target) {\n",
" targ = e.target;\n",
" } else if (e.srcElement) {\n",
" targ = e.srcElement;\n",
" }\n",
" if (targ.nodeType === 3) {\n",
" // defeat Safari bug\n",
" targ = targ.parentNode;\n",
" }\n",
"\n",
" // pageX,Y are the mouse positions relative to the document\n",
" var boundingRect = targ.getBoundingClientRect();\n",
" var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
" var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
" }\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",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * this.ratio;\n",
" var y = canvas_pos.y * this.ratio;\n",
"\n",
" this.send_message(name, {\n",
" x: x,\n",
" y: y,\n",
" button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event),\n",
" });\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",
" // Prevent repeat events\n",
" if (name === 'key_press') {\n",
" if (event.which === this._key) {\n",
" return;\n",
" } else {\n",
" this._key = event.which;\n",
" }\n",
" }\n",
" if (name === 'key_release') {\n",
" this._key = null;\n",
" }\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which !== 17) {\n",
" value += 'ctrl+';\n",
" }\n",
" if (event.altKey && event.which !== 18) {\n",
" value += 'alt+';\n",
" }\n",
" if (event.shiftKey && event.which !== 16) {\n",
" value += 'shift+';\n",
" }\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, 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",
"\n",
"///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
"// prettier-ignore\n",
"var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";/* global mpl */\n",
"\n",
"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 overridden (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 = document.getElementById(id);\n",
" var ws_proxy = comm_websocket_adapter(comm);\n",
"\n",
" function ondownload(figure, _format) {\n",
" window.open(figure.canvas.toDataURL());\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
" fig.cell_info[0].output_area.element.on(\n",
" 'cleared',\n",
" { fig: fig },\n",
" fig._remove_fig_handler\n",
" );\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function (fig, msg) {\n",
" var width = fig.canvas.width / fig.ratio;\n",
" fig.cell_info[0].output_area.element.off(\n",
" 'cleared',\n",
" fig._remove_fig_handler\n",
" );\n",
" fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
" '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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 width = this.canvas.width / this.ratio;\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] =\n",
" '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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 () {\n",
" fig.push_to_output();\n",
" }, 1000);\n",
"};\n",
"\n",
"mpl.figure.prototype._init_toolbar = function () {\n",
" var fig = this;\n",
"\n",
" var toolbar = document.createElement('div');\n",
" toolbar.classList = 'btn-toolbar';\n",
" this.root.appendChild(toolbar);\n",
"\n",
" function on_click_closure(name) {\n",
" return function (_event) {\n",
" return fig.toolbar_button_onclick(name);\n",
" };\n",
" }\n",
"\n",
" function on_mouseover_closure(tooltip) {\n",
" return function (event) {\n",
" if (!event.currentTarget.disabled) {\n",
" return fig.toolbar_button_onmouseover(tooltip);\n",
" }\n",
" };\n",
" }\n",
"\n",
" fig.buttons = {};\n",
" var buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'btn-group';\n",
" var button;\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",
" /* Instead of a spacer, we start a new button group. */\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
" buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'btn-group';\n",
" continue;\n",
" }\n",
"\n",
" button = fig.buttons[name] = document.createElement('button');\n",
" button.classList = 'btn btn-default';\n",
" button.href = '#';\n",
" button.title = name;\n",
" button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
" button.addEventListener('click', on_click_closure(method_name));\n",
" button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
" buttonGroup.appendChild(button);\n",
" }\n",
"\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = document.createElement('span');\n",
" status_bar.classList = 'mpl-message pull-right';\n",
" toolbar.appendChild(status_bar);\n",
" this.message = status_bar;\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = document.createElement('div');\n",
" buttongrp.classList = 'btn-group inline pull-right';\n",
" button = document.createElement('button');\n",
" button.classList = 'btn btn-mini btn-primary';\n",
" button.href = '#';\n",
" button.title = 'Stop Interaction';\n",
" button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
" button.addEventListener('click', function (_evt) {\n",
" fig.handle_close(fig, {});\n",
" });\n",
" button.addEventListener(\n",
" 'mouseover',\n",
" on_mouseover_closure('Stop Interaction')\n",
" );\n",
" buttongrp.appendChild(button);\n",
" var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
" titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
"};\n",
"\n",
"mpl.figure.prototype._remove_fig_handler = function (event) {\n",
" var fig = event.data.fig;\n",
" if (event.target !== this) {\n",
" // Ignore bubbled events from children.\n",
" return;\n",
" }\n",
" fig.close_ws(fig, {});\n",
"};\n",
"\n",
"mpl.figure.prototype._root_extra_style = function (el) {\n",
" el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
"};\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function (el) {\n",
" // this is important to make the div 'focusable\n",
" el.setAttribute('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",
" } else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\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",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which === 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" fig.ondownload(fig, null);\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(\n",
" 'matplotlib',\n",
" mpl.mpl_figure_comm\n",
" );\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
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"output_type": "display_data"
},
{
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6DowgOx9R076+Wwmx2Z2Ws5vkzgARkDLAuLml2MzOTaTi9Vmwdo6pG49gEEzs/DDF+tfh++FhPWN+70x2iwSbGan5fw2iQNgBLQsIG5+OTaTYzO5WGpWGazF/I378MznGbj6ey/b8qs3UlBc0bgGsdQsWtjMTsv5bRIHwAhoWUDc/HJsJsdmcrHS7GBpFX72WrLthZcHfpWF1K0HEBS8m0asNIsmNrPTcn6bxAEwAloWEDe/HJvJsZlcLDQLhUJ4/NP08NA3LHEDVuQUNvndNWKhWbSxmZ2W89sklQNgamoqbr31VnTq1Ak+nw+zZs1q8OsPPPAAfD5fg6tv374NPqeoqAj33HMPzjzzTLRv3x4PP/wwSktLRfehZQFx88uxmRybyTX3ZqFQCG8tzIblD+CSQbOxfndxxF+zuTczgc3stJzfJqkcAJOSkjBkyBDMnDnzuANgv379UFBQEL4OHTrU4HP69euHH/3oR1ixYgWWLl2KSy+9FHfffbfoPrQsIG5+OTaTYzO55tysqqYWz0zPCP+V77sp26LydZtzM1PYzE7L+W2SygHwu443AN5xxx3H/T2bNm2Cz+fD6tWrwx+bM2cO4uLisGfPnkY/tpYFxM0vx2ZybCbXnJsdHf4uHjQbny7fGbWv25ybmcJmdlrOb5Oa7QDYvn17dOzYEd26dcPjjz+OwsLC8K9PmjQJHTp0aPB7ampqcMopp2DmzJmNfmwtC4ibX47N5NhMrrk2O1xejUsHz4blD2DR5v1R/drNtZlJbGan5fw2qVkOgNOmTUNiYiKysrIwa9YsXHnllfjxj3+M2tr694ocMWIEunXrZvtaHTt2xHvvvXfcx6qqqkJJSUn4ys/Ph8/nQ2FhIYLBoGev8vJyJCQkoLy83PV70XKxGZuxWdOvdxdtheUPoO/YxWzmgYvN7FdhYSEHQLdvIFLHGgC/LycnBz6fDwsXLgTQ9AFw2LBhtieX+Hw+xMfHIyEhgRcvXrxi/npragIuHvgNLH8Az01MdP1+ePE61hUfH88B0O0biFRjBkAAOPfcczFhwgQATf8rYH4HMHYuNmMzNmvcVVpRhaVb9+G1pE24852luHhQ/V/9PvrJKlRXV7OZBy42s1/8DmCMDID5+fmIi4tDYmIigP8+CSQ9PT38OfPmzeOTQCiMzeTYTE5rs637juDfqTm4f9JKXDF0ToMXeLb8AfzuvW9RXG7m30lrMzexmZ2W89sklQNgaWkpMjIykJGRAZ/Ph7FjxyIjIwN5eXkoLS3Fv/71Lyxfvhy5ublYuHAhevTogcsuuwxVVVXhr9GvXz9ce+21WLlyJZYtW4bLLruMLwNDYWwmx2ZyGpst2rzfNvD1fHk+npq2Fp+v2oX8Q+VGH19jM7exmZ2W89sklQNgSkrKMX8W74EHHkBFRQVuueUWdOzYEaeddhosy8KAAQOwb9++Bl+jqKgId999N9q2bYt27drhoYce4gtBUxibybGZnMZmg2ZmwfIH0O/NJfhgSQ42F5QgFGrau3o0hcZmbmMzOy3nt0kqB0Cv0LKAuPnl2EyOzeQ0NrtpzGJY/gDmbihw5fE1NnMbm9lpOb9N4gAYAS0LiJtfjs3k2ExOW7NDZdXhv/YtKqt25R60NfMCNrPTcn6bxAEwAloWEDe/HJvJsZmctmZfr9sDyx/AjWMWu3YP2pp5AZvZaTm/TeIAGAEtC4ibX47N5NhMTlOzUCiE299eCssfwOi5W1y7D03NvILN7LSc3yZxAIyAlgXEzS/HZnJsJqep2aIt9c/+7TYkCQdLq07+GwzR1Mwr2MxOy/ltEgfACGhZQNz8cmwmx2ZyWpoFa+vwyzdSYPkDePmbje7ei5JmXsJmdlrOb5M4AEZAywLi5pdjMzk2k9PQLFhbh2c+z4DlD6DH8PkornD3XjU08xo2s9NyfpvEATACWhYQN78cm8mxmZzXm1VU1+Khj1fB8gdw8aDZmLN+r9u35PlmXsRmdlrOb5M4AEZAywLi5pdjMzk2k/N6s2dnrAv/3N/CTftO/hsc4PVmXsRmdlrOb5M4AEZAywLi5pdjMzk2k/Nys7V5h8Kv+bds20G3byfMy828is3stJzfJnEAjICWBcTNL8dmcmwm5+Vmj3yyGpY/gP/7fJ3bt9KAl5t5FZvZaTm/TeIAGAEtC4ibX47N5NhMzsvNbvzPW7556bt/gLebeRWb2Wk5v03iABgBLQuIm1+OzeTYTM6rzQ4cqcLlQ5Ng+QPYtv+I27fTgFebeRmb2Wk5v03iABgBLQuIm1+OzeTYTM6LzUKhEP4WvxaWP4DfjF+CurqQ27fUgBebeR2b2Wk5v03iABgBLQuIm1+OzeTYTM5rzY5UBvHUtPrhr+vAALLyi92+JRuvNdOAzey0nN8mcQCMgJYFxM0vx2ZybCbnpWbrdxfj/722KPyafx8v2+H2LR2Tl5ppwWZ2Ws5vkzgARkDLAuLml2MzOTaT81KzvuNSYfkD6D0qGek7i9y+nePyUjMt2MxOy/ltEgfACGhZQNz8cmwmx2ZybjarqwuhsLQKm/aWIHXrAVwxdA4sfwCbC/jnWXPDZnZazm+TOABGQMsC4uaXYzM5NpMz0ayiuhY7C8uwOrcISVl7MTktF6PnbsFzMzLx0Mer8Nu3luAnIxbg4kGzwy/0/N2roLgyavdiAteZHJvZaTm/TeIAGAEtC4ibX47N5NhMrqnNdhWV492UbRiWuAFPfLYGd01Iwy9Gp+CqF+Yec6g70dVj+Hz0HZeKP3+4Au8s2mbo3zR6uM7k2MxOy/ltEgfACGhZQNz8cmwmx2ZyTW12xzvLTjjUXT40Cf/vtUX43Xvf4rEp6Xg+YT3eWpiNaSvzkLx5H7Lyi1FQXIlgbZ2hfzNzuM7k2MxOy/ltEgfACGhZQNz8cmwmx2ZyTWmWc6A0/EzdUUmbMWnpDny9bg9W5BQi50ApjlQGEQp567X7oonrTI7N7LSc3yZxAIyAlgXEzS/HZnJsJidtFqytw58/XAHLH8D9k1Yavjtv4jqTYzM7Lee3SRwAI6BlAXHzy7GZHJvJNbZZYWkVlm07iCc+WwPLH8AVQ+dg/W7vvUizE7jO5NjMTsv5bRIHwAhoWUDc/HJsJsdmct9vVl1Th017S/DVmnyMmL0Jf/5wBa57ZUGDn+/rOjCAhZv2uXzn7uE6k2MzOy3nt0kcACOgZQFx88uxmRybyQWDQQz9IBF//2wN+o5LxSXHeWmWLgMD+MXoFDz+aTqSN8fu8AdwnTUFm9lpOb9N4gAYAS0LiJtfjs3k2Exu0+5DtmGv+7C5uOv9NLyQsB7xK/OwNu8Qyqpq3L5Vz+A6k2MzOy3nt0kcACOgZQFx88uxmRybyU1cvA2WP4Bfv5mKhZv2Yffhimb9DN5o4DqTYzM7Lee3SRwAI6BlAXHzy7GZHJvJHC6vRu9RC2H5A5iQku327ajBdSbHZnZazm+TOABGQMsC4uaXYzM5Nmu8UCiEBz9aCcsfQM8XvkHRkXK3b0kNrjM5NrPTcn6bxAEwAloWEDe/HJvJsVnjpW0vhOUPoNuQJLzzGZtJcJ3JsZmdlvPbJA6AEdCygLj55dhMjs0a76WvN8LyB/CP6WvZTIjrTI7N7LSc3yZxAIyAlgXEzS/HZnJs1ni/fnMJLH8AiWt3sZkQ15kcm9lpOb9N4gAYAS0LiJtfjs3k2KzxrnlpHix/AOt3FbGZENeZHJvZaTm/TeIAGAEtC4ibX47N5NiscQ6XV4df86+wpJzNhLjO5NjMTsv5bRIHwAhoWUDc/HJsJsdmjTNvQwEsfwC/eiOFzZqAzeTYzE7L+W0SB8AIaFlA3PxybCbHZo3zzOcZsPwBDJ6ZxWZNwGZybGan5fw2iQNgBLQsIG5+OTaTY7OT23+kEpcNSYLlD2Bt3iE2awI2k2MzOy3nt0kcACOgZQFx88uxmRybndj+kkrcPHYxLH8At7+9FKFQiM2agM3k2MxOy/ltEgfACGhZQNz8cmwmx2bHV1BciT6vL4LlD+AnIxZg2/4jANisKdhMjs3stJzfJqkcAFNTU3HrrbeiU6dO8Pl8mDVrVvjXgsEgnnvuOXTv3h1t2rRBp06dcN9992HPnj0NvoZlWfD5fA2uUaNGie5DywLi5pdjMzk2O7ayqhr0+8/r/t3wajLyCv/7tm9sJsdmcmxmp+X8NknlAJiUlIQhQ4Zg5syZtgGwuLgYN910Ez7//HNs2bIFy5cvx09+8hP07NmzwdewLAvDhw9HQUFB+CorKxPdh5YFxM0vx2ZybGYXCoXwyCer69/z9+X52FXU8D1/2UyOzeTYzE7L+W2SygHwu74/AB7LqlWr4PP5kJeXF/6YZVkYN25cRI+tZQFx88uxmRyb2S3bdhCWP4DLhiRhbd4h26+zmRybybGZnZbz26SYGAAXLFiAuLi4Bv9HW5aF8847D2effTauueYavP7666ipqTnh16mqqkJJSUn4ys/Ph8/nQ2FhIYLBoGev8vL6F5stLy93/V60XGzGZtG4HvxoBSx/AENmZrJZlC42Y7NoXIWFhRwA3b6BSJ1sAKysrESPHj1wzz33NPj4mDFjkJKSgszMTLz//vvo0KEDnnnmmRM+1rBhw2w/N+jz+RAfH4+EhARevHjxCl8zZyXg4oHfwPIH8H68+/fDixev/17x8fEcAN2+gUidaAAMBoO47bbbcO211570/+RJkybh1FNPRVVV1XE/h98BjJ2Lzdgs0mtX4RFY/gAuHjQblVXVbBali83YLBoXvwPYjAfAYDCIO++8Ez/84Q9RWFh40q+zYcMG+Hw+bNmypdGPreVnCIJB/vyHFJvJsVlD6TuLYPkD6D0q+bifw2ZybCbHZnZazm+TmuUAeHT4u+qqq3DgwIFGfZ2pU6eiRYsWOHTI/oPax6NlAXHzy7GZHJs1lLhuDyx/AHdNSDvu57CZHJvJsZmdlvPbJJUDYGlpKTIyMpCRkQGfz4exY8ciIyMDeXl5CAaDuP3223HRRRdh3bp1DV7mpbq6GgCQlpaGcePGYd26dcjJycHUqVPRsWNH3H///aL70LKAuPnl2EyOzRp6L2U7LH8A/5iecdzPYTM5NpNjMzst57dJKgfAlJSUYz4Z44EHHkBubu4xf83n8yElJQUAsGbNGlx//fVo3749WrVqhSuvvBIjR4484c//HYuWBcTNL8dmcmzW0MCvMmH5Axg99/g/VsJmcmwmx2Z2Ws5vk1QOgF6hZQFx88uxmRyb/de8DQW4eNBsWP4AApl7j/t5bCbHZnJsZqfl/DaJA2AEtCwgbn45NpNjs3qrc4tw2eAkWP4Anvk8A3V1oeN+LpvJsZkcm9lpOb9N4gAYAS0LiJtfjs3k2AyoqqnFL0enwPIHMGDyatTU1p3w89lMjs3k2MxOy/ltEgfACGhZQNz8cmwmx2bAuynbYPkDuO6VBSipPHkHNpNjMzk2s9NyfpvEATACWhYQN78cm8mxGXD3v5fD8gcwOS23UZ/PZnJsJsdmdlrOb5M4AEZAywLi5pdjMzk2A+6akAbLH8DsrOM/8eO72EyOzeTYzE7L+W0SB8AIaFlA3PxybCYX681CoRB6j0qG5Q9gafbBRv2eWG/WFGwmx2Z2Ws5vkxwbAEOhEL744gv89a9/xe9//3v079+/waWRlgXEzS/HZnKx3mzBxn2w/AFcMmg2yqtrGvV7Yr1ZU7CZHJvZaTm/TXJsAHzqqafQsmVL9OvXDw888AAefPDBBpdGWhYQN78cm8nFarOismo8Mz0Dlj8QfvZvY8Vqs0iwmRyb2Wk5v01ybAA866yzMHv2bKcezhFaFhA3vxybycVis52FZegxfD4sfwBdBgYwLHEDSqsa990/IDabRYrN5NjMTsv5bZJjA2CXLl2wefNmpx7OEVoWEDe/HJvJxWKzz1fvguUPoNfIhVibd0j8+2OxWaTYTI7N7LSc3yY5NgB+8skn+NOf/oSKigqnHtI4LQuIm1+OzeRisdnHy3bA8meuwHMAACAASURBVAfwxNQ1Tfr9sdgsUmwmx2Z2Ws5vkxwbACsqKtC3b1+0bdsW3bt3x7XXXtvg0kjLAuLml2MzuVhsdvSFn//5xbom/f5YbBYpNpNjMzst57dJjg2Ad911F84991w8/vjjGDZsGF588cUGl0ZaFhA3vxybycViszHztsDyB/B8wvom/f5YbBYpNpNjMzst57dJjg2Abdq0wdKlS516OEdoWUDc/HJsJheLzV7+ZiMsfwAjkzY16ffHYrNIsZkcm9lpOb9NcmwAvPzyy5GZmenUwzlCywLi5pdjM7lYbDZoZhYsfwBvLshu0u+PxWaRYjM5NrPTcn6b5NgAGAgE0LdvX+Tm5jr1kMZpWUDc/HJsJheLzf7xn9f/+3dqTpN+fyw2ixSbybGZnZbz2yTHBsAOHTrg9NNPR4sWLdC2bVucddZZDS6NtCwgbn45NpOLxWZ/mbIalj+AT5fvbNLvj8VmkWIzOTaz03J+m+Toy8Cc6NJIywLi5pdjM7lYbPbrN5fA8gcwd0NBk35/LDaLFJvJsZmdlvPbJEcGwGAwiIceegg7duxw4uEco2UBcfPLsZlcrDXbc7gClw6eDcsfwK6i8iZ9jVhrFg1sJsdmdlrOb5Mc+w5gu3btOAC6hJtfjs3kYqnZ/iOV+MXoFFj+APq9uQShUKhJXyeWmkULm8mxmZ2W89skxwbA+++/H2PHjnXq4RyhZQFx88uxmVwsNbvr/TRY/gB6j0rGnsNNf3ejWGoWLWwmx2Z2Ws5vkxwbAF9++WV06NABv//97zFy5EiMHz++waWRlgXEzS/HZnKx0qyuLoQuAwOw/AFsLohs78dKs2hiMzk2s9Nyfpvk2ADYpUuX415du3Z16jaiSssC4uaXYzO5WGm2bf8RWP76AbAyWBvR14qVZtHEZnJsZqfl/DbJsQGwOdKygLj55dhMLhaa7SupxM9eS4blD+B3730b8deLhWbRxmZybGan5fw2yZUBMBQKNfmHpr1EywLi5pdjM7nm3qy6pg6/GV//si8/f30R9h+pjPhrNvdmJrCZHJvZaTm/TXJ0AJw8eTK6d++Oli1bomXLlrj66qsxZcoUJ28hqrQsIG5+OTaTa+7Nxs7fCssfwDUvzWvyy758X3NvZgKbybGZnZbz2yTHBsAxY8agTZs2eO6555CYmIjExEQ8++yzaNOmjdpnB2tZQNz8cmwm15ybJWXtxSWD6l/z75vMPVH7us25mSlsJsdmdlrOb5McfRLI5MmTbR//5JNP0KVLF6duI6q0LCBufjk2k2uuzT5etiP8rN9/TM+I6o+vNNdmJrGZHJvZaTm/TXJsAGzZsiW2bdtm+3h2djZatmzp1G1ElZYFxM0vx2ZyzbHZ7Ky94Wf8DpmVhdq66P7scnNsZhqbybGZnZbz2yTHBsCrrroKI0aMsH385ZdfRvfu3Z26jajSsoC4+eXYTK45Nvvzhytg+QN4PmG9kSeuNcdmprGZHJvZaTm/TXJsAPzyyy9xyimnoG/fvhg+fDiGDx+Ovn374tRTT8XMmTOduo2o0rKAuPnl2EyuuTUrKqvGxf/5ub/cg2VGHqO5NXMCm8mxmZ2W89skR58FnJ6ejnvvvRc9evRAjx49cO+992Lt2rVO3kJUaVlA3PxybCbX3JolrtsDyx9A33Gpxh6juTVzApvJsZmdlvPbJKMD4DPPPIOysvr/ck5NTUVNTY3Jh3OclgXEzS/HZnLNrZn/y0xY/gBGzN5k7DGaWzMnsJkcm9lpOb9NMjoAnnrqqdi3bx8AoEWLFti/f7/Jh3OclgXEzS/HZnLNrdnRn//7Mj3f2GM0t2ZOYDM5NrPTcn6bZHQAvPTSSzF48GAsXrwYcXFxSEhIQGpq6jEvjbQsIG5+OTaTa27Njr7rx6It5v7Dtbk1cwKbybGZnZbz2ySjA+CsWbNw3nnnIS4uDi1atEBcXNwxrxYtWpi8DWO0LCBufjk2k2tuzXqNXAjLH8C6XYeNPUZza+YENpNjMzst57dJjjwJpLS0FHFxccjOzkZxcfExL420LCBufjk2k2tuzS4fmgTLH4ja274dS3Nr5gQ2k2MzOy3nt0mOPQt48eLFfBKIS7j55dhMrjk1q6iuDb8AdGmVuT+3mlMzp7CZHJvZaTm/TXL0ZWCiJTU1Fbfeeis6deoEn8+HWbNmNfj1UCiE559/Hueffz5atWqFG2+8EdnZ2Q0+p6ioCPfccw/OPPNMtG/fHg8//DBKS0tF96FlAXHzy7GZXHNqtm1/KSx/AJcNTjLyAtBHNadmTmEzOTaz03J+m6RyAExKSsKQIUMwc+bMYw6Ar776Ktq3b4+EhARkZmbi9ttvR9euXVFZWRn+nH79+uFHP/oRVqxYgaVLl+LSSy/F3XffLboPLQuIm1+OzeSaU7PX5myG5Q/gng+WG32c5tTMKWwmx2Z2Ws5vk1QOgN/1/QEwFArh/PPPx+jRo8MfKy4uRsuWLTFt2jQAwKZNm+Dz+bB69erw58yZMwdxcXHYs2dPox9bywLi5pdjM7nm0iwUCuHHryyA5Q9gzvq9Rh+ruTRzEpvJsZmdlvPbpGY3AObk5MDn8yEjI6PB5/385z/HU089BQCYNGkSOnTo0ODXa2pqcMopp4jelk7LAuLml2MzuebSbF9JJSx/ABcPmo3KYK3Rx2ouzZzEZnJsZqfl/Dap2Q2A3377LXw+H/bubfhf7nfddRf+8Ic/AABGjBiBbt262b5Wx44d8d577x33saqqqlBSUhK+8vPz4fP5UFhYiGAw6NmrvLwcCQkJKC8vd/1etFxsFrvNFmyofwu4X45OYTMPXmzGZtG4CgsLOQCa/OL9+/dv9NVUTg6Aw4YNg8/ns13x8fFISEjgxYtXM7geGv81LH8Af3jja9fvhRcvXmau+Ph4DoAmv/iDDz7Y6KupnPwrYH4HMHYuNovdZr8ZnwrLH8CM1Xls5sGLzdgsGhe/A9gM/wr46JNA3njjjfDHSkpKjvkkkPT09PDnzJs3j08CoTA2k2sOzQ6VVaPLwPrX/9t/pPLkvyFCzaGZ09hMjs3stJzfJqkcAEtLS5GRkYGMjAz4fD6MHTsWGRkZyMvLA1D/MjAdOnRAYmIisrKycMcddxzzZWCuvfZarFy5EsuWLcNll13Gl4GhMDaTaw7NpizfCcsfwM1jFzvyeM2hmdPYTI7N7LSc3yY5OgDOmDEDd911F66//npce+21DS6JlJSUY/4s3gMPPADgvy8Efd5556Fly5a48cYbsXXr1gZfo6ioCHfffTfatm2Ldu3a4aGHHuILQVMYm8lpb1ZcEUSP4fNh+QOYtHSHI4+pvZkb2EyOzey0nN8mOTYAjh8/Hm3btsXf/vY3nH766Xjsscdw0003oX379hg8eLBTtxFVWhYQN78cm8lpb/bv1BxY/gB+9UYKgrV1jjym9mZuYDM5NrPTcn6b5NgAePnllyM+Ph4A0LZtW+Tk5AAAnn/+eTz55JNO3UZUaVlA3PxybCanvdljU9Jh+QOYsHi7Y4+pvZkb2EyOzey0nN8mOTYAtm7dGjt37gRQ/3Ir69atAwBkZ2fj7LPPduo2okrLAuLml2MzOe3Neo9KhuUPIG17oWOPqb2ZG9hMjs3stJzfJjk2AHbt2hVr164FAPTs2RMTJkwAUP/s27POOsup24gqLQuIm1+OzeQ0NztYWgXLH0CXgQGUVtU49riam7mFzeTYzE7L+W2SYwPgI488ghdffBEA8M4776B169a46aab0KFDBzz88MNO3UZUaVlA3PxybCanudmizfth+QO4cYwzz/49SnMzt7CZHJvZaTm/TXJsAKyrq0NNzX//y3ratGn4+9//jrfeegvV1dVO3UZUaVlA3PxybCanudm4BVth+QN4ZnrGyT85ijQ3cwubybGZnZbz2ySVrwPoFVoWEDe/HJvJaW529AkgTr38y1Gam7mFzeTYzE7L+W0SB8AIaFlA3PxybCanudmfJi6H5Q8gcV3j3wkoGjQ3cwubybGZnZbz2yQOgBHQsoC4+eXYTE5zs9vfXgrLH8DCTfscfVzNzdzCZnJsZqfl/DaJA2AEtCwgbn45NpPT3OzGMYsdfwkYQHczt7CZHJvZaTm/TeIAGAEtC4ibX47N5DQ36/nyAlj+ANbvLnb0cTU3cwubybGZnZbz2yTHBsCKigqUl5eH/3nnzp0YN24c5s2b59QtRJ2WBcTNL8dmclqbFZcHYfkDsPwBHKl09t61NnMTm8mxmZ2W89skxwbAm2++Ge+//z4A4PDhwzjvvPNw0UUXoVWrVnjvvfecuo2o0rKAuPnl2ExOa7OMXYdh+QP4yYgFjj+21mZuYjM5NrPTcn6b5NgAeM4552DDhg0AgA8++AA//OEPUVdXhy+++AJXXHGFU7cRVVoWEDe/HJvJaW22eOsBWP4A+r25xPHH1trMTWwmx2Z2Ws5vkxx9L+C8vDwAwF133RV+V5Bdu3ahdevWTt1GVGlZQNz8cmwmp7VZIHMvLH8Ad72f5vhja23mJjaTYzM7Lee3SY4NgFdffTXGjx+PXbt2oV27dkhLq//DNj09Heedd55TtxFVWhYQN78cm8lpbTZ9VR4sfwAPf7zK8cfW2sxNbCbHZnZazm+THBsAZ8yYgdNOOw0tWrTAzTffHP74yJEj0a9fP6duI6q0LCBufjk2k9PabOx8d94GDtDbzE1sJsdmdlrOb5McfRmYgoICrF27FnV1deGPrVy5Eps3b3byNqJGywLi5pdjMzmtzfq/uwyWP4D4lXmOP7bWZm5iMzk2s9NyfpvE1wGMgJYFxM0vx2ZyGpvlHixDl4H1LwGz53CF44+vsZnb2EyOzey0nN8mOToArl69Gs8++yz++Mc/on///g0ujbQsIG5+OTaT09hsyKwsWP4AHnLh5/8Anc3cxmZybGan5fw2ybEBcNq0aTjttNNw66234vTTT8ett96Kbt26oX379njwwQeduo2o0rKAuPnl2ExOW7PC0ip0G5LkylvAHaWtmRewmRyb2Wk5v01y9FnA77zzDgCgbdu2yMnJQSgUwoABA/DCCy84dRtRpWUBcfPLsZmctmbjFtQ/+eO2t5ciFAq5cg/amnkBm8mxmZ2W89skxwbANm3aIDc3FwBw9tlnIysrCwCwadMmnH/++U7dRlRpWUDc/HJsJqet2W/fWgLLH8BXa/JduwdtzbyAzeTYzE7L+W2SYwPghRdeGB76rr76asTHxwMA0tLS0K5dO6duI6q0LCBufjk2k9PW7IZXk2H5A1iTd8i1e9DWzAvYTI7N7LSc3yY5NgDefffdGDNmDABg+PDh6NixIx599FFYlsUngRjGzS/HZnKamlUGa3HZf37+b2dhmWv3oamZV7CZHJvZaTm/TXJsACwqKsKePXsAAHV1dRg1ahRuu+02/N///R8OHXLvv8AjoWUBcfPLsZmcpmZLsw/C8gfwkxELXPv5P0BXM69gMzk2s9NyfpvE1wGMgJYFxM0vx2ZympqNnL0Jlj+Af36xztX70NTMK9hMjs3stJzfJjk2ALZo0QL79++3fbywsBAtWrRw6jaiSssC4uaXYzM5Tc36vVn/BJCEjN2u3oemZl7BZnJsZqfl/DbJsQEwLi7umAPgnj170KpVK6duI6q0LCBufjk2k9PS7MCRKlj++nf/KCytcvVetDTzEjaTYzM7Lee3ScYHwPHjx2P8+PFo0aIFRowYEf7n8ePHY+zYsbjzzjtxzTXXmL4NI7QsIG5+OTaT09Csri6Et5OzYfkD+O1bS9y+HRXNvIbN5NjMTsv5bZLxAbBLly7o0qUL4uLi8IMf/CD8z126dEG3bt1wyy23YMWKFaZvwwgtC4ibX47N5LzebNm2g7j1raXh7/69tTDb7VvyfDMvYjM5NrPTcn6b5NhfAf/iF79Q+2zf49GygLj55dhMzqvNqmpq8fDHq8KD3/88PwdvLshGsLbO7VvzbDMvYzM5NrPTcn6b5NqzgGtra5GRkaF6KNSygLj55dhMzqvNVuUWwfIHcMmg2RiWuAEHXf65v+/yajMvYzM5NrPTcn6b5NgA+PTTT+PDDz8EUD/89e7dG3FxcTjjjDOQkpLi1G1ElZYFxM0vx2ZyXm2WkLEblj+AP0xIc/tWbLzazMvYTI7N7LSc3yY5NgBecMEFWL16NQBg1qxZuOCCC7B161YMHToUvXv3duo2okrLAuLml2MzOa82e23OZk+85t+xeLWZl7GZHJvZaTm/TXJsAGzZsiXy8+vfdH3AgAF4+umnAQA7duzAmWee6dRtRJWWBcTNL8dmcl5tdvvb9U/8+DI93+1bsfFqMy9jMzk2s9Nyfpvk2ADYuXNnzJs3D7W1tfjBD36AQCAAANiwYQM6dOjg1G1ElZYFxM0vx2ZyXmy253BF+Mkf+0oq3b4dGy828zo2k2MzOy3nt0mODYDDhg1D+/btccUVV6Bz586oqqr/QexJkybhpz/9qVO3EVVaFhA3vxybyXmx2YTF22H5A7jLgz//B3izmdexmRyb2Wk5v01y9FnAM2bMwNixY8N/FQwAn3zyCRISEpy8jajRsoC4+eXYTM6LzR6dvBqWP4APluS4fSvH5MVmXsdmcmxmp+X8NsmVl4GprPTeX8U0hZYFxM0vx2ZyXmz26/+852/y5n1u38oxebGZ17GZHJvZaTm/TXJsAKytrcXw4cNxwQUX4JRTTkFOTv1/kQ8dOjT88jDRZFkWfD6f7XriiScAAH369LH92mOPPSZ6DC0LiJtfjs3kvNjshy/Og+UPYEvBEbdv5Zi82Mzr2EyOzey0nN8mOTYAvvTSS7j44osxdepUtG7dOjwATp8+3cjPAB44cAAFBQXha8GCBfD5fOHXHOzTpw8GDBjQ4HOkC0HLAuLml2MzOa81K62qCT8BpLSqxu3bOSavNdOAzeTYzE7L+W2SYwPgJZdcgoULFwIA2rZtGx4AN2/e7MizgJ9++mlccsklCIVCAOoHwKMvRdNUWhYQN78cm8l5rdnOwjJY/gCufH6O27dyXF5rpgGbybGZnZbz2yTHBsBWrVph586dABoOgBs3bsQZZ5xh9LGrq6txzjnnYMSIEeGP9enTB+eeey7OOeccXHXVVRg4cCDKy8tP+HWqqqpQUlISvvLz8+Hz+VBYWIhgMOjZq7y8HAkJCSgvL3f9XrRcbKa/Wdau+reA6zF8vuv3oqWZhovN2CwaV2FhIQdApx6oR48e+PTTTwE0HABfeukl/OxnPzP62J9//jlOOeUU7NmzJ/yxiRMnYu7cucjKysLUqVNx4YUXon///if8OsOGDTvmzxXGx8cjISGBFy9eHrr+8X4iLH8A/2/4N67fCy9evLx1xcfHcwB06oESEhLQvn17vPrqq2jTpg1Gjx6NRx99FKeffjrmz59v9LFvueUW3HrrrSf8nOTkZPh8Pmzfvv24n8PvAMbOxWa6m1VWVaPP64tg+QP4ZFmO6/ejoZmWi83YLBoXvwPo8MvALFmyBDfddBM6duyI1q1b44YbbsC8efOMPubOnTvRokULJCSc+LUGy8rK4PP5MHfu3EZ/bS0/QxAM8uc/pNhMzkvNvlqTD8sfwNXD5qLMo08AAbzVTAs2k2MzOy3nt0muvA6gk4YNG4bzzz8fNTUnPgSWLVsGn8+HzMzMRn9tLQuIm1+OzeS80qyguBK9RyXD8gfwbso2V+/lZLzSTBM2k2MzOy3nt0nNegCsq6tD586d4ff7G3x8+/btGD58ONLT05Gbm4vExERcfPHF+PnPfy76+loWEDe/HJvJeaFZ9r4j6DVyISx/AL1HJaO82rvf/QO80UwbNpNjMzst57dJjg2AcXFxaNGixXEvE+bNmwefz4etW7c2+PiuXbvw85//HGeffTZatmyJSy+9FM8++yxfB5DC2EzO7WaHyqrDL/z8yzdSsKvoxM/q9wK3m2nEZnJsZqfl/DbJ0SeBfPeaMWMGBg8ejAsvvNDIO4E4QcsC4uaXYzM5t5st2rwflj+An45ciENl1a7cg5TbzTRiMzk2s9Nyfpvk+l8Bf/bZZ7j99tvdvo0m0bKAuPnl2EzO7WZJWXth+QP43/e/deXxm8LtZhqxmRyb2Wk5v01yfQDMyckx/kLQpmhZQNz8cmwm53azUUmbYfkDeGraWlcevyncbqYRm8mxmZ2W89skVwfAiooKPP300+jWrZubt9FkWhYQN78cm8m53ezGMYth+QP4et2ek3+yR7jdTCM2k2MzOy3nt0mODYAdOnTAWWedFb46dOiAU045BWeeeSYSExOduo2o0rKAuPnl2EzO7WaXD02C5Q8gr9D7T/44yu1mGrGZHJvZaTm/TXJsAPzkk08aXFOmTMGcOXNw6NAhp24h6rQsIG5+OTaTc7NZbV0Ilw6eDcsfwJ7DFY4/flNxncmxmRyb2Wk5v01y/WcANdOygLj55dhMzs1mCRm7YfkD+OGL81BVU+v44zcV15kcm8mxmZ2W89skRwfAQ4cOYfTo0Xj44Yfx8MMP44033kBRUZGTtxBVWhYQN78cm8m51awyWItfvpECyx/AWwuzHX3sSHGdybGZHJvZaTm/TXJsAExNTUW7du3wgx/8AP3790f//v3RuXNntGvXDqmpqU7dRlRpWUDc/HJsJudWsxe/3gDLH0DPl+ejpFLX/19cZ3JsJsdmdlrOb5McGwC7d++OAQMGoLb2v389U1tbi7/85S/o3r27U7cRVVoWEDe/HJvJOdXswJEqfJO5B0NnrcfNY+uf+Wv5A1i0Zb/RxzWB60yOzeTYzE7L+W2SYwNgq1atsGXLFtvHt2zZglatWjl1G1GlZQFx88uxmZypZgeOVCFx3R4MnpmFX/3nr3q/f42Zv/XkX8iDuM7k2EyOzey0nN8mOTYA9u7dG7NmzbJ9fNasWbj++uuduo2o0rKAuPnl2EzORLMvVu8KP7v3u1e/N5dgWOIGzFm/F0VK3vbtWLjO5NhMjs3stJzfJjk2AE6fPh2dO3fG6NGjsXTpUixduhSjR49Gly5dMH36dGRmZoYvLbQsIG5+OTaTi2azuroQPly6Izzw9R2Xipe+3oh5GwpwuFzvwPd9XGdybCbHZnZazm+THBsA4+LiTni1aNEi/L9aaFlA3PxybCbX1GbFFUGs3FGEyWm5GPhVFu58dxmufH5OePh7+ZuNCIVChu7aXVxncmwmx2Z2Ws5vkxwbAHfu3NnoSwstC4ibX47N5BrTLK+wHF+v24PX527Gwx+vQu9Rycf8mT7LH8DlQ5PwzqJtzXb4A7jOmoLN5NjMTsv5bRJfCDoCWhYQN78cm8mdrFnKlv3HHfZ6j0rGwx+vwutzN+PrdXuwbf8R1NTWOfxv4DyuMzk2k2MzOy3nt0mOvhVcIBAI//Ozzz6L9u3bo1evXqq+6/ddWhYQN78cm8mdrNmbC7Jh+QPoNXIhBn6VhclpuVi5owjFFbHbmOtMjs3k2MxOy/ltkmMDYLdu3ZCcnAwASEtLQ+vWrTFx4kTcdttt6N+/v1O3EVVaFhA3vxybyZ2s2T+/WAfLH8DoufaXg4pVXGdybCbHZnZazm+THBsAW7dujby8PADAc889h/vuuw8AsGHDBpx77rlO3UZUaVlA3PxybCZ3oma7ispx2eAkWP4Alm076MLdeRPXmRybybGZnZbz2yTHBsCOHTti7dq1AIBrrrkGU6ZMAQBs374dZ5xxhlO3EVVaFhA3vxybyR2vWXVNHf784QpY/gDu+WB5s35ShxTXmRybybGZnZbz2yTHBsB77rkHPXr0wCOPPII2bdqgsLAQAJCYmIirrrrKqduIKi0LiJtfjs3kjtWsri6Ep6atheUP4Iqhc7Bxj7f3itO4zuTYTI7N7LSc3yY5NgAePnwYTz75JG6//XbMmTMn/PEXXngBr7zyilO3EVVaFhA3vxybyX2/WW1dCM/NyITlD+CSQbNVvlevaVxncmwmx2Z2Ws5vk/gyMBHQsoC4+eXYTO67zYK1dfh7fP13/roODCAhY7fbt+dJXGdybCbHZnZazm+THB0AlyxZgnvvvRe9evXC7t31B8KUKVOwdOlSJ28jarQsIG5+OTaT+26z0XO3hL/zNztrr9u35llcZ3JsJsdmdlrOb5McGwC//PJLtG7dGo8++ihatmyJnJwcAMDbb7+NX//6107dRlRpWUDc/HJsJne0WcHhsvBbufE7fyfGdSbHZnJsZqfl/DbJsQHwmmuuweTJkwEAbdu2DQ+Aa9euxXnnnefUbUSVlgXEzS/HZnJHm721oP67f7e9vZTP+D0JrjM5NpNjMzst57dJjr4OYG5uLoCGA2BOTg5atmzp1G1ElZYFxM0vx2ZyR5v98/MMWP4A3k7OdvuWPI/rTI7N5NjMTsv5bZJjA2DXrl2xYMECAA0HwMmTJ+PKK6906jaiSssC4uaXYzO5o83u/WA5LH8A01bmuX1Lnsd1Jsdmcmxmp+X8NsmxAXDkyJH4n//5H6xYsQJnnnkmli5diqlTp6Jjx4546623nLqNqNKygLj55dhMLhgM4suZCeGf/1u/u9jtW/I8rjM5NpNjMzst57dJjg2AoVAIr7zyCs444wzExcUhLi4OrVq1wtChQ526hajTsoC4+eXYTC4YDOKNKQmw/AFc98oC/vxfI3CdybGZHJvZaTm/TXL8dQCrq6uxceNGrFy5EqWlpQCAiooKp28jKrQsIG5+OTaTCwaDGDYpEZY/gLsmpLl9OypwncmxmRyb2Wk5v01y9YWgq6qqMGbMGD4L2DBufjk2kwsGg/BPrB8AH/54ldu3owLXmRybybGZnZbz2yTjA2BVVRUGDhyInj17olevXpg1axYA4KOPPkKnTp1w0UUX4dVXXzV9G0ZoWUDc/HJsJhcMBvH0e/UD4FPT1rp9OypwncmxmRyb2Wk5v00yPgA+99xzaN++IrF98wAAIABJREFUPX7/+9+jU6dOOPXUUzFgwABcffXVmDZtGmpra03fgjFaFhA3vxybyQWDQTz+dv0AOPCrLLdvRwWuMzk2k2MzOy3nt0nGB8CuXbsiMTERALB+/XrExcXhoYceahY/IK5lAXHzy7GZXDAYxIC3voblD+CFhPVu344KXGdybCbHZnZazm+TjA+Ap512Wvh9fwGgVatWyMpqHt8d0LKAuPnl2EwuGAziwTfrB8Dh32x0+3ZU4DqTYzM5NrPTcn6bZHwAbNGiBQ4cOBD+57Zt22LHjh2mH9YRWhYQN78cm8kFg0HcPLJ+APxsBV8EujG4zuTYTI7N7LSc3yYZHwDj4uLwm9/8Bv3790f//v1x6qmn4pZbbgn/89FLIy0LiJtfjs3kqqurccXgb/gi0AJcZ3JsJsdmdlrOb5OMD4APPvhgoy6NtCwgbn45NpPbe6gUlj+ALgMDqAzqfXKXk7jO5NhMjs3stJzfJrn6OoAmDRs2DD6fr8F1+eWXh3+9srISTzzxBM4++2ycccYZ+N3vfod9+/aJHkPLAuLml2MzubRt+2H5A7hh1EK3b0UNrjM5NpNjMzst57dJzXoAvOqqq1BQUBC+Dh48GP71xx9/HD/4wQ+QnJyM9PR0/PSnP0Xv3r1Fj6FlAXHzy7GZ3GfLc2H5A7j3g+Vu34oaXGdybCbHZnZazm+TmvUA+KMf/eiYv1ZcXIzTTjsNM2bMCH9s8+bN8Pl8WL688YeXlgXEzS/HZnIjAhtg+QMYMjPT7VtRg+tMjs3k2MxOy/ltUrMeANu0aYNOnTqha9euuOeee5CXV//MxOTkZPh8Phw+fLjB7+ncuTPGjh3b6MfQsoC4+eXYTG7A5FWw/AF8kLrN7VtRg+tMjs3k2MxOy/ltUrMdAJOSkvDFF18gMzMTc+fORa9evdC5c2ccOXIEn332GU4//XTb7/nxj3+M55577rhfs6qqCiUlJeErPz8fPp8PhYWFCAaDnr3Ky8uRkJCA8vJy1+9Fy8Vmwl6VVfjxK/Nh+QOYl7nL9fvRcnGdsRmbuXMVFhZyAHT7Bpxy+PBhtGvXDh9++GGTB8BjPbHE5/MhPj4eCQkJvHg1+2vmrATEf5mAf09LwLhPE/DKx4kY9O9E3DO2/vX/ug/9BjNmun+fvHjx4nWiKz4+ngOg2zfgpOuuuw4DBw5s8l8B8zuAsXPFQrPq6moUHSnH9n3FWL3jIOZv2IPPV+7EhJRsjJq9Ec/NyMCAyavwv+9/ixvfSEGP4fNx8aDZsPyB415PvpvYrJtxnbl/sRmbRePidwBjaAAsLS3FWWedhfHjx4efBPLll1+Gf33Lli18EgiFNZdmoVAIU9JyMXhmFv46NR1/nJiGW8am4rpXFuCSkwxzJ7quemEufvZaMm5/eyke+GglnpmegdFzNmHGTP3NnNRc1pmT2EyOzey0nN8mNdsB8J///CcWL16M3NxcfPvtt7jppptw7rnnht+W7vHHH0fnzp2xaNEipKeno1evXujVq5foMbQsIG5+uebSbOba/JMOc1c+Pwe9RyXj1reW4r5JK/H0tLUYlrgB4xdmY8rynQhk7sW32w9ic0EJ9pdUoqrm2C/y3FyaOYnN5NhMjs3stJzfJjXbAfCPf/wjOnXqhNNPPx0XXngh/vjHP2L79u3hXz/6QtBnnXUW2rRpg/79+6OgoED0GFoWEDe/XHNoVlFdi5+OXFj/V7OfrcHktFx8vW4Plm07iA17irG3uCKq79jRHJo5jc3k2EyOzey0nN8mNdsB0AlaFhA3v5z2ZqFQCH+LXwvLH0DvUcmOvDWb9mZuYDM5NpNjMzst57dJHAAjoGUBcfPLaW1WU1uHjF2HMWhmFix/AJcMmo3lOYWOPLbWZm5iMzk2k2MzOy3nt0kcACOgZQFx88tpaRYKhbCl4Ag+WrYDj3yyGt2HzW3w833TV+U5di9amnkJm8mxmRyb2Wk5v03iABgBLQuIm1/Oi80qg7XYUnAESVl78c6ibfhb/Fr0fHmB7UkdVw+biwGTV+ObzD2O3p8Xm3kdm8mxmRyb2Wk5v03iABgBLQuIm1/OrWZ1dSHkHyrHkuwD+OTbXAxL3IA/f7gCN7yajC4Dj/0s3suHJuHPH67AeynbkZl/GLV1IUfv+SiuMzk2k2MzOTaz03J+m8QBMAJaFhA3v1xTm9XWhVBaVYP9JZXIPViGDXuKsSq3CIu3HkBS1l58mZ6PKWm5eH/xdoyZvxUvf7MRg2Zm4fFP09F3XCq6DUk64Uu2dB82F7e/swzPTM/A28nZWJ5TeNyXZXEa15kcm8mxmRyb2Wk5v03iABgBLQuIm9+uprYOJZVB7CupRM6BUqzfXYyVO4qwaMt+zM7ai2krcvHPCYl4e+FWjJm3BcO/2YiBX2XiqWlr8cgnq3HPB8txxzvLcMvYVNzwajKuHT4flw898fDW2OvSwbNx45jFeHTyaoxM2oTpq/KwKrcIB0urEAq58929xuA6k2MzOTaTYzM7Lee3SRwAI6BlAcXa5i+uCGLQzCw88skq/Gnictz+zjLcNGYxeo9KxjUvzTvpd9micXUdWP9uGT9+ZQF+MToFv35zCf73/W9x36SVePzTdDzzeQaGzlqPkUmb8OaCbHy0bAcWbd6PnYVlqKmtczthk8TaOosGNpNjMzk2s9NyfpvEATACWhZQrG3+V+dsbvSgdvGg2eg+bC5+MmIBfjk6Bb8ZvwR3vZ+G+z9cgTte+xrPTF+L5xPWY1TSZoxfmI0PluQgfmUeEjJ2Y/7GfVi27SDW5h3C1n1HsKuoHEVl1agM1nr6O3WmxNo6iwY2k2MzOTaz03J+m8QBMAJaFlCsbP5DZdVIyNiNH744D5Y/gFfnbEbiuj1YsHEfvt1+EBm7DiN73xHkHyrHobJqVNUcf1CLlWbRxGZybCbHZnJsZqfl/DaJA2AEtCyg5rr56+pCyMw/jPELs3Hnu8vQ9TvPkv3F6JSIng3bXJuZxGZybCbHZnJsZqfl/DaJA2AEtCyg5rb5y6tr8ELCevR8eb7tr3T7jkvFyKRN2FtcEdFjNLdmTmAzOTaTYzM5NrPTcn6bxAEwAloWUHPa/LsPV+DXby4JD3xXvTAXf5myGvEr8yIe+r6rOTVzCpvJsZkcm8mxmZ2W89skDoAR0LKAtG/+ymAtvt1+EGPmbw1/16/ny/OxcNM+VNeYecas9mZuYDM5NpNjMzk2s9NyfpvEATACWhaQts3/3YHvrglpuGxww5dt6ffmEuw+HL3v9h2LtmZewGZybCbHZnJsZqfl/DaJA2AEtCwgLZt/b3EF7pu00jbwWf4AfjJiAZ6athbTVuahotr8O19oaeYlbCbHZnJsJsdmdlrOb5M4AEZAywLSsvn/MmW1beCLX5mHHQfLHH9dPS3NvITN5NhMjs3k2MxOy/ltEgfACGhZQBo2f2b+4fA7aKzOLXL9hZQ1NPMaNpNjMzk2k2MzOy3nt0kcACOgZQFp2PxvJ2fD8gfw2JR0t28FgI5mXsNmcmwmx2ZybGan5fw2iQNgBLQsIA2b/+jbt7309Ua3bwWAjmZew2ZybCbHZnJsZqfl/DaJA2AEtCwgr2/+UCiEO99dBssfwITF292+HQDeb+ZFbCbHZnJsJsdmdlrOb5M4AEZAywLy+uZfuaMIlj+AywYn4cCRKrdvB4D3m3kRm8mxmRybybGZnZbz2yQOgBHQsoC8vPmrampx05jFsPwBDPwqy+3bCfNyM69iMzk2k2MzOTaz03J+m8QBMAJaFpCXN/+YeVvC7+xRVFbt9u2EebmZV7GZHJvJsZkcm9lpOb9N4gAYAS0LyMubv/eoZFj+ABIydrt9Kw14uZlXsZkcm8mxmRyb2Wk5v03iABgBLQvIq5s/bXshLH8AXQYGcKTSW/fm1WZexmZybCbHZnJsZqfl/DaJA2AEtCwgr23+Q2XVeG5GZvhdP/40cbnbt2TjtWYasJkcm8mxmRyb2Wk5v03iABgBLQvIS5t/2/5SXDt8fnj483+ZicPl3vnZv6O81EwLNpNjMzk2k2MzOy3nt0kcACOgZQF5afP/3+frYPkD+NUbKVidW+T27RyXl5ppwWZybCbHZnJsZqfl/DaJA2AEtCwgr2z+ksogLh+aBMsfwJq8Q67ey8l4pZkmbCbHZnJsJsdmdlrOb5M4AEZAywLyyuZfvPUALH8AP399EUKhkKv3cjJeaaYJm8mxmRybybGZnZbz2yQOgBHQsoC8svknLN4Oyx/AE5+tcfU+GsMrzTRhMzk2k2MzOTaz03J+m8QBMAJaFpBXNv/QWeth+QMYPXeLq/fRGF5ppgmbybGZHJvJsZmdlvPbJA6AEdCygNze/KFQCKtyi8Jv+fbRsh2u3IeE2800YjM5NpNjMzk2s9NyfpvEATACWhaQW5u/prYOgcy9uP2dZeGXfbl40Gyk7/Tus3+P4h+Ycmwmx2ZybCbHZnZazm+TOABGQMsCcnLzl1QGkZS1F8/OWIcfv7IgPPhdNiQJA7/KxLb9R4zfQzTwD0w5NpNjMzk2k2MzOy3nt0kcACOgZQGZ3PyhUAib9pbgvZTt+MOENFwyaHZ46LP8AVw7fD7GzN+Kg6VVUX9sk/gHphybybGZHJvJsZmdlvPbJA6AEdCygKK5+SuDtUjfWYSJqdvxlymr0fPlBQ0GPssfwC/fSMFLX2/EkuwDqKqpjcK/gfP4B6Ycm8mxmRybybGZnZbz2yQOgBHQsoAi2fwHjlRhzvoCjJi9Cb9771tcNjjJNvBdPjQJD328ClPScrGrqNzAv4Hz+AemHJvJsZkcm8mxmZ2W89ukZjsAjhw5Etdddx3atm2Ljh074o477sCWLQ1ffqRPnz7w+XwNrscee6zRj6FlATVl85dX1+CZzzNsw57lD6Dny/MxYPJqTEzdjvSdRagM6vwu34nwD0w5NpNjMzk2k2MzOy3nt0nNdgDs27cvPv74Y2zYsAHr1q3Db37zG3Tu3BllZWXhz+nTpw8GDBiAgoKC8CVZDFoWkHTzb9t/JPySLV0GBtB3XCoGzczCV2vysbOwzPPv4hEN/ANTjs3k2EyOzeTYzE7L+W1Ssx0Av+/AgQPw+XxITU0Nf6xPnz54+umnm/w1tSygxmz+Q2XV+GL1LjzyyarwX/P++JUFWLnD+y/ZYgL/wJRjMzk2k2MzOTaz03J+mxQzA+C2bdvg8/mwfv368Mf69OmDc889F+eccw6uuuoqDBw4EOXljf8ZNi0L6Hibf19JJaak5eKeD5bj4u89e/feD1bgwBFdz9yNJv6BKcdmcmwmx2ZybGan5fw2KSYGwLq6Ovz2t7/FDTfc0ODjEydOxNy5c5GVlYWpU6fiwgsvRP/+/Y/7daqqqlBSUhK+8vPz4fP5UFhYiGAw6NmrvLwcCQkJKC8vx7Z9xXhvUTbufGep7Wf7+o5LxZh5m7EhvwjV1dWu37dXmrl9L1ouNmMzNvPmxWb2q7CwkAOg2zfghMcffxyWZSE/P/+En5ecnAyfz4ft27cf89eHDRtme9KIz+dDfHw8EhISPHu9F5+Ax99ORO+XvrENfX1e/gZPvZeID6e7f5+8ePHixYuXE1d8fDwHQLdvwLQnn3wSF110EXbsOPn7z5aVlcHn82Hu3LnH/HVt3wEsLqvAU/FrGgx8Fw+ajbsnpuHjpduRX3jE9Xv06sX/YmYzNvPmxWZsFo2L3wFsxgNgKBTCk08+iQsuuADZ2dmN+j3Lli2Dz+dDZmZmoz7fyz9DkHuwDH3HpYaHvt+++jWmrcjFobJqt29NhWCQPzMjxWZybCbHZnJsZufl89spzXYA/Otf/4r27dtj8eL/397dB0V133scZ4sjY2sSG6xtgWQjaszDlYjSpZrWaCxjx5qkCSYVFBNDZFRsSyclG6LGqpMarzU2SZN0xsSQ3IZom1TRvRIxTjSpIooPgAtqCCIPSmAlIIq7POzn/sFldT0E+e5xOWc5n9fMmUaWpWfe8/txvsA+7PF6mZeWlhYAQFlZGVauXImCggKcPn0a2dnZiIyMxKRJk3r9/6HXBeRodmLsip3//5p9u/CfU7Xc/EL8hinHZnJsJsdmcmympNfrd1/qtwNgd4/VCwoKwrvvvgsAqKysxKRJk3DrrbciJCQEI0eORHp6er94HcA1OaUwW22Yum4Papsuc/P7gM3k2EyOzeTYTI7NlPR6/e5L/XYA7At6XEDl9Rdxz7IcmK027Dx+DgA3vy/YTI7N5NhMjs3k2ExJj9fvvsYBUAW9LaDGS62YsvYzmK02xL+5z/OOHdz8cmwmx2ZybCbHZnJspqS367cWOACqoLcF9Ow/j8FstWHi6t1eL+LMzS/HZnJsJsdmcmwmx2ZKert+a4EDoAp6W0CxL30Ks9WGvSfrvD7OzS/HZnJsJsdmcmwmx2ZKert+a4EDoAp6WkAVjoswW22443kbLjrbvG7j5pdjMzk2k2MzOTaTYzMlPV2/tcIBUAU9LaDffXgEZqsNc94+oLiNm1+OzeTYTI7N5NhMjs2U9HT91goHQBX0soC+qmv2/PavuLpRcTs3vxybybGZHJvJsZkcmynp5fqtJQ6AKuhlAf3zUCXMVhse//v+bm/n5pdjMzk2k2MzOTaTYzMlvVy/tcQBUAW9LKAV2+wwW21Ysc3e7e3c/HJsJsdmcmwmx2ZybKakl+u3ljgAqqCXBbTog8MwW2149z/l3d7OzS/HZnJsJsdmcmwmx2ZKerl+a4kDoAp6WEBldc2wvLQLZqsNW49Wd/s53PxybCbHZnJsJsdmcmympIfrt9Y4AKqg9QLa92U9xiz/BGarDfe/vBuNLd1vbm5+OTaTYzM5NpNjMzk2U9L6+q0HHABV0HIBFVScx6gXdsBsteHRN/6D+mbnt34uN78cm8mxmRybybGZHJspcQDkAKiKVguo5psWjF/V+Wff5MyDuNza3uPnc/PLsZkcm8mxmRybybGZEgdADoCqaLGA3G43EjfkwWy1Ydr6vYp3/egON78cm8mxmRybybGZHJspcQDkAKiKFgtoz8k6mK02jHphByocF3t1H25+OTaTYzM5NpNjMzk2U+IAyAFQlb5eQJdb2zF13R6YrTas3N79a/51h5tfjs3k2EyOzeTYTI7NlDgAcgBUpa8X0J//twRmqw3jV+1Cw0VXr+/HzS/HZnJsJsdmcmwmx2ZKHAA5AKrSlwuo4aILw5+3wWy1IddeK7ovN78cm8mxmRybybGZHJspcQDkAKhKXy6g0nNNMFttGLcyV3xfbn45NpNjMzk2k2MzOTZT4gDIAVCVvlxA+8scMFttmLz2M/F9ufnl2EyOzeTYTI7N5NhMiQMgB0BV+nIB/U9eBcxWG57amC++Lze/HJvJsZkcm8mxmRybKXEA5ACoSl8tILfbjTlvH4DZasOfd5SI78/NL8dmcmwmx2ZybCbHZkocADkAqtJXCygr/0zna/8t2YEvv24W35+bX47N5NhMjs3k2EyOzZQ4AHIAVKUvFlDdBSf+68VPYLbasOHzr3z6Gtz8cmwmx2ZybCbHZnJspsQBkAOgKn2xgKwfFcJsteGh179AR4fbp6/BzS/HZnJsJsdmcmwmx2ZKHAA5AKri7wV0ydXmee2/gorzPn8dbn45NpNjMzk2k2MzOTZT4gDIAVAVfy+gugtOmK2dA6Db7dtv/wBufl+wmRybybGZHJvJsZkSB0AOgKr4ewGdcVzyPPlDDW5+OTaTYzM5NpNjMzk2U+IAyAFQFX8voE9LamG22jBt/V5VX4ebX47N5NhMjs3k2EyOzZQ4AHIAVMXfC+jNz8pgttrwuw+PqPo63PxybCbHZnJsJsdmcmymxAGQA6Aq/l5Ay7YWw2y14b8/KVX1dbj55dhMjs3k2EyOzeTYTIkDIAdAVfy9gOa/dwhmqw3v7z+t6utw88uxmRybybGZHJvJsZkSB0AOgKr4ewE9/PoXMFtt2Hn8nKqvw80vx2ZybCbHZnJsJsdmShwAOQCq4s8F1NregTHLO98BpLi6Ud3X4uYXYzM5NpNjMzk2k2MzJQ6AHABV8ecC+vxUHcxWG8atzEW7j+8A0oWbX47N5NhMjs3k2EyOzZQ4AHIAVMWfC2jtJydgttrw7D+Pqf5a3PxybCbHZnJsJsdmcmymxAGQA6Aq/lxAK7fbYbbasHqHumcAA9z8vmAzOTaTYzM5NpNjMyUOgBwAVfHnAkr94DDMVhte/fSU6q/FzS/HZnJsJsdmcmwmx2ZKHAA5AKrirwV0ubUd9yzLgdlqw+EzDaq/Hje/HJvJsZkcm8mxmRybKXEA5ACIv/3tbzCbzQgJCYHFYkF+fn6v7+uvBZT3lQNmqw2Wl3bB7Vb3BBCAm98XbCbHZnJsJsdmcmymxAHQ4APgpk2bMHDgQGzcuBF2ux3z58/HkCFD8PXXX/fq/v5aQJsOnoHZakPSO70fRnvCzS/HZnJsJsdmcmwmx2ZKHAANPgBaLBakpqZ6/t3R0YGwsDCsXr26V/f31wJak1MKs9WGpVuKb8jX4+aXYzM5NpNjMzk2k2MzJQ6ABh4AXS4XgoODsWXLFq+Pz507Fw8//HC393E6nWhqavIcVVVVCAoKgsPhQGtr6w07Csrr8ffPTmFP6bkb8vUuXbqErVu34tKlSzf0PPvzwWZsxmb6PNiMzW7E4XA4OABqfQJaqampQVBQEPbv3+/18fT0dFgslm7vs3z5cgQFBSmOrKwsbN26lQcPHjx48OARAEdWVhYHQK1PQCu+DIB99RtA/vSn/cFmbMZm+jzYjM1uxMHfABp4APTlT8DXCpTHELS28vEfUmwmx2ZybCbHZnJsphQo129/MuwACHQ+CWTx4sWef3d0dCA8PFzzJ4HcaNz8cmwmx2ZybCbHZnJsphQo129/MvQAuGnTJoSEhCAzMxMlJSVISUnBkCFDUFtb26v7B8oC4uaXYzM5NpNjMzk2k2MzpUC5fvuToQdAAHj99ddx++23Y+DAgbBYLDhw4ECv7xsoC4ibX47N5NhMjs3k2EyOzZQC5frtT4YfANUIlAXEzS/HZnJsJsdmcmwmx2ZKgXL99icOgCoEygLi5pdjMzk2k2MzOTaTYzOlQLl++xMHQBUCZQFx88uxmRybybGZHJvJsZlSoFy//YkDoAqBsoC4+eXYTI7N5NhMjs3k2EwpUK7f/sQBUIVAWUDc/HJsJsdmcmwmx2ZybKYUKNdvf+IAqEKgLCBufjk2k2MzOTaTYzM5NlMKlOu3P3EAVCFQFhA3vxybybGZHJvJsZkcmykFyvXbnzgAqhAoC4ibX47N5NhMjs3k2EyOzZQC5frtTxwAVWhsbERQUBCqqqrQ1NSk28PhcCArKwsOh0PzcwmUg83YjM30ebAZm92Io6qqCkFBQWhsbNR6lNAMB0AVuhYQDx48ePDgwSPwjqqqKq1HCc1wAFSho6MDVVVVaGxs1Pynmd78pKP331Tq6WAzNmMzfR5sxmY34mhsbERVVRU6Ojq0HiU0wwHQAJqa+FgHKTaTYzM5NpNjMzk2o+5wADQAbn45NpNjMzk2k2MzOTaj7nAANABufjk2k2MzOTaTYzM5NqPucAA0AKfTieXLl8PpdGp9KgGDzeTYTI7N5NhMjs2oOxwAiYiIiAyGAyARERGRwXAAJCIiIjIYDoBEREREBsMBkIiIiMhgOAASERERGQwHQCIiIvKJy+XS+hTIRxwAySdnz55Ffn4+cnNzcfHiRa1PJyA4nU5Dv++kL9jMN/X19Th58iTy8vK0PpWAUV1djZycHGRlZeHs2bNan05AKC0tRUpKCg4ePKj1qZAPOACSWGFhIcLDwxEVFQWTyYT7778fa9as0fq0dM1ut+OJJ57Avn374Ha7tT6dgMBmvikuLsa4ceNw7733wmQyISEhAY2NjVqflq4VFRUhMjISEyZMgMlkQlxcHOrq6rQ+LV1zuVyYPn06br75ZqSkpKCgoMBzG/drYOAASCIOhwN33XUX/vjHP6K6uhoVFRVITk5GTEwMUlJStD49XSovL0dkZCRMJhMsFgsKCgr4DfI62Mw3J06cwA9+8AMsWbIEhw8fRl5eHgYPHoyVK1dqfWq6VVpaimHDhmHp0qVoaGhAeXk5TCYTduzYofWp6d7cuXMRGxsLi8WCpKQk5Ofne93O397rGwdAEikuLsaIESNw/Phxz8caGhrwl7/8BWPHjkVaWpqGZ6c/LpcLK1aswOOPPw673Y67774bUVFRXgMNBxtvbOab5uZmJCYmYuHChejo6PA0Wr58OSZPngyAF+RrNTU1YdasWUhNTYXb7fb0eeSRR7Bhwwa89tpr2Lt3r8ZnqT9da2v9+vXIzMzEzp07ER0djXnz5qGiogJr167lWgsAHABJ5PTp07j99tuRlZUF4Mo3ggsXLmDVqlWIiYmBzWbT8hR1pa2tDTk5Odi8ebPn31cPNPwmqcRmvmlqasKMGTOwceNGr4+/8847GDVqFFpbW9nuGs3NzcjMzERhYaHnY6tWrYLJZMJDDz2E0aNHY9y4cXjllVc0PEv9+uCDD/DII48AADZv3oyf/vSnGDVqFEwmk+dxlPxhTb84AJJIQ0MDHnjgATz22GOor6/3uq2pqQljx47FggULNDo7fbp8+bLXv51Op9dAA3R+k9yzZ48Wp6dLbOabqqoqz3+3t7cDALZs2YLx48d7fV51dXWfnpeeXbp0yfPfBw4cwE033YTs7Gy0t7ejra0Ns2bNQlxcHJxOp4ZnqS9dP0js3bsXsbGxno9PmjQJISEhmDFjBoqLi7U6PeolDoDUI4fDgWPHjsFut6OhoQEAcPDgQQwcOBCLFy9Gc3Oz1+e/8MILmDJlCtra2rQ4XV3oanb8+HF88803Xrd1dbl8+bJnoMnLy8OCBQtw3333GfaB59988w3KyspQV1fnuSB3/RmTzb5dV7evv/6XtWvNAAAKyElEQVTa69n4XcMfAGRnZ2P06NGef2dkZCAxMdFr8DGSntaa0+nEmTNnAFxpuGbNGowdOxYtLS2anbPWrm52dYcLFy5g6tSpcLlcmDNnDsLDw7F69WpMmDABjz76KI4dO6bhWdP1cACkb1VUVITo6GiMHj0aw4cPx6JFi3D+/HkAnReVgQMHIjk5GSdPnvTcZ/bs2UhMTPS6ABnJtc1SU1PR1NTk9Tmtra0AOn+rNWbMGAwYMACDBg3C4cOHtThlzRUWFiIqKgojR45EZGQkZs6cidLSUgBXftPAZkq96QYAW7duRUREBABg6dKlCA4ONuzLdvS22dVSUlIwb948zxo0mp6aNTQ0ICoqCiNGjEBYWBiOHj0KAHj//fcxZcoU1NTUaHnqdB0cAKlbdrsdoaGhSE9Ph91ux8svv4zIyEiUl5d7Pic3Nxe33norfv7zn+PBBx9EQkICBg8ejKKiIg3PXDu9adb1eJiu32otWLAAoaGhsNvtmpyz1qqqqvCjH/0If/jDH3DgwAG8+uqrmDZtGoYMGeJ5DbuuHybY7IredOsaWLKzs/Gzn/0ML774IkJCQgw7NEvWGtC53pYsWYIf/vCHKCkp0eq0NfVtzW655Rbs27cPQOdvSGNjY71eBgaA4gdf0h8OgKTgcDgwYcIEr2f0trW1YerUqdi5cycOHTrkeQxRSUkJ1q9fj6SkJDz33HOGvSj3ptm5c+cAXLnIrFu3DiaTCUeOHNHknPVg9+7dGD9+vOc3ywBQVlaGhIQEfPe73/W06frtDJt16m03ANi2bRtMJhOGDh2quEgbSW+bud1ubN68GbNmzUJ4eLih11pPzQYNGuT5E2/X9zaAT/oIJBwASaGyshKvvfaa14N4V61aheDgYNx5552IiorC8OHDFcOekZ9h2JtmkZGROHHiBIDOVkeOHMGpU6e0OmVd+PjjjxEcHIza2lqvj9fU1GDmzJkYMWIEKisrAXReWNisk6Tbl19+iYiICMP+Zr6LpFlFRQUyMjK8Ht5iRD01i4+Px8iRI72eeESBhQMgdevqB9a//fbbGDBgAP71r3/h3LlzOHLkCCZPnoxnnnkGLpfLsI/3u1Zvms2fP9+wjyXqzrlz52CxWJCRkYELFy543ZaXl4eYmBj84x//0Ojs9Evajc9glTcz8g+0Xbg/+zcOgHRdtbW1ivcUffjhhzFz5kyNzkj/2Kz30tPTMW7cOGRmZipe/iU6OhoLFy7U6Mz0TdKNf5brxLUmx2b9FwdA6lbXBePan4Ldbjfa29uRlJSEP/3pT3C73by4/D82k+nq1NHRgfj4eNx777146623vC4yjz32GFatWqXVKeoSu8mxmRyb9X8cAEmh60+6177GH9D5zWDZsmUIDw/nY7GuwmZy1z50YN68eRg3bhzi4uKwdu1aJCcn46abbvK85AR1Yjc5NpNjs/6PAyB5XP2iuxUVFZg6dSq++OILz+27d+9Gamoqhg4dauhnxl2Nza6vu8efXd1s0qRJKCoqgtvtxnvvvYfExETExsbi17/+tddbdBkNu8mxmRybGRcHQIMqLy/HK6+8goyMDGzbts3rtq+++gq33XYbUlJSvD7+/vvvY+HChYZ9TSw2k7Pb7Zg4caLXUNzl6mbXvnOM0+k09JNl2E2OzeTYzNg4ABpQYWEhIiIi8OCDD2LixIkwmUzIzs723B4XF4eEhIRuH6dm1LePYjPfzJs3DyaTCSNGjMD+/fsBXHnbrbi4OMyaNYuPh+wGu8mxmRybGRsHQIM5efIkIiIikJGRAZfLhYaGBkyfPh1vvPGG53O6+5OAkb8JsJnvNm7cCKvViuTkZISGhuLzzz/33MaXD/p27CbHZnJsZmwcAA3E5XIhMTERTz75pNfmjo+PR1JSEp5++mn89a9/RUNDg4ZnqS9sps6mTZswceJEtLS0YMaMGRg2bBhKSkqQkZGBTZs2aX16usVucmwmx2bGxgHQYI4fP47c3FzPv1966SV85zvfwezZs7Fo0SKYTCYsXrxYwzPUHzbz3alTpzB58mTPv3/zm98gJCQEoaGhfEZ0D9hNjs3k2MzYOAAaWFFREX7xi19gx44dnj9XfvTRRxgwYIDnLcvIG5vJ3XfffZ631EpMTMT3vvc9fP/738ehQ4c0PjN9Yzc5NpNjM+PiANjP1dTU4ODBg8jJyUFbW5viRYrPnj3r9e9///vfiIqKgsPh6MvT1BU2k7u6WXt7u6dZS0sLJk+ejIKCAvz2t79FWFgY8vPz8cQTTyA4OBj5+fkan7m22E2OzeTYjLrDAbAfKywsxG233YZ77rkHAwYMQHR0NN566y2vFyu+9okK6enpmD59uuJ9H42CzeS+rVlTUxMA4Nlnn8WgQYMQFhaGw4cPA+h8bOWcOXM8v3kwInaTYzM5NqNvwwGwn6qvr8fdd98Nq9WK06dPo66uDgkJCYiNjUVaWppiWDl79iyWLl2KIUOGoLi4WKOz1habyfXU7Pe//z1aWlqQnZ2NX/3qVzh69KjWp6sb7CbHZnJsRj3hANhPFRcX44477vB6pXaXy4UXX3wRFosFS5Ys8bynY0FBAebMmYPhw4cb+psAm8n11CwmJgYrVqwA0P1b5BkZu8mxmRybUU84APZTJ0+exPDhw7F9+3YAV97ap62tDenp6Rg7dqznNZ+qq6uxbds2lJeXa3a+esBmctdrFhUV5XmXAb4u4hXsJsdmcmxGPeEA2E85nU7ExMRgxowZntev69r8brcbY8aMQVJSkpanqDtsJtebZnPnztXyFHWJ3eTYTI7NqCccAPuhrmd4FRcX4+abb8b8+fM9t3X9lLdkyRL88pe/1OT89IjN5NjMN+wmx2ZybEbXwwGwn+r6aW/Lli0YPHgwkpKSUFtb69n4iYmJSEhI4Nv9XIXN5NjMN+wmx2ZybEY94QDYD3X9ir+5uRkOhwOffvophg0bhp/85CeYNm2a58U+i4qKND5T/WAzOTbzDbvJsZkcm9H1cAAMYNc+aNftdns2/enTp/HjH/8YOTk5ADpfDmD58uV4+umnkZaWBrvd3ufnqwdsJsdmvmE3OTaTYzPyFQfAAHXixAksW7YMTz75JDZs2IDS0lLPbWfOnEFoaCiSk5Phdrs9v97v+kZx7TtbGAWbybGZb9hNjs3k2IzU4AAYgOx2O2655RbEx8dj4sSJiI2NRUREBHbt2gUAePXVV5GWltbtT4ZX/6+RsJkcm/mG3eTYTI7NSC0OgAGmvb0dc+bMwezZsz0fO3r0KJ555hkEBwcjNzfX83nUic3k2Mw37CbHZnJsRjcCB8AA09raigceeADPP/+818fr6uqwYMECDBo0CHl5eRqdnT6xmRyb+Ybd5NhMjs3oRuAAGIBSU1MxYcIENDQ0eH28srIS8fHxmD59uueNvqkTm8mxmW/YTY7N5NiM1OIAGIA2b96M6OhorFu3DhcuXPC6LTMzE2FhYaisrNTo7PSJzeTYzDfsJsdmcmxGanEA1Lmamhps374dH3/8MQ4dOuT5+MKFC3HXXXfhzTffxPnz5z0ft9vtGDlypKGf3s9mcmzmG3aTYzM5NiN/4ACoY0VFRYiMjITFYsHQoUMRExODDz/80HP7U089hTFjxiAtLQ1lZWWor6/Hc889hzvvvBMOh0PDM9cOm8mxmW/YTY7N5NiM/OX/AJC5mssDhVk6AAAAAElFTkSuQmCC\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Traigo las fechas, y las paso a un tipo de dato datetime.\n",
"# De esta forma matplotlib lo plotea bien y no se queja\n",
"dates = [datetime.datetime.strptime(date_str, '%d/%m/%Y %H:%M:%S') for date_str in respuestas.Tiempo.values]\n",
"\n",
"# Esta linea de arriba es equivalente a escribir:\n",
"# dates = [] # armo una lista vacia\n",
"# for date_str in respuestas.Time.values: # recorro los timestamps\n",
"# new_date = datetime.datetime.strptime(date_str, '%d/%m/%Y %H:%M:%S') # paso a tipo de dato correcto\n",
"# dates.append(new_date) # guardo \n",
"\n",
"\n",
"# Ordeno las fechas por si alguna se mezcló en el camino, y ploteo contra el id.\n",
"# en realidad solo necesito que vaya de uno en uno con cada fecha de ingreso\n",
"# sería lo mismo hacer algo como:\n",
"# plt.plot(sorted(dates), np.arange(len(dates)))\n",
"\n",
"plt.plot(sorted(dates), respuestas.index+1)\n",
"plt.xticks(rotation=45) # Arreglo para que no se solapen las horas\n",
"plt.ylabel(\"Respuestas al form\")\n",
"plt.grid()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"---\n",
"Algo menos trivial sería ver como es el solape de inscripciones, para saber como distribuirnos los docentes los horarios. Si hay mucha gente a la mañana habrá que poner más gente en ese turno. Pero también es importante saber cuanta gente se anotó a mas de un turno, porque quizás se podría eliminar alguno de los turnos y distribuir a esa gente en los otros.\n",
"\n",
"Una rudimentaria forma de hacerlo sería recorrer cada uno de los dias y horarios para cada alumno e ir almacenando la id de ese alumno, cosa de que poder ver fácilmente quien se anotó a qué dia y horario"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {},
"outputs": [],
"source": [
"dias = respuestas.columns[1:6].values\n",
"solape = {}\n",
"turnos = ['Man', 'Tar', 'Noc', 'None']\n",
"\n",
"# Solape va a ser un diccionario con 5 entradas (una por cada dia)\n",
"# cada entrada va a tener otras tres (una por cada turno)\n",
"# y ahi guardaremos el id de la persona que se anotó a ese dia y horario\n",
"#\n",
"# Sería una cosa:\n",
"# Lun:\n",
"# Man: {Persona1, ...}\n",
"# Tar: {Persona25, ...}\n",
"# Noc: {...}\n",
"# None: {...}\n",
"#\n",
"# idem con los demas días\n",
"#\n",
"# Primero entonces inicializo todo vacio, pero para que ya esté la estructura armada\n",
"for dia in dias: \n",
" solape[dia] = {turn:set() for turn in turnos}\n",
"\n",
"for index, fila in respuestas.iterrows(): # recorro todas las filas\n",
" for dia in dias: # recorro todos los dias (que son las columnas 1:6)\n",
" for turno in fila[dia].split(\", \"): # Aca me fijo y separo todos los dias que se anotó la persona\n",
" solape[dia][turno].add(str(index)) # agrego a la persona a cada dia y turno apropiado"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ahora tenemos en `solape` todo distribuido ya con las personas ordenadas en los dias y turnos que eligió. Para ver efectivamente el solapamiento de horarios podemos ver la interseccion de cada set, justamente por este motivo es que elegimos usar un set, para poder intersecar fácilmente."
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Lun:\n",
" \t Man\tTar\tNoc\tNone\n",
"Man\t76\t35\t21\t0\t\n",
"Tar\t35\t68\t21\t0\t\n",
"Noc\t21\t21\t68\t0\t\n",
"None\t0\t0\t0\t20\t\n",
"\n",
"\n",
"\n",
"Mar:\n",
" \t Man\tTar\tNoc\tNone\n",
"Man\t76\t38\t21\t0\t\n",
"Tar\t38\t76\t24\t0\t\n",
"Noc\t21\t24\t67\t0\t\n",
"None\t0\t0\t0\t18\t\n",
"\n",
"\n",
"\n",
"Mie:\n",
" \t Man\tTar\tNoc\tNone\n",
"Man\t80\t40\t20\t0\t\n",
"Tar\t40\t76\t23\t0\t\n",
"Noc\t20\t23\t64\t0\t\n",
"None\t0\t0\t0\t17\t\n",
"\n",
"\n",
"\n",
"Jue:\n",
" \t Man\tTar\tNoc\tNone\n",
"Man\t72\t37\t22\t0\t\n",
"Tar\t37\t71\t21\t0\t\n",
"Noc\t22\t21\t65\t0\t\n",
"None\t0\t0\t0\t25\t\n",
"\n",
"\n",
"\n",
"Vie:\n",
" \t Man\tTar\tNoc\tNone\n",
"Man\t84\t47\t28\t0\t\n",
"Tar\t47\t84\t31\t0\t\n",
"Noc\t28\t31\t68\t0\t\n",
"None\t0\t0\t0\t17\t\n",
"\n",
"\n",
"\n"
]
}
],
"source": [
"for dia in dias: # Voy a armar una \"matriz de solape\" por dia\n",
" print(f\"{dia}:\")\n",
" print(\" \\t\",\"\\t\".join(turnos)) # Esta es la primer fila con los nombre de los turnos\n",
" for turno_i in turnos:\n",
" print(turno_i, end='\\t') # La primer columna con nombres de turnos\n",
" for turno_j in turnos: \n",
" # Aca interseco los dos sets con `&` y luego me fijo el largo del set\n",
" # como son sets, no tengo que preocuparme por doble conteos nunca!!\n",
" interseccion = len(solape[dia][turno_i] & solape[dia][turno_j])\n",
" print(f\"{interseccion}\", end='\\t') # printeo el valor y meto un tab\n",
" print(\"\") # agrego un salto de linea para ir a la siguiente fila\n",
" print(\"\\n\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Y bueno, así de fácil (fácil?) se pudo saber como es la distribución de inscriptos en cada horario, y cómo fue el solapamiento de turnos. Obvio que esto tambien se podría visualizar en un heatmap, o algo por el estilo, pero todas esas cosas extra se las dejamos para ustedes para experimentar.\n",
"\n",
"Esto puede parecer al pedo, pero quizas está bueno por si realmente se decide en base a estos datos sacar un turno. Por ejemplo, si se decide eliminar el turno tarde del Lunes, se le podría enviar un mail a aquellos que se anotaron a la tarde y noche, pidiendoles que se anoten de noche, y a los que se anotaron mañana-tarde que se anoten a la mañana!\n",
"\n",
"Abajo les dejamos eso hecho con el id del inscripto. El formulario tambien nos dice a nosotros sus mails, pero no divulgaríamos esa informacion! De cualquier manera el procedimiento sería el mismo, salvo que al final printeariamos los mails de forma tal de luego poder copiar y pegarlos y enviarles el mail a todos juntos."
]
},
{
"cell_type": "code",
"execution_count": 194,
"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>Tiempo</th>\n",
" <th>Lun</th>\n",
" <th>Mar</th>\n",
" <th>Mie</th>\n",
" <th>Jue</th>\n",
" <th>Vie</th>\n",
" <th>Nivel</th>\n",
" <th>Extras</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>29/03/2020 18:22:11</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>3</td>\n",
" <td>Programación orientada a objetos</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>29/03/2020 21:46:11</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>5</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>29/03/2020 22:51:49</td>\n",
" <td>Man, Tar</td>\n",
" <td>None</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>30/03/2020 04:35:31</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>30/03/2020 09:26:26</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man</td>\n",
" <td>Man</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>7</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>30/03/2020 12:44:55</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>3</td>\n",
" <td>Animaciones</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>30/03/2020 16:34:50</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>31/03/2020 08:34:47</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>5</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>01/04/2020 00:11:25</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>01/04/2020 00:30:13</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>01/04/2020 00:33:26</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>01/04/2020 02:42:35</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>None</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>None</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>3</td>\n",
" <td>Importar txt y que python lo interprete como u...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>01/04/2020 10:54:46</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>7</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>01/04/2020 11:35:54</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>01/04/2020 11:40:01</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>5</td>\n",
" <td>Ahroa estoy metiendome con netCDF4 y xArray pa...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>01/04/2020 11:52:56</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>None</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>None</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>2</td>\n",
" <td>panda</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>01/04/2020 13:15:16</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>01/04/2020 13:47:41</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>01/04/2020 14:21:19</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>01/04/2020 15:49:05</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>None</td>\n",
" <td>Tar</td>\n",
" <td>None</td>\n",
" <td>Tar, Noc</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>01/04/2020 15:55:30</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td>01/04/2020 16:10:48</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>01/04/2020 16:19:01</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>01/04/2020 16:29:33</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>None</td>\n",
" <td>Noc</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>01/04/2020 19:06:56</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>01/04/2020 19:08:07</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Tar, Noc</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td>01/04/2020 21:31:27</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man</td>\n",
" <td>Man, Tar</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129</th>\n",
" <td>01/04/2020 23:22:27</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>None</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>None</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>4</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134</th>\n",
" <td>02/04/2020 03:40:22</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>7</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136</th>\n",
" <td>02/04/2020 04:25:40</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>1</td>\n",
" <td>Html</td>\n",
" </tr>\n",
" <tr>\n",
" <th>137</th>\n",
" <td>02/04/2020 10:54:04</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>5</td>\n",
" <td>genomica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>142</th>\n",
" <td>02/04/2020 12:34:15</td>\n",
" <td>Man, Tar</td>\n",
" <td>None</td>\n",
" <td>Man, Tar</td>\n",
" <td>None</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>2</td>\n",
" <td>numpy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td>02/04/2020 14:10:59</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>154</th>\n",
" <td>02/04/2020 20:13:32</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man, Tar</td>\n",
" <td>Man</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>161</th>\n",
" <td>03/04/2020 11:47:59</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>Man, Tar, Noc</td>\n",
" <td>5</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Tiempo Lun Mar Mie \\\n",
"3 29/03/2020 18:22:11 Man, Tar, Noc Man, Tar, Noc Man, Tar, Noc \n",
"17 29/03/2020 21:46:11 Man, Tar, Noc Man, Tar, Noc Man, Tar, Noc \n",
"21 29/03/2020 22:51:49 Man, Tar None Man, Tar \n",
"26 30/03/2020 04:35:31 Man, Tar, Noc Man, Tar, Noc Man, Tar, Noc \n",
"28 30/03/2020 09:26:26 Man, Tar, Noc Man Man \n",
"35 30/03/2020 12:44:55 Man, Tar, Noc Man, Tar, Noc Man, Tar, Noc \n",
"40 30/03/2020 16:34:50 Man, Tar Man, Tar Man, Tar \n",
"43 31/03/2020 08:34:47 Man, Tar Man, Tar Man, Tar \n",
"50 01/04/2020 00:11:25 Man, Tar Man, Tar Man, Tar \n",
"53 01/04/2020 00:30:13 Man, Tar Man, Tar Man, Tar \n",
"55 01/04/2020 00:33:26 Man, Tar, Noc Man, Tar, Noc Man, Tar, Noc \n",
"63 01/04/2020 02:42:35 Man, Tar, Noc None Man, Tar, Noc \n",
"72 01/04/2020 10:54:46 Man, Tar Man, Tar Tar \n",
"76 01/04/2020 11:35:54 Man, Tar Man, Tar Man, Tar \n",
"77 01/04/2020 11:40:01 Man, Tar Man, Tar Man, Tar \n",
"78 01/04/2020 11:52:56 Man, Tar, Noc None Man, Tar, Noc \n",
"81 01/04/2020 13:15:16 Man, Tar Man, Tar Man, Tar \n",
"85 01/04/2020 13:47:41 Man, Tar Man, Tar Man, Tar \n",
"87 01/04/2020 14:21:19 Man, Tar, Noc Man, Tar, Noc Man, Tar, Noc \n",
"99 01/04/2020 15:49:05 Man, Tar, Noc None Tar \n",
"100 01/04/2020 15:55:30 Man, Tar Man, Tar Man, Tar \n",
"101 01/04/2020 16:10:48 Man, Tar Man, Tar Man, Tar \n",
"103 01/04/2020 16:19:01 Man, Tar Man, Tar Man, Tar \n",
"105 01/04/2020 16:29:33 Man, Tar, Noc None Noc \n",
"112 01/04/2020 19:06:56 Man, Tar, Noc Man, Tar, Noc Man, Tar, Noc \n",
"113 01/04/2020 19:08:07 Man, Tar, Noc Man, Tar, Noc Man, Tar, Noc \n",
"124 01/04/2020 21:31:27 Man, Tar Man Man, Tar \n",
"129 01/04/2020 23:22:27 Man, Tar, Noc None Man, Tar, Noc \n",
"134 02/04/2020 03:40:22 Man, Tar Man, Tar Man, Tar \n",
"136 02/04/2020 04:25:40 Man, Tar Man, Tar Man, Tar \n",
"137 02/04/2020 10:54:04 Man, Tar Man, Tar Man, Tar \n",
"142 02/04/2020 12:34:15 Man, Tar None Man, Tar \n",
"147 02/04/2020 14:10:59 Man, Tar, Noc Tar, Noc Man, Tar, Noc \n",
"154 02/04/2020 20:13:32 Man, Tar Man, Tar Man, Tar \n",
"161 03/04/2020 11:47:59 Man, Tar, Noc Man, Tar, Noc Man, Tar, Noc \n",
"\n",
" Jue Vie Nivel \\\n",
"3 Man, Tar, Noc Man, Tar, Noc 3 \n",
"17 Man, Tar, Noc Man, Tar, Noc 5 \n",
"21 Man, Tar Man, Tar 1 \n",
"26 Man, Tar, Noc Man, Tar, Noc 2 \n",
"28 Man, Tar, Noc Man, Tar, Noc 7 \n",
"35 Man, Tar, Noc Man, Tar, Noc 3 \n",
"40 Man, Tar Man, Tar 3 \n",
"43 Man, Tar Man, Tar 5 \n",
"50 Man, Tar Man, Tar 3 \n",
"53 Man, Tar Man, Tar 1 \n",
"55 Man, Tar, Noc Man, Tar, Noc 1 \n",
"63 None Man, Tar, Noc 3 \n",
"72 Man, Tar Man, Tar 7 \n",
"76 Man, Tar Man, Tar 1 \n",
"77 Man, Tar Man, Tar 5 \n",
"78 None Man, Tar, Noc 2 \n",
"81 Man, Tar Man, Tar 1 \n",
"85 Man, Tar Man, Tar 3 \n",
"87 Man, Tar, Noc Man, Tar, Noc 2 \n",
"99 None Tar, Noc 1 \n",
"100 Man, Tar Man, Tar 2 \n",
"101 Man, Tar Man, Tar 2 \n",
"103 Man, Tar Man, Tar 1 \n",
"105 None None 1 \n",
"112 Man, Tar, Noc Man, Tar, Noc 2 \n",
"113 Man, Tar, Noc Tar, Noc 2 \n",
"124 None None 1 \n",
"129 None Man, Tar, Noc 4 \n",
"134 Man, Tar Man, Tar 7 \n",
"136 Man, Tar Man, Tar 1 \n",
"137 Man, Tar Man, Tar 5 \n",
"142 None Man, Tar, Noc 2 \n",
"147 Noc Man, Tar, Noc 1 \n",
"154 Man Man, Tar, Noc 2 \n",
"161 Man, Tar, Noc Man, Tar, Noc 5 \n",
"\n",
" Extras \n",
"3 Programación orientada a objetos \n",
"17 NaN \n",
"21 NaN \n",
"26 NaN \n",
"28 NaN \n",
"35 Animaciones \n",
"40 NaN \n",
"43 NaN \n",
"50 NaN \n",
"53 NaN \n",
"55 NaN \n",
"63 Importar txt y que python lo interprete como u... \n",
"72 NaN \n",
"76 NaN \n",
"77 Ahroa estoy metiendome con netCDF4 y xArray pa... \n",
"78 panda \n",
"81 NaN \n",
"85 NaN \n",
"87 NaN \n",
"99 NaN \n",
"100 NaN \n",
"101 NaN \n",
"103 NaN \n",
"105 NaN \n",
"112 NaN \n",
"113 NaN \n",
"124 NaN \n",
"129 NaN \n",
"134 NaN \n",
"136 Html \n",
"137 genomica \n",
"142 numpy \n",
"147 NaN \n",
"154 NaN \n",
"161 NaN "
]
},
"execution_count": 194,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gente_man_tar = respuestas.loc[(respuestas.Lun == 'Man, Tar')]\n",
"gente_man_tar\n",
"# Aca si estuviera el campo Email podriamos hacer\n",
"# print(gente_man_tar[Email])\n",
"# Y mandarles el mail"
]
}
],
"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": "3.9.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
phobson/seaborn | doc/tutorial/regression.ipynb | 3 | 18286 | {
"cells": [
{
"cell_type": "raw",
"metadata": {},
"source": [
".. _regression_tutorial:\n",
"\n",
".. currentmodule:: seaborn"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Visualizing linear relationships\n",
"================================\n",
"\n",
".. raw:: html\n",
"\n",
" <div class=col-md-9>"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Many datasets contain multiple quantitative variables, and the goal of an analysis is often to relate those variables to each other. We :ref:`previously discussed <distribution_tutorial>` functions that can accomplish this by showing the joint distribution of two variables. It can be very helpful, though, to use statistical models to estimate a simple relationship between two noisy sets of observations. The functions discussed in this chapter will do so through the common framework of linear regression.\n",
"\n",
"In the spirit of Tukey, the regression plots in seaborn are primarily intended to add a visual guide that helps to emphasize patterns in a dataset during exploratory data analyses. That is to say that seaborn is not itself a package for statistical analysis. To obtain quantitative measures related to the fit of regression models, you should use `statsmodels <https://www.statsmodels.org/>`_. The goal of seaborn, however, is to make exploring a dataset through visualization quick and easy, as doing so is just as (if not more) important than exploring a dataset through tables of statistics."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.set(color_codes=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"hide"
]
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"np.random.seed(sum(map(ord, \"regression\")))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tips = sns.load_dataset(\"tips\")"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Functions to draw linear regression models\n",
"------------------------------------------\n",
"\n",
"Two main functions in seaborn are used to visualize a linear relationship as determined through regression. These functions, :func:`regplot` and :func:`lmplot` are closely related, and share much of their core functionality. It is important to understand the ways they differ, however, so that you can quickly choose the correct tool for particular job.\n",
"\n",
"In the simplest invocation, both functions draw a scatterplot of two variables, ``x`` and ``y``, and then fit the regression model ``y ~ x`` and plot the resulting regression line and a 95% confidence interval for that regression:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.regplot(x=\"total_bill\", y=\"tip\", data=tips);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.lmplot(x=\"total_bill\", y=\"tip\", data=tips);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"You should note that the resulting plots are identical, except that the figure shapes are different. We will explain why this is shortly. For now, the other main difference to know about is that :func:`regplot` accepts the ``x`` and ``y`` variables in a variety of formats including simple numpy arrays, pandas ``Series`` objects, or as references to variables in a pandas ``DataFrame`` object passed to ``data``. In contrast, :func:`lmplot` has ``data`` as a required parameter and the ``x`` and ``y`` variables must be specified as strings. This data format is called \"long-form\" or `\"tidy\" <https://vita.had.co.nz/papers/tidy-data.pdf>`_ data. Other than this input flexibility, :func:`regplot` possesses a subset of :func:`lmplot`'s features, so we will demonstrate them using the latter.\n",
"\n",
"It's possible to fit a linear regression when one of the variables takes discrete values, however, the simple scatterplot produced by this kind of dataset is often not optimal:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.lmplot(x=\"size\", y=\"tip\", data=tips);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"One option is to add some random noise (\"jitter\") to the discrete values to make the distribution of those values more clear. Note that jitter is applied only to the scatterplot data and does not influence the regression line fit itself:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.lmplot(x=\"size\", y=\"tip\", data=tips, x_jitter=.05);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"A second option is to collapse over the observations in each discrete bin to plot an estimate of central tendency along with a confidence interval:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.lmplot(x=\"size\", y=\"tip\", data=tips, x_estimator=np.mean);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Fitting different kinds of models\n",
"---------------------------------\n",
"\n",
"The simple linear regression model used above is very simple to fit, however, it is not appropriate for some kinds of datasets. The `Anscombe's quartet <https://en.wikipedia.org/wiki/Anscombe%27s_quartet>`_ dataset shows a few examples where simple linear regression provides an identical estimate of a relationship where simple visual inspection clearly shows differences. For example, in the first case, the linear regression is a good model:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"anscombe = sns.load_dataset(\"anscombe\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.lmplot(x=\"x\", y=\"y\", data=anscombe.query(\"dataset == 'I'\"),\n",
" ci=None, scatter_kws={\"s\": 80});"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"The linear relationship in the second dataset is the same, but the plot clearly shows that this is not a good model:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.lmplot(x=\"x\", y=\"y\", data=anscombe.query(\"dataset == 'II'\"),\n",
" ci=None, scatter_kws={\"s\": 80});"
]
},
{
"cell_type": "raw",
"metadata": {
"collapsed": true
},
"source": [
"In the presence of these kind of higher-order relationships, :func:`lmplot` and :func:`regplot` can fit a polynomial regression model to explore simple kinds of nonlinear trends in the dataset:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.lmplot(x=\"x\", y=\"y\", data=anscombe.query(\"dataset == 'II'\"),\n",
" order=2, ci=None, scatter_kws={\"s\": 80});"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"A different problem is posed by \"outlier\" observations that deviate for some reason other than the main relationship under study:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.lmplot(x=\"x\", y=\"y\", data=anscombe.query(\"dataset == 'III'\"),\n",
" ci=None, scatter_kws={\"s\": 80});"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"In the presence of outliers, it can be useful to fit a robust regression, which uses a different loss function to downweight relatively large residuals:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.lmplot(x=\"x\", y=\"y\", data=anscombe.query(\"dataset == 'III'\"),\n",
" robust=True, ci=None, scatter_kws={\"s\": 80});"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"When the ``y`` variable is binary, simple linear regression also \"works\" but provides implausible predictions:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tips[\"big_tip\"] = (tips.tip / tips.total_bill) > .15\n",
"sns.lmplot(x=\"total_bill\", y=\"big_tip\", data=tips,\n",
" y_jitter=.03);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"The solution in this case is to fit a logistic regression, such that the regression line shows the estimated probability of ``y = 1`` for a given value of ``x``:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.lmplot(x=\"total_bill\", y=\"big_tip\", data=tips,\n",
" logistic=True, y_jitter=.03);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Note that the logistic regression estimate is considerably more computationally intensive (this is true of robust regression as well) than simple regression, and as the confidence interval around the regression line is computed using a bootstrap procedure, you may wish to turn this off for faster iteration (using ``ci=None``).\n",
"\n",
"An altogether different approach is to fit a nonparametric regression using a `lowess smoother <https://en.wikipedia.org/wiki/Local_regression>`_. This approach has the fewest assumptions, although it is computationally intensive and so currently confidence intervals are not computed at all:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.lmplot(x=\"total_bill\", y=\"tip\", data=tips,\n",
" lowess=True);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"The :func:`residplot` function can be a useful tool for checking whether the simple regression model is appropriate for a dataset. It fits and removes a simple linear regression and then plots the residual values for each observation. Ideally, these values should be randomly scattered around ``y = 0``:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.residplot(x=\"x\", y=\"y\", data=anscombe.query(\"dataset == 'I'\"),\n",
" scatter_kws={\"s\": 80});"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"If there is structure in the residuals, it suggests that simple linear regression is not appropriate:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.residplot(x=\"x\", y=\"y\", data=anscombe.query(\"dataset == 'II'\"),\n",
" scatter_kws={\"s\": 80});"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Conditioning on other variables\n",
"-------------------------------\n",
"\n",
"The plots above show many ways to explore the relationship between a pair of variables. Often, however, a more interesting question is \"how does the relationship between these two variables change as a function of a third variable?\" This is where the difference between :func:`regplot` and :func:`lmplot` appears. While :func:`regplot` always shows a single relationship, :func:`lmplot` combines :func:`regplot` with :class:`FacetGrid` to provide an easy interface to show a linear regression on \"faceted\" plots that allow you to explore interactions with up to three additional categorical variables.\n",
"\n",
"The best way to separate out a relationship is to plot both levels on the same axes and to use color to distinguish them:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.lmplot(x=\"total_bill\", y=\"tip\", hue=\"smoker\", data=tips);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"In addition to color, it's possible to use different scatterplot markers to make plots the reproduce to black and white better. You also have full control over the colors used:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.lmplot(x=\"total_bill\", y=\"tip\", hue=\"smoker\", data=tips,\n",
" markers=[\"o\", \"x\"], palette=\"Set1\");"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"To add another variable, you can draw multiple \"facets\" which each level of the variable appearing in the rows or columns of the grid:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.lmplot(x=\"total_bill\", y=\"tip\", hue=\"smoker\", col=\"time\", data=tips);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.lmplot(x=\"total_bill\", y=\"tip\", hue=\"smoker\",\n",
" col=\"time\", row=\"sex\", data=tips);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Controlling the size and shape of the plot\n",
"------------------------------------------\n",
"\n",
"Before we noted that the default plots made by :func:`regplot` and :func:`lmplot` look the same but on axes that have a different size and shape. This is because :func:`regplot` is an \"axes-level\" function draws onto a specific axes. This means that you can make multi-panel figures yourself and control exactly where the regression plot goes. If no axes object is explicitly provided, it simply uses the \"currently active\" axes, which is why the default plot has the same size and shape as most other matplotlib functions. To control the size, you need to create a figure object yourself."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"f, ax = plt.subplots(figsize=(5, 6))\n",
"sns.regplot(x=\"total_bill\", y=\"tip\", data=tips, ax=ax);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"In contrast, the size and shape of the :func:`lmplot` figure is controlled through the :class:`FacetGrid` interface using the ``size`` and ``aspect`` parameters, which apply to each *facet* in the plot, not to the overall figure itself:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.lmplot(x=\"total_bill\", y=\"tip\", col=\"day\", data=tips,\n",
" col_wrap=2, height=3);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.lmplot(x=\"total_bill\", y=\"tip\", col=\"day\", data=tips,\n",
" aspect=.5);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Plotting a regression in other contexts\n",
"---------------------------------------\n",
"\n",
"A few other seaborn functions use :func:`regplot` in the context of a larger, more complex plot. The first is the :func:`jointplot` function that we introduced in the :ref:`distributions tutorial <distribution_tutorial>`. In addition to the plot styles previously discussed, :func:`jointplot` can use :func:`regplot` to show the linear regression fit on the joint axes by passing ``kind=\"reg\"``:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.jointplot(x=\"total_bill\", y=\"tip\", data=tips, kind=\"reg\");"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Using the :func:`pairplot` function with ``kind=\"reg\"`` combines :func:`regplot` and :class:`PairGrid` to show the linear relationship between variables in a dataset. Take care to note how this is different from :func:`lmplot`. In the figure below, the two axes don't show the same relationship conditioned on two levels of a third variable; rather, :func:`PairGrid` is used to show multiple relationships between different pairings of the variables in a dataset:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.pairplot(tips, x_vars=[\"total_bill\", \"size\"], y_vars=[\"tip\"],\n",
" height=5, aspect=.8, kind=\"reg\");"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Like :func:`lmplot`, but unlike :func:`jointplot`, conditioning on an additional categorical variable is built into :func:`pairplot` using the ``hue`` parameter:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.pairplot(tips, x_vars=[\"total_bill\", \"size\"], y_vars=[\"tip\"],\n",
" hue=\"smoker\", height=5, aspect=.8, kind=\"reg\");"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
".. raw:: html\n",
"\n",
" </div>"
]
}
],
"metadata": {
"celltoolbar": "Tags",
"kernelspec": {
"display_name": "Python 3.6 (seaborn-dev)",
"language": "python",
"name": "seaborn-dev"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| bsd-3-clause |
PMEAL/OpenPNM | examples/reference/data_management/interleaving_data.ipynb | 1 | 6405 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Interleaving Data\n",
"## Defining Multiple Subdomains\n",
"\n",
"One of the features in OpenPNM is the ability to model heterogeneous materials by applying different pore-scale models to different regions. This is done by (a) creating a unique **Geometry** object for each region (i.e. small pores vs big pores) and (b) creating unique **Physics** object for each region as well (i.e. Knudsen diffusion vs Fickian diffusion). One consequence of this segregation of properties is that a *single* array containing values for all locations in the domain does not exist. OpenPNM offers a shortcut for this, known as ``interleave_data``, which happens *automatically*, and makes it possible to query **Geometry** properties via the **Network** object, and **Physics** properties from the associated **Phase** object:\n",
"\n",
"Let's demonstrate this by creating a network and assigning two separate geometries to each half of the network:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:25:13.386101Z",
"iopub.status.busy": "2021-06-24T11:25:13.384478Z",
"iopub.status.idle": "2021-06-24T11:25:13.964400Z",
"shell.execute_reply": "2021-06-24T11:25:13.963829Z"
}
},
"outputs": [],
"source": [
"import openpnm as op"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:25:13.972408Z",
"iopub.status.busy": "2021-06-24T11:25:13.969085Z",
"iopub.status.idle": "2021-06-24T11:25:13.976698Z",
"shell.execute_reply": "2021-06-24T11:25:13.977198Z"
}
},
"outputs": [],
"source": [
"pn = op.network.Cubic([5, 5, 5])\n",
"geo1 = op.geometry.GenericGeometry(network=pn, pores=range(0, 75),\n",
" throats=range(0, 150))\n",
"geo2 = op.geometry.GenericGeometry(network=pn, pores=range(75, 125),\n",
" throats=range(150, 300))\n",
"geo1['pore.diameter'] = 1.0\n",
"geo2['pore.diameter'] = 0.1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each of the Geometry objects has a 'pore.diameter' array with different values. To obtain a single array of 'pore.diameter' with values in the correct locations, we can use the Network as follows:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:25:13.982468Z",
"iopub.status.busy": "2021-06-24T11:25:13.981793Z",
"iopub.status.idle": "2021-06-24T11:25:13.985983Z",
"shell.execute_reply": "2021-06-24T11:25:13.986467Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1. 1. 1. 1. 1. 0.1 0.1 0.1 0.1 0.1]\n"
]
}
],
"source": [
"Dp = pn['pore.diameter']\n",
"print(Dp[70:80])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As can be seen, the 'pore.diameter' array contains values from both Geometry objects, and they are in their correction locations in terms of the domain number system. This is referred to as ``interleave_data``. It also works to obtain Physics values via their associated Phase object.\n",
"\n",
"Interleaving of data also works in the reverse direction, so that data only present on the network can be accessed via the Geometry objects:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:25:13.991348Z",
"iopub.status.busy": "2021-06-24T11:25:13.990613Z",
"iopub.status.idle": "2021-06-24T11:25:13.994245Z",
"shell.execute_reply": "2021-06-24T11:25:13.994789Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0.5 0.5 0.5]\n",
" [0.5 0.5 1.5]\n",
" [0.5 0.5 2.5]]\n"
]
}
],
"source": [
"coords = geo1['pore.coords']\n",
"print(coords[0:3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, ``interleave_data`` works between objects of the same type, so that if 'pore.volume' is present on one but not another Geometry object, you will get an array of NaNs when asking for it on the object that does not have it:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-24T11:25:14.000868Z",
"iopub.status.busy": "2021-06-24T11:25:14.000097Z",
"iopub.status.idle": "2021-06-24T11:25:14.004911Z",
"shell.execute_reply": "2021-06-24T11:25:14.004240Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[nan nan nan nan nan]\n"
]
}
],
"source": [
"geo1['pore.volume'] = 3.0\n",
"print(geo2['pore.volume'][:5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Points to Note\n",
"\n",
"* Data **cannot** be written in this way, so that you cannot write 'pore.diameter' values to the Network if 'pore.diameter is already present on a Geometry (e.g. pn['pore.diameter'] = 2.0 will result in an error)\n",
"* Interleaving data is automatically attempted if the requested key is not found. For instance, when you request ``pn['pore.diameter']`` it is not found, so a search is made of the associated Geometry objects and if found an array is built.\n",
"* If an array named 'pore.foo' is already present on the Network or Phase, it cannot be created on a Geometry or Physics, resepctively, since this would break the automated ``interleave_data`` mechanism, which searches for arrays called 'pore.foo' on all associated objects"
]
}
],
"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": "3.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
jallen2/Research-Trend | .ipynb_checkpoints/CU_Funding-checkpoint.ipynb | 1 | 125294 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd \n",
"import glob\n",
"import re \n",
"import string\n",
"from bs4 import BeautifulSoup\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt \n",
"\n",
"%matplotlib inline\n",
"tag0 = 0\n",
"tag1 = 1 \n",
"tag2 = 2\n",
"tag3 = 3\n",
"tag4 = 4"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def dict_build(tags,tag3,tag4,store,sorted_keys,keyword_list,email='clemson.edu',\n",
"alt_email='g.clemson.edu'):\n",
" count = 0\n",
" value = 0 \n",
" for x in range(len(tags)):\n",
" #Store Id, Effective Date, Dollar Amount\n",
" if tags[x] != tags[tag3] and tags[x] != tags[tag4]:\n",
" try: \n",
" store[sorted_keys[x]].append(soup.find(tags[x]).string)\n",
" except AttributeError: \n",
" continue\n",
" elif tags[x] == tags[tag3]: \n",
" #User count is stored \n",
" try:\n",
" for e_mails in soup.find_all(tags[tag3]):\n",
" e_check = e_mails.string.split('@')[1]\n",
" if e_check == email or e_check == alt_email:\n",
" value+=1\n",
" store[sorted_keys[x]].append(value)\n",
" value = 0 \n",
" except AttributeError: \n",
" continue\n",
" elif tags[x] == tags[tag4]: \n",
" #Keyword check \n",
" try:\n",
" abst = soup.find(tags[tag4]).string\n",
" regex = re.compile('[%s]' % re.escape(string.punctuation))\n",
" abs_punc_free = regex.sub(' ', str(abst))\n",
" abs_word_list = abs_punc_free.split()\n",
" for words in abs_word_list: \n",
" if words in keyword_list:\n",
" count+=1\n",
" if count > 0: \n",
" store[sorted_keys[x]].append('Found')\n",
" count = 0 \n",
" else:\n",
" store[sorted_keys[x]].append('NOT Found')\n",
" count = 0 \n",
" except AttributeError:\n",
" continue"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 10min 50s, sys: 10.8 s, total: 11min 1s\n",
"Wall time: 11min 55s\n"
]
}
],
"source": [
"%%time\n",
" \n",
"keyword_list = ['computation', 'compute', 'simulation', 'computational', 'simulate', 'genome',\n",
" 'sequence', 'sequencing', 'molecule', 'large scale', 'large-scale', 'massive', \n",
" 'hpc', 'molecular', 'simulations', 'genomic']\n",
"\n",
"#*****Dictionary to set up for DataFrame************\n",
"store = {'ID':[],'Date':[],'Dollar_Amount':[],\n",
"'Inspector_Count':[],'Data_Word_Check':[]}\n",
"sorted_keys = ['ID','Date','Dollar_Amount',\n",
"'Inspector_Count','Data_Word_Check']\n",
"#***************************************************\n",
"\n",
"tags =['AwardID','AwardEffectiveDate','AwardAmount',\n",
" 'EmailAddress','AbstractNarration']\n",
"file = glob.glob('20*/*.xml')\n",
"\n",
"email = 'clemson.edu'\n",
"alt_email = 'g.clemson.edu'\n",
"\n",
" for x in file: \n",
" with open(x) as file2:\n",
" xml = file2.read()\n",
" soup = BeautifulSoup(xml,'xml')\n",
" try: \n",
" inspector_email = soup.find(tags[tag3]).string.split('@')[1]\n",
" except AttributeError:\n",
" pass\n",
" if inspector_email == email or inspector_email == alt_email:\n",
" dict_build(tags,tag3,tag4,store,sorted_keys,keyword_list,email,alt_email)\n",
" else:\n",
" continue\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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'600000', '75000', '8000', '990898', '308405', '2072668', '262654', '4000', '85540', '49995', '298870', '129386', '330202', '400000', '526216', '773996', '24900', '300000', '50000', '273320', '146968', '1095', '23175', '207000', '50000', '216000', '150000', '210000', '313127', '327824', '699843', '10967', '150000', '253554', '100000', '448497', '7500', '326972', '206248', '100000', '100002', '180000', '10999999', '499347', '584229', '242600', '330751', '358386', '98625', '1276249', '204633', '93255', '300000', '287754', '178018', '500000', '128216', '396983', '130560', '210459', '330000', '335187', '4000', '258235', '375000', '143956', '11499', '53844', '33224', '192469', '71870', '49980', '57172', '152268'], [1, 3, 1, 1, 3, 1, 1, 1, 4, 2, 1, 1, 2, 2, 4, 3, 2, 4, 4, 1, 1, 2, 5, 1, 1, 1, 3, 3, 1, 1, 5, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 3, 2, 1, 1, 1, 2, 1, 2, 3, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 5, 1, 5, 1, 4, 1, 3, 1, 4, 4, 1, 2, 4, 4, 2, 1, 1, 1, 3, 2, 1, 1, 4, 1, 1, 2, 3, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 5, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 4, 1, 2, 1, 1, 2, 1, 1, 2, 3, 1, 3, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 5, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 3, 1, 5, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 3, 3, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 1, 3, 1, 1, 3, 5, 1, 4, 2, 2, 1, 1, 1, 1, 1, 3, 1, 1, 2, 2, 2, 3, 3, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 2, 1, 5, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 3, 1, 1, 2, 1, 4, 6, 1, 3, 1, 3, 1, 1, 1, 2, 4, 1, 2, 2, 1, 3, 2, 2, 2, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 3, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 4]])\n"
]
}
],
"source": [
"print(store.values())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID</th>\n",
" <th>Date</th>\n",
" <th>Dollar_Amount</th>\n",
" <th>Inspector_Count</th>\n",
" <th>Data_Word_Check</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1500007</td>\n",
" <td>10/15/2014</td>\n",
" <td>151149</td>\n",
" <td>1.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1504619</td>\n",
" <td>07/01/2015</td>\n",
" <td>227369</td>\n",
" <td>3.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1507266</td>\n",
" <td>08/01/2015</td>\n",
" <td>375000</td>\n",
" <td>1.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1507529</td>\n",
" <td>08/01/2015</td>\n",
" <td>390000</td>\n",
" <td>1.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1510790</td>\n",
" <td>08/15/2015</td>\n",
" <td>359066</td>\n",
" <td>3.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID Date Dollar_Amount Inspector_Count Data_Word_Check\n",
"0 1500007 10/15/2014 151149 1.0 Found\n",
"1 1504619 07/01/2015 227369 3.0 NOT Found\n",
"2 1507266 08/01/2015 375000 1.0 Found\n",
"3 1507529 08/01/2015 390000 1.0 Found\n",
"4 1510790 08/15/2015 359066 3.0 NOT Found"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fund_frame = pd.DataFrame(dict([(k,pd.Series(v)) for k,v in store.items()]))\n",
"fund = fund_frame[['ID','Date','Dollar_Amount','Inspector_Count','Data_Word_Check']]\n",
"fund.head()\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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>Date</th>\n",
" <th>Dollar_Amount</th>\n",
" <th>Inspector_Count</th>\n",
" <th>Data_Word_Check</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1500007</td>\n",
" <td>10/15/2014</td>\n",
" <td>151149</td>\n",
" <td>1.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1504619</td>\n",
" <td>07/01/2015</td>\n",
" <td>227369</td>\n",
" <td>3.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1507266</td>\n",
" <td>08/01/2015</td>\n",
" <td>375000</td>\n",
" <td>1.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1507529</td>\n",
" <td>08/01/2015</td>\n",
" <td>390000</td>\n",
" <td>1.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1510790</td>\n",
" <td>08/15/2015</td>\n",
" <td>359066</td>\n",
" <td>3.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1512342</td>\n",
" <td>08/16/2014</td>\n",
" <td>206536</td>\n",
" <td>1.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1513875</td>\n",
" <td>10/01/2015</td>\n",
" <td>304924</td>\n",
" <td>1.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1517014</td>\n",
" <td>09/01/2015</td>\n",
" <td>282067</td>\n",
" <td>1.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1518455</td>\n",
" <td>09/01/2015</td>\n",
" <td>850000</td>\n",
" <td>4.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1522191</td>\n",
" <td>09/15/2015</td>\n",
" <td>234780</td>\n",
" <td>2.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1522751</td>\n",
" <td>07/15/2015</td>\n",
" <td>180000</td>\n",
" <td>1.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1527193</td>\n",
" <td>09/01/2015</td>\n",
" <td>246411</td>\n",
" <td>1.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>1527421</td>\n",
" <td>09/01/2015</td>\n",
" <td>307767</td>\n",
" <td>2.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1529927</td>\n",
" <td>08/01/2015</td>\n",
" <td>332654</td>\n",
" <td>2.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>1531127</td>\n",
" <td>09/01/2015</td>\n",
" <td>614582</td>\n",
" <td>4.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1534304</td>\n",
" <td>01/01/2016</td>\n",
" <td>969089</td>\n",
" <td>3.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>1537756</td>\n",
" <td>08/15/2015</td>\n",
" <td>349999</td>\n",
" <td>2.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>1537924</td>\n",
" <td>09/01/2015</td>\n",
" <td>255680</td>\n",
" <td>4.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>1539536</td>\n",
" <td>09/01/2015</td>\n",
" <td>396011</td>\n",
" <td>4.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>1539688</td>\n",
" <td>04/01/2015</td>\n",
" <td>50000</td>\n",
" <td>1.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>1540025</td>\n",
" <td>08/15/2015</td>\n",
" <td>90000</td>\n",
" <td>1.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>1540623</td>\n",
" <td>09/01/2015</td>\n",
" <td>799128</td>\n",
" <td>2.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1540702</td>\n",
" <td>08/01/2015</td>\n",
" <td>499855</td>\n",
" <td>5.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>1541944</td>\n",
" <td>03/15/2016</td>\n",
" <td>122306</td>\n",
" <td>1.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>1542727</td>\n",
" <td>06/01/2015</td>\n",
" <td>49991</td>\n",
" <td>1.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>1543373</td>\n",
" <td>04/01/2016</td>\n",
" <td>55083</td>\n",
" <td>1.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>1544910</td>\n",
" <td>10/01/2015</td>\n",
" <td>800000</td>\n",
" <td>3.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>1547107</td>\n",
" <td>09/01/2015</td>\n",
" <td>9808</td>\n",
" <td>3.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>1547164</td>\n",
" <td>01/01/2016</td>\n",
" <td>249915</td>\n",
" <td>1.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>1547236</td>\n",
" <td>08/15/2015</td>\n",
" <td>248703</td>\n",
" <td>1.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>350</th>\n",
" <td>1428620</td>\n",
" <td>08/15/2014</td>\n",
" <td>584229</td>\n",
" <td>1.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>351</th>\n",
" <td>1435261</td>\n",
" <td>08/01/2014</td>\n",
" <td>242600</td>\n",
" <td>2.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352</th>\n",
" <td>1437836</td>\n",
" <td>01/01/2015</td>\n",
" <td>330751</td>\n",
" <td>1.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>353</th>\n",
" <td>1438325</td>\n",
" <td>08/01/2014</td>\n",
" <td>358386</td>\n",
" <td>1.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>354</th>\n",
" <td>1442131</td>\n",
" <td>06/01/2014</td>\n",
" <td>98625</td>\n",
" <td>1.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>355</th>\n",
" <td>1444461</td>\n",
" <td>01/01/2016</td>\n",
" <td>1276249</td>\n",
" <td>1.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>356</th>\n",
" <td>1444962</td>\n",
" <td>08/15/2014</td>\n",
" <td>204633</td>\n",
" <td>1.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>357</th>\n",
" <td>1446323</td>\n",
" <td>09/15/2014</td>\n",
" <td>93255</td>\n",
" <td>1.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>358</th>\n",
" <td>1447771</td>\n",
" <td>09/01/2014</td>\n",
" <td>300000</td>\n",
" <td>2.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>359</th>\n",
" <td>1453607</td>\n",
" <td>09/01/2015</td>\n",
" <td>287754</td>\n",
" <td>2.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360</th>\n",
" <td>1453775</td>\n",
" <td>02/01/2015</td>\n",
" <td>178018</td>\n",
" <td>2.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361</th>\n",
" <td>1454139</td>\n",
" <td>02/01/2015</td>\n",
" <td>500000</td>\n",
" <td>2.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>362</th>\n",
" <td>1456582</td>\n",
" <td>03/01/2015</td>\n",
" <td>128216</td>\n",
" <td>1.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>363</th>\n",
" <td>1457909</td>\n",
" <td>05/01/2015</td>\n",
" <td>396983</td>\n",
" <td>2.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>364</th>\n",
" <td>1458177</td>\n",
" <td>08/01/2015</td>\n",
" <td>130560</td>\n",
" <td>1.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>365</th>\n",
" <td>1460110</td>\n",
" <td>07/15/2015</td>\n",
" <td>210459</td>\n",
" <td>1.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>366</th>\n",
" <td>1460863</td>\n",
" <td>03/15/2015</td>\n",
" <td>330000</td>\n",
" <td>1.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367</th>\n",
" <td>1460895</td>\n",
" <td>11/01/2015</td>\n",
" <td>335187</td>\n",
" <td>1.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>368</th>\n",
" <td>1462064</td>\n",
" <td>04/01/2015</td>\n",
" <td>4000</td>\n",
" <td>1.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>369</th>\n",
" <td>1462420</td>\n",
" <td>08/15/2014</td>\n",
" <td>258235</td>\n",
" <td>1.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>370</th>\n",
" <td>1462804</td>\n",
" <td>07/15/2015</td>\n",
" <td>375000</td>\n",
" <td>2.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>371</th>\n",
" <td>1463808</td>\n",
" <td>06/15/2015</td>\n",
" <td>143956</td>\n",
" <td>1.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>372</th>\n",
" <td>1464459</td>\n",
" <td>06/01/2015</td>\n",
" <td>11499</td>\n",
" <td>4.0</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>373</th>\n",
" <td>1705448</td>\n",
" <td>11/01/2016</td>\n",
" <td>53844</td>\n",
" <td>NaN</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>374</th>\n",
" <td>1705450</td>\n",
" <td>11/01/2016</td>\n",
" <td>33224</td>\n",
" <td>NaN</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>375</th>\n",
" <td>1710898</td>\n",
" <td>09/30/2016</td>\n",
" <td>192469</td>\n",
" <td>NaN</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>376</th>\n",
" <td>1719461</td>\n",
" <td>08/01/2016</td>\n",
" <td>71870</td>\n",
" <td>NaN</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>377</th>\n",
" <td>1722482</td>\n",
" <td>01/01/2017</td>\n",
" <td>49980</td>\n",
" <td>NaN</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>378</th>\n",
" <td>1722997</td>\n",
" <td>10/01/2016</td>\n",
" <td>57172</td>\n",
" <td>NaN</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>379</th>\n",
" <td>1725377</td>\n",
" <td>02/01/2017</td>\n",
" <td>152268</td>\n",
" <td>NaN</td>\n",
" <td>NOT Found</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>380 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" ID Date Dollar_Amount Inspector_Count Data_Word_Check\n",
"0 1500007 10/15/2014 151149 1.0 Found\n",
"1 1504619 07/01/2015 227369 3.0 NOT Found\n",
"2 1507266 08/01/2015 375000 1.0 Found\n",
"3 1507529 08/01/2015 390000 1.0 Found\n",
"4 1510790 08/15/2015 359066 3.0 NOT Found\n",
"5 1512342 08/16/2014 206536 1.0 NOT Found\n",
"6 1513875 10/01/2015 304924 1.0 NOT Found\n",
"7 1517014 09/01/2015 282067 1.0 Found\n",
"8 1518455 09/01/2015 850000 4.0 NOT Found\n",
"9 1522191 09/15/2015 234780 2.0 Found\n",
"10 1522751 07/15/2015 180000 1.0 Found\n",
"11 1527193 09/01/2015 246411 1.0 Found\n",
"12 1527421 09/01/2015 307767 2.0 NOT Found\n",
"13 1529927 08/01/2015 332654 2.0 NOT Found\n",
"14 1531127 09/01/2015 614582 4.0 NOT Found\n",
"15 1534304 01/01/2016 969089 3.0 Found\n",
"16 1537756 08/15/2015 349999 2.0 NOT Found\n",
"17 1537924 09/01/2015 255680 4.0 NOT Found\n",
"18 1539536 09/01/2015 396011 4.0 NOT Found\n",
"19 1539688 04/01/2015 50000 1.0 NOT Found\n",
"20 1540025 08/15/2015 90000 1.0 NOT Found\n",
"21 1540623 09/01/2015 799128 2.0 NOT Found\n",
"22 1540702 08/01/2015 499855 5.0 NOT Found\n",
"23 1541944 03/15/2016 122306 1.0 Found\n",
"24 1542727 06/01/2015 49991 1.0 Found\n",
"25 1543373 04/01/2016 55083 1.0 NOT Found\n",
"26 1544910 10/01/2015 800000 3.0 Found\n",
"27 1547107 09/01/2015 9808 3.0 Found\n",
"28 1547164 01/01/2016 249915 1.0 NOT Found\n",
"29 1547236 08/15/2015 248703 1.0 Found\n",
".. ... ... ... ... ...\n",
"350 1428620 08/15/2014 584229 1.0 NOT Found\n",
"351 1435261 08/01/2014 242600 2.0 NOT Found\n",
"352 1437836 01/01/2015 330751 1.0 NOT Found\n",
"353 1438325 08/01/2014 358386 1.0 Found\n",
"354 1442131 06/01/2014 98625 1.0 NOT Found\n",
"355 1444461 01/01/2016 1276249 1.0 Found\n",
"356 1444962 08/15/2014 204633 1.0 NOT Found\n",
"357 1446323 09/15/2014 93255 1.0 NOT Found\n",
"358 1447771 09/01/2014 300000 2.0 Found\n",
"359 1453607 09/01/2015 287754 2.0 NOT Found\n",
"360 1453775 02/01/2015 178018 2.0 Found\n",
"361 1454139 02/01/2015 500000 2.0 NOT Found\n",
"362 1456582 03/01/2015 128216 1.0 NOT Found\n",
"363 1457909 05/01/2015 396983 2.0 NOT Found\n",
"364 1458177 08/01/2015 130560 1.0 NOT Found\n",
"365 1460110 07/15/2015 210459 1.0 NOT Found\n",
"366 1460863 03/15/2015 330000 1.0 NOT Found\n",
"367 1460895 11/01/2015 335187 1.0 NOT Found\n",
"368 1462064 04/01/2015 4000 1.0 NOT Found\n",
"369 1462420 08/15/2014 258235 1.0 NOT Found\n",
"370 1462804 07/15/2015 375000 2.0 Found\n",
"371 1463808 06/15/2015 143956 1.0 NOT Found\n",
"372 1464459 06/01/2015 11499 4.0 NOT Found\n",
"373 1705448 11/01/2016 53844 NaN Found\n",
"374 1705450 11/01/2016 33224 NaN NOT Found\n",
"375 1710898 09/30/2016 192469 NaN NOT Found\n",
"376 1719461 08/01/2016 71870 NaN Found\n",
"377 1722482 01/01/2017 49980 NaN Found\n",
"378 1722997 10/01/2016 57172 NaN Found\n",
"379 1725377 02/01/2017 152268 NaN NOT Found\n",
"\n",
"[380 rows x 5 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fund"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"check = fund.loc[fund['Data_Word_Check'] == 'Found']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID</th>\n",
" <th>Date</th>\n",
" <th>Dollar_Amount</th>\n",
" <th>Inspector_Count</th>\n",
" <th>Data_Word_Check</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1500007</td>\n",
" <td>10/15/2014</td>\n",
" <td>151149</td>\n",
" <td>1.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1507266</td>\n",
" <td>08/01/2015</td>\n",
" <td>375000</td>\n",
" <td>1.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1507529</td>\n",
" <td>08/01/2015</td>\n",
" <td>390000</td>\n",
" <td>1.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1517014</td>\n",
" <td>09/01/2015</td>\n",
" <td>282067</td>\n",
" <td>1.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1522191</td>\n",
" <td>09/15/2015</td>\n",
" <td>234780</td>\n",
" <td>2.0</td>\n",
" <td>Found</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" ID Date Dollar_Amount Inspector_Count Data_Word_Check\n",
"0 1500007 10/15/2014 151149 1.0 Found\n",
"2 1507266 08/01/2015 375000 1.0 Found\n",
"3 1507529 08/01/2015 390000 1.0 Found\n",
"7 1517014 09/01/2015 282067 1.0 Found\n",
"9 1522191 09/15/2015 234780 2.0 Found"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"check.head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2006 239397.0\n",
"2007 4580307.0\n",
"2008 4568309.0\n",
"2009 29983040.0\n",
"2010 13833306.0\n",
"2011 9821422.0\n",
"2012 15521590.0\n",
"2013 8715603.0\n",
"2014 18506847.0\n",
"2015 13549741.0\n",
"2016 24084718.0\n",
"2017 702053.0\n",
"Name: Dollar_Amount, dtype: float64\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x2af607fdd048>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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kSZKkXm///ffnl7/8JX/84x9rmgBuyy23bJ35vNJjjz3W2qY9l112GUOGDOHC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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2af607b53898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"d1 = fund['Dollar_Amount']#series\n",
"d1 = d1.astype(float) #type conversion to float\n",
"d2 = pd.to_datetime(fund[\"Date\"]) #convert Date to datetime\n",
"d1.index = d2 #set d1's index to Date values \n",
"byyear = d1.groupby(d1.index.year).sum() #aggregate the date and sum it\n",
"print(byyear)\n",
"my_plot = byyear.plot.bar(figsize = (12,6),color='#F66733',legend=True,label='CU Award Amounts')\n",
"my_plot.set_title('NSF Award Totals (2007-2017)')\n",
"my_plot.set_ylabel('10 million ($)')\n",
"my_plot.set_xlabel('Year')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#csv converted to list \n",
"df = pd.read_csv('palmetto/palmetto_data/Users.csv') #type = dataframe\n",
"user_df_Series = df['LocalUserId'] #type = Series, palmetto user ids \n",
"user_df_list = list(user_df_Series)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-29-50916abccfe9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"\\nfiles = glob.glob('20*/*.xml')\\nusername = [] #The clemson userIDs in the NSF files \\n #length = 802\\npal_user_fund = []\\ntest = 0\\namount = {'Date':[],'Award_Amount':[]}\\n\\nfor xml_f in files:\\n#Open files\\n with open(xml_f) as new_file:\\n #Store Data\\n xml = new_file.read()\\n #Convert data to text \\n soup = BeautifulSoup(xml,'xml')\\n try:\\n #loop through the xml file and store all user email\\n for e in soup.find_all(tags[tag3]):\\n try: \\n mail = e.string.split('@')[1]\\n if mail == email or mail == alt_email: \\n username.append(e.string.split('@')[0])\\n else:\\n test = 'False'\\n except IndexError: \\n continue \\n except AttributeError:\\n continue\\n \\n #**********Adding new condition for the money earned by palmetto users \\n try:\\n mail_test = soup.find(tags[tag3]).string.split('@')[0]\\n if mail_test in user_df_list:\\n amount['Award_Amount'].append(soup.find(tags[tag2]).string)\\n amount['Date'].append(soup.find(tags[tag1]).string)\\n except AttributeError: \\n continue\\n \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/software/anaconda3/4.2.0/lib/python3.5/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m 2101\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2102\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2103\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2104\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<decorator-gen-59>\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
"\u001b[0;32m/software/anaconda3/4.2.0/lib/python3.5/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/software/anaconda3/4.2.0/lib/python3.5/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m 1174\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1175\u001b[0m \u001b[0mst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1176\u001b[0;31m \u001b[0mexec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_ns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1177\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1178\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m/software/anaconda3/4.2.0/lib/python3.5/site-packages/bs4/__init__.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, markup, features, builder, parse_only, from_encoding, exclude_encodings, **kwargs)\u001b[0m\n\u001b[1;32m 226\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 228\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_feed\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 229\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 230\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mParserRejectedMarkup\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/software/anaconda3/4.2.0/lib/python3.5/site-packages/bs4/__init__.py\u001b[0m in \u001b[0;36m_feed\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 287\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 288\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 289\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmarkup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 290\u001b[0m \u001b[0;31m# Close out any unfinished strings and close all the open tags.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 291\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendData\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/software/anaconda3/4.2.0/lib/python3.5/site-packages/bs4/builder/_lxml.py\u001b[0m in \u001b[0;36mfeed\u001b[0;34m(self, markup)\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmarkup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCHUNK_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 138\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparser\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[1;32m 139\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mUnicodeDecodeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLookupError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0metree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mParserError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32msrc/lxml/parser.pxi\u001b[0m in \u001b[0;36mlxml.etree._FeedParser.feed (src/lxml/lxml.etree.c:112202)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32msrc/lxml/parser.pxi\u001b[0m in \u001b[0;36mlxml.etree._FeedParser.feed (src/lxml/lxml.etree.c:112077)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32msrc/lxml/parsertarget.pxi\u001b[0m in \u001b[0;36mlxml.etree._TargetParserContext._handleParseResult (src/lxml/lxml.etree.c:128526)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32msrc/lxml/parsertarget.pxi\u001b[0m in \u001b[0;36mlxml.etree._TargetParserContext._handleParseResult (src/lxml/lxml.etree.c:128396)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32msrc/lxml/lxml.etree.pyx\u001b[0m in \u001b[0;36mlxml.etree._ExceptionContext._raise_if_stored (src/lxml/lxml.etree.c:10741)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32msrc/lxml/saxparser.pxi\u001b[0m in \u001b[0;36mlxml.etree._handleSaxTargetStart (src/lxml/lxml.etree.c:120346)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32msrc/lxml/saxparser.pxi\u001b[0m in \u001b[0;36mlxml.etree._callTargetSaxStart (src/lxml/lxml.etree.c:121259)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32msrc/lxml/parsertarget.pxi\u001b[0m in \u001b[0;36mlxml.etree._PythonSaxParserTarget._handleSaxStart (src/lxml/lxml.etree.c:127508)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m/software/anaconda3/4.2.0/lib/python3.5/site-packages/bs4/builder/_lxml.py\u001b[0m in \u001b[0;36mstart\u001b[0;34m(self, name, attrs, nsmap)\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnsmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[0;31m# Make sure attrs is a mutable dict--lxml may send an immutable dictproxy.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m \u001b[0mattrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 148\u001b[0m \u001b[0mnsprefix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;31m# Invert each namespace map as it comes in.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/software/anaconda3/4.2.0/lib/python3.5/_collections_abc.py\u001b[0m in \u001b[0;36mkeys\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 607\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[0;34m\"D.keys() -> a set-like object providing a view on D's keys\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 609\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mKeysView\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 610\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 611\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/software/anaconda3/4.2.0/lib/python3.5/_collections_abc.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, mapping)\u001b[0m\n\u001b[1;32m 629\u001b[0m \u001b[0m__slots__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'_mapping'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 630\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 631\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapping\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 632\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mapping\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 633\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"%%time \n",
"\n",
"files = glob.glob('20*/*.xml')\n",
"username = [] #The clemson userIDs in the NSF files \n",
" #length = 802\n",
"pal_user_fund = []\n",
"test = 0\n",
"amount = {'Date':[],'Award_Amount':[]}\n",
"\n",
"for xml_f in files:\n",
"#Open files\n",
" with open(xml_f) as new_file:\n",
" #Store Data\n",
" xml = new_file.read()\n",
" #Convert data to text \n",
" soup = BeautifulSoup(xml,'xml')\n",
" try:\n",
" #loop through the xml file and store all user email\n",
" for e in soup.find_all(tags[tag3]):\n",
" try: \n",
" mail = e.string.split('@')[1]\n",
" if mail == email or mail == alt_email: \n",
" username.append(e.string.split('@')[0])\n",
" test+=1\n",
" if test == 1: \n",
" try:\n",
" amount['Award_Amount'].append(soup.find(tags[tag2]).string)\n",
" amount['Date'].append(soup.find(tags[tag1]).string)\n",
" except AttributeError: \n",
" continue\n",
" except IndexError: \n",
" continue \n",
" except AttributeError:\n",
" continue\n",
" test = 0 "
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of Palmetto usernames: 282\n",
"Number of total Clemson usernames: 802\n"
]
}
],
"source": [
"similar = [userID for userID in username if userID in user_df_list] #length = 282\n",
"print('Number of Palmetto usernames: '+ str(len(similar)))\n",
"print('Number of total Clemson usernames: '+ str(len(username)))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Palmetto Users: 35%\n",
"Note: Value out of total number of Clemson usernames.\n"
]
}
],
"source": [
"palmetto = len(similar)\n",
"all_users = len(username)\n",
"\n",
"percentage = palmetto/all_users #35%\n",
"print(\"Palmetto Users: \"+ str(round(percentage*100)) + '%\\n'+'Note: ' + \"Value out of total number of Clemson usernames.\")"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Award_Amount Dollar_Amount\n",
"2006 547565.0 239397.0\n",
"2007 14977193.0 4580307.0\n",
"2008 17173581.0 4568309.0\n",
"2009 64442893.0 29983040.0\n",
"2010 40823264.0 13833306.0\n",
"2011 41480604.0 9821422.0\n",
"2012 43714433.0 15521590.0\n",
"2013 19862183.0 8715603.0\n",
"2014 32441117.0 18506847.0\n",
"2015 37781328.0 13549741.0\n",
"2016 59165119.0 24084718.0\n",
"2017 4274561.0 702053.0\n"
]
},
{
"ename": "AttributeError",
"evalue": "Unknown property columns",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-27-f38216d1d665>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 18\u001b[0m plot1 = stack_fund.plot.bar(stacked=True,figsize = (12,6),\n\u001b[1;32m 19\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'#522D80'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'#F66733'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m legend=True,columns = ['CU Awards: Palmetto Users Only','CU Awards'])\n\u001b[0m\u001b[1;32m 21\u001b[0m \u001b[0mplot1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'NSF Award Totals (2007-2017)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mplot1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'10 million ($)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/software/anaconda3/4.2.0/lib/python3.5/site-packages/pandas/tools/plotting.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, x, y, **kwds)\u001b[0m\n\u001b[1;32m 3777\u001b[0m \u001b[0maxes\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAxesSubplot\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3778\u001b[0m \"\"\"\n\u001b[0;32m-> 3779\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bar'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3780\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3781\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mbarh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/software/anaconda3/4.2.0/lib/python3.5/site-packages/pandas/tools/plotting.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)\u001b[0m\n\u001b[1;32m 3738\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfontsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3739\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3740\u001b[0;31m sort_columns=sort_columns, **kwds)\n\u001b[0m\u001b[1;32m 3741\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_frame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3742\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/software/anaconda3/4.2.0/lib/python3.5/site-packages/pandas/tools/plotting.py\u001b[0m in \u001b[0;36mplot_frame\u001b[0;34m(data, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)\u001b[0m\n\u001b[1;32m 2612\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2613\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort_columns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort_columns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2614\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2615\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2616\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/software/anaconda3/4.2.0/lib/python3.5/site-packages/pandas/tools/plotting.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1026\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1027\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1028\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\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 1029\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1030\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_legend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/software/anaconda3/4.2.0/lib/python3.5/site-packages/pandas/tools/plotting.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(cls, ax, x, y, w, start, log, **kwds)\u001b[0m\n\u001b[1;32m 1916\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1917\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1918\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1919\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1920\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/software/anaconda3/4.2.0/lib/python3.5/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1817\u001b[0m warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m 1818\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1819\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\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 1820\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1821\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/software/anaconda3/4.2.0/lib/python3.5/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, left, height, width, bottom, **kwargs)\u001b[0m\n\u001b[1;32m 2087\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'_nolegend_'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2088\u001b[0m )\n\u001b[0;32m-> 2089\u001b[0;31m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\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 2090\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interpolation_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2091\u001b[0m \u001b[0;31m#print r.get_label(), label, 'label' in kwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/software/anaconda3/4.2.0/lib/python3.5/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, props)\u001b[0m\n\u001b[1;32m 857\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'set_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 858\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Unknown property %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 860\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 861\u001b[0m \u001b[0mchanged\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: Unknown property columns"
]
},
{
"data": {
"image/png": 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z92/qwmE2r0ny+UzuxBiZfPZ88o+/b7d06KZ/DjcAAAAcDTylHAAAABoIbgAA\nAGgguAEAAKCB4AYAAIAGghsAAAAaCG4AAABoILgBAACggeAGAACABoIbAAAAGghuAAAAaCC4AQAA\noMH/B2opeDjqMlPVAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2af62dae75c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pal_user_fund = pd.DataFrame(amount) #Dateframe\n",
"pal_fund = pal_user_fund[['Date','Award_Amount']] #Dataframe\n",
"\n",
"pal_s = pal_fund['Award_Amount'] #Series\n",
"pal_s = pal_s.astype(float) #convert to float \n",
"\n",
"pal2 = pd.to_datetime(pal_fund['Date']) #convert to date type\n",
"pal_s.index = pal2 #Set index to date \n",
"\n",
"g_pal_s = pal_s.groupby(pal_s.index.year).sum() #sum dollar amounts\n",
"stack_fund = pd.concat([g_pal_s,byyear],axis=1) #combine series\n",
"\n",
"\n",
"stack_fund.columns = ['CU Awards: Palmetto Users Only','CU Awards']#set column names\n",
"print(stack_fund)\n",
"\n",
"#*****************************Graph Properties**************************************\n",
"plot1 = stack_fund.plot.bar(stacked=True,figsize = (12,6),\n",
" color=['#522D80','#F66733'],\n",
" legend=True)\n",
"plot1.set_title('NSF Award Totals (2007-2017)')\n",
"plot1.set_ylabel('10 million ($)')\n",
"plot1.set_xlabel('Year')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"celltoolbar": "Raw Cell Format",
"kernelspec": {
"display_name": "Python 3 (Anaconda)",
"language": "python",
"name": "anaconda_py3"
},
"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 |
evangelistalab/forte | tutorials/Tutorial_01.03_forte_sparse.ipynb | 1 | 25626 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Forte Tutorial 1.03: Forte's sparse operator class\n",
"---\n",
"\n",
"Forte exposes several functions to create and manipulate general second quantized operators and wave functions.\n",
"In this tutorial we will look at simple examples that illustrate how these classes work.\n",
"\n",
"## Preliminaries\n",
"Here we define a useful function to display equations in LaTeX format"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"import forte\n",
"from IPython.display import display, Math, Latex\n",
"\n",
"def latex(obj):\n",
" \"\"\"Call the latex() function on an object and display the returned value in LaTeX\"\"\"\n",
" display(Math(obj.latex()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In a previous tutorial we looked at how to define determinants in forte. Here we are going to use the utility function `forte.det()`, which creates a determinant from a string representation of the determinant. The occupation of each orbital is specified by the symbols `2` (doubly occupied), `+` (single alpha electron), `-` (single beta electron), `0` (empty).\n",
"\n",
"Here are some examples."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|22+-000000000000000000000000000000000000000000000000000000000000>\n",
"|22-+000000000000000000000000000000000000000000000000000000000000>\n",
"|+--+000000000000000000000000000000000000000000000000000000000000>\n"
]
}
],
"source": [
"print(forte.det('22+-'))\n",
"print(forte.det('22ba'))\n",
"print(forte.det('ABBA'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Depending on the size if the `Determinant` class, these commands will return a 64 bit or longer representation of the determinants."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The StateVector class\n",
"\n",
"Sparse collections of determinants can be manipulated using the `StateVector` class. The simplest way to create a `StateVector` object is by passing a dictionary of `determinants -> double`. For example, here we create a superposition of a determinant with two electrons and one that has no electrons, both with equal coefficients normalized to one\n",
"$$\n",
"|\\Psi\\rangle = \\frac{1}{\\sqrt{2}}\\left( |20\\rangle + |00\\rangle \\right)\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|2000000000000000000000000000000000000000000000000000000000000000> * 0.707107\n",
"|0000000000000000000000000000000000000000000000000000000000000000> * 0.707107\n",
"\n"
]
}
],
"source": [
"c = 1./ math.sqrt(2.0)\n",
"psi = forte.StateVector({ forte.det('20'): c, forte.det('00') : c})\n",
"print(psi)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An alternative way to print this wave function is by calling the `str` method on the `StateVector` object. The argument `2` here indicates that we want to show only the occupation numbers of only the first two orbitals."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|20> * 0.707107\n",
"|00> * 0.707107\n",
"\n"
]
}
],
"source": [
"print(psi.str(2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The `SparseOperator` class\n",
"\n",
"The `SparseOperator` class can handle operators of the form\n",
"$$\n",
"\\hat{O} = \\sum_{pqrs\\cdots} t_{pq\\cdots}^{rs\\cdots} \\hat{a}^\\dagger_p \\hat{a}^\\dagger_q \\cdots \\hat{a}_s \\hat{a}_r\n",
"$$\n",
"where each individual term in the summation can be an arbitrary order operator.\n",
"However, the amplitudes are assumed to be **real numbers**.\n",
"\n",
"At creation, the user can specify if this operator should be anti-Hermitian, that is if each term should be paired with minus its Hermitian conjugate\n",
"$$\n",
"\\hat{O} = \\sum_{pqrs\\cdots} t_{pq\\cdots}^{rs\\cdots} \\left( \\hat{a}^\\dagger_p \\hat{a}^\\dagger_q \\cdots \\hat{a}_s \\hat{a}_r \n",
"- \\hat{a}^\\dagger_r \\hat{s}^\\dagger_q \\cdots \\hat{a}_q \\hat{a}_p \\right)\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating `SparseOperator` objects\n",
"\n",
"After creation, a `SparseOperator` object is empty\n",
"```python\n",
"op = forte.SparseOperator()\n",
"latex(op)\n",
"# displays nothing\n",
"```\n",
"\n",
"The simplest way to populate a `SparseOperator` is by adding one term at a time using the `add_term_from_str` function.\n",
"\n",
"A generic operator\n",
"$$\n",
"\\hat{q}_1 \\hat{q}_2 \\cdots, \\quad \\text{ with } \\hat{q}_i \\in \\{ \\hat{a}_p, \\hat{a}^\\dagger_p\\}\n",
"$$\n",
"can be specified using the following syntax\n",
"```\n",
"add_term_from_str('[<orbital_1><spin_1><type_1> <orbital_2><spin_2><type_2> ...]', amplitude)\n",
"```\n",
"where\n",
"```\n",
"orbital_i: int\n",
"spin_i: 'a' (alpha) or 'b' (beta)\n",
"type_i: '+' (creation) or '-' (annihilation)\n",
"```\n",
"\n",
"For example, the operator $\\hat{a}^\\dagger_{1_\\alpha} \\hat{a}_{0_\\alpha}$ is encoded as `[1a+ 0a-]`. The following code generates the operators $\\hat{a}^\\dagger_{1_\\alpha} \\hat{a}_{0_\\alpha}$ and $\\frac{1}{2} (\\hat{a}_{0_\\alpha} - \\hat{a}^\\dagger_{0_\\alpha})$"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle +\\;\\hat{a}_{1 \\alpha}^\\dagger\\hat{a}_{0 \\alpha}$"
],
"text/plain": [
"<IPython.core.display.Math object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle +0.500000\\;\\hat{a}_{0 \\alpha} -0.500000\\;\\hat{a}_{0 \\alpha}^\\dagger$"
],
"text/plain": [
"<IPython.core.display.Math object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"op = forte.SparseOperator(antihermitian=False)\n",
"op.add_term_from_str('[1a+ 0a-]',1.0)\n",
"latex(op)\n",
"\n",
"op = forte.SparseOperator(antihermitian=True)\n",
"op.add_term_from_str('[0a-]',0.5)\n",
"latex(op)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ordering of operators in the `SparseOperator` object\n",
"\n",
"<div class=\"alert alert-block alert-warning\">\n",
"Note that `add_term_from_str` <b>assumes that the operators will match a specific order!</b>\n",
"\n",
"This canonical order is defined as\n",
"$$\n",
"(\\alpha \\text{ creation}) (\\beta \\text{ creation}) (\\beta \\text{ annihilation}) (\\alpha \\text{ annihilation})\n",
"$$\n",
"with the creation (annihilation) operators ordered within each group in increasing (decreasing) order.\n",
"The following operator satisfies the canonical order:\n",
"$$\n",
"+\\;\\hat{a}_{2 \\alpha}^\\dagger\\hat{a}_{3 \\alpha}^\\dagger\\hat{a}_{2 \\beta}^\\dagger\\hat{a}_{3 \\beta}^\\dagger\\hat{a}_{1 \\beta}\\hat{a}_{0 \\beta}\\hat{a}_{1 \\alpha}\\hat{a}_{0 \\alpha}\n",
"$$\n",
"</div>\n",
"\n",
"If you want to work with operators that do not follow this ordering, for example, $\\hat{a}_{1 \\alpha}\\hat{a}^\\dagger_{0 \\alpha}$, you will need to work out an equivalent representation, for example, $\\hat{a}_{0 \\alpha}\\hat{a}^\\dagger_{0 \\alpha} = 1 - \\hat{a}^\\dagger_{0 \\alpha}\\hat{a}_{0 \\alpha}$.\n",
"\n",
"These examples illustrate valid operators in canonical order"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle +\\;\\hat{a}_{1 \\beta}\\hat{a}_{0 \\beta}\\hat{a}_{1 \\alpha}\\hat{a}_{0 \\alpha}$"
],
"text/plain": [
"<IPython.core.display.Math object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle +\\;\\hat{a}_{0 \\alpha}^\\dagger\\hat{a}_{1 \\alpha}^\\dagger\\hat{a}_{0 \\beta}^\\dagger\\hat{a}_{1 \\beta}^\\dagger$"
],
"text/plain": [
"<IPython.core.display.Math object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle +\\;\\hat{a}_{2 \\alpha}^\\dagger\\hat{a}_{3 \\alpha}^\\dagger\\hat{a}_{2 \\beta}^\\dagger\\hat{a}_{3 \\beta}^\\dagger\\hat{a}_{1 \\beta}\\hat{a}_{0 \\beta}\\hat{a}_{1 \\alpha}\\hat{a}_{0 \\alpha}$"
],
"text/plain": [
"<IPython.core.display.Math object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# beta annihilation operators appear to the left of alpha annihilation\n",
"# within each group, orbital indices decrease going from left to right\n",
"op = forte.SparseOperator(antihermitian=False)\n",
"op.add_term_from_str('[1b- 0b- 1a- 0a-]',1.0)\n",
"latex(op)\n",
"\n",
"# beta creation operators appear to the right of alpha annihilation\n",
"# within each group, orbitals increase going from left to right\n",
"op = forte.SparseOperator(antihermitian=False)\n",
"op.add_term_from_str('[0a+ 1a+ 0b+ 1b+]',1.0)\n",
"latex(op)\n",
"\n",
"# creation operators appear to the left of annihilation operators\n",
"op = forte.SparseOperator(antihermitian=False)\n",
"op.add_term_from_str('[2a+ 3a+ 2b+ 3b+ 1b- 0b- 1a- 0a-]',1.0)\n",
"latex(op)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When the operator passed is out of order, an exception is thrown. For example, the following code\n",
"```python\n",
"op = forte.SparseOperator(antihermitian=False)\n",
"op.add_term_from_str('[0b- 1b- 1a- 0a-]',1.0)\n",
"latex(op)\n",
"```\n",
"leads to the following RuntimeError"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RuntimeError: Trying to initialize a SQOperator object with a product of\n",
"operators that are not arranged in the canonical form\n",
"\n",
" a+_p1 a+_p2 ... a+_P1 a+_P2 ... ... a-_Q2 a-_Q1 ... a-_q2 a-_q1\n",
" alpha creation beta creation beta annihilation alpha annihilation\n",
"\n",
"with indices sorted as\n",
"\n",
" (p1 < p2 < ...) (P1 < P2 < ...) (... > Q2 > Q1) (... > q2 > q1)\n",
"\n"
]
}
],
"source": [
"op = forte.SparseOperator(antihermitian=False)\n",
"try:\n",
" op.add_term_from_str('[0b- 1b- 1a- 0a-]',1.0)\n",
"except Exception as e:\n",
" print(f'RuntimeError: {e}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This error can be overriden. However, **this is recommended only if you understand what happens when you do so**. The function `add_term_from_str` has an extra option that allows it to reorder the operators to the canonical form. The final operator is multiplied by a sign factor that corresponds to the parity of the permutation that connects the initial and final ordering. This code illustrates how this reordering happens"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle -\\;\\hat{a}_{1 \\beta}\\hat{a}_{0 \\beta}\\hat{a}_{1 \\alpha}\\hat{a}_{0 \\alpha}$"
],
"text/plain": [
"<IPython.core.display.Math object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle -\\;\\hat{a}_{1 \\beta}\\hat{a}_{0 \\beta}\\hat{a}_{1 \\alpha}\\hat{a}_{0 \\alpha}$"
],
"text/plain": [
"<IPython.core.display.Math object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle -\\;\\hat{a}_{1 \\beta}\\hat{a}_{0 \\beta}\\hat{a}_{1 \\alpha}\\hat{a}_{0 \\alpha}$"
],
"text/plain": [
"<IPython.core.display.Math object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle +\\;\\hat{a}_{2 \\alpha}^\\dagger\\hat{a}_{3 \\alpha}^\\dagger\\hat{a}_{2 \\beta}^\\dagger\\hat{a}_{3 \\beta}^\\dagger\\hat{a}_{1 \\beta}\\hat{a}_{0 \\beta}\\hat{a}_{1 \\alpha}\\hat{a}_{0 \\alpha}$"
],
"text/plain": [
"<IPython.core.display.Math object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# the operators [0a- 0b- 1a- 1b-] are reordered and the final sign is -1. \n",
"op = forte.SparseOperator(antihermitian=False)\n",
"op.add_term_from_str('[0a- 0b- 1a- 1b-]',1.0,allow_reordering=True)\n",
"latex(op)\n",
"\n",
"# the operators [0a- 0b- 1a- 1b-] are reordered and the final sign is -1. \n",
"op = forte.SparseOperator(antihermitian=False)\n",
"op.add_term_from_str('[0a- 0b- 1a- 1b-]',1.0,allow_reordering=True)\n",
"latex(op)\n",
"\n",
"# The operator [0a- 0b- 1a- 1b-] (see above) is equivalent to -[1a- 1b- 0b- 0a-].\n",
"op = forte.SparseOperator(antihermitian=False)\n",
"op.add_term_from_str('[1a- 1b- 0b- 0a-]',-1.0,allow_reordering=True)\n",
"latex(op)\n",
"\n",
"# Another example that illustrates the reordering of operators\n",
"op = forte.SparseOperator(antihermitian=False)\n",
"op.add_term_from_str('[0a- 0b- 1a- 1b- 2a+ 2b+ 3a+ 3b+]',1.0,allow_reordering=True)\n",
"latex(op)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An exception is also thrown if two operators are repeated. For example, the following code\n",
"```python\n",
"op = forte.SparseOperator(antihermitian=False)\n",
"op.add_term_from_str('[0b- 0b-]',1.0)\n",
"```\n",
"gives to the following RuntimeError"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RuntimeError: Trying to initialize a SQOperator object with a product of\n",
"operators that contains repeated operators.\n",
"\n"
]
}
],
"source": [
"op = forte.SparseOperator(antihermitian=False)\n",
"try:\n",
" op = forte.SparseOperator(antihermitian=False)\n",
" op.add_term_from_str('[0b- 0b-]',1.0)\n",
"except Exception as e:\n",
" print(f'RuntimeError: {e}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Specifying a full operator with the `SparseOperator` class\n",
"\n",
"To form a full operator we can just keep adding terms to a `SparseOperator` object. For example"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle +0.300000\\;\\hat{a}_{1 \\alpha}^\\dagger\\hat{a}_{0 \\alpha} +0.300000\\;\\hat{a}_{1 \\beta}^\\dagger\\hat{a}_{0 \\beta} +0.100000\\;\\hat{a}_{1 \\alpha}^\\dagger\\hat{a}_{1 \\beta}^\\dagger\\hat{a}_{0 \\beta}\\hat{a}_{0 \\alpha}$"
],
"text/plain": [
"<IPython.core.display.Math object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"op = forte.SparseOperator(antihermitian=False)\n",
"op.add_term_from_str('[1a+ 0a-]',0.3)\n",
"op.add_term_from_str('[1b+ 0b-]',0.3)\n",
"op.add_term_from_str('[1a+ 1b+ 0b- 0a-]',0.1)\n",
"latex(op)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another way to create an operator is via the function `add_term` by providing a list of tuples that specifies the second quantized operators and the corresponding amplitude. This is useful when building operators with a large number of terms. Note, that **this function uses a different convention than `add_term_from_str` for expressing the ordering of the operators**. Here we specify the operator (in reversed order)\n",
"$$\n",
"\\cdots \\hat{q}_2 \\hat{q}_1, \\quad \\text{ with } \\hat{q}_i \\in \\{ \\hat{a}_p, \\hat{a}^\\dagger_p\\}\n",
"$$\n",
"with the following syntax\n",
"```\n",
"add_term([(type_1, spin_1, orb_1), (type_2, spin_2, orb_2), ...]', amplitude)\n",
"```\n",
"where\n",
"```\n",
"type_i: bool (true = creation, false = annihilation)\n",
"spin_i: bool (true = alpha, false = beta)\n",
"orb_i: int\n",
"```\n",
"For example, the operator $\\hat{a}^\\dagger_{1_\\alpha}\\hat{a}_{0_\\alpha}$ is generated in this way"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle +\\;\\hat{a}_{1 \\alpha}^\\dagger\\hat{a}_{0 \\alpha}$"
],
"text/plain": [
"<IPython.core.display.Math object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"op = forte.SparseOperator()\n",
"op.add_term([(False,True,0),(True,True,1)],1.0)\n",
"op.str()\n",
"latex(op)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Applying a `SparseOperator` to a `StateVector`\n",
"\n",
"To apply an operator to a state vector you can use the `forte.apply_operator(op,psi)` function. This function takes an operator (`op`) and a state (`psi`), and returns the state `|new_psi> = op |psi>`. For example, the following creates a CIS wave function using the operator\n",
"$$\n",
"\\hat{T} = 0.1\\; +0.3 \\left(\\hat{a}_{1 \\alpha}^\\dagger\\hat{a}_{0 \\alpha} + \\hat{a}_{1 \\beta}^\\dagger\\hat{a}_{0 \\beta} \\right)\n",
"$$\n",
"where the first term is just a scalar"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|+-0> * 0.300000\n",
"|-+0> * 0.300000\n",
"|200> * 0.100000\n",
"\n"
]
}
],
"source": [
"op = forte.SparseOperator(antihermitian=False)\n",
"op.add_term_from_str('[]',0.1)\n",
"op.add_term_from_str('[1a+ 0a-]',0.3)\n",
"op.add_term_from_str('[1b+ 0b-]',0.3)\n",
"psi = forte.StateVector({ forte.det('2'): 1.0})\n",
"new_psi = forte.apply_operator(op,psi)\n",
"print(new_psi.str(3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exponential operator\n",
"\n",
"To apply the exponential operator $\\exp(\\hat{T})$ we can use the class `SparseExp` class. This class provides the method `compute` which takes as arguments the operator and the state"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|020> * 0.099465\n",
"|-+0> * 0.331551\n",
"|+-0> * 0.331551\n",
"|200> * 1.105171\n",
"\n"
]
}
],
"source": [
"psi = forte.StateVector({ forte.det('2'): 1.0})\n",
"exp_op = forte.SparseExp()\n",
"new_psi = exp_op.compute(op,psi)\n",
"print(new_psi.str(3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are several variables that control the behavior of `compute`. For example, to compute the inverse, we can just apply $\\exp(-\\hat{T})$"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|200> * 1.000000\n",
"\n"
]
}
],
"source": [
"new_psi2 = exp_op.compute(op,new_psi,scaling_factor=-1.0)\n",
"print(new_psi2.str(3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default `compute` uses a caching algorithm that reuses information from previous applications of the exponential. A memory-light algorithm can be also invoked"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|020> * 0.099465\n",
"|-+0> * 0.331551\n",
"|+-0> * 0.331551\n",
"|200> * 1.105171\n",
"\n"
]
}
],
"source": [
"psi = forte.StateVector({ forte.det('2'): 1.0})\n",
"new_psi = exp_op.compute(op,psi,algorithm='onthefly')\n",
"print(new_psi.str(3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also control other parameters, like the order of the Taylor expansion used to approximate $\\exp(\\hat{T})$ (`maxk`) and a threshold used to screen term (`screen_thresh`). For example, to apply $1 + \\hat{T}$ we can call"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|-+0> * 0.300000\n",
"|+-0> * 0.300000\n",
"|200> * 1.100000\n",
"\n"
]
}
],
"source": [
"psi = forte.StateVector({ forte.det('2'): 1.0})\n",
"new_psi = exp_op.compute(op,psi,algorithm='onthefly',maxk=1)\n",
"print(new_psi.str(3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the most efficient algorithm to compute the exponential of an operator via `SparseExp`\n",
"assumes that the function is always called with the same operator.\n",
"For example, if `op1` and `op2` are two different `SparseOperator` obects, the following code will give\n",
"an incorrect result\n",
"```python\n",
"exp_op = forte.SparseExp()\n",
"psi1 = exp_op.compute(op1,psi0)\n",
"psi2 = exp_op.compute(op2,psi1)\n",
"```\n",
"However, if we ask the `SparseExp` class to use an on-the-fly algorithm via the following code\n",
"```python\n",
"exp_op = forte.SparseExp()\n",
"psi1 = exp_op.compute(op1,psi0,algorithm='onthefly')\n",
"psi2 = exp_op.compute(op2,psi1,algorithm='onthefly')\n",
"```\n",
"then the result will be correct."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Factorized exponential of an anti-Hermitian operator\n",
"\n",
"Another useful operator is the factorized exponential of an operator $\\hat{T}$. If $\\hat{T}$ is a sum of operators\n",
"$$\n",
"\\hat{T} = \\sum_\\mu t_\\mu \\hat{\\kappa}_\\mu\n",
"$$\n",
"the factorized exponential is defined as\n",
"$$\n",
"\\exp_\\mathrm{f}(\\hat{T}) = \\prod_\\mu \\exp(t_\\mu \\hat{\\kappa}_\\mu)\n",
"$$\n",
"This operation is implemented in the class `SparseFactExp` for the case of anti-Hermitian operators, that is, when $(\\hat{T})^\\dagger = - \\hat{T}$.\n",
"This class provides the method `compute` which takes as arguments the operator and the state. Here is a simple example:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|+-0> * 0.282321\n",
"|020> * 0.087332\n",
"|-+0> * 0.282321\n",
"|200> * 0.912668\n",
"\n"
]
}
],
"source": [
"op = forte.SparseOperator(antihermitian=True)\n",
"op.add_term_from_str('[1a+ 0a-]',0.3)\n",
"op.add_term_from_str('[1b+ 0b-]',0.3)\n",
"\n",
"psi = forte.StateVector({ forte.det('2'): 1.0})\n",
"factexp_op = forte.SparseFactExp()\n",
"new_psi = factexp_op.compute(op,psi)\n",
"print(new_psi.str(3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To compute the inverse of the factorized exponential, just pass the option `inverse=True` to `compute()`:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|200> * 1.000000\n",
"\n"
]
}
],
"source": [
"starting_psi = factexp_op.compute(op,new_psi,inverse=True)\n",
"print(starting_psi.str(3))"
]
}
],
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"source": [
"# format the book\n",
"%matplotlib inline\n",
"import sys\n",
"from __future__ import division, print_function\n",
"import sys\n",
"sys.path.insert(0,'../code')\n",
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# More Estimation \n",
"\n",
"\n",
"## The Euro problem\n",
"\n",
"In <span>*Information Theory, Inference, and Learning\n",
"Algorithms*</span>, David MacKay poses this problem:\n",
"\n",
"> A statistical statement appeared in \\`\\`The Guardian\" on Friday\n",
"> January 4, 2002:\n",
">\n",
"> > When spun on edge 250 times, a Belgian one-euro coin came up heads\n",
"> > 140 times and tails 110. ‘It looks very suspicious to me,’ said\n",
"> > Barry Blight, a statistics lecturer at the London School of\n",
"> > Economics. ‘If the coin were unbiased, the chance of getting a\n",
"> > result as extreme as that would be less than 7%.’\n",
">\n",
"> But do these data give evidence that the coin is biased rather than\n",
"> fair?\n",
"\n",
"To answer that question, we’ll proceed in two steps. The first is to\n",
"estimate the probability that the coin lands face up. The second is to\n",
"evaluate whether the data support the hypothesis that the coin is\n",
"biased.\n",
"\n",
"You can download the code in this section from\n",
"<http://thinkbayes.com/euro.py>. For more information see\n",
"Section [download].\n",
"\n",
"Any given coin has some probability, $x$, of landing heads up when spun\n",
"on edge. It seems reasonable to believe that the value of $x$ depends on\n",
"some physical characteristics of the coin, primarily the distribution of\n",
"weight.\n",
"\n",
"If a coin is perfectly balanced, we expect $x$ to be close to 50%, but\n",
"for a lopsided coin, $x$ might be substantially different. We can use\n",
"Bayes’s theorem and the observed data to estimate $x$.\n",
"\n",
"Let’s define 101 hypotheses, where $H_x$ is the hypothesis that the\n",
"probability of heads is $x$%, for values from 0 to 100. I’ll start with\n",
"a uniform prior where the probability of $H_x$ is the same for all $x$.\n",
"We’ll come back later to consider other priors.\n",
"\n",
"[Posterior distribution for the Euro problem on a uniform\n",
"prior.](figs/euro1.pdf)\n",
"\n",
"[fig.euro1]\n",
"\n",
"The likelihood function is relatively easy: If $H_x$ is true, the\n",
"probability of heads is $x/100$ and the probability of tails is $1-\n",
"x/100$."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from thinkbayes import Suite\n",
"class Euro(Suite):\n",
"\n",
" def Likelihood(self, data, hypo):\n",
" x = hypo\n",
" if data == 'H':\n",
" return x/100.0\n",
" else:\n",
" return 1 - x/100.0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here’s the code that makes the suite and updates it:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"suite = Euro(range(0, 101))\n",
"dataset = 'H' * 140 + 'T' * 110\n",
"\n",
"for data in dataset:\n",
" suite.Update(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The result may be plotted with:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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lsVjafGGLxdLsOYODgxUTE9Pm8wIA/M+5qtPuz30iOmfU/fJrlVWd6bTrAkBHaXXkPSQk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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2512839e5f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt \n",
"xs = list(range(0,100))\n",
"ps = suite.Probs(xs)\n",
"plt.plot(xs, ps);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summarizing the posterior\n",
"\n",
"Again, there are several ways to summarize the posterior distribution.\n",
"One option is to find the most likely value in the posterior\n",
"distribution. `thinkbayes` provides a function that does that:\n",
"\n",
"```python\n",
"def MaximumLikelihood(pmf):\n",
" \"\"\"Returns the value with the highest probability.\"\"\"\n",
" prob, val = max((prob, val) for val, prob in pmf.Items())\n",
" return val\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"56"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"suite.MaximumLikelihood()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case the result is 56, which is also the observed percentage of\n",
"heads, $140/250 = 56\\%$. So that suggests (correctly) that the observed\n",
"percentage is the maximum likelihood estimator for the population.\n",
"\n",
"We might also summarize the posterior by computing the mean and median:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean 55.952380952380956\n",
"Median 56\n"
]
}
],
"source": [
"import thinkbayes\n",
"print('Mean', suite.Mean())\n",
"print('Median', thinkbayes.Percentile(suite, 50))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we can compute a credible interval:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CI (51, 61)\n"
]
}
],
"source": [
"print('CI', thinkbayes.CredibleInterval(suite, 90))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, getting back to the original question, we would like to know\n",
"whether the coin is fair. We observe that the posterior credible\n",
"interval does not include 50%, which suggests that the coin is not fair.\n",
"\n",
"But that is not exactly the question we started with. MacKay asked, “ Do\n",
"these data give evidence that the coin is biased rather than fair?” To\n",
"answer that question, we will have to be more precise about what it\n",
"means to say that data constitute evidence for a hypothesis. And that is\n",
"the subject of the next chapter.\n",
"\n",
"But before we go on, I want to address one possible source of confusion.\n",
"Since we want to know whether the coin is fair, it might be tempting to\n",
"ask for the probability that <span>x</span> is 50%:\n",
"\n",
"```python\n",
"print(suite.Prob(50))\n",
"```\n",
"\n",
"The result is 0.021, but that value is almost meaningless. The decision\n",
"to evaluate 101 hypotheses was arbitrary; we could have divided the\n",
"range into more or fewer pieces, and if we had, the probability for any\n",
"given hypothesis would be greater or less."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Swamping the priors\n",
"\n",
"We started with a uniform prior, but that might not be a good choice. I\n",
"can believe that if a coin is lopsided, $x$ might deviate substantially\n",
"from 50%, but it seems unlikely that the Belgian Euro coin is so\n",
"imbalanced that $x$ is 10% or 90%.\n",
"\n",
"It might be more reasonable to choose a prior that gives higher\n",
"probability to values of $x$ near 50% and lower probability to extreme\n",
"values.\n",
"\n",
"As an example, I constructed a triangular prior. Here’s the code that constructs the prior:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def TrianglePrior(suite):\n",
" for x in range(0, 51):\n",
" suite.Set(x, x)\n",
" for x in range(51, 101):\n",
" suite.Set(x, 100-x) \n",
" suite.Normalize()\n",
" return suite"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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OyQVxtyI5Ow6jJBPhYR/AIC4RtTts8ImIFPSsAO0I53EYxgBtu2Nk2AtvBf8D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\nIiIi0iFc8ImIiIiIdAgXfCIiIiIiHfL/6vyVi+YfKfwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x25128551080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xs = list(range(0,100))\n",
"\n",
"ps = thinkbayes.MakeUniformPmf(0, 100, 101).Probs(xs)\n",
"plt.plot(xs, ps, label='uniform', color='k')\n",
"\n",
"suite = TrianglePrior(Euro())\n",
"ps = suite.Probs(xs)\n",
"plt.plot(xs, ps, label='triangle');\n",
"plt.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This shows the result (and the uniform prior for\n",
"comparison). Updating this prior with the same dataset yields the\n",
"posterior distribution shown in Figure [fig.euro3]. Even with\n",
"substantially different priors, the posterior distributions are very\n",
"similar. The medians and the credible intervals are identical; the means\n",
"differ by less than 0.5%."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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KePbyETmiRs6OrnB38QQA6HQNKC7XiBwREVHnMHknIiKL0RTfqOTi6xkIiUQi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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x251287e3dd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for data in dataset:\n",
" suite.Update(data)\n",
" \n",
"ps = thinkbayes.MakeUniformPmf(0, 100, 101).Probs(xs)\n",
"plt.plot(xs, ps, label='uniform', color='k')\n",
"\n",
"ps = suite.Probs(xs)\n",
"plt.plot(xs, ps, label='triangle');\n",
"plt.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is an example of **swamping the priors** with enough\n",
"data, people who start with different priors will tend to converge on\n",
"the same posterior."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Optimization\n",
"\n",
"The code I have shown so far is meant to be easy to read, but it is not\n",
"very efficient. In general, I like to develop code that is demonstrably\n",
"correct, then check whether it is fast enough for my purposes. If so,\n",
"there is no need to optimize. For this example, if we care about run\n",
"time, there are several ways we can speed it up.\n",
"\n",
"The first opportunity is to reduce the number of times we normalize the\n",
"suite. In the original code, we call `Update` once for each spin.\n",
"\n",
"```python\n",
"dataset = 'H' * heads + 'T' * tails\n",
"\n",
"for data in dataset:\n",
" suite.Update(data)\n",
"```\n",
"\n",
"And here’s what `Update` looks like:\n",
"\n",
"```python\n",
"def Update(self, data):\n",
" for hypo in self.Values():\n",
" like = self.Likelihood(data, hypo)\n",
" self.Mult(hypo, like)\n",
" return self.Normalize()\n",
"```\n",
"\n",
"Each update iterates through the hypotheses, then calls `Normalize`,\n",
"which iterates through the hypotheses again. We can save some time by\n",
"doing all of the updates before normalizing.\n",
"\n",
"`Suite` provides a method called `UpdateSet` that does exactly that.\n",
"Here it is:\n",
"\n",
"```python\n",
"def UpdateSet(self, dataset):\n",
" for data in dataset:\n",
" for hypo in self.Values():\n",
" like = self.Likelihood(data, hypo)\n",
" self.Mult(hypo, like)\n",
" return self.Normalize()\n",
"```\n",
"\n",
"And here’s how we can invoke it:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2.38031546401283e-75"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"heads, tails = 140, 110\n",
"\n",
"dataset = 'H' * heads + 'T' * tails\n",
"suite.UpdateSet(dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This optimization speeds things up, but the run time is still\n",
"proportional to the amount of data. We can speed things up even more by\n",
"rewriting `Likelihood` to process the entire dataset, rather than one\n",
"spin at a time.\n",
"\n",
"In the original version, `data` is a string that encodes either heads or\n",
"tails:\n",
"\n",
"```python\n",
"def Likelihood(self, data, hypo):\n",
" x = hypo / 100.0\n",
" if data == 'H':\n",
" return x\n",
" else:\n",
" return 1-x\n",
"```\n",
"\n",
"As an alternative, we could encode the dataset as a tuple of two\n",
"integers: the number of heads and tails. In that case `Likelihood` looks\n",
"like this:\n",
"\n",
"```python\n",
"def Likelihood(self, data, hypo):\n",
" x = hypo / 100.0\n",
" heads, tails = data\n",
" like = x**heads * (1-x)**tails\n",
" return like\n",
"```\n",
"\n",
"And then we can call `Update` like this:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.4413448096175153"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"heads, tails = 140, 110\n",
"suite.Update((heads, tails))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we have replaced repeated multiplication with exponentiation, this\n",
"version takes the same time for any number of spins."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The beta distribution\n",
"\n",
"There is one more optimization that solves this problem even faster.\n",
"\n",
"So far we have used a Pmf object to represent a discrete set of values\n",
"for $x$. Now we will use a continuous distribution,\n",
"specifically the beta distribution (see\n",
"<http://en.wikipedia.org/wiki/Beta_distribution>).\n",
"\n",
"The beta distribution is defined on the interval from 0 to 1 (including\n",
"both), so it is a natural choice for describing proportions and\n",
"probabilities. But wait, it gets better.\n",
"\n",
"It turns out that if you do a Bayesian update with a binomial likelihood\n",
"function, as we did in the previous section, the beta distribution is a\n",
"<span>**conjugate prior**</span>. That means that if the prior\n",
"distribution for <span>x</span> is a beta distribution, the posterior is\n",
"also a beta distribution. But wait, it gets even better.\n",
"\n",
"The shape of the beta distribution depends on two parameters, written\n",
"$\\alpha$ and $\\beta$, or <span>alpha</span> and <span>beta</span>. If\n",
"the prior is a beta distribution with parameters <span>alpha</span> and\n",
"<span>beta</span>, and we see data with <span>h</span> heads and\n",
"<span>t</span> tails, the posterior is a beta distribution with\n",
"parameters <span>alpha+h</span> and <span>beta+t</span>. In other words,\n",
"we can do an update with two additions.\n",
"\n",
"So that’s great, but it only works if we can find a beta distribution\n",
"that is a good choice for a prior. Fortunately, for many realistic\n",
"priors there is a beta distribution that is at least a good\n",
"approximation, and for a uniform prior there is a perfect match. The\n",
"beta distribution with <span>alpha=1</span> and <span>beta=1</span> is\n",
"uniform from 0 to 1.\n",
"\n",
"Let’s see how we can take advantage of all this.\n",
"<span>thinkbayes.py</span> provides a class that represents a beta\n",
"distribution:\n",
"\n",
"```python\n",
"class Beta(object):\n",
"\n",
" def __init__(self, alpha=1, beta=1):\n",
" self.alpha = alpha\n",
" self.beta = beta\n",
"```\n",
"\n",
"By default `__init__` makes a uniform distribution. <span>Update</span>\n",
"performs a Bayesian update:\n",
"\n",
"```python\n",
"def Update(self, data):\n",
" heads, tails = data\n",
" self.alpha += heads\n",
" self.beta += tails\n",
"```\n",
"\n",
"`data` is a pair of integers representing the number of heads\n",
"and tails.\n",
"\n",
"So we have yet another way to solve the Euro problem:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5595238095238095\n"
]
}
],
"source": [
"beta = thinkbayes.Beta()\n",
"beta.Update((140, 110))\n",
"print(beta.Mean())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`Beta` provides `Mean,` which computes a simple\n",
"function of `alpha` and `beta`:\n",
"\n",
"```python\n",
"def Mean(self):\n",
" return float(self.alpha) / (self.alpha + self.beta)\n",
"```\n",
"\n",
"For the Euro problem the posterior mean is 56%, which is the same result\n",
"we got using Pmfs.\n",
"\n",
"`Beta` also provides `EvalPdf`, which evaluates\n",
"the probability density function (PDF) of the beta distribution:\n",
"\n",
"```python\n",
"def EvalPdf(self, x):\n",
" return x**(self.alpha-1) * (1-x)**(self.beta-1)\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"111"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"beta.beta"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3.192662744667652e-75"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"beta.EvalPdf(.55)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, `Beta` provides `MakePmf`, which uses\n",
"`EvalPdf` to generate a discrete approximation of the beta\n",
"distribution."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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ZX0/QnpqaGr+dq9057WFhYcrJyVFRUZHX/qKiIuXl5bV6zPjx47V582Y1Nja6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\nI7QDAAAAQY7QDgAAAAQ5QjsAAAAQ5P5/Sa9sV+qpDpwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x25128892cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pmf = beta.MakePmf()\n",
"xs = [i / 100. for i in range(101)]\n",
"ps = pmf.Probs(xs)\n",
"plt.plot(xs, ps);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Discussion\n",
"\n",
"In this chapter we solved the same problem with two different priors and\n",
"found that with a large dataset, the priors get swamped. If two people\n",
"start with different prior beliefs, they generally find, as they see\n",
"more data, that their posterior distributions converge. At some point\n",
"the difference between their distribution is small enough that it has no\n",
"practical effect.\n",
"\n",
"When this happens, it relieves some of the worry about objectivity that\n",
"I discussed in the previous chapter. And for many real-world problems\n",
"even stark prior beliefs can eventually be reconciled by data.\n",
"\n",
"But that is not always the case. First, remember that all Bayesian\n",
"analysis is based on modeling decisions. If you and I do not choose the\n",
"same model, we might interpret data differently. So even with the same\n",
"data, we would compute different likelihoods, and our posterior beliefs\n",
"might not converge.\n",
"\n",
"Also, notice that in a Bayesian update, we multiply each prior\n",
"probability by a likelihood, so if $\\mathrm{p}(H)$ is 0,\n",
"$\\mathrm{p}(H|D)$ is also 0, regardless of $D$. In the Euro\n",
"problem, if you are convinced that $x$ is less than 50%, and you assign\n",
"probability 0 to all other hypotheses, no amount of data will convince\n",
"you otherwise.\n",
"\n",
"This observation is the basis of **Cromwell’s rule**, which\n",
"is the recommendation that you should avoid giving a prior probability\n",
"of 0 to any hypothesis that is even remotely possible (see\n",
"<http://en.wikipedia.org/wiki/Cromwell's_rule>).\n",
"\n",
"Cromwell’s rule is named after Oliver Cromwell, who wrote, “I beseech\n",
"you, in the bowels of Christ, think it possible that you may be\n",
"mistaken.” For Bayesians, this turns out to be good advice (even if it’s\n",
"a little overwrought)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises\n",
"\n",
"Suppose that instead of observing coin tosses directly, you measure the\n",
"outcome using an instrument that is not always correct. Specifically,\n",
"suppose there is a probability <span>y</span> that an actual heads is\n",
"reported as tails, or actual tails reported as heads.\n",
"\n",
"Write a class that estimates the bias of a coin given a series of\n",
"outcomes and the value of <span>y</span>.\n",
"\n",
"How does the spread of the posterior distribution depend on\n",
"<span>y</span>?\n",
"\n",
"This exercise is inspired by a question posted by a “redditor” named\n",
"dominosci on Reddit’s statistics “subreddit” at\n",
"<http://reddit.com/r/statistics>.\n",
"\n",
"Reddit is an online forum with many interest groups called subreddits.\n",
"Users, called redditors, post links to online content and other web\n",
"pages. Other redditors vote on the links, giving an “upvote” to\n",
"high-quality links and a “downvote” to links that are bad or irrelevant.\n",
"\n",
"A problem, identified by dominosci, is that some redditors are more\n",
"reliable than others, and Reddit does not take this into account.\n",
"\n",
"The challenge is to devise a system so that when a redditor casts a\n",
"vote, the estimated quality of the link is updated in accordance with\n",
"the reliability of the redditor, and the estimated reliability of the\n",
"redditor is updated in accordance with the quality of the link.\n",
"\n",
"One approach is to model the quality of the link as the probability of\n",
"garnering an upvote, and to model the reliability of the redditor as the\n",
"probability of correctly giving an upvote to a high-quality item.\n",
"\n",
"Write class definitions for redditors and links and an update function\n",
"that updates both objects whenever a redditor casts a vote."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
castelao/CoTeDe | docs/notebooks/profile_CTD.ipynb | 1 | 27498 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# [WIP] Quality Controlling a CTD profile\n",
"Quality control of a shipboard CTD profile (Temperature & Salinity)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Objective:\n",
"Walk throught the QC process using CoTeDe."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from bokeh.io import output_notebook, show\n",
"from bokeh.layouts import row\n",
"from bokeh.plotting import figure\n",
"import numpy as np\n",
"\n",
"import cotede\n",
"from cotede import datasets, qctests"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"output_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data\n",
"We'll use a CTD profile in the Tropical Atlantic for this tutorial.\n",
"If curious about this dataset, check [CoTeDe's documentation](https://cotede.readthedocs.io) for more details.\n",
"\n",
"Let's load the data and check which variables are available."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = cotede.datasets.load_ctd()\n",
"\n",
"print(\"The variables are: \", \", \".join(sorted(data.keys())))\n",
"print(\"There is a total of {} observed depths.\".format(len(data[\"TEMP\"])))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This CTD was equipped with backup sensors to provide more robustness.\n",
"Measurements from the secondary sensor are identified by a 2 in the end of the name. For instance, TEMP2 is the secondary temperature sensor.\n",
"Here, we will focus on the primary sensors.\n",
"\n",
"To visualize this profile we will use Bokeh which allows to make interactive plots."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"p1 = figure(plot_width=420, plot_height=600)\n",
"p1.circle(data['TEMP'], -data['PRES'],\n",
" size=8, line_color=\"seagreen\", fill_color=\"mediumseagreen\", fill_alpha=0.3)\n",
"p1.xaxis.axis_label = \"Temperature [C]\"\n",
"p1.yaxis.axis_label = \"Depth [m]\"\n",
"\n",
"p2 = figure(plot_width=420, plot_height=600)\n",
"p2.y_range = p1.y_range\n",
"p2.circle(data['PSAL'], -data['PRES'],\n",
" size=8, line_color=\"seagreen\", fill_color=\"mediumseagreen\", fill_alpha=0.3)\n",
"p2.xaxis.axis_label = \"Salinity\"\n",
"p2.yaxis.axis_label = \"Depth [m]\"\n",
"\n",
"p = row(p1, p2)\n",
"show(p)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Considering the unusual magnitudes and variability near the bottom, there are clearly bad measurements in this profile.\n",
"Let's start with one of the most fundamental QC test and restrict the profile to feasible values."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Global Range: Check for Feasible Values\n",
"Let's use the thresholds recommended by the [GTSPP](https://cotede.readthedocs.io/en/latest/qctests.html):\n",
" - Temperature between -2 and 40 $^\\circ$C\n",
" - Salinity between 0 and 41"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ToDo: Include a shaded area for unfeasible values\n",
"\n",
"idx_valid = (data['TEMP'] > -2) & (data['TEMP'] < 40)\n",
"\n",
"p1 = figure(plot_width=420, plot_height=600, title=\"Global Range Check (-2 <= T <= 40)\")\n",
"p1.circle(data['TEMP'][idx_valid], -data['PRES'][idx_valid], size=8, line_color=\"seagreen\", fill_color=\"mediumseagreen\", fill_alpha=0.3, legend_label=\"Good values\")\n",
"p1.triangle(data['TEMP'][~idx_valid], -data['PRES'][~idx_valid], size=8, line_color=\"red\", fill_color=\"red\", fill_alpha=0.3, legend_label=\"Bad values\")\n",
"p1.xaxis.axis_label = \"Temperature [C]\"\n",
"p1.yaxis.axis_label = \"Depth [m]\"\n",
"\n",
"\n",
"idx_valid = (data['PSAL'] > 0) & (data['PSAL'] < 41)\n",
"\n",
"p2 = figure(plot_width=420, plot_height=600, title=\"Global Range Check (0 <= S <= 41)\")\n",
"p2.y_range = p1.y_range\n",
"p2.circle(data['PSAL'][idx_valid], -data['PRES'][idx_valid], size=8, line_color=\"seagreen\", fill_color=\"mediumseagreen\", fill_alpha=0.3, legend_label=\"Good values\")\n",
"p2.triangle(data['PSAL'][~idx_valid], -data['PRES'][~idx_valid], size=8, line_color=\"red\", fill_color=\"red\", fill_alpha=0.3, legend_label=\"Bad values\")\n",
"p2.xaxis.axis_label = \"Pratical Salinity\"\n",
"p2.yaxis.axis_label = \"Depth [m]\"\n",
"\n",
"p = row(p1, p2)\n",
"show(p)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great, we already identified a fair number of bad measurements.\n",
"The global range test is a simple and light test, and there is no reason to always apply it in normal conditions, but it is usually not enough.\n",
"We will need to apply more tests to capture the rest of the bad measurements.\n",
"\n",
"Several QC tests were already implemented in CoTeDe, so you don't need to code it again.\n",
"For instance, the global range test is available as `qctests.GlobalRange` and we can use it like"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y = qctests.GlobalRange(data, varname='TEMP', cfg={\"minval\": -2, \"maxval\": 40})\n",
"y.flags"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's use that to check what are the unfeasible values of temperature."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"flag = y.flags[\"global_range\"]\n",
"data[\"TEMP\"][flag==4]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Global Range is a trivial one to implement, but there are other checks that are more complex and CoTeDe provides a solution for that.\n",
"For instance, let's consider another traditional procedure, the Spike check."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Spike\n",
"The spike check is a quite traditional one and is based on the principle of comparing one measurement with the tendency observed from the neighbor values.\n",
"We could implement it as follows:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def spike(x):\n",
" \"\"\"Spike check as defined by GTSPP\n",
" \n",
" Notes\n",
" -----\n",
" - Check CoTeDe's manual for more details.\n",
" \"\"\"\n",
" y = np.nan * x\n",
" y[1:-1] = np.abs(x[1:-1] - (x[:-2] + x[2:]) / 2.0) - np.abs((x[2:] - x[:-2]) / 2.0)\n",
" return y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is already implemented in CoTeDe as `qctests.spike`, and we could use it as shown below:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"temp_spike = qctests.spike(data[\"TEMP\"])\n",
"\n",
"print(\"The largest spike observed was: {:.3f}\".format(np.nanmax(np.abs(temp_spike))))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The same could be done for salinity, such as: ``sal_spike = qctests.spike(data[\"PSAL\"])``\n",
"\n",
"The traditional approach to use the spike check is by comparing the \"spikeness magnitude\" with a threshold.\n",
"The measurement is considered bad (flag 4) if the spike is larger than that threshold.\n",
"Similar to the global range check, we could hence use the `spike()` and compare the output with acceptable limits.\n",
"This procedure is already available in CoTeDe as `qctests.Spike` and we can use it as follows,"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_spike = qctests.Spike(data, \"TEMP\", cfg={\"threshold\": 2.0})\n",
"y_spike.flags"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Like the Global Range, it provides the quality flags obtained from this procedure.\n",
"Note that the standard flagging follows the IOC recommendation, thus 1 means good data while 0 is no QC applied.\n",
"To customize the flags, check the manual for custom configuration.\n",
"The spike check is based on the previous and following measurements, thus it can't evaluate the first nor the last values, returning flag 0 for those two measurements.\n",
"\n",
"Some procedures provide more than just the flags, but also include features derived from the original measurements.\n",
"For instance, if one was interested in the \"spike intensity\" of one measurement, that could be inspected as:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_spike.features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The magnitudes of the tests are stored in `features`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## More tests\n",
"QC checks are usually focused on specific characteristics of bad measurements, thus to cover a wider range of issues we typically combine a set of checks.\n",
"Let's apply the Gradient and the Tukey53H checks"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_gradient = qctests.Gradient(data, \"TEMP\", cfg={\"threshold\": 10})\n",
"y_gradient.flags"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_tukey53H = qctests.Tukey53H(data, \"TEMP\", cfg={\"threshold\": 2.0})\n",
"y_tukey53H.flags"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These already implemented tests are useful, but it could be easier.\n",
"We usually don't apply one test at a time but a set of tests.\n",
"We could do that by defining a QC configuration like"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cfg = {\n",
" \"TEMP\": {\n",
" \"global_range\": {\"minval\": -2, \"maxval\": 40},\n",
" \"gradient\": {\"threshold\": 10.0},\n",
" \"spike\": {\"threshold\": 2.0},\n",
" \"tukey53H\": {\"threshold\": 1.5},\n",
" },\n",
" \"PSAL\": {\n",
" \"global_range\": {\"minval\": 0, \"maxval\": 41},\n",
" \"gradient\": {\"threshold\": 5.0},\n",
" \"spike\": {\"threshold\": 0.3},\n",
" \"tukey53H\": {\"threshold\": 1.0},\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pqc = cotede.ProfileQC(data, cfg=cfg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's it, the temperature and salinity from the primary sensor were evaluated.\n",
"Let's explore this pqc object.\n",
"\n",
"The same variables in the input are available in the output object."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Variables available in data: {}\\n\".format(\", \".join(data.keys())))\n",
"print(\"Variables available in pqc: {}\\n\".format(\", \".join(pqc.keys())))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But only the variables in the `cfg` dictionary were QC'd"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Variables flagged in pqc: {}\\n\".format(\", \".join(pqc.flags.keys())))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Flags available for temperature {}\\n\".format(pqc.flags[\"TEMP\"].keys()))\n",
"print(\"Flags available for salinity {}\\n\".format(pqc.flags[\"PSAL\"].keys()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"flag = pqc.flags[\"TEMP\"][\"overall\"]\n",
"print('Overall flags for TEMP:', flag)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The flags are on IOC standard, thus 1 means good while 4 means bad.\n",
"0 is used when the QC there was no QC. For instance, the spike test is defined so that it depends on the previous and following measurements, thus the first and last data point of the array will always have a spike flag equal to 0."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using CoTeDe QC framework\n",
"CoTeDe automates many procedures for QC. Let's start using the standard procedure."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's it, the primary and secondary sensors were evaluated. First the same variables in the input are available in the output object."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Variables available in data: {}\\n\".format(data.keys()))\n",
"print(\"Variables available in pqc: {}\\n\".format(pqc.keys()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Flags available for temperature {}\\n\".format(pqc.flags[\"TEMP\"].keys()))\n",
"print(\"Flags available for salinity {}\\n\".format(pqc.flags[\"PSAL\"].keys()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The flags are on IOC standard, thus 1 means good while 4 means bad.\n",
"0 is used when the QC there was no QC. For instance, the spike test is defined so that it depends on the previous and following measurements, thus the first and last data point of the array will always have a spike flag equal to 0.\n",
"\n",
"Let's check the salinity with feasible values:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ToDo: Include a shaded area for unfeasible values\n",
"\n",
"idx_valid = (pqc.flags[\"TEMP\"][\"overall\"] <= 2)\n",
"\n",
"p1 = figure(plot_width=420, plot_height=600, title=\"Global Range Check (-2 <= T <= 40)\")\n",
"p1.circle(data['TEMP'][idx_valid], -data['PRES'][idx_valid], size=8, line_color=\"seagreen\", fill_color=\"mediumseagreen\", fill_alpha=0.3, legend_label=\"Good values\")\n",
"p1.triangle(data['TEMP'][~idx_valid], -data['PRES'][~idx_valid], size=8, line_color=\"red\", fill_color=\"red\", fill_alpha=0.3, legend_label=\"Bad values\")\n",
"p1.xaxis.axis_label = \"Temperature [C]\"\n",
"p1.yaxis.axis_label = \"Depth [m]\"\n",
"\n",
"\n",
"idx_valid = (pqc.flags[\"PSAL\"][\"overall\"] <= 2)\n",
"\n",
"p2 = figure(plot_width=420, plot_height=600, title=\"Global Range Check (0 <= S <= 41)\")\n",
"p2.y_range = p1.y_range\n",
"p2.circle(data['PSAL'][idx_valid], -data['PRES'][idx_valid], size=8, line_color=\"seagreen\", fill_color=\"mediumseagreen\", fill_alpha=0.3, legend_label=\"Good values\")\n",
"p2.triangle(data['PSAL'][~idx_valid], -data['PRES'][~idx_valid], size=8, line_color=\"red\", fill_color=\"red\", fill_alpha=0.3, legend_label=\"Bad values\")\n",
"p2.xaxis.axis_label = \"Pratical Salinity\"\n",
"p2.yaxis.axis_label = \"Depth [m]\"\n",
"\n",
"p = row(p1, p2)\n",
"show(p)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## More tests: GTSPP Spike and Gradient tests\n",
"OK, let's apply more tests beyond the global range.\n",
"Some common ones are the gradient and spike, and we could use CoTeDe to run that like"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_gradient = qctests.Gradient(data, 'TEMP', cfg={\"threshold\": 10})\n",
"y_gradient.flags"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_spike = qctests.Spike(data, 'TEMP', cfg={\"threshold\": 2.0})\n",
"y_spike.flags"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Easiest Way: High level\n",
"Let's evaluate this profile using EuroGOOS standard tests."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pqced = cotede.ProfileQCed(data, cfg='eurogoos')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"p = figure(plot_width=500, plot_height=600)\n",
"p.circle(pqced['TEMP'], -pqced['PRES'], size=8, line_color=\"green\", fill_color=\"green\", fill_alpha=0.3)\n",
"show(p)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## QC with more control: \"medium\" level"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pqc = cotede.ProfileQC(data, cfg='eurogoos')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pqc.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pqc.flags[\"TEMP\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data.keys()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Low level"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from cotede import qctests\n",
"y = qctests.GlobalRange(data, 'TEMP', cfg={'minval': -4, \"maxval\": 45 })\n",
"y.flags"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y = qctests.Tukey53H(data, 'TEMP', cfg={'threshold': 6, \"l\": 12})\n",
"y.features[\"tukey53H\"]\n",
"p = figure(plot_width=500, plot_height=600)\n",
"p.circle(y.features[\"tukey53H\"], -data['PRES'], size=8, line_color=\"green\", fill_color=\"green\", fill_alpha=0.3)\n",
"show(p)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cfg = {'TEMP': {'global_range': {'minval': -4, 'maxval': 45}}}\n",
"\n",
"pqc = ProfileQC(data, cfg)\n",
"\n",
"pqc.flags['TEMP']\n",
"pqc.flags['TEMP']['overall']\n",
"\n",
"idx_good = pqc.flags['TEMP']['overall'] <= 2\n",
"idx_bad = pqc.flags['TEMP']['overall'] >= 3\n",
"\n",
"p = figure(plot_width=500, plot_height=600)\n",
"p.circle(data['TEMP'][idx_good], -data['PRES'][idx_good], size=8, line_color=\"green\", fill_color=\"green\", fill_alpha=0.3)\n",
"p.triangle(data['TEMP'][idx_bad], -data['PRES'][idx_bad], size=8, line_color=\"red\", fill_color=\"red\", fill_alpha=0.3)\n",
"show(p)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cfg['TEMP']['spike'] = {'threshold': 6}\n",
"\n",
"pqc = ProfileQC(data, cfg)\n",
"\n",
"pqc.flags['TEMP']\n",
"pqc.flags['TEMP']['overall']\n",
"\n",
"idx_good = pqc.flags['TEMP']['overall'] <= 2\n",
"idx_bad = pqc.flags['TEMP']['overall'] >= 3\n",
"\n",
"p = figure(plot_width=500, plot_height=600)\n",
"p.circle(data['TEMP'][idx_good], -data['PRES'][idx_good], size=8, line_color=\"green\", fill_color=\"green\", fill_alpha=0.3)\n",
"p.triangle(data['TEMP'][idx_bad], -data['PRES'][idx_bad], size=8, line_color=\"red\", fill_color=\"red\", fill_alpha=0.3)\n",
"show(p)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cfg['TEMP']['woa_normbias'] = {'threshold': 6}\n",
"\n",
"\n",
"pqc = ProfileQC(data, cfg)\n",
"\n",
"pqc.flags['TEMP']\n",
"pqc.flags['TEMP']['overall']\n",
"\n",
"idx_good = pqc.flags['TEMP']['overall'] <= 2\n",
"idx_bad = pqc.flags['TEMP']['overall'] >= 3\n",
"\n",
"p = figure(plot_width=500, plot_height=600)\n",
"p.circle(data['TEMP'][idx_good], -data['PRES'][idx_good], size=8, line_color=\"green\", fill_color=\"green\", fill_alpha=0.3)\n",
"p.triangle(data['TEMP'][idx_bad], -data['PRES'][idx_bad], size=8, line_color=\"red\", fill_color=\"red\", fill_alpha=0.3)\n",
"show(p)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cfg['TEMP']['spike_depthconditional'] = {\"pressure_threshold\": 500, \"shallow_max\": 6.0, \"deep_max\": 2.0}\n",
"\n",
"pqc = ProfileQC(data, cfg)\n",
"\n",
"pqc.flags['TEMP']\n",
"pqc.flags['TEMP']['overall']\n",
"\n",
"idx_good = pqc.flags['TEMP']['overall'] <= 2\n",
"idx_bad = pqc.flags['TEMP']['overall'] >= 3\n",
"\n",
"p = figure(plot_width=500, plot_height=600)\n",
"p.circle(data['TEMP'][idx_good], -data['PRES'][idx_good], size=8, line_color=\"green\", fill_color=\"green\", fill_alpha=0.3)\n",
"p.triangle(data['TEMP'][idx_bad], -data['PRES'][idx_bad], size=8, line_color=\"red\", fill_color=\"red\", fill_alpha=0.3)\n",
"show(p)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"## The Easiest Way: High level\n",
"Let's evaluate this profile using EuroGOOS standard tests."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pqced = cotede.ProfileQCed(data, cfg='eurogoos')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"p = figure(plot_width=500, plot_height=600)\n",
"p.circle(pqced['TEMP'], -pqced['PRES'], size=8, line_color=\"green\", fill_color=\"green\", fill_alpha=0.3)\n",
"show(p)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"## QC with more control: \"medium\" level"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pqc = cotede.ProfileQC(data, cfg='eurogoos')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pqc.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pqc.flags[\"TEMP\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"### Low level"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from cotede import qctests\n",
"y = qctests.GlobalRange(data, 'TEMP', cfg={'minval': -4, \"maxval\": 45 })\n",
"y.flags"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y = qctests.Tukey53H(data, 'TEMP', cfg={'threshold': 6, \"l\": 12})\n",
"y.features[\"tukey53H\"]\n",
"p = figure(plot_width=500, plot_height=600)\n",
"p.circle(y.features[\"tukey53H\"], -data['PRES'], size=8, line_color=\"green\", fill_color=\"green\", fill_alpha=0.3)\n",
"show(p)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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"display_name": "Python 3",
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"name": "python3"
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"file_extension": ".py",
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}
},
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| bsd-3-clause |
AtmaMani/pyChakras | udemy_ml_bootcamp/Machine Learning Sections/Natural-Language-Processing/NLP Project - Solutions.ipynb | 4 | 77307 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"\n",
"<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
"___"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Natural Language Processing Project\n",
"\n",
"Welcome to the NLP Project for this section of the course. In this NLP project you will be attempting to classify Yelp Reviews into 1 star or 5 star categories based off the text content in the reviews. This will be a simpler procedure than the lecture, since we will utilize the pipeline methods for more complex tasks.\n",
"\n",
"We will use the [Yelp Review Data Set from Kaggle](https://www.kaggle.com/c/yelp-recsys-2013).\n",
"\n",
"Each observation in this dataset is a review of a particular business by a particular user.\n",
"\n",
"The \"stars\" column is the number of stars (1 through 5) assigned by the reviewer to the business. (Higher stars is better.) In other words, it is the rating of the business by the person who wrote the review.\n",
"\n",
"The \"cool\" column is the number of \"cool\" votes this review received from other Yelp users. \n",
"\n",
"All reviews start with 0 \"cool\" votes, and there is no limit to how many \"cool\" votes a review can receive. In other words, it is a rating of the review itself, not a rating of the business.\n",
"\n",
"The \"useful\" and \"funny\" columns are similar to the \"cool\" column.\n",
"\n",
"Let's get started! Just follow the directions below!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports\n",
" **Import the usual suspects. :) **"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Data\n",
"\n",
"**Read the yelp.csv file and set it as a dataframe called yelp.**"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"yelp = pd.read_csv('yelp.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Check the head, info , and describe methods on yelp.**"
]
},
{
"cell_type": "code",
"execution_count": 96,
"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>business_id</th>\n",
" <th>date</th>\n",
" <th>review_id</th>\n",
" <th>stars</th>\n",
" <th>text</th>\n",
" <th>type</th>\n",
" <th>user_id</th>\n",
" <th>cool</th>\n",
" <th>useful</th>\n",
" <th>funny</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9yKzy9PApeiPPOUJEtnvkg</td>\n",
" <td>2011-01-26</td>\n",
" <td>fWKvX83p0-ka4JS3dc6E5A</td>\n",
" <td>5</td>\n",
" <td>My wife took me here on my birthday for breakf...</td>\n",
" <td>review</td>\n",
" <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ZRJwVLyzEJq1VAihDhYiow</td>\n",
" <td>2011-07-27</td>\n",
" <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n",
" <td>5</td>\n",
" <td>I have no idea why some people give bad review...</td>\n",
" <td>review</td>\n",
" <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>6oRAC4uyJCsJl1X0WZpVSA</td>\n",
" <td>2012-06-14</td>\n",
" <td>IESLBzqUCLdSzSqm0eCSxQ</td>\n",
" <td>4</td>\n",
" <td>love the gyro plate. Rice is so good and I als...</td>\n",
" <td>review</td>\n",
" <td>0hT2KtfLiobPvh6cDC8JQg</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>_1QQZuf4zZOyFCvXc0o6Vg</td>\n",
" <td>2010-05-27</td>\n",
" <td>G-WvGaISbqqaMHlNnByodA</td>\n",
" <td>5</td>\n",
" <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\n",
" <td>review</td>\n",
" <td>uZetl9T0NcROGOyFfughhg</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>6ozycU1RpktNG2-1BroVtw</td>\n",
" <td>2012-01-05</td>\n",
" <td>1uJFq2r5QfJG_6ExMRCaGw</td>\n",
" <td>5</td>\n",
" <td>General Manager Scott Petello is a good egg!!!...</td>\n",
" <td>review</td>\n",
" <td>vYmM4KTsC8ZfQBg-j5MWkw</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" business_id date review_id stars \\\n",
"0 9yKzy9PApeiPPOUJEtnvkg 2011-01-26 fWKvX83p0-ka4JS3dc6E5A 5 \n",
"1 ZRJwVLyzEJq1VAihDhYiow 2011-07-27 IjZ33sJrzXqU-0X6U8NwyA 5 \n",
"2 6oRAC4uyJCsJl1X0WZpVSA 2012-06-14 IESLBzqUCLdSzSqm0eCSxQ 4 \n",
"3 _1QQZuf4zZOyFCvXc0o6Vg 2010-05-27 G-WvGaISbqqaMHlNnByodA 5 \n",
"4 6ozycU1RpktNG2-1BroVtw 2012-01-05 1uJFq2r5QfJG_6ExMRCaGw 5 \n",
"\n",
" text type \\\n",
"0 My wife took me here on my birthday for breakf... review \n",
"1 I have no idea why some people give bad review... review \n",
"2 love the gyro plate. Rice is so good and I als... review \n",
"3 Rosie, Dakota, and I LOVE Chaparral Dog Park!!... review \n",
"4 General Manager Scott Petello is a good egg!!!... review \n",
"\n",
" user_id cool useful funny \n",
"0 rLtl8ZkDX5vH5nAx9C3q5Q 2 5 0 \n",
"1 0a2KyEL0d3Yb1V6aivbIuQ 0 0 0 \n",
"2 0hT2KtfLiobPvh6cDC8JQg 0 1 0 \n",
"3 uZetl9T0NcROGOyFfughhg 1 2 0 \n",
"4 vYmM4KTsC8ZfQBg-j5MWkw 0 0 0 "
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yelp.head()"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 10000 entries, 0 to 9999\n",
"Data columns (total 10 columns):\n",
"business_id 10000 non-null object\n",
"date 10000 non-null object\n",
"review_id 10000 non-null object\n",
"stars 10000 non-null int64\n",
"text 10000 non-null object\n",
"type 10000 non-null object\n",
"user_id 10000 non-null object\n",
"cool 10000 non-null int64\n",
"useful 10000 non-null int64\n",
"funny 10000 non-null int64\n",
"dtypes: int64(4), object(6)\n",
"memory usage: 781.3+ KB\n"
]
}
],
"source": [
"yelp.info()"
]
},
{
"cell_type": "code",
"execution_count": 99,
"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>stars</th>\n",
" <th>cool</th>\n",
" <th>useful</th>\n",
" <th>funny</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>10000.000000</td>\n",
" <td>10000.000000</td>\n",
" <td>10000.000000</td>\n",
" <td>10000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>3.777500</td>\n",
" <td>0.876800</td>\n",
" <td>1.409300</td>\n",
" <td>0.701300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.214636</td>\n",
" <td>2.067861</td>\n",
" <td>2.336647</td>\n",
" <td>1.907942</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\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>3.000000</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>4.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>5.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>5.000000</td>\n",
" <td>77.000000</td>\n",
" <td>76.000000</td>\n",
" <td>57.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" stars cool useful funny\n",
"count 10000.000000 10000.000000 10000.000000 10000.000000\n",
"mean 3.777500 0.876800 1.409300 0.701300\n",
"std 1.214636 2.067861 2.336647 1.907942\n",
"min 1.000000 0.000000 0.000000 0.000000\n",
"25% 3.000000 0.000000 0.000000 0.000000\n",
"50% 4.000000 0.000000 1.000000 0.000000\n",
"75% 5.000000 1.000000 2.000000 1.000000\n",
"max 5.000000 77.000000 76.000000 57.000000"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yelp.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Create a new column called \"text length\" which is the number of words in the text column.**"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"yelp['text length'] = yelp['text'].apply(len)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# EDA\n",
"\n",
"Let's explore the data\n",
"\n",
"## Imports\n",
"\n",
"**Import the data visualization libraries if you haven't done so already.**"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"sns.set_style('white')\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Use FacetGrid from the seaborn library to create a grid of 5 histograms of text length based off of the star ratings. Reference the seaborn documentation for hints on this**"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x121e705f8>"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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8Ltaz01qKsJKZ64DHgHljXJcZwgy1oYkZMj+Frn+vmp8RNTE/YIZ26vr3qxka\nURMzZH4KXf9eNT8jamJ+YIwz1KQGxreAkwEi4lXA97pQwx0Rsbj8/CRgFXArsCgiZkTEHOAQYA1w\ny856y4+rhg62u8rrgr4KvDszryo339nlmt4YEeeVD7dSLBRzW0Qc042aMvOYzDwuM48Dvgu8Cbi+\nm1+j0tuAjwNExPMpwvnPY/x1MkNmaEQNzZD5KZifn63J/LTHDBXM0M/WZIZGZn4K5udnazI/7RnT\nDPUMDg7WVHdn4pnVd19ebnprZq4dg+MeCHwhM4+KiJ+nWMxmOnA3sDQzByPiDODtFFNhlmXmlyNi\nJnAV8D8pVqM9LTMf7rCWTwCnAj8ojzUIvAP4ZBdrmgX8FfA/KO5as7ys74pu1dRS200UqwEP0sX/\nb2Ut0ym+TgdSdIvfTdF9HLOvkxkyQxVqa0SGzI/5eZaazE97tZghM7SrmszQyHWYH/Ozq5rMT3u1\njGmGGtPAkCRJkiRJ2pUmXUIiSZIkSZI0LBsYkiRJkiSp8WxgSJIkSZKkxrOBIUmSJEmSGs8GhiRJ\nkiRJajwbGJIkSZIkqfFsYDRYRMyOiOsqvvaVEXHRMNtPj4i/6ry64Y81GuNLVZkhqTrzI3XGDEnV\nmR/tig2MZtsPOLTia18CzN3Fc4MVx2z3WHWPL1VlhqTqzI/UGTMkVWd+NKxp3S5Az+oS4PkR8aXM\nfH1EvBl4B9AD3A6cDbwUuL78OAjcAfwG8CFgr4h4b2YuH27wiHglcDEwE3gUeHtm3h8R3wC+A7wa\neC5wTmZ+NSJeAHwO2AdYAxxTHvcnxwIeBH6+HOOFwI2ZeWbdXxipTWZIqs78SJ0xQ1J15kfDcgZG\ns/0h8GAZ2pcAS4CFmXkY8AhwbmbeCVwGfAy4FPjLzLwLuBD4yrOEdjpwOfA7mXk4RYCvaNllemYe\nBfwx8JFy2yXAFzLzF4FrgednZt8wxzoA+E3gxcBJEfHiOr4YUgVmSKrO/EidMUNSdeZHw3IGxvhx\nHPAi4N8iogeYTtFlBFgG3AZszsw3tjneAmA+8JVyPIC9W56/ofy4hmIKF8AJwOkAmfnliNiwi7Fv\nzsyNABFxL0X3Uuo2MyRVZ36kzpghqTrzo5+wgTF+TAX+NjPfCRARs3jm/98+QC+wd0Tsl5mPtzne\nvWUXkzK881qe31p+HKSYqgWwg/Zm7fS3fN76eqmbzJBUnfmROmOGpOrMj37CS0iarZ9nwvkvwCkR\n8bwyZJ/RjxoNAAABOUlEQVQG3lk+95fAJ4FPUUyj2vna6c8y9g+A/SJiUfl4CfD5Eer5Z+B3ASLi\nJIp/MIbWKTWJGZKqMz9SZ8yQVJ350bBsYDTbQ8API+LG8nquDwE3Ad8rn78oIn4b+DmK67IuoVg4\n5rcoFp85MiI+OtzAmbkNOBX4eER8F3gT8Lby6V2tnvtHwOsj4vbytTunTn0HeFV5rKGvdSVedZMZ\nkqozP1JnzJBUnfnRsHoGB/26qj0RcQ7wtcz8QUS8AvhMZr6y23VJ44UZkqozP1JnzJBUnflpDqe7\naHesA74YEQPAFmBpl+uRxhszJFVnfqTOmCGpOvPTEM7AkCRJkiRJjecaGJIkSZIkqfFsYEiSJEmS\npMazgSFJkiRJkhrPBoYkSZIkSWo8GxiSJEmSJKnxbGBIkiRJkqTG+2/qcrM3MzgSxAAAAABJRU5E\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x121e70ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = sns.FacetGrid(yelp,col='stars')\n",
"g.map(plt.hist,'text length')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Create a boxplot of text length for each star category.**"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x121283470>"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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fTro8STKzn1KcMPysu3cnWZ4wShI1ZmZ/CVwJDCVdloRdAfS5+4fN7FTg50Am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\nkYqUJEREpCIlCRERqej/AwbzFS0Gcw8CAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x122037b70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(x='stars',y='text length',data=yelp,palette='rainbow')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Create a countplot of the number of occurrences for each type of star rating.**"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x12578fc88>"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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8APhyRBwFzAcez8wzgTuAhU3WKknaX9OXm34GfKhl/7TM7K23VwDnAKcDfZm5MzM3AWuA\nWUA3cH9L37MbrlWSNECjIZGZ3wV2tjSNadl+AZgMdAEbW9o3A1MGtO/tK0lqo3ZPXO9u2e4Cnqea\nb5g8oH1D3d41oK8kqY3aHRL/OyLOrLfPBXqBVUB3REyIiCnATGA1sBKYW/edW/eVJLVRu0PiM8D1\nEfEPwFHA3Zm5DlgM9AHfp5rY3g4sAf5VRPQCnwS+2OZaJWnUa/wW2Mz8JfDOensNcNYgfXqAngFt\n24ALmq5vtNm1axf9/f2dLqMR06dPZ9y4cZ0uQxpRGg8JDS/9/f388d/+LRNPOKHTpRxR2555hmVz\n5zJjxoxOlyKNKIbEKDTxhBOY9KY3dboMSa8ALsshSSoyJCRJRYaEJKnIkJAkFRkSkqQiQ0KSVGRI\nSJKKRsVzEj5lLEmHZ1SERH9/P3+yaCUTjzup06UcUds2/JpvfQ6fMpbUmFEREgATjzuJY1/3Lzpd\nhiS9ojgnIUkqMiQkSUWGhCSpyJCQJBUZEpKkIkNCklRkSEiSijrynEREPApsrHd/AXwJuB3YDazO\nzCvqfpcB84AdwKLMXN7+aiVp9Gp7SETE0QCZ+d6WtnuBBZnZGxFLIuI84IfAlcBsYBLQFxEPZuaO\ndtcsSaNVJ0YSs4BjI+IBYBzwOWB2ZvbWx1cAf0A1qujLzJ3ApohYA/xr4NEO1CxJo1In5iS2Al/J\nzPcD84G7gDEtx18AJgNdvHxJCmAzMKVdRUqSOhMST1EFA5m5BlgPTGs53gU8D2yiCouB7ZKkNulE\nSHwc+BpARJxAFQQPRsSc+vi5QC+wCuiOiAkRMQWYCazuQL2SNGp1Yk6iB7gtInqp5h0upRpN3BoR\nRwFPAndn5p6IWAz0UV2OWpCZ2ztQrySNWm0PifrupIsHOXTWIH17qEJFktQBPkwnSSoyJCRJRYaE\nJKnIkJAkFRkSkqQiQ0KSVGRISJKKDAlJUpEhIUkqMiQkSUWGhCSpyJCQJBUZEpKkIkNCklRkSEiS\nigwJSVKRISFJKjIkJElFhoQkqciQkCQVje90AQcSEWOAvwRmAS8Cn8zMn3e2KkkaPYb7SOJ84OjM\nfCdwLXBTh+uRpFFluIdEN3A/QGb+CHhrZ8uRpNFluIfEZGBjy/7OiBjuNUvSiDGs5ySATUBXy/7Y\nzNx9gP7jANauXbtP47p169jy7E/YtXX9ka+wg17c+Azr1k1g0qRJQz5n3bp1bPnpT9m1YUODlbXf\ni+vWse4Nbzjkn8X/yX62/HbjwTu/gmx4+jnWRRzyz+KnTzzDht9tabCy9lu3diNTj1t3yD+Lnzy7\nkee27miwsvZ7euNWfn/d4D+Llt+Z4wYeG7Nnz56GSzt8EfFHwB9m5scj4h3Awsz8twfo3w30tq1A\nSRpZ3p2Zfa0Nw30k8V3gnIj4h3r/Ywfpvwp4N/AssKvJwiRpBBkHvIHqd+g+hvVIQpLUWU4CS5KK\nDAlJUpEhIUkqMiQkSUXD/e6mV5yIeDtwQ2a+p9O1dFJEjAe+CZwCTAAWZeb3OlpUh9QPgN4CBLAb\nuDwzn+hsVZ0TEVOBR4CzM/OpTtfTSRHxKC8/MPyLzPxEJ+sZjCFxBEXE1cBHgM2drmUYuBh4LjM/\nGhHHAT8GRmVIAB8E9mRmd0TMAb5EtS7ZqFP/8fANYGuna+m0iDgaIDPf2+laDsTLTUfWz4APdbqI\nYeLbwMJ6eywwsh5fPQSZeS8wr949BRhZj7sfmq8CS4BnOl3IMDALODYiHoiI79dXIYYdQ+IIyszv\nAjs7XcdwkJlbM3NLRHQBy4DPdbqmTsrM3RFxO/DnwF0dLqcjIuJS4DeZ+XfAmA6XMxxsBb6Sme8H\n5gN3Dce16YZdQRo5IuIk4CFgaWb+j07X02mZeSkwA7g1IiZ2uJxO+BjVCgo/AE4F/rqenxitnqL+\ngyEz1wDrqZ56Hlack2jGqP8rKSKmAQ8AV2TmDzpdTydFxMXAiZl5A9XLs3ZRTWCPKpk5Z+92HRR/\nmpm/6WBJnfZx4PeBKyLiBKrFTJ/tbEn7MySa4Von1UuiXg0sjIjrqH4m52bmS50tqyO+A9wWEQ9T\n/T/3qVH6c2jl/yPQQ/XfRS/VHw0fP8gq1x3h2k2SpCLnJCRJRYaEJKnIkJAkFRkSkqQiQ0KSVGRI\nSJKKDAnpCIqI/xIR7+p0HdKRYkhIR9YcqpfKSyOCD9NJhyki3ki19s4kqidmlwPXUC2t8CHgdcCf\nAROB44BrMvNvIuI24LXA9Lr/WcDZVMt1/M/MvL6934lU5khCOnyfAL6XmadT/bLfAqwCPpGZ/wRc\nUW+/FfgkcF3Luc9l5luAn1AtV/JvgHcBb46ICe38JqQDce0m6fB9H/ibiJgN3Af8N6oXDO1d4PEj\nwB9GxAXAO4BXtZz7o/rfTwNbI6Kv/ozPZ+b2dhQvDYUjCekwZeZK4F8C9wMXUr15r/X6bR/wNqpX\ndS5i39WBt9WfsYsqQD4PvAb4YUS8ufHipSEyJKTDFBE3Ah/NzDuAK4HZVC+dGl+/svXNwHWZeT/w\nfgaZ0I6IU4GHgb/PzGuAJ6jehS0NC4aEdPhuBv5dRDxGtRz45VTv0PgG1S/6W4En6pfdvw6YWL9s\n6P+NNjLzx8BK4J8i4hHgF8CKtn4X0gF4d5MkqciRhCSpyJCQJBUZEpKkIkNCklRkSEiSigwJSVKR\nISFJKjIkJElF/xcZofnjpeOA5AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12265cf98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x='stars',data=yelp,palette='rainbow')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Use groupby to get the mean values of the numerical columns, you should be able to create this dataframe with the operation:**"
]
},
{
"cell_type": "code",
"execution_count": 105,
"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>cool</th>\n",
" <th>useful</th>\n",
" <th>funny</th>\n",
" <th>text length</th>\n",
" </tr>\n",
" <tr>\n",
" <th>stars</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.576769</td>\n",
" <td>1.604806</td>\n",
" <td>1.056075</td>\n",
" <td>826.515354</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.719525</td>\n",
" <td>1.563107</td>\n",
" <td>0.875944</td>\n",
" <td>842.256742</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.788501</td>\n",
" <td>1.306639</td>\n",
" <td>0.694730</td>\n",
" <td>758.498289</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.954623</td>\n",
" <td>1.395916</td>\n",
" <td>0.670448</td>\n",
" <td>712.923142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.944261</td>\n",
" <td>1.381780</td>\n",
" <td>0.608631</td>\n",
" <td>624.999101</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cool useful funny text length\n",
"stars \n",
"1 0.576769 1.604806 1.056075 826.515354\n",
"2 0.719525 1.563107 0.875944 842.256742\n",
"3 0.788501 1.306639 0.694730 758.498289\n",
"4 0.954623 1.395916 0.670448 712.923142\n",
"5 0.944261 1.381780 0.608631 624.999101"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stars = yelp.groupby('stars').mean()\n",
"stars"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Use the corr() method on that groupby dataframe to produce this dataframe:**"
]
},
{
"cell_type": "code",
"execution_count": 106,
"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>cool</th>\n",
" <th>useful</th>\n",
" <th>funny</th>\n",
" <th>text length</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>cool</th>\n",
" <td>1.000000</td>\n",
" <td>-0.743329</td>\n",
" <td>-0.944939</td>\n",
" <td>-0.857664</td>\n",
" </tr>\n",
" <tr>\n",
" <th>useful</th>\n",
" <td>-0.743329</td>\n",
" <td>1.000000</td>\n",
" <td>0.894506</td>\n",
" <td>0.699881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>funny</th>\n",
" <td>-0.944939</td>\n",
" <td>0.894506</td>\n",
" <td>1.000000</td>\n",
" <td>0.843461</td>\n",
" </tr>\n",
" <tr>\n",
" <th>text length</th>\n",
" <td>-0.857664</td>\n",
" <td>0.699881</td>\n",
" <td>0.843461</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cool useful funny text length\n",
"cool 1.000000 -0.743329 -0.944939 -0.857664\n",
"useful -0.743329 1.000000 0.894506 0.699881\n",
"funny -0.944939 0.894506 1.000000 0.843461\n",
"text length -0.857664 0.699881 0.843461 1.000000"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stars.corr()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Then use seaborn to create a heatmap based off that .corr() dataframe:**"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x120edb828>"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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4DrgpMS8GbEp3UMYYMxjRHBxGbU/NEZ3A+yLyInB0r0XdIrJGVV9Ie3TGGDMA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\nMcZ4yJKwMcZ4yJKwMcZ4yJKwMcZ46P8BCWGXIaznedoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x120edb048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(stars.corr(),cmap='coolwarm',annot=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NLP Classification Task\n",
"\n",
"Let's move on to the actual task. To make things a little easier, go ahead and only grab reviews that were either 1 star or 5 stars.\n",
"\n",
"**Create a dataframe called yelp_class that contains the columns of yelp dataframe but for only the 1 or 5 star reviews.**"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"yelp_class = yelp[(yelp.stars==1) | (yelp.stars==5)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Create two objects X and y. X will be the 'text' column of yelp_class and y will be the 'stars' column of yelp_class. (Your features and target/labels)**"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X = yelp_class['text']\n",
"y = yelp_class['stars']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Import CountVectorizer and create a CountVectorizer object.**"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import CountVectorizer\n",
"cv = CountVectorizer()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Use the fit_transform method on the CountVectorizer object and pass in X (the 'text' column). Save this result by overwriting X.**"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X = cv.fit_transform(X)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train Test Split\n",
"\n",
"Let's split our data into training and testing data.\n",
"\n",
"** Use train_test_split to split up the data into X_train, X_test, y_train, y_test. Use test_size=0.3 and random_state=101 **"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.3,random_state=101)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training a Model\n",
"\n",
"Time to train a model!\n",
"\n",
"** Import MultinomialNB and create an instance of the estimator and call is nb **"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.naive_bayes import MultinomialNB\n",
"nb = MultinomialNB()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Now fit nb using the training data.**"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
]
},
"execution_count": 123,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nb.fit(X_train,y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Predictions and Evaluations\n",
"\n",
"Time to see how our model did!\n",
"\n",
"**Use the predict method off of nb to predict labels from X_test.**"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"predictions = nb.predict(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Create a confusion matrix and classification report using these predictions and y_test **"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.metrics import confusion_matrix,classification_report"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[159 69]\n",
" [ 22 976]]\n",
"\n",
"\n",
" precision recall f1-score support\n",
"\n",
" 1 0.88 0.70 0.78 228\n",
" 5 0.93 0.98 0.96 998\n",
"\n",
"avg / total 0.92 0.93 0.92 1226\n",
"\n"
]
}
],
"source": [
"print(confusion_matrix(y_test,predictions))\n",
"print('\\n')\n",
"print(classification_report(y_test,predictions))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Great! Let's see what happens if we try to include TF-IDF to this process using a pipeline.**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using Text Processing\n",
"\n",
"** Import TfidfTransformer from sklearn. **"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import TfidfTransformer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Import Pipeline from sklearn. **"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.pipeline import Pipeline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Now create a pipeline with the following steps:CountVectorizer(), TfidfTransformer(),MultinomialNB()**"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pipeline = Pipeline([\n",
" ('bow', CountVectorizer()), # strings to token integer counts\n",
" ('tfidf', TfidfTransformer()), # integer counts to weighted TF-IDF scores\n",
" ('classifier', MultinomialNB()), # train on TF-IDF vectors w/ Naive Bayes classifier\n",
"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using the Pipeline\n",
"\n",
"**Time to use the pipeline! Remember this pipeline has all your pre-process steps in it already, meaning we'll need to re-split the original data (Remember that we overwrote X as the CountVectorized version. What we need is just the text**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train Test Split\n",
"\n",
"**Redo the train test split on the yelp_class object.**"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X = yelp_class['text']\n",
"y = yelp_class['stars']\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.3,random_state=101)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Now fit the pipeline to the training data. Remember you can't use the same training data as last time because that data has already been vectorized. We need to pass in just the text and labels**"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Pipeline(steps=[('bow', CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
" dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
" lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
" ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
" strip_...f=False, use_idf=True)), ('classifier', MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))])"
]
},
"execution_count": 159,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# May take some time\n",
"pipeline.fit(X_train,y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Predictions and Evaluation\n",
"\n",
"** Now use the pipeline to predict from the X_test and create a classification report and confusion matrix. You should notice strange results.**"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"predictions = pipeline.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 228]\n",
" [ 0 998]]\n",
" precision recall f1-score support\n",
"\n",
" 1 0.00 0.00 0.00 228\n",
" 5 0.81 1.00 0.90 998\n",
"\n",
"avg / total 0.66 0.81 0.73 1226\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/marci/anaconda/lib/python3.5/site-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n"
]
}
],
"source": [
"print(confusion_matrix(y_test,predictions))\n",
"print(classification_report(y_test,predictions))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks like Tf-Idf actually made things worse! That is it for this project. But there is still a lot more you can play with:\n",
"\n",
"**Some other things to try....**\n",
"Try going back and playing around with the pipeline steps and seeing if creating a custom analyzer like we did in the lecture helps (note: it probably won't). Or recreate the pipeline with just the CountVectorizer() and NaiveBayes. Does changing the ML model at the end to another classifier help at all?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Great Job!"
]
}
],
"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 |
liuhanfei0615/liupengyuan.github.io | chapter2/homework/computer/middle/201611680862.ipynb | 15 | 6872 | {
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"请输入行数:5\n",
" * \n",
" * * \n",
" * * * \n",
" * * * * \n",
" * * * * * \n"
]
}
],
"source": [
"def fun(n):\n",
" for i in range(1,n+1):\n",
" for j in range(0,n+1-i):\n",
" print(' ',end = '')\n",
" for k in range(1,i+1):\n",
" print('* ',end = '') \n",
" print()\n",
" \n",
" \n",
"n=int(input('请输入行数:'))\n",
"fun(n)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1*1=1 \n",
"2*1=2 2*2=4 \n",
"3*1=3 3*2=6 3*3=9 \n",
"4*1=4 4*2=8 4*3=12 4*4=16 \n",
"5*1=5 5*2=10 5*3=15 5*4=20 5*5=25 \n",
"6*1=6 6*2=12 6*3=18 6*4=24 6*5=30 6*6=36 \n",
"7*1=7 7*2=14 7*3=21 7*4=28 7*5=35 7*6=42 7*7=49 \n",
"8*1=8 8*2=16 8*3=24 8*4=32 8*5=40 8*6=48 8*7=56 8*8=64 \n",
"9*1=9 9*2=18 9*3=27 9*4=36 9*5=45 9*6=54 9*7=63 9*8=72 9*9=81 \n"
]
}
],
"source": [
"for i in range(1,10):\n",
" for j in range(1,i+1):\n",
" print('{}*{}={:<3}'.format(i,j,i*j),end = '')\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"请输入2-10000内的一个整数：18\n"
]
},
{
"ename": "TypeError",
"evalue": "'float' object cannot be interpreted as an integer",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-22-23c5b12932ac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0mnumber\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'请输入2-10000内的一个整数：'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mfun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-22-23c5b12932ac>\u001b[0m in \u001b[0;36mfun\u001b[0;34m(number)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mfun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[1;32mfor\u001b[0m \u001b[0mnumber1\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnumber\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mnumber2\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnumber\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mnumber1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnumber1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: 'float' object cannot be interpreted as an integer"
]
}
],
"source": [
"\n",
"def fun(number):\n",
" for number1 in range(2,number/2+1):\n",
" number2=number-number1\n",
" for k in range(2,number1):\n",
" if(number1%k==0):\n",
" print('此数不能表示成两个质数之和。') \n",
" break; \n",
" if(k==number1-1):\n",
" n=1;\n",
" for j in range(2,number2):\n",
" if(number2%j==0):\n",
" print('此数不能表示成两个质数之和。')\n",
" break;\n",
" if(j==number2-1):\n",
" m=1;\n",
" if(m==1 and n==1):\n",
" print('能表示成两个质数之和分别为',number1 ,number2)\n",
" \n",
" \n",
"number=int(input('请输入2-10000内的一个整数：'))\n",
"fun(number)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 [1*,2,3,4,5]\n",
"2 [1,2*,3,4,5]\n",
"3 [1,2,3*,4,5]\n",
"4 [2,3,4*,5,6]\n",
"5 [3,4,5*,6,7]\n",
"6 [4,5,6*,7,8]\n",
"7 [5,6,7*,8,9]\n",
"8 [6,7,8*,9,10]\n",
"9 [7,8,9*,10,11]\n",
"10 [8,9,10*,11,12]\n",
"11 [9,10,11*,12,13]\n",
"12 [10,11,12*,13,14]\n",
"13 [11,12,13*,14,15]\n",
"14 [12,13,14*,15,16]\n",
"15 [13,14,15*,16,17]\n",
"16 [14,15,16*,17,18]\n",
"17 [15,16,17*,18,19]\n",
"18 [16,17,18*,19,20]\n",
"19 [16,17,18,19*,20]\n",
"20 [16,17,18,19,20*]\n"
]
}
],
"source": [
"list=['1','2','3','4','5','6','7','8','9','10','11','12','13','14','15','16','17','18','19','20']\n",
"print(1,'['+list[0]+'*'+','+list[1]+','+list[2]+','+list[3]+','+list[4]+']')\n",
"print(2,'['+list[0]+','+list[1]+'*'+','+list[2]+','+list[3]+','+list[4]+']')\n",
"for i in range(3,19):\n",
" print(i,'['+list[i-3]+','+list[i-2]+','+list[i-1]+'*'+','+list[i]+','+list[i+1]+']')\n",
"print(19,'['+list[15]+','+list[16]+','+list[17]+','+list[18]+'*'+','+list[19]+']')\n",
"print(20,'['+list[15]+','+list[16]+','+list[17]+','+list[18]+','+list[19]+'*'+']') "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
katelynneese/dmdd | Testing Simulation_AM.ipynb | 1 | 19830 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:root:pymultinest not imported!\n",
"WARNING:root:DMDD_MAIN_PATH environment variable not defined, defaulting to: ~/.dmdd\n"
]
}
],
"source": [
"import dmdd\n",
"import numpy as np\n",
"%matplotlib inline\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<module 'dmdd' from 'dmdd/__init__.pyc'>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reload(dmdd)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# \n",
"This cell demonstrates that dRdQ_AM has the same value as dRdQ at day 0 and day 365, when the v_lag is = 220."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 2.80527965e-17]\n",
"[ 2.80527965e-17]\n",
"[ 2.80527965e-17]\n"
]
}
],
"source": [
"print dmdd.dRdQ_AM(Q = [100.], sigma_si = 75.5)\n",
"print dmdd.rate_UV.dRdQ(Q = np.asarray([100.]), sigma_si = 75.5)\n",
"print dmdd.dRdQ_AM(Q = [100.], sigma_si = 75.5, time = 365)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# \n",
"This cell demonstates the rate_UV.dRdQ can recieve multiple Qs and return them as an array.\n",
"The following cell shows the same thing, but for dRdQ_AM. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 3.41365358e-12, 8.46638177e-13, 1.77757110e-13,\n",
" 2.84875973e-14, 2.61626746e-15, 2.80527965e-17])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dmdd.rate_UV.dRdQ(Q = np.array([50., 60., 70., 80., 90., 100.]), sigma_si = 75.5)\n",
"\n",
"# demonstrats that rate_UV.dRdQ can take multiple Q's"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 3.36242431e-10, 3.99546567e-11, 3.41365358e-12,\n",
" 2.80527965e-17])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dmdd.dRdQ_AM(Q = [10., 30., 50., 100.], sigma_si = 75.5, time = 0)\n",
"# demonstrates that dRdQ_AM can take multiple Q's as a list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# \n",
"This cell is showing the relative progression of rate over various times for fixed parameters."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 4.82670411e-17]\n"
]
},
{
"ename": "ValueError",
"evalue": "Buffer has wrong number of dimensions (expected 1, got 0)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-0b423515c327>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mdmdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdRdQ_AM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mQ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m100.\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msigma_si\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m75.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mdmdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdRdQ_AM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msigma_si\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m75.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\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;32mprint\u001b[0m \u001b[0mdmdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdRdQ_AM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msigma_si\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m75.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m150\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mdmdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdRdQ_AM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msigma_si\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m75.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mdmdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdRdQ_AM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msigma_si\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m75.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m250\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/katelynneese/dmddACT/dmdd/dmdd.pyc\u001b[0m in \u001b[0;36mdRdQ_AM\u001b[0;34m(mass, sigma_si, sigma_anapole, Q, time, element, vlag_mean, v_amplitude)\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[0;31m#print type(v_lag) #vlag must be a number not an array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m \u001b[0mrate_QT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrate_UV\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdRdQ\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mQ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menergy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv_lag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv_lag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmass\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msigma_si\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msigma_si\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msigma_anapole\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msigma_anapole\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0melement\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0melement\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[0;34m\"Return a 1D array with the rate based on the time and energy given\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mrate_UV.pyx\u001b[0m in \u001b[0;36mrate_UV.dRdQ (dmdd/rate_UV.c:14430)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Buffer has wrong number of dimensions (expected 1, got 0)"
]
}
],
"source": [
"print dmdd.dRdQ_AM(Q = [100.], sigma_si = 75.5, time = 50)\n",
"print dmdd.dRdQ_AM(sigma_si = 75.5, time = 100)\n",
"print dmdd.dRdQ_AM(sigma_si = 75.5, time = 150)\n",
"print dmdd.dRdQ_AM(sigma_si = 75.5, time = 200)\n",
"print dmdd.dRdQ_AM(sigma_si = 75.5, time = 250)\n",
"print dmdd.dRdQ_AM(sigma_si = 75.5, time = 300)\n",
"print dmdd.dRdQ_AM(sigma_si = 75.5, time = 350)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# \n",
"Demonstration of integral function"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2.9231845333250124e-06"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dmdd.integral(1., 100., 0., 365., sigma_si = 75.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# \n",
"Testing integral function for a known integral with an exact value of 1/6 ."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def funct1(Q,time, sigma_si, sigma_anapole, mass, element, v_amplitude):\n",
" return (Q**2)*(time)\n",
"#have to define like this due to the way integral is defined, but still returns correct answer"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.16675008341675007"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dmdd.integral(0, 1, 0, 1, function = funct1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# \n",
"Model and experiment to be used in simulations"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"model: Anapole, parameters: ['mass', 'sigma_anapole'].\n",
"model: SI, parameters: ['mass', 'sigma_si'].\n"
]
}
],
"source": [
"# shortcut for scattering models corresponding to rates coded in rate_UV:\n",
"anapole_model = dmdd.UV_Model('Anapole', ['mass','sigma_anapole'])\n",
"SI_model = dmdd.UV_Model('SI', ['mass','sigma_si'])\n",
"\n",
"print 'model: {}, parameters: {}.'.format(anapole_model.name, anapole_model.param_names)\n",
"print 'model: {}, parameters: {}.'.format(SI_model.name, SI_model.param_names)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# intialize an Experiment with XENON target, to be passed to Simulation_AM:\n",
"xe = dmdd.Experiment('1xe', 'xenon', 5, 150, 1000, dmdd.eff.efficiency_unit, energy_resolution=True)\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# \n",
"Attempting to run Simulation_AM"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Simulation data and/or pickle file does not exist. Forcing simulation.\n",
"\n",
"\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-11-5729ae255812>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mQmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m150.\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 4\u001b[0m \u001b[0mTmin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m365\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msigma_si\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m75\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m element = 'xenon', force_sim = True)\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/katelynneese/dmddACT/dmdd/dmdd.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, experiment, model, parvals, Qmin, Qmax, element, sigma_si, sigma_anapole, mass, v_amplitude, Tmin, Tmax, path, force_sim, asimov, nbins_asimov, plot_nbins, plot_theory, silent)\u001b[0m\n\u001b[1;32m 1085\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Asimov simulations not yet implemented!'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1086\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1087\u001b[0;31m \u001b[0mQ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimulate_data\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 1088\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavetxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatafile\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mQ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1089\u001b[0m \u001b[0mfout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpicklefile\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/katelynneese/dmddACT/dmdd/dmdd.pyc\u001b[0m in \u001b[0;36msimulate_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1148\u001b[0m if U < (PDF(Q_rand, T_rand, element = self.element, mass = self.mass,\n\u001b[1;32m 1149\u001b[0m \u001b[0msigma_si\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msigma_si\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msigma_anapole\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msigma_anapole\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1150\u001b[0;31m Qmin = np.asarray([self.Qmin]), Qmax = np.asarray([self.Qmax]), Tmin = self.Tmin, Tmax = self.Tmax)/env):\n\u001b[0m\u001b[1;32m 1151\u001b[0m \u001b[0;31m#increment matches\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1152\u001b[0m \u001b[0mmatches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmatches\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/katelynneese/dmddACT/dmdd/dmdd.pyc\u001b[0m in \u001b[0;36mPDF\u001b[0;34m(Q, time, element, mass, sigma_si, sigma_anapole, Qmin, Qmax, Tmin, Tmax)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0mTmin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTmax\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0melement\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0melement\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msigma_si\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msigma_si\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m sigma_anapole = sigma_anapole, mass = mass)\n\u001b[0m\u001b[1;32m 120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdrdq\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mnorm\u001b[0m \u001b[0;31m#for now removed efficiency and made it 1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/katelynneese/dmddACT/dmdd/dmdd.pyc\u001b[0m in \u001b[0;36mintegral\u001b[0;34m(Qmin, Qmax, Tmin, Tmax, Qpoints, Tpoints, function, sigma_si, sigma_anapole, mass, element, v_amplitude)\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mT\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mT_box\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 103\u001b[0m a_sum = np.sum(function(Q = np.asarray(Q), time = T, sigma_si = sigma_si, sigma_anapole = sigma_anapole,\n\u001b[0;32m--> 104\u001b[0;31m mass = mass, element = element, v_amplitude = v_amplitude))\n\u001b[0m\u001b[1;32m 105\u001b[0m \u001b[0mtotal_sum\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_sum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/katelynneese/dmddACT/dmdd/dmdd.pyc\u001b[0m in \u001b[0;36mdRdQ_AM\u001b[0;34m(mass, sigma_si, sigma_anapole, Q, time, element, vlag_mean, v_amplitude)\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[0;31m#print type(v_lag) #vlag must be a number not an array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m \u001b[0mrate_QT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrate_UV\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdRdQ\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mQ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menergy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv_lag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv_lag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmass\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msigma_si\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msigma_si\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msigma_anapole\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msigma_anapole\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0melement\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0melement\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[0;34m\"Return a 1D array with the rate based on the time and energy given\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"xe = dmdd.Simulation_AM('AM_xenon', xe, SI_model, \n",
" {'mass':50.,'sigma_si':75}, Qmin = np.asarray([5.]), \n",
" Qmax = np.asarray([150.]), \n",
" Tmin = 0, Tmax = 365, sigma_si = 75, \n",
" element = 'xenon', force_sim = True)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
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| mit |
Automating-GIS-processes/2017 | source/codes/Lesson3-geocoding.ipynb | 2 | 10868 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Geocoding & Table Join\n",
"\n",
"**Sources**\n",
"\n",
"*Following materials are partly based on documentation of [Geopandas](http://geopandas.org/geocoding.html), [geopy](http://geopy.readthedocs.io/en/1.11.0/#) and [Pandas](http://pandas.pydata.org/)\n",
"\n",
"## Overview of Geocoders\n",
"\n",
"Geocoding, i.e. converting addresses into coordinates or vice versa, is a really common GIS task. Luckily, in Python there are nice libraries that makes the geocoding really easy. One of the libraries that can do the geocoding for us is [geopy](http://geopy.readthedocs.io/en/1.11.0/) that makes it easy to locate the coordinates of addresses, cities, countries, and landmarks across the globe using third-party geocoders and other data sources. \n",
"\n",
"As said, **Geopy** uses third-party geocoders - i.e. services that does the geocoding - to locate the addresses and it works with multiple different service providers such as:\n",
"\n",
"- [ESRI ArcGIS](http://resources.arcgis.com/en/help/arcgis-rest-api/)\n",
"- [Baidu Maps](http://developer.baidu.com/map/webservice-geocoding.htm)\n",
"- [Bing](http://www.microsoft.com/maps/developers/web.aspx)\n",
"- [geocoder.us](http://geocoder.us/)\n",
"- [GeocodeFarm](https://www.geocodefarm.com/)\n",
"- [GeoNames](http://www.geonames.org/)\n",
"- [Google Geocoding API (V3)](https://developers.google.com/maps/documentation/geocoding/)\n",
"- [IGN France](http://api.ign.fr/tech-docs-js/fr/developpeur/search.html)\n",
"- [Mapquest](http://www.mapquestapi.com/geocoding/)\n",
"- [Mapzen Search](https://mapzen.com/projects/search/)\n",
"- [NaviData](http://navidata.pl)\n",
"- [OpenCage](http://geocoder.opencagedata.com/api.html)\n",
"- [OpenMapQuest](http://developer.mapquest.com/web/products/open/geocoding-service)\n",
"- [Open Street Map Nominatim](https://wiki.openstreetmap.org/wiki/Nominatim)\n",
"- [SmartyStreets](https://smartystreets.com/products/liveaddress-api)\n",
"- [What3words](http://what3words.com/api/reference)\n",
"- [Yandex](http://api.yandex.com/maps/doc/intro/concepts/intro.xml)\n",
"\n",
"Thus, there is plenty of geocoders where to choose from! However, to be able to use these services you might need to request so called API access-keys from the service provider to be able to use the service. You can get your access keys to e.g. Google Geocoding API from [Google APIs console](https://code.google.com/apis/console) by creating a Project and enabling a that API from [Library](https://console.developers.google.com/apis/library). Read a short introduction about using Google API Console from [here](https://developers.googleblog.com/2016/03/introducing-google-api-console.html).\n",
"\n",
"*There are also other Python modules in addition to geopy that can do geocoding such as* [Geocoder](http://geocoder.readthedocs.io/).\n",
"\n",
"## Geocoding in Geopandas\n",
"\n",
"It is also possible to do geocoding in Geopandas using its integrated functionalities of geopy. Geopandas has a function called `geocode()` that can geocode a list of addresses (strings) and return a GeoDataFrame containing the resulting point objects in `geometry` column. Nice, isn't it! Let's try this out. \n",
"\n",
"Download a text file called [addresses.txt](data/addresses.txt) that contains few addresses around Helsinki Region. The first rows of the data looks like following:\n",
"\n",
" ```\n",
" id;address\n",
" 1000;Itämerenkatu 14, 00101 Helsinki, Finland\n",
" 1001;Kampinkuja 1, 00100 Helsinki, Finland\n",
" 1002;Kaivokatu 8, 00101 Helsinki, Finland\n",
" 1003;Hermanstads strandsväg 1, 00580 Helsingfors, Finland\n",
" ```\n",
"\n",
"- Let's first read the data into a Pandas DataFrame using `read_csv()` -function:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" id address\n",
"0 1000 Itämerenkatu 14, 00101 Helsinki, Finland\n",
"1 1001 Kampinkuja 1, 00100 Helsinki, Finland\n",
"2 1002 Kaivokatu 8, 00101 Helsinki, Finland\n",
"3 1003 Hermanstads strandsväg 1, 00580 Helsingfors, F...\n",
"4 1004 Itäväylä, 00900 Helsinki, Finland\n"
]
}
],
"source": [
"# Import necessary modules\n",
"import pandas as pd\n",
"import geopandas as gpd\n",
"from shapely.geometry import Point\n",
"\n",
"# Import the geocoding function\n",
"from geopandas.tools import geocode\n",
"\n",
"# Filepath\n",
"fp = r\"/home/geo/addresses.txt\"\n",
"fp = r\"C:\\HY-Data\\HENTENKA\\KOODIT\\Opetus\\Automating-GIS-processes\\AutoGIS-Sphinx\\source\\data\\addresses.txt\"\n",
"\n",
"# Read the data\n",
"data = pd.read_csv(fp, sep=';')\n",
"\n",
"# Let's take a look of the data\n",
"print(data.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" - Now we have our data in a Pandas DataFrame and we can geocode our addresses\n",
" \n",
" - **Notice**: *here we will be using my API key that has a limitation of 2500 requests / hour. Because of this, only the computer instances of our course environment have access to Google Geocoding API for a short period of time. Thus, the following key will NOT work from your own computer, only from our cloud computers. If you wish, you can create your own API key to Google Geocoding API V3 from* [Google APIs console](https://code.google.com/apis/console). *See the notes from [above](#Geocoders)*"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" address geometry\n",
"0 Itämerenkatu 14, 00180 Helsinki, Finland POINT (24.9146767 60.1628658)\n",
"1 Kampinkuja 1, 00100 Helsinki, Finland POINT (24.9301701 60.1683731)\n"
]
}
],
"source": [
"from geopandas.tools import geocode\n",
"\n",
"# Key for our Google Geocoding API \n",
"# Notice: only the cloud computers of our course can access and successfully execute the following\n",
"key = 'AIzaSyAwNVHAtkbKlPs-EEs3OYqbnxzaYfDF2_8'\n",
"\n",
"# Geocode addresses\n",
"geo = geocode(data['address'], api_key=key)\n",
"\n",
"print(geo.head(2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And Voilà! As a result we have a GeoDataFrame that contains our original address and a 'geometry' column containing Shapely Point -objects that we can use for exporting the addresses to a Shapefile for example. However, the `id` column is not there. Thus, we need to join the information from `data` into our new GeoDataFrame `geo`, thus making a **Table Join**. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Table join\n",
"\n",
"Table joins are again something that you need to really frequently when doing GIS analyses. Combining data from different tables based on common `key` attribute can be done easily in Pandas/Geopandas using [.merge()](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html) -function. \n",
"\n",
"- Let's join the `data` and `geo` DataFrames together based on common column `address`. Parameter `on` is used to determine the common key in the tables. If your key in the first table would be named differently than in the other one, you can also specify them separately for each table by using `left_on` and `right_on` -parameters. "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" address \\\n",
"0 Kampinkuja 1, 00100 Helsinki, Finland \n",
"1 Kaivokatu 8, 00101 Helsinki, Finland \n",
"2 Hermanstads strandsväg 1, 00580 Helsingfors, F... \n",
"3 Itäväylä, 00900 Helsinki, Finland \n",
"4 Tyynenmerenkatu 9, 00220 Helsinki, Finland \n",
"\n",
" geometry id \n",
"0 POINT (24.9301701 60.1683731) 1001 \n",
"1 POINT (24.9418933 60.1698665) 1002 \n",
"2 POINT (24.9774004 60.18735880000001) 1003 \n",
"3 POINT (25.0919641 60.21448089999999) 1004 \n",
"4 POINT (24.9214846 60.1565781) 1005 \n"
]
},
{
"data": {
"text/plain": [
"geopandas.geodataframe.GeoDataFrame"
]
},
"execution_count": 22,
"output_type": "execute_result",
"metadata": {}
}
],
"source": [
"# Join tables by using a key column 'address'\n",
"join = geo.merge(data, on='address')\n",
"\n",
"# Let's see what we have\n",
"print(join.head())\n",
"\n",
"# Let's also check the data type\n",
"type(join)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As a result we have a new GeoDataFrame called `join` where we now have all original columns plus a new column for `geometry`. \n",
"\n",
"- Now it is easy to save our address points into a Shapefile"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"# Output file path\n",
"outfp = r\"/home/geo/addresses.shp\"\n",
"outfp = r\"C:\\HY-Data\\HENTENKA\\KOODIT\\Opetus\\Automating-GIS-processes\\AutoGIS-Sphinx\\source\\data\\addresses.shp\"\n",
"\n",
"# Save to Shapefile\n",
"join.to_file(outfp)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's it. Now we have successfully geocoded those addresses into Points and made a Shapefile out of them.\n",
"\n",
"**Task**: Make a map out of the points. What do you think that the addresses are representing?"
]
}
],
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"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
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} | mit |
prisae/blog-notebooks | MX_MexicoMaps.ipynb | 1 | 302104 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Mexico Maps\n",
"\n",
"Maps for <https://mexico.werthmuller.org>, but not yet used."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import travelmaps as tm\n",
"from adashof import cm2in\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.basemap import Basemap\n",
"\n",
"# Disable DecompressionBombWarning\n",
"from PIL import Image\n",
"Image.MAX_IMAGE_PIXELS = None\n",
"\n",
"%matplotlib inline\n",
"from matplotlib import rcParams\n",
"# Adjust dpi, so figure on screen and savefig looks the same\n",
"dpi = 200\n",
"rcParams['figure.dpi'] = dpi\n",
"rcParams['savefig.dpi'] = dpi"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tulum"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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K/jgajRpDn2IYqLH9iqFm0c4KhUKTPRptLbxY4txCSLrdbkPYCA+R2+2mq6vLaN/ipi0E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+giOtylGeVprpmwJQSCYotx4Z4LXL7lMtO7Mr7WLctE9QTGgJGmnzdR1l1GwBbEVeYibFbWDARJ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\nRkVEPBGCtUEu7b0EgPeyl40nNrLl9S3seGkH+57Zx+7ndqOPV2roxvyI2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lEAqVSKM2fO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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9be6f98e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig_x = plt.figure(figsize=(cm2in([11, 6])))\n",
"\n",
"MDF = [19.433333, -99.133333] # Mexico City\n",
"TUL = [20.214722, -87.428889] # Tulum\n",
"CAN = [21.160556, -86.8475] # Cancun\n",
"\n",
"# Create basemap\n",
"m_x = Basemap(width=3500000, height=2300000, resolution='c',\n",
" projection='tmerc', lat_0=24, lon_0=-102)\n",
"m_x.drawmapboundary(fill_color='#99ccff')\n",
"\n",
"# Fill non-visited countries (fillcontinents does a bad job)\n",
"countries = ['USA', 'BLZ', 'GTM', 'HND', 'SLV', 'NIC', 'CUB']\n",
"tm.country(countries, m_x, fc='.8', ec='.7', lw=.5)\n",
"\n",
"# Fill states\n",
"stateclrs = 32*['g']\n",
"stateclrs[22] = 'r'\n",
"tm.country('MEX', bmap=m_x, fc=stateclrs, ec='.2', lw=.5, adm=1)\n",
"\n",
"# Add visited cities\n",
"tm.city(TUL, 'Tulum', m_x, offs=[1, -3], halign=\"right\")\n",
"tm.city(MDF, 'Mexiko Stadt', m_x, offs=[.5, .5], halign=\"right\")\n",
"tm.city(CAN, 'Cancun', m_x, offs=[1, 1], halign=\"right\")\n",
"\n",
"# Save-path\n",
"#fpath = '../mexico.werthmuller.org/content/images/tulum/'\n",
"#plt.savefig(fpath+'MapTulum.png', bbox_inches='tight')\n",
" \n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Malinalco"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Qsiyj7VFhG6WmMPHeBL6EH1mTKAcq5EZz1Dw1yv4yVX+1t0FxCiy+sMji2UXG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\nOXXYAsXGxsbGxsbm1PH/A+fGd/rMdzT2AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9bd9457c50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig_x = plt.figure(figsize=(cm2in([11, 6])))\n",
"\n",
"MDF = [19.433333, -99.133333] # Mexico City\n",
"MAL = [18.95, -99.5] # Oaxaca\n",
"\n",
"# Create basemap\n",
"m_x = Basemap(width=700000, height=500000, resolution='c',\n",
" projection='tmerc', lat_0=20, lon_0=-99)\n",
"m_x.drawmapboundary(fill_color='#99ccff')\n",
"\n",
"# Fill states\n",
"stateclrs = 32*['g']\n",
"stateclrs[14] = 'r'\n",
"stateclrs[8] = 'c'\n",
"tm.country('MEX', bmap=m_x, fc=stateclrs, ec='.2', lw=.5, adm=1)\n",
"\n",
"# Add visited cities\n",
"tm.city(MAL, 'Malinalco', m_x, offs=[0, -0.3], halign=\"right\")\n",
"\n",
"# Save-path\n",
"#fpath = '../mexico.werthmuller.org/content/images/malinalco/'\n",
"#plt.savefig(fpath+'MapMalinalco.png', bbox_inches='tight')\n",
" \n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Teotihuacán"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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Qsiyj7VFhG6WmMPHeBL6EH1mTKAcq5EZz1Dw1yv4yVX+1t0FxCiy+sMji2UXG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2O2a5bF1sUbfoeIKZHOZ7uQtFm87YukjCnSpjTh6lw7NTJ0PttJ3BLHvmgnbo0Tt++emI3ZYggI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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9be5d2ceb8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig_x = plt.figure(figsize=(cm2in([11, 6])))\n",
"\n",
"MDF = [19.433333, -99.133333] # Mexico City\n",
"TEO = [19.6925, -98.8438] # Teotihuacán\n",
"\n",
"# Create basemap\n",
"m_x = Basemap(width=700000, height=500000, resolution='c',\n",
" projection='tmerc', lat_0=20, lon_0=-99)\n",
"m_x.drawmapboundary(fill_color='#99ccff')\n",
"\n",
"# Fill states\n",
"stateclrs = 32*['g']\n",
"stateclrs[14] = 'r'\n",
"stateclrs[8] = 'c'\n",
"tm.country('MEX', bmap=m_x, fc=stateclrs, ec='.2', lw=.5, adm=1)\n",
"\n",
"# Add visited cities\n",
"tm.city(TEO, 'Teotihuacan', m_x, offs=[0, .7], halign=\"center\")\n",
"\n",
"# Save-path\n",
"#fpath = '../mexico.werthmuller.org/content/images/teotihuacan/'\n",
"#plt.savefig(fpath+'MapTeotihuacan.png', bbox_inches='tight')\n",
" \n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| cc0-1.0 |
babrodtk/equelle | examples/iPython_demos/2D_heat_implicit_flux_version.ipynb | 3 | 10837 | {
"metadata": {
"name": "2D_heat_implicit_flux_version"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Implicit heat equation on Cartesian grid"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Se ogs\u00e5 notebook for den eksplisitte l\u00f8sningen samt 1D implisitt l\u00f8sning. Vi \u00f8nsker \u00e5 l\u00f8se varmelikningen implisitt,\n",
"\n",
"$\\frac{\\delta u}{\\delta t} = \\kappa \\frac{\\delta^2u}{\\delta x^2}$,\n",
"\n",
"fortsatt p\u00e5 kartesisk grid, men n\u00e5 i 2D. Diskretisert implisitt med hensyn p\u00e5 tid f\u00e5r vi\n",
"\n",
"$\\frac{1}{\\Delta t}\\left(u_i^{k} - u_i^{k-1}\\right) = \n",
"\\kappa\\left[\n",
"\\frac{1}{\\Delta x^2} \\left(u^k_{i, j-1} - 2u_{i, j}^k + u_{i, j+1}^k \\right) +\n",
"\\frac{1}{\\Delta y^2} \\left(u^k_{i-1, j} - 2u_{i, j}^k + u_{i+1, j}^k \\right)\n",
"\\right]$,\n",
"\n",
"og tilh\u00f8rende stensil\n",
"\n",
"$u_{i, j}^{k-1} = \n",
"-s_x u^k_{i, j-1} +2s_x u_{i, j}^k - s_x u_{i, j+1}^k \n",
"-s_y u^k_{i-1, j} +2s_y u_{i, j}^k - s_y u_{i+1, j}^k + u_{i, j}^k,\n",
"\\quad s_x=\\frac{\\kappa\\Delta t}{\\Delta x^2}, s_y=\\frac{\\kappa\\Delta t}{\\Delta y^2}$.\n",
"\n",
"Om vi reorganiserer, kan vi skrive dette som \n",
"\n",
"$u_{i, j}^{k-1} = \n",
"- s_x ( u_{i, j-1}^k - u_{i, j}^k )\n",
"- s_x ( u_{i, j+1}^k - u_{i, j}^k )\n",
"- s_y ( u_{i-1, j}^k - u_{i, j}^k )\n",
"- s_y ( u_{i+1, j}^k - u_{i, j}^k )\n",
"+ u_{i, j}^k$,\n",
"\n",
"eller\n",
"\n",
"$u_{i, j}^{k-1} = - \\text{LeftFlux} - \\text{RightFlux} - \\text{DownFlux} - \\text{UpFlux} + u_{i, j}^k$,\n",
"\n",
"som illustrerer sammenhengen mellom matrisa for likningssystemet og fluksene samt verdiene $u_i^n$ for l\u00f8sningen."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import time\n",
"import matplotlib.pyplot as plt\n",
"from IPython.display import clear_output\n",
"from mpl_toolkits.mplot3d.axes3d import Axes3D\n",
"\n",
"# Grid spacing\n",
"dx = 0.5\n",
"dy = 0.5\n",
"\n",
"# Heat diffusion constant\n",
"kappa = 0.3\n",
"\n",
"# Maximum time step size (constrained by CFL)\n",
"#dt = (dx*dx)/(4*kappa) # ok when dx==dy\n",
"dt = (dx*dx*dy*dy)/(dx*dx+dy*dy) / (2*kappa) # if dx!=dy\n",
"\n",
"#dt *= 10\n",
"\n",
"#dt = 1000;\n",
"\n",
"# Number of grid cells\n",
"n = 5\n",
"\n",
"# Boundary conditions\n",
"ub = np.zeros( (n, n) )\n",
"ub[0] = np.linspace(0.5, 1.5, n) # Top row\n",
"ub[n-1] = np.linspace(0.5, 1.5, n) # Bottom row\n",
"ub[:, 0] = np.linspace(0.5, 0.5, n) # Left column\n",
"ub[:, n-1] = np.linspace(1.5, 1.5, n) # Right column\n",
"\n",
"# Grid (fikse denne, bruke dx og dy)\n",
"x = np.outer( np.linspace(1, 1, n), np.linspace(0.0, 1.0, n) )\n",
"y = np.outer( np.linspace(1.0, 0.0, n), np.linspace(1, 1, n) )"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Initial temperatures\n",
"u = np.zeros( (n, n) )\n",
"u[0] = ub[0]\n",
"u[n-1] = ub[n-1]\n",
"u[:, 0] = ub[:, 0] \n",
"u[:, n-1] = ub[:, n-1]\n",
"\n",
"# Data structure containing neigbhour information, nbr[i, j, k] contains the displacement of neigbhour cell number\n",
"# k of cell (i, j) (with coordinates x[j], y[i]), i.e. the neighbour cell is cell (i, j) + nbr[i, j, k].\n",
"# The number of neighbours of cell (i, j) is stored in num_of_nbr[i, j].\n",
"nbr = np.zeros( (n, n, 4, 2), dtype=int )\n",
"num_of_nbr = np.zeros( (n, n), dtype=int )\n",
"for i in range(n):\n",
" for j in range(n):\n",
" # left\n",
" if (j>0):\n",
" nbr[i, j, num_of_nbr[i, j]] = np.array( [0, -1] )\n",
" num_of_nbr[i, j] += 1\n",
" # right\n",
" if (j<n-1):\n",
" nbr[i, j, num_of_nbr[i, j]] = np.array( [0, 1] )\n",
" num_of_nbr[i, j] += 1\n",
" # up (in indices, \"down\" in the sense that y is less for the neighbour)\n",
" if (i>0):\n",
" nbr[i, j, num_of_nbr[i, j]] = np.array( [-1, 0] )\n",
" num_of_nbr[i, j] += 1\n",
" # down\n",
" if (i<n-1):\n",
" nbr[i, j, num_of_nbr[i, j]] = np.array( [1, 0] )\n",
" num_of_nbr[i, j] += 1"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# print or plot?\n",
"pl = True"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def assembleA(rx, ry):\n",
" A = np.zeros( (n*n, n*n) )\n",
"\n",
" # Assemble the matrix one cell at a time.\n",
" for i in range(n):\n",
" for j in range(n):\n",
" \n",
" ii = i*n + j # Index of cell (i, j)\n",
" A[ii, ii] = 1;\n",
" \n",
" # Looping over neighbours.\n",
" for k in range(num_of_nbr[i, j]):\n",
" di, dj = nbr[i, j, k]\n",
" nbr_ii = (i+di)*n + (j+dj)\n",
" \n",
" # flux = r*(u[i, j] - u[i+di, j+dj])\n",
" if ( di == 0 ):\n",
" r = rx\n",
" else:\n",
" r = ry\n",
" A[ii, ii] += r\n",
" A[ii, nbr_ii] += -r\n",
" \n",
" # Apply Dirichlet boundary conditions, # u_i^n = u_i^n-1, for i=0 and i=n-1.\n",
" for i in range(0, n, n-1):\n",
" for j in range(n):\n",
" ii = i*n + j;\n",
" A[ii] = np.zeros(n*n)\n",
" A[ii, ii] = 1\n",
" for j in range(0, n, n-1):\n",
" for i in range(n):\n",
" ii = i*n + j;\n",
" A[ii] = np.zeros(n*n)\n",
" A[ii, ii] = 1\n",
"\n",
" return A;"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sx = kappa * dt / (dx*dx)\n",
"sy = kappa * dt / (dy*dy)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Rendering a matrix A and its inverse, for visual inspection\n",
"fig2 = plt.figure(figsize=(16, 4))\n",
"plt.subplot(121); # Denne g\u00e5r inn i current figure, som n\u00e5 er fig2?\n",
"plt.imshow(assembleA(sx, sy), interpolation='nearest');\n",
"plt.colorbar();\n",
"plt.subplot(122)\n",
"plt.imshow(pylab.inv(assembleA(sx, sy)), interpolation='nearest')\n",
"qqq=plt.colorbar() # Trenger \u00e5 putte den i en variabel for \u00e5 unng\u00e5 utskrift av \"handle\"\n",
"#plt.sca(ax) # Setting current axis to 'ax' and current figure to the parent figure of 'ax'.\n",
"#plt.figure(fig.number); # Setting the current figure"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Preparing figure for later rendering\n",
"if (pl):\n",
" fig = plt.figure(figsize=(10, 6))\n",
" ax = fig.add_subplot(1, 1, 1, projection='3d')\n",
" ax.view_init(50, -110)\n",
" plt.close()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Initializing with zero in the interior and the Dirichlet boundary conditions. Rendering the initial condition.\n",
"u = np.zeros( (n, n) )\n",
"u[0] = ub[0]\n",
"u[n-1] = ub[n-1]\n",
"u[:, 0] = ub[:, 0] \n",
"u[:, n-1] = ub[:, n-1]\n",
"if (pl):\n",
" ax.cla()\n",
" clear_output()\n",
" #ax.set_title( \"Time = \" + str(dt*k) )\n",
" ax.grid(False) # When 'True', the grid \"shines through\". How to avoid this?\n",
" ax.plot_surface( x, y, u, rstride=1, cstride=1, antialiased=False, linewidth=1, shade=True );\n",
" ax.set_xlabel(\"x\")\n",
" ax.set_ylabel(\"y\");\n",
" ax.set_zlabel(\"temp\");\n",
" ax.set_zlim(0.5, 1.5)\n",
" display(fig)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Setting up the matrix A, and then performing som iterations\n",
"A = assembleA(sx, sy)\n",
"for k in range(0, 20):\n",
" # Indexing convention for the unknowns u^{n+1}_{i, j} ( =v[i, j] ) in the solution vector: v[i*n+j].\n",
" v = np.linalg.solve(A, reshape(u, (n*n, 1)));\n",
" u, v = reshape(v, (n, n)), u;\n",
" \n",
" if (not(pl)):\n",
" print \"u =\\n\", u\n",
" else:\n",
" ax.cla()\n",
" clear_output()\n",
" ax.set_title( \"Time = \" + str(dt*k) )\n",
" ax.grid(False) # When 'True', the grid \"shines through\". How to avoid this?\n",
" ax.plot_surface( x, y, u, rstride=1, cstride=1, antialiased=False, linewidth=1, shade=True );\n",
" ax.set_xlabel(\"x\")\n",
" ax.set_ylabel(\"y\");\n",
" ax.set_zlabel(\"temp\");\n",
" ax.set_zlim(0.5, 1.5)\n",
" display(fig)\n",
"\n",
"plt.close()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | agpl-3.0 |
4dsolutions/Python5 | Graphing Equations.ipynb | 1 | 5726 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Graphing Equations\n",
"\n",
"Lets revisit high school and think about those tiny screens a lot of us had to use.\n",
"\n",
"We may recreate those kinds of plots, of mathematical functions, quite straightforwardly.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets define a domain from -5 to 5, of 100 points, and plot some XY curves that show some functions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"domain = np.linspace(-5.0,5.0,100)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y = np.power(domain, 2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline \n",
"# \"magic\" command telling Jupyter NB to embed plots\n",
"\n",
"# always label and title your plot, at minimum\n",
"plt.xlabel(\"X\")\n",
"plt.ylabel(\"Y\")\n",
"plt.title(\"Parabolic Curve\")\n",
"p = plt.plot(domain, y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x3 = np.power(domain, 3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.xlabel(\"X\")\n",
"plt.ylabel(\"Y\")\n",
"plt.title(\"X to the 3rd Power\")\n",
"p = plt.plot(domain, x3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def poly(x):\n",
" return (x - 3) * (x + 5) * (x - 1) * x"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Poly = np.vectorize(poly)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y = Poly(domain)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.xlabel(\"X\")\n",
"plt.ylabel(\"Y\")\n",
"plt.title(\"4th Degree Polynomial\")\n",
"plt.grid()\n",
"p = plt.plot(domain, y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y0 = np.sin(domain)\n",
"y1 = np.cos(domain)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.xlabel(\"X\")\n",
"plt.ylabel(\"Y\")\n",
"plt.title(\"Sine & Cosine\")\n",
"plt.grid()\n",
"plt.plot(domain, y0, color = \"orange\", label=\"Sine\")\n",
"plt.plot(domain, y1, color = \"green\", label=\"Cosine\")\n",
"p = plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we've plotted some data, lets organize the data into a data table, or \"data frame\" to be more precise. Pandas is all about the DataFrame object.\n",
"\n",
"Our domain is a 1-D ndarray lets remember. As such, we may turn it into a Series, which is the one dimensional equivalent in pandas. DataFrames are 2-dimensional and above."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"domain.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"col0 = pd.Series(domain)\n",
"col1 = pd.Series(np.power(domain,2))\n",
"col2 = pd.Series(x3)\n",
"col3 = Poly(domain)\n",
"\n",
"datadict = {\"Input\":col0, \"Parabola\":col1, \"3rd Power\":col2, \"Polynomial\":col3}\n",
"df = pd.DataFrame(datadict, columns = [\"Input\", \"Parabola\", \"3rd Power\", \"Polynomial\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Without the ```columns``` argument, there's no guarantee that ```datadict``` will gives us the left-to-right column order we desire."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we're starting to introduce how data may be selected by numeric indexes, yes, but also by labels."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.loc[:,\"3rd Power\"].head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.loc[:10,[\"Input\", \"3rd Power\"]] # rows 0-10 inclusive, two columns"
]
}
],
"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 |
connorsempek/splice | notebooks/examples.ipynb | 1 | 23318 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### TODOs\n",
"* Should set many parameters to None (e.g., slice_by and idx)\n",
"* Bug with empty string hover_text\n",
"* update traces method\n",
"* drop_attr method which performs an update by removing an attribute\n",
"* access trace list as attribute\n",
"* annotation methods, expecially in bar charts\n",
"* break up splicer functions\n",
"* method to return traces indexed by name (might be useful)\n",
"* add points/lines e.g. add y=3 line, and allow to add by date or categorical point too\n",
"* Ribbon plots\n",
"* Error bars\n",
"* Subplots shouldn't have dedicated figure, should call an update method, or, have methods that return static figure that doesn't get updated"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import sys\n",
"import numpy as np\n",
"import pandas as pd\n",
"import plotly\n",
"import plotly.graph_objs as go\n",
"\n",
"sys.path.append('/Users/csempek/root/repos/personal/splice/')\n",
"import splice\n",
"import splice.splice_objs as so\n",
"\n",
"import plotly.offline as off\n",
"off.init_notebook_mode(connected=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.random.seed(111)\n",
"df = pd.DataFrame({\n",
" 'idx': np.arange(1000) % 100,\n",
" 'num': np.random.random(1000),\n",
" 'cat1': ['ABCD'[np.random.randint(0, 4)] for _ in range(1000)],\n",
" 'cat2': [['male', 'female'][np.random.randint(0, 2)] for _ in range(1000)],\n",
" 'cat3': [['child', 'teen', 'adult'][np.random.randint(0, 3)] for _ in range(1000)],\n",
" })"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bar = so.GroupedBars(df=df, x='cat1', y='num', \n",
" slice_by=['cat2', 'cat3'],\n",
" idx=['cat2', 'cat3'],\n",
" hover_text=['cat1', 'cat2', 'cat3'],\n",
" marker={'line': {'color':'gray', 'width':0.5}}\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"off.iplot(bar.figure)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This is the format of your plot grid:\n",
"[ (1,1) x1,y1 ] [ (1,2) x2,y2 ]\n",
"[ (2,1) x3,y3 ] [ (2,2) x4,y4 ]\n",
"[ (3,1) x5,y5 ] [ (3,2) x6,y6 ]\n",
"\n"
]
},
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"bar = so.StackedBars(df=df, x='cat1', y='num', \n",
" slice_by=['cat2', 'cat3'],\n",
" idx=['cat2'],\n",
" hover_text=['cat1', 'cat2', 'cat3'],\n",
" marker={'line': {'color':'gray', 'width':0.5}}\n",
" )"
]
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{
"cell_type": "code",
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"collapsed": false
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{
"name": "stdout",
"output_type": "stream",
"text": [
"This is the format of your plot grid:\n",
"[ (1,1) x1,y1 ] [ (1,2) x2,y2 ]\n",
"\n"
]
},
{
"data": {
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"<div id=\"4db0b76b-398b-44fe-97ec-d1b00a425e8f\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"4db0b76b-398b-44fe-97ec-d1b00a425e8f\", [{\"legendgroup\": \"male|adult\", \"name\": \"male|adult\", \"yaxis\": \"y1\", \"text\": [\"cat1: A<br>cat2: male<br>cat3: adult\", \"cat1: B<br>cat2: male<br>cat3: adult\", \"cat1: C<br>cat2: male<br>cat3: adult\", \"cat1: D<br>cat2: male<br>cat3: adult\"], \"marker\": {\"color\": \"rgba(25, 181, 254, 1)\", \"line\": {\"color\": \"gray\", \"width\": 0.5}}, \"xaxis\": \"x1\", \"hoverinfo\": \"text\", \"y\": [15.72101973379144, 19.587735455966396, 22.46989391225016, 23.334960734307646], \"x\": [\"A\", \"B\", \"C\", \"D\"], \"type\": \"bar\"}, {\"legendgroup\": \"male|child\", \"name\": \"male|child\", \"yaxis\": \"y1\", \"text\": [\"cat1: A<br>cat2: male<br>cat3: child\", \"cat1: B<br>cat2: male<br>cat3: child\", \"cat1: C<br>cat2: male<br>cat3: child\", \"cat1: D<br>cat2: male<br>cat3: child\"], \"marker\": {\"color\": \"rgba(102, 204, 153, 1)\", \"line\": {\"color\": \"gray\", \"width\": 0.5}}, \"xaxis\": \"x1\", \"hoverinfo\": \"text\", \"y\": [18.703014296985877, 24.451667843427405, 18.47765650057995, 22.417882467550907], \"x\": [\"A\", \"B\", \"C\", \"D\"], \"type\": \"bar\"}, {\"legendgroup\": \"male|teen\", \"name\": \"male|teen\", \"yaxis\": \"y1\", \"text\": [\"cat1: A<br>cat2: male<br>cat3: teen\", \"cat1: B<br>cat2: male<br>cat3: teen\", \"cat1: C<br>cat2: male<br>cat3: teen\", \"cat1: D<br>cat2: male<br>cat3: teen\"], \"marker\": {\"color\": \"rgba(243, 156, 18, 1)\", \"line\": {\"color\": \"gray\", \"width\": 0.5}}, \"xaxis\": \"x1\", \"hoverinfo\": \"text\", \"y\": [20.588190242010306, 24.463632428994377, 20.74811816224883, 16.887216009844796], \"x\": [\"A\", \"B\", \"C\", \"D\"], \"type\": \"bar\"}, {\"legendgroup\": \"female|adult\", \"name\": \"female|adult\", \"yaxis\": \"y2\", \"text\": [\"cat1: A<br>cat2: female<br>cat3: adult\", \"cat1: B<br>cat2: female<br>cat3: adult\", \"cat1: C<br>cat2: female<br>cat3: adult\", \"cat1: D<br>cat2: female<br>cat3: adult\"], \"marker\": {\"color\": \"rgba(103, 65, 114, 1)\", \"line\": {\"color\": \"gray\", \"width\": 0.5}}, \"xaxis\": \"x2\", \"hoverinfo\": \"text\", \"y\": [14.315842614846789, 18.074745094614343, 19.40114627442219, 24.991385240654857], \"x\": [\"A\", \"B\", \"C\", \"D\"], \"type\": \"bar\"}, {\"legendgroup\": \"female|child\", \"name\": \"female|child\", \"yaxis\": \"y2\", \"text\": [\"cat1: A<br>cat2: female<br>cat3: child\", \"cat1: B<br>cat2: female<br>cat3: child\", \"cat1: C<br>cat2: female<br>cat3: child\", \"cat1: D<br>cat2: female<br>cat3: child\"], \"marker\": {\"color\": \"rgba(58, 83, 155, 1)\", \"line\": {\"color\": \"gray\", \"width\": 0.5}}, \"xaxis\": \"x2\", \"hoverinfo\": \"text\", \"y\": [23.70538321388523, 22.188830435490498, 20.13595000562476, 14.714808741590392], \"x\": [\"A\", \"B\", \"C\", \"D\"], \"type\": \"bar\"}, {\"legendgroup\": \"female|teen\", \"name\": \"female|teen\", \"yaxis\": \"y2\", \"text\": [\"cat1: A<br>cat2: female<br>cat3: teen\", \"cat1: B<br>cat2: female<br>cat3: teen\", \"cat1: C<br>cat2: female<br>cat3: teen\", \"cat1: D<br>cat2: female<br>cat3: teen\"], \"marker\": {\"color\": \"rgba(34, 49, 63, 1)\", \"line\": {\"color\": \"gray\", \"width\": 0.5}}, \"xaxis\": \"x2\", \"hoverinfo\": \"text\", \"y\": [26.750690529336975, 18.15374863496241, 20.485470692520586, 20.504159255918808], \"x\": [\"A\", \"B\", \"C\", \"D\"], \"type\": \"bar\"}], {\"yaxis1\": {\"domain\": [0.0, 1.0], \"anchor\": \"x1\"}, \"xaxis2\": {\"domain\": [0.55, 1.0], \"anchor\": \"y2\"}, \"xaxis1\": {\"domain\": [0.0, 0.45], \"anchor\": \"y1\"}, \"annotations\": [{\"yanchor\": \"bottom\", \"xref\": \"paper\", \"xanchor\": \"center\", \"yref\": \"paper\", \"text\": \"male\", \"y\": 1.0, \"x\": 0.225, \"font\": {\"size\": 16}, \"showarrow\": false}, {\"yanchor\": \"bottom\", \"xref\": \"paper\", \"xanchor\": \"center\", \"yref\": \"paper\", \"text\": \"female\", \"y\": 1.0, \"x\": 0.775, \"font\": {\"size\": 16}, \"showarrow\": false}], \"barmode\": \"stack\", \"yaxis2\": {\"domain\": [0.0, 1.0], \"anchor\": \"x2\"}, \"legend\": {\"tracegroupgap\": 0}}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>"
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"off.iplot(bar.subplot_figure)"
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| mit |
whitead/numerical_stats | unit_7/hw_2017/problem_set_2.ipynb | 1 | 6927 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true,
"nbgrader": {
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"grade_id": "1",
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}
},
"source": [
"Instructions\n",
"----\n",
"----\n",
"\n",
"Compute the sample statistics on the given data using `numpy`. Write the equation in LaTeX first and then complete the computation in Python second. You may refer to equations in other problems. For example, to compute sample variance you could refer to the sample mean computed in problem 1.\n",
"\n",
"#### example\n",
"\n",
"The sum of the data is\n",
"\n",
"$$\n",
"\\sum x_i = 16\n",
"$$\n",
"\n",
"**Note that your answer must appear in the Markdown cell and Python cell**"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
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"grade": false,
"grade_id": "2",
"locked": true,
"schema_version": 1,
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}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"16\n"
]
}
],
"source": [
"#example\n",
"example_data_do_not_use = [4,3,6,3]\n",
"print(sum(example_data_do_not_use))"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "3",
"locked": true,
"schema_version": 1,
"solution": false
}
},
"source": [
"#### Data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"nbgrader": {
"grade": false,
"grade_id": "4",
"locked": true,
"schema_version": 1,
"solution": false
}
},
"outputs": [],
"source": [
"data=[13,13,11,11,12,10,14,14,8,11,14,10,16,11,11,15,12,13,12,11,13,12,14,10,9,12,13,14,14,10,15,13,12,12,13,10,12,10,13,13,14,8,14,11,9,13,10,11,9,9,15,12,14,10,16,14,9,10,12,13,8,11,16,13,10,10,13,10,11,11,14,7,12,14,13,13,9,9,13,10,12,12,13,12,10,10,13,11,15,13,13,17,9,12,12,9,12,9,10,12]"
]
},
{
"cell_type": "markdown",
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"nbgrader": {
"grade": false,
"grade_id": "5",
"locked": true,
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}
},
"source": [
"#### Problem 1\n",
"\n",
"Compute the mean"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": true,
"grade_id": "6",
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}
},
"source": [
"The mean is given by\n",
"\n",
"$$\n",
"\\bar{x} = \\frac{1}{N} \\sum_i^N x_i = 11.82\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"nbgrader": {
"grade": true,
"grade_id": "7",
"locked": false,
"points": 2,
"schema_version": 1,
"solution": true
}
},
"outputs": [
{
"data": {
"text/plain": [
"11.82"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(data)"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": false,
"grade_id": "11",
"locked": true,
"schema_version": 1,
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}
},
"source": [
"#### Problem 2\n",
"Compute the sample standard deviation"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": true,
"grade_id": "22",
"locked": false,
"points": 2,
"schema_version": 1,
"solution": true
}
},
"source": [
"Sample standard deviation is given by:\n",
"\n",
"$$\n",
"\\sigma_x = \\sqrt{\\frac{1}{N - 1} \\sum_x (x - \\bar{x})^2}\n",
"$$\n",
"\n",
"and is $\\sigma_x = 2.04$"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"nbgrader": {
"grade": true,
"grade_id": "33",
"locked": false,
"points": 2,
"schema_version": 1,
"solution": true
}
},
"outputs": [
{
"data": {
"text/plain": [
"2.0418846513191995"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.std(data, ddof=1)"
]
},
{
"cell_type": "markdown",
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"nbgrader": {
"grade": false,
"grade_id": "44",
"locked": true,
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}
},
"source": [
"#### Problem 3\n",
"Compute the correlation of the original data with the given data below"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {
"grade": true,
"grade_id": "55",
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},
"source": [
"The sample correlation is given by\n",
"\n",
"$$\n",
"r = \\frac{\\sigma_{xy}}{\\sigma_x\\sigma_y}\n",
"$$\n",
"\n",
"where $\\sigma_x$ is the sample standard deviation defined above and the sample covariance, $\\sigma_{xy}$ is\n",
"\n",
"$$\n",
" \\sigma_{xy}= \\frac{1}{N - 1} \\sum_i^N (x - \\bar{x})(y - \\bar{y})\n",
"$$\n",
"\n",
"In this case $r = 0.865$"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"nbgrader": {
"grade": true,
"grade_id": "66",
"locked": false,
"points": 2,
"schema_version": 1,
"solution": true
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1. , 0.86469286],\n",
" [ 0.86469286, 1. ]])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data2=[16,15,14,13,16,12,15,15,9,13,17,13,19,14,16,18,15,14,14,14,14,14,15,14,13,14,16,18,15,13,17,16,14,16,17,13,16,13,17,16,16,11,18,12,12,16,13,15,14,11,15,17,17,15,20,16,11,14,14,15,11,14,19,16,13,11,13,11,13,15,16,9,13,15,15,15,10,11,17,11,15,15,16,15,12,12,16,13,17,17,15,18,11,16,15,11,15,12,14,16]\n",
"### BEGIN SOLUTION\n",
"np.corrcoef(data, data2)\n",
"### END SOLUTION"
]
}
],
"metadata": {
"celltoolbar": "Create Assignment",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"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"
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| gpl-3.0 |
fastai/fastai | nbs/index.ipynb | 1 | 10250 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#|hide\n",
"#|skip\n",
"! [ -e /content ] && pip install -Uqq fastai # upgrade fastai on colab"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Welcome to fastai\n",
"> fastai simplifies training fast and accurate neural nets using modern best practices\n",
"\n",
"- image: /images/layered.png"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" [](https://pypi.org/project/fastai/#description) [](https://anaconda.org/fastai/fastai) [](https://github.com/fastai/docker-containers) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Installing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can use fastai without any installation by using [Google Colab](https://colab.research.google.com/). In fact, every page of this documentation is also available as an interactive notebook - click \"Open in colab\" at the top of any page to open it (be sure to change the Colab runtime to \"GPU\" to have it run fast!) See the fast.ai documentation on [Using Colab](https://course.fast.ai/start_colab) for more information.\n",
"\n",
"You can install fastai on your own machines with conda (highly recommended), as long as you're running Linux or Windows (NB: Mac is not supported). For Windows, please see the \"Running on Windows\" for important notes.\n",
"\n",
"If you're using [miniconda](https://docs.conda.io/en/latest/miniconda.html) (recommended) then run (note that if you replace `conda` with [mamba](https://github.com/mamba-org/mamba) the install process will be much faster and more reliable):\n",
"```bash\n",
"conda install -c fastchan fastai\n",
"```\n",
"\n",
"...or if you're using [Anaconda](https://www.anaconda.com/products/individual) then run:\n",
"```bash\n",
"conda install -c fastchan fastai anaconda\n",
"```\n",
"\n",
"To install with pip, use: `pip install fastai`. If you install with pip, you should install PyTorch first by following the PyTorch [installation instructions](https://pytorch.org/get-started/locally/).\n",
"\n",
"If you plan to develop fastai yourself, or want to be on the cutting edge, you can use an editable install (if you do this, you should also use an editable install of [fastcore](https://github.com/fastai/fastcore) to go with it.) First install PyTorch, and then:\n",
"\n",
"``` \n",
"git clone https://github.com/fastai/fastai\n",
"pip install -e \"fastai[dev]\"\n",
"``` "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Learning fastai"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The best way to get started with fastai (and deep learning) is to read [the book](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527), and complete [the free course](https://course.fast.ai).\n",
"\n",
"To see what's possible with fastai, take a look at the [Quick Start](https://docs.fast.ai/quick_start.html), which shows how to use around 5 lines of code to build an image classifier, an image segmentation model, a text sentiment model, a recommendation system, and a tabular model. For each of the applications, the code is much the same.\n",
"\n",
"Read through the [Tutorials](https://docs.fast.ai/tutorial) to learn how to train your own models on your own datasets. Use the navigation sidebar to look through the fastai documentation. Every class, function, and method is documented here.\n",
"\n",
"To learn about the design and motivation of the library, read the [peer reviewed paper](https://www.mdpi.com/2078-2489/11/2/108/htm)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## About fastai"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying patterns of many deep learning and data processing techniques in terms of decoupled abstractions. These abstractions can be expressed concisely and clearly by leveraging the dynamism of the underlying Python language and the flexibility of the PyTorch library. fastai includes:\n",
"\n",
"- A new type dispatch system for Python along with a semantic type hierarchy for tensors\n",
"- A GPU-optimized computer vision library which can be extended in pure Python\n",
"- An optimizer which refactors out the common functionality of modern optimizers into two basic pieces, allowing optimization algorithms to be implemented in 4–5 lines of code\n",
"- A novel 2-way callback system that can access any part of the data, model, or optimizer and change it at any point during training\n",
"- A new data block API\n",
"- And much more...\n",
"\n",
"fastai is organized around two main design goals: to be approachable and rapidly productive, while also being deeply hackable and configurable. It is built on top of a hierarchy of lower-level APIs which provide composable building blocks. This way, a user wanting to rewrite part of the high-level API or add particular behavior to suit their needs does not have to learn how to use the lowest level."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img alt=\"Layered API\" src=\"images/layered.png\" width=\"345\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Migrating from other libraries"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's very easy to migrate from plain PyTorch, Ignite, or any other PyTorch-based library, or even to use fastai in conjunction with other libraries. Generally, you'll be able to use all your existing data processing code, but will be able to reduce the amount of code you require for training, and more easily take advantage of modern best practices. Here are migration guides from some popular libraries to help you on your way:\n",
"\n",
"- [Plain PyTorch](https://docs.fast.ai/migrating_pytorch)\n",
"- [Ignite](https://docs.fast.ai/migrating_ignite)\n",
"- [Lightning](https://docs.fast.ai/migrating_lightning)\n",
"- [Catalyst](https://docs.fast.ai/migrating_catalyst)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Windows Support\n",
"\n",
"When installing with `mamba` or `conda` replace `-c fastchan` in the installation with `-c pytorch -c nvidia -c fastai`, since fastchan is not currently supported on Windows.\n",
"\n",
"Due to python multiprocessing issues on Jupyter and Windows, `num_workers` of `Dataloader` is reset to 0 automatically to avoid Jupyter hanging. This makes tasks such as computer vision in Jupyter on Windows many times slower than on Linux. This limitation doesn't exist if you use fastai from a script.\n",
"\n",
"See [this example](https://github.com/fastai/fastai/blob/master/nbs/examples/dataloader_spawn.py) to fully leverage the fastai API on Windows."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tests"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To run the tests in parallel, launch:\n",
"\n",
"`nbdev_test_nbs` or `make test`\n",
"\n",
"For all the tests to pass, you'll need to install the dependencies specified as part of dev_requirements in settings.ini\n",
"\n",
"`pip install -e .[dev]` \n",
"\n",
"Tests are written using `nbdev`, for example see the documentation for `test_eq`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Contributing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After you clone this repository, please run `nbdev_install_git_hooks` in your terminal. This sets up git hooks, which clean up the notebooks to remove the extraneous stuff stored in the notebooks (e.g. which cells you ran) which causes unnecessary merge conflicts.\n",
"\n",
"Before submitting a PR, check that the local library and notebooks match. The script `nbdev_diff_nbs` can let you know if there is a difference between the local library and the notebooks.\n",
"\n",
"- If you made a change to the notebooks in one of the exported cells, you can export it to the library with `nbdev_build_lib` or `make fastai`.\n",
"- If you made a change to the library, you can export it back to the notebooks with `nbdev_update_lib`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Docker Containers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For those interested in official docker containers for this project, they can be found [here](https://github.com/fastai/docker-containers#fastai)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"jekyll": {
"keywords": "fastai"
},
"jupytext": {
"split_at_heading": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| apache-2.0 |
soudegesu/deep_study | src/chapter1/3.matplotlib/10.seaborn_hex.ipynb | 1 | 1515 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
":0: FutureWarning: IPython widgets are experimental and may change in the future.\n",
"/Users/takaaki-suzuki/.pyenv/versions/anaconda3-2.2.0/lib/python3.4/site-packages/matplotlib/tight_layout.py:222: UserWarning: tight_layout : falling back to Agg renderer\n",
" warnings.warn(\"tight_layout : falling back to Agg renderer\")\n"
]
}
],
"source": [
"import numpy as np\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"\n",
"mean, cov = [0, 1], [(1, .5), (.5, 1)]\n",
"data = np.random.multivariate_normal(mean, cov, 200)\n",
"df = pd.DataFrame(data, columns=[\"x\", \"y\"])\n",
"sns.jointplot(x=\"x\", y=\"y\", kind=\"hex\", data=df)\n",
"sns.plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
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"name": "python3"
},
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"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.5"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
CompPhysics/ThesisProjects | doc/MSc/msc_students/former/AudunHansen/Audun/Notebooks/Benchmarking.ipynb | 1 | 80725 | {
"metadata": {
"name": "Benchmarking.ipynb",
"signature": "sha256:204c690b9ac13e8ad28edadf3ed6e0b213deff4a4b15b90583dc0038299f44be"
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"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Benchmarking the CCSolver\n",
"\n",
"In this notebook I will do some Coupled-Cluster benchmark calculations using a Slater Determinant produced from a Restricted Hartree-Fock (RHF) calculation on an STO-3G basis set for the hydrogen gas (H2) and a water molecule (H2O). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##The water molecule\n",
"\n",
"We have previously done some calculations on H2O using RHF and STO-3G to determine geometry and ground state energy. The results corresponded rather nice to the ones presented in the literature (see Report from FYS4411 - G\u00f8ran Brekke Svaland and Audun Skau Hansen). \n",
"\n",
"We now use the geometry and energy presented in the following document to compare our Coupled Cluster Singles Doubles (CCSD) solver to the corresponding calculations performed by NWChem: <url>http://institute.loni.org/NWChem2012/documents/tce-session.pdf</url>. \n",
"\n",
"Prior to the CCSD calculation the different algorithms starts out with the following parameters:\n",
"\n",
"HF-energy (NWChem) : -74.962663062148\n",
"\n",
"HF-energy (CCSolve): -74.962677575226\n",
"\n",
"HF-limit : -76.067 (<url>http://chemistry.illinoisstate.edu/standard/che460/handouts/460performance.pdf</url>)\n",
"\n",
"The results from each iteration is presented in the table below. The rightmost coulomn contains the results obtained by CCSolve."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
" Iter | Correlation(NWChem) | Correlation (CCsolve) | Rel.Error\n",
"----|----|----|----\n",
" 1|-0.0358672469179 | -0.035897626185118| 0.0008469919\n",
" 2| -0.0454068882657 |-0.04544804888198|0.000906484\n",
" 3| -0.0483870059027 |-0.048432634274069|0.0009429881\n",
" 4| -0.0494370597647 | -0.049484831256215|0.0009663093\n",
" 5| -0.0498391184890 | -0.049888079084351|0.0009823728\n",
" 6| -0.0500021724029 | -0.050051825093841|0.0009930107\n",
" 7| -0.0500711904756 | -0.050121251581979|0.0009997986\n",
" 8| -0.0501014381364 |-0.050151741527804|0.0010040309\n",
" 9| -0.0501150974135 | -0.05016554465157|0.0010066276\n",
" 10| -0.0501214303300 | -0.050171962746426|0.0010081998\n",
" 11| -0.0501244348663 | -0.050175017473014|0.0010091407\n",
" 12| -0.0501258887096 | -0.050176500709333|0.0010096978\n",
" 13| -0.0501266039080 | -0.050177233008851|0.0010100246\n",
" 14| -0.0501269605251 | -0.050177599511074|0.0010102146\n",
" 15| -0.0501271402835 | -0.050177784948453|0.0010103242\n",
" 16| -0.0501272316751 | -0.050177879584002|0.0010103871\n",
"17| -0.0501272784536 | -0.05017792820593|0.0010104229\n",
" 18| -0.0501273025229 | -0.050177953317881|0.0010104433\n",
" 19| -0.0501273149581 | -0.050177966340254|0.0010104547\n",
" 20| -0.0501273214031 | (converged)| (none)\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"CCSolve obtains a lower ground state energy already at the onset from the SCF-procedure. This is also reflected in both the first iteration of the CCSD procedure and the final converged correlation energy. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Revisiting the Hydrogen Molecule\n",
"\n",
"We spent some time dealing with this simple diatomic system in FYS4411, and found our code to produce some nice results. (See report from FYS4411 - G\u00f8ran Brekke Svaland and Audun Skau Hansen). As the RHF procedure forces the electrons into spin-orbitals, the method is not well suited for calculating the energy of H2 as the bondlength increase beyond the point of electron interaction.\n",
"\n",
"In our new calculation we wish to perform a CCSD or (CCD) for H2 to investigate how the energy compares to RHF as we increase the bondlength. The results are shown in the plot below."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.display import Image\n",
"h2 = Image(filename='H2bondlength.png')\n",
"h2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"png": 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Ou0OStP94tgjxAAAAAPiiNm3a6A9/+EOdtWnSpIl+8pOf6Cc/+Um1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PS1VUQKjRolaxBVPYrXN79O3xp9eestV2rXhtmz4YknzE4nIrbk/HmYMAGmTwd7e3j6aXj2WShZ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\np6pvVcAo7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"prompt_number": 7,
"text": [
"<IPython.core.display.Image at 0x7f108c12ded0>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As the plot above shows, we see that we get the expected behaviour from the CCSD solver as we increase the distance between the nucelis. When comparing the plot above to Norlis results for the same system, one should consider the following:\n",
"\n",
"(1) The plot above spans a larger region along both axes compared to Norli's plot.\n",
"\n",
"(2) The basis sets used differ (STO-3G vs. 6-311++G(2p,2d)) \n",
"\n",
"This may account for the fact that the correlation converges above -1 a.u., but a more comparable calculations could possibly shed some more light on the issue."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##General result comparison\n",
"\n",
"\n",
" System | Total energy (a.u.) | Correlation (a.u.) | Parameters | Comparison\n",
"----|----|----|----|----\n",
"H2|-1.13728|-0.0205616|Bond length: 1.4, STO-3G, RHF+CCSD|HF-limit: -1.132 (2), Exact correlation -0.03969 (1)\n",
"O2|-147.696|-0.145003 |Bond lenght: 2.287, STO-3G, RHF+CCSD|HF-limit: -, Exact correlation -0.37 (1)\n",
"O|-73.7092|-0.047385|STO-3G, RHF+CCSD|HF-limit-74.729 (2), Correlation: -0.262 (2)\n",
"Be|-14.4037|-0.0517703|STO-3G, RHF+CCSD|General HF: -14.67 (3)\n",
"Be|-14.5199|-0.00484722|Hydrogenlike (4 functions), RHF+CCSD|General HF: -14.67 (3)\n",
"He|-2.84228|-0.00868762|Hydrogenlike (4 functions), RHF+CCSD|General HF: -2.904 (3)\n",
"\n",
"\n",
"(1) Results from Thijssen, p84, obtained from variational calculus\n",
"\n",
"(2) results from 460performance.pdf (see text above)\n",
"\n",
"(3) Source given in \"Report from FYS4411 - G\u00f8ran Brekke Svaland and Audun Skau Hansen\""
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for(k=0; k<nElectron, k++)\n",
"for(a=nElectron, k<nStates, k++)"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | cc0-1.0 |
dobestan/data-science-school | database/add-data-to-table.ipynb | 1 | 6946 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MySQL 테이블을 생성하고 데이터를 넣기"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os\n",
"\n",
"import pandas as pd\n",
"\n",
"import MySQLdb"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"DATABASE_HOST = \"localhost\" # \"localhost\" == \"127.0.0.1\"\n",
"\n",
"DATABASE_USERNAME = os.environ.get(\"DATABASE_USERNAME\", \"YOUR_USERNAME\")\n",
"DATABASE_PASSWORD = os.environ.get(\"DATABASE_PASSWORD\", \"YOUR_PASSWORD\")\n",
"\n",
"DATABASE_NAME = \"fastcampus_data_science_db\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"connection = MySQLdb.connect(\n",
" DATABASE_HOST,\n",
" DATABASE_USERNAME,\n",
" DATABASE_PASSWORD,\n",
" DATABASE_NAME,\n",
" charset='utf8',\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"connection\n",
"\n",
"cursor = connection.cursor()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/dobestan/.pyenv/versions/datascience/lib/python3.5/site-packages/ipykernel/__main__.py:12: Warning: Table 'fastroom' already exists\n"
]
},
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SQL_QUERY = \"\"\"\n",
" CREATE TABLE IF NOT EXISTS fastroom \n",
" (\n",
" email varchar(255),\n",
" phonenumber varchar(255),\n",
" address varchar(255),\n",
" deposit int,\n",
" rent int\n",
" );\n",
"\"\"\"\n",
"\n",
"cursor.execute(SQL_QUERY)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(('fastroom',),)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SQL_QUERY = \"\"\"\n",
" SHOW TABLES;\n",
"\"\"\"\n",
"\n",
"cursor.execute(SQL_QUERY)\n",
"cursor.fetchall()"
]
},
{
"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>email</th>\n",
" <th>phonenumber</th>\n",
" <th>address</th>\n",
" <th>deposit</th>\n",
" <th>rent</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [email, phonenumber, address, deposit, rent]\n",
"Index: []"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SQL_QUERY = \"\"\"\n",
" SELECT *\n",
" FROM fastroom;\n",
"\"\"\"\n",
"\n",
"pd.read_sql(SQL_QUERY, connection)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 지금까지 추가된 row 데이터 제거\n",
"\n",
"# SQL_QUERY\n",
"SQL_QUERY = \"\"\"\n",
" DELETE FROM fastroom;\n",
"\"\"\"\n",
"\n",
"cursor.execute(SQL_QUERY)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SQL_QUERY = \"\"\"\n",
" INSERT INTO fastroom (email, phonenumber, address, deposit, rent)\n",
" VALUES (\n",
" \"[email protected]\",\n",
" \"010-2220-5736\",\n",
" \"서울시 강남구 논현1동 2-9 대기빌딩 1,4층\",\n",
" 1000,\n",
" 50\n",
" );\n",
"\"\"\"\n",
"\n",
"cursor.execute(SQL_QUERY)"
]
},
{
"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>email</th>\n",
" <th>phonenumber</th>\n",
" <th>address</th>\n",
" <th>deposit</th>\n",
" <th>rent</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>[email protected]</td>\n",
" <td>010-2220-5736</td>\n",
" <td>서울시 강남구 논현1동 2-9 대기빌딩 1,4층</td>\n",
" <td>1000</td>\n",
" <td>50</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" email phonenumber address deposit \\\n",
"0 [email protected] 010-2220-5736 서울시 강남구 논현1동 2-9 대기빌딩 1,4층 1000 \n",
"\n",
" rent \n",
"0 50 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SQL_QUERY = \"\"\"\n",
" SELECT *\n",
" FROM fastroom;\n",
"\"\"\"\n",
"\n",
"pd.read_sql(SQL_QUERY, connection)"
]
}
],
"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 |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/dev/.ipynb_checkpoints/n17_training_a_volume_estimator-checkpoint.ipynb | 1 | 242864 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# In this notebook an estimator for the Volume will be trained. No hyperparameters will be searched for, and the ones from the 'Close' values estimator will be used instead."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n",
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"source": [
"# Basic imports\n",
"import os\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import datetime as dt\n",
"import scipy.optimize as spo\n",
"import sys\n",
"from time import time\n",
"from sklearn.metrics import r2_score, median_absolute_error\n",
"\n",
"%matplotlib inline\n",
"\n",
"%pylab inline\n",
"pylab.rcParams['figure.figsize'] = (20.0, 10.0)\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"sys.path.append('../../')\n",
"\n",
"from sklearn.externals import joblib\n",
"import utils.preprocessing as pp\n",
"import predictor.feature_extraction as fe"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Let's generate the datasets"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def generate_one_set(params):\n",
" # print(('-'*70 + '\\n {}, {} \\n' + '-'*70).format(params['base_days'].values, params['ahead_days'].values))\n",
" tic = time()\n",
" \n",
" train_val_time = int(params['train_val_time'])\n",
" base_days = int(params['base_days'])\n",
" step_days = int(params['step_days'])\n",
" ahead_days = int(params['ahead_days'])\n",
" \n",
" print('Generating: base{}_ahead{}'.format(base_days, ahead_days))\n",
" pid = 'base{}_ahead{}'.format(base_days, ahead_days)\n",
" \n",
" # Getting the data\n",
" data_df = pd.read_pickle('../../data/data_train_val_df.pkl')\n",
" today = data_df.index[-1] # Real date\n",
" print(pid + ') data_df loaded')\n",
"\n",
" # Drop symbols with many missing points\n",
" data_df = pp.drop_irrelevant_symbols(data_df, params['GOOD_DATA_RATIO'])\n",
" print(pid + ') Irrelevant symbols dropped.')\n",
" \n",
" # Generate the intervals for the predictor\n",
" x, y = fe.generate_train_intervals(data_df, \n",
" train_val_time, \n",
" base_days, \n",
" step_days,\n",
" ahead_days, \n",
" today, \n",
" fe.feature_volume_one_to_one,\n",
" target_feature=fe.VOLUME_FEATURE) \n",
" print(pid + ') Intervals generated')\n",
" \n",
" # Drop \"bad\" samples and fill missing data\n",
" x_y_df = pd.concat([x, y], axis=1)\n",
" x_y_df = pp.drop_irrelevant_samples(x_y_df, params['SAMPLES_GOOD_DATA_RATIO'])\n",
" x = x_y_df.iloc[:, :-1]\n",
" y = x_y_df.iloc[:, -1]\n",
" x = pp.fill_missing(x)\n",
" print(pid + ') Irrelevant samples dropped and missing data filled.')\n",
" \n",
" # Pickle that\n",
" x.to_pickle('../../data/x_volume_{}.pkl'.format(pid))\n",
" y.to_pickle('../../data/y_volume_{}.pkl'.format(pid))\n",
" \n",
" toc = time()\n",
" print('%s) %i intervals generated in: %i seconds.' % (pid, x.shape[0], (toc-tic)))\n",
" \n",
" return pid, x, y"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"GOOD_DATA_RATIO 0.99\n",
"SAMPLES_GOOD_DATA_RATIO 0.9\n",
"ahead_days 1\n",
"base_days 112\n",
"step_days 7\n",
"train_val_time -1\n",
"Name: 1.0, dtype: object"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"best_params_df = pd.read_pickle('../../data/best_params_final_df.pkl').loc[1,:]\n",
"to_drop = [\n",
" 'model',\n",
" 'mre',\n",
" 'r2',\n",
" 'x_filename',\n",
" 'y_filename',\n",
" 'train_days'\n",
"]\n",
"best_params_df.drop(to_drop, inplace=True)\n",
"best_params_df"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generating: base112_ahead1\n",
"base112_ahead1) data_df loaded\n",
"base112_ahead1) Irrelevant symbols dropped.\n",
"base112_ahead1) Intervals generated\n",
"base112_ahead1) Irrelevant samples dropped and missing data filled.\n",
"base112_ahead1) 219281 intervals generated in: 168 seconds.\n"
]
},
{
"data": {
"text/plain": [
"('base112_ahead1',\n",
" 0 1 2 3 4 5 \\\n",
" 1993-01-29 AAPL 1.0 0.799399 1.100301 0.391059 0.706612 0.841097 \n",
" ABT 1.0 1.737416 2.302661 2.362296 1.290798 1.972427 \n",
" ADBE 1.0 2.796128 1.733207 2.519306 2.074187 1.665204 \n",
" ADM 1.0 1.307959 0.440623 0.615481 1.301435 1.294688 \n",
" ADP 1.0 0.712254 1.190919 1.558534 1.658917 1.347101 \n",
" ADSK 1.0 0.799576 0.553022 1.047897 1.136179 1.069282 \n",
" AEP 1.0 2.275556 1.133333 1.462222 2.337778 1.511111 \n",
" AES 1.0 2.347038 2.316376 1.839721 2.841115 5.972125 \n",
" AET 1.0 2.097744 1.658237 3.170882 3.940533 5.034860 \n",
" AFL 1.0 2.545356 0.305185 3.473929 5.266079 1.714198 \n",
" AIG 1.0 1.026964 1.941007 2.961265 1.820695 1.402409 \n",
" AJG 1.0 2.315911 1.027314 1.097471 0.938503 0.829349 \n",
" ALK 1.0 2.562933 2.416789 6.761893 9.916177 3.885006 \n",
" AMAT 1.0 2.032120 1.736617 1.421842 3.229122 0.520343 \n",
" AMD 1.0 1.087518 0.786743 2.667666 1.303023 0.936548 \n",
" AME 1.0 1.066548 0.846488 1.859798 9.660963 2.243993 \n",
" AMGN 1.0 0.734142 0.782556 1.522897 1.418300 2.823373 \n",
" AN 1.0 1.060365 3.055209 2.577014 2.670462 1.811386 \n",
" APA 1.0 1.798526 1.238329 0.838657 1.647011 0.805897 \n",
" APC 1.0 2.659827 4.132586 1.513314 1.245628 1.656787 \n",
" APD 1.0 2.747712 2.135011 16.065789 8.041762 5.586957 \n",
" APH 1.0 1.474860 0.240223 3.511173 0.480447 0.511173 \n",
" ARNC 1.0 1.354223 1.653951 8.166213 3.051771 2.929155 \n",
" AVY 1.0 3.367521 5.145299 3.649573 2.170940 4.239316 \n",
" AXP 1.0 0.957552 1.545388 1.289967 1.536200 2.015803 \n",
" AZO 1.0 0.439237 0.447180 1.380461 0.530580 0.785544 \n",
" BA 1.0 0.753015 1.812349 1.694645 3.768934 12.678244 \n",
" BAC 1.0 1.175926 1.753704 1.672222 2.070370 0.903704 \n",
" BAX 1.0 0.544790 0.767950 0.638758 1.001350 1.598582 \n",
" BBBY 1.0 0.189542 0.265069 0.774873 0.329702 0.362382 \n",
" ... ... ... ... ... ... ... \n",
" 2014-07-18 TWX 1.0 0.775236 0.920909 1.067082 1.180822 1.063247 \n",
" TXN 1.0 0.903685 1.132094 0.807083 1.893544 1.691031 \n",
" TXT 1.0 0.979728 0.796844 1.001587 0.957226 0.843836 \n",
" UDR 1.0 1.488939 1.616842 1.330744 1.897088 1.040619 \n",
" UHS 1.0 0.637570 0.782777 0.560014 0.558266 0.685587 \n",
" UNH 1.0 0.728199 0.697939 0.754300 0.748640 0.827890 \n",
" UNM 1.0 1.222187 2.480161 1.646877 1.604697 1.024687 \n",
" UNP 1.0 1.034173 1.122000 0.667085 1.033498 0.863975 \n",
" USB 1.0 0.697080 2.277888 1.965622 1.958388 4.901866 \n",
" UTX 1.0 0.861043 0.897089 1.114190 1.029418 0.732781 \n",
" VFC 1.0 0.478337 0.479296 0.502592 0.781046 0.302908 \n",
" VLO 1.0 1.175617 1.002927 0.910263 0.836980 0.753943 \n",
" VMC 1.0 1.119121 1.484201 1.175821 1.738728 1.091950 \n",
" VNO 1.0 0.586860 0.763256 0.742285 0.998913 0.640917 \n",
" VRTX 1.0 0.497778 0.677025 0.529962 1.397894 0.893703 \n",
" VZ 1.0 0.655098 0.832476 1.655119 2.091069 0.896350 \n",
" WDC 1.0 0.883370 0.692727 0.698990 1.003498 0.733375 \n",
" WEC 1.0 0.983828 0.769730 0.588174 0.641791 0.648891 \n",
" WFC 1.0 0.764054 1.304965 0.893999 0.993471 0.731476 \n",
" WFM 1.0 1.158300 0.567337 0.713110 0.947244 0.682447 \n",
" WHR 1.0 0.622109 0.571671 0.479058 0.490595 0.316556 \n",
" WMB 1.0 0.582959 0.966706 8.392179 2.581877 1.896077 \n",
" WMT 1.0 0.392280 0.449307 0.357566 0.560103 0.342033 \n",
" WY 1.0 0.703713 0.892094 0.812959 1.471157 0.803616 \n",
" XEL 1.0 0.808009 0.904747 0.854875 0.967681 0.948396 \n",
" XLNX 1.0 1.475966 1.713026 1.349345 1.371480 1.972339 \n",
" XOM 1.0 0.576020 0.536759 0.827962 0.832970 0.630647 \n",
" XRAY 1.0 0.871061 1.030509 0.984077 0.787750 0.857157 \n",
" XRX 1.0 0.852697 1.016390 0.666632 0.511420 0.743556 \n",
" ZION 1.0 0.860937 1.896578 0.915797 0.704707 3.152652 \n",
" \n",
" 6 7 8 9 ... 102 \\\n",
" 1993-01-29 AAPL 0.788505 1.025169 0.988355 1.099925 ... 0.580766 \n",
" ABT 1.265470 0.427060 1.345303 0.667842 ... 2.384739 \n",
" ADBE 2.393327 1.605166 3.158544 2.404181 ... 0.980768 \n",
" ADM 0.429101 0.249023 0.411915 0.466721 ... 0.350154 \n",
" ADP 1.377462 0.826039 0.678063 0.439278 ... 1.276258 \n",
" ADSK 1.146341 0.823082 0.868770 1.552050 ... 0.419318 \n",
" AEP 0.937778 0.906667 0.706667 0.795556 ... 1.506667 \n",
" AES 3.558885 5.029268 3.219512 4.489199 ... 7.278746 \n",
" AET 2.576213 2.711552 2.138072 2.304853 ... 0.652085 \n",
" AFL 1.792150 2.831077 3.921967 6.707591 ... 4.019382 \n",
" AIG 1.098549 0.933616 1.329729 2.179031 ... 0.807282 \n",
" AJG 0.455045 0.431965 0.499201 0.672396 ... 0.644797 \n",
" ALK 3.499347 2.603529 2.710445 2.161036 ... 1.197572 \n",
" AMAT 0.312634 1.361884 4.813704 0.758030 ... 0.659529 \n",
" AMD 0.525606 0.640187 0.599800 0.894246 ... 0.993671 \n",
" AME 2.016851 1.383316 0.602472 1.932549 ... 1.425906 \n",
" AMGN 1.886279 2.027503 1.230541 0.747632 ... 0.603571 \n",
" AN 1.991622 1.499463 0.918582 1.178303 ... 1.896241 \n",
" APA 2.275184 1.040131 1.162162 1.814087 ... 1.548731 \n",
" APC 0.828897 0.906466 0.653741 0.721168 ... 1.092525 \n",
" APD 4.342677 3.789474 5.086957 7.761442 ... 2.484554 \n",
" APH 0.977654 0.667598 0.731844 0.145251 ... 0.620112 \n",
" ARNC 1.346049 2.024523 2.893733 2.544959 ... 0.803815 \n",
" AVY 1.786325 3.025641 1.188034 2.290598 ... 2.000000 \n",
" AXP 0.744763 0.863102 0.657295 0.670526 ... 1.350423 \n",
" AZO 1.169976 0.924543 2.016680 0.699762 ... 0.992851 \n",
" BA 4.681138 4.806561 4.543174 2.771346 ... 1.134588 \n",
" BAC 1.722222 0.633333 1.279630 0.757407 ... 0.816667 \n",
" BAX 0.727774 0.517893 0.452960 0.818479 ... 0.367207 \n",
" BBBY 0.437908 0.309368 1.033406 0.389252 ... 0.620189 \n",
" ... ... ... ... ... ... ... \n",
" 2014-07-18 TWX 0.725303 1.029746 0.853268 1.055154 ... 0.974789 \n",
" TXN 1.736169 2.019143 3.112889 2.082898 ... 1.525969 \n",
" TXT 1.057867 0.785366 1.155752 1.427635 ... 1.315725 \n",
" UDR 1.777035 2.499819 1.804504 3.605088 ... 2.130185 \n",
" UHS 0.800004 0.719126 0.705439 1.069736 ... 0.816538 \n",
" UNH 0.871167 0.792160 1.338165 2.275105 ... 1.835890 \n",
" UNM 1.270091 1.291093 1.408312 1.538039 ... 1.245264 \n",
" UNP 0.567020 1.258420 1.842716 1.373468 ... 0.755533 \n",
" USB 3.677410 1.902763 1.500695 1.748765 ... 0.732848 \n",
" UTX 0.990825 1.257383 1.467129 1.408352 ... 1.232453 \n",
" VFC 0.305436 0.224019 0.528657 0.493044 ... 0.365596 \n",
" VLO 0.984043 1.473754 1.489158 1.639548 ... 0.839997 \n",
" VMC 0.947637 3.253781 1.260333 2.098196 ... 1.813770 \n",
" VNO 0.599826 0.738174 1.168563 1.257432 ... 0.661668 \n",
" VRTX 1.099272 1.169215 1.257466 1.255074 ... 0.958279 \n",
" VZ 0.919368 2.151405 1.523302 1.799840 ... 1.673329 \n",
" WDC 0.787632 0.796086 1.207637 2.949817 ... 2.976357 \n",
" WEC 0.869618 0.937009 0.738642 1.268273 ... 1.098167 \n",
" WFC 1.016783 1.758063 3.661011 4.191226 ... 0.484308 \n",
" WFM 1.077936 0.905906 1.217219 1.926337 ... 1.166428 \n",
" WHR 0.756542 0.563684 0.588885 0.841606 ... 0.466000 \n",
" WMB 1.262235 1.222985 1.328080 1.798648 ... 0.802450 \n",
" WMT 0.586186 0.603281 0.917365 0.779255 ... 0.671835 \n",
" WY 1.121423 1.280896 2.032363 1.960516 ... 1.634902 \n",
" XEL 1.120560 0.981374 1.348900 2.229193 ... 2.621996 \n",
" XLNX 2.083613 1.476547 1.890565 2.148335 ... 0.860465 \n",
" XOM 0.603227 0.538388 0.945515 0.742207 ... 0.281240 \n",
" XRAY 1.240143 1.285577 1.415018 1.566664 ... 2.118458 \n",
" XRX 1.134552 1.472962 1.757815 2.219839 ... 1.372078 \n",
" ZION 10.202257 3.781212 4.898003 2.921387 ... 1.786106 \n",
" \n",
" 103 104 105 106 107 108 \\\n",
" 1993-01-29 AAPL 1.502254 3.806161 1.927874 2.796769 2.639745 5.151766 \n",
" ABT 1.766592 1.795127 2.333440 4.712408 2.440846 5.756973 \n",
" ADBE 4.448833 3.672625 3.513949 2.623441 5.208416 4.821548 \n",
" ADM 0.287081 1.650929 1.666158 1.728794 1.025446 0.484122 \n",
" ADP 1.379376 1.092177 1.199125 2.351751 1.195295 1.156729 \n",
" ADSK 0.916490 0.734624 1.211205 0.452103 0.378667 0.554878 \n",
" AEP 4.986667 3.271111 1.253333 1.426667 1.946667 1.497778 \n",
" AES 7.183275 4.519861 2.747735 2.487805 2.187456 2.221603 \n",
" AET 1.105263 0.908407 1.257690 1.160629 0.864662 0.936432 \n",
" AFL 2.869988 1.772686 15.057936 25.687494 5.201162 1.266160 \n",
" AIG 1.176020 3.317547 2.014919 1.291952 1.480564 1.895702 \n",
" AJG 0.453936 0.589864 1.013270 0.468600 0.357859 0.464896 \n",
" ALK 5.208425 4.503420 1.488501 2.416803 1.840344 1.870101 \n",
" AMAT 3.256959 1.254818 0.952891 2.413276 1.580300 1.154176 \n",
" AMD 0.778583 0.540761 0.474394 0.831959 1.187693 0.817887 \n",
" AME 0.686774 2.270612 1.150826 1.042591 0.787926 0.989352 \n",
" AMGN 0.690400 0.911293 0.535819 0.691928 0.258393 0.340333 \n",
" AN 1.236305 1.163265 0.885285 1.397852 0.801504 1.095596 \n",
" APA 2.028665 2.748567 0.873874 1.036855 1.625717 1.679771 \n",
" APC 0.365022 1.421803 0.660334 0.795186 0.807353 3.172378 \n",
" APD 2.356979 5.458238 4.847826 2.440503 1.323227 1.929062 \n",
" APH 0.089385 0.259777 0.055866 0.335196 0.782123 0.055866 \n",
" ARNC 1.435967 0.765668 1.427793 1.103542 1.803815 3.542234 \n",
" AVY 2.632479 4.188034 2.598291 2.017094 2.068376 5.290598 \n",
" AXP 0.810731 1.164094 1.754686 0.986218 0.351158 0.789416 \n",
" AZO 1.171565 1.095314 1.182685 0.884829 0.424940 2.100079 \n",
" BA 1.037144 1.922335 1.113845 1.479016 0.993247 1.833575 \n",
" BAC 0.812037 0.497222 2.137037 1.698148 1.004630 1.927778 \n",
" BAX 0.579226 0.866532 0.309926 0.218659 0.329845 0.599257 \n",
" BBBY 0.767611 0.583878 0.578794 0.390704 0.709513 0.573711 \n",
" ... ... ... ... ... ... ... \n",
" 2014-07-18 TWX 1.087740 0.981904 1.062421 0.919032 1.058277 1.277802 \n",
" TXN 1.345164 2.280436 2.445929 2.630392 3.254262 3.054662 \n",
" TXT 0.921984 1.255432 1.537701 1.065384 1.250150 1.392120 \n",
" UDR 2.007327 3.165001 3.086554 2.817447 2.225871 4.892918 \n",
" UHS 1.070097 0.969129 1.158963 0.917131 1.093273 1.490912 \n",
" UNH 2.669333 2.735266 3.490730 2.381830 2.231478 2.942789 \n",
" UNM 1.382166 1.084988 1.212371 1.185419 1.116609 1.854339 \n",
" UNP 0.793017 0.937622 1.244535 1.601986 1.202715 2.052006 \n",
" USB 2.007901 1.609199 1.439298 1.765746 1.224808 2.122626 \n",
" UTX 1.512070 1.691523 1.513280 1.735906 1.655187 1.920690 \n",
" VFC 0.376155 0.491196 0.469023 0.452464 0.513898 0.712792 \n",
" VLO 1.124771 1.011446 1.430038 1.220308 1.944494 1.687013 \n",
" VMC 2.346027 2.143014 2.186996 2.338878 2.260090 3.090988 \n",
" VNO 0.676942 1.088367 1.075682 1.300243 1.093597 3.121243 \n",
" VRTX 1.109188 1.173068 1.432007 1.225368 1.190475 2.166913 \n",
" VZ 2.082797 2.094902 1.822235 1.818064 1.758582 2.626470 \n",
" WDC 5.677609 2.808052 2.597441 4.577213 3.212802 2.692380 \n",
" WEC 1.213431 1.604715 1.565360 1.277337 1.382076 2.336493 \n",
" WFC 0.741768 0.886767 0.629119 0.580302 0.961370 0.825384 \n",
" WFM 1.209162 1.747931 1.633308 1.849937 1.687606 2.493296 \n",
" WHR 0.488568 0.610531 0.913509 0.801408 0.919314 1.007623 \n",
" WMB 0.641483 1.119062 1.506732 3.196091 1.641704 1.784513 \n",
" WMT 0.564901 0.519555 0.834421 0.821836 1.126930 1.324858 \n",
" WY 1.482519 1.285832 1.513722 1.351630 1.759683 2.456335 \n",
" XEL 2.993106 2.603893 3.212892 2.841394 2.787749 4.686587 \n",
" XLNX 1.028636 1.067717 1.554001 0.956018 0.893221 1.785408 \n",
" XOM 0.401707 0.502457 0.445220 0.384164 0.548983 0.820247 \n",
" XRAY 2.053616 2.629364 2.070099 3.131564 2.763502 2.478919 \n",
" XRX 2.138218 1.985444 1.381174 2.532877 1.712842 2.828343 \n",
" ZION 1.984792 2.013023 2.487787 2.411735 1.583735 6.660535 \n",
" \n",
" 109 110 111 \n",
" 1993-01-29 AAPL 1.170173 1.052968 0.614576 \n",
" ABT 2.494069 2.722026 2.975313 \n",
" ADBE 3.721397 3.279440 2.370246 \n",
" ADM 0.519792 0.785994 0.458892 \n",
" ADP 1.640591 1.743435 1.394694 \n",
" ADSK 0.418169 0.511400 0.820343 \n",
" AEP 1.040000 1.511111 6.648889 \n",
" AES 1.894077 7.043902 13.678049 \n",
" AET 0.876965 1.515379 1.613807 \n",
" AFL 9.473751 21.252589 1.090890 \n",
" AIG 1.399808 1.077744 0.805502 \n",
" AJG 0.528805 0.444818 0.327604 \n",
" ALK 2.093377 2.523691 2.067680 \n",
" AMAT 1.111349 6.631692 1.443255 \n",
" AMD 0.886085 1.149138 0.726289 \n",
" AME 0.606916 0.482686 8.897897 \n",
" AMGN 0.215262 0.467586 0.318156 \n",
" AN 1.258861 0.703545 4.933835 \n",
" APA 1.298935 1.561016 1.793612 \n",
" APC 1.024086 2.892273 0.545249 \n",
" APD 3.754577 3.188215 2.048627 \n",
" APH 0.796089 0.064246 0.189944 \n",
" ARNC 1.079019 1.103542 1.607629 \n",
" AVY 1.897436 4.487179 2.888889 \n",
" AXP 0.698089 1.233921 0.426314 \n",
" AZO 1.111199 1.337569 2.293487 \n",
" BA 3.261939 2.932947 1.431259 \n",
" BAC 1.665741 1.121296 1.491667 \n",
" BAX 0.415147 0.659352 0.696826 \n",
" BBBY 0.945534 2.437908 0.426289 \n",
" ... ... ... ... \n",
" 2014-07-18 TWX 0.530034 0.591024 0.223461 \n",
" TXN 3.168576 2.555643 0.841740 \n",
" TXT 0.797579 0.613077 0.318867 \n",
" UDR 1.684727 1.042907 0.702218 \n",
" UHS 0.801403 0.629013 0.242555 \n",
" UNH 1.954080 1.066165 0.422193 \n",
" UNM 0.720394 0.639780 0.240268 \n",
" UNP 0.616161 0.780114 0.244534 \n",
" USB 0.812164 1.163426 0.462780 \n",
" UTX 0.998914 0.692105 0.313967 \n",
" VFC 0.249998 0.242735 0.144441 \n",
" VLO 1.157524 0.777383 0.541410 \n",
" VMC 1.510328 1.209514 0.774067 \n",
" VNO 1.068055 2.067989 0.264214 \n",
" VRTX 1.037646 0.941286 0.653042 \n",
" VZ 2.131411 1.336311 0.563970 \n",
" WDC 1.570191 1.876370 0.962263 \n",
" WEC 1.123205 0.852678 0.277451 \n",
" WFC 0.484006 0.346320 0.226190 \n",
" WFM 1.154334 1.223340 1.743624 \n",
" WHR 0.648346 0.371935 0.168273 \n",
" WMB 0.637206 0.465016 0.178788 \n",
" WMT 0.758578 0.740589 0.275550 \n",
" WY 0.931290 1.130115 0.507147 \n",
" XEL 2.177277 1.669837 0.837650 \n",
" XLNX 0.749315 0.697184 0.230843 \n",
" XOM 0.281015 0.336757 0.108314 \n",
" XRAY 1.688812 1.603256 0.523903 \n",
" XRX 1.248227 1.076201 0.782831 \n",
" ZION 1.341415 1.490635 0.795542 \n",
" \n",
" [219281 rows x 112 columns],\n",
" 1993-01-29 AAPL 0.641247\n",
" ABT 1.764027\n",
" ADBE 1.508452\n",
" ADM 0.346670\n",
" ADP 0.760394\n",
" ADSK 0.365677\n",
" AEP 2.342222\n",
" AES 9.845296\n",
" AET 1.326726\n",
" AFL 13.012597\n",
" AIG 1.119765\n",
" AJG 0.452715\n",
" ALK 1.393792\n",
" AMAT 1.289079\n",
" AMD 1.007078\n",
" AME 0.383316\n",
" AMGN 0.227223\n",
" AN 2.084425\n",
" APA 1.739558\n",
" APC 1.680103\n",
" APD 1.787185\n",
" APH 0.215084\n",
" ARNC 0.724796\n",
" AVY 1.358974\n",
" AXP 0.935134\n",
" AZO 2.343129\n",
" BA 1.957549\n",
" BAC 0.399074\n",
" BAX 0.310713\n",
" BBBY 0.400145\n",
" ... \n",
" 2014-07-18 TWX 0.300524\n",
" TXN 2.001688\n",
" TXT 0.401045\n",
" UDR 0.668886\n",
" UHS 0.397621\n",
" UNH 0.512206\n",
" UNM 0.376141\n",
" UNP 0.518985\n",
" USB 0.834650\n",
" UTX 0.523019\n",
" VFC 0.190948\n",
" VLO 0.782874\n",
" VMC 0.694705\n",
" VNO 0.291869\n",
" VRTX 0.765249\n",
" VZ 0.644282\n",
" WDC 0.954004\n",
" WEC 0.376179\n",
" WFC 0.395389\n",
" WFM 1.403008\n",
" WHR 0.261707\n",
" WMB 0.316105\n",
" WMT 0.224937\n",
" WY 0.661314\n",
" XEL 1.289547\n",
" XLNX 0.381192\n",
" XOM 0.128445\n",
" XRAY 0.828852\n",
" XRX 0.630608\n",
" ZION 0.686132\n",
" Name: 112, Length: 219281, dtype: float64)"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"generate_one_set(best_params_df)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(219281, 112)\n"
]
},
{
"data": {
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" <th>111</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">1993-01-29</th>\n",
" <th>AAPL</th>\n",
" <td>1.0</td>\n",
" <td>0.799399</td>\n",
" <td>1.100301</td>\n",
" <td>0.391059</td>\n",
" <td>0.706612</td>\n",
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" <td>3.806161</td>\n",
" <td>1.927874</td>\n",
" <td>2.796769</td>\n",
" <td>2.639745</td>\n",
" <td>5.151766</td>\n",
" <td>1.170173</td>\n",
" <td>1.052968</td>\n",
" <td>0.614576</td>\n",
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" <th>ABT</th>\n",
" <td>1.0</td>\n",
" <td>1.737416</td>\n",
" <td>2.302661</td>\n",
" <td>2.362296</td>\n",
" <td>1.290798</td>\n",
" <td>1.972427</td>\n",
" <td>1.265470</td>\n",
" <td>0.427060</td>\n",
" <td>1.345303</td>\n",
" <td>0.667842</td>\n",
" <td>...</td>\n",
" <td>2.384739</td>\n",
" <td>1.766592</td>\n",
" <td>1.795127</td>\n",
" <td>2.333440</td>\n",
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" <td>2.440846</td>\n",
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" <td>2.722026</td>\n",
" <td>2.975313</td>\n",
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" <th>ADBE</th>\n",
" <td>1.0</td>\n",
" <td>2.796128</td>\n",
" <td>1.733207</td>\n",
" <td>2.519306</td>\n",
" <td>2.074187</td>\n",
" <td>1.665204</td>\n",
" <td>2.393327</td>\n",
" <td>1.605166</td>\n",
" <td>3.158544</td>\n",
" <td>2.404181</td>\n",
" <td>...</td>\n",
" <td>0.980768</td>\n",
" <td>4.448833</td>\n",
" <td>3.672625</td>\n",
" <td>3.513949</td>\n",
" <td>2.623441</td>\n",
" <td>5.208416</td>\n",
" <td>4.821548</td>\n",
" <td>3.721397</td>\n",
" <td>3.279440</td>\n",
" <td>2.370246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADM</th>\n",
" <td>1.0</td>\n",
" <td>1.307959</td>\n",
" <td>0.440623</td>\n",
" <td>0.615481</td>\n",
" <td>1.301435</td>\n",
" <td>1.294688</td>\n",
" <td>0.429101</td>\n",
" <td>0.249023</td>\n",
" <td>0.411915</td>\n",
" <td>0.466721</td>\n",
" <td>...</td>\n",
" <td>0.350154</td>\n",
" <td>0.287081</td>\n",
" <td>1.650929</td>\n",
" <td>1.666158</td>\n",
" <td>1.728794</td>\n",
" <td>1.025446</td>\n",
" <td>0.484122</td>\n",
" <td>0.519792</td>\n",
" <td>0.785994</td>\n",
" <td>0.458892</td>\n",
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" <th>ADP</th>\n",
" <td>1.0</td>\n",
" <td>0.712254</td>\n",
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" <td>1.558534</td>\n",
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" <td>0.439278</td>\n",
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" <td>1.276258</td>\n",
" <td>1.379376</td>\n",
" <td>1.092177</td>\n",
" <td>1.199125</td>\n",
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" <td>1.195295</td>\n",
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" <td>1.640591</td>\n",
" <td>1.743435</td>\n",
" <td>1.394694</td>\n",
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],
"text/plain": [
" 0 1 2 3 4 5 \\\n",
"1993-01-29 AAPL 1.0 0.799399 1.100301 0.391059 0.706612 0.841097 \n",
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" ADM 1.0 1.307959 0.440623 0.615481 1.301435 1.294688 \n",
" ADP 1.0 0.712254 1.190919 1.558534 1.658917 1.347101 \n",
"\n",
" 6 7 8 9 ... 102 \\\n",
"1993-01-29 AAPL 0.788505 1.025169 0.988355 1.099925 ... 0.580766 \n",
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" ADBE 2.393327 1.605166 3.158544 2.404181 ... 0.980768 \n",
" ADM 0.429101 0.249023 0.411915 0.466721 ... 0.350154 \n",
" ADP 1.377462 0.826039 0.678063 0.439278 ... 1.276258 \n",
"\n",
" 103 104 105 106 107 108 \\\n",
"1993-01-29 AAPL 1.502254 3.806161 1.927874 2.796769 2.639745 5.151766 \n",
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" ADBE 4.448833 3.672625 3.513949 2.623441 5.208416 4.821548 \n",
" ADM 0.287081 1.650929 1.666158 1.728794 1.025446 0.484122 \n",
" ADP 1.379376 1.092177 1.199125 2.351751 1.195295 1.156729 \n",
"\n",
" 109 110 111 \n",
"1993-01-29 AAPL 1.170173 1.052968 0.614576 \n",
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" ADM 0.519792 0.785994 0.458892 \n",
" ADP 1.640591 1.743435 1.394694 \n",
"\n",
"[5 rows x 112 columns]"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_volume = pd.read_pickle('../../data/x_volume_base112_ahead1.pkl')\n",
"print(x_volume.shape)\n",
"x_volume.head()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(219281,)\n"
]
},
{
"data": {
"text/plain": [
"1993-01-29 AAPL 0.641247\n",
" ABT 1.764027\n",
" ADBE 1.508452\n",
" ADM 0.346670\n",
" ADP 0.760394\n",
"Name: 112, dtype: float64"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_volume = pd.read_pickle('../../data/y_volume_base112_ahead1.pkl')\n",
"print(y_volume.shape)\n",
"y_volume.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Let's generate the test dataset, also"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def generate_one_test_set(params, data_df):\n",
" # print(('-'*70 + '\\n {}, {} \\n' + '-'*70).format(params['base_days'].values, params['ahead_days'].values))\n",
" tic = time()\n",
" \n",
" train_val_time = int(params['train_val_time'])\n",
" base_days = int(params['base_days'])\n",
" step_days = int(params['step_days'])\n",
" ahead_days = int(params['ahead_days'])\n",
" \n",
" print('Generating: base{}_ahead{}'.format(base_days, ahead_days))\n",
" pid = 'base{}_ahead{}'.format(base_days, ahead_days)\n",
" \n",
" # Getting the data\n",
" today = data_df.index[-1] # Real date\n",
" print(pid + ') data_df loaded')\n",
"\n",
" # Drop symbols with many missing points\n",
" y_train_df = pd.read_pickle('../../data/y_volume_{}.pkl'.format(pid))\n",
" kept_symbols = y_train_df.index.get_level_values(1).unique().tolist()\n",
" data_df = data_df.loc[:, (slice(None), kept_symbols)]\n",
" print(pid + ') Irrelevant symbols dropped.')\n",
" \n",
" # Generate the intervals for the predictor\n",
" x, y = fe.generate_train_intervals(data_df, \n",
" train_val_time, \n",
" base_days, \n",
" step_days,\n",
" ahead_days, \n",
" today, \n",
" fe.feature_volume_one_to_one,\n",
" target_feature=fe.VOLUME_FEATURE) \n",
" print(pid + ') Intervals generated')\n",
" \n",
" # Drop \"bad\" samples and fill missing data\n",
" x_y_df = pd.concat([x, y], axis=1)\n",
" x_y_df = pp.drop_irrelevant_samples(x_y_df, params['SAMPLES_GOOD_DATA_RATIO'])\n",
" x = x_y_df.iloc[:, :-1]\n",
" y = x_y_df.iloc[:, -1]\n",
" x = pp.fill_missing(x)\n",
" print(pid + ') Irrelevant samples dropped and missing data filled.')\n",
" \n",
" # Pickle that\n",
" x.to_pickle('../../data/x_volume_{}_test.pkl'.format(pid))\n",
" y.to_pickle('../../data/y_volume_{}_test.pkl'.format(pid))\n",
" \n",
" toc = time()\n",
" print('%s) %i intervals generated in: %i seconds.' % (pid, x.shape[0], (toc-tic)))\n",
" \n",
" return pid, x, "
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generating: base112_ahead1\n",
"base112_ahead1) data_df loaded\n",
"base112_ahead1) Irrelevant symbols dropped.\n",
"base112_ahead1) Intervals generated\n",
"base112_ahead1) Irrelevant samples dropped and missing data filled.\n",
"base112_ahead1) 15957 intervals generated in: 2 seconds.\n"
]
},
{
"data": {
"text/plain": [
"('base112_ahead1',\n",
" 0 1 2 3 4 5 \\\n",
" 2015-01-02 AAPL 1.0 1.309379 0.881915 0.502535 0.741084 0.551628 \n",
" ABT 1.0 1.020744 1.228717 1.009282 1.113661 0.734277 \n",
" ADBE 1.0 1.876632 2.380451 1.396930 3.661908 1.810396 \n",
" ADM 1.0 2.079629 1.857814 1.529286 1.885960 1.604243 \n",
" ADP 1.0 2.157365 2.960348 2.136902 1.499220 1.718951 \n",
" ADSK 1.0 1.170121 1.162677 0.832090 1.048707 0.711736 \n",
" AEP 1.0 1.073527 1.259167 0.866142 1.414134 0.822234 \n",
" AES 1.0 2.362325 2.152819 1.351209 1.449337 1.495302 \n",
" AET 1.0 1.314205 2.073317 1.391448 1.229979 1.200142 \n",
" AFL 1.0 1.034931 1.371505 0.943251 1.276478 1.014083 \n",
" AJG 1.0 1.422405 1.966099 2.316243 1.546083 1.505282 \n",
" ALK 1.0 1.674931 2.044477 1.283825 1.611856 2.105850 \n",
" AMAT 1.0 1.519000 2.641575 1.029301 1.247451 0.781427 \n",
" AMD 1.0 1.748260 1.372503 1.110396 1.793936 0.935558 \n",
" AME 1.0 1.331017 2.177782 1.852193 2.440179 1.672840 \n",
" AMGN 1.0 1.567243 1.947129 1.343569 1.749368 1.514921 \n",
" AN 1.0 1.698041 1.742035 1.524749 1.425072 1.061121 \n",
" APA 1.0 2.002836 1.521676 1.400045 1.392676 0.949827 \n",
" APC 1.0 1.430615 1.354038 1.370842 1.087004 1.065589 \n",
" APD 1.0 1.665231 2.118093 1.526939 2.047976 1.365097 \n",
" APH 1.0 0.678916 1.630449 1.153657 1.470564 0.712215 \n",
" ARNC 1.0 2.116942 4.007535 2.146717 3.307453 3.039781 \n",
" AVY 1.0 2.247686 2.443505 2.278373 2.489746 1.619325 \n",
" AXP 1.0 1.652595 2.848836 2.273496 2.416261 1.264385 \n",
" AZO 1.0 1.217239 1.229300 1.619671 2.554008 4.271811 \n",
" BA 1.0 1.052354 1.019497 0.608791 0.810926 0.555651 \n",
" BAC 1.0 0.984574 1.639317 0.974967 0.950229 0.640009 \n",
" BAX 1.0 1.996102 2.129876 1.406360 1.579985 1.000849 \n",
" BBBY 1.0 1.900836 2.102104 1.420279 2.106673 2.002082 \n",
" BBT 1.0 1.283045 1.943430 2.543561 4.389168 2.623263 \n",
" ... ... ... ... ... ... ... \n",
" 2016-07-14 TWX 1.0 0.909714 0.922579 1.052514 1.070886 0.858737 \n",
" TXN 1.0 2.251900 1.154166 1.040112 1.512894 1.658280 \n",
" TXT 1.0 0.962747 1.455920 0.976870 0.912868 0.843559 \n",
" UDR 1.0 1.645542 0.930940 0.835749 0.524865 0.608243 \n",
" UHS 1.0 0.817897 0.707206 0.556051 0.404186 0.423550 \n",
" UNH 1.0 1.048697 0.865690 0.826834 0.679675 0.625269 \n",
" UNM 1.0 0.857625 0.660638 1.056228 0.903143 1.157153 \n",
" UNP 1.0 1.249276 1.388450 1.775000 1.829523 1.167174 \n",
" USB 1.0 0.720443 0.773807 1.022645 1.000011 0.994290 \n",
" UTX 1.0 0.755958 0.510331 0.628513 0.796116 1.249229 \n",
" VFC 1.0 1.997235 1.452215 2.265087 1.608391 2.752920 \n",
" VLO 1.0 0.958964 0.849966 0.816881 0.754629 0.762427 \n",
" VMC 1.0 1.256897 0.867078 0.925360 1.082007 0.896877 \n",
" VNO 1.0 1.322490 1.138209 1.123220 1.322277 1.800812 \n",
" VRTX 1.0 1.838026 1.708751 1.676503 1.568943 0.922000 \n",
" VZ 1.0 0.762170 0.771703 0.901242 0.764880 1.087305 \n",
" WDC 1.0 0.855560 0.822979 0.776716 0.684046 0.911343 \n",
" WEC 1.0 1.997958 1.373229 0.909236 0.942627 0.906728 \n",
" WFC 1.0 1.374261 1.125240 1.335525 0.821102 0.796342 \n",
" WFM 1.0 0.883729 0.741493 0.621663 0.661923 0.828346 \n",
" WHR 1.0 1.054215 0.798190 0.778727 0.851870 0.687997 \n",
" WMB 1.0 1.192526 1.471499 1.663737 1.320881 1.057600 \n",
" WMT 1.0 0.865936 0.815470 1.053907 1.152948 0.867580 \n",
" WY 1.0 0.901961 0.821093 0.721907 0.794713 0.666862 \n",
" XEL 1.0 1.112015 1.061592 0.635049 0.794515 0.834956 \n",
" XLNX 1.0 0.987284 0.545648 0.642176 0.861321 0.521535 \n",
" XOM 1.0 1.663253 1.827840 1.750169 3.845671 1.274750 \n",
" XRAY 1.0 1.081037 0.965152 0.944194 1.165436 1.474943 \n",
" XRX 1.0 0.926467 0.877605 0.588330 0.767208 0.597233 \n",
" ZION 1.0 0.787273 0.841850 0.646830 0.740828 0.967934 \n",
" \n",
" 6 7 8 9 ... 102 \\\n",
" 2015-01-02 AAPL 0.393675 0.513118 0.759931 0.683728 ... 0.490166 \n",
" ABT 0.567758 0.892810 0.817798 1.204868 ... 0.971316 \n",
" ADBE 1.897997 2.174064 2.080942 2.087752 ... 1.638939 \n",
" ADM 2.916509 3.623164 5.267184 2.536432 ... 1.344322 \n",
" ADP 1.895002 2.060124 3.366209 3.947636 ... 1.285805 \n",
" ADSK 0.827306 0.801458 0.828909 0.908657 ... 0.907499 \n",
" AEP 0.916453 1.406367 1.297242 1.349148 ... 1.506619 \n",
" AES 1.670273 1.762120 1.468321 1.252290 ... 3.013902 \n",
" AET 1.199016 1.313512 1.292515 1.297105 ... 1.058216 \n",
" AFL 1.756458 1.191076 0.894483 1.436965 ... 1.043639 \n",
" AJG 1.320893 1.446357 2.710073 2.384031 ... 1.928542 \n",
" ALK 1.255452 1.173873 1.512098 1.609072 ... 1.646459 \n",
" AMAT 0.826378 0.952945 1.126522 0.859464 ... 1.372170 \n",
" AMD 0.708281 1.027661 0.957231 0.766359 ... 0.863920 \n",
" AME 2.303601 1.763586 1.578466 1.769249 ... 1.048180 \n",
" AMGN 1.036040 1.753277 1.314420 1.110955 ... 1.093323 \n",
" AN 1.174986 1.337484 1.253217 0.939827 ... 2.200300 \n",
" APA 1.679392 1.235779 1.682862 0.934154 ... 0.632823 \n",
" APC 1.438755 1.202554 1.338463 1.615223 ... 1.258033 \n",
" APD 2.593475 2.349888 1.995157 1.718512 ... 2.772246 \n",
" APH 0.649977 1.488225 1.261445 0.984633 ... 0.696905 \n",
" ARNC 1.711586 2.395383 2.180967 3.017904 ... 2.845901 \n",
" AVY 1.354080 1.743732 3.172775 2.675464 ... 1.392422 \n",
" AXP 1.820718 2.086664 3.177137 3.838873 ... 1.713364 \n",
" AZO 1.542512 1.147446 0.971196 1.600778 ... 0.869252 \n",
" BA 0.513660 0.597992 0.623627 1.512184 ... 0.552441 \n",
" BAC 0.627417 1.197538 1.197772 0.845683 ... 0.667984 \n",
" BAX 2.075219 2.068944 2.648916 1.893051 ... 0.572115 \n",
" BBBY 2.028663 1.910683 0.960977 0.871603 ... 2.015447 \n",
" BBT 1.944492 2.548825 1.464028 1.707577 ... 1.222109 \n",
" ... ... ... ... ... ... ... \n",
" 2016-07-14 TWX 0.844474 1.486436 2.883391 2.451845 ... 1.278672 \n",
" TXN 1.135886 1.268350 1.510837 1.487067 ... 1.444330 \n",
" TXT 0.735406 1.127198 1.335242 1.298180 ... 1.333617 \n",
" UDR 0.948457 0.652131 1.138983 1.780780 ... 1.116154 \n",
" UHS 0.412161 0.390108 0.400502 0.450081 ... 0.784591 \n",
" UNH 0.463672 0.545566 1.048538 0.881222 ... 0.682370 \n",
" UNM 1.062287 1.046381 1.334607 1.207831 ... 1.288378 \n",
" UNP 0.898964 0.908317 0.889174 0.894496 ... 1.517103 \n",
" USB 0.819061 1.088907 1.223291 5.383950 ... 8.276785 \n",
" UTX 0.789276 0.514712 0.542020 0.660736 ... 0.925733 \n",
" VFC 4.076905 2.254501 1.523020 1.988893 ... 1.627736 \n",
" VLO 1.029497 1.281862 2.137143 1.531252 ... 1.070784 \n",
" VMC 0.988708 0.977494 1.689763 1.538456 ... 1.466914 \n",
" VNO 1.247626 1.930719 1.674133 2.321748 ... 2.594304 \n",
" VRTX 1.625360 1.340915 0.904854 1.057631 ... 1.712049 \n",
" VZ 0.645844 1.042509 1.319077 1.036631 ... 1.483997 \n",
" WDC 0.359111 0.570000 0.345004 0.447628 ... 0.409431 \n",
" WEC 0.773023 0.689605 0.917605 1.017542 ... 2.651941 \n",
" WFC 1.403939 3.159954 1.827070 2.347811 ... 1.425058 \n",
" WFM 0.833515 0.489713 0.728079 0.746453 ... 0.804177 \n",
" WHR 0.657342 0.991694 1.036558 1.037470 ... 1.569378 \n",
" WMB 0.951178 1.173560 3.108479 2.460970 ... 2.598472 \n",
" WMT 0.917551 0.812924 1.403023 1.001051 ... 4.272631 \n",
" WY 0.649469 0.712258 0.789643 0.869330 ... 1.048123 \n",
" XEL 0.768249 1.167047 1.306677 1.080103 ... 1.203281 \n",
" XLNX 0.377718 0.646707 0.704621 0.734384 ... 4.545990 \n",
" XOM 1.114495 1.736627 1.501408 2.123204 ... 1.706351 \n",
" XRAY 0.794543 0.738303 0.887803 1.105778 ... 1.572767 \n",
" XRX 0.822698 0.469397 0.687939 1.032828 ... 1.433052 \n",
" ZION 1.021068 0.826939 1.414639 2.633002 ... 0.991185 \n",
" \n",
" 103 104 105 106 107 108 \\\n",
" 2015-01-02 AAPL 0.743715 0.886662 1.044498 0.628821 0.421408 0.687093 \n",
" ABT 0.650973 0.836879 0.704147 0.562672 0.688186 0.891585 \n",
" ADBE 1.171208 1.258540 2.449889 1.714750 1.326784 1.353690 \n",
" ADM 1.820245 1.415031 1.345540 1.654624 1.692824 1.441385 \n",
" ADP 1.338339 1.830701 1.246661 2.432781 1.424882 1.677282 \n",
" ADSK 1.161206 0.874610 0.754384 0.748825 0.641940 0.616540 \n",
" AEP 0.783943 0.661938 0.804946 0.788033 1.265916 3.110682 \n",
" AES 2.137534 3.179703 3.043151 3.160738 2.973048 2.546015 \n",
" AET 1.104412 1.320581 1.623483 1.687550 1.387921 1.422960 \n",
" AFL 1.084495 1.419733 1.137749 1.714690 0.863867 1.749220 \n",
" AJG 1.716100 1.766974 2.223401 1.969470 2.405547 1.842425 \n",
" ALK 1.531179 1.162292 1.235399 1.275089 1.327137 1.049528 \n",
" AMAT 1.222216 0.930921 1.072350 0.666572 0.905208 0.710751 \n",
" AMD 1.086093 0.928488 0.902415 0.723140 0.818217 0.838268 \n",
" AME 1.144184 1.132519 1.786602 1.653829 1.914811 0.877774 \n",
" AMGN 0.957967 0.828776 1.502877 1.316446 0.799001 1.061087 \n",
" AN 2.218631 3.116026 2.643243 2.004636 1.742433 2.458795 \n",
" APA 0.669607 0.974809 0.510852 0.670743 0.860038 1.009563 \n",
" APC 1.109946 0.681452 0.923985 1.052093 0.849384 1.063525 \n",
" APD 1.869295 2.204768 1.356630 1.024508 0.959527 1.464339 \n",
" APH 0.819324 1.094102 0.628818 0.768747 0.590878 0.907470 \n",
" ARNC 2.663311 2.522955 1.303511 1.924036 1.743410 1.828948 \n",
" AVY 1.579532 1.591538 1.623651 1.987663 1.316808 1.334504 \n",
" AXP 2.308777 1.321807 2.559223 1.849814 2.185394 1.840905 \n",
" AZO 0.394840 0.668001 0.395058 0.554487 0.528401 0.474417 \n",
" BA 0.988967 0.752157 0.422797 1.073541 0.576859 0.839358 \n",
" BAC 0.765025 0.658804 0.580024 0.705498 0.742461 0.924713 \n",
" BAX 0.580392 0.787629 0.666715 1.028478 0.577361 0.696948 \n",
" BBBY 1.764496 2.148264 2.727530 1.646108 3.729650 2.773730 \n",
" BBT 1.993510 1.312110 1.564020 1.022638 0.795519 1.290839 \n",
" ... ... ... ... ... ... ... \n",
" 2016-07-14 TWX 1.960651 2.525270 1.608312 1.594163 1.386166 0.910013 \n",
" TXN 1.602180 1.396580 4.022354 0.891923 1.611158 4.142033 \n",
" TXT 1.097432 1.056981 1.465539 1.470776 1.364459 1.159663 \n",
" UDR 1.332316 0.657397 0.618274 1.160657 1.383365 1.199580 \n",
" UHS 0.855549 0.706938 0.501787 0.603988 0.767997 0.623963 \n",
" UNH 0.678409 0.783438 0.987583 0.971746 1.429747 1.642043 \n",
" UNM 0.957890 1.036618 1.353941 1.151066 0.834745 1.091664 \n",
" UNP 1.135583 1.335436 1.156882 1.289334 1.294925 1.473983 \n",
" USB 9.353526 4.241497 2.371774 3.661436 11.518788 5.263343 \n",
" UTX 0.724290 0.768687 0.775758 0.777231 0.665695 0.840202 \n",
" VFC 2.223378 2.247950 2.256141 2.467401 2.248724 4.024276 \n",
" VLO 0.929882 0.857652 1.133009 0.725744 0.758870 0.854703 \n",
" VMC 1.036436 0.910164 1.458431 1.100285 1.794873 1.258146 \n",
" VNO 1.790825 1.634471 1.748667 1.221944 3.231469 1.991508 \n",
" VRTX 1.302011 0.920159 1.699940 1.029280 2.089041 1.415647 \n",
" VZ 1.672523 1.240792 1.578066 1.595321 1.587301 1.057529 \n",
" WDC 0.461011 0.480078 0.426969 0.352054 0.638083 0.649299 \n",
" WEC 2.265765 1.156598 1.586385 1.385536 3.105546 1.783201 \n",
" WFC 1.242240 1.252415 1.817262 1.521335 1.010848 1.373482 \n",
" WFM 0.782704 0.710426 0.782432 0.800580 0.955876 0.656267 \n",
" WHR 0.825200 0.758019 1.239069 1.510097 1.432990 1.546284 \n",
" WMB 2.154583 1.743191 1.539412 1.670824 2.209782 6.003044 \n",
" WMT 1.317290 0.856201 1.004358 0.980214 0.706317 0.774204 \n",
" WY 1.029763 1.618417 1.366766 1.337191 1.304371 1.073708 \n",
" XEL 0.977755 0.863154 1.672727 2.026088 1.756652 1.200320 \n",
" XLNX 3.182349 1.824059 1.784862 3.449086 3.651118 2.385607 \n",
" XOM 1.807545 7.007270 4.504553 2.952029 3.770322 2.544200 \n",
" XRAY 1.279357 1.555748 1.156926 2.109645 1.215521 1.663749 \n",
" XRX 1.381666 1.240640 0.986018 0.803058 1.118755 1.297604 \n",
" ZION 1.622891 0.807117 0.986565 1.012151 1.375305 1.260563 \n",
" \n",
" 109 110 111 \n",
" 2015-01-02 AAPL 0.489222 0.486098 0.450118 \n",
" ABT 0.753670 0.814058 0.761690 \n",
" ADBE 1.481891 1.386150 1.122632 \n",
" ADM 1.564289 1.477611 1.394270 \n",
" ADP 2.096849 1.576910 1.085765 \n",
" ADSK 0.648402 0.681021 0.557779 \n",
" AEP 1.306764 1.188326 1.306306 \n",
" AES 2.639939 2.195160 1.633802 \n",
" AET 1.502647 1.240896 0.936814 \n",
" AFL 1.125791 0.949057 0.696193 \n",
" AJG 2.398097 1.640902 2.556784 \n",
" ALK 1.014248 1.212392 1.185089 \n",
" AMAT 0.573959 0.674200 1.300083 \n",
" AMD 0.868155 0.768263 0.611322 \n",
" AME 0.816372 1.683801 1.649667 \n",
" AMGN 2.291228 1.393825 0.919942 \n",
" AN 4.475812 5.914504 4.578586 \n",
" APA 0.473995 0.412513 0.800065 \n",
" APC 1.161302 0.890748 1.040878 \n",
" APD 1.130471 1.190375 1.240924 \n",
" APH 1.065276 1.169516 0.689181 \n",
" ARNC 2.753110 3.015002 1.951408 \n",
" AVY 1.886899 1.400147 1.149400 \n",
" AXP 2.060932 2.400297 1.769591 \n",
" AZO 0.681198 0.679871 0.554133 \n",
" BA 1.597921 0.655561 0.620938 \n",
" BAC 1.060734 0.698427 0.702154 \n",
" BAX 0.705262 0.449595 0.513686 \n",
" BBBY 1.990361 1.750695 2.905341 \n",
" BBT 1.610808 1.354875 1.104143 \n",
" ... ... ... ... \n",
" 2016-07-14 TWX 3.046425 1.095342 1.273785 \n",
" TXN 3.856722 1.059564 0.936854 \n",
" TXT 2.139374 1.134609 0.942961 \n",
" UDR 2.039415 0.739796 0.903213 \n",
" UHS 0.905603 0.446458 0.504321 \n",
" UNH 1.488921 0.640478 0.786685 \n",
" UNM 2.414300 1.160345 0.681996 \n",
" UNP 2.534513 1.577183 1.043481 \n",
" USB 3.735727 1.747135 1.178516 \n",
" UTX 1.239713 0.536248 0.567640 \n",
" VFC 5.154066 2.553056 2.366960 \n",
" VLO 1.340557 0.604560 0.462774 \n",
" VMC 1.915799 1.207996 0.934084 \n",
" VNO 2.302574 1.570365 1.340294 \n",
" VRTX 2.484558 1.613962 1.094170 \n",
" VZ 1.596910 1.453010 1.190689 \n",
" WDC 0.872577 0.361997 0.378750 \n",
" WEC 1.887073 0.955798 1.094826 \n",
" WFC 4.276194 1.247334 0.821547 \n",
" WFM 0.988080 0.542170 0.722354 \n",
" WHR 1.951285 0.922996 1.375329 \n",
" WMB 3.884168 1.782162 2.455847 \n",
" WMT 1.339566 0.703908 1.183441 \n",
" WY 2.684885 0.886093 0.919004 \n",
" XEL 2.212358 1.040995 0.777003 \n",
" XLNX 4.939276 1.347456 2.359739 \n",
" XOM 1.985215 0.987633 0.860738 \n",
" XRAY 2.864758 0.981340 1.175850 \n",
" XRX 1.749127 0.723255 1.136066 \n",
" ZION 1.923845 0.771420 1.122944 \n",
" \n",
" [15957 rows x 112 columns])"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_test_df = pd.read_pickle('../../data/data_test_df.pkl')\n",
"generate_one_test_set(best_params_df, data_test_df)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(15957, 112)\n"
]
},
{
"data": {
"text/html": [
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"<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></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" <th>...</th>\n",
" <th>102</th>\n",
" <th>103</th>\n",
" <th>104</th>\n",
" <th>105</th>\n",
" <th>106</th>\n",
" <th>107</th>\n",
" <th>108</th>\n",
" <th>109</th>\n",
" <th>110</th>\n",
" <th>111</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">2015-01-02</th>\n",
" <th>AAPL</th>\n",
" <td>1.0</td>\n",
" <td>1.309379</td>\n",
" <td>0.881915</td>\n",
" <td>0.502535</td>\n",
" <td>0.741084</td>\n",
" <td>0.551628</td>\n",
" <td>0.393675</td>\n",
" <td>0.513118</td>\n",
" <td>0.759931</td>\n",
" <td>0.683728</td>\n",
" <td>...</td>\n",
" <td>0.490166</td>\n",
" <td>0.743715</td>\n",
" <td>0.886662</td>\n",
" <td>1.044498</td>\n",
" <td>0.628821</td>\n",
" <td>0.421408</td>\n",
" <td>0.687093</td>\n",
" <td>0.489222</td>\n",
" <td>0.486098</td>\n",
" <td>0.450118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABT</th>\n",
" <td>1.0</td>\n",
" <td>1.020744</td>\n",
" <td>1.228717</td>\n",
" <td>1.009282</td>\n",
" <td>1.113661</td>\n",
" <td>0.734277</td>\n",
" <td>0.567758</td>\n",
" <td>0.892810</td>\n",
" <td>0.817798</td>\n",
" <td>1.204868</td>\n",
" <td>...</td>\n",
" <td>0.971316</td>\n",
" <td>0.650973</td>\n",
" <td>0.836879</td>\n",
" <td>0.704147</td>\n",
" <td>0.562672</td>\n",
" <td>0.688186</td>\n",
" <td>0.891585</td>\n",
" <td>0.753670</td>\n",
" <td>0.814058</td>\n",
" <td>0.761690</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADBE</th>\n",
" <td>1.0</td>\n",
" <td>1.876632</td>\n",
" <td>2.380451</td>\n",
" <td>1.396930</td>\n",
" <td>3.661908</td>\n",
" <td>1.810396</td>\n",
" <td>1.897997</td>\n",
" <td>2.174064</td>\n",
" <td>2.080942</td>\n",
" <td>2.087752</td>\n",
" <td>...</td>\n",
" <td>1.638939</td>\n",
" <td>1.171208</td>\n",
" <td>1.258540</td>\n",
" <td>2.449889</td>\n",
" <td>1.714750</td>\n",
" <td>1.326784</td>\n",
" <td>1.353690</td>\n",
" <td>1.481891</td>\n",
" <td>1.386150</td>\n",
" <td>1.122632</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADM</th>\n",
" <td>1.0</td>\n",
" <td>2.079629</td>\n",
" <td>1.857814</td>\n",
" <td>1.529286</td>\n",
" <td>1.885960</td>\n",
" <td>1.604243</td>\n",
" <td>2.916509</td>\n",
" <td>3.623164</td>\n",
" <td>5.267184</td>\n",
" <td>2.536432</td>\n",
" <td>...</td>\n",
" <td>1.344322</td>\n",
" <td>1.820245</td>\n",
" <td>1.415031</td>\n",
" <td>1.345540</td>\n",
" <td>1.654624</td>\n",
" <td>1.692824</td>\n",
" <td>1.441385</td>\n",
" <td>1.564289</td>\n",
" <td>1.477611</td>\n",
" <td>1.394270</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADP</th>\n",
" <td>1.0</td>\n",
" <td>2.157365</td>\n",
" <td>2.960348</td>\n",
" <td>2.136902</td>\n",
" <td>1.499220</td>\n",
" <td>1.718951</td>\n",
" <td>1.895002</td>\n",
" <td>2.060124</td>\n",
" <td>3.366209</td>\n",
" <td>3.947636</td>\n",
" <td>...</td>\n",
" <td>1.285805</td>\n",
" <td>1.338339</td>\n",
" <td>1.830701</td>\n",
" <td>1.246661</td>\n",
" <td>2.432781</td>\n",
" <td>1.424882</td>\n",
" <td>1.677282</td>\n",
" <td>2.096849</td>\n",
" <td>1.576910</td>\n",
" <td>1.085765</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 112 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 \\\n",
"2015-01-02 AAPL 1.0 1.309379 0.881915 0.502535 0.741084 0.551628 \n",
" ABT 1.0 1.020744 1.228717 1.009282 1.113661 0.734277 \n",
" ADBE 1.0 1.876632 2.380451 1.396930 3.661908 1.810396 \n",
" ADM 1.0 2.079629 1.857814 1.529286 1.885960 1.604243 \n",
" ADP 1.0 2.157365 2.960348 2.136902 1.499220 1.718951 \n",
"\n",
" 6 7 8 9 ... 102 \\\n",
"2015-01-02 AAPL 0.393675 0.513118 0.759931 0.683728 ... 0.490166 \n",
" ABT 0.567758 0.892810 0.817798 1.204868 ... 0.971316 \n",
" ADBE 1.897997 2.174064 2.080942 2.087752 ... 1.638939 \n",
" ADM 2.916509 3.623164 5.267184 2.536432 ... 1.344322 \n",
" ADP 1.895002 2.060124 3.366209 3.947636 ... 1.285805 \n",
"\n",
" 103 104 105 106 107 108 \\\n",
"2015-01-02 AAPL 0.743715 0.886662 1.044498 0.628821 0.421408 0.687093 \n",
" ABT 0.650973 0.836879 0.704147 0.562672 0.688186 0.891585 \n",
" ADBE 1.171208 1.258540 2.449889 1.714750 1.326784 1.353690 \n",
" ADM 1.820245 1.415031 1.345540 1.654624 1.692824 1.441385 \n",
" ADP 1.338339 1.830701 1.246661 2.432781 1.424882 1.677282 \n",
"\n",
" 109 110 111 \n",
"2015-01-02 AAPL 0.489222 0.486098 0.450118 \n",
" ABT 0.753670 0.814058 0.761690 \n",
" ADBE 1.481891 1.386150 1.122632 \n",
" ADM 1.564289 1.477611 1.394270 \n",
" ADP 2.096849 1.576910 1.085765 \n",
"\n",
"[5 rows x 112 columns]"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_volume_test = pd.read_pickle('../../data/x_volume_base112_ahead1_test.pkl')\n",
"print(x_volume_test.shape)\n",
"x_volume_test.head()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(15957,)\n"
]
},
{
"data": {
"text/plain": [
"2015-01-02 AAPL 0.578212\n",
" ABT 1.043824\n",
" ADBE 1.406943\n",
" ADM 1.222931\n",
" ADP 1.390231\n",
"Name: 112, dtype: float64"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_volume_test = pd.read_pickle('../../data/y_volume_base112_ahead1_test.pkl')\n",
"print(y_volume_test.shape)\n",
"y_volume_test.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Let's train a predictor with the same hyperparameters as for the 'Close' one."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"best_params_df = pd.read_pickle('../../data/best_params_final_df.pkl')"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean metrics: \n",
" train test\n",
"r2 0.539703 0.480612\n",
"mre 0.277789 0.277234\n",
"----------------------------------------------------------------------\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f37fe481c18>"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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tZRIAjOoYtCygBEkfvnZj2tchjRImFb9ayKeusW5Om1swmsBkJF70x1tJ/OEY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z48f2UasWS6bFPcctnekl2gldPd1uV1IJXmMRGQnlxytN+HvgYnOuvXZ3T6YF8DbGZtJxkYbJpb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\nADA0YRIAAAAAQ/v/AWe2TegHJ0hgAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f37fa18e780>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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AQBAIAQAAdGBpGQMAAIEhEAIAAOggtv62QiAEAAACQSAEAADQQWx9IkTLGAAACAWBEAAA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\nDSFJkiRJkqTG/B8vxAyRJNqKAAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f37fa151fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import predictor.feature_extraction as fe\n",
"from predictor.linear_predictor import LinearPredictor\n",
"import utils.misc as misc\n",
"import predictor.evaluation as ev\n",
"\n",
"ahead_days = 1\n",
"\n",
"# Get some parameters\n",
"train_days = int(best_params_df.loc[ahead_days, 'train_days'])\n",
"GOOD_DATA_RATIO, \\\n",
"train_val_time, \\\n",
"base_days, \\\n",
"step_days, \\\n",
"ahead_days, \\\n",
"SAMPLES_GOOD_DATA_RATIO, \\\n",
"x_filename, \\\n",
"y_filename = misc.unpack_params(best_params_df.loc[ahead_days,:])\n",
"\n",
"pid = 'base{}_ahead{}'.format(base_days, ahead_days)\n",
"\n",
"# Get the datasets\n",
"x_train = pd.read_pickle('../../data/x_volume_{}.pkl'.format(pid))\n",
"y_train = pd.read_pickle('../../data/y_volume_{}.pkl'.format(pid))\n",
"x_test = pd.read_pickle('../../data/x_volume_{}_test.pkl'.format(pid)).sort_index()\n",
"y_test = pd.DataFrame(pd.read_pickle('../../data/y_volume_{}_test.pkl'.format(pid))).sort_index()\n",
"\n",
"# Let's cut the training set to use only the required number of samples\n",
"end_date = x_train.index.levels[0][-1]\n",
"start_date = fe.add_market_days(end_date, -train_days)\n",
"x_sub_df = x_train.loc[(slice(start_date,None),slice(None)),:]\n",
"y_sub_df = pd.DataFrame(y_train.loc[(slice(start_date,None),slice(None))])\n",
"\n",
"# Create the estimator and train\n",
"estimator = LinearPredictor()\n",
"estimator.fit(x_sub_df, y_sub_df)\n",
"\n",
"# Get the training and test predictions\n",
"y_train_pred = estimator.predict(x_sub_df)\n",
"y_test_pred = estimator.predict(x_test)\n",
"\n",
"# Get the training and test metrics for each symbol\n",
"metrics_train = ev.get_metrics_df(y_sub_df, y_train_pred)\n",
"metrics_test = ev.get_metrics_df(y_test, y_test_pred)\n",
"\n",
"# Show the mean metrics\n",
"metrics_df = pd.DataFrame(columns=['train', 'test'])\n",
"metrics_df['train'] = metrics_train.mean()\n",
"metrics_df['test'] = metrics_test.mean()\n",
"print('Mean metrics: \\n{}\\n{}'.format(metrics_df,'-'*70))\n",
"\n",
"# Plot the metrics in time\n",
"metrics_train_time = ev.get_metrics_in_time(y_sub_df, y_train_pred, base_days + ahead_days)\n",
"metrics_test_time = ev.get_metrics_in_time(y_test, y_test_pred, base_days + ahead_days)\n",
"plt.plot(metrics_train_time[2], metrics_train_time[0], label='train', marker='.')\n",
"plt.plot(metrics_test_time[2], metrics_test_time[0], label='test', marker='.')\n",
"plt.title('$r^2$ metrics')\n",
"plt.legend()\n",
"plt.figure()\n",
"plt.plot(metrics_train_time[2], metrics_train_time[1], label='train', marker='.')\n",
"plt.plot(metrics_test_time[2], metrics_test_time[1], label='test', marker='.')\n",
"plt.title('MRE metrics')\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['../../data/best_volume_predictor.pkl']"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"joblib.dump(estimator, '../../data/best_volume_predictor.pkl')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
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| mit |
mwickert/scikit-dsp-comm | docs/source/nb_examples/Convolutional_Codes.ipynb | 1 | 25630 | {
"cells": [
{
"cell_type": "raw",
"metadata": {},
"source": [
"\\tableofcontents\n",
"% These TeX commands run at the start to remove section numbering\n",
"\\renewcommand{\\thesection}{\\hspace*{-1.0em}}\n",
"\\renewcommand{\\thesubsection}{\\hspace*{-1.0em}}\n",
"\\renewcommand{\\thesubsubsection}{\\hspace*{-1.0em}}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%pylab inline\n",
"#%matplotlib qt\n",
"import sk_dsp_comm.sigsys as ss\n",
"import scipy.signal as signal\n",
"from IPython.display import Audio, display\n",
"from IPython.display import Image, SVG"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pylab.rcParams['savefig.dpi'] = 100 # default 72\n",
"#pylab.rcParams['figure.figsize'] = (6.0, 4.0) # default (6,4)\n",
"#%config InlineBackend.figure_formats=['png'] # default for inline viewing\n",
"%config InlineBackend.figure_formats=['svg'] # SVG inline viewing\n",
"#%config InlineBackend.figure_formats=['pdf'] # render pdf figs for LaTeX"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import scipy.special as special\n",
"import sk_dsp_comm.digitalcom as dc\n",
"import sk_dsp_comm.fec_conv as fec"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Convolutional Coding"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rate 1/2 "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A convolutional encoder object can be created with the `fec.FECConv` method. The rate of the object will be determined by the number of generator polynomials used. Right now, only rate 1/2 and rate 1/3 are supported, so 2 or three generator polynomials can be used. The following table shows ideal rate 1/2 generator polynomials. These are also included in the docstring. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Table 1: Weight spectra $c_k$ for bounding the codedrate 1/2 BEP**.\n",
"\n",
"| CL |Polynomials |$D_{free}$|$d_f$|$d_f+1$|$d_f+2$|$d_f+3$|$d_f+4$|$d_f+5$|$d_f+6$|$d_f+7$|\n",
"|:----:|:--------------------------------:|:-------:|:---:|:-----:|:-----:|:-----:|-------|-------|-------|-------|\n",
"|3 |(5,7) = ('101','111') |5 |1 |4 |12 |32 |80 |192 |488 |1024 |\n",
"|4 |(15,17) = ('1101','1111') |6 |2 |7 |18 |49 |130 |333 |836 |2069 |\n",
"|5 |(23,35) = ('10011','11101') |7 |4 |12 |20 |72 |225 |500 |1324 |3680 |\n",
"|6 |(53,75) = ('101011','111101') |8 |2 |36 |32 |62 |332 |701 |2342 |5503 |\n",
"|7 |(133,171) = ('1011011','1111001') |10 |36 |0 |211 |0 |1404 |0 |11633 |0 |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In addition to the generator polynomials, you can specify a decision depth for the object. This will determine how many state transitions will be used for the traceback. The following shows how to create a rate 1/2 `fec_conv` object with contraint length 3 and decision depth 10."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cc1 = fec.FECConv(('111','101'),10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `trellis_plot()` method can be used to see the state transitions of the `fec_conv` object."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cc1.trellis_plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Rate 1/2 Hard Decision Decoding"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we would like to know the theoretical bit error probability bounds of our convolutional encoding/decoding setup. We can do this using the `fec.conv_Pb_bound` method. The method takes the rate, degrees of freedom, $c_k$ values, SNR, hard or soft decisions, and order M for an MPSK modulation scheme as arguments. It returns the BEP. The following shows theoretical bounds for rate 1/2 encoding/decoding BPSK system. Compare with Ziemer pg 667."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Weight Structure Bounds BEP"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"SNRdB = arange(0,12,.1)\n",
"Pb_uc = fec.conv_Pb_bound(1/2,7,[4, 12, 20, 72, 225],SNRdB,2)\n",
"Pb_s_half_3_hard = fec.conv_Pb_bound(1/2,5,[1, 4, 12, 32, 80, 192, 448, 1024],SNRdB,0)\n",
"Pb_s_half_5_hard = fec.conv_Pb_bound(1/2,7,[4, 12, 20, 72, 225, 500, 1324, 3680],SNRdB,0)\n",
"Pb_s_half_7_hard = fec.conv_Pb_bound(1/2,10,[36, 0, 211, 0, 1404, 0, 11633, 0],SNRdB,0)\n",
"Pb_s_half_9_hard = fec.conv_Pb_bound(1/2,12,[33, 0, 281, 0, 2179, 0, 15035, 0],SNRdB,0)\n",
"figure(figsize=(5,5))\n",
"semilogy(SNRdB,Pb_uc)\n",
"semilogy(SNRdB,Pb_s_half_3_hard,'--')\n",
"semilogy(SNRdB,Pb_s_half_5_hard,'--')\n",
"semilogy(SNRdB,Pb_s_half_7_hard,'--')\n",
"semilogy(SNRdB,Pb_s_half_9_hard,'--')\n",
"axis([0,12,1e-7,1e0])\n",
"title(r'Hard Decision Rate 1/2 Coding Theory Bounds')\n",
"xlabel(r'$E_b/N_0$ (dB)')\n",
"ylabel(r'Symbol Error Probability')\n",
"legend(('Uncoded BPSK','R=1/2, K=3, Hard',\\\n",
" 'R=1/2, K=5, Hard', 'R=1/2, K=7, Hard',\\\n",
" 'R=1/2, K=9, Hard'),loc='upper right')\n",
"grid();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### BEP Simulation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we can determine our BEP bounds, we can test the actual encoder/decoder using dummy binary data. The following code creates a rate 1/2 fec_conv object. It then generates dummy binary data and encodes the data using the `conv_encoder` method. This method takes an array of binary values, and an initial state as the input and returns the encoded bits and states. We then adds nois to the encoded data according to the set $E_b/N_0$ to simulate a noisy channel. The data is then decoded using the `viterbi_decoder` method. This method takes the array of noisy data and a decision metric. If the hard decision metric is selected, then we expect binary input values from around 0 to around 1. The method then returns the decoded binary values. Then the bit errors are counted. Once at least 100 bit errors are counted, the bit error probability is calculated. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"N_bits_per_frame = 10000\n",
"EbN0 = 4\n",
"total_bit_errors = 0\n",
"total_bit_count = 0\n",
"cc1 = fec.FECConv(('11101','10011'),25)\n",
"# Encode with shift register starting state of '0000'\n",
"state = '0000'\n",
"while total_bit_errors < 100:\n",
" # Create 100000 random 0/1 bits\n",
" x = randint(0,2,N_bits_per_frame)\n",
" y,state = cc1.conv_encoder(x,state)\n",
" # Add channel noise to bits, include antipodal level shift to [-1,1]\n",
" yn_soft = dc.cpx_awgn(2*y-1,EbN0-3,1) # Channel SNR is 3 dB less for rate 1/2\n",
" yn_hard = ((sign(yn_soft.real)+1)/2).astype(int)\n",
" z = cc1.viterbi_decoder(yn_hard,'hard')\n",
" # Count bit errors\n",
" bit_count, bit_errors = dc.bit_errors(x,z)\n",
" total_bit_errors += bit_errors\n",
" total_bit_count += bit_count\n",
" print('Bits Received = %d, Bit errors = %d, BEP = %1.2e' %\\\n",
" (total_bit_count, total_bit_errors,\\\n",
" total_bit_errors/total_bit_count))\n",
"print('*****************************************************')\n",
"print('Bits Received = %d, Bit errors = %d, BEP = %1.2e' %\\\n",
" (total_bit_count, total_bit_errors,\\\n",
" total_bit_errors/total_bit_count))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y[:100].astype(int)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The simulated BEP can then be compared to the theoretical bounds that were shown earlier. Some values were simulated for the constraint length 3 and constraint length 5 cases."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"SNRdB = arange(0,12,.1)\n",
"Pb_uc = fec.conv_Pb_bound(1/2,7,[4, 12, 20, 72, 225],SNRdB,2)\n",
"Pb_s_half_3_hard = fec.conv_Pb_bound(1/2,5,[1, 4, 12, 32, 80, 192, 448, 1024],SNRdB,0)\n",
"Pb_s_half_5_hard = fec.conv_Pb_bound(1/2,7,[4, 12, 20, 72, 225, 500, 1324, 3680],SNRdB,0)\n",
"Pb_s_half_7_hard = fec.conv_Pb_bound(1/2,10,[36, 0, 211, 0, 1404, 0, 11633, 0],SNRdB,0)\n",
"Pb_s_half_9_hard = fec.conv_Pb_bound(1/2,12,[33, 0, 281, 0, 2179, 0, 15035, 0],SNRdB,0)\n",
"Pb_s_half_5_hard_sim = array([3.36e-2,1.04e-2,1.39e-3,1.56e-04,1.24e-05])\n",
"Pb_s_half_3_hard_sim = array([2.59e-02,1.35e-02,2.71e-03,6.39e-04,9.73e-05,7.71e-06])\n",
"figure(figsize=(5,5))\n",
"semilogy(SNRdB,Pb_uc)\n",
"semilogy(SNRdB,Pb_s_half_3_hard,'y--')\n",
"semilogy(SNRdB,Pb_s_half_5_hard,'g--')\n",
"semilogy(SNRdB,Pb_s_half_7_hard,'--')\n",
"semilogy(SNRdB,Pb_s_half_9_hard,'--')\n",
"semilogy([3,4,5,6,7,8],Pb_s_half_3_hard_sim,'ys')\n",
"semilogy([3,4,5,6,7],Pb_s_half_5_hard_sim,'gs')\n",
"axis([0,12,1e-7,1e0])\n",
"title(r'Hard Decision Rate 1/2 Coding Measurements')\n",
"xlabel(r'$E_b/N_0$ (dB)')\n",
"ylabel(r'Symbol Error Probability')\n",
"legend(('Uncoded BPSK','R=1/2, K=3, Hard',\\\n",
" 'R=1/2, K=5, Hard', 'R=1/2, K=7, Hard',\\\n",
" 'R=1/2, K=9, Hard', 'R=1/2, K=3, Simulation',\\\n",
" 'R=1/2, K=5, Simulation'),loc='lower left')\n",
"grid();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can look at the surviving paths using the `traceback_plot` method."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cc1.traceback_plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Soft Decision Decoding BEP Simulation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Soft decision decoding can also be done. In order to simulate the soft decision decoder, we can use the same setup as before, but now we specify 'soft' in the `viterbi_decoder` method. We also have to pick a quantization level when we do this. If we want 3-bit quantization we would specify that the quant_level=3. When we use soft decisions we have to scale our noisy received values to values on $[0,2^{n}-1]$. So for a three-bit quantizaiton, we would scale to values on $[0,7]$. This helps the system to get better distance metrics for all possible paths in the decoder, thus improving the BEP. The following shows how to simulate soft decisions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"N_bits_per_frame = 10000\n",
"EbN0 = 2\n",
"total_bit_errors = 0\n",
"total_bit_count = 0\n",
"cc1 = fec.FECConv(('11101','10011'),25)\n",
"# Encode with shift register starting state of '0000'\n",
"state = '0000'\n",
"while total_bit_errors < 100:\n",
" # Create 100000 random 0/1 bits\n",
" x = randint(0,2,N_bits_per_frame)\n",
" y,state = cc1.conv_encoder(x,state)\n",
" # Add channel noise to bits, include antipodal level shift to [-1,1]\n",
" yn = dc.cpx_awgn(2*y-1,EbN0-3,1) # Channel SNR is 3dB less for rate 1/2\n",
" # Scale & level shift to three-bit quantization levels [0,7]\n",
" yn = (yn.real+1)/2*7\n",
" z = cc1.viterbi_decoder(yn.real,'soft',quant_level=3)\n",
" # Count bit errors\n",
" bit_count, bit_errors = dc.bit_errors(x,z)\n",
" total_bit_errors += bit_errors\n",
" total_bit_count += bit_count\n",
" print('Bits Received = %d, Bit errors = %d, BEP = %1.2e' %\\\n",
" (total_bit_count, total_bit_errors,\\\n",
" total_bit_errors/total_bit_count))\n",
"print('*****************************************************')\n",
"print('Bits Received = %d, Bit errors = %d, BEP = %1.2e' %\\\n",
" (total_bit_count, total_bit_errors,\\\n",
" total_bit_errors/total_bit_count))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"SNRdB = arange(0,12,.1)\n",
"Pb_uc = fec.conv_Pb_bound(1/3,7,[4, 12, 20, 72, 225],SNRdB,2)\n",
"Pb_s_third_3 = fec.conv_Pb_bound(1/3,8,[3, 0, 15],SNRdB,1)\n",
"Pb_s_third_4 = fec.conv_Pb_bound(1/3,10,[6, 0, 6, 0],SNRdB,1)\n",
"Pb_s_third_5 = fec.conv_Pb_bound(1/3,12,[12, 0, 12, 0, 56],SNRdB,1)\n",
"Pb_s_third_6 = fec.conv_Pb_bound(1/3,13,[1, 8, 26, 20, 19, 62],SNRdB,1)\n",
"Pb_s_third_7 = fec.conv_Pb_bound(1/3,14,[1, 0, 20, 0, 53, 0, 184],SNRdB,1)\n",
"Pb_s_third_8 = fec.conv_Pb_bound(1/3,16,[1, 0, 24, 0, 113, 0, 287, 0],SNRdB,1)\n",
"Pb_s_half = fec.conv_Pb_bound(1/2,7,[4, 12, 20, 72, 225],SNRdB,1)\n",
"figure(figsize=(5,5))\n",
"semilogy(SNRdB,Pb_uc)\n",
"semilogy(SNRdB,Pb_s_third_3,'--')\n",
"semilogy(SNRdB,Pb_s_third_4,'--')\n",
"semilogy(SNRdB,Pb_s_third_5,'g')\n",
"semilogy(SNRdB,Pb_s_third_6,'--')\n",
"semilogy(SNRdB,Pb_s_third_7,'--')\n",
"semilogy(SNRdB,Pb_s_third_8,'--')\n",
"#semilogy(SNRdB,Pb_s_half,'--')\n",
"semilogy([0,1,2,3,4,5],[9.08e-02,2.73e-02,6.52e-03,\\\n",
" 8.94e-04,8.54e-05,5e-6],'gs')\n",
"axis([0,12,1e-7,1e0])\n",
"title(r'Soft Decision Rate 1/2 Coding Measurements')\n",
"xlabel(r'$E_b/N_0$ (dB)')\n",
"ylabel(r'Symbol Error Probability')\n",
"legend(('Uncoded BPSK','R=1/3, K=3, Soft',\\\n",
" 'R=1/3, K=4, Soft','R=1/3, K=5, Soft',\\\n",
" 'R=1/3, K=6, Soft','R=1/3, K=7, Soft',\\\n",
" 'R=1/3, K=8, Soft','R=1/3, K=5, Sim', \\\n",
" 'Simulation'),loc='upper right')\n",
"grid();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The decoder can also do unquantized soft decisions. This is done by specifying 'unquant' for the metric type. The system will then expect floating point numbers on $[0,1]$ at the decoder input."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rate 1/3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rate 1/3 convolution encoding/decoding can be done very similarly to the rate 1/2 code. The difference when instantiating, is that the rate 1/3 uses 3 generator polynmials instead of 2. The following table shows ideal generator polynomials at different constraint lengths for rate 1/3 convolutional codes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Table 2: Weight spectra $c_k$ for bounding the coded rate 1/3 BEP**.\n",
"\n",
"\n",
"| CL |Polynomials |$d_{free}$|$d_f$|$d_f+1$|$d_f+2$|$d_f+3$|$d_f+4$|$d_f+5$|$d_f+6$|$d_f+7$|\n",
"|:----:|:-------------------------------------------------:|:--------:|:---:|:-----:|:-----:|:-----:|-------|-------|-------|-------|\n",
"|3 |(7,7,5) = ('111','111','101') |8 |3 |0 |15 |0 |58 |0 |201 |0 |\n",
"|4 |(15,13,11) = ('1111','1101','1011') |10 |6 |0 |6 |0 |58 |0 |118 |0 |\n",
"|5 |(31,27,21) = ('11111','11011','10101') |12 |12 |0 |12 |0 |56 |0 |320 |0 |\n",
"|6 |(61,43,39) = ('111101','101011','100111') |13 |1 |8 |26 |20 |19 |62 |86 |204 |\n",
"|7 |(121,101,91) = ('1111001','1100101','1011011') |14 |1 |0 |20 |0 |53 |0 |184 |0 |\n",
"|8 |(247,217,149) = ('11110111','11011001','10010101') |16 |1 |0 |24 |0 |113 |0 |287 |0 |"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cc2 = fec.FECConv(('111','111','101'),10)\n",
"cc2.trellis_plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Rate 1/3 Hard Decision Decoding"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Weight Structure Bounds BEP\n",
"Compare with Ziemer pg 668."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"SNRdB = arange(0,12,.1)\n",
"Pb_uc = fec.conv_Pb_bound(1/3,7,[4, 12, 20, 72, 225],SNRdB,2)\n",
"Pb_s_third_3_hard = fec.conv_Pb_bound(1/3,8,[3, 0, 15, 0, 58, 0, 201, 0],SNRdB,0)\n",
"Pb_s_third_4_hard = fec.conv_Pb_bound(1/3,10,[6, 0, 6, 0, 58, 0, 118, 0],SNRdB,0)\n",
"Pb_s_third_5_hard = fec.conv_Pb_bound(1/3,12,[12, 0, 12, 0, 56, 0, 320, 0],SNRdB,0)\n",
"Pb_s_third_6_hard = fec.conv_Pb_bound(1/3,13,[1, 8, 26, 20, 19, 62, 86, 204],SNRdB,0)\n",
"Pb_s_third_7_hard = fec.conv_Pb_bound(1/3,14,[1, 0, 20, 0, 53, 0, 184],SNRdB,0)\n",
"Pb_s_third_8_hard = fec.conv_Pb_bound(1/3,16,[1, 0, 24, 0, 113, 0, 287, 0],SNRdB,0)\n",
"figure(figsize=(5,5))\n",
"semilogy(SNRdB,Pb_uc)\n",
"semilogy(SNRdB,Pb_s_third_3_hard,'--')\n",
"#semilogy(SNRdB,Pb_s_third_4_hard,'--')\n",
"semilogy(SNRdB,Pb_s_third_5_hard,'--')\n",
"#semilogy(SNRdB,Pb_s_third_6_hard,'--')\n",
"semilogy(SNRdB,Pb_s_third_7_hard,'--')\n",
"#semilogy(SNRdB,Pb_s_third_8_hard,'--')\n",
"axis([0,12,1e-7,1e0])\n",
"title(r'Hard Decision Rate 1/3 Coding Theory Bounds')\n",
"xlabel(r'$E_b/N_0$ (dB)')\n",
"ylabel(r'Symbol Error Probability')\n",
"legend(('Uncoded BPSK','R=1/3, K=3, Hard',\\\n",
" #'R=1/3, K=4, Hard', 'R=1/3, K=5, Hard',\\\n",
" #'R=1/3, K=6, Hard', 'R=1/3, K=7, Hard',\\\n",
" #'R=1/3, K=7, Hard'),loc='upper right')\n",
" 'R=1/3, K=5, Hard', 'R=1/3, K=7, Hard'),\\\n",
" loc='upper right')\n",
"grid();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### BEP Simulation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"N_bits_per_frame = 10000\n",
"EbN0 = 3\n",
"total_bit_errors = 0\n",
"total_bit_count = 0\n",
"cc1 = fec.FECConv(('11111','11011','10101'),25)\n",
"# Encode with shift register starting state of '0000'\n",
"state = '0000'\n",
"while total_bit_errors < 100:\n",
" # Create 100000 random 0/1 bits\n",
" x = randint(0,2,N_bits_per_frame)\n",
" y,state = cc1.conv_encoder(x,state)\n",
" # Add channel noise to bits, include antipodal level shift to [-1,1]\n",
" yn_soft = dc.cpx_awgn(2*y-1,EbN0-10*log10(3),1) # Channel SNR is 10*log10(3) dB less\n",
" yn_hard = ((sign(yn_soft.real)+1)/2).astype(int)\n",
" z = cc1.viterbi_decoder(yn_hard.real,'hard')\n",
" # Count bit errors\n",
" bit_count, bit_errors = dc.bit_errors(x,z)\n",
" total_bit_errors += bit_errors\n",
" total_bit_count += bit_count\n",
" print('Bits Received = %d, Bit errors = %d, BEP = %1.2e' %\\\n",
" (total_bit_count, total_bit_errors,\\\n",
" total_bit_errors/total_bit_count))\n",
"print('*****************************************************')\n",
"print('Bits Received = %d, Bit errors = %d, BEP = %1.2e' %\\\n",
" (total_bit_count, total_bit_errors,\\\n",
" total_bit_errors/total_bit_count))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"SNRdB = arange(0,12,.1)\n",
"Pb_uc = fec.conv_Pb_bound(1/3,7,[4, 12, 20, 72, 225],SNRdB,2)\n",
"Pb_s_third_3_hard = fec.conv_Pb_bound(1/3,8,[3, 0, 15, 0, 58, 0, 201, 0],SNRdB,0)\n",
"Pb_s_third_5_hard = fec.conv_Pb_bound(1/3,12,[12, 0, 12, 0, 56, 0, 320, 0],SNRdB,0)\n",
"Pb_s_third_7_hard = fec.conv_Pb_bound(1/3,14,[1, 0, 20, 0, 53, 0, 184],SNRdB,0)\n",
"Pb_s_third_5_hard_sim = array([8.94e-04,1.11e-04,8.73e-06])\n",
"figure(figsize=(5,5))\n",
"semilogy(SNRdB,Pb_uc)\n",
"semilogy(SNRdB,Pb_s_third_3_hard,'r--')\n",
"semilogy(SNRdB,Pb_s_third_5_hard,'g--')\n",
"semilogy(SNRdB,Pb_s_third_7_hard,'k--')\n",
"semilogy(array([5,6,7]),Pb_s_third_5_hard_sim,'sg')\n",
"axis([0,12,1e-7,1e0])\n",
"title(r'Hard Decision Rate 1/3 Coding Measurements')\n",
"xlabel(r'$E_b/N_0$ (dB)')\n",
"ylabel(r'Symbol Error Probability')\n",
"legend(('Uncoded BPSK','R=1/3, K=3, Hard',\\\n",
" 'R=1/3, K=5, Hard', 'R=1/3, K=7, Hard',\\\n",
" ),loc='upper right')\n",
"grid();"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cc1.traceback_plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Soft Decision Decoding BEP Simulation\n",
"Here we use 3-bit quantization soft decoding."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"N_bits_per_frame = 10000\n",
"EbN0 = 2\n",
"total_bit_errors = 0\n",
"total_bit_count = 0\n",
"cc1 = fec.FECConv(('11111','11011','10101'),25)\n",
"# Encode with shift register starting state of '0000'\n",
"state = '0000'\n",
"while total_bit_errors < 100:\n",
" # Create 100000 random 0/1 bits\n",
" x = randint(0,2,N_bits_per_frame)\n",
" y,state = cc1.conv_encoder(x,state)\n",
" # Add channel noise to bits, include antipodal level shift to [-1,1] \n",
" yn = dc.cpx_awgn(2*y-1,EbN0-10*log10(3),1) # Channel SNR is 10*log10(3) dB less\n",
" # Translate to [0,7]\n",
" yn = (yn.real+1)/2*7\n",
" z = cc1.viterbi_decoder(yn,'soft',quant_level=3)\n",
" # Count bit errors\n",
" bit_count, bit_errors = dc.bit_errors(x,z)\n",
" total_bit_errors += bit_errors\n",
" total_bit_count += bit_count\n",
" print('Bits Received = %d, Bit errors = %d, BEP = %1.2e' %\\\n",
" (total_bit_count, total_bit_errors,\\\n",
" total_bit_errors/total_bit_count))\n",
"print('*****************************************************')\n",
"print('Bits Received = %d, Bit errors = %d, BEP = %1.2e' %\\\n",
" (total_bit_count, total_bit_errors,\\\n",
" total_bit_errors/total_bit_count))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"SNRdB = arange(0,12,.1)\n",
"Pb_uc = fec.conv_Pb_bound(1/3,7,[4, 12, 20, 72, 225],SNRdB,2)\n",
"Pb_s_third_3 = fec.conv_Pb_bound(1/3,8,[3, 0, 15, 0, 58, 0, 201, 0],SNRdB,1)\n",
"#Pb_s_third_4 = fec.conv_Pb_bound(1/3,10,[6, 0, 6, 0, 58, 0, 118, 0],SNRdB,1)\n",
"Pb_s_third_5 = fec.conv_Pb_bound(1/3,12,[12, 0, 12, 0, 56, 0, 320, 0],SNRdB,1)\n",
"#Pb_s_third_6 = fec.conv_Pb_bound(1/3,13,[1, 8, 26, 20, 19, 62, 86, 204],SNRdB,1)\n",
"Pb_s_third_7 = fec.conv_Pb_bound(1/3,14,[1, 0, 20, 0, 53, 0, 184, 0],SNRdB,1)\n",
"#Pb_s_third_8 = fec.conv_Pb_bound(1/3,16,[1, 0, 24, 0, 113, 0, 287, 0],SNRdB,1)\n",
"figure(figsize=(5,5))\n",
"semilogy(SNRdB,Pb_uc)\n",
"semilogy(SNRdB,Pb_s_third_3,'--')\n",
"#semilogy(SNRdB,Pb_s_third_4,'--')\n",
"semilogy(SNRdB,Pb_s_third_5,'g')\n",
"#semilogy(SNRdB,Pb_s_third_6,'--')\n",
"semilogy(SNRdB,Pb_s_third_7,'r--')\n",
"#semilogy(SNRdB,Pb_s_third_8,'--')\n",
"#semilogy(SNRdB,Pb_s_half,'--')\n",
"semilogy([0,1,2,3,4,5],[9.08e-02,2.73e-02,6.52e-03,\\\n",
" 8.94e-04,8.54e-05,5e-6],'gs')\n",
"axis([0,12,1e-7,1e0])\n",
"title(r'Soft Decision Rate 1/3 Coding Measurements')\n",
"xlabel(r'$E_b/N_0$ (dB)')\n",
"ylabel(r'Symbol Error Probability')\n",
"legend(('Uncoded BPSK','R=1/3, K=3, Soft',\\\n",
" #'R=1/3, K=4, Soft','R=1/3, K=5, Soft',\\\n",
" 'R=1/3, K=5, Soft','R=1/3, K=7, Soft',\\\n",
" #'R=1/3, K=8, Soft','R=1/2, K=5, Soft', \\\n",
" 'R-1/3, K=5, Simulation'),loc='upper right')\n",
"grid();"
]
}
],
"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
}
| bsd-2-clause |
wtsi-medical-genomics/team-code | python-club/notebooks/python-club-14-solutions.ipynb | 1 | 16344 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Exercise 1 (Sarah)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/dr9/Developer/team-code/python-club/notebooks/a.txt\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/anagram_sets.py\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/anagram_sets.pyc\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/anagrams.db.db\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/b\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/c06d\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/patterns.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-1.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-10-solutions.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-10.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-11-solutions-final.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-11-solutions.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-11.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-12-solutions-final.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-12-solutions.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-12.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-14-solutions-Copy1.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-14-solutions.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-14.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-2-solutions.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-2.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-3-solutions.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-3.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-4-solutions.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-4.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-5-solutions.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-5.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-6-solutions.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-6.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-7-solutions.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-7.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-8-solutions.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-8.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-9.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/python-club-solutions-9.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/stopwatch.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/test.txt\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/Untitled.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/Untitled2-Copy0.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/Vagrantfile\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/words.txt\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/parsing-hits-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/patterns-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-1-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-10-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-10-solutions-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-11-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-11-solutions-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-11-solutions-final-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-12-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-12-solutions-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-12-solutions-final-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-14-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-14-solutions-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-14-solutions-Copy1-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-2-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-2-solutions-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-3-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-3-solutions-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-4-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-4-solutions-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-5-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-5-solutions-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-6-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-6-solutions-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-7-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-7-solutions-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-8-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-8-solutions-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-9-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/python-club-solutions-9-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/stopwatch-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.ipynb_checkpoints/Untitled-checkpoint.ipynb\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.vagrant/machines/default/virtualbox/action_provision\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.vagrant/machines/default/virtualbox/action_set_name\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.vagrant/machines/default/virtualbox/id\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.vagrant/machines/default/virtualbox/index_uuid\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.vagrant/machines/default/virtualbox/private_key\n",
"/Users/dr9/Developer/team-code/python-club/notebooks/.vagrant/machines/default/virtualbox/synced_folders\n"
]
}
],
"source": [
"import os\n",
"\n",
"def go_for_a_walk(d):\n",
" for root, dirs, files in os.walk(d, topdown=True):\n",
" for name in files:\n",
" print os.path.join(root, name)\n",
"\n",
"go_for_a_walk(os.getcwd())"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"##Exercise 2 (Wendy)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\"\"\"Exercise 14.2. Write a function called sed that takes as arguments a pattern string, a replacement string, and two filenames; it should read the first file and write the contents into the second file (creating it if necessary). If the pattern string appears anywhere in the file, it should be replaced with the replacement string.\r\n",
"\"\"\"\r\n",
"\r\n",
"import os\r\n",
"import sys\r\n",
"import string\r\n",
"\r\n",
"def main():\r\n",
" # Enter pattern string and replacement string\r\n",
" pattern_string = sys.argv [1]\r\n",
" replacement_string = sys.argv [2]\r\n",
"\r\n",
" #Open the file to read (fin) and the file to output to. Raise exceptions if there are problems. If no output file has been created then create this.\r\n",
"\r\n",
" try:\r\n",
" fin = open(sys.argv[3])\r\n",
" except:\r\n",
" print 'Something went wrong with opening the first file.'\r\n",
"\r\n",
" try:\r\n",
" fout = open(sys.argv[4], 'w')\r\n",
" except:\r\n",
" print 'Something went wrong with opening the second file it has been created'\r\n",
" fout = open('output.txt', 'w')\r\n",
"\r\n",
" #Read through the input file and replace any of the pattern strings found with a replacement string.\r\n",
" for line in fin:\r\n",
" str(line)\r\n",
" new_line = string.replace(line, pattern_string, replacement_string)\r\n",
" fout.write(new_line)\r\n",
"\r\n",
" #Close the input file and the output file\r\n",
" fin.close()\r\n",
" fout.close()\r\n",
"\r\n",
"if __name__ == \"__main__\":\r\n",
" main()"
]
}
],
"source": [
"!cat exercise14-2.py"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This is a testing string that does work\r\n"
]
}
],
"source": [
"!echo 'This is a testing string that may work' > file_in\n",
"!python exercise14-2.py may does file_in file_out\n",
"!cat file_out"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Exercise 3 (Dan)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import anagram_sets\n",
"import shelve\n",
"\n",
"def store_anagrams(filename):\n",
" shelf = shelve.open(filename, 'c')\n",
" d = anagram_sets.all_anagrams('words.txt')\n",
" for word, word_list in d.iteritems():\n",
" shelf[word] = word_list\n",
" shelf.close()\n",
"\n",
"def read_anagrams(filename, word):\n",
" shelf = shelve.open(filename)\n",
" sig = signature(word)\n",
" try:\n",
" return shelf[sig]\n",
" except KeyError:\n",
" return []"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"store_anagrams('anagrams.db')"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['act', 'cat']"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"read_anagrams('anagrams.db', 'cat')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Exercise 4 (Liu)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dups found with md5sum \"d41d8cd98f00b204e9800998ecf8427e\":\n",
"\t/Users/dr9/Developer/team-code/python-club/notebooks/a.txt\n",
"\t/Users/dr9/Developer/team-code/python-club/notebooks/output.txt\n",
"\t/Users/dr9/Developer/team-code/python-club/notebooks/test.txt\n"
]
}
],
"source": [
"import os\n",
"\n",
"def walk(workdir):\n",
" \"\"\"\n",
" 'walk' take a specified Folder and return all the files in a List within it.\n",
" \"\"\"\n",
" list_all_files = []\n",
" for root, dirs, files in os.walk(workdir, topdown=True):\n",
" for name in files:\n",
" list_all_files.append(os.path.join(root, name))\n",
" return list_all_files\n",
"\n",
"def make_md5sum(list_files, suffix):\n",
" \"\"\"\n",
" 'make_md5sum' take a List of files and a suffix, then return a dictonary with the md5sums \n",
" of the files with the specified sufix as the keys\n",
" \"\"\"\n",
" d_md5sum = {}\n",
" for each_file in list_files:\n",
" if not each_file.endswith(suffix):\n",
" continue\n",
" cmd_md5 = 'md5 ' + each_file\n",
" fp = os.popen(cmd_md5)\n",
" res = fp.read()\n",
" fp.close()\n",
" md5_sum = res.strip().split(' ')[-1]\n",
" if md5_sum not in d_md5sum:\n",
" d_md5sum[md5_sum] = [each_file]\n",
" else:\n",
" d_md5sum[md5_sum].append(each_file)\n",
" return d_md5sum\n",
"\n",
"def find_duplicates (dict_md5sum):\n",
" \"\"\"\n",
" 'find_duplicates' take a Dictionary of md5sum and output those with more than 1 files attached to it\n",
" \"\"\"\n",
" for each_md5sum in dict_md5sum:\n",
" if len(dict_md5sum[each_md5sum]) > 1:\n",
" print 'Dups found with md5sum \"' + each_md5sum + '\":'\n",
" for each_file in dict_md5sum[each_md5sum]:\n",
" print '\\t' + each_file"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dups found with md5sum \"eb1a3227cdc3fedbaec2fe38bf6c044a\":\n",
"\t/Users/dr9/Developer/team-code/python-club/notebooks/testing.txt\n",
"\t/Users/dr9/Developer/team-code/python-club/notebooks/testing_dup.txt\n",
"Dups found with md5sum \"d41d8cd98f00b204e9800998ecf8427e\":\n",
"\t/Users/dr9/Developer/team-code/python-club/notebooks/a.txt\n",
"\t/Users/dr9/Developer/team-code/python-club/notebooks/output.txt\n",
"\t/Users/dr9/Developer/team-code/python-club/notebooks/test.txt\n"
]
}
],
"source": [
"!echo testing > testing.txt\n",
"!cp testing.txt testing_dup.txt\n",
"all_files = walk(os.getcwd())\n",
"dict_md5sum = make_md5sum(all_files, '.txt')\n",
"find_duplicates(dict_md5sum)"
]
}
],
"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-2.0 |
HAOzj/Classic-ML-Methods-Algo | .ipynb_checkpoints/第一节Kmeans（LLoyd算法)-checkpoint.ipynb | 2 | 4572 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## 简介\n",
"我们的第一讲贡献给K-means方法，这是一种聚类方法，用于讲数据点进行划分。我们常会把它和Lloyd算法，也就是K-means方法的一种实现算法，混淆。\n",
"\n",
"## 历史和背后思想\n",
"K-means方法是1957年由Hugo Steinhaus提出，而“K-means”这个术语是James MacQueen在1967年第一次使用。它的思想是将数据点分到K个聚类（Clusters），使得每个点和所在聚类的中心的距离的平方和最小，也就是最小化intra-cluster variance,这里我们把这个intra-class variance叫做成本函数（cost function）。数学上就是\n",
"$ \\underset{S}{\\arg\\min} \\sum\\limits_{k}\\sum\\limits_{x_i \\in S(k)} |x_i - \\mu_k| ^2 $\n",
"其中 $\\mu_k $是各个聚类的中心，$ S(k)$是每个聚类的点的集合。\n",
"\n",
"## Lloyd算法\n",
"我们以最常用的一种启发式算法（Heuristics）-Lloyd算法为例，介绍K-means方法的一般步骤:\n",
"1.初始化K个聚类的中心，一般是在n个数据点中随机选择,n为数据集的基数\n",
"2.根据每个数据点到每个聚类中心的距离，将它分配到最近的聚类，然后更新聚类的中心，迭代直到收敛，也就是每个点的聚类不再改变\n",
"\n",
"## 复杂度和收敛性\n",
"因为聚类一共有 $n^K$ 种情况，每次迭代都会降低成本函数（聚类内所有点到x点的距离平方和是个二次函数，这个函数在x为聚点中心是取到最小值），所以我们总可以在有限时间内收敛。但是现实操作中，我们往往将迭代次数或者成本函数的改善用于终止函数。简单来讲，就是迭代i次终止，或者当某次迭代的结果对上次迭代的结果改善度小于某个阈值时终止。所以Lloyd的复杂度在固定迭代次数的情况下复杂度为O(n*K*d*i),其中d为数据点的维度。\n",
"\n",
"K-means方法一定会收敛，但不一定收敛到全局最优点（Lloyd算法就是一种启发式算法）。初始化的K个聚点中心起着决定性作用，所以人们试着改进在选取初始聚点中心的方法。比如K-means++算法，就是想让初始K个聚类中心相互尽量离得远。它的具体步骤是：\n",
"1.随机选择第一个聚点中心\n",
"2.对数据集中剩下的每个点x，计算它和最近的聚点中心的距离$d（x）$，将 所有的$d^2(x）$归一化求得概率$g（x）$，这时所以剩下的点就对应（0,1）上不重复的线段\n",
"3.随机得在（0,1）上取值，该值落在的x点就成为新的聚点中心\n",
"4.重复2,3步，直到找到K个聚点中心\n",
"5.K-means一般步骤2\n",
"\n",
"## 超参数和优缺点\n",
"K-means方法中聚类的数量K，作为超参数，可以是提前给定的，也可以是以输入形式得到的。我们必须在训练前有一个K，一个坏的K会带来不好的结果，所以一般都会多训练几次来确定一个合适的K。\n",
"\n",
"K-means方法处理球面或者超球面的数据集时表现很好，也就是数据呈现比较明显的围绕几个中心分布的情况。但面对其他分布的数据集时表现一般，并且每次运行（run）时结果不一定相同。\n",
"## 相似方法\n",
"类似的方法有K-medoid和GMM（高斯混合模型）。K-medoid和K-means的区别在于一般步骤2时，我们选择聚类的中心点，也就是离中心最近的那个数据点，而不是中心。这样做的好处是减少了极端值对聚类的影响，但加大了计算复杂度，因为每次更新都要计算聚类内每个点到聚类中心的距离，不适合于大规模的数据集。至于GMM，留待第三节讲。\n",
"\n"
]
},
{
"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": "3.4.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
CompPhysics/ComputationalPhysics2 | doc/LectureNotes/basicquantumcomputing.ipynb | 2 | 41243 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Quantum Computing\n",
"\n",
"**text to come**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto1\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\Psi(x,t)=\\Psi(x,t)_a+\\Psi(x,t)_b,\n",
"\\label{_auto1} \\tag{1}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto2\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" |q\\rangle = \\alpha \\vert 0 \\rangle + \\beta |1\\rangle,\n",
"\\label{_auto2} \\tag{2}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto3\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" |q\\rangle = \\frac{1}{\\sqrt{2}}(\\vert 0 \\rangle + |1\\rangle),\n",
"\\label{_auto3} \\tag{3}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"eq:dataord\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" |\\psi\\rangle = a_0|\\psi\\rangle_0+a_1|\\psi\\rangle_1+a_2|\\psi\\rangle_2+a_3|\\psi\\rangle_3+a_4|\\psi\\rangle_4+a_5|\\psi\\rangle_5+a_6|\\psi\\rangle_6+a_7|\\psi\\rangle_7,\n",
"\\label{eq:dataord} \\tag{4}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto4\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\hat{H}\\vert 0 \\rangle=\n",
" \\frac{1}{\\sqrt{2}}(\\vert 0 \\rangle +|1\\rangle),\n",
"\\label{_auto4} \\tag{5}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto5\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\hat{H}|1\\rangle=\n",
" \\frac{1}{\\sqrt{2}}(\\vert 0 \\rangle -|1\\rangle).\n",
"\\label{_auto5} \\tag{6}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto6\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\hat{H}\\hat{H}\\vert 0 \\rangle=\n",
" \\hat{H}\\frac{1}{\\sqrt{2}}(\\vert 0 \\rangle +|1\\rangle)\n",
" =\\vert 0 \\rangle,\n",
"\\label{_auto6} \\tag{7}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto7\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\hat{H}\\hat{H}|1\\rangle=\n",
" \\hat{H}\\frac{1}{\\sqrt{2}}(\\vert 0 \\rangle -|1\\rangle)\n",
" =|1\\rangle.\n",
"\\label{_auto7} \\tag{8}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto8\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\vert 0 \\rangle=\\left(\\begin{array}{c} 1 \\\\ 0\\end{array} \\right),\n",
"\\label{_auto8} \\tag{9}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto9\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" |1\\rangle=\\left(\\begin{array}{c} 0 \\\\ 1\\end{array} \\right),\n",
"\\label{_auto9} \\tag{10}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"og"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto10\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\hat{H}=\\frac{1}{\\sqrt{2}}\n",
" \\left(\\begin{array}{cc} 1 & 1 \\\\ 1& -1\\end{array} \\right).\n",
"\\label{_auto10} \\tag{11}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto11\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\hat{H}_{\\mathrm{NOT}}= \n",
" \\left(\\begin{array}{cc} 0 & 1 \\\\ 1& 0\\end{array} \\right).\n",
"\\label{_auto11} \\tag{12}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto12\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\hat{H}_{\\mathrm{NOT}}\\vert 0 \\rangle= \n",
" \\left(\\begin{array}{cc} 0 & 1 \\\\ 1& 0\\end{array}\\right)\n",
" \\left(\\begin{array}{c} 1 \\\\ 0\\end{array} \\right)=\n",
" \\left(\\begin{array}{c} 0 \\\\ 1\\end{array} \\right)=|1\\rangle,\n",
"\\label{_auto12} \\tag{13}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto13\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\hat{H}_{\\mathrm{NOT}}|1\\rangle= \n",
" \\left(\\begin{array}{cc} 0 & 1 \\\\ 1& 0\\end{array}\\right)\n",
" \\left(\\begin{array}{c} 0 \\\\ 1\\end{array} \\right)=\n",
" \\left(\\begin{array}{c} 1 \\\\ 0\\end{array} \\right)=\\vert 0 \\rangle.\n",
"\\label{_auto13} \\tag{14}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto14\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\hat{H}_{\\Phi}\\vert 0 \\rangle=e^{i\\phi}\\vert 0 \\rangle,\n",
"\\label{_auto14} \\tag{15}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"og"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto15\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\hat{H}_{\\Phi}|1\\rangle=|1\\rangle,\n",
"\\label{_auto15} \\tag{16}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto16\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\hat{H}_{\\Phi}= \n",
" \\left(\\begin{array}{cc} e^{i\\phi} & 0 \\\\ 0& 1\\end{array} \\right).\n",
"\\label{_auto16} \\tag{17}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto17\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" |q\\rangle_{in} = \\alpha \\vert 0 \\rangle_{in} + \\beta |1\\rangle_{in},\n",
"\\label{_auto17} \\tag{18}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto18\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" |q\\rangle_{ut}=\\hat{H}|q\\rangle_{in} = \n",
" \\frac{1}{\\sqrt{2}}((\\alpha+\\beta)\\vert 0 \\rangle_{ut} + \n",
" (\\alpha-\\beta)|1\\rangle_{ut}),\n",
"\\label{_auto18} \\tag{19}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto19\"></div>\n",
"\n",
"$$\n",
"\\begin{equation} \n",
" |q\\rangle_{ut}=\\hat{H}\\hat{H}|q\\rangle_{in}=|q\\rangle_{in}.\n",
"\\label{_auto19} \\tag{20}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto20\"></div>\n",
"\n",
"$$\n",
"\\begin{equation} \n",
" \\hat{H}\\hat{\\Phi}\\hat{H}\\vert 0 \\rangle,\n",
"\\label{_auto20} \\tag{21}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto21\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\frac{1}{\\sqrt{2}}((e^{i\\phi}+1)\\vert 0 \\rangle + \n",
" (e^{i\\phi}-1)|1\\rangle).\n",
"\\label{_auto21} \\tag{22}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"eq:superent\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\frac{\\vert 0 \\rangle_1 |1\\rangle_2 + |1\\rangle_1\\vert 0 \\rangle_2}{\\sqrt{2}},\n",
"\\label{eq:superent} \\tag{23}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"eq:superent1\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\frac{|+\\rangle_1 |-\\rangle_2 - |-\\rangle_1|+\\rangle_2}{\\sqrt{2}},\n",
"\\label{eq:superent1} \\tag{24}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto22\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" |q\\rangle_{1} = \\alpha_1 \\vert 0 \\rangle_{1} + \\beta_1 |1\\rangle_{1},\n",
"\\label{_auto22} \\tag{25}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto23\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" |q\\rangle_{12}= |q\\rangle_{1} |q\\rangle_{2}\n",
"\\label{_auto23} \\tag{26}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"eller"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto24\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" |q\\rangle_{12}=\\alpha_1 \\alpha_2\\vert 0 \\rangle_1\\vert 0 \\rangle_2 +\n",
" \\alpha_1 \\beta_2\\vert 0 \\rangle_1|1\\rangle_2 +\n",
" \\beta_1 \\alpha_2|1\\rangle_1\\vert 0 \\rangle_2 +\n",
" \\beta_1 \\beta_2|1\\rangle_1|1\\rangle_2,\n",
"\\label{_auto24} \\tag{27}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto25\"></div>\n",
"\n",
"$$\n",
"\\begin{equation} \n",
" |q\\rangle_{ut}=\\hat{H}|q\\rangle_{in},\n",
"\\label{_auto25} \\tag{28}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$\\hat{H}^{\\dagger}=\\hat{H}$ og $\\hat{H}^{\\dagger}\\hat{H}=1$,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto26\"></div>\n",
"\n",
"$$\n",
"\\begin{equation} \n",
" \\hat{H}^{\\dagger}|q\\rangle_{ut}=\\hat{H}^{\\dagger}\\hat{H}|q\\rangle_{in}=\n",
" |q\\rangle_{in}.\n",
"\\label{_auto26} \\tag{29}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto27\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\vert 0 \\rangle_{c} \\vert 0 \\rangle_{t}\\rightarrow\n",
" \\vert 0 \\rangle_{c} \\vert 0 \\rangle_{t},\n",
"\\label{_auto27} \\tag{30}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto28\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\vert 0 \\rangle_{c} |1\\rangle_{t}\\rightarrow\n",
" \\vert 0 \\rangle_{c} |1\\rangle_{t},\n",
"\\label{_auto28} \\tag{31}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto29\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" |1\\rangle_{c} \\vert 0 \\rangle_{t}\\rightarrow\n",
" |1\\rangle_{c} |1\\rangle_{t},\n",
"\\label{_auto29} \\tag{32}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto30\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" |1\\rangle_{c} |1\\rangle_{t}\\rightarrow\n",
" |1\\rangle_{c} \\vert 0 \\rangle_{t}.\n",
"\\label{_auto30} \\tag{33}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto31\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\hat{H}_{\\mathrm{CNOT}}=\n",
" \\left(\\begin{array}{cccc} 1 & 0 & 0 &0 \\\\ \n",
" 0 & 1 & 0 &0 \\\\\n",
" 0& 0 & 0 &1 \\\\\n",
" 0 & 0 & 1 &0\\end{array}\\right),\n",
"\\label{_auto31} \\tag{34}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$\\vert 0 \\rangle_c\\vert 0 \\rangle_t$, $\\vert 0 \\rangle_c|1\\rangle_t$, $|1\\rangle_c\\vert 0 \\rangle_t$ \n",
"$|1\\rangle_c|1\\rangle_t$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto32\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\vert 0 \\rangle_c\\vert 0 \\rangle_t=\\left(\\begin{array}{c} 1 \\\\ \n",
" 0 \\\\\n",
" 0 \\\\\n",
" 0 \\end{array}\\right),\n",
"\\label{_auto32} \\tag{35}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto33\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\vert 0 \\rangle_c|1\\rangle_t=\\left(\\begin{array}{c} 0 \\\\ \n",
" 1 \\\\\n",
" 0 \\\\\n",
" 0 \\end{array}\\right),\n",
"\\label{_auto33} \\tag{36}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto34\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" |1\\rangle_c\\vert 0 \\rangle_t=\\left(\\begin{array}{c} 0 \\\\ \n",
" 0 \\\\\n",
" 1 \\\\\n",
" 0 \\end{array}\\right),\n",
"\\label{_auto34} \\tag{37}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"og"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto35\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" |1\\rangle_c|1\\rangle_t=\\left(\\begin{array}{c} 0 \\\\ \n",
" 0 \\\\\n",
" 0 \\\\\n",
" 1 \\end{array}\\right).\n",
"\\label{_auto35} \\tag{38}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto36\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\hat{H}_{\\mathrm{CNOT}}|1\\rangle_c|1\\rangle_t=\n",
" \\left(\\begin{array}{cccc} 1 & 0 & 0 &0 \\\\ \n",
" 0 & 1 & 0 &0 \\\\\n",
" 0& 0 & 0 &1 \\\\\n",
" 0 & 0 & 1 &0\\end{array}\\right)\n",
" \\left(\\begin{array}{c} 0 \\\\ \n",
" 0 \\\\\n",
" 0 \\\\\n",
" 1 \\end{array}\\right)=\n",
"\\left(\\begin{array}{c} 0 \\\\ \n",
" 0 \\\\\n",
" 1 \\\\\n",
" 0 \\end{array}\\right),\n",
"\\label{_auto36} \\tag{39}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$|1\\rangle_c\\vert 0 \\rangle_t$, dvs. target-biten forandrer verdi fra bit '1'\n",
"til bit '0' når kontroll-biten har verdi bit '1'.\n",
"\n",
"\n",
"\n",
"\n",
"## Hamiltonians\n",
"\n",
"A general two-body Hamiltonian for fermionic system \n",
"can be written as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"eq:twobodyH\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"\\label{eq:twobodyH} \\tag{40}\n",
"H = E_0 + \\sum_{ij=1} E_{ij} a^\\dag_i a_j\n",
"+\\sum_{ijkl = 1} V_{ijkl} a^\\dag_i a^\\dag_j a_l a_k,\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $E_0$ is a constant energy term, $E_{ij}$ represent all the\n",
"one-particle terms, allowing for non-diagonal terms as well. The\n",
"one-body term can represent \n",
"a chosen single-particle potential, the kinetic energy or other more\n",
"specialized terms such as \n",
"those discussed in connection with the Hubbard model or the pairing Hamiltonian \n",
"discussed below.\n",
"The two-body interaction part is given by $V_{ijkl}$ and can be any\n",
"two-body interaction, from \n",
"Coulomb interaction to the interaction between nucleons. \n",
"The sums run over all possible single-particle levels $N$. \n",
"Note that\n",
"this model includes particle numbers from zero to the number of\n",
"available quantum levels, $n$. To simulate states with fixed numbers\n",
"of fermions one would have to either rewrite the Hamiltonian or\n",
"generate specialized input states in the simulation.\n",
"\n",
"The algorithm which we will develop in this section and in\n",
"However, \n",
"in our demonstrations of the quantum computing algorithm, we will limit ourselves to\n",
"two simple models, which however capture much of the important physics\n",
"in quantum mechanical \n",
"many-body systems. We will also limit ourselves to spin $j=1/2$\n",
"systems, although our algorithm \n",
"can also simulate higher $j$-values, such as those which occur in nuclear, atomic and\n",
"molecular physics, it simply uses one qubit for every available\n",
"quantum state. \n",
"These simple models are the Hubbard model and a pairing\n",
"Hamiltonian.\n",
"We start with the spin $1/2$ Hubbard model, described by the following Hamiltonian"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto37\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"H_H = \\epsilon \\sum_{i, \\sigma} a_{i\\sigma}^\\dag a_{i\\sigma} \n",
"-t \\sum_{i, \\sigma} \\left(a^\\dag_{i+1, \\sigma}a_{i, \\sigma}\n",
"+a^\\dag_{i, \\sigma}a_{i+1, \\sigma} \\right) \\notag \n",
"\\label{_auto37} \\tag{41}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"eq:hubbard\"></div>\n",
"\n",
"$$\n",
"\\begin{equation} \n",
" + U \\sum_{i=1} a_{i+}^\\dag a_{i-}^\\dag a_{i-}a_{i+},\n",
"\\label{eq:hubbard} \\tag{42}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $a^{\\dagger}$ and $a$ are fermion creation and annihilation operators, respectively.\n",
"This is a chain of sites where each site has room for one spin up\n",
"fermion and one spin down fermion. \n",
"The number of sites is $N$, and the sums over $\\sigma$ are sums over\n",
"spin up and down only.\n",
"Each site has a single-particle\n",
"energy $\\epsilon$. There is a repulsive term $U$ if there is a pair\n",
"of particles at the same site. It is energetically favourable to tunnel to\n",
"neighbouring sites, described by \n",
"the hopping terms with coupling constant $-t$.\n",
"\n",
"The second model-Hamiltonian is the simple pairing Hamiltonian"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"eq:pairing\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" H_P=\\sum_i \\varepsilon_i a^{\\dagger}_i a_i -\\frac{1}{2} g\\sum_{ij>0}\n",
" a^{\\dagger}_{i}\n",
" a^{\\dagger}_{\\bar{\\imath}}a_{\\bar{\\jmath}}a_{j},\n",
"\\label{eq:pairing} \\tag{43}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The indices $i$ and $j$ run over the number of levels $N$, and the label $\\bar{\\imath}$ \n",
"stands for a time-reversed state. The parameter $g$ is the strength of the pairing force \n",
"while $\\varepsilon_i$ is the single-particle energy of level $i$. \n",
"In our case\n",
"we assume that the single-particle levels are equidistant (or\n",
"degenerate) with a fixed spacing $d$. \n",
"Moreover, in our simple model, the degeneracy of the single-particle\n",
"levels is set to $2j+1=2$, with $j=1/2$ \n",
"being the spin of the particle. This gives a set of single-particle\n",
"states with the same spin projections as \n",
"for the Hubbard model. Whereas in the Hubbard model we operate with\n",
"different sites with \n",
"spin up or spin down particles, our pairing models deals thus with\n",
"levels with double degeneracy. \n",
"Introducing the pair-creation operator \n",
"$S^+_i=a^{\\dagger}_{im}a^{\\dagger}_{i-m}$,\n",
"one can rewrite the Hamiltonian in \n",
"Eq. ([43](#eq:pairing)) as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"H_P=d\\sum_iiN_i+\n",
" \\frac{1}{2} G\\sum_{ij>0}S^+_iS^-_j,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $N_i=a^{\\dagger}_i a_i$\n",
"is the number operator, and \n",
"$\\varepsilon_i = id$ so that the single-particle orbitals \n",
"are equally spaced at intervals $d$. The latter commutes with the \n",
"Hamiltonian $H$. In this model, quantum numbers like seniority \n",
"$\\cal{S}$ are good quantum numbers, and the eigenvalue problem \n",
"can be rewritten in terms of blocks with good seniority. \n",
"Loosely \n",
"speaking, the seniority quantum number $\\cal{S}$ is equal to \n",
"the number of unpaired particles.\n",
"Furthermore, in a series of papers, Richardson\n",
"obtained the exact solution of the pairing Hamiltonian, with \n",
"semi-analytic (since there is still the need for a numerical solution) \n",
"expressions for the eigenvalues and eigenvectors. The exact solutions\n",
"have had important consequences for several fields, from Bose condensates to\n",
"nuclear superconductivity and is currently a very active field of studies, see for example\n",
"Finally, for particle numbers up to $P \\sim 20$, the above model can be \n",
"solved exactly through numerical diagonalization and one can obtain all eigenvalues.\n",
"It serves therefore also as an excellent ground for comparison with our algorithm based\n",
"on models from quantum computing.\n",
"\n",
"\n",
"## Basic quantum gates\n",
"\n",
"Benioff showed that one could make a quantum mechanical Turing machine\n",
"by using various unitary operations on a quantum system.\n",
"Benioff demonstrated \n",
"that a quantum computer can calculate anything a\n",
"classical computer can. To do this one needs a quantum system and\n",
"basic operations that can approximate all unitary operations\n",
"on the chosen many-body system. We describe in this subsection the basic ingredients entering \n",
"our algorithms.\n",
"\n",
"### Qubits, gates and circuits\n",
"\n",
"In this article we will use the standard model of quantum information,\n",
"where\n",
"the basic unit of information is the qubit, the quantum bit. \n",
"As mentioned in the introduction, any\n",
"suitable \n",
"two-level quantum system can be a qubit, \n",
"it is the smallest system there is with the\n",
"least complex dynamics.\n",
"Qubits are both abstract measures of information and physical objects.\n",
"Actual physical qubits can be ions trapped in magnetic fields where\n",
"lasers can access only two energy levels or the nuclear spins of some of\n",
"the atoms in molecules accessed and manipulated by an NMR machine.\n",
"Several other ideas have been proposed and some tested.\n",
"\n",
"The computational basis for one qubit is ${\\ensuremath{\\vert 0 \\rangle}}$ (representing for example bit $0$) \n",
"for the first state\n",
"and ${\\ensuremath{|1\\rangle}}$ (representing bit $1$) for the second, and for a set of qubits \n",
"the tensor products of\n",
"these basis states for each qubit form a product basis. Below we write out the different\n",
"basis states for a system of $n$ qubits."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"eq:compBasis\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"\\label{eq:compBasis} \\tag{44}\n",
"{\\ensuremath{\\vert 0 \\rangle}} \\equiv {\\ensuremath{\\vert 00\\cdots 0 \\rangle}} =\n",
" {\\ensuremath{\\vert 0 \\rangle}} \\otimes {\\ensuremath{| 0 \\rangle}} \\otimes\n",
" \\cdots \n",
"\\otimes {\\ensuremath{\\vert 0 \\rangle}} \n",
"\\notag \n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto38\"></div>\n",
"\n",
"$$\n",
"\\begin{equation} \n",
"{\\ensuremath{|1\\rangle}} \\equiv {\\ensuremath{\\vert 00\\cdots 1\\rangle}} =\n",
" {\\ensuremath{\\vert 0 \\rangle}} \\otimes {\\ensuremath{| 0 \\rangle}} \\otimes\n",
" \\cdots \n",
"\\otimes {\\ensuremath{|1\\rangle}} \n",
"\\notag \n",
"\\label{_auto38} \\tag{45}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto39\"></div>\n",
"\n",
"$$\n",
"\\begin{equation} \n",
"\\vdots \\notag \n",
"\\label{_auto39} \\tag{46}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto40\"></div>\n",
"\n",
"$$\n",
"\\begin{equation} \n",
"{\\ensuremath{|2^n-1\\rangle}} \\equiv {\\ensuremath{|11\\cdots 1\\rangle}} =\n",
" {\\ensuremath{|1\\rangle}} \\otimes {\\ensuremath{| 1\\rangle}} \\otimes \n",
"\\cdots \\otimes {\\ensuremath{|1 \\rangle}}.\n",
"\\notag \n",
"\\label{_auto40} \\tag{47}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto41\"></div>\n",
"\n",
"$$\n",
"\\begin{equation} \n",
"\\label{_auto41} \\tag{48}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a $2^n$-dimensional system and we number the different basis\n",
"states using binary numbers corresponding to the order in which they appear in the\n",
"tensor product.\n",
"\n",
"Quantum computing means to manipulate and measure qubits in such a\n",
"way that the results from a measurement yield the solutions to a given problem. \n",
"The quantum operations we need to be able to perform our simulations are \n",
"a small set of elementary single-qubit\n",
"operations, or single-qubit gates, and one universal two-qubit gate,\n",
"in our case the so-called CNOT gate defined below.\n",
"\n",
"To represent quantum computer algorithms graphically we use circuit\n",
"diagrams. In a circuit diagram each qubit is represented by a line,\n",
"and operations on the different qubits are represented by boxes.\n",
"\n",
"## Number of work qubits versus number of simulation qubits\n",
"\n",
"The largest possible amount of different eigenvalues is $2^s$, where\n",
"$s$ is the number of simulation qubits. The resolution in the energy\n",
"spectrum we get from measuring upon the work qubits is $2^w$, with $w$ the number of\n",
"work qubits.\n",
"Therefore the resolution per eigenvalue in a non-degenerate system is\n",
"$2^{w-s}$. The higher the degeneracy the less work qubits are needed. \n",
"\n",
"\n",
"\n",
"\n",
"## Number of operations\n",
"\n",
"Counting the number of single-qubit and $\\sigma_z\\sigma_z$ operations\n",
"for different sizes of systems simulated gives us an indication of the\n",
"decoherence time needed for different physical realizations of a\n",
"quantum simulator or computer. The decoherence time is an average time\n",
"in which the state of the qubits will be destroyed by noise, also called \n",
"decoherence, while the operation time is the average time an operation takes\n",
"to perform on the given system. Their fraction is the number of\n",
"operations possible to perform before decoherence destroys the\n",
"computation. In table we have listed the number of\n",
"gates used for the pairing model, $H_P$, and the Hubbard model, $H_H$,\n",
"for different number of simulation qubits. \n",
"\n",
"<table border=\"1\">\n",
"<thead>\n",
"<tr><th align=\"center\"> </th> <th align=\"center\">$s=2$</th> <th align=\"center\">$s=4$</th> <th align=\"center\">$s=6$</th> <th align=\"center\">$s=8$</th> <th align=\"center\">$s=10$</th> <th align=\"center\">$s=12$</th> </tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr><td align=\"left\"> $H_P$ </td> <td align=\"center\"> 9 </td> <td align=\"center\"> 119 </td> <td align=\"center\"> 333 </td> <td align=\"center\"> 651 </td> <td align=\"center\"> 1073 </td> <td align=\"center\"> 1598 </td> </tr>\n",
"<tr><td align=\"left\"> $H_H$ </td> <td align=\"center\"> 9 </td> <td align=\"center\"> 51 </td> <td align=\"center\"> 93 </td> <td align=\"center\"> 135 </td> <td align=\"center\"> 177 </td> <td align=\"center\"> 219 </td> </tr>\n",
"</tbody>\n",
"</table>\n",
"Number of two-qubit gates used in simulating the time\n",
" evolution operator of the pairing model, $H_P$, and the Hubbard\n",
" model, $H_H$, for different number of simulation qubits $s$.\n",
"\n",
"\n",
"\n",
"We list here some useful relations involving different $\\sigma$ matrices,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto42\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"\\sigma_x \\sigma_z = -i\\sigma_y, \\quad\n",
"\\sigma_z \\sigma_x = i\\sigma_y, \\quad [\\sigma_x, \\sigma_z]=-2i\\sigma_y,\n",
"\\label{_auto42} \\tag{49}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto43\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"\\sigma_x \\sigma_y = i\\sigma_z, \\quad\n",
"\\sigma_y \\sigma_x = -i\\sigma_z, \\quad [\\sigma_x, \\sigma_y]=2i\\sigma_z,\n",
"\\label{_auto43} \\tag{50}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto44\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"\\sigma_y \\sigma_z = i\\sigma_x, \\quad\n",
"\\sigma_z \\sigma_y = -i\\sigma_x, \\quad [\\sigma_y, \\sigma_z]=2i\\sigma_x.\n",
"\\label{_auto44} \\tag{51}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For any two non-equal $\\sigma$-matrices $a$ and $b$ we have"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto45\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"aba = -b.\n",
"\\label{_auto45} \\tag{52}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Hermitian $\\sigma$-matrices $\\sigma_x$, $\\sigma_y$ and $\\sigma_z$\n",
"result in the identity matrix when squared"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto46\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"\\sigma_x^2 = _1_,\\quad \n",
"\\sigma_y^2 = _1_,\\quad \n",
"\\sigma_z^2 = _1_,\\quad \n",
"\\label{_auto46} \\tag{53}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"which can be used to obtain simplified expressions for exponential functions involving $\\sigma$-matrices"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto47\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"e^{\\pm i\\alpha \\sigma}=\\cos(\\alpha) _1_ \\pm i \\sin(\\alpha) \\sigma. \n",
"\\label{_auto47} \\tag{54}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The equations we list below are necessary for the relation between a general unitary\n",
"transformation on a set of qubits with a product of two-qubit unitary\n",
"transformations. We have the general equation for $a,b \\in \\{\\sigma_x,\\sigma_y, \\sigma_z\\}$, where $a\\neq b$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto48\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" e^{-i\\pi/4a} b e^{i\\pi/4a} = \\frac{1}{2} (_1_ -ia) b ( _1_ + ia)\n",
" \\notag\n",
"\\label{_auto48} \\tag{55}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto49\"></div>\n",
"\n",
"$$\n",
"\\begin{equation} \n",
" = \\frac{1}{2} (b + aba + i[b,a]) \\notag\n",
"\\label{_auto49} \\tag{56}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto50\"></div>\n",
"\n",
"$$\n",
"\\begin{equation} \n",
" = \\frac{i}{2}[b,a].\n",
"\\label{_auto50} \\tag{57}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The more specialized equations read"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"eq:rotations1\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"\\label{eq:rotations1} \\tag{58}\n",
" e^{-i\\pi/4 \\sigma_x} \\sigma_z e^{i\\pi/4 \\sigma_x} = -\\sigma_y, \n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"eq:rotations2\"></div>\n",
"\n",
"$$\n",
"\\begin{equation} \n",
"\\label{eq:rotations2} \\tag{59}\n",
" e^{-i\\pi/4 \\sigma_y} \\sigma_z e^{i\\pi/4 \\sigma_y} = \\sigma_x, \n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"eq:rotations3\"></div>\n",
"\n",
"$$\n",
"\\begin{equation} \n",
"\\label{eq:rotations3} \\tag{60}\n",
" e^{-i\\pi/4 \\sigma_z} \\sigma_x e^{i\\pi/4 \\sigma_z} = \\sigma_y, \n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"eq:rotations4\"></div>\n",
"\n",
"$$\n",
"\\begin{equation} \n",
"\\label{eq:rotations4} \\tag{61}\n",
" e^{-i\\pi/4 \\sigma_z} \\sigma_y e^{i\\pi/4 \\sigma_z} = -\\sigma_x. \n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We need also different products of the operator$\\sigma_z$ with the raising and lowering operators"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"eq:pmzs\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"\\label{eq:pmzs} \\tag{62}\n",
" \\sigma_+ \\sigma_z = -\\sigma_+ \n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto51\"></div>\n",
"\n",
"$$\n",
"\\begin{equation} \n",
" \\sigma_z \\sigma_+ = \\sigma_+, \n",
"\\label{_auto51} \\tag{63}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto52\"></div>\n",
"\n",
"$$\n",
"\\begin{equation} \n",
" \\sigma_- \\sigma_z = \\sigma_-, \n",
"\\label{_auto52} \\tag{64}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto53\"></div>\n",
"\n",
"$$\n",
"\\begin{equation} \n",
" \\sigma_z \\sigma_- = -\\sigma_-. \n",
"\\label{_auto53} \\tag{65}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto54\"></div>\n",
"\n",
"$$\n",
"\\begin{equation} \n",
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"\\end{equation}\n",
"$$"
]
}
],
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| cc0-1.0 |
caganze/wisps | notebooks/Crossmatches.ipynb | 1 | 327568 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Adding 2404 sources from /Users/caganze/research/splat//resources/Spectra/Public/SPEX-PRISM/ to spectral database\n",
"Adding 89 sources from /Users/caganze/research/splat//resources/Spectra/Public/MAGE/ to spectral database\n",
"Adding 145 sources from /Users/caganze/research/splat//resources/Spectra/Public/LRIS-RED/ to spectral database\n"
]
}
],
"source": [
"from astropy.io import fits\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import wisps\n",
"from tqdm import tqdm\n",
"from astropy.table import Table\n",
"%matplotlib inline\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def get_source(grism_id):\n",
" return wisps.Source(filename=grism_id.replace('g141', 'G141'))\n",
"\n",
"def invert_parallax(plx, plx_er):\n",
" plxs=np.random.normal(plx, plx_er, 1000)\n",
" return np.nanmedian(1/plxs)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#Crossmatches using topcat\n",
"hdus=fits.open(wisps.LIBRARIES+'/master_crossmatch.fits')\n",
"ucds=pd.read_pickle(wisps.LIBRARIES+'/new_real_ucds.pkl')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"#gaia"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"simbad=Table(hdus[1].data).to_pandas()\n",
"tmass=Table(hdus[2].data).to_pandas()\n",
"gaia=Table(hdus[3].data).to_pandas()\n",
"panstarrs=Table(hdus[4].data).to_pandas()\n",
"sdss=Table(hdus[5].data).to_pandas()\n",
"yise=Table(hdus[6].data).to_pandas()\n",
"#closest objects\n",
"gaia['gaia_distance']=np.nanmedian(1000/np.random.normal(gaia.parallax, gaia.parallax_error, size=( 1000, len(gaia))), \n",
" axis=0)\n",
"gaia['gaia_distance_er']=np.nanstd(1000/np.random.normal(gaia.parallax, gaia.parallax_error, size=( 1000, len(gaia))), \n",
" axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"gaia['spectra']=gaia.grism_id.apply(get_source)\n",
"gaia['designation']=gaia.spectra.apply(lambda x: x.designation )"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#gaia"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"merged_yise=ucds.merge(yise, on='grism_id')\n",
"merged_ps1=ucds.merge(panstarrs, on='grism_id')\n",
"merged_gaia=ucds.merge(gaia, on='grism_id')\n",
"merged_tmass=ucds.merge(tmass, on='grism_id')\n",
"merged_sdss=ucds.merge(sdss, on='grism_id')\n",
"merged_simbad=ucds.merge(simbad, on='grism_id')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
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"2 J15451481+1155008 17.0 0.555157\n",
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"7 J16252493+5721274 24.0 0.389257"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_sdss[['designation', 'spt', 'angDist']]"
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{
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" <td>J23333951+3925052</td>\n",
" <td>18.0</td>\n",
" <td>0.214139</td>\n",
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" <th>9</th>\n",
" <td>J14024558+5410246</td>\n",
" <td>18.0</td>\n",
" <td>0.176452</td>\n",
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" <tr>\n",
" <th>10</th>\n",
" <td>J23333572+3922141</td>\n",
" <td>18.0</td>\n",
" <td>0.104383</td>\n",
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"11 J09275744+6027467 21.0 0.133171\n",
"12 J16252493+5721274 24.0 0.580792"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_ps1[['designation', 'spt', 'angDist']]"
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{
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"metadata": {},
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{
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" <th>6</th>\n",
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" <td>23.0</td>\n",
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" <td>34.0</td>\n",
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},
"execution_count": 11,
"metadata": {},
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}
],
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"merged_simbad[['designation', 'spt', 'angDist', 'plx']]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
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{
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"</div>"
],
"text/plain": [
" designation spt angDist\n",
"0 J09275744+6027467 21.0 0.335232"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_tmass[['designation', 'spt', 'angDist']]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Text(0.5, 0, 'G-BP'), Text(0, 0.5, 'RP-BP')]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"findfont: Font family ['serif'] not found. Falling back to DejaVu Sans.\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax=plt.subplots()\n",
"ax.scatter(gaia.phot_g_mean_mag-gaia.phot_bp_mean_mag, \\\n",
" gaia.phot_rp_mean_mag-gaia.phot_bp_mean_mag, s=50, c='k')\n",
"ax.set(xlabel='G-BP', ylabel='RP-BP')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Text(0.5, 0, 'i-z'), Text(0, 0.5, 'spectral type')]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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MDAwwMDDAhg0bYMsdorpsrwL+fYSrBAgtuRZHP7XMAD4YZ+fVzOwywlD2V7n7oopFY/1Zdq6x2S5V62xl5syZLFu2LElYIiJtYeyf7Xnz5rF69eqH4my7vWSyPJrOICSV7wC/rVqnDGwEfuHuP4+z80pmdglwIbAUeFfV4nXRtNatrLF5tW6BiYhIE0yYTNx9BeEhWJjZfsCV7v7LtIMws4sJlffXAgtrNPG9i3CL61U1Np8dTe9IOy4REalP3RXw7n5mgxLJRYSWYsuAM919tMa+NwI3AXPM7PCKbTuBhYShXVanHZuIiNSn7k6LZnY2cLK7Hz/O8h8C33L3L8d8z0uBPwK3AaeHEVM2e8Tdb41+/xBwHPBDM/ss8AShB/ws4PXqsCgikp04w6nMZ+JbSX8gDI9SdzJhS9+QfYGv1li+ArgVwN2Hzexo4BPA+WwZm+t1GkpFRCRbcZJJN6FyfDy/B06Ps3N3n09IUvWuvxY4Kc4+RESk8eJ0WtyRLc1wa9llO8tFRGSaipNM/gC8ZoLlJxCGjxcRkTYTJ5lcD5xgZpeZ2U5jM81sRzO7lJBMrht3axERmbbi1Jl8FjiRMDT8v5jZ3YQOi4cQhlNZCXw69QhFRGTKi9PP5BnC1cf5wEPAEcDLCeNxfRA43t03NSJIERGZ2uJcmYwllCuiHxERESDdIehFRKRNxboyMbO/I/RYPwHYi9Bh8MdmtifwSeBL7v6r9MMUEZGpLM5jew8g9IA/hdBBcYexZe7+KPBKwjhZIiLSZuJcmXwcGCU8nvdJ4P9VLR8E3phSXCIi0kLi1JkcD3zR3R8kNAmu9gAxn8wlIiLTQ5xksiuwfoLlOxGzDkZERKaHOMnkQeDQCZbPBoaThSMiIq0oTjL5NtBvZodVzCsDmNkpwNuAb6YYm4iItIg4yeTjhJ7vvwS+Rkgk55vZ7YQk8hs0nIqISFuKM5zKE4RnsF9NaAY8gzCKsAFfBI5196caEaSIiExtcYdTeQI4Bzgn6qg4A3hUj8wVEWlvk259FXVUFBERiZ9MzOxU4GTgwGjWvcB33F2V7yIibaruZGJmzwO+B/wvwu2tDdH0SOBUMzsLeJO7lxoQp4iITGFxWnNdDhwHfA7Y291f4O67A3tH844ltPgSEZE2E+c2VwG4wd3PrZzp7n8CzjWzWdE65267qYiITGdxh1P5yQTLfxytIyIibSZOMvkt0D3B8m7grmThiIhIK4qTTC4E3mFm2wwzb2YnEZ5lckFagYmISOuIU2fyduA+4Ltm5sBawpAqLyH0gr8L6DOzvoptyu6+IK1gRURkaoqTTOZX/H5w9FPpZdFPpTKgZCIiMs3VnUzcPc4tMRERaSNKECIikliiJyOa2XOAk4AXADdFfU5ERKTN1H1lYmZXmNmvKl7PAG4jPMvky8BdZnZQ+iGKiMhUF+c21+uAlRWv3wj8E/DvwOnRvPNTiktERFpInNtcfwcMVbx+I3Cfu58PYGaHEpoPi4hIm4lzZbIT8LeK18cSbnONuReYmUZQIiLSWuIkkweB2bD5KuRAYEXF8r2AjemFJiIirSLOba5vAB8xs72AQ4EngMGK5UcA96QYm4iItIg4VyaLgGuAVxF6tp/h7hsAzGw34E3Aj1KOT0REWkCcHvBPE4ZGqTU8ygihvuSvKcUlIiItJFGnxTHuPgo8nsZ7iYhI69FwKiIikpiSiYiIJKZkIiIiiSmZiIhIYqlUwE+WmX0IeDnwCuAA4AF333+C9V8FjG2zB7AO+DGwyN3vbXjAIiJSU6bJBLgc+DNwJ/D8iVY0s9cBNxM6Rn4eeIzQefKdwClm9lJ3f7ih0Yq0gJGREYrFIkNDQ3R3d1MoFMjn81mHNSVk9dk0c7+ZHf9yuZzZT09Pz4EVv/+up6fn/gnW/UFPT8+mnp6eF1bNX9jT01Pu6ek5t8Y2y/v6+soi7WLlypXlfD5fzuVyZaCcy+XK+Xy+vHLlyqxDy1xWn00z95vWvvr6+so9PT3LyzH+nmdaZxLz1tSuwFPAX6rmr4umpVSCEmlRIyMjzJ07l5GREUqlcDqUSqXN8zdubN+h87L6bJq536yPfytVwP8AyANfNbPDzWyWmb0W+DSwljB2mEjbKhaLjI6O1lw2OjpKsVhsckRTR1afTTP3m/Xxz7rOJI5FhJGJ+9n6uSmDwGnuPlJro/Xr19Pb27v5dX9/P/39/Y2MUyQTQ0NDm/8jrVYqlRgeHm5yRFNHVp9NM/ebxr4GBgYYGBhgw4YNAPvE2X8rXZn8DXiY8AyVhcBbCFclxwPfMLMda200c+ZMVq1atflHiUSmq+7ubnK5XM1luVyOrq6uJkc0dWT12TRzv2nsq7+/n1WrVnHEEUcAPBRn/62UTK4hDDJ5qrt/xd2/4+7/BzgHOBH45yyDE8laoVCgo6P2Kd3R0UGhUGhyRFNHVp9NM/eb9fFviWRiZvsSbm3d7O7VIxPfEE2PaW5UIlNLPp9ncHCQfD6/+T/UXC63eX5nZ2fGEWYnq8+mmfvN+vi3Sp3JrGi6Q41lz6mairSt3t5e1q1bR7FYZHh4mK6uLgqFQlsnkjFZfTbN3G+Wx79V/gA7oc7kzWZ2wdhDuSLzo+mvmh2UyFTU2dnJggW1HjskWX02zdxvVmXMejiVecB+0cs9gZ3M7MLo9QPuvgzA3f9sZouB9wNrzGwJoef80YTbX/cAVzczdhER2SLrK5MFbFvXcVk0XQEsq5j/AcIVykLgAmBnQuuuLwGXuPsTjQ1VRETGk2kycfc5MdYtA0uiHxERmUJaojWXiIhMbUomIiKSmJKJiIgkpmQiIiKJKZmIiEhiSiYiIpKYkomIiCSmZCIiIokpmYiISGJKJiIikpiSiYiIJKZkIiIiiSmZiIhIYkomIiKSmJKJiIgklvXDsaakkZERisUiQ0NDdHd3UygUyOfzWYfVFBOVvZ0/l1Yz2WOlYyyTVi6Xp+1PT0/P8r6+vnIcK1euLOfz+XIulysD5VwuV87n8+WVK1fGep9WNFHZ2/lzaTWTPVY6xjKmr6+v3NPTs7wc4+/tjHK5nGkyayQzW37UUUcds2zZsu2vTPivbNasWYyMjGyzLJ/Ps27dOjo7O9MOc0qYqOxjZd64ceM2y6b759JqJvsdbufvvmxr3rx5rF69ekWcp+GqzqRCsVhkdHS05rLR0VGKxWKTI2qeicr+zDPPsGnTpprLpvvn0mom+x1u5+++pEN1JhWGhoYolUo1l5VKJYaHh5scUfNMVPann3563O2m++fSaib7HW7n776kQ1cmFbq7u8nlcjWX5XI5urq6mhxR80xU9p133pmddtqp5rLp/rm0msl+h9v5uy/pUDKpUCgU6Oio/ZF0dHRQKBSaHFHzTFT2HXfccdxkMt0/l1Yz2e9wO3/3JR1KJhXy+TyDg4Pk8/nN/6XlcrnN86dzBeREZb/lllu45ZZb2vJzaTWT/Q6383df0qE6kyq9vb2sW7eOYrHI8PAwXV1dFAqFtjiZtlf2dv1cWs1kv8Pt/N2X5NQ0WEREtqKmwSIikgklk5QMDAxkHULTqKzTU7uUtV3KCc0tq5JJSvQFnZ5U1umnXcoJzS3rdK8zeSifz8865JBDGr6vNWvWcMQRRzR8P1OByjo9tUtZ26WcMPmyrl27lpGRkYfdfZ96t5nuyWQNsCfQjO67+wAPNWE/U4HKOj21S1nbpZww+bJ2AY+6e92ZaFonExERaQ7VmYiISGJKJiIikpiSiYiIJNb2w6mYWQdwDnAWsD/wKPBN4CJ3rz0m95ZtdwfOAF4PHAK8EPgjsAK4zN0frFp/DvCTcd7uZnd/w6QLUockZY22Xw4cM87iI939jqr1dwM+BrwF2AO4B/g8cKW7N7SyLuFxncP4x2lMr7v/rI71G3pczexDwMuBVwAHAA+4+/6TeJ+5wIXA4cDTwI+AD7r7fTXWzeS4Ji1rK52vaRzXZp+vbZ9MgM8C7wW+A3ya8CV7L3CEmR3v7rWfGBT8Q7TNjwgf+mPAYYQ/YKea2avd/b9rbHcVsLJqXjNalyQp65jHgPfVmH9v5Qsz2wm4FTgC+BywFjgR+CLwIuCSyRWhbknKuhaYV2P+zoRj9xiwusbyLI7r5cCfgTuB50/mDczsLcCNwG+ADwC7AecCPzOzV7r7uop1szyuScvaSudr4uMaadr52tbJxMwOBd4DfNvdT6mYfx/wn8D/Bq6b4C3uBszd76l635sJB+ajwFtrbHe7u38tYfixpFDWMaU6Y18IHAm8190/F81bYmbfAi4ws6Xu/kCsQtQpaVnd/RFgmzKa2WmEW8PXuvszNTZt+nEFDnL3ewHM7HdArFEZzWxHwh+PB4F/dPeN0fxbgP8i/BF5Z8UmmR1XEpaVFjpfSV7WMU07X9u9zuQ0YAawuGr+EuCvQN9EG7v7/dVfzGj+bYT/Kg4bb1szy5nZLnEDTiBRWSuZWYeZ7WpmMyZY7fTofZdUzV8M7Ag08gEZqZW1ysJoevV4KzT7uI79wUngGGBv4OqxRBK976+B5UAhSjhjMjuuScvaSudrCsd1s2adr+2eTI4ERqm6ZeHuTwG/jpbHFt17zAOPjLPKfwAbgSfN7A9mds52DnQa0irrLELsjwMbzezbZnZw5QpRfcXLgTXR+1daHcUxqc+2TqkfVzM7ADgWWOXuPs5qWRzXpMY+i9trLPsFsCvQA1PiuDbEFD1f09K087Xdk8newGPuXush5w8DL4zuJcZ1ISGbf7Vq/jPA94EPAm8C3gVsIGT/Rg+ik0ZZ7wOuAM4E3ka4n3oi8Esze2nFersDz43edyvR/v8/4UveKI04rv2Eq51aVyVZHtek9o6m2xyrinljxyrr49ooU/F8TUNTz9e2rjMBnkdouVLLUxXrbKr3Dc3srcD7gR8ASyuXRa1/TqpafwkwCMw3s6+4+6p69xVT4rK6+5lVs240s+8Tbod8BnhNxfuwnf09b5xlaUj1uJrZDsB84AnghurlGR/XpCY6Vk9VrZP1cU3dFD5fE2v2+druVyZ/JbTQqWWXinXqEjWv/Dqh4vLUeprTRa2KFkUv59a7r0lItaxj3H0l8FPgWDN7btX7TLS/2PuKIe2yvpYwxtH17l7Xdk08rklNdKyqP6usj2uqpvj52hCNPF/bPZmsI9zyqPUhziLcKqn3v9fXAd8Gfg+c4O5PxIjj/mj6whjbxJVaWWu4H9iBcLkM8BfgSWpcGkf734Pat1XSknZZF0TTcSvex3F/NG3kcU1qrNlvrdsYY/PGjlXWxzU1LXC+NtL9NOB8bfdk8ivCZ3BU5cyo1cbfA3fU2GYbZvZaQn+Gu4Hj3f0vMePojqbjVQCmIZWyjqMbeJbQImbsv7c7CX06qv+gHxXFkWR/25NaWc1sL+CNwG+rO3nVoRnHNalfRdNX1Vg2m3Br7w8wJY5rKlrkfG2khpyv7Z5MikCZ0EGr0jsI9wi/PjbDzGaa2cFmttW9QzM7Afgu4YQ7zt3/PN7OzGyPGvN2ZkuHoJtil6B+icpqZrtFdQdbMbPXA0cDt1a1BLk+et93Vm1yLuGL/M1Jl2T7Eh/XCmcQKmcnag6c5XGt2zhlXQGsBxaaWWfFuocDc4AbqvrUZHlc6zYNzte6TZXzta0r4N39LjP7AvBuM/s2oWJtrKf0Crbu2LYI+GdC89DlAGb2SuB7hFY+S4ETzax6H5Udhv6vma0j3KNdR2hJ00f4T+Fz7l6rV3UqkpY1+v0zZnYToffss4T/WvoIvWzPrdrlEkIrks+Y2f6EHrVzgZOBj9UapiMtKZS1Uj+hAnKijl+ZHVczmwfsF73cE9jJzC6MXj/g7ssqVt+mrO7+jJmdQ0jAK6MK5l0JvaYfBS6u2mVmxzVpWVvpfE1aVjI4X9s6mUTOJdxDfCdhzJ7HCD2CL6pjeJHD2FJJ+dlx1qn8ct4IvJnQO/v5QAlYA1zs7tfHjjy+c5l8WZ1wUr2BMLzCjoQhJa4ELnf3re6puvsmMzueMNbPaWwZ6+c9wBfSKc6EzmXyZQXAzF5NSELXbedWSJbHdQHbjr90WTRdASxjO9z9BjN7ktBE9lNsGZvr36bYcU1a1lY6X5OWtennqx6OJSIiibV7nYmIiKRAyURERBJTMhERkcSUTEREJDElExERSUzJREREElMyERGRxJRMRBrAzOaYWdnM5mcdi0gzKJmIiEhiGk5FpDF+Snh63TPbW1FkOtBwKiIikpiuTEQawMzmAD8BznT3ayZYbz5Vj4utcqy7L08zNpFGUDIRydZPgXlV82YAlxOGPH+s6RGJTIKSiUiG3P1ewvMmNjOzKwjPnH+fu/8uk8BEYlJrLpEpxMwWAh8ArnT3xRmHI1I3XZmINIGZvbhq1qbqR8aa2XHAl4AfEh5KJNIylExEmmN91esVhGesA2BmhwDfIjwh723u/mzzQhNJTslEpDleU/V682OAzWwv4GbC43Lf4O5PNDMwkTQomYg0gbvfVmu+me0CfA+YSWgGfH8z4xJJi5KJSLYuBWYTbnF1mVlX1fJb3f2R5oclEo+SiUi2XhRNT4l+qh0LKJnIlKfhVEREJDH1MxERkcSUTEREJDElExERSUzJREREElMyERGRxJRMREQkMSUTERFJTMlEREQSUzIREZHElExERCSx/wFLqrgk8XACPQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax=plt.subplots()\n",
"ax.scatter(merged_ps1.imag-merged_ps1.zmag, \\\n",
" merged_ps1.spt, s=50, c='k')\n",
"ax.set(xlabeL='i-z', ylabel='spectral type')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"#splat."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"#gaia.sort_values('distance')[['grism_id', 'ra', 'dec', 'angDist', 'distance', 'ruwe', 'pmra', 'pmdec']]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"#mask=(abs(gaia.gaia_distance_er/gaia.gaia_distance) <0.5).values\n",
"mask=np.ones_like(gaia.gaia_distance).astype(bool)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"gaia['distance']=[x.value for x in\\\n",
" np.array(gaia.spectra.apply(lambda x: pd.Series(x.distance)).val)]\n",
"gaia['distance_er']=np.nanmedian(gaia.spectra.apply(lambda x: pd.Series(x.distance['er'])),axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"#gaia['distance']]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib\n",
"import numpy as np\n",
"from astropy.table import Table\n",
"cmap =matplotlib.cm.get_cmap(\"cividis\")\n",
"normalize = matplotlib.colors.Normalize(vmin=15, vmax=21)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"#normalized spectral types\n",
"norm_spts=gaia.spectra.apply(lambda x: x.spectral_type[0])[mask].values"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"#read missed \n",
"missed= Table.read('/users/caganze/research/wisps/libraries/candidates_missed.tex').to_pandas()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"mask= np.logical_and.reduce([norm_spts>=15, gaia.gaia_distance.values>0.0, \\\n",
" gaia.designation != 'J16252493+5721274', \\\n",
" gaia.designation.isin(missed.designation)])\n",
"mask2=np.logical_and.reduce([merged_gaia.spt.values>=17, merged_gaia.spt.values>0.0,\\\n",
" merged_gaia.designation_y != 'J16252493+5721274'])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"merged_gaiax=merged_gaia[mask2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"#"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"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": [
"fig, ax=plt.subplots()\n",
"\n",
"ax.errorbar(gaia.gaia_distance[mask], gaia.distance.values[mask],\\\n",
" xerr= gaia.gaia_distance_er[mask], \\\n",
" yerr=gaia.distance_er[mask], zorder=0., fmt='o', c='#0074D9')\n",
"#c=ax.scatter(gaia.gaia_distance[mask], gaia.distance.values[mask],\\\n",
"# c=norm_spts[mask], vmin=15, vmax=21, cmap='viridis_r', zorder=100, marker='o')\n",
"c=ax.errorbar(merged_gaiax.gaia_distance, merged_gaiax.distance, \n",
" xerr=merged_gaiax.gaia_distance_er, \n",
" yerr=np.nanmedian(merged_gaiax.distance_er.values[0]), \n",
" mec='#111111', mfc='#FF4136', zorder=0., fmt='^', ms=20)\n",
"ax.errorbar(50.5, 67,xerr=0.5, yerr=17, mfc='#FF4136', mec='#111111', zorder=0., fmt='^', ms=20)\n",
"\n",
"#manually add the second source\n",
"\n",
"#add the etra\n",
"#add the \n",
"ax.plot((0, 400), (0, 400), c='k')\n",
"ax.minorticks_on()\n",
"#cbar=plt.colorbar(c)\n",
"#cbar.ax.set_yticks([15, 16, 17, 18, 19, 20, 21], major=True)\n",
"#cbar.ax.set_yticklabels(['M5', 'M6', 'M7', 'M8', 'M9', 'L0', 'L1'])\n",
"ax.set( xlabel='Gaia distance (pc)', \\\n",
" ylabel='Photometric distance (pc)')\n",
"plt.tight_layout()\n",
"plt.savefig(wisps.OUTPUT_FIGURES+'/gaia_distance_comparison.pdf')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"findfont: Font family ['serif'] not found. Falling back to DejaVu Sans.\n",
"findfont: Font family ['serif'] not found. Falling back to DejaVu Sans.\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"_=gaia[mask].spectra.apply(lambda x: x.plot(compare_to_sds=True, comprange=[[1.2, 1.65]]))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"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>designation</th>\n",
" <th>gaia_distance</th>\n",
" <th>gaia_distance_er</th>\n",
" <th>distance</th>\n",
" <th>distance_er</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>J14190738+5246567</td>\n",
" <td>305.280921</td>\n",
" <td>42.027866</td>\n",
" <td>347.843417</td>\n",
" <td>320.367388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>J10002943+0217108</td>\n",
" <td>230.511063</td>\n",
" <td>40.747216</td>\n",
" <td>172.901973</td>\n",
" <td>50.197478</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" designation gaia_distance gaia_distance_er distance distance_er\n",
"0 J14190738+5246567 305.280921 42.027866 347.843417 320.367388\n",
"2 J10002943+0217108 230.511063 40.747216 172.901973 50.197478"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gaia[mask][['designation', 'gaia_distance', 'gaia_distance_er', 'distance', 'distance_er']]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
GetsDrawn/getsdrawndotcom | .ipynb_checkpoints/gotdrawn-checkpoint.ipynb | 1 | 23415 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"GotDrawn\n",
"\n",
"Updating GetsDrawnDotCom with title feature.\n",
"\n",
"Taking getsdrawndotcom code and adding in features such as comments.\n",
"Getting rid of folder structure. "
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"import requests\n",
"\n",
"import re\n",
"import json\n",
"import arrow\n",
"\n",
"import praw\n",
"\n",
"import dominate\n",
"\n",
"from dominate.tags import * \n",
"\n",
"import shutil\n",
"\n",
"from time import gmtime, strftime\n"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gotdrndir = ('/home/wcmckee/gotdrawn/imgs')"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Version 2.1.19 of praw is outdated. Version 2.1.20 was released Friday January 23, 2015.\n"
]
}
],
"source": [
"r = praw.Reddit(user_agent='getsdrawndotcom')"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fulcom = []\n",
"fuldic = dict()"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"getrn = r.get_subreddit('redditgetsdrawn')"
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rnew = getrn.get_new()"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for rnc in rnew:\n",
" fulcom.append(rnc)"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"artes = arrow.utcnow()"
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"yrpath = artes.strftime('%Y')"
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mntpath = artes.strftime('%m')"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dapth = artes.strftime('%d')"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"its true\n"
]
}
],
"source": [
"if os.path.isdir(gotdrndir) == True:\n",
" print 'its true'\n",
"else:\n",
" print 'its false'\n",
" os.mkdir(gotdrndir)"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"imgszpath = (gotdrndir)\n",
"yrzpat = (imgszpath + '/' + yrpath)\n",
"mntzpat = (yrzpat + '/' + mntpath)\n",
"datzpat = (mntzpat + '/' + dapth)"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'/home/wcmckee/gotdrawn/imgs'"
]
},
"execution_count": 181,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"imgszpath"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dayform = ('imgs/' + yrpath + '/' + mntpath + '/' + dapth)"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'imgs/2015/02/21'"
]
},
"execution_count": 183,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dayform"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"its false\n"
]
}
],
"source": [
"if os.path.isdir(yrzpat) == True:\n",
" print 'its true'\n",
"else:\n",
" print 'its false'\n",
" os.mkdir(yrzpat)"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"its false\n"
]
}
],
"source": [
"if os.path.isdir(mntzpat) == True:\n",
" print 'its true'\n",
"else:\n",
" print 'its false'\n",
" os.mkdir(mntzpat)"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"its false\n"
]
}
],
"source": [
"if os.path.isdir(datzpat) == True:\n",
" print 'its true'\n",
"else:\n",
" print 'its false'\n",
" os.mkdir(datzpat)"
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'/home/wcmckee/gotdrawn/imgs/2015/02/21'"
]
},
"execution_count": 187,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datzpat"
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'/home/wcmckee/gotdrawn/imgs/2015/02'"
]
},
"execution_count": 188,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mntzpat"
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'/home/wcmckee/gotdrawn/imgs/2015/02/21'"
]
},
"execution_count": 189,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datzpat"
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"its true\n"
]
}
],
"source": [
"if os.path.isdir(yrpath) == True:\n",
" print 'its true'\n",
"else:\n",
" print 'its false'\n",
" os.mkdir(yrpath)"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#mthzpath = (yrzpat + '/' + "
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#if os.path("
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2015-02-21\n"
]
}
],
"source": [
"print artes.date()"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"23:27:05.587817\n"
]
}
],
"source": [
"print artes.time()"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dati = artes.datetime"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Random logo from logo folder.\n",
"#5 to choose from. import random, random choice out of a list"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import random"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"imglis = ['img1', 'img2', 'img3', 'img4', 'img0']"
]
},
{
"cell_type": "code",
"execution_count": 199,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'img4'"
]
},
"execution_count": 199,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"random.choice(imglis)"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 200,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.listdir('/home/wcmckee/gotdrawn/logo')"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import PIL"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"imed = PIL"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'2.6.1'"
]
},
"execution_count": 203,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"imed.PILLOW_VERSION"
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#import pillow"
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 205,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"os.chdir('/home/wcmckee/gotdrawn/')"
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"imlocdir = (dayform + '/' + str(flc.author) + '-reference.png') "
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'imgs/2015/02/21/ShittyDuckFace-reference.png'"
]
},
"execution_count": 207,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"imlocdir"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"doc = dominate.document(title='GetsDrawn')\n",
"\n",
"with doc.head:\n",
" link(rel='stylesheet', href='style.css')\n",
" script(type ='text/javascript', src='script.js')\n",
" #str(str2)\n",
" \n",
" with div():\n",
" attr(cls='header')\n",
" h1('GetsDrawn')\n",
" p(img('imgs/getsdrawn-bw.png', src='imgs/getsdrawn-bw.png'))\n",
" #p(img('imgs/15/01/02/ReptileLover82-reference.png', src= 'imgs/15/01/02/ReptileLover82-reference.png'))\n",
" h1('Updated ', str(artes.datetime))\n",
" #p(panz)\n",
" #p(bodycom)\n",
" \n",
" \n",
"\n",
"with doc:\n",
" with div(id='body').add(ol()):\n",
" for flc in fulcom:\n",
" if 'http://i.imgur.com' in flc.url:\n",
" p(h1(flc.title))\n",
" p(img(imlocdir, src= imlocdir))\n",
" #p(img(flc.url, src = flc.url))\n",
" p(str(flc.author))\n",
" #res = requests.get(flc.url, stream=True)\n",
" #with open(str(flc.author) + '-' + str(artes.date()) + '-reference.png', 'wb') as outfil:\n",
" # shutil.copyfileobj(res.raw, outfil)\n",
" # del res\n",
" \n",
" \n",
" \n",
" for flcz in flc.comments:\n",
" p(flcz.body)\n",
" \n",
" \n",
" #for rdz in reliz:\n",
" #h1(rdz.title)\n",
" #a(rdz.url)\n",
" #p(img(rdz, src='%s' % rdz))\n",
" #print rdz\n",
" #p(img(rdz, src = rdz))\n",
" #p(rdz)\n",
"\n",
"\n",
" \n",
" #print rdz.url\n",
" #if '.jpg' in rdz.url:\n",
" # img(rdz.urlz)\n",
" #else:\n",
" # a(rdz.urlz)\n",
" #h1(str(rdz.author))\n",
" \n",
" #li(img(i.lower(), src='%s' % i))\n",
"\n",
" with div():\n",
" attr(cls='body')\n",
" p('GotDrawn is open source')\n",
" a('https://github.com/getsdrawn/getsdrawndotcom')\n",
" a('https://reddit.com/r/redditgetsdrawn')\n",
"\n",
"#print doc"
]
},
{
"cell_type": "code",
"execution_count": 209,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"docre = doc.render()\n",
"#s = docre.decode('ascii', 'ignore')\n",
"yourstring = docre.encode('ascii', 'ignore').decode('ascii')\n",
"indfil = ('/home/wcmckee/gotdrawn/index.html')\n",
"mkind = open(indfil, 'w')\n",
"mkind.write(yourstring)\n",
"mkind.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 210,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fertz = datzpat + '/' "
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'/home/wcmckee/gotdrawn/imgs/2015/02/21/'"
]
},
"execution_count": 211,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fertz"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<!DOCTYPE html>\n",
"<html>\n",
" <head>\n",
" <title>GotDrawn</title>\n",
" <link href=\"style.css\" rel=\"stylesheet\"><script src=\"script.js\" type=\"text/javascript\"></script>\n",
" <div class=\"header\">\n",
" <h1>GetsDrawn</h1>\n",
" <p>\n",
" <img src=\"imgs/getsdrawn-bw.png\">\n",
" </p>\n",
" <h1>Updated 2015-02-21 21:26:42.626446+00:00</h1>\n",
" </div>\n",
" </head>\n",
" <body>\n",
" <div id=\"body\">\n",
" <ol>\n",
" <p>\n",
" <h1>Would you be willing to draw my cousin and her husband for his birthday? I know they'd love it! :)</h1>\n",
" </p>\n",
" <p>\n",
" <img src=\"imgs/ShittyDuckFace-2015-02-21-reference.png\">\n",
" </p>\n",
" <p>ram1n</p>\n",
" <p>\n",
" <h1>My wife almost 19 years of marriage, can someone draw her?</h1>\n",
" </p>\n",
" <p>\n",
" <img src=\"imgs/ShittyDuckFace-2015-02-21-reference.png\">\n",
" </p>\n",
" <p>tubarao2002</p>\n",
" <p>\n",
" <h1>Can anyone draw this photo of my grandma taken when she was dating my grandpa?</h1>\n",
" </p>\n",
" <p>\n",
" <img src=\"imgs/ShittyDuckFace-2015-02-21-reference.png\">\n",
" </p>\n",
" <p>sailorchell</p>\n",
" <p>\n",
" <h1>Care to draw me viewing the White Cliffs of Dover?</h1>\n",
" </p>\n",
" <p>\n",
" <img src=\"imgs/ShittyDuckFace-2015-02-21-reference.png\">\n",
" </p>\n",
" <p>sentimentation</p>\n",
" <p>\n",
" <h1>My sister, unaware she had sniffed the flowers so closely. I thought it might be fun to draw.</h1>\n",
" </p>\n",
" <p>\n",
" <img src=\"imgs/ShittyDuckFace-2015-02-21-reference.png\">\n",
" </p>\n",
" <p>moulinroux</p>\n",
" <p>\n",
" <h1>I'd love to see you guys draw my son in some cool outfits! (any and all styles welcome!)</h1>\n",
" </p>\n",
" <p>\n",
" <img src=\"imgs/ShittyDuckFace-2015-02-21-reference.png\">\n",
" </p>\n",
" <p>seriousherenow</p>\n",
" <p>\n",
" <h1>Can someone draw this older photo of my daughter?</h1>\n",
" </p>\n",
" <p>\n",
" <img src=\"imgs/ShittyDuckFace-2015-02-21-reference.png\">\n",
" </p>\n",
" <p>zombreness</p>\n",
" <p>\n",
" <h1>Can anyone draw my neice? So cute we put her in the bin (not a real bin)</h1>\n",
" </p>\n",
" <p>\n",
" <img src=\"imgs/ShittyDuckFace-2015-02-21-reference.png\">\n",
" </p>\n",
" <p>emotionalbeever</p>\n",
" <p>\n",
" <h1>Hey RGD! I would love to have my beautiful bestfriend drawn. It would make an amazing gift. Thanks. :)</h1>\n",
" </p>\n",
" <p>\n",
" <img src=\"imgs/ShittyDuckFace-2015-02-21-reference.png\">\n",
" </p>\n",
" <p>Dispersonated</p>\n",
" <p>\n",
" <h1>It's lambing season! Here's me and yesterday's newborn!</h1>\n",
" </p>\n",
" <p>\n",
" <img src=\"imgs/ShittyDuckFace-2015-02-21-reference.png\">\n",
" </p>\n",
" <p>obsessedwithmydog</p>\n",
" </ol>\n",
" </div>\n",
" <div class=\"body\">\n",
" <p>GotDrawn is open source</p>\n",
" <a>https://github.com/getsdrawn/getsdrawndotcom</a>\n",
" <a>https://reddit.com/r/redditgetsdrawn</a>\n",
" </div>\n",
" </body>\n",
"</html>\n"
]
}
],
"source": [
"print doc"
]
},
{
"cell_type": "code",
"execution_count": 212,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"os.chdir(fertz)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for flc in fulcom:\n",
" if 'http://i.imgur.com' in flc.url:\n",
" print flc.title\n",
" print flc.url\n",
" print flc.author\n",
" res = requests.get(flc.url, stream=True)\n",
" with open(str(flc.author) + '-' + str(artes.date()) + '-reference.png', 'wb') as outfil:\n",
" shutil.copyfileobj(res.raw, outfil)\n",
" del res\n",
" \n",
" for flcz in flc.comments:\n",
" print flcz.body"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"u'[Bahh :3](http://ycsketch.blogspot.com/2015/02/rredditgetsdrawn-uobsessedwithmydog.html)'"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flcz.author\n",
"\n",
"flcz.body"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 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 |
gdhungana/desispec | doc/nb/Boot_Calibs_firsttry.ipynb | 3 | 262405 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bootstrap Calibrations"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/xavier/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:872: 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"
]
}
],
"source": [
"# imports\n",
"try:\n",
" import seaborn as sns; sns.set(context=\"notebook\",font_scale=2)\n",
"except:\n",
" pass\n",
"\n",
"from desispec import bootcalib as desiboot\n",
"from desiutil import funcfits as dufits\n",
"from astropy.io import fits\n",
"from astropy.stats import sigma_clip\n",
"\n",
"import numpy as np\n",
"from astropy.modeling import models, fitting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Find peaks"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Read flat\n",
"flat_hdu = fits.open('/Users/xavier/DESI/Wavelengths/pix-b0-00000001.fits')\n",
"header = flat_hdu[0].header\n",
"flat = flat_hdu[0].data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:bootcalib.py:660:find_fiber_peaks: starting\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:DESI:starting\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:bootcalib.py:697:find_fiber_peaks: Found 500 fibers\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:DESI:Found 500 fibers\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:bootcalib.py:705:find_fiber_peaks: Found 20 bundles\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:DESI:Found 20 bundles\n"
]
}
],
"source": [
"reload(desiboot)\n",
"xpk, ypos, cut = desiboot.find_fiber_peaks(flat)#,debug=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x12451f890>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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TawvXugZPuHP0mdbP4J/ecY9zbbMdxJIY4VGtdwDYt28v+/2EAwe8fUu23t3W\niv2dLoyr9m1HgDZg1puz2I5IsVh0orLbwQVpSdqqhauxYqsXXDj5lZmi4IILA9Geh1oQf5OyKE3G\nvHLbPOr6bPr9JPsaYMYg2Zv/eM3z/r7214kvVk2zQYpRc8yLxSL+5V/+BY2NjVi5ciXe9a534d/+\n7d+wcuVKnxre0dGB5ubmsp/R1NQEwExuR0cH0uk00ul02Wvtmmk6sMt9dmNjY+BzKUNXybXHE2zH\nXJLVcqkxj4ouY/dPlxietjPf3c13aqPKNkcVHaV6Sy5WLVyN64+u8qizT2aEkXbXFiXe/Eky5gSq\np0UbMDU7jf1+GkM+nxdlWmkM1cyYS+s1w9/Z29srDlbZxnE1Mmj2XkDGNhc2Y0CyP7pQ2b3vNGPY\nv1/mmB8+fNgv73KnsteiY+6WMZcqmhNaiuP8kolkt0yuh2jsgLv4GyezRJgQn+jvmUXh1tvV1enf\nuyy4YMYgmQf7d5M65vl83okh47pnBTPm5bsMVQJKSkkDVbRfuNLxXWrMd/1pB9AGTN4yWZTldGEg\nBpM1fLs/KpvQlGi60cCryWZ1rzGX9zGf0XCyv6/tf01mA1QTo+KYJxIJzJgxAz/+8Y9x55134otf\n/CLuvfde/OM//iO6urrwta99DYC3eZRztAH4r9OmXcm1tJnQplTuepdrjycEqewSp9Z2zKtDl7EV\nJyWbsB2NlDghlG2GJX7tQr1yz5jLHHMA2PvqHqANSG5IiA5DFwoc4FZjTti50zw/rg6hzKmlMXSI\nDCMA2L37TevzquGYmzUhzTbbjqQrlV1iqEfh1FJttvd51XuWALmzYY/BlX1Rm1R2Y/y7ir9JnFr7\nOdu27c/s9wNBJ8xd/M3tOdu1ayf7/UBwn5DNg+ueZdbn3r2ytXLo0CFfGd5135WV1bnVmAPefe/e\nvWv47/w9y3aAJPNoP0tS5ls+n8drr20e/jz+GHK5nP/7u2b9JY75rObZuPncvwesxLPEJnRpl2Yn\nFji2+YhBBcjqs91rzPmq7AR7Db/66threz0qjvnNN9+MJ554AkuWLAm8vmLFCpx77rl49dVXsW3b\nNmSz2REfPFpURHN/u2tjsZgvCpfNev2cy11Pnxu+ttwiDl97PME9Yy6rMY+SLmO3s3DdhKWsiIvG\nfQC4A8B6YPLzU9jZ5kKh4NTH3H6fi2NOm5fUoYyqTt4lY75jx3b/7xJHxDZMXGjggDzrEaSySxxC\nVyq7+d2Ojr6mAAAgAElEQVRsx44Ddyq77Wy4resDB2RjCDrmx76kIBhckEX83R0mN6dvtGHvFa7i\nb66shu3bt73FlSMjmPV3a/kmC2KZ79y1S1ZSduiQcSRlDqHrGNyDWDazxl2vwO1Zkp4de/fu8c8M\n2fkXTRkUIM+Y79ixzbeHZGeHW3DBTtZIbcL6lxqAOwGsB1qeGSdqU0l7k2vHJE6Nebn6bBSAyc9O\nYSdrcrmcU3ChUChY4m8Sx9x85+bNr1bcOrZWMGrt0kbCGWecgU2bNmH37t1obm4e8eGn14nS3tzc\n7CuJh1sAlbuWXp8wISicQ5lRoqnTteUypuFr3wqTJze97TW1hFjMbHrpdJx9/3V19qOTq/j9jQ2Z\nt/x/nPvYs8fURScS/Dno7DQsiaGhftEc/uQnvwU6ALQBE04bj8Xz3sl6vx3UKBQq/x0JtlhJT0+3\naAw9PT1+tjmX498DYA7ywcFB0fsLhfzwvXRj3LhsxW2+CJMnN2H3bpPxaWnJsu8jnzebeVNTmv3+\nuHWeFYt9ot9h/35jWDY28u8hFjNBr/r6JPv9qZRZU4ODsuepq8sYlvX1qbf9jPD/TybNIZrN8vem\ndNpMRHd3u2gMfX2mtCWT4d9DJmPuIZnk7032/trZeVg0hoEBc7amUpI93nRKSSSKI76/WmdfLmcM\ntmyW/6yn0+ZZT6Xc5mj//jdFv8PQkNn/U6kY+zOy2crmaCQ0NJh99tChfaIx5HLG+Jc86/X1Zgyx\nWIH9/sZGc463tx8Unl9mrUhsiV273MbQ1GTG0NNztOL329e98oopoYrF+M9CV5cJ5BWLeYE9ZWy7\n3t4u0Tz84Q92gIv/OxYKbvbU0JB5Dvr6ekT22E9/+iMku5No2tyE+vr6t7QJy32+HdSQzIPtUg0O\n8uzar77vi3jfye/F0uuX4uxzzkb/c/3Y88oe9j3YwaVCgT8GO9A1NMS3KRMJOzjRjd7eI5gzZw7r\nM6qJyB3zfD6PV155BcViEWeddVbJ/6eoXCaTwZw5c7Bp0yYMDg6WUMnffPNNxONxzJ7tRWrmzJmD\n559/Hrt37y75gd9806N/zp0717+WXqf329fGYrHAtcVi0f+Mt/rct8LBg2OrDn33bpMR6ujoZt//\noUNHrb+3V/z+/3nSxYjHvlyaNS96/49zH2++aQ6Srq5e9hj27zcb4KFDR0Rz+NBDP0cikUA2W4eB\ngUH2Z9hCL319/ez3ByPtlc+Djf/+7+f8iGKhUMD+/UcRj/PINLY6vuQe7Mzoli27MHny5IrfO3ly\nEw4c6MTWrYZOun9/B7JZXpurri5jYO7ZcxiJRAPr/b29JuOwZcsOnHQSvySAagQB4NChTvZv2dFh\nrt+3j/9Md3UZw+aNN3aI5nLbNpN5O3Kk6y0/Y/LkppL/395u/n3wYAf7Hjo7zTxu27ZTOAYzD0eP\n9rA/4+BBs7ccPnyU/f72dhMo3rZNNg9bt5oxdHf3CfZHY1y1t5efx3Lzd6zw5pumTEFyhh0+bJ9h\n/OfMnqOXX94s+h127tzj/11yhrk8Z5MnN+HwYROAeu21N6r+nB096maL7NixSzSGLVu2+3/v7eWf\nw3v3mjF0dfH3iwMHzDzu3bu/oveH196LL77q/72vb4B9D7t3G8e+p0cyj2YMBw8eFs3DH/+4yf+7\nxJ7atcvYtb29/DHs22cYLO3t/D1hx47tePHFF3HJJf8bb7zxOo4eHXlNjrR37t1r2BsSm9Auhejs\nfOvztxzSvU1AGzB/xmnY0vc6crkc+zPsfU0yj3br3+5ut/MXAP7rvzagoYHfunUkjHYwOnIqez6f\nxzXXXIO/+qu/KksfeO6555BIJLBgwQIsXrwYhULBb4VGGBwcxAsvvID58+f7NPLFixeP2Lpsw4YN\naGpqwrx58wLXbtiwoeTaZ555BvF43A8aLF682P+MctfGYjGcc845zF+h9mEzBFxFZzhU9nIq4CgA\n79h6mqjVDMGVyiipMd+zZzeefXYT/sf/eC8mTJggoh7bAnYSBc5gyzdZH/PNm18N/Fs2DmqPlfNr\n9TgI0uD4dPb29iOB8VebyiepE+zt7XWuOY1K/A1wqTGPTpXdVYRISgO3xd9c67NdWw/VBpW99mrM\n7TpW2fjchNOioLK7ir+5zpE9BmmNuS12KduzohuDtMY8qItx7Ne7feZKa8zt+XMt5XJvlyYrSaOS\nunQ6LSqLsNexzJ6ybUJ+YIG+f9q0aUgkEj4TkAObfu5KZZeIv9Hzn0wmkUwmRPagaztme+4kpSH0\n/NfXe8mVsI1b64jcMU+n03jf+96Hzs5O/PCHPwz8v/vvvx9btmzBpZdeisbGRqxYsQLxeBx33XVX\nwEm6++670dPTg6uuusp/bfny5WhoaMB9990X6Nf8yCOPYPv27bjyyiv911pbWzF9+nQ8/PDD2L3b\n1Gy2tbXh6aefxvvf/36MHz8egNd+bdGiRVi/fj1efvll/9rXX38djz/+OBYuXIgFCxZE9wPVCGwV\ncndVdl67tFULV+Pvxn8TWA/8r+SlwJ3AtF3T2fdgbzrVEPqgerZ3vnMhEgnZBhY8SNzEe/r6+kS/\nA21ajY1Nw5/JO9S9Niv2OPj3YBsSkjpzu74ckAp22caV2/N06BBfmX3v3t2Bf7uOQbYmoqgxj9LI\ndTPU5arsZuyy/tKu/eTdW0C5ir+5BqpGG1GqsrsYf4An/iapYwwG4o69yKB9ZslrzO0OBm518pJ2\nacFA3F7RPNj7hLu2h5sauLRdmu2Yy2r9Xc8O9xrzV199GS0t4zB16jTRnhXs1OMmwidJ1thObSKR\nFM2DbU+7Js04NeYE+t2TySTi8YRob7Udc1cNK1mNuXfPZ57plRGMNQG4Uakx/8pXvoLnnnsO3/ve\n9/DMM8/gtNNOw8svv4wNGzbg1FNPxVe/+lUAwCmnnIIbbrgB9913Hy677DJceOGF2LJlC5566iks\nWbIk4Gy3tLTgS1/6Em655RZ8+MMfxiWXXIL9+/fjt7/9LU455RSsXm3EFeLxOL7xjW/g05/+NC6/\n/HJceuml6Onpwbp16zBx4kR86UtfCtzvmjVr8NGPfhQf/ehH8cEPfhCJRAKPPeYJkX3jG98YjZ+o\n6nCPaMky5oTxsQlAG3DRVZfg/3Q8LjoMo9zAZJuwd8+pVAqJhGwDcz9IguPu7OzEpEmTWJ/x2mue\nY75gwRnYuPEZ9oE4NDQUmL/BwQFfVLHyz7BbrfDFb8gxTyaTyOVyoueBsv6ANEhifgNJy7Q9ezz6\nV0vLOBw9KlN2d80+2b+bnTXmwO677a407WYgRqPKfuyDC9Fk/d2CC0FxtFp0zKPrY+6yVmi9Hjhw\nACeddBLrM4LBBckZ5hZcsNd7e3s7uru7/ABtpbCDkNXJ+pvvHBgYQHv7EUyYwKOt2iKR7vuuW7a5\nvf0ICoUCu5xs584diMViqKurd2ZbuQYXuru7kMvlkExW7mL09fVh27Y/o7X13di/f5/oHoKJDrcx\nDAwMYGBgAJnMyLpIYZDtlEgkfVuEC9ueds+Y96BYLCIWi73FO4IgOyaRSPoJJ+5n2Ik/ydkTVceM\nU06Zh5df/pNmzAFgxowZ+NWvfoXLL78cW7ZswU9/+lPs3r0bq1atwkMPPYSWlhb/2i984Qv427/9\nW8TjcTzwwAN444038Jd/+Zf453/+5xIBqKuvvhrf+c53MGHCBDz00EN49tln8ZGPfAQ/+clPSvqQ\nX3DBBbj33nsxf/58PPLII/j973+Pv/iLv8DPfvYzzJgxI3DtmWeeiZ/97GdYsmQJ1q1bh3/913/F\nokWL8OCDD+LMM88cjZ+o6ghS2d16gLvQZWiOJY65K+XH3jQlCpxEU0ok4kgmk6IIbzDr77YJA0Bn\nJz9S/dprm3HSSVMxcaLn0HMPk3BEUzIO12g7OeazZ88BEAWVzzXrwXfMqVXarFleSUc16Ij2d0qo\n7N3dXejt7fEPcXfmgjxjns1mcfDgAVEGzZXKHlTLljNhEokEenq6RYwe22FyLymoPSq77dS6OoQu\nGfNTT30HABmd3Z3KbgcX5KUrxCDctWsX+zPs58yVmeEyBrIl7MBgpdi/fx9isRji8XhVWr7Zv1s+\nnxd1WNm1ayemTp2G+vr6CFq+uTEXAASYrZVgy5bXUCgUsGDBGWKnNpjocMvUAvyETTBjHhdS2W3b\n3M0O8diMvL3NjCHhB1a45YnuGfNggIT7/bSe0uk0TjvtdGzZ8prIx6gWRk2VfcqUKfjmN79Z0bXX\nXnstrr322oqu/cAHPoAPfOADFV173nnn4bzzzqvo2gULFuDee++t6NrjAcGMuVsPcC6VHQhvYLJs\nczCyKBlDsJ6IH1k00VE5bSn6jDkH3d1dePPNXXjvey/0N2HuOOxMMxBFqxU5lX3evPnYuvUN55Y3\nrmUFkjpBapU2a9Zs/OlPL0QwBvnz1NDQKKKyU4b6pJOmYt++vc7ZJxc6/vTpM/DnP29Fd3cXmpqa\n3+ZdBsViEQcPHkBdXR36+vpEBp5rBs0ew65dO7Fv3z7Mn8/LZB48eAATJkzAkSNHhHXybvMw2rAD\neK7BE1lWxtsnTz31Hdi0aQO2bduKpUvfzfqMI0eOIB6Po1AoOI9BQvmk75w79xS0tz+LXbt2YMGC\nM1ifcfjwIaTTaQwODgqZa9Fk/adPn4EdO7Zj3749OOMMXkLlwIH9mDRpMrq6OiNYK+5O7ZEjRzBu\n3PiK35/L5bBnz24sXnwudu7c4dxqzJXKDgBHj7Zj4sTKmQtU1nHSSVMdEh2uLU+D39nV1ckaQz7v\nOZCJRGLYJnS1a92YQPR51Ha6EoSp7N5reSQSibd6WwCujNzwmdnf389qW22y/glMnDgJg4OD6O/v\nR0MDT9C3WhiVjLmi9mELZblGFiUZc3vxyzdhO2POf799kBQKBfY4giIZ0ghvdNQrgB+lJkd+0qRJ\nSCZpE3bNmPPGUSwWkcvl/KCIS4353LmnAJDW2Ln3MadDXFInSHoYlDGvhggR/W7Tpk0TUdmJdk2s\nJFfhNJeewDNmnAyAX2fe3d2F/v5+TJ06DYB7zakLtZXGwKXkF4tFHDp0EFOnTg98Hgeu8zDa6Oho\n90tmXEuZXIIn8+dTxvzPb3V5WbS3t2PSJK8DhcwIj6Y+e84cb9+UCMAdOnQQJ500FYBsrbgG4ug7\nZ86cBUCWMT9w4ACmTDlJHGB3z/p7zy85H9zA7p49u5HP5zFz5iykUinhPERHZQf4zDfKLrvUZ7uK\n6YaDC1wmpaGyJ8Q2YZAJKpmH4HvsDHwloHuOxxO+TcgdRzBjnmOz1sJj4AYdw4k/ACL2QrWgjvkJ\nCpui41qf50Jlp2wzRRo5cN3Awhs3l7ZkqOyJYfVKN+qVlzXhbR7hueNS4MJiJeU+8+0QNtq5hzp9\nHzm10oz51KnT0NDQCIDvEBaLRefa5nw+j/r6BjQ1NYvE3/bsISr7LP/zuIjKQJw2bTqOHu1gfwYJ\nlc2YMROAe7TchX0xfboXHODWaBNTYNo0z6l1ZS5IaOA09+SYcwXgjh7twNDQEKZN84IL1RAWG030\n9fWhv7/fd2pdxycTGPKeMymVPZ/P4+jRDkyePGX48yTPmVudvF2LCQTbNVaCYrGII0cOY8oUbwzu\nNHB5xvzkk709Z+/ePW91eQl6enrQ3d2FKVOmIJWSOVOu7BL6zilTPI0CrmNOAZXZs2c76N1EI0Tb\n0uK1KeWe42GHUGZPmTG4MBApSSB1askhLBaLbBq2uyp78HdzSThJnVpK/BkWJm8uw/YXn45PpWBy\nu7aaUMf8BEV3d5cvLhKFwAQX5IhTHYuM8mO+NwrqlS1YUdn7w8EFN1V2gD8OmgcS7OFS2emeKcIL\n8DcwO7gAyMdABqokY75795uYMeNk8RhKgwsyhzCZTGLixIlC8bfdaGpq9imM1akTHEIikfD7yHPH\nQU4wOcXVqDml76SsPTdjfuCAxxSgjLm7oJU8uEDOBj+44I2BggvVoOeOJqhVGgldVkM4zdRnTwDA\nN4Apo0hrTSaUGM1aIaYRN2Pe2XkUQ0NDmDRpMlKplHOQ36XWX5oxp/3BhUIdVGWXr3fjmPMEUCkY\nMW3aDLE9Fdx35YrmtCa5GXPbJpTSwIMZc3m2mTQXuC1obXuKHEKuXWjvI5JkTTggwbXP6ftoHgC+\nLUKaKLQ3cs/Q0ciYS5J/1YI65icourq6/M3HtQe4a6/ERCLuJJwWi8WcDAKqO+ELfdgbmLsqO8CP\n8tLcUbaZS2U3m3DSd2r5VHY3p5bGMGmS55jb/YkrAR1edXV1Do65W4DE+84hpFIpTJw4CUeOHGbT\nt44cOYKJEyf6Y6iWCFEqlfKDJNw6c6Jcn3yyl+mtRqaWDMTp02U0cBqzS3DBdQxhOj43Y05jmDx5\nMhKJxHFHZSej39DAq9HH3Hu2s9nM8L95zwkJv7lkzINOLX8M9LtNnToNqVSqpGXj24EEIidOnDTs\nELqpgbsEsYxjzsuYkyK7obK7ObWSkoJwxpxbCkXvz2Qy4uCCaymXOce9Ncl1zE2iQ56ssddxPp8X\nOLXBYJucyp4U08ApSy8t06E9IZ1OA+Db5ya4IHdqKUE0YYL3O3L3hfDzF9YxejsY29zzLwAZA7Fa\nUMf8BIXnmHuLxiXb3NTUjN7eXrFqYjyeEGebifLT0tLiVBNFTi3XMQ9nmyW0JTpIKOPNrYsKj4FL\nZS8X4eWLv3ljIPoXfwzes9Dc3Ix0Ou1EgTNj4N1D2CCUPU85JBJJTJo0CblcTjQXXus9WYAEiMap\n9cbgGVdcx5wcSHKKpQ4hPUvRZMx5YzBUdhcauKuyPDkblDGXOuZTHLKAtUtlpz2CnlPJ+CgIGo/H\nnZ6zdDoz/G/ec0JZUXLGXCnULtnmVCqNVCrNrnM/dMhzICdNmoxkMiVaK2SEx2Ix4VrxvnPChAmo\nq6sTZMy966dMmTLsEMqDifF43CljTu32uFR226mV1mfTbx+Px5HL5di2TJj5xg2wh1uNFQoFsT1F\nJW3c84f2XaljHnQIjXAaB+RINzd73au4yRq7jSPA72Ue1k6yX6sU9LvR78h9P9mQ9P3hBNbbwa71\nd7GnqgV1zE9AFItFdHd3OVFmaeGMG+ctfm7GIQrhNKLoNDePc6onIqfWJTpKm7CUBk4tBKWbMLU6\nk9aYU528/VqlIIOQDhJpxjyVSmLcuPFsKrtNvUql3IILdKBLs5ypVMrvoculgefzOSQSCb/tD3cM\n1BrFBHmkGfOklTHnCcARLXT6dDmFur9/wGkMtCamTaMac56hTllAF+E0yprV19cLM2gkwjcDsViM\nTWWnMXiOucxhonXd0NBYc1T20oy5vD67ubnZKdtMfY65WaWODs8xnzhxEmKxmJOieX19gxO7RFpS\nFsyYJ4R0fO++m5tbnHQxkskUJk8+iR1MpD2LMuYuVPampmYMDg6y2VL0nSSix3XMw0kCyTxQVpJs\nEWlJmpzKbsZgq4Fz4GpP0Zgp08u3CU0LXalDSI452dZ8GnjQNudT2YNsVkBSYx6msnMdc9oTvG4q\n8pZv8gBJNaGO+QmInp4eFItFjBs3Tty3kzY8isrx6TLBhSNZNL29vYjFYmhqanJqK2Ey5tzoqHtk\nsacnGB2VHobkDHJrzG0lVDmV3TNqaROVBhcSiSTGjx8vzpi71MmTQSg1SrzvHEIqlfSDJJRNqhT5\nfMFJCZUMQpoHaS92z8CljDnPMe/q6kJdXR3q673yEFnQb8Aag5wWKg24mfpsUmWXGeqxWEzs1JJh\nnc1mkclk2BkDck68TGbSSbW8paVFFFwYTdAeQQEkqVMbi8XQ2NjkVGNuHHPec0IZ8/Hjx4vrs+3g\ngov4m8fU4fddpuCj19VDNgb67VtapI65OYdTKf6zTvtDc3Mzkklp2Yfb+eFaYx60RWRldeYclwbY\nqcbcOzu4GfPwGABJttkbA9ml3DOQ5oEcSq7uULjbEMAPsBOVncbAtW1dbfOwdhLAn4fu7k5kMhm/\nywCfyu6NgdqcSmvMvX1NHXPFGAA5oE1NTcMGgdxok0flgv0epVT2+voGZDJpUcY8XJ8tj44mxE4t\nZWpdD3TXjHmQBi4bAx3o0sMwlUph3Ljx6OjoYFHYyguuuAUX5MJpxjHnZsxzuVxAHZ87BjLM6VmS\nKhwnk4bKThmxSuH1O01azAWZU9vU1DT8d7mhTg6T1NlwEX8bHBxANptFNpt1djbi8QQ7G0usk/Hj\nJ4gVjun5kTp9owlyzGnvlvYAz2QyyGazQhq491ylUmnEYjH2GRacIxlrzM4suVDZifXFHUO4xtwl\nANTU1Ix8Ps/+HQzjKiUq27BrauX12SZjDvDPD3K+Jk6chHg8zs6Y0x5H57hLgMQ45rJMLZ0dXOYb\nnflRCNFKM+ZhQUe+7lApi1JKZXfN+ktt87B2kvcan8pO/gUgr5On4AK3xjxcFmG/NhagjvkJCNps\nGhubkEymRNnmcB2LWw9wWYS3t7cH9fX1w7VxEoMguhrzqKhX/FZjFOH1yhK4GfMosv7h4IL0MPQc\n83EoFAos9oJ9GLqqspvgAs+wono4T/zNe54kxlUiEXcYg6GEAlIqu1fnns3WAeA7955jLg/yeN85\n4GeKZdnmHOLxOJLJlH9PHNDzS9F+aW/mTCaDTCYjcpjsYJXEaSPjynu/WyazublF5DCNJsI15lLx\nt3Q6g2y2ziljTgYs3zH3sqITJkwQz5FNA+/v7xf0Cza1nBKHjsYwceJEcf/scECRT1s1jrUkiBUO\n7LrUmEvZSkavII3x48cLaswN8y2VSonqs12D00b8zQtMSzPmLgF210RHmEXJrzEvR6HmUtlJO8mN\nyi6tMS+X9ZdQ2Rsbm6yyPD77D7Cp7NyMuQkuGNtcVdkVNQxqAeEtnKSwB7hrVM7ehGUZnd7eXjQ0\nNPgHEb8HeNgxj8KpldU2m02Ym2327iGbzaKxsUmgym5HqWUbmKtTaxuHpHvAibZH0fLNjIGMEllw\nIZlM+vVpXDoiZZujKimQU9nlz3M+n/PrVb3388ZAdfKZTBaZTFZYnz3kVFtG15OolzTrn05nkMlk\nRWURQWVcPsWYrqdAj4vT58JeGC2UthqTZZvT6fRwxlzex5zWC3e90h43btx4p/rseDyOhoYGFItF\nthFvsv6UMeft/fRMZDJZsfq/CSjKyldMEEuW9bcdEXlJQfD84AY07TNwwoSJDjXmdm0z1xZxPceJ\nqZRFU1OzU4253J4KC6cdW/G3YJBHqsreg2QyKRawK60xl1PZyanlzoOXMW8Wd5gxTCBpsK60Tl6p\n7IqaBm02TU2UMa9ejbmhj8mp7BSVc42Ouqmyy6KjpRlz2UGSTKbQ0tIi6LvpHqUujbTLsgVEZQfA\nqjMPZ668z+Q9T1EFF1KpFFKp9PB9cecyFzrQeWMwjpScjm+cWpnoSz6fRzxuC9jxf4NCoYBMJot0\nOi0cQx7JZEps3IUDbtKaU4/KnhGXFAByZyNcJyh1XL2sv9e2p5bo7LQ/kLaGtNUYUdlzuZyAlUDP\nSUqUqaUxjB8/XnwO22MA+HMUpt7y17sJ7HoZc9lzFo/HfV0K/hjsrL98DKTvEQWVnXt+2HR8iQCq\nCcTJ23SVasXIbBFPxHWcWJXdrjGXsvcomMgvDQxS2V36mJsAO5/K7tm1Mqe21DaXq7JLnNp8Po+e\nnu4AlZ1fY05Zf88e42qs0Pe5sCirCXXMT0AYKnsj0mk3GrhrVM5Q6PgK1ERlp36NEicAiEqV3Y0G\nTgeJVDgtlUqhubk5kj7m0myza518MpnyKfmcaDuNwTOsZIdZmE4ppfFJM7WFQgHFYjFwoHPHEJ4H\nqRo4ORoA36gI18nzgwveGLLZjLg+m7L+NA8uzoa81Vg/MpkM0ukMBgYGxCrNpsZclvX3HCZpzekA\nMpms36dbkvkfLdD+MG6cJ5wmDZ5QxhxwV/7lzpHJNmeGM7WyVmN28IS75oNUdv4YbGZGIpEUquP3\n+3oMgMs8pERlceFss5Rd4gkJellOPuPKnMPpdJpNRS8ngMoPsFNwQcaQsc/xlpZxDhlzuc5KX1/v\nsE2YGb4nri3iXd/Q4NmVUQgCS6jsHhM0HbinSuFqm7syEEm8jhJ/gDy4EEXGnGwZrh1QTahjfgKC\nooAe1URG3QovHKn4m6nP49eg5HI5NDQ0+Iufm22OKmPuVp/di7q6Ot+wkjq1nmPuZcyP9YFO0UxX\n4Rs7Y061i5UgqMpLdcVuAnZywyolehbCAkTeazLxt4aGRqd+ui408EKh4Nh6z1Bj0+m0uL90KpVE\nPC6jsIVZJLKa08FhOn5m+N9yI1fSW9k4TPL2SRRcME5f7WTMOzs7kUwmhzVG5JlaL9vs6Slwxxfc\n//lnmD1H3nMmDS5k/OeMS8m32UqJBN+ptTPuchq4FyChMbiUEUmCWOVat3IDaf39fcO6GHSOyxlX\nkqBoOQFUfmeS/sjGMG7cOPT0dLMcMqMV4CL+1oe6ujo/WSO3p9JoamqKRHeIr8re4+9r9j1VCtpH\nyJYiR7ny95vzT+LUUoLLrjHnBuzCmg3cdsz23qyq7IoxgaAqu1xJNZVK+QJJblR2vkFAgYD6+gak\n01LabFiVnVtjHqzjBGQOYV1dnTg6amc8mpubUSgUWBuxTYGTZ/0pU+tFaOV0/CTq6vhGcjRGSbCm\nSU5FtCO0EmV5I1omZS64CKeRUyt9nkn8zQivyYIL5BDKM+be98tqTvOIxWKIx+PD2WapU2uysXwG\nRtCw4Eb7bUEsF0EryvrTv2sFXmvCFGKxmEOmdtDJqR0aGvKfE+lzBmC49ENabjAYCC7w9y37OZHQ\nwIPlXNLgAmlKeP8+tswFW9HciF3xa+1J7JH+zUGQessPapYT7OKuif5+csxlTq09D2QLcBh8xply\ndczrxY65XVLQ2NjkxKKUt3zrDZRouiqauwkz8+chWCpL75cK2LllzF30CqoJdcxPQNhUdo8GKBOJ\nSmhZVlQAACAASURBVKXSaGjw6sLcxN/4Nea02dTX11sZcxnlh2ph+NHRUroMt86wt7cXdXX11mEo\n28AoYw7wlNmDUWpZhJcMWikNPJgtoNpmSdY/6TwGaZ28neGU1mV575cLrxkaeBbpdEZEZSenVvo8\nU528a0kBGYjyGnPv+6XtkyjKLhFOKxQKww5T1ndquXNhB6vi8bgjlV2uyu45fZTJrCXHPO8H4aTB\nExJ/o2CgxCEk4zmaTK0LHZ+eM6nomOwcDjIz5Blzr2TCjY5v9yyWBEXt7JqkjIhYPoDcqZX2XQ7X\nyXuv8Z3abNYkCVzYe4b9UPlc0v3G4/IuN5495ZIxN2uyqalZoMpeeo5z5oFKNEnU2LunYyv+Vj7b\nXPl6IvuzubnFIbgQFn+T9TF30cupJtQxPwFhqCbNkLdLG0Q6Lc+Yl4tyc+hj1AKioaHR34T5bbrM\nQeLRlmQKnPZhyM/89zplzG3xN3IqOVFqWzjNtTbNKKHKxyAzStyZC6Wq7G61mvZ9VYIgfVpmWBka\neEaUMae6Rjcqe94pSh0cQ1bUAooE7ADKmHPbJ+Ws9/Mdc7t22GTQ+E5fIpEYzgjL62a9dS3LZFJw\noRap7MHgCd8hLBQKyOVyAeE0Sa9cOwAkuQfAdmplrAZS//f+Lev3S3svXygxWILDPccBwy6RrhU7\nQC5hTEVBoTZZf/l6B4zYI8BzJILnuJxxRUFdwEVnRXaOh1voct8PmIy5nIHoXZ9Op32bUMp8M1T2\nyuehr68PxWLRbwNs31OloGAEBSj4Zablsv6Vj4G6G7lkzE27NFnG3C7RkQZ5qgl1zE9AmBrzJods\nwyBSqbSvpMpf/OVqaitfOIbKbtfi8OvUAW8Tbmzk1xNF1QPchXpFY06nUz51qbOzcsfcznhIDhLA\nvU2XXWMuaTESFhLkvh8oFU7jUhGNI5QSBQfKCdhJagQBIJutG3bMpVREWdafPsOlhyuNIZ32KNTF\nYlFU90q/oUw4reCvBUm2mQxaOwsoMXJpX0smkwKKcbhOXirKlfH3plqisnsBIO8Z9ZTzZcETL9tM\n5TP8Xrm033g0cHn/bJesv+3UyjPmUVDZZQ4h6TGYkgnuGNz2rXBrQe/9kvpsu+xDlm22W1RJa8zl\nIq59ASq7dAzSc9x1DMVi0dfsofJGKQMxmfSSNZTBrhThbkPeaxy7lpigDWK71g6Q1NfXs/uY21l/\nybNo15gb8TeZbW5qzOUt31SVXTEmEKSyy1TZh4ZySKfTDjXmJqMjia7aVHZpuzSKRKZSKTQ0NIrr\niWyHkEtbooPEVegjmUyJVG2j2MDC2WY5c8HcA8dAjMKpDbe7kdP4ZIdZOQqcdAxeXXDaidYqrzH3\nnNpYLAZJqzFymLLZjGUg8tsn0XqSCEvajr0kW01Zf28MUip70Olzq19OsVWe7X7y0uDCaILa8gGU\nMZdRJW1WA7+OccjPKElZDbFYzKqT542hWCyW0MD52WabtupSMhH3fwtJ+YpdMsFfKyZb7JKp9c5A\nCi7wmT52xpwfiDPBaQnbKNguTc4aszPm/HPcZP1dznGpKvvg4CAKhYKjZo+ZB1LY5yRsymX9OfNI\n+kANDUY7SarKnk6n0dDQ6MBmldlTdo05tXyTZsxNuzTu3uzmX1Qb6pifgAgunBTy+Tw72j80ROJv\nlDF3o7Lbr1UCs4E1Om/CNm2JQ8ML1nXxNzA6SOyWby79syUUuHK1zZK6Lk8hmfrQyuuzZU6te8ac\nDNr6+gYkEgmnPrSydmnBlj3e++Xib+k0P2MeBf0rn3er7zY0cFsMir+u6Tf01LIldHzvaJRkzMNZ\nf/u1SuFpeNhZf26tv+1wyfYmAAG1bIlmwWjBDp5IhNPomUqnM0415i4ig8Ex8J8zUg9PpzPi+uyw\n1gufjh/UMgB4+5bRYzB0/Cgo1DKHUF6SZjoYSMXfSrP+Eiq7XQrFcQgpEJfN1jnbIlKNknIlaTwa\nuGeDujEQTbKmsdEL0nMSNuXadHHWQzntJGmrMRJndqGyS9YTfV9dXT2kLJpSKrus24S2S1OMGVAt\ntUc1kYk0eTXmJmPOpcvYQh+SCG+5jDmXLhPchBtRKBRYAYZgZJF/ENkHiasqezRtVqRU9v7AYegy\nBonYSJgSCsizzXV1WREN3KYiuo1Brqprss11yGb5NeZBSqjUMc/5hqVEDdyosmfFGXNSlgekVPaw\n+Jss6+/SAzxY5+7WikvibNCzY2fQuPMwmqC2fIBsjmh8dh9zflYmOEdcw4/0GABvDMVikfWsmucs\nLa4xp/aIpGUgFbCzs5wcW4LWRTROrZ1plZ6BfFskl8shl8sNrxU34bRkUiZ8RmeNlAZuzg73MXjn\nOL+koHxpYOXvp5ZaNgPRZQwkbCzpchO0ayUlmg2WPSXLNnv2eYNj0kyynqgVchJSJiiNoampCYCL\nKrtcTLeaUMf8BERPTzcymYzjwhkarjGPQvxNXosTVK+UbcJextyLjkppS5LoqH2QuNdEuTqE7rVp\n0kh7MNrvomju1mYFMP2zpe2tbKNEmvGQqupSVJlaXHGF02wlcMmz5F2ft+qzXans2cBrlWJoaMiJ\nBu4Jr9nib1xaKzEX5D3A7ZZvkqy/obLHRZlMu5+8oUnXTsY8LP7Gr2EMigwCkqxM3nLM+dlmW8tA\nxrii4EJGPEdeEMtk/QFpm0eTMeesF7s9oqQUy/u+cholMgq1i1PrElwI95O374vzflLXt1+rBPTs\nkz4JIKeySwO7djBRolFCiY76+nqrpEA+BsmzYFOoJe83osYN4qRZuMa8t7eXuabLsVndxHhddVok\nrSyBoCq7UtkVNQ2vnRAZfbIe4EaVXUpld2sPFazFkUUW7eioqSeqvNVYueiohHrlosAZpFDLo9S2\nYy6hUHvBBTrQ5fMgOZBpDPG4zCgBgtnmdDojprJLqcPRiCgFqexc4bRy5SUu9dkSKrupk7fVgfnG\nlS2cJhF/o/GnUvwx2EaFtAe47fRJW3EZVXe+gRfsJ197fcy9AJARf+Ovd5NVkjq1XgDI1JhLWgva\na4U+s1KYbLMpN6AAY+X3kA8Eoei+KoWtLG+MeM5zRmOQrxWz/8dFQVHX4LTN8pGOwaaBS+6hnM4K\n51myGWNki8jF39yU5aUBkt5eO2PupmieTsvYe9FR2V36mFPCKWW1M+YwQUvZVtIAi/sY0sOOuUw7\nQ0rHrzbUMT8BYWcbjFPLrwFJpdJIp9NIJBLsOpZgdJQcStdaHP5B4h3m9gYic2Rk0VGistdFQANP\nixzraBTNKWNOWX+X+mw5FdFNOI0yBm5Udnm7tCjGYDI3RKHmGIjhfvKxWIx5IBsj3fvTrdWYGUPl\nhzK1a3JxaguFYCZUmvWXjgEwFGPvHqR18uFsrCyTWYtU9rBTK+2Ta9eYS3rlBkUG5VoGkgA5Gate\nxlxaJ28L2LkFdk3GXOLUylsLenoMKcRiMcdyrrjoDLRZPoYGLi0jcm2X5kYDt2vMXWr9XeZBegYG\na8xlDETbFqHAH2cegiVEZMtU7thTwsklWWNKNNOixJm9piXBBTvAYvYE/v5MArLZbJ2TgJ3Urq0m\n1DE/ARFuNQPwFk4+n0c+n0c6nUYsFhPXsYQzOvJaHCkNfLCExidV0pZkag2V3VaW547BTbCrfNaf\nZ2D29fX5mWZA3vLNtf+pS//scI3dsRZ/K5fx4Br6drs0SS9am8IG8J0NO0oNuAmnBWubKx+DbaB6\n98JXms7lcoFsrIs6vpSOH3Y8i8Uim2IcdsxlVHY7uMBb16MJO/CQSqUwNDTEKtsolzF362PO1wGw\n50hCAzfZPbf6bFvAju6rUhBzgYxo7zM5GXP7OZNT2e09i+6rUtj7nkSPwWb5SNkXNuPKTThNxnyz\nhUOlNHA7sOuS9Zc6hMEac7dEh7stInuWgiWaMq2ZYI05lZryW75JBYHLsf+kGlaxWAx1dXXi8hZp\noqTaUMf8BEQ5g4CzCdsbMACR8iM55oCsvs6uxaFNmE/Hz/nvlUSpjVMbh6Q1honwutR1uWVqgyUF\n/MPQa/nWF8j6S+u6bAqc1CGUUrD7+vp8p1qiaG5+R5kysB3hJadQ6hAGW41xMualjrmMikjib/JW\nY1IKtc1coHtwEX9LpVJsp9gWf5PrLpiWb9L2e0aZXkKTLqeOXzsZ83L12Zw5on3WU52X1Zjbv7FU\nZNCMQUIDN5naKJxaiUMXrvX3PlMiYJcRO4R2cCEqGjjnN7DH4HIGGhE+eS92aVmd0ScxVHb5Oe4u\n4irJNtuJjmiFaGX2lIzKbhJO8vJGMw8SceagoKNbP3p5cGHID8xns3zH3AQX5EK21YQ65icgggJN\nfKqJbdQAnmPOrW0Lqw7Ta5WiXC2OpDWGrd7s3YOMPiZZ/MGMeRRKqC70MRnlZ3BwEMViEdlsdjhA\nkRQc6KUUOI6RXS5jzo3QUs9mwKvZ5FMR7fo6lz60pge4PFObFWVqS51anvCZfSDT50gz5tKewPaB\nTPfAz2SW7k0yp9YW5eL3YTW1vzJBxNJsrJSeW5s15i712cFMbV3gtUoR7mPOZ2bYY+DPkU3Hl/di\nN8+6lHFl1jt/rQTXu3StmNaC7jRwFzq+Sx/zoZLnWSY6JqOBB7uSyNulxeNx/z/AXQBVRmWvc2Ag\nmvJG17ankmwzOdDBbkN8RXMK8jQ0eNpJnMRZkAnKt8foWi9IJKeyExO2ro4v/qYZc8WYQ9Ag4Ee0\naLOjRSejspeKzvBqcUo3MK4T4NWmBTPmrv2zpSqitAlFIZwmUeCUCt/Qhkl1mp6iuXubFUmENpmU\n9dIFPOOKnChZxtxulyZXlreznK4CdgAv0BN2avlU9mDG3aOyc+uzS41cTm/mcNZf0gPcVS07SAuV\n9WIv52xwDUTjNPINxGD9ci065qYtn2T/t2ngdXXSjHkwU8tlVpTTAZCLv0md2nIBck5Q1GZm8Pde\nu+WbSy92m7ng3QPfkZCfgaWtBbljGBoyz5Kk73K5OnkJDVxaBgUEWT6uOitupYFudfJhptKxFLCj\nMQQz5jIauPc5/K5JNF4vWeOSMY87BRdo/NlsHQYGBpjJmtLggjrmipqGbRBIFM1NxjxIZefU+HkZ\njzCNT3KQZJ16VtL4JUZJOaEPyRjsmii56JgRK5E6hK61aYDMMS/XokQSILFV2SUtrii4kMlkUCgU\nWHNZPkAiyxZ4fyYFfczt2mZ+D/BwiUoiERcZh2TQyFqNGUPdpU7edmolFGM7uGB/biUgZ8OjScsE\nrWxnQyrqGKZJyzLKtUplzwfWCsDLyhhWQ9rPmLv0MZeKXRktg5T/WqUwz5kJLrhpGcgcQpfgQvmy\nD674Wy6wZ9F9VYoghZo/j0GhRJlD6LWti7pOnv8seY65NEngqlfgliSwBYFd9G5KxyBTZZf8Bjbz\nztDA+YrmNIa6Os8x5zBay1HZXWvM+e3SBi0qO79Mx36W1DFXjAmUMwh4Ro1RfQS8jTCfz7Ozc3av\nYe8eOFE5U0fjIvRhi0QBXGeq1KmVtktzVUJNpWT3EOwhzj+I6DAk4zaVcqWBy7MFUvEegKjs3kEg\nqQume5CK8NFv7kIDt4MkEvqxzTyge5HW19GfLlR2yYFMz5JNA+e2SbGzsabVmBu1leNsFItFX2ka\nkDt9YSo7x7GnNWwrTddWxry0HIs3R6U0cM4cFQqFgPq/VNHcBIDkTq03BmmN+VBJcIHXLs0Iycr6\nmJcLAPGdWjcaeM4XsJOpsruLv3mt91wc83I15hyFf5PocGn55lZS4GpP2YkOF9GxYJBHYpe6dmfx\n6rPlLXRpDBJ7yLXbkP0byIMLZh7ItuQwmsqNgctArCbUMT8BUV78TV5jLqmjtMXf6ECSZuekLd9s\nyo/EKAm2ZOBvwsG+m/KaKMBziN0j7S61aSbbzN2Egy1KolFll/TPpgNAkqkt1y5Ncpi59M/u7+/3\nfwOJkJLNvvDuhedY2/V13ufwlarLtx6qfAy28I13L/zgQLksoIzamhHR8Ymy50IxtrP+EmcjCqXp\n0UKxWAyxGvhr3lYuNoafJAAkf07Kt7STZf3l6v95pyynzeyQ9TGn5ywdSWtBKQ08qvUuz9SaQJwk\na+/a9tQ+x6Vj8II8bsFEgOj4Es0eu12anIEYFhLkPUt2fbVsT6D3R0EDlyU6zLPkUlYhDRIB5TPm\nvKx/uS4HmjFX1DDcDYIg5VWqGuwS5S6v/CinLUko1K5UdhLkcFERtR0R2SZaepDwqOymNo3uQxJp\nB+QKmvaz4NKLnZwoooHLnFo7QitTpPX+lPUAp3mQiEG5KpqHqezJZMqhB7iMQl3aLk0iyuVGIy+n\naC4N8gAubazCFGOZ0rTU6Rst2DXB3p9u4m+SGvPSAJCrQygX6POCJ7TeuXXyRsBO2hopCpHBqDLm\nUj0Gl7KIIENGSmXPO9lTrnXydmmg5PwDwsEFfkmZa5IgmnZpOUe7tlyyRmbXuugO0filtrl3D0nh\nPJZS2V2YC1Qnz9ufzRgkge1qQx3zExBBCh1/4dCh5VKfXZ7KLqP+yuuzbdqSS6bWVZXdvcZcWksT\nPEjc1FwBz9DlU9mNkSt5Fsr3MecdhkNDQ5aAnYQGXtqeQ0rHpz/59dl9voEelfibRB3fHkOhUBC3\nGjN18vyMebDlW6Fi/QsS8IqqblbS8q3cGAC+gWeYC5IWUG56BaOJcABIcoYFFc35fczDQofS8hWX\n4En5rL9cOE0WACpdK5KWb0E9BrlwmrTFVTgQJ2355kIDN8EBFyq7EdySiD1ms7Ytwn+Woqj1l2ab\ngxlzKQNx0FHArpwgsMyulTu1bsKhQZtQMo+2bc4vMwK89VOaMZcE6M0YuImOakId8xMQtkEg6QEe\nrjGX9L+2xd8khqedrZa0fAOorYS7AqdURdTONstrorwotVcfJ6O8AuEoNT+4EKwxl1HZg+05pBFa\nNxVtAEIauM1ckBxmpeJvfCr7gNXyTULHDwqncXszl8v6269XAnL+PBo4P2MeFrDjZgHD2VgXQUSP\nYsxnLtjtA+174T5PLowkI8InV5YfLdiUV+9P/nNmK5qn02nEYjERM8PFoSs3R9L+2R5lMy4SGXTL\n1Lr2MTd7bywWQyaTcVKWl9DAg1l/t/ODfktJ6z3pnuW93ysNtO0AWR9zeT/5csEFqT0l6QEeTcY8\nGmX5YHBB4tgnxCxKu0TTtUZcQucv3zKO5xsUCgWrVJZfY14uQMLVmqkm1DE/AVHuIOJEtMj4paik\nlPJTapS4RYg5DmGxWEQul3NUZXejj9liWdIe4MGDRO4QegeJ/DAMqrJLxd9kVPbgge71UJWK9wAy\n8TdbhA+IggaeEAmnGTq+i0NojFxZeYlc0CroEMpV2U37JN6aCKvjuzgbQSo7J+sfZE9IDUS3Vlzu\nTt9oobQtn5twWiwWQ11dHbPsI8xq4IuH2qwG16x/LBZDNptlzVE+n49EwM48Z3yHkII9Zu/NCLL+\nQ4FgIsAPLpS2FpTR8Sm4wM82m1p/iVPqXidvasxpvUtqzKOg4wfLwfjMhXQ6bZ3h8jFI2s8GkzXy\n1q92D3Butnlw0Kay077EH0MiYQIk0jFI9jV7HgGZKrvd6UdV2RVjAuWUVDlRuXDGXJrRCUdXucJr\ntHnTAuZkzEuFrmRGCYBhp5pPwQ6LZaXTfOE0W6xEYlTYGXMZlZ2i1F4dUDqdxtDQEKt1ni3+5hql\nBvhq4KUt3+Tib9I+5MYZM2OQ9WI3/eQBmXBakMouE+/xPkdGoaYglcS4Km35xnuewll/t1ZjGZGg\nVbkgD8DXjrBr/b3X+MEql0zmaCE8RxLGlN3HHPDGKc3IADLx0HI6ADKnNj38Z0ZovMqz9uXr5Pn1\n2bTWJc+Zq6J5sLWgiyo7BUWzbIewfKaWVxroMo/l254e65ICN3sqqAbOt2uB8k6ttDOJi6hxsMac\nnzF3yfqXF9OVJUoke3OpuLSbKrvEv6g21DE/ARE0CPgRLVo4Lo65XdclcSjLUegkYl2uNebJZDLQ\nZkXiFNNcpNMpYZTaxSiJlgYucQgpIiyt1S9nYLoYJabW0UUNPCEqKbBp4NxIuacsH8yYc7L+5Zza\nKKjsXCPXOIN8KrsJNMkolXRdFBRju/WQxGEK749c3YUwvZfzfspaBh2m2qKy074pe86Morn3ZzYi\np5a3Xsz7XZxa4xC6jMGdyi53amnv9bL+vA4GxWLRmX4cxRiMRgnfqbVF+KR1vS7rISzimk7z13u5\nbLNkHuT2lBGyjcViw/NwbO0p117srsJp4VabLokO26mVCthJsv703NG+RvpFHFX2ciWaSmVX1DRc\nxd+IMh52arktGUrVYDmOjBGdkahXUnDBpcbc1SgpzfykhTVRbswFeq9kEzYZ82CmljMOO0Po2mbF\n+5MnnDZycEEi2GWeJ5nCvy3+Vvk8FIvF4V7sFFzgU6jDz6M3Bl7WBkCZvYXnMIXp+K4t34DK16XJ\n2kSjlh1NgIRvqNusKJkoV7C8w8sC1krGPKgDIHnObBo4QBlzN/V/794q1zIoFotlmBkS8TczBslz\n5lIyUU44jccuoay/WfNuY5AFyF1+A1N+Y4+B312ltEMMl0Eof5aiyZgPOf2ONovSzZ4iNmjakYEo\ns0Xi8bi41t/WTpK0AabyFLekmWEumPXEKfG09Z/4Z49J/HnzQGxMXru0HGKx2HBpI5+BUm2oY36C\noVgsBgwCSYuTcMZc8uAHWxK50QCN0Adn8Xvf5SKSEcz6u2d6vcOQK5yWK4mOSqlTkswcKWWGaeAy\nJe2UM3XK+5PXP7tctgCQtrgymVopfQzg9zEnIz0cXHAVTpPWlgEyUcdgcEGebS41ECvbm8JOn6zV\nmBF/k9SclmbMpdmncDZW8jzJHKbRhC2Q5P3Jd2Rs8TfAW/turAZZyYRbuYHpn+39yQuehIOJ0tZI\npUKJfD0GO+sfTQcDHlvJrQtDqUYJZ71T54roRPjkQrTBrP+xz9S6CFbaDiHgJWw4CQLj1IbHICux\nNFR2XsIJ8J4B+n5JmWlY/4lrExomqKRO3m7BK68xp/NfWmMe1s5QKruiZjFSfR4vYx6sAZG2xnBv\nBxQeAyezRhlzN8faJVIfPkhSKd5BAgSj1NIACUCUH36AhAwQCo5I6qKC7dLc2qwAlDGXZDiDfcx5\nwYWwU8sLDpSjgbtQQmVtusLZp7hYVdf+kzsXZKRLWr65KppH0arMFrAD+IJWpixCXmMeFOWSl6jY\nQZLaccxLNSUAufgb4Dm3sj7mbo55mNUgFX8DXKjsQQq1K2tMkjE3VHZejXl435WeH1RPLN2zgHAQ\nix8QdW2X5sLycc2Y07261TbnS+j4LqVUXocYiXZSWLOHywSVJ5zsNUl0fFmZaTQdPVyo8IlEXMRm\nMgkCsillLEyXMqNqQx3zEwy2mjlg15jLBZa4h1mxWAw55vyFExSw41N+TI15kPLDiW4G6aISsRIT\nWaR7kdSmhY0SSZQ5qIQqE48DZD3Ay4m/uTqEnMPMbrMCyDLmrlT28O9I2epKRfSIuVDaLk0u/sYf\nw0jZZl72yWQAJcJp4RpznrMRFmSUMIoGBwd8ATuAb6iXBkh4+2spTVrubEgzmaOJ0RF/q8PAwEDF\n6y1ctuEaAJI9Z+XF3yodQ1iwUlpGFF4rEpFB2q+8IFblYzAaKdULsJcGsXjib66ClUBYU4K/3sNt\nT7ksnyjKIoLljTIWJRC0pzhnT2mQR8K+KJQwxlyy/slkSsQELaWyc5I17vpP9N0uezPta7KuT/mS\nfU1rzEO4/fbbcfrpp2Pjxo0l/+/Xv/41LrvsMpxzzjm44IIL8K1vfQu9vb1lP+fJJ5/EVVddhUWL\nFmHZsmVYs2YNjhw5Uvba559/Htdffz1aW1uxdOlS3HTTTdi1a1fZa7du3YpPfepTWLZsGZYsWYJP\nfvKTeOWVV+QDrmGMnDHnGwRSCvVIGQ85lZ0cAPlhKFPgtOn40dREcansthKqjAZu5kIiGhN+nsjQ\n5R0mXv9TrybIJUJrnicenbK0RtB+vRLYWX+6F1nWP5i5qTRQVKoMzHdqw33MuWOg9R8O+nHrNe1M\nMyAP8gB8p9Y8S+F1zQ0ueAJ2gJeFclXHt+/t7TDyGCQMDMMiqZUa89JAnKSlXVD8jUuXDFPZuUHN\nUgM8muBJoVBg6ylIA+x0bdiId2EucAOKpR0MZI5EaZBH8ixJs83lSwp4e6/R3JEkOsL2ENcWCe+7\nUgq1S6eecratTK/ArVNPuPWetLQQIBalS8ZcOgb3Es1EIunX2kuYQC7BBTvIo6rsZfDiiy/iJz/5\niW+k2Ljnnnvw1a9+FcViER/72MewYMEC/PjHP8aqVatKfsR169bhxhtvRHt7O6699lqcd955WLt2\nLa655hp0d3cHrt2wYQM+/vGP44033sBHPvIRLF++HL/73e+wcuVK7NmzJ3Dt1q1bcfXVV2Pjxo24\n5JJL8KEPfQgvvPACrrnmGrz00kvR/yBVxkg9YF3aGXCdqVIKnVuEmBawRCQqqih1FFR2bk0UEGyN\nIT0MAW8uJDWG5DjayvIA35kKz4OE/iUVTiutMZe0GiulI/Jqy4IZc+7vYOiUwaw/J3MT3huSyaSf\nfa0E5Wr97dcrQble7JKaU2kGLVzrL1X8pnsHvOfJrUc2t07enSYddjYyGS+4wGmDOFqgdVUqnCY3\n/rgdAMJzxD0Do6Djh7P+pF5c6RhGKtuQOlPGluA7tXadvPe6LEAiy9TarQVlexYQpLLncrmK9//S\nZ+nY95PP5/O+WBbgBaxkGXM3e8otUxvcFzKZjChZ41af7cZcCO8L3PLGcJmpfAzhgKFbcEHSypiC\npq7BBYm4dLWRHM0PHxoawte+9rWyG9SePXtw1113YdGiRXjggQf8Sbzzzjtx99134+GHH8Z1110H\nAOjt7cWtt96K2bNnY+3ataiv91T6KGv+gx/8AF/+8pcBeDTpm2++GfX19fjVr36FKVOmAABWAKY1\n5AAAIABJREFUrFiBG264AbfffjvuuOMO/z5uu+029PX14dFHH8Vpp50GALj66quxcuVK3HLLLfjl\nL385ej9QFVC68PnCaRRJlSo/uiraAsEIsUS9MppanIJ1GLpnm7k1UUA0Sqj0Xhcqu92L3bsvXo15\nabZA0mrMOCI9PT0Vv3+kjLnEMJFmzMMOof082U7eSDDBBaKE8pXl38q4ovt5K5RjLtAYKkE+n0cu\nl/ON83g8Ls56SNd1qe4D3zDp7+/31wHgOX2HDx+u+P2lPbJ5dPxyegX251YCk8k0NGl6neanWhhJ\nOE1Gl/TGxQ9+uJ1h9D1hKruMBh4MLvT3D6Cxselt3+9KZQ87QiaI5dbyzXu9svOjFgLspZoSpFEy\n4JdHvfX3h7P+MtYYrUupLULfC3jzQYG4ckm1MMoxxrzPdRPhc68x5zuE7qrs4UztscyYR7MeTICE\nrztRWtKWYndmAUr9C255Y5j5oFT2Ydx9993YuXMnli1bVvL/Hn74YeTzeaxevTqwIdx4441oaGjA\nI4884r+2bt06dHZ24hOf+ITvlAPA5Zdfjrlz52Lt2rV+JL+trQ3bt2/HFVdc4TvlAHDeeedh2bJl\neOKJJ3D06FEAwI4dO/D0009j+fLlvlMOAKeeeiouvfRSvPTSS9i8eXN0P0gNYKRWM7KMuWwTLtca\nyrsH2UEiUa8MC0zIsgX2BiY7iIBgTVShUKh4Ey0Wi8jlciV18hLxt0Qi6bf5cKOyy4TTXISuytVn\nuwiv0TMhyTbbB6Kktqy0vKOydRmuc+RmnuzvkmZuTJAnTDGubAzhAIn3d15t88hOaaVBwyio7IaO\nD3jBEpd6TX6AY6T6ZU4mM0jHl5RGjBZKS4BkLe1sHQDX4DI3s2TWimnxCLiyGqiLQWUidmEBO25Q\nNJytls4DIC/BGSlAIteKkZV92M8SV6PEtXTFu9advRd0zHkJm/DZQRlXads6SaY2l8v5NgzAZyBG\nX+vPt2XCwS5uyzdX/Se6VqpvApQPbsvapclrzO1nid6vVHYAmzdvxg9/+EOsXr0a8+bNK/n/mzZt\nAgC0trYGXk+n0zj77LOxefNmn6JO1y5durTkc1pbW9HR0YHXX38dALBx40bEYrGSz6X35/N5PPvs\ns2977bvf/W4Ui8WydfFjGSNltSSRRePU8iJSI2XM+TXm3vca9Uo5bUmyAdn1RNKDyPvuoFNbeX1d\n+QPdpaZJ2iKrlL3AmwsaQywWQywWE2Wbg6rs8t9Alm0unQtJrb+0bjZs4Mra1tE9SGuby4u/VU7H\nDwYXvL/LKJXSGruw0yfZFwYHB3zmAmDUsrklAVIDsTSjzKd69/cPlGT9Ad7zNFooV3IB8OZoaGgw\nEAByPcOkrIawTgpn3wpn/bl18uGMOVc8dKSSCW4QK51O+78fN6A4ctkHl44v0wqgey0XxKq0E0NU\nvdhdRDdt9h/APwNLNXuk8xAub5Rn/YmByBVDdOkBXk7U2I1FyROyDTu1UpswClV2WtOJBG8MdMYY\nKrs06y9nX1Qbo+KYFwoFrFmzBnPnzsXq1avLXrNz505MnDixLNVnxowZAIDt27f71wLAzJkzK752\n1qxZJdeefPLJKBaL/rUkBlfu2vDnHi8YSXXYZfHza8zLZ7WktW0Any5DRk2pEyKjXrlswibSznNq\nzUHiFuCw74GbbQ6zH6ROLT2H3mdFo2jOfb9dmwZIW43ZNeaS4EBY5b+ycYRry+hQkzm1MiPV1iuw\n/6x0LsP1pgA5tcdOHbgclRDgrSlbwA7wgiTU57cSGL0CGYskHCCRUdn7Ea6Tp9erjSjqswcGBgOB\nB/4ZVn6OpM+ZhI4/MDCAZDLpG8Dcs3ykAJC0ZEKS5bTbIwLmOat0zUcR5LeNeMO+k2tKcEuhosmY\nF0rWg5SCDfADu67BRPoMl9LAcNY/lUr7HYAqQfj8k6mB5yyHVPYsBrP+vITTSDXmXBZlWIRW8hvQ\nGDw6vqTMNB24B3nWnz+P1cao1Jjfd9992Lx5Mx566KERaxM7OjrKOtoA0NTk1Ud1dXX516bTaf9h\nC19bLBYD1wJAc3NzybWNjY2Bz21vb6/42uMFpUJZkvq8cLaZa5SMVE/LofyEqVc82hKN160Xe1RC\nH8G2b5WyF0Zu7yHPFicSSSEVnrLNZJTwFF3pOaTP4jgRpfXZCdbzXCrCJxcTDLYa49EpvffLsiZh\nKrxdb1opSmubXdc1z1EI0/EBvnBaWOGY72yUF5PiUlvts8pu+1buDAuD9sGw0jSXJk2BEWkrLruW\n3GQyq98yzVXLAPCcJnsu+FT24Bxx72FkKjxvjmyn1pWOz68xLz8P0vaIAF+EzwREZWdgsVgM6NVI\n58E+v6SOeWmNOe8cdrVFbHuKArvcJIGbblA5KjtnDIVQcMEI0VaikVL6LElswtIxHMuEU2myxk04\nTRKcsOfRuxee+FuY/WcE7Liq7OFn8QSmsm/btg3f//73ce211+Kss84a8Tq7NjaMsGFcybU0mbSQ\ny13vcu3xgpGpx+4RrUof/NIoN7//tt2nkO5FImDnQgO370EqnBaOLAKcw9A90h6ObnpOLe8wtL+b\nDkNuppYOEu+zeMGBcKbWo7LLlVAl4m/lWg9JlOWlLQTL0chjsVhE4m88NXDpGEwbRtupzQpLCmR0\nwigE7IaGhgJOLZcGXtrHXEbHl+oVAF6QpFwWsBbOw3LiQgBf/C1IZXdzarnnR2kdpmQMA74DJbkH\nQ2WXPesjBfl5zIzBEj0G73UZhVqa9S9VcHYRTuNplLiud8C9Tt5+P2DrrFQaXHCbB+8zbOE093kw\nwsaVzUM42yxjHpSrzz52Cado6PjlFM1dggs8W8i1HA0I2ubmWRo7quyRO+Zr1qzBpEmT8IUvfOEt\nr8tmsyM6UrRAiOb+dtfGYjFfFI4MonLX0+eGry2XGQtfe7xgZMNT0qZFRmUPH4ZRUNlTqZSoTj7c\nGkO6ARmBCfkYuJlaEyCRGYd0D3Z0U05lD6qyc1uNuVDZw05tKpVCoVCoOMJaGlyQ1GeXBptca/3t\nz638/UZ3IZPhiY650hFLfwNpwC5IqZRk/aUKx+G9iZttLidgx6WBu1JbS/dX/t5USjGuJfG3kRgB\n3PHJndows4J/BgbbTEop1G50fFctg/KaEtyMuT0P5jmrbM2XC8rar1f6fkPd5bNLwmcoV/ytNCDK\nd0ptJW15kqCUyl65LVLKGPM+lxdgdxF/CwcXTICdW9oht6eCDiE/4RTO+nsZ8yFGnXwUNeaFSMfg\nmjGXBBeC3Yr4jn21ESmV/ac//Smee+45/PCHPwxkDMo9VM3NzSPSxOl1orQ3NzdjYGBgOLOWettr\n6fX/n71vjbW0Ku9/9uXc55wztwMKCANiDUGtzNDBoU3QiBYioIBcBRE0SrWpHwzESpBQa6NJ/yZK\nKxJoYiBgKChGx0Y/UC9toAwQikUlCC2KoMPMnDmXObd99uX/YZ2139uz1noua++z5ez1hWGfd797\nrfddl+f5Pb/n92zdujVzrRWTszR1e22+Djp2bahNTYXLk/RCm542YMfY2DBMTY3D0tIWAAAol1vk\nMVhf8nWv2wJTU+MwOWnAi02bhkj3OHx4eO36EZiaGoft2817GBqqkPvQbDZgaGigff3w8BCsrKyQ\nvz8yYgaxZYu5fts2e58q+R6NRh2GhwdhamocKhV7MJXI3y+XzaZjr5+YGAMAgPHxQdI9FhenAQBg\n06ZRmJoah2Zzca0PZXIfSqVWpg8DAwPQajXJ3x8YMJve1NQkTE2Nw7Zt9l3S+2BKgiVjrlYrUCrR\n56PdcI8+ejNMTY3DyIjZ0LduHS3sF1gbHjZzYdu2cZiaGofZWbNvVCr0PgA0oVwuw9FHTwKAoQI2\nGg3y94eGTB+2b5+Aqalx2LTJrNPJyWHSPTZtGly7fiy1Joah0aiT+1CtGtbEUUeZdzk2Ztbp5s20\nPoyNDa5db/qwZYvZO0dHB7zft3+bnMzuC+bfo1Cr0df14KCZj9u2mec4Pm6e48QEbQzj40Nr/zV9\n2LrVPkva3jQz02j3216/ebP579gYbW8ZGTHH8pYtm9b217G1e9L2hbm54bXfM2M+6qjNAGCeDfU5\nrq7WYHQ0eWZbt06s9a34HLp99iXvaHRtzzG/z9lzzPiSeWbn+uTkiOgdbd48tnYf2jvav394bQwj\nyneU9Jc7162xbfcM7hjqdWMjjY6ac3962oxhYIA+BrO2t6fOwNG1sdBsibGxgbUxbFpbr2bPoZ7j\ni4tm3x0ZMb936NAkewytVhMGBpI9ztoUo6O0Ptj5bN8Dxxaxf280Gm1bpNUyqvyVCt0WabWaMDiY\njGHzZvMcqXtW/vzZvt2uSbpN12jU2zZdtWqBL87+0oJqtZpaD2YuTUzQ5nN+Llm7dHBQZpcODtqS\niO73kP+8VGpBtZr83tjYCLRaLdi6dZREx7f70tat4+IxpO1a68Zx7Nr8GIaHh6Bep9shQ0PmDN++\nfVLsH6THYN8Dby6tb4vqmP/oRz+CUqkEH//4xwt/K5VKcPXVV0OpVIKHH34YduzYAU888cRanlSW\nSv673/0OyuUynHDCCQAAsGPHDnjqqafg5Zdfhh07dhSuBQA48cQT29faz+3309eWSqXMta1Wq30P\n331D7cCBP45c9AMH5gAAoFZrwoED8zA3Z9CpI0cWyWOYmzM1oufna3DgwDwsLRmndHr6COker746\nu9aHBhw4MA/z86YPs7ML5D40Gg1otZLnXqlUYWVlnvz9Q4fMc1hZaa6NZWVtbPTnUK/XodmEtTGY\nw3BxcZn8/ZWVVSiXK+3rLaD3+99Pw9at4Xv84Q9GI6HZLMGBA/Nw+LDtwwq5D8vLNahUqu3ry+UK\nrKyskr+/sGAiaDMzS3DgwHz7eR46NEe+x+rqKpRK5VQfylCr0ftgDcyZmSUYGZkHGyj//e8Pk+rI\nzs0ZQGN+3jy3I0fMfJ6dpc1nAIClpWUYGBhoX99smjlK/b6dP3Nzpg+rq2YQr746C9u2he9x+LAx\nkhcXk+c2MDAICwv0+Tw/v+jsw+BgUYcj32ZmFjJ9WF427+XQIfe6nJoab//twAG7LzRTc8HQ4P7w\nh5kMu8TVZmdNHxYWVtfmY7gP6XbokLlmebkOBw7Mw8LCantstO8bjZNGI9mbbLBh//4Z2LyZ/i6X\nlupr+6u5wfQ0bQz2Oa6uZvf4uTn5/rq6asD1V1+dydwj/f661dzviL5eV1ZWoFIZSI0vmevbt9Pf\n0fJyI9IZaCJdnHe0vLwMIyOj7etXVswBcvAgbZ7YKKcdw+LiantslO/v32/mer3eggMH5mF21pwF\n8/P0PWdlpQblcnL+LC+bMVDPj4MH7TneWNu7rS1B68P8vPm+PcftGI4cWWKcX3UYGBhMneMG4PzD\nH6ZZ69WOgWqL2LXXbDah1WpBq2XsgJkZc5YsLNBtkdXVeuYMTvasw6wx2L1bYk+ZPae09h7NGJaW\n6LZMrZa1I5pN8x5+//tpGB7eHPz+wYN232yJ54KxRUpr+5LfJsT2zlptFcrlZAytlhnDK69Mk2yZ\nxK5tRBnD4uKidwxYy4+hVCrD6irdnpueNmOw+5J0DHYu2ffAmUuh1mkHP6pjfvHFF6Mlzf7jP/4D\nfv7zn8OFF14Ixx13HExMTMCuXbtg37598MQTT2TqnNdqNXj66afh5JNPbtPId+3aBd/5znfg8ccf\nLzjm+/btg/Hx8XZJtl27dkGr1YJ9+/bBn//5n2eufeyxx6BcLrdz33ft2tW+x6WXXlq4tlQqwWmn\nnaZ7KD3W8nTRRKBJnmPOpcu4Ka9yFVFL+aE2d4kSumhMjNIYmpyoPOVVXjs06YPJ79aU9+CrgRux\nkSSyXS5z89yzyvBSNfGigB2v1Fh6DCYloAGtVqudv0/pQ7IueSkm+TEAwBqVnV+LXVvbWaoG7hoD\ngHFCxsbGyH3I00KlolxJGSv5GLhUOq2QkrsWu5za2kvKttp0AwA3lV061xPqrkysUVY/Oy/+Jiv5\npk370FQwyNOPuboWecGuRChKq8egyTGXUdm1efJFVXaN+BsvnctVLo1HA68Xzg6pur7pC68Wu0t3\niCvCpxEOdaU3mnkedszzOi2xxsBX10+nR/Jsc/cYeLa5RhR5vVtUx/wDH/gA+vnc3Bz8/Oc/h4su\nugj+7M/+DAAAzjvvPLjjjjvgtttug9NPP709AW+//XZYWFiAyy67rP39s88+G/7hH/4B7rrrLnjv\ne98Lk5OGbvTggw/Ciy++CB/96Efb1+7evRuOOeYYuP/+++HSSy9tlz179NFH4ZFHHoG//Mu/hC1b\nDH37DW94A+zcuRN+9KMfwbXXXgunnnoqAAA899xz8P3vfx/e+ta3wimnnBLzEa17KypQ61XZ5erN\n1nDU5XebvvDE35Icc5kqu81f1qiI5o2SJMecNg5XuTSu0EdWqKPCyiPNq4lbMSLqgd5qtaBez5ZL\ns04ttbkcQqkzJS01lqaaJcZVtj6sqxWV5XlGbt6hBDDzCUvTcTWtmKArT566LvM5qwB8Uaw88Kgd\nQzKXaL+f11yQ9MFdxoonwpcviUnN18yDjgAyQ7tTzZ4feaEoznpvNBoZHQDu+Fyq6nKnVqYsnxV/\n04m3xSuXxqmfnQeAeOewndPy8oh4nrymTBf3DEx0MbLrnV6FIbve7fvk1TFvZM5gbtnTWOr4uhzz\nZu78s0K00vKzPKALIGuXJnuCzCk297Bj4NmEebuWOob83h9D/M3kmNfJQQobmLIAl1SEtgiQrP/Z\nRW1RHXNOO+mkk+C6666Du+66Cy688EJ417veBb/+9a/hpz/9KZx++ulwySWXtK+dnJyEG264AW69\n9Vb4wAc+AOeccw7s378ffvjDH8JJJ52UqZVeLpfhlltugU996lNw8cUXw/nnnw8LCwuwd+9e2LZt\nG9xwww2Zftx0001w1VVXwVVXXQUXXHABVCoV+N73vgcAALfcckt3HkYXWxFd1dQxjy1ORI02GOpW\nXrSMp17pqmPOM8y09R7zZSXSfQt/3yW4wt1E5eJvdrNLhNd0UX8A8y54pTHwaDOVBeKKmHPrmKeV\n5dPvguKY5+vBcw1E+7yyBuIwHDp0iPR981t5w4TrbOSjzValmatUnTi10vrS0uhT/j1wo4D5/TX9\nb7rTl62RbfcYObhg91cquFCcSxIjs1Mt70zZ+Uo9w+y61kXM47Aa8owr6hharVZB/I3bBxdrjL7n\n5B1zvghffn/ks++ye5a2bJ2M+ZYv08WNmOMAiVR0U+LUuiK13AoxRZuQ60zlAVUu+y+xI7jsPa09\nlbdLpWPIq7Kn+xZqFoSQMkHzTi337DHXNnNnR3KGUjR/LKBVLMcsE5GVCOCtd4uuys5pn/nMZ+Dm\nm2+GcrkM99xzDzz//PNw7bXXwje+8Y3CC7z88svhK1/5CmzduhW+9a1vwZNPPgkXXXQR3H333YU6\n5GeddRbceeedcPLJJ8ODDz4IP/vZz+Dd73433Hfffe0Ium2nnnoq3HfffXD66afD3r174Qc/+AHs\n3LkT7r333nYE/bXUXPQzDdUkVg1XLuU1exgOMtUrbam8OGVWZBtYNlqQqIjK6GNy2lGWyi6jkZvx\nc0uN5VFqAFsDXB4x51L6rSNi34Wkjnmejs+P/LjGIFMXBjDzgrOuixE0XZkuLuCGrWs+tTU/Bu7e\nhEfMNXR8LminrZFdZCTJGCRYJJOzN3SquRkB1LJ81jGPUS5NtvfmQSjuGJLIWHEMfCq7zJkqpkxU\nMn0LtXyUFECuLC+N+uffQ5wa4NKSbzLH3FVPnltdBXPMuSXfpIrmxdSQMpTLZcF7kDMQ89WGuOdX\nfgylUok9hjzDLomY82xC7RjsWi6VSlCp8FIL83OJC5y6I+YyoMqWA96wVHZX+9znPgef+9zn0L9d\neeWVcOWVV5Luc+6558K5555LunbPnj2wZ88e0rWnnHIK3HnnnaRr/9hbPtqQlGnh08BjUS3lqF6x\n7iYVlctHzKV0/HzUvps55q4DnRNtzhtGZgwc+hgO9NBLlGTnkrlXGWo1HsABUGSBSEuNlctlGBgY\nYEbM67kx6CiVWjo+gDlcOSixFuixY8gDbnQqe3EMXCpevMiNNdTt/qjJMedR6bTRWBf4ygcXinT8\nXog6FLUQeBRq62zgdcx5Tqk2ZUJaa96CC2kqO/cMy9cxlwJxyVqRzjMMiOONIZ/bLKW9ynRa6hmd\nFguOUs9hbZ58HpyQO7WaqH9+DDqWj7mXzp7iRv2L7AkpSJQdAzdYkwb4kzHwnNpY+k/2Hlw6f5qN\nxE1HSyLmshzzPDBt79ELoDK1rWvEvN+635Kc4AQR40bWarUaVKvV9ubL3YTzBwmXeoVt4tY51SKL\nfCp7fhOWo6NJThQvn0gatQEw40hHWSuVMpt6lf5tfsQcp7JzRV/MPXQRwrxhwomYr66uOqKkvChn\nUfxNPgauiJ7LMOEbiPEMdbl+hdTZwOn43PeQNWxkolz5dU1n8+AgjwZckKTJdKrlndqE9cWNmKed\nWt47yht/2nQuLg08f4Zm+8Bjl+QBDm2OOReIy54/uueoZyrJ6pjrGDJZMJG/9+OpJxo6fiz2Hpfl\nkw8SxIj68wMdWeFQLvtCM4Z81J+baurWf6ICVTi4oEmL4ApMJxFzqW2Ov4deAJWpre+Yb7CGOZRW\nnIF+j9UoirbFw1SW2waQjphrnVrqBpaNDpp78Dew7HvgIrxZcEFCncrnuUup7Pkcc+qBnqfAAfA3\nUZeByHeKkz4MDQ0yleVXMxFzvkOZp4HzxoCtiUqFnxJgqWvpPshzTnXfT99DGkGTpqgUhcW4zobc\nqc3n/srZPNm0CM17kIB+nWrF9c6LcmKOuT4/W0d7TcTf5EKJUqdU60zl9Rg0UVIu6yv/HrR7TswK\nBvI8d5kzlY7ayxzCrE0IwDnHXemJ3LmgsaeaqD1FD3ToFM0xsMyA43KnVkoDl2snFd8DN7XQMEjS\n2klcRlN2f5bqTmRZLBXye+yF1nfMN1jDo808RfNabbW9eaXvxc+J0glU5HPMbd8oLYmY2w2MBw64\nDsM4OVE8wZV8tJmziTabjQKyKMsxN8+BP4YsQALA30TzQnxc6jA2n7glPoo55jJKpVTAx5Wfberb\n0nQX8uCCnMqXpYXSo4CYcaY1cnXMAzlzIUYUMB9Bk80FKZW918ul5aOc1PWaRFT0c10bXdMoy5vv\nYU4tl8oeJ2XC7r/U9+ADgLjrXVqmy1VakEs/jvEe8oEKKZho7sF1arN0fHmevAwQzWt7mH/z2XsY\nA1EuYKerEGD+zbfHYqQ3SqsNYVR2MwYNg4S3P+f1M7hRfxebqE9l77eebS4aOFfRHDPgtaVmtNQt\n2zdKK6KjMvpZ1pnTbWBcIT5MOI2bD1Sv5+vBV9uiPJSWV3CWOub5+SQxjGwpjhg0cG4fVlfzOeYy\n+nI+CqgpNSZBy7GSb1Jl3YQWys0xl+c2a0W58nuTXVsxooD8aKyUjp+n9/KiLpiByY1kdrK5cuhj\nzDN+2oXUqc0ry/P2frwsn0yENS8yKE2ZsGlxMcAF+nqNBS5k0/I01TCkAnbFPHn5niOxRXBleS7z\nTUahzqdYmn/z8rNdTq2UvSdlW2Wp6PwqN2mHkpumowVIYjDv8swFbopLwmiKc4abf/Ps4vVufcd8\ng7U8Ug/Ajw7WarVcfp6ULpONcPLLISQLjyuSkSjLxyn5BsCn/LhyorgHiSbabMRGsvQxc28ZJZN/\noGNR/zITXCjS8c3nOqeW44TU66sZoyJW6SFpiRAAvvaDqcUuBxfyOXax8uTN37h5s9ap5bJ5rEK/\nrCYwtr/yDTxdFDCfJ8iNuPtqsfdC1CE/T5K1Jt+7ucBsETzhskN0mhL+HHOZAWvnmWaeGMaVPO2D\nD2LhdHwpmFgqlVisMf8YeOJv0ioKuGBXlV3HHIs2S7VipHOxGPXnpuXJ7al8tSG5vkmeyh4jWMMb\ngx27FCTKzyUNg4RfNSMfNJPpxGiCPevd+o75BmtYbjQ3x3x11UV55eZE5ansPHQ1q8rOpTOaPsRF\n5bgHSR4d5dXdjBNtrucMK54BbpD2cjtanRzoMmV5AD7tyCW+w3eKs44IL2Ked2p1uV3rkZ+dr8Uu\nLTVWVJrmjQEr08WNoCVOLU8h2Z1SoIkC8hzHWBTj/Ht4rVLZuXmY9jlonKn8GcYtb5hfr1wl7ZjM\nDGl5RAwcqFYHGACJO9rMBaHyegrclLTsGPjgQvbs4AKiLnCBCyZmnSHqXLLpTtiexdf2kM0lDCyT\niI7lnwGAPOofyybkB2vkdq0+xxxzzCXl0rJgXfreoeYqWydNTwHgAyTr3fqO+QZreeVkALNwNBFz\nrjPnzkWVG47cTdheJ1c+xpgHOmRRqsqep1BzhdPyRon5nP4usmPgqbLj5dJ4z7Fer6P0L21+NofO\n2Gw2leXSsgZenNrTfCaKJgIXaww6amt+b9ExQOSlxjTgQiPz2/wxZI1cK+inK1vXy475eryjbISQ\n71DiZxjfIcSYGTx2iZY1lqdQ08dQZPnI0z7i0cA5+dk+II7LdtLWAJfSwPH3yN1zcDFdaSqX+bdO\nwM7aItwc81hlKu29dNFmbo65DTjJBOzyKToAZgwc9qALIKGXgqxBpVJBGFFy9iD3Pax36zvmG6xh\nkbXBwUHyogGwEfMilZ1rlBQ3QLlhxVVlzyuaS+uf5o0KDbIor2OeOIT8qH3dEd2jRzk1dPx81Mb8\nW5JjHiNfNE9ll0eOpLldMY0CCY1tfbUj9EauPvqE06S5pcY0zkZCbc3SpLuV6+h3+tbfuCmWKuNG\nzN16DNyojHauS6t64LnNuog3d63hjgg/x1xHA4/jTEmZb1hFD22evLwWu8wWSd6DXNvDVWqMv/dn\n84I1uf5yQWBp/WwsUsuvJ591annnT6KdlAV1NSCPttIPv+RbTVUxw7Ume0Efhdr6jvnLxpf1AAAg\nAElEQVQGa3ikd4C8eQGYxW+juwB6uoxUlR3LY6FHzPMIbwz1SokqezIGq4TKLZeWL9NFHYMVecuP\nwfSNXjYuZo5g+h6cyA82BmmU1P5bSuMz3+fRl/M5dnGjzfTnmC9Xk753qOWp6FIKtSZSW6xjLo3c\nxGPz8JWi8RxzfqpP3mGSgRPZPvSCY44Lp2lSV/ggFp5jzneKs2cYvwZ4jGizbAwuGriOmaFjLmhZ\nPgBmH+9m2kfeqeXvOcW5JMmTx9c7FyCJ9x4kYCIWqeUKAifsC+lclEVqW62WOmKe107iihlq6fhY\nWgRXwM7Yczp70HxPDpCsd+s75hus4U4tPacKwCx+Tbm0PELLz0XF0FGecZZEzKVU9mKeoqR2KE7H\nl9ViB+A5lC6U2vxNRmW3tdQ16KaEjoipgPIN9byQH0+8B1Nll4JN0jWhNRDzuf6mDzrAjS6clhVe\nS/eBM4ZqtdrWPOCDftm9hSvCF4eOj5dPkkZC7b24EWVNJLOTLW/EJ/O0e85U/hlzwRfsDOMoaWOO\njHQMcuG04hgGBgZUALuUBp4X0dM51hynVl8NI4na69Lq8uud+gxwJW6+cCgARseXMR/MPSTggr78\nrF0PNgVIA7bx3gOu/2T6xk1vzNrnGrDNfF/DouGJ8eaZpNrAn/13L4DK1NZ3zDdYw6lbdKS+2WxC\nvV5HqSZSyqvUAchHhAA0OeZ6KqJEER2jXvHrbsooO5jegIzKnt1GOJugK0+e04dGA1eWpx8mePk9\nbk6wRvytmN4hjTYXRYjo6zIv6sgFF1yRTPmByncW8gr9vL0lT0XnlnqJ45hn51MM9gQnCohFfrhz\noZMtn/ZhlbS5wp86+nHWiE+inDJw2tyDEzF3j0HLDtGCAzHqmHNBLCm7xM18k4MLCUhD60OeuSBV\n0s4DHLo8ed170KYQAejfg7RcWn5N8ceQFx3j7glFm5C69yd0/KwtIrXN7felQTcAPriQt+dKpRKU\nSiWBLZWdz72QhkVtfcd8gzUXDbxer0Or1Qp+PzlEsAinNi9MbnhKnVppPhFW65dDH8MiJvKDREZl\nd+XXmXvLnGIAHsKKjYGvJl7PHegyIznv1PLHIKcvuyLm3NxmLPrEEQ2LkScvLYOIG7k8cGF1NU+D\nk5VaKdLxdQYm5x756JNWw8P0R0dt5QJFnWz5tA+A7gunFfPc9VEdSQ3wfKQ3fe9Qy5/lXIcQm+sm\nYs4DRGMyF7RMJXsvfrRZXtlE69QmlXbyTi0VqMIj7ube3LmURJs5zhSu00I/g3HtJFmwJh900ji1\nMhG+oqI5PeqfLTVm7seJ+rvODvl6snOCPh/rmT3B3o97fuZtmT6Vvd96tuGIFh2Vsws/u3nxDsM8\nsmhLxegMAuuY80Qy8rmompJtEipiFh2V0payjog0HxeAfyBjmyiHOeDPMacfBlrqVPp3AXgAhyul\nwPaN0gwdPyk7Z52y7iqa4+XSpMYVN9ocJ4JWR+n4UoAkBk2am6tYLCepSxWy9+KndsjfQyeby5ni\n5tBr3pFbwVm3XrutKQFQpB/rnKmqAEzUiI7hdHwumJhNhZIIp+nBhSJDprtjwHPMZQwSez9NkIBT\nnQV7D9zys27NHup6wgIdOhp4EnCiCqfpxpAA09k1yQfns2AdAC/IkP4+tw+utIheSMOitr5jvsGa\ni+YIQEPlEnGJdLk0fmQu/T3bH125NOsE0NXABwcHU7moOhqg/bempIN9pisrvDrm+Wgz/yCQi5a5\nNlE+BU6eY16MmPP0BlzGlYaOzzWuXOr4mnq8EgMR+77UwOOKcmHvQVK2B5/PMvEbLk06Rs5pfl3z\n6fwuWmj3nI1ONtcZRo+Y46krAHzBrUQ4TSeUCMAVf8PmmSwvWFpa0Me+k36fr0iO61romW8x2Bc6\nkEcXqaW/ByzIIN+z8ml1umixxqbkplIl70EGLrhEJTWAaKJo3h06Pj6X6GPA7OIkyEBPl8VtShmb\nKfn++p9d1NZ3zDdYcx2mALSF48phAZA4tfHK+SQRc3q0OUsBj6fKTkkJcBnPnD643gXXKMFp4HRK\nfn4TrVbpB7I/2kx3RLR0/PTv2n83m03Wu8Qo1BykWeco6Oi5rVZrTfxNE/XXGclxyljhJd+0VHRd\nfrYuTUaa+ysvAaV3NjrZMECRl5+td0RiqVAXaa9yZ0qq/i91CPEzjF/HPE9fTt871PIOoRxMzOqs\nxMgx54LTSUqBno5frZra05S93w/E8fLks3uv5Dlm1wN3DDqAxLJBs+e4JlgjobLjLErafLalxmzA\nyfSHPgZsX+KlR2KgJz/HvEhl54M0RSZpn8rebz3aMIGKZOFQqOxF1Ue+ijYebdaowUrqZ6c3YK6a\nuI/uSTlIYpQk0lKv8lEf82++6BhGZefmOeKiYzI6PT9HXQeS5MV7ACTvslE40M335QJ2nD7Ya7Jz\nyYILvHWZ5GfrjWQJe0IjiIXlvUpKjWFGLkeEL/09+f6aj5jLo7HcSGYnmza65jI+AbopYIqNYYBN\nA49JoY5RP1tSx1ybugIgV5Z3i45pWGfdpoEXNRc4oGgMpzY/l+w9+GkRsjHgQJUU5MmOQVtqzJal\nDTW/U0u3a9O2ub2fxja34II04CQTf8s75nyAJAuO91XZ+62HG7YBcqgmLoEMAG20mU/5wRxKesS8\nltmAAaQHiexAjmFYdaJcmkSdPq/KrqVOScql4UrccgoZ51DHy6Xxx4AZRvza00VVdtoYfHoDMrSd\nw8QBCOU20yM3cYQppVHAGICbARfyaTaayIusjrlsLnW66SnUmCOjO8Okuc1SOn4MZfl8tDlhZugi\ntRojXptSFiMlTVLHHBPsooM0Rfoxj0FYLLPFAUV974EPsGfZD5pIrWQMOoC+CJDwosXFM9T2hzaG\n4tnDzTFfXa1l3oHtgya1QxZwwtYDfX/WpEf2qez99kfX8Nqj9MUfi3ps7pFfODxkEisrwRHJSIML\ntj/0PrjBAdomjNexNX/TIO0S6pWOQq3JB/LlZ/NyozUHgT3UZdRWLFrA7UOz2RDPpXQ/ccMkfA8f\nuMDPL7Oq7PoxSGp4S4279O/oI2hycCFftk7KAJFS2bWgY6dbvCin5h1lRZJigCd65XyZU1oUf9Os\nFbquBJ66wtszihUM4qiy6/Lk+ZUkADCAXV72lLP3d6Jsnb2HJurPGUMscMHWLk/fQxPo4Nh0LkDV\n9o3SsIg5h06vTcXCxiCJmOdtSh64gIOWVOZCL7S+Y77BGobwJgsnTJfBFy7vIHJFanVIPV8kIx8x\n59HAcePQ/E13kPBLlMgE6HwoNYfSny+XZnLDdAc6pw9mI9fQwM1BkM3Lohu5mLK8hBorNUrMdbpI\nrYuKCCCPAvKF29xRQM580qjju1J9dI49F+DAy9bponi6qD93PnayuXI5dYJdfABoYGCgwGrQzHUr\n/kaJNuMpE3zByfT3JEwl8/3iXKcY4ckYMEBUJv5mHSvdGUjXionh1GLRZo4jkjAIZcw3X9k6jVaM\nPlLLGYMeqDLR5oGMHWDz3CnNF6yR2oSJqLE84FSpVMRlINP9kb4HbpWcRqOpKpfmUmVP96/XW98x\n32ANL61BzzGPkxuNI7wa4zepWUndwGqZHHN7Px1dhn6Q+MR7+Ae6jsquqYVbr2M55mUGSq13CF2K\n5hqElhcxz+YEZ79PB3q0Y3D3gQ4uaFIK8oZ+HAE7/nzUOfau/Gwq88EaufISUPmydVw6vmuP16V2\n9E7E3AXMUvOz7TvCxkcHl/PMjCrz+24DVpqPqhVO46euYDTwgczf/N+P4UzhDD7ue8iXFjR90Aqn\n8Z6jNiUNA3YpawID8qQpQFpbBBNkpABNvjXNKd1aDNboys/yqOyYY26ZoPQ65vlACccec+XJA9DW\npKtCQPpvoeayx+jggh54Xe/Wd8w3WMMQYg4qF+MgckXGuAgxVrOSnqOHiWRIBCaKGxjlIImRE+U2\nSrhq4nL6l5vKrosWmD6E72HpSTh9mT6fimOgv0u/gB3dMNGUetHmZ2Nl65L5KDsQy+UyyyhInFod\npVJTi92lDK+jtvIBN41StWtviqPSvP6GDZ6OxWc1SFNXAIwRn35HfPAEExnkn8MxlOXzwml0JW03\nFZ03hiINXJsTq6Oy06P+WkA0/Tv5M4xfCaJo09HSmNy2DHcM+eegidRyqlHg9hSfyq4L1mBsK/oZ\nGqMGeL6yir2f5uzggAsYQJL4F/Qc86L4G9+m1OgOrXfrO+YbrOERIbpIk38T54nvSMUZXIYZAF2V\nvVbTbWB+VE52GEoPdLx0HcUZc+eYc8r2YOJvfAVqmSPiS2vgiOdg1Cl6H4rRAimdvvj7PM0D7Dly\nkG7s+1zHWl+mS+ds4DQ8bvRK5/Tp8jXr6rlU7INO/4LLAOlkwwWG+FR9zd6bNx4lgKb5nkwbI0a0\nuV6vt6uRSL7vS4ujRTn17wETTuOkpPkBdtlakTKN5KJjuvWOp0Xw9n7rEGbTwbSaCXRwwHd2cKjs\nMQSB5QJ2RbCOk2Zq7pFl8gDIGCS4TSgDdTn+hb0HxsLUAtMAvVHuk9L6jvkGaxgqxxFOi1FKx5fX\nxfl+1iDgib/lBZYA4h2GlHt0SnCFVyLLR8enPwcs2kz9vlb8zZ/TpIn6051aiwTjETTZc5TTwLP1\neKl9cKVFAPBp4HlaaBwhJVot9rxTK3U28pFMbm1mnbNRLzganO9jefJJ5Q0ZtZWrI9LJhueY0+uY\nY4AkHwjL0l6lwp045ZNyDuv3nGIFAy7Lxz3XOSC/ln1XqVQKecGatA1eXnARyJOIl8YQHcPo+N0E\neYqBjjjOFE/RXBfowII1GhZlsiZl+y5fOK0T+dkSOr5mDEV7jGdH6PfG9W59x3yDNbxOL51+hpfS\n4R6mLvoZb+FJRctarRbUarXolB8J0p5+D6VSCUqlkoCyI6NQ4wAJP/KjOQiSPsiE0+Ioy2dV3dN9\n4Dm1OmdMMwYf0EO5Byb+JgEXyuVyzkjuXrQZoxJKVZqLgopyxz6Zz/Qons6xd0djpSrNvWTY4LmY\nA2TVXXyt8NI2XO9IVy6NTlvtBDMDQAakYRE+LTODp8dQdAg1QBwHnMa/z8/1x6OcukCH+ZtsLslA\nHrk95WJbAfAcQmktd/s7eLCGm56C7bt0Wyb9DJKgGTViXhTjlTBIMCYoZU35GIyU92j3cDzHnHf+\n4RVm1v/8orS+Y77BmhbRwoxffj5tMbJmVNl5BxGGtHM2cU1ZCT/lR4YscvuAUeA4fUgMeDmVHY82\n0zdRrVPrN7I50eqiYUS9R0Kn1KmB42PojlOLVVuQiEEVFfrpgBtOqdQBJBKQJv279n7cmsLZNcUD\nu4yyPLYeNOI3dPAVT2voHcfcB0LJdVJ0Zfk4eZiuPkhyajXpBnktA3uPGLRXWtTfzfKhK8sX926t\nGriMXRI72iwpb4jZQxRnype6wgFI5HMJY1vJqOyyZwAAzmBNDOE0niBw0anl5GdjqYVc21wrapwF\n6ziBvyJAY+/HT08p7q29wPiitL5jvsEahq4mKDfHaNMdRPk+aCmvdvOg9MHmoecPEpkCp4wGjo3B\n/j+nXBpGgUvf39eSHD8sWkAX+iii/RwKm65+Nk5r5RvJGHWK2wecQk0vtYKJxsSJ1NLBBbxsnQak\nqaqU5WW0Uk3uMCZAJxmDLk8+S5OWgTRSMUHte+h0SxyJ4vik6VixGC4xqOwcA1Ya2bJ9wEWWNCVD\n+TnmnXAI+QA75hB2J9qcX++2P9zUFa1DiEd66SBPjPKz2vzs9WRfaHWDtGCdvUeRwcgZA8YYkwD8\nOv8iPRdtHzS2eS8xviit75hvsJZEq2WoXMxyadnFX44SHaQ5UkUnxN5DZ1jR+4A9RwAuMojnyQPw\nNlEsrYHjkBYPAjrI4lKWB6AZBXiEko7Qmnu4y6VxRPQ0KqD5PkjAhfTvpv9NU2X3HWZU46pZiF5x\nhNMwo0CSUqAtvZfvgzZnldsHU7ZHn/uLMw+0zsb6i79pRSsTJyBelJMLxPn2LR51F4s2y/Lk7T34\ne07xDOwuc6FIx+c6tTgoKosQcvd+w5DBABI5KKsFF7igLB7159tT0vWAl5/lphRg64HvEEoZX76A\nE2dNYmkR/FQu6d6K7WsSRq6chRnDHlvv1nfMN1jDJr5V9ZZT2fm03eI9OIepTjUYc0LsPeg0PtsH\nWWQMO0wBeEaF6yBJ94/SB1y9kv59DXXKgkFSwS5MfIdLA8dymmQOoW5N4MwFGrjgi6DRxoC9B76R\njDEP+PsCtqYo69rn2HOjgNlUH2r+so+5wDHw4kQBZWCVf39d/4gD9o444DJmQEveEQae8AGc4hi0\nFGpdbjM/PxsbQzfrmGscEa1D6ANYNNHmarWqinJy1jv2HiR6N/h74K6HIgNRWi7NsgnpY6gh5dL4\n6Y0xFf45a9qdn80Zgw6gx/Y1jnaGK2DFYZDgGlQ8RtN6t75jvsGaH9EKC0z48pI5eWHp79l/8/Pz\nsuVyAKjOnIuCzUdH8yJR6b/5v+/bgKjRZuwg4Qh1+HLTNGPgRJt1Ti12IHMEiOw9itQpjlPro4HT\nDUTtGNK/C8DVG8Bq6fIOM1fpPC4NXBpBw8fAQ8pj5S9rHSYNaIjnzfKdvmw0lgfSdLLhwmmxIoR0\nQDH9jrhOAL5v8SPmGgG6fJ68vUe3AKAYNPBGo4GwxjhGvC5K6Utd4axXjPnGTWPSlq3D0+qoYyiy\n94y+CG/P6oQtwkkN1JVLczNxeKKbspQC/zOQR5t5KQXFfU2mO4ExSfVzqZ9j3m892fxqsFLBFild\nNLtwqFEpX1kKTmkNTImbr14pQ0cx49ncj4Pw4nldADwqu5bGFyPaLHVqfQcJxzjDIr3UeyQgja5M\nl8YpbjQa7SiHbZK5gAMkdDojRmWPkaLCUejXOivme9J17XuOtJJvmLNhgEsN86C7gladbLg4nc4h\n5Lwjc52L1RBDKIpTLk2eboA75vyyRDG1DGQgltwRwfOzdQ4hN9pcr+NAXLdSV1wpaZyoPcbek5Sf\nxSjUes0eCjjRhGazidLx7d/DfcACHfRz3C+62a1ASZw8eb1/obOLAVz+wfqfX5TWd8w3WMOjzRJn\nTJ8DqUepseigLMpq76eLFtAXP2Y8m//nUNkxlJpPnZLnE+HPkWPYaEXH4uRlFSnYnPqjlqKFsSek\nJUK41FhX9AuAF21OvwdtLXbThziijt1iLvgMC1rUAxOPo7NYXGk2EiMXExGS7q+9JJ6jNf5wVgRf\nOC3/jmRpGxhzTZqfHYPKrs1ttvOsOwAJHqmlV1GwZzUW4eMITmodiSLIw08p0FKoNUrYrhrgOpuQ\nw4Isfh+APgbfvmvuz3FKpe8B23fLZJAHS8sw95Ds/UVbRgqO88Tf3OACPZ0Ms8d6h/FFaX3HfIM1\n38LhldLR02Uw0Rgauuk70GVRLXMPjgBdHCo7Bg7QUwJwwZX0/UPfT3/H/FuXE8XtQ+LUyhxCrVFi\n76EZQ+LUyqKkrqgLV3RMNwb7HDUl35qFNSWL9OrojNkxcOn4RSM1icZKqa38iLsmx9y3x0udDe5c\n6GTDcuCT3GbKnlEET2RK2nJHBusDL0Ko05Qwv4NrlHDrZ2MAuT5PXg5IStTAY+b62/+nO9Z4uTS+\nPaV9D8U0JI6AHbZnAfAitXKntlipwd6Dd3bk3yMnlUrnEPpSCjQMRtkYZGcwJoYoE5cuBqwA9EzQ\nXji/KK3vmG+w5o82hHPM4xxERWdKEiXFDStNLg7nMPRFC7pzoLsiHgA0dBNXpKVH/d2HGYd+jNWe\n5r9LacTD/o67jnm4D7gaOP0wdM0FTuSn0WiiIA8AzTBKxoA9Rzo1FmNPUAXs8FJlHJCnOIZkTfKM\n3DSjKFZdYw7IoxHlwgEOHdWbqyPSyeZjK3HGh9NmaawIDITiOTJaKjsGANH3XdsHXcS8CGJxomPa\nCCGAXknb/x405zgdkMzrFdj78XUxZEECn0PHs0XkaXXxQJ6iVowm4s6jcbtTbLRMTtoYivPAfp/a\nB996kFfJ4bNosJJv6b/7ml13OPuPtqbWu/Ud8w3WfEabNEJp76ehi2ojtRxELIm46MeAOzJyZ4wj\nXOMvl8ZBqWVOLYaOmvvx6cfSSC2O8NIjnPZ3XCgzh76FHQQ8+rLcuMPGwEkxSRzC9Bjs9znPMXuk\nVKv0cmk+CjWPwqbL9bfOQXIPPhNG7pgXx2D7QH+Oxf1NH43lPcdONgMAZd+RpFaudHwuxlW1ygen\nccEumTPFTeExzpQsOgeQrAe8ZBsdGNallOG12LkgFl47mmNLaBiEeLS52WxCq9UKfh+rECMB2KXv\nodFoQKvVQmuAp+/vv0ccBiJOZZfbY7yAEfYeYjAQq0R7zj8GHkCBRf1lAEksBkr67/57uPf3fsS8\n33qy4RQ6ySauE04rl8tonjsv4l1UDebQRTHqFvUwxGlLeqdWXy5NS73i025dmyhlPmFlujhOLZ6r\nSWcu2Ht0ijrFMbJxGrh8DPFEyzhGMj4GmoGJgSx8dXysjJV2DOn7+5qvrjFlPmN6BfYeMaitUuOK\n+xw72TAQimP8YXuGBNDEKdRyVoMsYi5fr24quxxgtw5mt2oW46XGOOCCNkiA20Oc8k4uNXAAuWBX\nrKi/dN/l98EXaZWDPFR7yhVt7qaAqjalwAdOpP/ua770RinAwSmX5gv8AciBUy77b71b3zHfYM0X\n0eHRZeRIuxHOKVJe7d9CzWfUaMdg7kGPzskBDh/Srs/rkqL9cQ50TrTZXS5NKlbCyae193DRwCl9\nSETHZEayWxWXQwPX5pgXARK+Y65Vt9dGm4sUNlvGSqM3wMvP1jl9WOTI3o+f+1sEPqXggu1DLzjm\nmJ5CMj5ZKpQkIqOhsuMpOHQD1hf1p6r/Y1R2DjDsc2p5EUZsntErGGgAEl8N8G6kpLneA28++sAF\nHUAiFXBN30/KGov1Hjhnh/sMlYqOcZiceB+oTBxfjrrpAz3qLrXHsLNDm96Svp8cIOGx/9a79R3z\nDdbwqBR/A9TR+HDBlnT/fA2LUHKUJ10Rym6KjvnQUZ4jlD/Q6YaRHx2V56Zxy6VVKpUcdTgOwtt9\n8TcZhdploHJp4O4ccy3KLM/Vl6wJqV6AfQ+aCByuN0Cn5+KGjS7qb+6hLQHVPWej080/z6Rq4Px3\nhPWBCp7g600HTksAduwMpKfPFMegLb1q+6BL+5AwFzA6fufrLttrXE4tbb0WgV1eWoQbIOHpk+hz\nzGNT2alzIdkz8YCRFGDnBGtcyvB0gCRmnrws6IXtaxYcl1abSPeHA5Bg/kEvAMuU1nfMN1jDNjBe\nVAtf/LxyPm7DqjuRNTxCKRGYkB8krqgLLZ/I9KHIPJAg7bhxSHcoMfoYvQ9FRVre94sCRJx8Wht1\ncYEL0hJXsihp8Tnqcsx5AIn9zeL35fNRXy5GF/W39+PQe11sHl4pLixNh25cYWPgCoth0dhuGLmd\nbvp5hinvc9Y77kzxqOxuZXmaQ4gpy+vSmGx/OOd4vg+89+B2RHjr3UV75aTFSfUYXHoDtDQkHxAH\nIAdZJPYUnp5IOTvce5a5v4yBKGPO5c9AWkqByybk2AE4uBBjDHHy5KVjkJVbK7LOpKKW6ftJbfO+\nKnu/9XTz0/hkUS17D15um9yw8lHZpQ5p+n4cgCIrfMPfwPDnGP6+rekYYwx6+hi+idIMk2Keo32m\nvJSC4nvg1J3W0L8ww0Siyo6J6HHo+O65QKGw6UT4APy5+pRDGXsXEuYCTm2lgQt4rj7fqZWW4vJF\nTTQU4wRc6A5o2MmGvSMeXbK4Z/Cie67IVFmV29xNxlUM9f94Yyg+R2kalPk+3RbAKdRxznFpzeV0\nf6Q07BgOITVP3v0eusdAtM86Dy5QAZKQU8sbQ5G9p0mxpAL0LiaoJGiF58mH57PbnqKdHW7bnC/G\ni1Vn6VPZ+60nmz8vTFYOwf4/vXaoNqdKGy1wb4C2f+E+xEGppYdh+CCRRQskII1OqEOXJ48jvEZY\nUGNYcYwzzDCRGKhYeoiGys6j4WFoP78uct6hTBwmLROGI4JUZGDw9C/0dHwtmwdXaeY5TNK0BBcT\nhhO172TD2CFa5V+t8Wr/n7NWXMry3RBOcwNAHPX/oiMSS3SMJ1iJC6BK+8AD4nB7iB5txmng2txk\nrfCn/X8d+4LjTLnXpFQAz96Do0Yew6mVMkj8KQX0Z+imgXNAR1mKjYt9QT2DQ5V+OPZYn8qOtJmZ\nGfj7v/97eM973gN/+qd/Cu973/vgrrvuQh/Md7/7XbjwwgvhtNNOg7POOgu+9KUvweLiInrfn/zk\nJ3DZZZfBzp074cwzz4SbbroJpqen0Wufeuop+MhHPgK7d++GM844Az796U/DSy+9hF77wgsvwCc/\n+Uk488wz4fTTT4ePfexj8Mtf/lL+AHq04VEp/kGmKVFijBI9MpmOFtg8Fs5BhlG3qH3Qqlj7jBLN\n95Nos/Qw1DEXTB840Wa3Iq0mr8ocBLKaxun7rWe5NGMYcfLkXfOZTqnM1pPn5phjDhMP6CmVSjm9\nAfp8ThTNZRE42wcXYMcRFpODC/qoP2YcyRxXWU3gTjfzjoogFkB33pGPfhxjnkmdMfv/HCBOk/aB\nMQc4QonaMfgqGJg+cJ5jbK0YGsAeooFLAU3O3u0Xe5QDyxx7Sgsu+Gng9IBTDLtUS2UvKvxzqexx\naeASOj8ecOpOnnxfld3RFhYW4IorroB7770X3vSmN8FVV10F4+Pj8I//+I/w13/915lr77jjDvjs\nZz8LrVYLrr76ajjllFPgm9/8Jnz0ox8tTOa9e/fC9ddfD4cPH4Yrr7wS9uzZAw899BBcccUVcOTI\nkcy1+/btgw9/+MPw/PPPw0UXXQRnn302/PjHP4ZLL70UXnnllcy1L7zwAlx++eXw+OOPwznnnAPv\nf//74emnn4YrrrgCnnnmmU48onVrPvVLHoW76BDyqOxxo1L2/2NEenkCE/HR0btETGQAACAASURB\nVG4JfWCUH05+nU9NnNMHTcQdM6xMH2j0L9+Bnv67r/mU5XVGNi96paPhFaNPPMPKlBl00UqpUcA4\n70GX++vam6TOhgTk0UT9DXsiG41NwIXOC1p1utXrvog5RwdARnVMzo/iPKNqQviV5Tm12LGIN2et\nFPcMmyYVahjbiLfe3c5QDBq4FJzWptXZ/+ftuzGou0W9m+44tfj3JVH79XsPOEAfK1jD0zuQ2YQx\nABK/TahjwXDYsDFSCrDUwl5gfFFaNXwJv91xxx3wf//3f3DzzTfDhz70ofbnn/nMZ+Df/u3f4Kc/\n/SmcddZZ8Morr8Btt90GO3fuhHvuuaf98L/2ta/B7bffDvfff3/7+4uLi/CFL3wBTjjhBHjooYdg\ndHQUAKAdNf/6178ON954IwCY/NvPf/7zMDo6Ct/5znfgqKOOAgCA8847D6677jr48pe/DF/96lfb\n/friF78IS0tL8O1vfxve/OY3AwDA5ZdfDpdeeinceuut8MADD3TiMa1Lw4w2TmTN7xTLjV+eYeQ+\nkKV5Zeb/6YdhsoHokHYMHTV9aBb+lm6+2tfUPmBIOTfCme6zbRyUGJ8LnBxBl1FAUzQPlxjhRG6w\n/Gx6iRIpuACAG/q8MejABVdKAEcduNHQiXrFohhjTjG1D9o0G3cUkBP1dwOf3YjGdrphc503z2IJ\np2EMF+o7aiKApr6sEPcM9FGofeePqw+WccMDRWURQjdzQZJShmmUyPc9apDANQZ9tJkTcfelJ2qi\npBJAsgguaLSPqlXaewjZhLznoGVfFPcVDh1fI2qMUdllAH/RNufZY267ONSSgFFxTfcC44vSOhIx\nf/nll+GYY46BK664IvP5+973Pmi1WvDf//3fAABw//33Q6PRgE984hOZyXj99dfD2NgYPPjgg+3P\n9u7dC3Nzc3DNNde0nXIAgIsvvhhOPPFEeOihh6DVagEAwKOPPgovvvgifPCDH2w75QAAe/bsgTPP\nPBMefvhhmJ2dBQCA3/zmN/DII4/A2Wef3XbKAQDe9KY3wfnnnw/PPPMMPPvssxGfzvo2LYXOhcpx\naLe4mji/D1geCkckQxedwwQm+AeJ9Dm4leXjHIYcA9f1HKl9cIETPLEQ2YHsM6zSf/e1pFxaMS+L\nNxfyeVmcOuZFQ18bqZWh/cXcMvN32nzCRM/S9/e1hI6voedi4m92TdAjmen5WCqVyIwiLeho7uHe\n4/XClOtPBcScRgkjIP2MOalQvpxaTjqXjjXmcqaowmn+SK2cuktfK750LN6+q6GBu2mv+gghPXXF\nVZmEBkhiALsE5JGlrricWk5Km5/K3nlwwcW8k4A0GBNHU6WAysQJASRaGriURWPvIa2SkL4fh4WZ\nTYmj+xe90DrimP+///f/4N///d8Lh+cLL7wAAADbt28HAIDHH38cAAB2796duW5wcBDe/va3w7PP\nPtumqD/xxBMAAHDGGWcUfm/37t0wMzMDzz33XPu+pVKpcF/7/UajAU8++WTw2ne84x3QarXa/Xwt\nNF9kjXOQaaIpnaWyc6Ksmk24+By0eWHm/2mGEVbyB6C7tdhD6Ca1Dzrj0DUfqWqsrhrinKi9jgbu\nBrt4dcw1hhEmOsYxKnziO+bvtOegibrEYPNg85GTn+0C/ajApS8KyInGugTsuiEs1ummPT8SR6K4\nb3HUwHEDmrZecWV5upaBa+/l1wDXOyJpO49XP7tT5ziH+YY5tXHWCmcMLp0VKQ1by5wzfaDSj/Ug\nD/YuJaJj2vUQJ+pffA/dOL/iMBeKtohd39pgjS5Fk2cTuu2QPw4qe1dU2aenp+Hee++Ff/qnf4Jj\njz0WLrjgAgAAeOmll2Dbtm0wMjJS+M6xxx4LAAAvvvgiAAD89re/BQCAN7zhDeRrjz/++MK1xx13\nHLRarfa1VgwOuzZ/39dCwyl0HOMXzwvjiRNhEXPOwnM5U1QFzjgOpfkOFiXVRcYAwhtI4pDqo82Y\nUSI1atL3oAI9urQG3Dij5uS6niOPfqylgbty9c0YLBPI1/CcVfphhI2BE+n1OaTp+4fu4Rawo4ty\nFetL0/UvcDp9DMCNl3PqivpT5oKfkaTT4OgFw0ZLZdc7hPg74lHZ3Xny3Shb6kv74PTBpWXAYZdg\nADlN7LHIVAKQgaJZgF3CfJOxJ5J91+VMdV7vxrdnaRk26b9T7iFlIPoB0fC+6WZvSII1cRmIXPaf\nJk/eN5ekqSG2T9q5SO2D0SCRA4690DqSY55uX/3qV+H2228HABMp/5d/+RcYHx8HAKPcjjnaANC+\nZn5+vn3t4OAgDA4Oote2Wq3MtQAAExMThWs3bdqUue/hw4fJ174WGhZtsAtXq8pOj5hjC0cfqaWi\ncj46vvm7LEIYIyeKim76DIL03yn3wA5DjhOiE64plvmS1X3GD+RQi0NlLxrqWgocQJZClv8b1geX\nccehhWKRG957wKPN1DWhRcrNb8pAGnMPDLjk03OlwKWNlroMC8pc8Nf5lkVCbR96wbDxU/U7/458\nZ6AVQUw7q1gz4ElnWA280oI6p9bNXJAD7NSUNN96t/0L38PHGtOB/Jp9VwYuYLaIDkykzOcQ+4KT\nHy2ngYfsKf++6VrTEuacnkWpU2XX2jJ5sI3zHnwACUfM12UTUpkL7rm4/qlYlNbxiPnxxx8PH//4\nx+G9731vW039V7/6FQCYB4g52gDQ/rxWq5GvXVlZaV+b/jzWta+Fhhlt2s3H/D+vJFGMw1QaLXAr\ny/OjzXKU2m2UpO/vau73wAcXsmOQ0Mc0CG2cuYBtxFoDl9qHer3eji7bxmEeYPSxdB+o70KXY+7u\nA08M0eUwdX4M7prAvFKOGiq7ntpaTCkA4K8pdzmxP34qu084TVouzf6/BoTigXFFR4GXF+x2hjR0\nfB7jyi3WSGXIpH8z3QceU0lebhObC4mAne4M1AlW6sbA+X5IADXkzIS0ZnhjKAIkGnCBum+GnVp6\nybUsyEPfE/QpBSFbhramXWKKGhYlnX0Rw45wByl6gfFFaR2PmF944YXtf//kJz+Bv/qrv4Ibb7wR\nvv/978Pw8LBzA7cOuaW5Dw8Pw8GDB53Xlkqltijc8PAwAOCHg71v/lr7ue/a10LDo1J24crzkTil\ndHwLRxqttv+v28D40WbMGdPkRFERVp9Dmv67/x5uoQ9t/VNOH2KkFGBiUBr2BA/tL4ILiVFCP5Dd\nomN1GBoaCvZBA5BgAnamTzxBRVfEgeowuYAmjggSRuWjptn4gEtqlQEAOXDpcpjShrYLoE7ugTlM\nfJq01GHqdMONR34qlFS8zVfH3PYh/7d886We0NaKu9TY8vJy8PtuOj4PXHABJDzWmDTX31zjEn+j\n7t3FCGE8CnWoxRgDBqpKxON8QJM/2uxmrdnvh/uARZslOi+yfSEG+w97DjErHkmZC7xUVUzzh27L\nuN8DTQzRx0Yyf6eltPWp7Iz2zne+E/bs2QOPPvoo/Pa3v4WJiQknTdx+bintExMTsLKyAqurq4VN\nDLvWfr5169bMtVZMztLU7bX5OujYtb42NTUevKY3mqk1nO5vs7kZAAAqlVJwHIODZoIfddRk5trh\n4UFoNBqk51Cv12F4eChz7eTkGAAAjI0NBu8xMFBG+zAwUIXV1dXg90dHzfzZsmVT+9qpqXHYtGlk\nrS/DwXuUSuY5HnVUkgKxfbv599BQJfj9kRGz9LZuHc9cOzo6tNa3Ee89pqcNoDQ2lu3r5s3052jf\n5fbtE+1rN20y/SqXw3N6bMw4CJOTo+i73LQp3IdGo1GYC9u2mX8PD1dF79J8dwiazfB8nJkZWRtL\n9jlu3Urvg50L6evKZQPqVavhNTU+bt75xMRYbi4Mr41tBCYn3fewzuDwcPZ5T02Z+Tg4GJ6P1ao5\n8I8+ekvm2mq1AqVSK/j95eWZdp+xuTA+7p4LyectGBjIPkc7Hyl709CQ2Re2b8/uC0NDA6S5AIDP\nxy1bzP4/MhKeC9bHeN3rNiO5t+HnaOdzfl8YGRla+3y0fc65W/E5cvYm1/46ODgAzWaz8P1un32N\nRgOGhgZye4YZ3/BweHzW7pW+o02bzL63efMm9B1t2TICY2Nj3nu0Wk0YHMzOs6OOMucw5R0l67X4\njpaWFsljmJgYRfeczZupZ2AlN4bJdv+k82xoaIBkS2zaNICOYWzMjGFy0n+G2jFUKhXxWsHOUACz\nFwMAbNs25i07Nz4+uPbfkdy+N7I2Nv97mJoaz8xn+1tTU5Nr/SsT9k07Bveeg+lAJWMw101OZs+v\n8XHaGADM/g4A8PrXb2k7dcn5FR7D8LD5zrZteXsqOUN9+6ZrPYyPj7bHSJlLpVIJjj56sv1ZaC5l\n5314LvkAksSOkI8B8w+sPUY5/1zvwazpOsGmNGvaPZfCYzB7a/Z8sPbc6OgA4Rmsf4vumDcaDdi3\nbx+0Wi0488wzC38/5phjAMDkge/YsQOeeOIJqNVqhSjA7373OyiXy3DCCScAAMCOHTvgqaeegpdf\nfhl27NhRuBYA4MQTT2xfaz+3309fWyqVMte2Wq32PXz39bUDB/448tBrtVUolcqZ/s7MLAEAwOLi\nSnAc8/Pm2tnZ5cy1zaZxEF59dc6L6rVaLajX69BsZp/Z8rJB2qanjwT7sLhoUgsOH16CwcHk2lKp\nDKur9eD3Z2YW1u6zCgcOzMPU1DgcODAPtZpB0w4enAveY3l5BSqVSua6ubnltf8uBr8/N7cIAADz\n89lnXq8bkZJXX52FatUNCB04MAcAAKurzcz3FxdNNOfw4fBztO9ybi7pg40GUebC9LQBrpaWVjPX\nLi3ZdzkfvAc2FzjP8fDhI2v9Xc1dS5sL9jnWatnneOSIcaxnZ8N9WF6uQbmcnQuzs+b9Li2Fn+PB\ng3Nr98n214K7f/jDYajV3MadfWeNBuT6sLw2lmXCXFhsfyd9bblchlot/2yL7dVXZ9f6kn2OKyv+\nNWXXHoDZm6rVauY6y1paWqoF+zA7a9b1wkK+vyWo18NzAQCfj3Y+U9bU8rJhbx08mAV6y+UKrKyE\nx3Do0Fz7N7NzwewL+/fPQCggap7jgGM+y/fXVqsE9Xoj8/30++tWazQahXdkx0fZM5aWVtB3VCrR\n5vqhQ+bvy8sN5zsaH/dHdmq1VRgeHkHX6/x8eAzJO1oEAP5ct3vOykp2DKurpt+vvjoLQ0OT6Hdt\nW1mpFc7A+fna2n+Xgn1YWDDjnZnJXttsmndMHUN+77bn+IEDc7B9u/8e2BiSubQQ7MORI7g9lOzd\nM4VAEjaG/HsI7ZsAydpbWjJz4dChhfbf5uZWyGNI7ID8GFprYzgMmza5I76u82tlpb7Wr7AdYMcw\nPb3Yth/n5+0YwushNIbQvjk9ja9pa5fSbMJa4fzy7Uv5vdOewXmb0GYS/P73h73MucQeq6NjoLwH\nfD2Y9zA7y5lLebunRFrTLnuOM4ZazUTMsfcwMxM+/yit0859R3LMr7/+erjhhhtQJcRf/epXUCqV\n4LjjjoNdu3ZBs9lsl0KzrVarwdNPPw0nn3xym0a+a9cuZ+myffv2wfj4OLzxjW/MXLtv377CtY89\n9hiUy2V429ve1r7W3gO7tlQqwWmnncZ8Ar3bfFQVHnVLlqduo3vafNr0d9L30FCGeHmc7pJEMfKB\nQs/BXTvUUq/otUOz4m+SuSCjHbVaLWg2myo6vptKyJ0LxRqu1D7415R8DNTySaG0CN67LI5DQyXk\ninK58xzpNHDsXdDegxE60uTNYjnqSR84VD5tjrmLjq9L9emNHHOf6JgsZcLeg0fXlOmDALio7BIB\nVK2WAV4/mzrPXGlMlPWuHwO+3rk0bhftVSdgp6NQc9dr/vv27NCcP9QzzHV2cPcsTUqB1qZL6Py6\ntDzdvu1OT6Hcw70vcVLaiqKUMiHCvC3CSy3Uav5oqPC90KI75pVKBd7znvfA9PQ03HXXXZm/3Xff\nffCLX/wC3vnOd8LWrVvhvPPOg3K5DLfddlsmx/v222+HhYUFuOyyy9qfnX322TA2NgZ33XUXzM7O\ntj9/8MEH4cUXX4RLLrmk/dnu3bvhmGOOgfvvvx9efvnl9uePPvooPPLII/Ce97wHtmzZAgCm/NrO\nnTvhRz/6EfziF79oX/vcc8/B97//fXjrW98Kp5xySrwHtM7NboDpJlPwzC9e3kGkES1zLX56XpdL\nOI27+HGRDG0+EaUPiVMtdwjtc0gfBOVyGcrlslIJlTsXcKOEUzsUU/nX1M3kqIDimgkc49A9Bnt/\n/+/HOMxc84n2HF251YmgFdWhxIEmzRio+heu+cjNOcWcPmrerCt/mWuoa5w+n+O53uI5LvCEC0Jp\n3lFIOE37jqSlLu3/01TZQ+JvMoCDU7bOl9vcarWCJa5cNcB5e7f7PfAE7FxaMaG927X383KbNdoc\nvrmU/rurxXBqcV0MvQgf1SFz2ZQ8NXC36KZGd4hqD7kAFk6eOy5+Guc92Dx5yve12kmuPPsNnWN+\nww03wBNPPAFf+cpX4LHHHoM/+ZM/gV/96lfw6KOPwvHHHw+33norAACcdNJJcN1118Fdd90FF154\nIbzrXe+CX//61/DTn/4UTj/99IyzPTk5CTfccAPceuut8IEPfADOOecc2L9/P/zwhz+Ek046CT7x\niU+0ry2Xy3DLLbfApz71Kbj44ovh/PPPh4WFBdi7dy9s27YNbrjhhkx/b7rpJrjqqqvgqquuggsu\nuAAqlQp873vfAwCAW265pROPaN2aLyqlcWT4qJ482uzbgDQRIZ7xWxQtk21gMqcUc6qlfZCWlwqh\n/aGDIJlLrtrV8ogFdQyh+ttUoyKGmqvUMNFGPNK/gZdBlFUpSN+P+i7y3+fUUvftTRTxHJdhxCs1\nVhTEArApAUWB0eL38Ugm11B3gYbdiGR2srnBQN56y++75p60dxRLOE3jFPtKVGlBLNu/UGs0imwn\nSdk6377lE9EL1QCnMxdc7D85qEo/xzvL3tM4U1SgKCzYRXsPGjDSJUSbrEkaQOJ2CGklCDV2RCjq\nHwZ5YuyNbvYfp9yZ6zmEyta5xVM5AIkv6v/HocreESr70UcfDd/+9rfhkksugeeeew7uvvtu+M1v\nfgPXXnstPPDAAzA1NdW+9jOf+QzcfPPNUC6X4Z577oHnn38err32WvjGN75ROPguv/xy+MpXvgJb\nt26Fb33rW/Dkk0/CRRddBHfffXehDvlZZ50Fd955J5x88snw4IMPws9+9jN497vfDffddx8ce+yx\nmWtPPfVUuO++++D000+HvXv3wg9+8APYuXMn3HvvvXDqqad24hGtW/NR6DQGOHXhhGqPamtW8hx7\nPOpPR9p1US3TB5dTSj0M5ZuovwZ45+ljMdBRl0NZrVah2WwSSr34DzPqfNSqups+yxzz0BikirL2\n/zVzieMQupxa+ny0cwGn51Lngrt0kYyOD0Dfm6yzIY1emWuKz5ETyfTNJ0oks5MtFFXSUNm1aRtc\nlo0GIPdRqDnrtViFgZPC447U8myJ2NFmbqTWBWJ18wyUO7V+9oVuLgHQbToNC7JeL6ZFJGyp7r0H\nKfsPwIIL8mh1OEjgX1NaZXp7D/cZLmcw2ndJTdHMs3p5FWZ0qYW90DoSMQcA2LZtG/zd3/0d6dor\nr7wSrrzyStK15557Lpx77rmka/fs2QN79uwhXXvKKafAnXfeSbr2j7n5IubaXFR7f19zRXp5lFV3\nWQhOeZH4VHb9QWLvEd7AYiDtbuouDaX2o5vhw9DPvtAg5WnjyldeKgbAgTnmnPlsn4NrDFQqoS76\n5QarKFFEd548zyGM4TD51oQPrQ8ZmPSofxHvpgIc9l0VHSbeutZS4dO/ifUhVA6sUy3MuKIBOLp3\n5H8+9LmuKcvn3v9pa8VVlo/nlLpYAzHqZ0uBXR5zwUfHj7FWQmCgP+Iunc8cZ8pFx+dGat02IS3a\nrAvWaG2REPtPBvJIIubSIEGclLZ6QYFfUkZYyhwI761UxpicudALrSMR837r3YblwVjRDQ4FTuoQ\nupwI3gaGb8KGys6hYMdFqbkGfPo3uX3QCq+lfwOjH3NofFgd2vT9w78vz68L1e4MH2buuZT+e6gP\n+WdQKpWgUuHNRykNXHsYmt9wr0tNnjzX2cAcZ3rerAtk4WkeaKPN2Bio+hdxGEWYoc6J+rvWBN1A\n61QL5VF2AwAK6QBQzw9XGhIHHMjvO9z1mnesY+Vn8+qYy5g+IeYC1ZaII4Yri7RSwMRQw4E43npI\n/6Zt3GCL9BkAhFIK5Iwv6j1iaM348+TlZzDVHnIFSvh6OfFtc+qaCtlCchYM/Rn0Qus75hus+YwS\njUAF1SHElMDN/+udWq7xq8lj8VHZdegoL8dccxhqRZ60DqFPcIzyfV8frMEZOgiSiHtcVXbbJx24\nwM0xx8dAdeZsn7P3WN8oor0H1bgDcO8tYcfcVTGCF0XEc8xp+hduIUC7N9EANxe4wBO0kgOXnWqu\nfVPrFNt70gBNlw4Az4h3zzOZU2z7QBNOC+XUyox4LkCS/s38PbTRNSkdXxKpdaee0BwRbW6zez3o\n9ArSfXS1GKwzX0pBd8R07XuUBRnsPTQsSve+S5uPcUSNsZQCCVDlYnBQbRlZmlCr1YLV1VXPvtR3\nzPutB5uLamlycjnGr4s+Rt085Ci1K1pgjRJpXjEvj8VHl9EfJNQoaQyhDq34mzSyljiDmsPQFXXn\n5WfrjDNfiSzagY71gavKrskxr9frbaG1/D04SLkm0osZmKYPvLxZ6d7imo92nevLpXHGoN2b8Peg\nicb2QtTBLZSoYzvZe/Lekcz4c1cwoDu1PmV5AHmEkJfyUNQyiEs/lubU8uYCxjpI3z/0ffObOtaY\nJi8YA4bt3NQAcdQ9w7Vf6KP++vQbOpNTP5d8+dm8YI2Wgahhzmkj5iHglKqOrwNYNOktvdD6jvkG\na9qIjtapdW+AvMXvorxS7hEDpfaXKIlB+aHlpvlyq0PNle/PjVBq87p04j24YUQ91EMHOlWzQBeB\n80f9peCC1jCy9+BEEfNGBZcGjju1vDKIUmprjGisS3WWCi641YFphrqNlmr2Jje4QN9bOtWS5yPf\n97DIlrkHFQByMTN084wDTmMCR+aePAq1Tv3fTWWnMDu050dIcJIKkGsYMtqqHq50MC7I7z476LaI\ny6ajnqFagEQDVLkdOm0ZX51jLgN5dACJZgyYgF0SLJIDJFTmgFb/KQbA0gut75hvsOZyaqk0cC1d\nxp0LRF/8rmgBPSfXTwOXOhEy4Rs8Okcts6I9DPF7cCPmMoQ1Vn5d+jeTPnCjpPLDzOVQateUNsdc\nS0s1fdAp9FNV2V1RRPuZlo5v/i6NwPGMZDdIIwfsqIa627iLGY0NO12daiGnVpNuQJ1ndi7rqb+6\n6JprDLQ+6PdeXIWafoa6WTrUnNrOAOxclo/5TRlzzXV+cc9A957Xeac2BoUaHwN93w0DJDQKtUbE\nFSv5FuM90AESF5PHridqyTfcltIBy9R0Mh0DJQzOrx+ozGl9x3wDNUvzdkULdKU1qAsnFCWVRwvo\nRgmOEHPonpiIHm8MITok1QCX08B9RryGihgvv45+ELgO9RCVLxy9ohl3eQPV3IO6ptwACQAlL0vH\nvjC/4TL0eTXE3TQ4WXqJ6QNP/M1lWMidWi6bR54nr436h41k2rrW0KQ72WLRNfF3xGVm4MJp9HQu\nXXTN947owml4aUFphLBUKrFSofAxcHNqNTRwjI4fz5nqFh2/+PtlKJfLXaHju9LJtHMpbq4/lbmg\niZhjNiHt/APwgQs8u1Yb9dfpRuDvgQqcuqrsUMGFkHhqP2Lebz3XXIeI+Yx2mIboZ3SEOH60gOpY\nu0TPeCh1nHwiLfPAtYlS0VHznSKNjmeU6HKi3IYR5znKcptDhxk1t1kXJcWNAvoY9NRYP5WdFi1I\n/6Zt/Ki/voyVdF37NBcA9KJczWYzKMrljjjw1pSbuaCJKNNpzp1qofFJHRl7T8o7CoNQVEdGDi74\nyvJR7uGi48dgHlDPD32evH8MUkeEt1b8NcCp7D2NQ4jZIgB0rZiQ6JhUiFYbbY6RY84N1mhzzDUs\nSq1TG7LHKAB/q9WKkmMuBVm0wZqwzsz6sb04re+Yb6CWGPC6iA5GP9MixDGiBfxos456pVGW10Z+\n3M4YXzSmVCrl7sFTt5dGm90gDc8pTn/HNu5z1BioWsGvsLNBixxhtFJqGUR31J87BhkN3Acalsu0\nqL3WqbUGoAbgcKfZcHNOZWsqDC7o99f1jZj733EMqj5VPNRl/FFzcl1RIa36P60PIWDX//1Wq+Wd\nJ9TcZo1jHmJLUQVQ844MRxdDr7OidwjNe9CnIcnLpcUBSOLQ8WXVghI7Qi546StTqVHHp67J0N4o\nr/QT4z3QtCe0tnm4ZGqfyt5vPdZ8xi81H9YdbeAqcctp4OFoAc141YmOYbk4vIhH+jdto27CcShw\nPqRdg47y0E1dfp1ODVxfTx5Hme09aFR4OwYX0COLwCV9kM1n2wdKpYOwCJ/sPdjPOBRjqYHpMiq4\nlErcYaLma7oca9r+6HoGXFEuTTS2k82dhsQxHpvoXOenC8hyo13v2H6mAU/4+h6y1BOX+rH9TMN8\niweQyJ6jTIXa5YhQz0CNU+tOCYgRbZaePzxbpLgmbQBIJ8JH2xdcFGo+E0eWW52+RlrP3Q346eyx\nGHR8LoPEvSZl3+e8h15ofcd8AzW/8Us7TDHKEQDdeA0pqVIPEpxCxzNe3fRl/wbkihbYCGUc2pKM\nsspTc8UN1Gq1wkRHXQeBjsbHEfySlrzRRgtc37d9oD1H12HEY4C4jFx6HVl5lFQ7H2M8Ry3FOJSf\npmVPUPrgqsVOfw8u3Qd9NLY3HHN/RKYb4LJb0ZxLZZeXLXWlntDBAZcRTjWgfeuVfn5o9pwY4DQG\nSPLXShlhnfGi/hoqOybCB8B5D346Pj3SKovUmj64gwQcUNal2aOfS7TUeqE5rgAAIABJREFUQM3Z\nEQI4pDYhncGoZyO5wDL6GRxHK6Cvyt5vfzTNtXmZz2jIpK80FAAnWiCLstprdE6E33ilRElNn3UR\nD9MHXJVd6ghx6x27kfZu0PhctCP6Yei+h45CzaV/YYYRnYLtivzoaeDUqL1PFDL9G+4++I2zkFHh\nKpNiP9OwJ7Tl0rjqwL5os9bIlaZmcES5XOBCLwjouNY71wDWMALCc10DpNGjnLozUOdMuc4ve0/d\ne+DtOW7qbhhgx8obxhGwi3UGys/xanWAVMc8lNbQLVsEBxe0VUF0Y6DORcuc07EG8H2BqoquLbUZ\nQ0xRS4d3nV/clAQNuNALre+Yb6DmckgBYlDZeREhHf0MdyL4kTHZJurafOxnmvIefMOoE0g71Six\n80mX0yt9BuYefuqTlMFB1UxwGVb2nprDjF7H3L2uqeJtbpAmTsRcKvoCoK+lzq1l2ynxN0ofYkX9\ndaJceO5vL+WYS5lG5hr/XO90GlFoz+BEarHvU/rgzgvmanPIwWmXaGas80MPsIedWm1pwTB9mAoO\n6IME7lrqOoCECrBrgCpXZRK+IKMsxdINrvMdc5ctQrUDXHujNMWGy8JM/2bSB96+IgVI3OACXXei\nF1rfMd9ALRxZozmULpGo9G+4WpyDyG9YSSMWWmfOfFZlRXqlm3As6pULpKHkFYdopfQ6tEXmAlW0\nLCR2tboayi1zHejUueQHaThq4i6jIhT18FNjaevaNxdMH2WONfX7vjFwAA5MzJCed+t6Dzwqn49R\nRI9kuoAiDXtCF43lRE461cKAZOffkVtgiJdXjK83HQ1cfw7r9z0OyI+DE7Q9IxSdo56hOMBBWytY\niaz0PaUpPNRz3CfC161giwugp+6bdgw4QNKdtDqXRgqfQl1kX9DBNl25s5A9Rj97ZM8AgELH16U3\nUvfWgQG5XdwLre+Yb6DmN35pDmU4sqZTZaceJL5oAdWhlKpfupxB+xk1WmD6LENYQyIb9Lxidz6s\nXN3e5ujRNlF37WqaYYTdg4t0F6OsPJQZf45c8R1dBE7zHF3aEVo6I7X0kH8MZVWeItU4c+2P/OiV\nG+CgU/plhoVrf7WfdSMa28nmNsDLrNQRzRnmFhiiRe1jab1oov5hEVb5nkMX7NKqsrsAEt2eZe/Z\nTRG+InOBl1bneo6aKgV0p9bv0IXtKTdzgZoW0Wg0oFQqeXL9ZTYhfS7pWZR64TPdGEKCkBxbpsi+\n0K1JfY45XTeiF1rfMd9ALWTA6yJCNIfQZRDEpIvKBeh4i18nfKOjXmnFTmwfNEJ+ncoxt/fgIOXS\n0nXalIAYVHYX0q0dAwCHyo5HfriGjVuET24kxxsDtQ+y/GW/kUzdH/H5TDfUfc5GHFGuXqhjLk3B\n8dOXeY6INJfTRTm199Q4hNqypXRwuxtpcVSARJYn7wfY6QxCnV6B35mSprTZe8bJk++sU+tjLujH\nwNVI0UarcXuKuh78pYi7MwbpXLT3cAGOtD5oWQN+/6JPZe+3nmv+aAqddqtRHXZThuh1BsN5XdJo\nM/Ug0iO87nJpPOPO5dhTFc019GUXQMHdRN1OLT2vOD8Om58tRZm1h6H9jENllwpahVNUaOwJ3bp2\nMWF4zAWtgekfA7WWrWtNaaKIujVBNSx8Th9PlEu+v3ayhdkhcmaGNipDfUc+wUgOU8hVUSP9G77v\np/uc/n3K9106Lfae9JQCt/ibPNefB27jAMlAFKeWHqiQMY38aRHU94CXeOQ6tdrKJq4ggYZBwo02\nu9TAqeVC3Tah3KlNxkAFx11jkJ09VJvU3sN3BlODPVr9J6k91yut75hvoBaKSq2nCimXyu7bwKT0\nY74arM6JAHBTqKV94BjPIXRTHm3WOfb2M160GTfOqAeBS2+AzlzADRvqYYbdg67KjqPE9p6U2tUG\npPFFfnRzQRdtrkCr1YJWqxXog0vZlzaGMGBHjaTK98cQE4auPdHJCNr6O+ZuGjgVfNFHOaXvKASk\ndQM8ceWTxqCyd09Z3r9ew0CaP+qvKd3KBXm0KW2ddQg7HfV3n6F0u1QnWKllwfj3/rIy4MQrYege\nAw1cwOrJ0xm1+HvgziVpPXm3qCVtb+6V1nfMN1DzGb8cuo2LcmT+LnMieJFeHOHlG0aynKgYudH2\nOUqFqtwbGJ12FBL8kho2dOPOMg9wZ4oa6cX6kESOdMadjspOX1MAPjFCuYHJ6YOGBu5SteVG/XUq\nzbjKs9ZIphomIYeL1wfdfNTs8aFobK865tVqd9+RFBR1gYHmnhzwpHgOc88wbY65huGiBRfca4Xr\nTLn2bj2IRd279TRw7XvQzCUX+8+OgSoii+85mhRLri3iYktJWQP2HrQz2KW5QGVLhYAqHahLZf/5\nz2BpmqmubCuVddArre+Yb6AWMn6pStwakaiwmjiNgq2h3bpQ5jjOGN0xj2Mcuig7lNrVbiVUcw9t\nhFFGHbaf0Q6C0IEaMtR1Ne1DdHyaEKCLR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9w1oG4oPzvdT9fvA/hBHpregY9BQi2B6LKL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\nIiKyof8HeO3xTYyTup0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10fcac250>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Check\n",
"plt.clf()\n",
"plt.figure(figsize=(16,7))\n",
"xplt = np.arange(cut.size)\n",
"plt.plot(xplt,cut, 'k-')\n",
"plt.plot(xpk, cut[xpk],'go')\n",
"plt.xlim(100,500)\n",
"plt.show()\n",
"plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Trace the flat spectra"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Crude estimate (flux weighted centroid)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reload(desiboot)\n",
"xset, xerr = desiboot.trace_crude_init(flat,xpk[0:50],ypos)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Polynomial fits (test)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"yval = np.arange(4096)\n",
"ii=4\n",
"xval = xset[:,ii]\n",
"gdval = xerr[:,ii] < 999.\n",
"dfit0 = dufits.func_fit(yval[gdval],xval[gdval],'legendre',6)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fitv = dufits.func_val(yval,dfit0)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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tenisP0YP6oW8i5X4cOspaHU6qUsiMimGBSKi2yQIAp6YFIiQvq44efYq1u3M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\nERERKeJYICIiIkUcC0RERKToA5c0j34HvtWFAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12451f590>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Fit\n",
"xdb.xplot(yval,fitv, xtwo=yval,ytwo=xset[:,ii],mtwo='+')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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r9nury6fDWECRj53TPncc6gOXYOfce25bl7ptFWGPlrd0+7AOG6v0GDYe5DGs\na8x7WgQ6dsxkxIi7NCaimkQ9iIiLi6Nx48ZkZmZWOD8zM5P69etTp06dCuefe+65pct91+7duyks\nLKRZs2ZhpUUDK49txozF/Pe/BViB8XusBWEJdloUYRf6auzCP4gVcmnY41vvYwVypls+yf19MUc2\nt5e49fdhBW5d7K6jAAtY1mOVaX1CTeh1CFV+XjAR4/7vVWpe4Q6hgtyrrGphBXMtQhVaA6yQ8+78\n8wlV8BBqSk122/K6V7yK9gys8Dzstpvl0tPQ/ZY3ePEMt78tsdaUp7AxA0nud70uBa+7Lt6t0wir\ncD5y+RJHaMBrInZH/Qe3jTpu+jVYd4j3Ho/6bn+8iqceoUosHxuHku6WScYq8AvctCeAX7vfynT7\ntZ9QUHaIUAXqDQ6t4dK+3e1LAqE7XO8dH16ws5fQ3bkXiEGodakEC3r8hAIW7+kg7+7YqzS9ICvo\n0utVNrlY4LuXUPN7iUu3NwA2F2tmz3LLJRCq8LzK+QxCAxG9gZ6H3HHztvM4ds0cxsY4nI61ZpU9\nL+thx9VrUfsP1oIS59I9ARtDcZVL93qsZQaXxy9jd+XnuPUaYRV2rEvbvdjgVrDuoU/dtpNcuutg\nT/Ocgd3Z4363FhbwLyxzfHzYIM973O96LRmnY8fXa3nx8gnsxiGW0Lihmtg5+K3LhyRCg6WDWGC7\nxeXj+24fzsCunwy3r82wt+F+jAWk2VhQ0RMrm6Zh3Wmz8D4F/uWXj7N7dy5+f8Wt16eSH8XASoDL\nL7+cPXv2kJ6efsT0Xbt2kZaWRps2bY66bqNGjWjcuDEbNmwoN897LLRt27bl5kmkkrGC669YhXMu\ndlfwH2wE/zlYIZuMFchnYQVGLlZ4notVut2xvuV/YhXDPqywr4f1Se/ACpgktx3vscNa7t+dCDXf\ntsIKoV2E+l4LsEfcviJUWO3HKoQANujPa90ocL9zGVZQNccKsMaEAosErLLzCkSvgjuA3b3FummX\nE7rLv979TpH7fwOskPOarEvc/H5Y4dgMG4A2zqWnFlaIX+5+o4XLu3Yurxpjj4dmu7Rc6rZzBTZg\nc6RLdzwWON3ujtkVLi0J7ticjVUgjd1+NMLuQEtcmhLd+t4TAruwu7vnsErD75avTeixz8YuTV4r\nCGX+Xc9tM8EdywEujd4TPQ0IDUzEpclLQwmhVptY7AkJb9zMYZe3EGqN8I5TLHYuxmMBgXfMgi79\ntQjd3XuluMZ7AAAgAElEQVQtBTdgg/XGYOdva5fesl+l9Y7r6W5//ujWrVkmnQ2xAPEBQuMUCrHz\nrAZ2bl6EHfN8t5//5/LeGyx5C3btNMG6SHa4bSQSaoXwu+OyG6tIvZaUlm4/OmOBXyFWAZe436+H\njW/qS2jAZIzbdpzbxpfYgOi+hLrMtmEBxAiXLw2x62G/2++zsKcv4twfLwj3ubw8hJ17p7m8a4Cd\nh1nu7zgsQPCC+EysS+MQoQCoO9ZN8gnWzeTHbmY+wcYyfYaNiRhSmg77FLjGRFSXKgkiBgwYQDAY\n5LnnnqPsE6QTJkzA5/Nx8803H3P9/v37s2PHDv72t7+VTsvLy2PatGkkJCTQv3//qkj2KSclpSud\nOp2BFVifE2oNaI21KPzdzUvCKpVYrEJ8Gyu4vJHhD2N3zfWxSu9sLCjwHiXbiBUsbbFCYTP2hswa\nWIHYCxsUV99Ny8MK3Lruz5WEHrXzXr+djFXq3qDA9ljAU5fQXWRrrCLejnWLdHPza2KFGoQq9sNY\n5R6PBSre+gWEnlsvxAIfr8XjPKxQvQF7MuQMQmMoDhF6iiQTaEu9egXUrHm6S29tt506WLD2W6wg\nXYm1OFxIQsJ/qVvXawFpAbxBTEwM99xzCTVqJGCDP3+PVR7Nsbu8NGrWLHbHaiuwjri4jcTHb+OC\nC7Jo0WIf/fqdzZgxF9K6dT79+3fmyy/n8vHHKTRpMpLGjb9m+PD2+HzbXV43dGl9wh3Xy7FgA3es\nilzeN3J5Vscdp4FY/783eO4srMJq45arh935xmAV7tlueobbF29+XUJ3xbEuX/1uem+sonoUO3e8\nR1zbueOT4NY5AxuE543zmIMFejHY+d4Pe7LAe7zY+8jTIeyJid+6bbVw2/K5452DDfi7Cju/umDv\nLyhw+1PLHZN1xMev5cwz/8/9Tjv3m69g184UrOKuhQVDddzv/M5Nu8alp5/b52xsfMFfXN4/gbWM\nDMSuswK3XABrAfHyL95tf4eb90tClbfXutcB6y4cjJ2HXl485/LyIHZetMGumZZYMNSS0IDsAvcn\ngL3+vg92zX2OBSTnEnpipNitG4dd8/uw87ouocD8AyyAnoQFqB+5bS/Aexz6aGPopOpV2afAR44c\nycKFC2nVqhUdOnRgw4YNbNiwgT59+hzxKuwpU6bg8/kYPnx46bS8vDxuvPFGvvnmG3r16sXZZ5/N\n4sWLyczM5NFHH+WWW24JKw3qzqhcYmIcTz01m9dfX8h//hPA7kRexC76fYQq9TzspVHTsUq5K6G7\nPQjdTWRglfUC7G4rB2uGvBYrAGZig6q8r4UedOtnYgWwN8r+D9j7ELKwZ90vwwZa/QkrsJ/GCqc1\nbhuHsErfexHVaViBuQsrPE/HCqxabvnNWHBUB6sA/401ld6HFXwLsApiB3bX6o3a9wKUIvenNVag\nJ2PBxyNYpfAVUIcePWpxzTXtj3ih0osv/pNXXlnCgQO5HDoEhYXvEOp3f4fWrT/g5pu7cvvt1wAc\n8QrjZ58dVvpq46O9eRGO//PqoRcztQE24PMVEwx2xAr2/th5YG+KtLy+BDvO8ViFfq5bthOhtzTu\nw4KeJ7BKawehj701xYKeApev2fh8UKuWj2AwSH7+BGyszTdYIDMMG2x3psu3e4F3iIlZx+HDDVya\nPnLHJRY7n17Dgo7N+P2bSU6+iIwMbzDjtVir0RnYuzoeAwq48842tGhxOa+99j6ff/4Edt56Axt/\nD0CbNn9m+vRr6Np1Cvn5z2KB9sVuvwC+5Ykn6hMXF8+DD/Z2eXcAa0VYTmzs21x8sZ+YGB81alzA\nxo37XZq+AUpITEzk4ou78PHHd2DvHMnHAonWwH8455x9DB58EU8+uRsLvDq53xgFLCIhYR7t29dn\n5Urvg3qJWMtIbSxgy8auOT8WlHyDBTezsOtkH1Yu/B0LLg5i1+E67FqYTujlU/e6ffa6RO9wf//L\nHdu6WMtNC3fcviXUCtUCC9K97rhHXB5dgrW2ed1J2Vg5MxprlXiPMWNaM2rU7ZzqfhQvm/KUlJTw\n0ksvMXfuXLKysmjUqBEDBgxg2LBhxMXFlS7XvHlzYmJi2Lx58xHrZ2dn89xzz7FixQry8/NLv+LZ\nt2/fsNOgIKJyycmJ7N6dy8svL2DcuM3YHWMKVoE/TOjjON4Hb77F7vzOwwqT3xJ6vbX3NsZx2B3H\ndDfvEqxgaokFBHux5tkRbplCrAujNxZkfI0FKd6yz2OVUiyhD3WlYXdqT2LBTU9Cnwqeg929FGJ3\nZwGsMMRt42vsTvFerCI6DSu0L8SCmSZYBVeEtW40AuDss1eQkfF7rDLy3mFQghVwH+L3Z+H3X0pO\nzmMAtG8/nbfeuqnS1zDba6OHANCx44wf5ANL4fjuh87mzPmX+yZHA+wcmYEFbZ8TE3OYnj1bMWHC\nMFas+IylS/+PkpLD1KgRS6tWZ7FxYxpffbWNbdsaUVT0IHZMP8YqpTXY3WfF34ewl6L1x86FkVhX\nRz2sBecFYCO9ezenW7c23HRTp9JXgzdtWovJkzdj58h0LEBcTv36S1m7dgx+v59Zs1axf382Eyd+\nTH7+77G7XXvvwFln1WH16pH4/X5efnk+48YdxM6x54BN1KlTk/vv78/dd19f+h0Le+35z7AXT1mr\nWYcO3zJnjrW+2rFOAd6hdu35dOt2IRMn/rz09etHe5U3cMQ3WTZs+Krc/C5dniI9/Xzs+rjZ7esy\n1q59kHr16nHnnX9k4cKHyuTzfurU+SexsZeSnX0aVjnXJNSdOcrl3a+xIOJSrDVkMXat/her6N8g\n9Dr3fVhgcbpb7lrs2q+JBVaZWEBxMdZyEXTn0GfYjcHz2I3LFuwmJBW71pLd8uuwAOJl97tnAlCr\n1ods3TrmhLhuqtOPKog4ESiIqFxyciIZGbvp1m0C27aNwipVe9OfDQj0CgyvkLgQCyqmY4V/b6xg\nfRW7C/ofrBK/EgsIhmMFag5WuD2OBSZlC6MJWAE/C2smfgd7gsN7A6DdobdqtZTc3Gakpdlz+lde\n+QK7dxeQlnahW67su/kP0KrVaD799FmssCnECqOviYvLobj4TKzp9gasIvqMevUuICcnHguSSrC7\nvnepWfMQv/51fxo3bsAvf9nPzVsIbKJFi0zOP/+scoU9hN8KcDJ9WTOcb094genR1vf2tW/fy7j6\n6r+Rm1sba4EaB8BVV71yxIfHvhtoXX75ZPLzP2f79lyuvvri0tdaVyQrayfdu89k794HsO+ArK3w\nmymhAOB/K1wu9IE0a13wAoOKvrdR9tsr382bsvs/YkS/0m+MRENOTg69er1GevpFwH9KP7zm7UNO\nTg5XXPEaBw7YlzTr1Hmajz++E7/fX+514/37X0mtWqfx5pur2LRpMqFz/kssAOiHXWvrsceex2PX\n+BgsGPg1dm3vx1r83sVaOfZgZcvrWOvFVVjL31OEBrWOwQL1De7/X2Kte+OxN7x+6tJwkLKfAn/i\nidNO+VdfK4iIMgURlUtOTuSZZ952zaxzsLEKL5OQsJkWLTq6JtQ3sWbJAKef/mv27p2EVfgNsALA\nu7MpoH79z8jOPhsrYHoDT5GQkMUvftGZ117bzr59dbCL/3GODFCmYi0Ty6lZczHLlw/jttuWsm3b\nr4HQHTocWUkDZb5aeaZLp1VEf/1rP+68c6G781vE6ae/z4gR3Rk6tA/79+e45vr2gFUKM2f2Z8yY\nGcydO5aKvg4Y+ornkCPSdCJX+tXhWEHEd+Xk5PDAAy+zbdtOmjZtyNVXt6jwI1pV+Qn2cJeLdrD3\nffIpXJWlsaKvfh5LqAXG++DhX+jX71s2b04svTavvPIFGjXKIT19F5s2PYWVDTWxrguvfFhP6DXr\n7xPqynwBK0fqYe9/mIIFLI9jT4E9jHVf7cfG53iP+G7CBoOHrtOBA5/ixRd/9T1y68dHQUSUKYio\nXCiI8LoCQh+0uf32nuXuqgYMaM+1105l27bfueXfwPuSXseOM7j22gQee6zij+MEAgGGD5/KO++M\nIvT1Pi9AyeH00x+iU6ezj+j3/z6fo67os8zH2kZF847VveC12pwsrQbVpSoqxx+jkyGfjtYCA+Vb\n3ELdTl6rxWdYV6DdgNSocS8lJTdh3RIvE/oI3K+54IJCvvqqBAsMFmDjOl7Cuk13Y2M4OmEtnY9j\nT3F4H/oCKOKJJ/7B3Xf3q8LcOPEpiIiyE/0CPRF4FeP36Ze38RM3EupqWMTAgZ8yefI9zJq1yhUk\n5e/kge8UNMuAg/Tvv5nOnVudMJXy0QKPk6HQPxEon8JzsuTT92nJKVuOdOjwMn37JrJpUzpXXHEh\n117bkk6d/k5hYTO8VspatfawZMmDXH/9n8jOfhobsFmABQzXYa0Vj2CtEGOx1stcrDtlFl53RtOm\nT/Ovf/3ihCg/qpOCiCg7GS7Q6uYVZN/3rv9oQUdlAwVP5IGElTlZCv3qpnwKz48xn8LpTnnggZdJ\nT99T+ml3u/HYjAUJb2JPrnhPiMzBukU+xwZve6/oPgtv8Cik0q+fj1dffegH2ccTmYKIKPuxXaBV\nIdKC7Pt2E4S77onsx1joVwXlU3iUT8ZaJw9j4x6ysAHd3pNfz2Ktlt5jsynYy7mex8ZaAHShf//n\nmD591Hc3fcqpjiAi6q+9lpNLIBBgxozFwPer0P1+f2kXxfeZF858ETl1pKR05e233yQ11XsbKdhA\ny5+4fwcIvel2ETZAczahwZ5/pV27837AFEtZCiJOYTZoag4rV9pLWubOffWk6VoQkR8Hv9/PnDk3\nu/dj7GDTpmdIT38A6EHt2r8jL68YG0xZgr3H43rsqY8lbgs3U6vWkoo3LlVO3RmnsNAgx4oHQcqR\n1PwcHuVTeJRPFSvb3Zmfn89jj9Uh9A6YBdiL6ObgPRXWrNlEVq4cqpsffkQf4BIREYmE1905ZEhv\natWqhbU6zMTeRdMFv/9Z7PHw92jW7De8//4tCiCqkYKIU1hKSle6dfMuziI6dpxR+gInEZHqlpLS\nlY4d/04oaPgD69ffxtNPL+Hpp0tYuXJUpS/Mkqql7oxTXGJiHFOmLABOriclqoOan8OjfAqP8ik8\ngUCABQvWkZsbUBlVCT2dIT84PSkhIicyv9/PPff0U8B1glJ3hoiIiEREQYSIiIhEREGEiIiIRERB\nhIiIiEREQYSIiIhEREGEiIiIRERBhIiIiEREQYSIiIhEREGEiIiIRERBhIiIiEREQYSIiIhEREGE\niIiIRERBhIiIiEREQYSIiIhEREGEiIiIRERBhIiIiEREQYSIiIhEREGEiIiIRERBhIiIiEREQYSI\niIhEREGEiIiIRERBhIiIiEREQYSIiIhEJOpBxM6dOxk9ejRdu3albdu23HrrraxZsybi7f3yl79k\n4MCBUUyhiIiIRENUg4i9e/cyePBg3n//fbp06cKgQYNIT09n6NChrFix4ntv7y9/+QuLFy+OZhLl\nOwKBADNmLGbGjMUEAoHqTo6IiJxEYqO5sUmTJrFz506mTZtGt27dABg2bBgDBw5k/PjxdO7cmbi4\nuEq3c/jwYZ599lleeeUVfD5fNJMoZQQCAW66aQ4rV94OwNy5rzJ79kD8fn81p0xERE4GUWuJyM/P\nZ968ebRs2bI0gABITk7mjjvuICsri1WrVlW6nc2bNzNw4EBeffVVOnfuTDAYjFYS5TtmzVrlAog4\nII41a4Ywa1blx0hERASiGERs2rSJoqIi2rdvX25ehw4dCAaDrF+/vtLtLF++nIyMDEaPHs1LL70U\nreSJiIhIlEUtiMjIyACgadOm5eY1adIEgLS0tEq306NHD5YuXcrQoUOJidHDI1UpJaUr3brNBIqA\nIjp2nEFKStfqTpaIiJwkojYmIicnB5/PR2JiYrl53rTc3NxKt3PppZdGK0lSCb/fz6JFtzBlyrsA\npKRoPISIiISv0iCiR48ebN++/ZjL3HbbbSQlJQEQHx9fbr43rbCwMJI0ShXy+/0MGdK7upMhIiIn\noUqDiN69e5OdnX3MZVq1asWePXsAKC4uLje/qKgIgISEhEjSKCIiIiegSoOIsWPHhrWhOXPmEAwG\nycvLKzfP68aoqKujKiUn/7C/d7JSPoVPeRUe5VN4lE/hU16dmKI2JqJZs2YAZGZmlpvnTfOW+aHs\n3l35GIxTXXJyovIpTMqr8CifwqN8Cp/yKjzVEWhF7fGHFi1a4Pf7WbduXbl5qamp+Hw+2rZtG62f\nExERkWoWtSAiISGBXr16sXHjxiNecZ2VlcXMmTNp2LAh3bt3j9bPiYiISDWL6muvR44cyUcffcSI\nESPo168fSUlJzJ8/n3379jF16lRiY0M/t3XrVpYuXUrz5s3p2bNnNJMhIiIiP4Covs2pUaNGzJ49\nm549e/LBBx/w1ltv0axZM6ZPn16uFWLLli1MnTqVZcuWHXObPp9P388QERE5AfmCP+KPU2ggTuU0\nYCl8yqvwKJ/Co3wKn/IqPCf1wEoRERE5tSiIEBERkYgoiBAREZGIKIgQERGRiCiIEBERkYgoiBAR\nEZGIKIgQERGRiCiIEBERkYgoiBAREZGIKIgQERGRiCiIEBERkYgoiBAREZGIKIgQERGRiCiIEBER\nkYgoiBAREZGIKIgQERGRiCiIEBERkYgoiBAREZGIKIgQERGRiCiIEBERkYgoiBAREZGIKIgQERGR\niCiIEBERkYgoiBAREZGIKIgQERGRiCiIEBERkYgoiBAREZGIKIgQERGRiCiIEBERkYgoiBAREZGI\nKIgQERGRiCiIEBERkYgoiBAREZGIKIgQERGRiCiIEBERkYgoiBAREZGIRD2I2LlzJ6NHj6Zr1660\nbduWW2+9lTVr1nyvbbzxxhvccMMNtG7dmrZt2zJ48GCWLFkS7aSKiIjIcYhqELF3714GDx7M+++/\nT5cuXRg0aBDp6ekMHTqUFStWhLWNRx55hMcff5y8vDxuuukmrr/+etLS0hgxYgQzZsyIZnJFRETk\nOPiCwWAwWht79NFHeeutt5g2bRrdunUDYPfu3QwcOJDY2FiWLFlCXFzcUdffuHEjKSkptG3blhkz\nZlCzZk0AsrOzGThwIDk5OSxbtowzzjgjrPTs3p17/Dv1I5ecnKh8CpPyKjzKp/Aon8KnvApPcnLi\nD/6bUWuJyM/PZ968ebRs2bI0gABITk7mjjvuICsri1WrVh1zG0uWLMHn83HPPfeUBhAA9evXJyUl\nhaKiItauXRutJIuIiMhxiI3WhjZt2kRRURHt27cvN69Dhw4Eg0HWr1/PNddcc9RtdOrUiYSEBFq1\nalVuXnx8PGDBioiIiFS/qAURGRkZADRt2rTcvCZNmgCQlpZ2zG1cffXVXH311RXO8wZWXnDBBceR\nShEREYmWqHVn5OTk4PP5SEws3yfjTcvNjaxPa+7cuWzcuJGLLrqIdu3aHVc6RUREJDoqbYno0aMH\n27dvP+Yyt912G0lJSUCo26Esb1phYeH3TuDq1av57W9/S1xcHL///e+/9/oiIiJSNSoNInr37k12\ndvYxl2nVqhV79uwBoLi4uNz8oqIiABISEr5X4lasWMGvfvUrSkpKeOaZZyocK3Es1TFS9WSkfAqf\n8io8yqfwKJ/Cp7w6MVUaRIwdOzasDc2ZM4dgMEheXl65eV43RkVdHcfa3mOPPYbP5+Opp57iuuuu\nC3tdjx4JqpwenQqf8io8yqfwKJ/Cp7wKT3UEWlEbWNmsWTMAMjMzy83zpnnLVGbatGlMmjQJv9/P\npEmT6N69e7SSKSIiIlEStSCiRYsW+P1+1q1bV25eamoqPp+Ptm3bVrqdv/71r0yaNIk6deowbdo0\nDaQUERE5QUXt6YyEhAR69erFxo0bj3jFdVZWFjNnzqRhw4aVtihs3ryZp59+mpo1azJ9+nQFECIi\nIiewqLVEAIwcOZKPPvqIESNG0K9fP5KSkpg/fz779u1j6tSpxMaGfm7r1q0sXbqU5s2b07NnTwCe\nf/55Dh06xMUXX8yqVasqfMNlly5daN26dTSTLSIiIhGIahDRqFEjZs+ezYQJE/jggw8oKSnhkksu\n4ZlnnqFjx45HLLtlyxamTp3KgAEDSoOIDRs24PP52Lx5M5s3b67wN+rUqaMgIooCgQAzZiwGICWl\nK36/v5pTJCIiJ4uofoDrRKPRvMcWCAS4/fZ3WbnydgA6dnyV2bMHKpA4Co0QD4/yKTzKp/Apr8Jz\nUn+AS04+s2atcgFEHBDHmjVDmDXr2B9JExER8SiIEBERkYgoiDiFpaR0pVu3mUARUETHjjNISela\n3ckSEZGTRFQHVsrJxe/3s2jRLUyZ8i4AKSkaDyEiIuFTEHGK8/v9DBnSu7qTISIiJyF1Z4iIiEhE\nFESIiIhIRBREiIiISEQURIiIiEhEFESIiIhIRBREiIiISEQURIiIiEhEFESIiIhIRBREiIiISEQU\nRIiIiEhEFESIiIhIRBREiIiISEQURIiIiEhEFESIiIhIRBREiIiISEQURIiIiEhEFESIiIhIRBRE\niIiISEQURIiIiEhEFESIiIhIRBREiIiISEQURIiIiEhEFESIiIhIRBREiIiISEQURIiIiEhEFESI\niIhIRBREiIiISEQURIiIiEhEFESIiIhIRBREiIiISESiHkTs3LmT0aNH07VrV9q2bcutt97KmjVr\nvtc2/vnPf3LjjTfSrl07OnXqxJgxY/j222+jnVQRERE5DlENIvbu3cvgwYN5//336dKlC4MGDSI9\nPZ2hQ4eyYsWKsLYxceJExo4dSyAQYNCgQXTu3JkFCxZwww03kJ6eHs3kioiIyHGIjebGJk2axM6d\nO5k2bRrdunUDYNiwYQwcOJDx48fTuXNn4uLijrr+tm3bePHFF2nTpg2vv/46NWrUAOD666/nrrvu\nYuLEiUyaNCmaSRYREZEIRa0lIj8/n3nz5tGyZcvSAAIgOTmZO+64g6ysLFatWnXMbWzdupXGjRsz\nbNiw0gACoHPnztStW5eNGzdGK7kiIiJynKIWRGzatImioiLat29fbl6HDh0IBoOsX7/+mNvo27cv\ny5cvp1evXkdM37NnDwcOHOCMM86IVnJFRETkOEUtiMjIyACgadOm5eY1adIEgLS0tO+1zUAgQGpq\nKnfffTc+n49f/OIXx51OERERiY6ojYnIycnB5/ORmJhYbp43LTc3N+ztZWRklLZI+Hw+HnrooXIt\nFCIiIlJ9Kg0ievTowfbt24+5zG233UZSUhIA8fHx5eZ70woLC8NO2KFDh/jZz35GQUEBy5Yt449/\n/CN5eXnce++9YW9DREREqk6lQUTv3r3Jzs4+5jKtWrViz549ABQXF5ebX1RUBEBCQkLYCWvWrBlj\nxowB4Ne//jUpKSlMmTKFLl260KpVq7C3IyIiIlWj0iBi7NixYW1ozpw5BINB8vLyys3zujEq6uoI\nR926dbn33nsZPXo0y5cvDzuISE6O7PdONcqn8CmvwqN8Co/yKXzKqxNT1MZENGvWDIDMzMxy87xp\n3jJH89VXX7F161Z69+5drlukcePGAOzbty/sNO3eHf4YjFNVcnKi8ilMyqvwKJ/Co3wKn/IqPNUR\naEXt6YwWLVrg9/tZt25duXmpqan4fD7atm17zG289tprPPDAA6xevbrcvK1btwIVP/0hIiIiP7yo\nBREJCQn06tWLjRs3HvGK66ysLGbOnEnDhg3p3r37MbfRt29fAJ5//vkjBmFmZGTwwgsv4Pf76dev\nX7SSLCIiIschqq+9HjlyJB999BEjRoygX79+JCUlMX/+fPbt28fUqVOJjQ393NatW1m6dCnNmzen\nZ8+eAFx99dXccMMNzJ07l379+tGjRw9yc3NZvHgxhYWFPPXUUzRs2DCaSRYREZEIRTWIaNSoEbNn\nz2bChAl88MEHlJSUcMkll/DMM8/QsWPHI5bdsmULU6dOZcCAAaVBBMAf/vAHWrVqxaxZs5g1axZ+\nv58rr7ySe+65hzZt2kQzuSIiInIcfMFgMFjdiagqGohTOQ1YCp/yKjzKp/Aon8KnvArPST2wUkRE\nRE4tCiJEREQkIgoiREREJCIKIkRERCQiCiJEREQkIgoiREREJCIKIkRERCQiCiJEREQkIgoiRERE\nJCIKIkRERCQiCiJEREQkIgoiREREJCIKIkRERCQiCiJEREQkIgoiREREJCIKIkRERCQiCiJEREQk\nIgoiREREJCIKIkRERCQiCiJEREQkIgoiREREJCIKIkRERCQiCiJEREQkIgoiREREJCIKIkRERCQi\nCiJEREQkIgoiREREJCIKIkRERCQiCiJEREQkIgoiREREJCIKIkRERCQiCiJEREQkIgoiREREJCIK\nIkRERCQiCiJEREQkIlEPInbu3Mno0aPp2rUrbdu25dZbb2XNmjURb2/Lli20bNmShx56KIqpFBER\nkeMV1SBi7969DB48mPfff58uXbowaNAg0tPTGTp0KCtWrPje2yspKeHhhx+mpKQkmskUERGRKIiN\n5sYmTZrEzp07mTZtGt26dQNg2LBhDBw4kPHjx9O5c2fi4uLC3t7LL7/Mli1b8Pl80UymiIiIREHU\nWiLy8/OZN28eLVu2LA0gAJKTk7njjjvIyspi1apVYW/v66+/5oUXXqB79+4Eg8FoJVNERESiJGpB\nxKZNmygqKqJ9+/bl5nXo0IFgMMj69evD2lYwGGTcuHGcffbZ3HvvvdFKooiIiERR1LozMjIyAGja\ntGm5eU2aNAEgLS0trG299tprfPLJJ/ztb38jPj4+WkkUERGRKIpaS0ROTg4+n4/ExMRy87xpubm5\nlW4nIyODyZMnk5KSQrt27aKVPBEREYmySlsievTowfbt24+5zG233UZSUhJAhS0H3rTCwsJKE/TI\nI49Qt25dRo0aVemyIiIiUn0qDSJ69+5Ndnb2MZdp1aoVe/bsAaC4uLjc/KKiIgASEhKOuZ0333yT\nddnteQQAABXxSURBVOvW8ec//5nTTjutsqSJiIhINao0iBg7dmxYG5ozZw7BYJC8vLxy87xujIq6\nOjxZWVk888wz9OnTh+7du5dOP54nM5KTj/57EqJ8Cp/yKjzKp/Aon8KnvDoxRW1gZbNmzQDIzMws\nN8+b5i1TkdWrV5Obm8uiRYtYuHDhEfN8Ph9z585l7ty5DB8+nOHDh4eVpt27Kx+DcapLTk5UPoVJ\neRUe5VN4lE/hU16FpzoCragFES1atMDv97Nu3bpy81JTU/H5fLRt2/ao619yySUVBgd79uxh1qxZ\nXHLJJfTs2bPCR0hFRETkhxe1ICIhIYFevXoxf/58VqxYwU9+8hPAuilmzpxJw4YNj+im+K7mzZvT\nvHnzctO3bt3KrFmzaN68Offdd1+0kisiIiLHKaqvvR45ciQfffQRI0aMoF+/fiQlJTF//nz27dvH\n1KlTiY0N/dzWrVtZunQpzZs3p2fPntFMhoiIiPwAovoBrkaNGjF79mx69uzJBx98wFtvvUWzZs2Y\nPn16uVaILVu2MHXqVJYtW1bpdn0+n76fUUUCgQAzZixmxozFBAKB6k6OiIicRHzBH/GHKTQQ59gC\ngQC33/4uK1feDkDHjq8ye/ZA/H5/NafsxKTBXeFRPoVH+RQ+5VV4qmNgZVRbIuTkMmvWKhdAxAFx\nrFkzhFmzwv9ImoiInNoURIiIiEhEFEScwlJSutKt20ygCCiiY8cZpKR0re5kiYjISSKqT2fIycXv\n97No0S1MmfIuACkpGg8hIiLhUxBxivP7/QwZ0ru6kyEiIichdWeIiIhIRBREiIiISEQURIiIiEhE\nFESIiIhIRBREiIiISEQURIiIiEhEFESIiIhIRBREiIiISEQURIiIiEhEFESIiIhIRBREiIiISEQU\nRIiIiEhEFESIiIhIRBREiIiISEQURIiIiEhEFESIiIhIRBREiIiISEQURIiIiEhEFESIiIhIRBRE\niIiISEQURIiIiEhEFESIiIhIRBREiIiISEQURIiIiEhEFESIiIhIRBREiIiISEQURIiIiEhEFESI\niIhIRBREiIiISEQURIiIiEhEYqO9wZ07dzJhwgRSU1PJzc3l0ksvZfjw4XTs2DGs9TMyMujVq1eF\n83w+H5s2bSI+Pj6aSRYREZEIRDWI2Lt3L4MHD2bv3r1cf/31JCYmMn/+fIYOHcoLL7zAT37yk0q3\nsXXrVgD69etHs2bNjpjn8/mIjY163CMiIiIRiGqNPGnSJHbu3Mm0adPo1q0bAMOGDWPgwIGMHz+e\nzp07ExcXd8xtfPHFF/h8Pu655x4uvPDCaCZPREREoihqYyLy8/OZN28eLVu2LA0gAJKTk7njjjvI\nyspi1apVlW7niy++IDY2lvPOOy9aSRMREZEqELUgYtOmTRQVFdG+ffty8zp06EAwGGT9+vWVbueL\nL77gvPPOo0aNGtFKmoiIiFSBqAURGRkZADRt2rTcvCZNmgCQlpZ2zG0UFBSQkZFBUlISjz/+OD16\n9KB169bccMMNvPvuu9FKqoiIiERB1MZE5OTk4PP5SExMLDfPm5abm3vMbXz55ZcEg0FSU1PJycmh\nb9++ZGdns3z5ckaPHk16ejrDhw+PVpJFRETkOFQaRPTo0YPt27cfc5nbbruNpKQkgAofv/SmFRYW\nHnM7ubm5nHfeeXTq1Ilx48aVTt+1axcpKSm88MIL9OrVi4svvriyZIuIiEgVqzSI6N27N9nZ2cdc\nplWrVuzZsweA4uLicvOLiooASEhIOOZ2OnfuzHvvvVdueoMGDRg+fDgPP/ww7733noIIERGRE0Cl\nQcTYsWPD2tCcOXMIBoPk5eWVm+d1Y1TU1RGuSy+9FIDMzMyw10lOjvz3TiXKp/Apr8KjfAqP8il8\nyqsTU9QGVnovhqqokvemffflUd+VkZHB2rVrK+z28KbpbZUiIiInhqgFES1atMDv97Nu3bpy81JT\nU/H5fLRt2/aY2/jTn/7EkCFD+PDDD8vN+/jjjwHrOhEREZHqF7UgIiEhgV69erFx40ZWrFhROj0r\nK4uZM2fSsGFDunfvfsxt9OnTB4CpU6dSUFBQOv2///0vL730EvXq1eOnP/1ptJIsIiIix8EXDAaD\n0drYjh07uPHGGzlw4AD9+vUjKSmJ+fPns2/fPqZOnXpEELF161aWLl1K8+bN6dmzZ+n0UaNG8d57\n79G4cWN69OjBgQMHWLJkCUVFRUydOvWIt2GKiIhI9YlqEAE2rmHChAmsWbOGkpISLrnkEu69995y\nX/GcO3cuDz/8MAMGDOCPf/zjEfP+9re/8eabb5KWlkZCQgLt2rXjvvv+f3vnHhRV+f/x10Lihdso\nhAKCYClLmkDqMmClOII0IImaBOZlNBEGM/NWXnCmMZuv06CSGUVYhOBtNDSZoSwTqZFEINQcEccg\nBBQDRATkfn5/8NsT6y5oSrawz2vGGefzvD3wefvs2c85zy2KsWPH9uSvKhAIBAKB4DHo8SJCIBAI\nBAKBYdBjcyIEAoFAIBAYFr2miEhOTkapVOrchwLgzp078nkb7u7uzJo1S+fGVQCNjY3s3LkTPz8/\n3NzcCAgIICUlRae2ra2NxMREAgICcHNzY9q0aXz66ae0trb2WG5Pmr6YU3dUVFQwYcIEkpKSdLYf\nPXqU4OBgPDw8mDx5Mv/73/9oaGjQqc3IyCAkJIQXXngBb29vNm7c2OVmbL/99huLFi1CpVLh6enJ\n22+/LZ8xo09UVlayefNmpkyZwtixY3nxxRdZu3atzt/V0L2qqanhgw8+wNfXV753JCQk0NbWpqU1\ndK86s23bNpRKpc5DGA3dp507d6JUKnX+Wb16tYZWH73qFcMZ586dY+nSpTQ1NXHu3DnMzMw02u/d\nu8e8efO4cuUK/v7+2NracuLECUpKSoiOjmbevHmytr29nYiICH7++WcmT57M6NGjyczMpKCggMWL\nF7Nu3TqNa2/evJlDhw4xceJEPDw8yMvLIycnh+nTpxMbG/tE8u9p+mJOXdHQ0MCiRYu4ePEi69ev\nZ8GCBRrtn3/+OTt27ECpVPLyyy9TWFhIRkYGHh4e7N27l6ee+ns/trS0NNasWYOjoyN+fn7cuHGD\n9PR0HBwcOHLkiEa/zM7OZsmSJVhaWhIYGMjdu3c5fvw4pqamHDlyBDs7uyfmQXdUVlYyZ84cKioq\n8Pb2RqlUUlRUxKlTp7C0tOTQoUPyoXqG7lV9fT1z5syhuLgYHx8fnJ2dyc3NJT8/Hx8fH+Li4mSt\noXvVmQsXLhAaGkp7eztJSUlMnDhRbhM+QWRkJGfOnCE8PJz7v45Hjx6Nn58foMdeSXpOWlqa5O7u\nLimVSkmpVEp3797V0sTFxUlKpVLat2+fHKuvr5cCAwMld3d3qaqqSo5/++23kouLi/TRRx/JsdbW\nVmnhwoWSq6urVFhYKMdzc3MlFxcXaeXKlRo/791335WUSqWUkZHRk6k+EfpiTl1RWloqBQcHSy4u\nLpJSqZS+/vprjfaysjJpzJgxUmhoqNTa2irHY2NjJaVSKSUnJ8ux+vp6SaVSSX5+flJ9fb0cP3z4\nsOTi4iJt27ZNjrW3t0vTp0+XVCqVVFFRIcfPnDkjKZVKacWKFf9Guo9EdHS0pFQqpcTERI34sWPH\nJBcXFykyMlKSpA4vDd2rmJgYycXFRSNXSZKkVatWaXx2RL/6m+bmZikgIEC+f2dnZ8ttwqcOfHx8\npODg4G41+uyV3g5n3L59m6ioKFavXo2VlZXOI8bV7N+/HysrK15//XU5NmjQICIiIrh37x5paWly\nPCUlhaeeeoply5bJMWNjY1auXEl7ezuHDx/W0CoUCq2TQ1etWgV0bPXd2+iLOekiMTGRoKAgCgsL\ntVYGqTl48CBtbW0sW7YMY2NjOR4REYGpqalGX0hLS6O2tpaFCxcyaNAgOT579mycnZ1JTU2VnyKy\nsrIoLi5mzpw52NjYyFovLy+8vb05efIkd+7c6emUH4mTJ09iZWXFwoULNeJBQUE4Ojryyy+/AHDo\n0CGD96qsrAw7OztCQ0M14gEBAUiSRH5+PiD6VWfi4uIoKSnB29tbq034BHV1dZSXlz/wPCh99kpv\ni4irV69y6tQpZs+ezdGjRzUS7Mz169flMW+FQqHR5unpCSCPwzU3N/P777/j6uqqdY7HuHHjGDhw\noMaYXW5uLoMHD+aZZ57R0NrY2ODk5KRzfE/f6Ys56SIpKYnhw4eTkpJCUFCQ1mtC+HsXVJVKpRE3\nMTHB3d2dgoICeQ6OWqvuU51RqVTU1NRQWFgIdPQ3hUKhdV31v29rayM3N/fxEuwB1EN7UVFROttN\nTExoaWmhpaVF7heG6hVATEwMP/30E0ZGmrfNa9euAWBtbQ0gvPp/CgoKiI+PZ9myZVr3GxCfP4Ar\nV64APLCI0Gev9LaIGDFiBMeOHWPr1q1acyA6U1JSAqDzTYW1tTX9+/enuLgYgPLyclpbW3VqjYyM\nGDZsGEVFRUBHwXHz5s0u34DY29tTW1vL7du3/2lq/xl9Maeu2LJlC0ePHsXNza1LTUlJCVZWVjpP\nl7W3tweQ+466nzk4ODy0VpfPw4cPR5IkWftfYmRkxPz587WerKHji/GPP/7A0dGRfv36cf36dYP2\nShfV1dWkpKTwySefYG9vT1BQEIDwio4CdePGjTg7O2u89e2MoX/+oKOIUCgUVFdXs3jxYlQqFSqV\nihUrVsjfRaDfXultETF06FBGjRr1QF1NTQ3Q9QmhZmZm8imiD9Kam5vT2NhIe3u7/AqnOy3Q5WoR\nfaQv5tQVkyZN0nozdT81NTVYWFjobFN70bnvmJiY6DwAztzcHEmStPqZrmurC2K1Vh+RJIktW7Yg\nSRIhISGA8Op+YmNj8fb2ZsuWLZibm7Nnzx7ZB+EVJCQkUFBQwNatWzUm/HVG+NRRREiSxJdffomZ\nmRlz587Fzc2NH374gblz51JQUADot1cPPAq8J5k6dSrl5eXdat544w02bdr00NdsaWkBuj7d08TE\nhMbGRgB5CWN3Wug4MfSfaHsLfTGnx6G1tfWBXjQ3Nz+0Vu1bdz73Bo+jo6P59ddfGTdunLyaRXil\niaOjI+Hh4RQXF3Py5EnCwsLYs2cPrq6uBu9VUVERu3fvJiwsjHHjxnWpM3SfoGM+nr29Pdu2bWPC\nhAlyXL26YsOGDXzzzTd67dUTLSL8/Py6XKeq5p+e0jlgwADg72Lifpqbm+VXQP3793+gVqFQMHDg\nQPkAsO60gM7XS/rKw+QPvSunx2HAgAEP7cWAAQOorKzsUqtQKORJTN31SfV1O0940hfa2trYtGkT\nqampjBgxgt27d8tPkcIrTYKDg+W/Z2RkEBkZybp16zh+/LjBe7Vx40asra219ji4H0P3CTqW2+si\nMDCQgwcPkpOTQ1FRkV579USLiPfee6/Hr2lpaQl0/cqlrq5OnvD0IO3du3dlw8zNzTEyMupWq9b1\nFvpiTo+DhYXFQ3thYWFBU1MTLS0t9OvX74FadXzIkCEaWvVQUXfzfP4LGhsbWbFiBZmZmTg7O5OY\nmMjTTz8ttwuvumbKlCl4eXmRlZVFSUmJQXuVnJxMXl4e8fHx8hcUoHNisyH79DA899xz5OTkUFZW\nptde6e2ciIfFyckJgNLSUq22v/76i6amJpydnYGOSSX9+vXTqW1vb+fmzZuytl+/ftjZ2enUqn/e\nkCFDuhyn0kf6Yk6Pg5OTE1VVVXLF3ZnS0lKMjIwYMWKErIWOZX66tIDcd7rrk6WlpSgUClmrD9TW\n1rJgwQIyMzMZM2YMKSkpDB06VENj6F61tbWRlZXFmTNndLarN+SpqakxaK++//57FAoF4eHhGjsv\n7t27F4D58+fj6upKeXm5QfsEHX3q4sWLXLhwQWe7ehi+f//+eu1Vry8ibG1tsbOzIy8vT6vt7Nmz\nAHh4eAAd409ubm5cvnxZa6vQ8+fPc+/ePVkLMH78eCorK/nzzz81tLdu3aK4uBh3d/eeTudfpy/m\n9KiMHz+e9vZ2eUmUmubmZs6fP8+zzz4rv5kaP348kiTpXAKbnZ2Nubm5vIxNrc3OztbSnj17FiMj\no27Hip8kzc3NhIeHc/HiRTw9PUlKStJ6IgHhFXSsyV+7dq3Op+rLly+jUCgYPny4QXs1e/ZsoqKi\nWL58ucYf9Sqp4OBgli9fjoWFhUH7BB1FRGhoKEuXLtXZp/Ly8jA2NsbV1VWvver1RQR0bIxz48YN\nkpOT5VhdXR2fffYZAwcOlJdeAbz66qs0NTWxa9cuOdba2kpsbCwKhYLXXntNjs+cORNJkti+fbvG\nf3JMTAwKhYK5c+f+y5n1PH0xp0clMDAQIyMjdu3apVHhx8XFUV9fL69MAJg2bRqmpqYkJCRobL5y\n+PBhiouLNfqNSqXCzs6OgwcPajwNqJ9kfX19GTx48L+c3cMRExNDfn4+Hh4efPHFF5iamurUGbpX\nxsbG+Pr6Ul1dTUJCgkbbvn37uHTpElOmTGHIkCEG7dXMmTO1CojORcSsWbOIiorCzMzMoH2CjomL\nU6dOpba2lvj4eI22PXv2cPXqVWbMmKH3XvWKszOg4zVYTk6OzrMz6urqmD17NiUlJfj6+uLg4MCJ\nEycoLS0lOjqasLAwWdve3s68efPIz8/Hy8uLMWPGkJmZSWFhIUuWLGHNmjUa1161ahXp6ek8//zz\neHp6kpeXR15eHv7+/uzYseOJ5N7T9MWcuiM1NZX169ezYcMGrbMzYmJiSEhIYOTIkfj4+HD16lVO\nnz7NhAkT+OqrrzTGFA8cOMD777/PsGHD8Pf3p6Kigu+++w4nJycOHDigMQx0+vRp+WY5Y8YM6uvr\nSUtLw8LCgoMHD8rrtf9LKisr8fHxobW1lVmzZmFra6tTFx4ejomJiUF7BR0HuYWEhFBRUcGkSZMY\nPXo0ly9fJisrC0dHR1JSUuR5JIbu1f18+OGH7N27V+vsDEP3qaysjJCQEKqqqvDy8sLFxYVLly6R\nnZ3NqFGjSE5Olufy6atXvaqIyM3NJTs7W+dEj+rqarZv386pU6doaGhg5MiRvPnmm7zyyita2oaG\nBnbt2kV6ejo1NTU4ODgQFhamc9OdtrY24uPjSU1NpaKiAltbW2bOnMmSJUu0Jq30FvpiTt2RmprK\nhg0bdB7ABR1Pkvv376ekpARra2v8/PzkD9X9pKenk5CQwLVr17C0tOSll15i5cqV8uTdzmRlZbF7\n924uXbqEqakpEydO5J133ul2C/cnyY8//shbb731QF3nwt1QvVJTVVVFbGwsGRkZVFdXY2Njw/Tp\n04mIiJBv9moM3avOdFVEgPDp1q1bfPzxx5w+fZqamhpsbGzw9/cnMjJSywN99KrXFBECgUAgEAj0\niz4xJ0IgEAgEAsGTRxQRAoFAIBAIHglRRAgEAoFAIHgkRBEhEAgEAoHgkRBFhEAgEAgEgkdCFBEC\ngUAgEAgeCVFECAQCgUAgeCREESEQCAQCgeCREEWEQCAQCASCR+L/AEwhTHChHyVTAAAAAElFTkSu\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x125680350>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Residuals\n",
"xdb.xplot(yval[gdval],fitv[gdval]-xval[gdval], scatter=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Polynomial fits (True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reload(desiboot)\n",
"xfit, fdicts = desiboot.fit_traces(xset,xerr)#[:,0:5],xerr[:,0:5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### QA"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing fiber_trace_qa.pdf QA for fiber trace\n"
]
}
],
"source": [
"reload(desiboot)\n",
"desiboot.qa_fiber_trace(flat,xfit)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PSF"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sigma for each fiber (initial guess)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:bootcalib.py:564:fiber_gauss: fiber_gauss uses astropy.modeling. Consider an alternative\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:DESI:fiber_gauss uses astropy.modeling. Consider an alternative\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:bootcalib.py:583:fiber_gauss: Working on fiber 0 of 50\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:DESI:Working on fiber 0 of 50\n",
"WARNING: AstropyDeprecationWarning: The \"sig\" keyword is now deprecated, use the \"sigma\" keyword instead. [astropy.stats.sigma_clipping]\n",
"WARNING:astropy:AstropyDeprecationWarning: The \"sig\" keyword is now deprecated, use the \"sigma\" keyword instead.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:bootcalib.py:583:fiber_gauss: Working on fiber 25 of 50\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:DESI:Working on fiber 25 of 50\n"
]
}
],
"source": [
"reload(desiboot)\n",
"gauss = desiboot.fiber_gauss(flat,xfit,xerr)#,verbose=True)#,debug=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fit a 2nd Order Polynomial"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fiber = np.arange(gauss.size)\n",
"gfdict,mask = dufits.iter_fit(fiber, gauss, 'polynomial', 2)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'coeff': array([ 1.13912985e+00, -6.26732640e-03, 1.93205792e-04]),\n",
" 'func': 'polynomial',\n",
" 'order': 2,\n",
" 'xmax': 49,\n",
" 'xmin': 0}"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gfdict"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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wbXr37k6XLveyb98J4DjWTrtcFVM1azpoEduwuuvB19cXX19fi8uDgoJKfVnU\nyJEjGTlyZJka1bVrV3744YdSl7m7uzN//vwybU9EKq527do89FAPHnqoB6++Oo+dO7cTG7uWLVs2\nU/TkBHQDhpCVVfMHQIrIZU4mR3ntnJWMOvIVjD3yF4wdf2XGfubMGQYMmE1Kyi/AJ0AhtWrVMj85\n4evrT4MGxccuFXUL/Hkwqa0nfLL1PmryBF1GvvbB2PHb4qkHJQoGYuQPCxg7/sqOvehLNCsrgzp1\n0omPj+Prr78CLncZPvroXwgKGkyfPn2LzYlSUwYz/i/pGA2Al5f9zGRpDSNf+2Ds+JUolINRLxYw\n9ocFKjd+e/+1WR3n/siRVNavjyEmZg0///wTAG5uDfD3H0BgYDAPPdTDPKtqZSst/rKcs8jIbUyd\nOoCiCbogn/DwjTVmqml99o0bv0PMoyBS0135a9Oe3ptQnVq2bMWkSVOYOPEfHDp0kNjYtcTFrSM6\n+iOioz+iceMmBAQEERQ0mHvvvd/ie14qg86ZiPXs4l0PIjWZrUfX2+NbHSvCycmJDh3uZubMWXz9\n9UHi4z82v3Pi/fcX4ePTm65d72Hu3Fn89NOPVdKmsp6za73+2tHOmcif6Y6CiB1x9F+6tWrV4oEH\nHuSBBx7k5ZfD+fTTncTGrmPr1s3Mn/8v5s//F3fddTeBgcEEBg6iefPbqrvJwNUn6HL0cyaiMQoG\nYuR+Oqi8+G05ut7WfeE1ZWbCCxcusG3bVmJj17Fz5zbzhG5duz5AYGAwAwYElvqKeWtdee7t+ZxV\nBn32jRu/xiiI2AF7nQ66Jo3Uv/766wkIGERAwCAyMs6xaVM8cXHr2Lt3D0lJX/DSS8/To8cjBAYG\n2+RFVfZ6zkTske4oGIiRs2qoGfEb7ZfutZw6dZING2KJi1vHN998DVx+3LJPn34EBQXTp08/XF1d\nr7mdyn7ipbLnhKiomnDtVyYjx687CiIORr90i2va9GbGjn2WsWOfJTU1hfXrY4iNXcvmzfFs3hxP\n/fpu+Pr6ExQUTPfuj1C3bt1rb9TGdM7E0emOgoEYOasG48VfE37plofJZOLQoYPExa1j/foYfvnl\nOACNGjUyv92ya9cHir3d0mjn/kqK37jxa8KlcjDqxQLG/rCAMeOvKYMZy8tkMvHVV0nExa1lw4Y4\nfvvtDAC33tqMgQODCAoK5u6776FJkwaGO/d/ZsRr/8+MHL8ShXIw6sUCxv6wgLHjN0Lsly5dYu/e\nPcTFrWPEfHgAAAAgAElEQVTz5o1kZp4HoHXrNgwf/hh9+/bnjjvaVnMrL6vqmTyNcP6vxsjxK1Eo\nB6NeLGDsDwsYO35bxW6vU1Vf2S6AXbt2EBe3jm3btpKbmwtAhw4dCQwMJiAgqNrmaKiOp1GMfO2D\nseNXolAORr1YwNgfFjB2/LaI3V4ft7xWu7Kzs9m37xOiopaza9cOLl26BECXLt0ICgqmf/9AmjRp\nUmXtvdbTKJWRjBn52gdjx2+LREFTOIuIVWw9VbWtXKtd9evXZ9iwYXz00RoOHTrMvHlv89BDPdi/\nP4kXXphCx45tCQ4eyIoVy8jIOFdtccD/kp6pUwcwdeoAQkLiNCW0VDslCiJiGDfe6M6IEaOIjd3E\nd9/9xJw5r9K5833s2fMJkyZN4K672jBiRAgxMWvIzs6ulDZc7b0R9pqMibEpURARq1zrxUjVpbzt\n8vBoypgxz7B1606++uo7Xnrp/7jjjnYkJGxl/PinuOuu1jz99Ci2bNlk01/1RfMuhIdvJDx8o110\n31hDL74yLo1RMBAj99OBseM32mDGK9tVlvh/+ulH8xwNqakpALi5NcDX15/AwEGVOrGTree+sNXj\nsfY6PsVaRv/sV5QSBQMx8ocFjB2/kWOH8sVvMpk4cODfxMXFsH59DGlpvwKXJ3by9w8gMHAQ3bp5\nUbt2bZu21VbJmC2/3Gv6dOBGvv41mFFEpJI4OTnRsWMn/vnP2Xz99UE2btzGk0+OwcmpFlFRHxAQ\n4EvnzncyY8Y0vv76K2z1m8vFxYVRo/oyalTfCv1i13gHsRUlCiIi11CrVi26dXuAuXP/xXff/cS6\ndfEMHz6SvLxcFi9eiI9Pb7p06cicOf/HgQPf2SxpsBf2Oj5Fqoa6HgzEyLffwNjxGzl2qLz48/Pz\n2b17F3FxMWzdupmcnMtPSrRpcwcBAYMIDAyuttkgK2u8A9jX+BRrGPn61xiFcjDqxQLG/rCAseN3\nhNgr8kVVFfHn5uayY8c2NmyIZdu2reYnA+66624CAoIYODCIFi1aVmobruTo7/qwliNc/+WlRKEc\njHqxgLE/LGDs+Gt67BUdmFfV8WdnZ5GQsJX162PYtWsHBQUFAHTufC8BAcEMHBjILbfcWmXtqenn\nv6KMHL8ShXIw6sUCxv6wgLHjr67YbXW7uqKj7qvz3GdknGPr1s3Exa3js89288cffwDQrZsXAQFB\n+PsH4OHhUaltMPK1D8aO3xaJQh0btENEpIQr7wLExdWsZ+9tpWHDGxk6dDhDhw7nt99+Y9OmDWzY\nEMvnn+/lyy/3MX3683h7d2fgwCD8/Qfg7t6oupssUoyeehCRSmHLx/McZdT9TTfdxKhRTxIXt5l/\n//tH5sx5lXvvvZ/PPtvNP/7xNzp0uIPQ0CBWrVrB+fMZ1d1cEUB3FESkBiia9njVqo0AhIbW/DsT\nTZvezJgxzzBmzDP88stx4uPXm8c07Nq1A2dnZ3r16sPAgUH06+dD/foVv4VsL2ryExRGpDEKBmLk\nfjowdvzVEbutH8+riJp07lNTU4iPj2P9+li+//4gcDlR6tOnHwEBQfTp04/rr7++TNu0p/irYzpo\ne4q/qlX5YMb09HSWL19OYmIisbGxVu/k2LFjREZGcuLECRYvXlxqnaioKFauXEl6ejotW7Zk0qRJ\ndO/evdS669at46WXXipW9vLLLzNo0KBrtsWoFwsY+8MCxo6/pg9mrKiaeu5//vkn1q+PYcOGWP7z\nn58BuP76evTr9xcGDAiid+9HcXFxsem7LipbdUwHbU/xV7UqHcyYnJzM3r17Wbp0aZlG6CYmJrJn\nzx6io6Pp2rVrqXWWLFkCwJtvvsmRI0d45ZVXGDduHDExMXh6eharW1hYSFRUFK1atTKXubi44O/v\nb3WbRKRqFE1HLOXTtm07pk59kSlTXuD77w+xYUMs69fHEBd3+V/9+m48+mg/fvjBgx9/fA1wrvJB\no/aSDErlKXPXQ3BwMOfOnWPnzp1l2pGXlxd33HEHy5YtK1aen5/Pl19+Wezuwfbt23nuueeYNm0a\no0aNKlY/Pj6e1NRUJk6cWKb9FzFqVgnGzqrB2PEbOXYoW/z2/sVnMpn47rtk1q+PJT4+jl9+Of7f\nJQ2BQCCIuXNzePJJX/M6lXX+y9ONUB1dUka+/qvlpVCurq7l2pGl9ZydnUt0MbRq1QonJyfuueee\nYuUmk4nFixfj4eHB6dOny9UOERFLir7Epk4dwNSpAwgJiTPPsGgvLv9t7Mw//zmb/fsP8OyzrwB/\nBeoBS4H+zJ79NH//+1/Zs+dTLl26VGltKc+TLUUDU8PDNxIevrFEkpCXl0dk5DYiI7fZ3bE3Krt8\nPHLfvn2MHj2azp07FytPSEggJSWFsLAwevbsybhx4zh69Gj1NFJEHE5Ne+Oik5MTzz//FF5edwGH\ngV00bfoI9eq5snx5JMHBA+jYsR3PPPMMn3++1zzZU3Wz9IbMmpCoGZFdJQomk4kNGzYQERFB7969\nSyxv27YtCxcuZPLkyXh6erJ7926Cg4NJTk6uhtaKiFS///1C30x4+O8kJa3hu+9+Ii5uM6NGPQmY\neO+99wgI8KVTp/a8+OIUvvhiH4WFhRXet63nt6hpiZpR2E2ikJuby5IlS/jwww85ffo0I0aMYMuW\nLcXqtGrVil69ejFmzBhiY2OZPn06OTk5TJ482TyXuohIedXUiZ2u/IVeu3ZtvL27Ex4+n++++5kd\nO3YwYsRoCgryiYhYzIAB/ejc+U5mzJjGV199We6k4VrdCOIYyjyYccSIEZw4caLMgxl79epFs2bN\nSgxmLM2GDRt44YUXaNSoEZ999tlV67799tu89957fPjhh3h5eZWpTSIiV7rcR37579uoUb0d6ouv\noKCATz75hNWrVxMXF8e5c+cAaN68OYMHD2bIkCF07doVJyenamlfXl4ef/nLSnbvHg7Aww9/xMcf\nD3Ooc1AT2WWiADB58mS2bt1KYmIi7u7uFuvl5OTQpUsX5s2bh4+PzzW3a9SRr2Dskb9g7PiNHDso\n/tLiz8/PZ8+eT9iwIY6tWzeTmXkegObNb6N//wACAoK4557OVZ40VMZTJ0Y+/w79UqguXbqwfft2\n6tevf9V69erVw83NjbZt21ZRy0REaj5nZ2f69OlHnz79uHjxIrt37zInDQsXvs3ChW9z220tGDgw\nkIEDA7n77nuqJGnQ3Bv2p1xjFCzdhDh//ny51itNWloaffv2xdnZ+ar1UlJSePDBB2ndurXV2xYR\nkf+57rrr6NvXh3ffXcL336cQGbmSoKDBnD37GwsWzKdPnx5069aJl18O48CB78r0t1xqvjIlCiaT\nibNnz5KdnV1i8GBERATdunUjISGhxHr5+flkZmaSkVHybWinT5/mtddeIykpyVz27bffkpSUxPTp\n081lBQUFTJ8+nbVr15oH3qSlpbFixQrmzJlTljBERMQCFxcXfH39WbTogz8lDcGkp6fz1lvz6N37\nIby87uWVV2Zx8OABJQ0GYHWisGXLFnx9fTly5AhZWVn4+voSHR1tXu7u7o6bmxsNGjQotl5UVBQ+\nPj7k5ORw+PBh/Pz82LFjh3l5bm4uiYmJPPXUUwQGBjJt2jSSkpKIjIwsNjahTp06XLx4kfDwcHx8\nfJg5cyaff/45M2bMoF69ehU5BiIilaKmTx7k6ur636ThQ374IZUPPlhOQEAQp06d5M03/0WvXt54\ned3L3LmzOHTooJIGB6W3RxqIkQf0gLHjN3LsUN1vz6y6tyRaYuv4L1y4wM6d24iPX8/27R9z4cIF\nAFq3bsPAgYEMGBBE+/Z3VtvTE1cy8vVf5W+PdARGvVjA2B8WMHb8Ro4dqif+6nhLoiWVGX9OTg47\nd25jw4Y4duxIIDc3F4A2be5gwIBABgwIrPakwcjXf7W860FERBxXWbtL6tWrx4ABgXzwwTK+/z6V\n99+PxN9/IGlpv/LGG+E88ogXDz3UhVdfncP33x9S90QNpDsKBmLkrBqMHb+RY4fq7noYBVTNWxIt\nsTZ+W3aXZGdns2NHAhs2xLFz5/+Sjst3GgKqtHvCyNe/uh7KwagXCxj7wwLGjt/IsUP1xW8vr6y2\nNv7K6i4pShri49ezc+e2K7onAujfP5A777yr0pIGI1//Dj3hkohITafJgy6rX78+AQGDCAgYRHZ2\ntnlMw86d23jjjdd5443Xad26jflOQ2UmDVJ2uqNgIEbOqsHY8Rs5dlD8Ze96GAVUfndJTk6O+U7D\nnwdCtm7dhv79A+jfP4AOHe6ucNJg5POvrodyMOrFAsb+sICx4zdy7KD4yxJ/dXWXFD09UZQ0FD1y\n2bJlq/8+PRFAhw4dSyQN1rTXyOdfiUI5GPViAWN/WMDY8Rs5dlD8NS3+nJwcdu3aXmKehhYtWjJg\nQCD9+w+kY8dOXLx40arBlzUtfltSolAORr1YwNgfFjB2/EaOHRR/TY7/8uRO29m0aT0JCR9z4UIO\nALfd1oKWLe9h9+7ngQcAJywNvqzJ8VeUBjOKiIhDu/766+nffyD9+w8kNzeXXbt2sHHjehIStnL8\n+AZgA3A78DgwrXob66A04ZKIiNQIrq6u+Pn1Z9GiD/jhh1QiIqK46aauwO/AXO67711CQ3tUdzMd\nju4oiIjUAPYyJ4O9cHFxYcCAQPr29eGjj3Zy4UI2Y8YMNPxxqQxKFERE7NyVMybGxVXfC6bsjYuL\nC0895VfdzXBo6noQEbFzq1bt+W+SUBeoy759o8x3F+xZTX/NtlymOwoiImJzugviOHRHQUTEzoWG\n9sDLaymQD+Tj5RVZoUF7VfFLv6beBZGSdEdBRMTOubi4sHp1IKtWbQQgNLT8v8z1S1/KSncURERq\ngKIXTI0a1bdCX+pV9Uvf1ndBpProjoKIiNicLe+CSPVSoiAiYiChoT2Ii1ta7A2RoaGBlbIvvWbb\nMShREBExEP3Sl7JSoiAiYjD6pS9locGMIiIiYpESBREREbFIiYKIiIhYpERBRERELFKiICIiIhYp\nURARERGLlCiIiIiIRWVKFNLT05k3bx5BQUFl2smxY8cICwtj7NixFutERUXRr18/OnfuTFBQEJ99\n9lmp9XJzcwkLC2PIkCEMGTKEBQsWUFhYWKb2iIiIiHWsThSSk5NZs2YNS5cu5fz581bvIDExkZUr\nVxIdHU1ubm6pdZYsWcLFixd58803efnll0lPT2fcuHH8+OOPxeoVFBQwevRo8vLyWLNmDdHR0SQl\nJTFz5kyr2yMiIiLWs3pmxk6dOtGpUyc+/fRTzp07Z/UOvL298fb2Jj4+vtTl+fn5tG/fnu7duwPQ\nvn176taty3PPPccXX3yBp6enuW5kZCQHDhxg0aJFANSuXZsJEyYwcuRI/Pz88PLysrpdIiJSc+Tl\n5Znfchka2kPTTlehMo9RcHV1LdeOLK3n7OxsThKKtGrVCicnJ+65555i5dHR0bRv356GDRuayzp3\n7oyzszMrV64sV7tERMS+5eXlERISx9SpA5g6dQAhIXHk5eVVd7MMwy4HM+7bt4/Ro0fTuXNnc1lK\nSgonTpygRYsWxeo6OzvTrFkzkpKSqriVIiJSFVat2sO+faOBukBd9u0bZb67IJXPrhIFk8nEhg0b\niIiIoHfv3sWWpaWlAdC4ceMS67m5uZGZmUlWVlaVtFNERMQo7CZRyM3NZcmSJXz44YecPn2aESNG\nsGXLFvPyogGUpXVh1KlzeaiFbkWJiDie0NAeeHktBfKBfLy8IgkN7VHdzTIMu0kUXF1dGTt2LBs2\nbODVV18FYO7cueblRQNXSksGispuuOGGKmipiIhUJRcXF1avDiQ8fCPh4RtZvTpQgxmrkNVPPVSl\ngQMHsnv3brZu3crvv/+Ou7s7zZs3ByAjI6NE/YyMDNzd3XF2dr7mths3drN5e2sSxW/c+I0cOyj+\nmh+/G1OmDCr32jU//upjl4kCQJcuXdi+fTv169cHoF27djRq1IjU1NRi9fLz8zl16hR+fn5WbffM\nGeOOY2jc2E3xGzR+I8cOil/xGzd+WyRI5ep6MJlMpZZfayImS+uVJi0tjb59+5rvEjg5OREaGsrB\ngwfJzMw010tKSqKwsJCQkBCrty0iIiLWKVOiYDKZOHv2LNnZ2RQUFBRbFhERQbdu3UhISCixXn5+\nPpmZmaV2G5w+fZrXXnut2OON3377LUlJSUyfPr1Y3TFjxtCyZUvef/99AC5cuMA777zD0KFDue++\n+8oSioiIiFjB6kRhy5Yt+Pr6cuTIEbKysvD19SU6Otq83N3dHTc3Nxo0aFBsvaioKHx8fMjJyeHw\n4cP4+fmxY8cO8/Lc3FwSExN56qmnCAwMZNq0aSQlJREZGYm7u3uxbV133XVERUXx66+/EhoayujR\no/Hx8dEUziIiIpXEyVSW/gAHYNR+KjB2Px0YO34jxw6KX/EbN/5qG6MgIiIixqBEQURERCxSoiAi\nIiIWKVEQERERi5QoiIiIiEVKFERERMQiJQoiIiJikRIFERERsUiJgoiIiFikREFEREQsUqIgIiIi\nFilREBEREYuUKIiIiIhFShRERETEIiUKIiIiYpESBREREbFIiYKIiIhYpERBRERELFKiICIiIhYp\nURARERGLlCiIiIiIRUoURERExCIlCiIiImKREgURERGxSImCiIiIWKREQURERCxSoiAiIiIWKVEQ\nERERi5QoiIiIiEVKFERERMSiMiUK6enpzJs3j6CgoDLt5NixY4SFhTF27NhSl1+6dInFixfTr18/\nOnbsiL+/P+vXr7e4vXXr1uHp6VnsX0xMTJnaJCIiItdWx9qKycnJ7N27l6VLl+Lh4WH1DhITE9mz\nZw/R0dF07dq11Dpz587FxcWF119/nfPnz7NgwQKmTZtGdnY2w4cPL1a3sLCQqKgoWrVqZS5zcXHB\n39/f6jaJiIiIdaxOFDp16kSnTp349NNPOXfunNU78Pb2xtvbm/j4+FKXp6en07RpU55++mlz2b33\n3oufnx9vvfUWQ4cOpXbt2uZlmzZtonfv3kycONHqNoiIiEj5lHmMgqura7l2ZGm9goICRo4cWays\nXr16PPLII2RnZ5ORkWEuN5lMLF68GA8PD06fPl2udoiIiIj1qn0w46233oqzs3OJcldXV+rXr4+7\nu7u5LCEhgZSUFMLCwujZsyfjxo3j6NGjVdhaERERY6n2RMGS/fv3M3DgQJycnMxlbdu2ZeHChUye\nPBlPT092795NcHAwycnJ1dhSERERx2WXicI333zD8ePHmTBhQrHyVq1a0atXL8aMGUNsbCzTp08n\nJyeHyZMnU1BQUE2tFRERcVx2lygUFBQwZ84cXnnlFRo2bHjVusOHD2f8+PGcPHmS/fv3V1ELRURE\njMPqpx6qyuzZs+nbty+9e/e2qv6TTz7JokWLig16vJrGjd0q0rwaT/EbN34jxw6KX/EbO/6KsKtE\nYcmSJdStW5dx48ZZvU69evVwc3Ojbdu2VtU/cyarvM2r8Ro3dlP8Bo3fyLGD4lf8xo3fFglSuboe\nTCZTqeXnz58v13oAy5cvJzU1lRkzZhQrT09Pv+o2U1JSePDBB2nduvVV64mIiEjZlSlRMJlMnD17\nluzs7BKDByMiIujWrRsJCQkl1svPzyczM9Ni98CHH37I5s2befrpp0lNTSU1NZWff/6Z+Ph4IiMj\ngctjF6ZPn87atWspLCwEIC0tjRUrVjBnzpyyhCEiIiJWsrrrYcuWLSxYsMA8b4Gvry9PPPEEQ4cO\nBcDd3R03NzcaNGhQbL2oqCiWLVtGTk4Ohw8fxs/Pj0mTJtGnTx/gcnfD/PnzAUqdhnn16tWXG1qn\nDhcvXiQ8PNyclNx9993MmDGj2COUIiIiYjtOpqv1Bzggo/ZTgbH76cDY8Rs5dlD8it+48VfbGAUR\nERExBiUKIiIiYpESBREREbFIiYKIiIhYpERBRERELFKiICIiIhYpURARERGLlCiIiIiIRUoURERE\nxCIlCiIiImKREgURERGxSImCiIiIWKREQURERCxSoiAiIiIWKVEQERERi5QoiIiIiEVKFERERMQi\nJQoiIiJikRIFERERsUiJgoiIiFikREFEREQsUqIgIiIiFilREBEREYuUKIiIiIhFShRERETEIiUK\nIiIiYpESBREREbFIiYKIiIhYpERBRERELFKiICIiIhbVKUvl9PR0li9fTmJiIrGxsVavd+zYMSIj\nIzlx4gSLFy8usfzSpUt88MEHxMbGcvLkSW677TaeeuopAgICStTNzc0lPDycQ4cOAdC9e3eeffZZ\natVSziMiImJrVn+7Jicns2bNGpYuXcr58+et3kFiYiIrV64kOjqa3NzcUuvMnTuXzMxMXn/9dd59\n912uv/56pk2bxkcffVSsXkFBAaNHjyYvL481a9YQHR1NUlISM2fOtLo9IiIiYj2rE4VOnToxYcIE\nPD09y7QDb29vXnjhBW688cZSl6enp9O0aVOmTJlCx44d6d69O0uXLqVp06a89dZb/PHHH+a6kZGR\nHDhwgOeffx6A2rVrM2HCBNatW8e+ffvK1C4RERG5tjLfr3d1dS3XjiytV1BQwMiRI4uV1atXj0ce\neYTs7GwyMjLM5dHR0bRv356GDRuayzp37oyzszMrV64sV7tERETEsmrv2L/11ltxdnYuUe7q6kr9\n+vVxd3cHICUlhRMnTtCiRYti9ZydnWnWrBlJSUlV0VwRERFDqfZEwZL9+/czcOBAnJycAEhLSwOg\ncePGJeq6ubmRmZlJVlZWlbZRRETE0dllovDNN99w/PhxJkyYYC4rGkBZWhdGnTqXH97Iy8urmgaK\niIgYhN0lCgUFBcyZM4dXXnml2FgEFxcXoPRkoKjshhtuqJpGioiIGESZ5lGoCrNnz6Zv37707t27\nWHnz5s0Big1uLJKRkYG7u3upYx2u1Lixm20aWkMpfuPGb+TYQfErfmPHXxF2dUdhyZIl1K1bl3Hj\nxpVY1q5dOxo1akRqamqx8vz8fE6dOoW3t3dVNVNERMQwypUomEymUsuvNRGTpfUAli9fTmpqKjNm\nzChWnp6eDoCTkxOhoaEcPHiQzMxM8/KkpCQKCwsJCQmxtvkiIiJipTIlCiaTibNnz5KdnU1BQUGx\nZREREXTr1o2EhIQS6+Xn55OZmVlqtwHAhx9+yObNm3n66adJTU0lNTWVn3/+mfj4eCIjI831xowZ\nQ8uWLXn//fcBuHDhAu+88w5Dhw7lvvvuK0soIiIiYgWrxyhs2bKFBQsWcPToUQB8fX154oknGDp0\nKADu7u64ubnRoEGDYutFRUWxbNkycnJyOHz4MH5+fkyaNIk+ffoAl7sb5s+fD4C/v3+J/a5evdr8\n39dddx1RUVHMnj2b0NBQTCYTvr6+JSZsEhEREdtwMl2tP0BEREQMza4GM4qIiIh9UaIgIiIiFtnd\nPAqVIS8vj5iYGJYtW8bSpUu55ZZbSq23adMmli9fTt26dXFzc+Oll17i1ltvreLW2l5hYSFvv/02\ne/fupXbt2tx999384x//ME9i5UjS09NZvnw5iYmJxMbGlljuyMdi9erVfPTRRxw/fpwmTZrw2GOP\nMWrUqGJ1HDn+jRs3smTJEn799VduvfVWnnrqKQICAorVceT4i5w7d46goCAGDRpUbHZbR4993759\njB49uljZM888w1//+lfz/zv6MQDYu3cv8fHxNGrUiGbNmvHYY48BFYzd5OB++eUX09KlS00PP/yw\nydPT05SWllZqveXLl5u8vb1Nv/32m8lkMplWr15t6tGjh+ns2bNV2dxK8dxzz5lGjx5tunTpkslk\nMpn+/ve/m5544olqbpXtffvtt6YFCxaY7rrrLlOvXr1KreOox+L99983vfDCC6avv/7a9OWXX5qe\nfvppU7t27UyvvvpqsXqOGn9cXJwpPDzcdPDgQdMnn3xi6tevn8nT09O0a9euYvUcNf4/e/rpp02e\nnp6mBQsWFCt39NhHjhxp8vHxMf/z8/MznTx5slgdRz4GWVlZpueee840bNgw06lTp0osr0jsDp8o\nFHnttdcsJgppaWmme+65xxQZGWkuKywsNPXq1cs0ZcqUqmymzW3evNnk6elp+uGHH8xlv/zyi6ld\nu3amtWvXVmPLKs+gQYNKTRQc9Vjk5+ebXnvttWJlf/zxhykwMNB05513mpNfR43fZDKZtm/fXuz/\nDx06ZGrXrp1p9uzZ5jJHjr/IokWLTOHh4aZ27doVSxQcPfavv/76mn+rHfkYZGVlmQIDA03Dhg0z\nXbx4scTyisZumDEKpb1MqsjatWu5ePFisdkdnZyc6Nq1K1u3brU4/0NNsGLFCm688UY8PT3NZc2a\nNeOWW25h5cqV1diyymPpXDvqscjOzuapp54qVlarVi18fHwoLCw0v3nVUeMHzI9bF2ndujUAnTp1\nMpc5cvwAX375JYcPH2bYsGElljl67AsXLuS2227jl19+sVjHkY/BP/7xD44dO8Ybb7xR6qsMKhq7\nYRKFotdVlyYxMREnJyduv/32YuWtWrXi0qVLfPPNN5XdvEqRk5NDcnJyibjg8h/SH3/8kezs7Gpo\nWdVz5GNx44034u7uXqLc1dWVWrVq0axZM4eOvzSJiYn4+PiY52Zx9PjPnDnDu+++y6xZs0osc/TY\nDxw4wN69e3n33Xd59NFHeeyxxzhw4ECxOo58DHbt2sWnn37KkCFD8PDwKLHcFrEbJlG4mrS0NBo0\naEDdunWLlbu5uWEyma6apdqzU6dO8ccff9C4ceMSy4pi+/XXX6uhZVXPiMdi//799OjRA3d3d0PF\nv3v3bmbNmoWfn5+5zJHjLywsZMaMGfzzn/8s9W6aI8cOlxPl9957j2nTptGlSxe++eYbhg0bxo4d\nO8x1HPkYrF69GicnJ2677TZmzZrFsGHDGDFihHkwty1ir3FPPcyfP5/du3df9Q6ByWTipptuIiIi\nwtIsACkAAAVHSURBVKptnj9/vtSDWKfO5cNz8eLF8jW2mhW9e6O0Px5FsZX22m5HZLRjkZaWxu7d\nu4mLiwOMEb/JZGL58uVs2bKF06dPM2HCBKZMmcKTTz7p0PG//fbb+Pj4mLtbruTIscPlW+jNmjUD\nYOTIkSQkJDBlyhSef/55tm/fjru7u0Mfgy+++IIGDRpwyy23MHToUC5dusQrr7zCiy++SHp6Ol27\ndgUqFnuNSxQmTZrEpEmTbLrN6667rtQDdfHiRZycnLjhhhtsur+qUvTYS2mxFZXV1NjKymjHYtas\nWfz973+nRYsWgDHid3Jy4vHHH+fxxx/n888/59lnn2XBggWEhIQ4bPx79uzh999/Z+LEieYy0xWT\n7Tpq7Jb069eP7OxsXnrpJXbt2kVwcLDDHoPff/+dixcv0rFjRx5++GHg8pf/tGnT+Pjjj1m4cKE5\nUahI7Op6AG677bZib6Qscu7cOQBuvvnmqm6STdx2220ApQ7GzMjIoHbt2jRp0qSqm1UtjHQsFi9e\nTOPGjRk+fLi5zEjxAzz44IMMHz6cixcvcuTIEYeN/4MPPiA2Npa77rrL/K9fv344OTnx7rvv0qFD\nBy5cuAA4XuxXM2jQIBo1amSO2VHPf9EdgXr16hUrd3Z2pkePHhQUFFBYWAhULPYad0ehMjzwwAP8\n+OOPHD161PwLDODYsWPUrVvXnJHVNPXr1+euu+7iyJEjJZYdO3aMe+65p8QF5qiMciw2bdrEwYMH\neeutt4qVGyX+P+vSpQsRERE0bNjQYeN/5ZVXyM3NLVaWnp7OE088wbBhw3jssce45ZZbHDL2a2nS\npAl33HEH4LjXf4MGDWjatCknT54ssayoO7158+YVjt0wdxSKbsddeVsOIDQ0lNq1a/PZZ58Vq//V\nV18xYMCAGj1r12OPPcaZM2f46aefzGVHjhzh9OnThISEVGPLKldp59nRj8W2bduIj49n/vz51Kr1\nv4/2mTNnAMeP/0q//vor99xzD82bNwccM/5bb72VNm3aFPtXNLrd3d2d1q1b4+rq6pCxX825c+do\n2LAh3bt3N5c56jEICgriP//5D8ePHy9Wfvz4cTp06ICHh0eFYzdMolD0x/Ls2bMllt1+++2MHz+e\nFStWmB8TiYyMpE6dOkyePLlK22lrgYGBeHl5sWTJEgAuXbrE/PnzeeSRRxgwYEA1t872TCYTZ8+e\nJTs7m4KCgmLLHPlYbNmyhbfffpuJEydy/PhxUlNTOXz4MDt27DDfXXDU+HNycnj99dfZuXOnuSwl\nJYW4uDjCw8PNZY4avzUcOfa5c+cSERFBfn4+cDlJePfdd5k7d26xhNlRj8HTTz9N+/btmTlzpvkY\nfPXVVyQmJjJz5kyg4rE7/Gumjxw5wpQpU/jhhx8oLCykYcOG9OnTh9mzZ5eoGxkZSXx8PK6urtxy\nyy1MmTKlRvZbXSkvL4+5c+dy6NAhateujZeXFxMmTDD3bzmKLVu2sGDBAo4ePQpcHg39xBNPMHTo\nUHMdRzwWGzdu5Pnnny/1LgrAG2+8gY+PD+CY8Z87d45nnnmGH374gZtvvpkOHTpw66238vjjj5eY\nX8IR479SWloaffr0YcKECTz77LPmckeN/bXXXiMuLg4XFxe8vLzw9PRk6NChpU485KjHICsri3/9\n618kJyfj5ubG9ddfz9/+9jfuuusuc52KxO7wiYKIiIiUn2G6HkRERKTslCiIiIiIRUoURERExCIl\nCiIiImKREgURERGxSImCiIiIWKREQURERCxSoiAiIiIWKVEQERERi5QoiIiIiEX/D7TA0grQ0QXT\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12dfec050>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.clf()\n",
"plt.scatter(fiber,gauss)\n",
"plt.plot(fiber, dufits.func_val(fiber,gfdict),'k-')\n",
"#plt.xlim(100,500)\n",
"plt.show()\n",
"plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extract Arc (one fiber at a time)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load Image"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"arc_hdu = fits.open('/Users/xavier/DESI/Wavelengths/pix-b0-00000000.fits')\n",
"arc = arc_hdu[0].data\n",
"header = arc_hdu[0].header"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Extract"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"10\n",
"20\n",
"30\n",
"40\n"
]
},
{
"data": {
"text/plain": [
"(4096, 50)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reload(desiboot)\n",
"all_spec = desiboot.extract_sngfibers_gaussianpsf(arc,xfit,gauss)\n",
"all_spec.shape"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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/QIPPyNgbZpNB9xj8sB2tcC+psbYZxeez9NRL7+ndD85Eeyi2s/tNL+D2bLrp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\npnyHhQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x126aa2210>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xdb.xplot(np.arange(all_spec[:,0].size), all_spec[:,0])#, sv_aspec[1], sv_aspec[2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Wavelength info"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Dispersion\n",
"import desimodel.io"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"desi_psf = desimodel.io.load_psf('b')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"desi_psf.wdisp(0,4500.) # fiber, wavelength"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"wave0 = desi_psf.wavelength(0,np.arange(desi_psf.npix_y))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.max(wave0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"med_bdisp = np.median(desi_psf.wdisp(0,wave0))\n",
"med_bdisp"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.median(np.abs(wave0-np.roll(wave0,1)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"xdb.xplot(wave0,desi_psf.wdisp(0,wave0))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"desi_psf.npix_x, desi_psf.npix_y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"poly_fit = dufits.func_fit(wave0,np.arange(desi_psf.npix_y), 'polynomial',2,xmin=0.,xmax=1.)\n",
"poly_fit"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"xdb.xplot(wave0, np.arange(desi_psf.npix_y),dufits.func_val(wave0,poly_fit))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Find Lines"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reload(desiboot)\n",
"spec = all_spec[:,0]\n",
"pixpk = desiboot.find_arc_lines(spec)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"19"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(pixpk)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.clf()\n",
"yspec = np.log10(np.maximum(spec,1))\n",
"xplt = np.arange(spec.size)\n",
"plt.plot(xplt,yspec,'b-')\n",
"plt.scatter(pixpk,yspec[np.round(pixpk).astype(int)],color='red')\n",
"plt.ylim(0.,np.max(yspec)*1.05)\n",
"plt.xlim(0,spec.size)\n",
"plt.xlabel('pixel')\n",
"plt.ylabel('log Flux')\n",
"plt.show()\n",
"plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Identify Arc Lines"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Init"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"camera = header['CAMERA']\n",
"print(camera)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load line list "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reload(desiboot)\n",
"#llist = pypit_alines.load_arcline_list(None,None,['CdI','ArI','NeI','HgI'],wvmnx=aparm['wvmnx'])\n",
"llist = desiboot.load_arcline_list(camera)#['CdI','ArI','HgI','NeI'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"llist"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Grab lines"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reload(desiboot)\n",
"dlamb, wmark, gd_lines = desiboot.load_gdarc_lines(camera)\n",
"dlamb, wmark, gd_lines"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Match a set of 5 gd_lines to detected lines"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reload(desiboot)\n",
"id_dict = desiboot.id_arc_lines(pixpk,gd_lines,dlamb,wmark)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"id_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.clf()\n",
"yspec = np.log10(np.maximum(spec,1))\n",
"xplt = np.arange(spec.size)\n",
"plt.plot(xplt,yspec,'b-')\n",
"plt.scatter(xpk,yspec[np.round(xpk).astype(int)],color='red')\n",
"# Guesses\n",
"for jj,xpixpk in enumerate(id_dict['first_id_pix']):\n",
" plt.text(xpixpk, yspec[np.round(xpixpk)], '{:g}'.format(id_dict['first_id_wave'][jj]),\n",
" ha='center',color='red')\n",
"#\n",
"plt.ylim(0.,np.max(yspec)*1.05)\n",
"plt.xlim(0,spec.size)\n",
"plt.xlabel('pixel')\n",
"plt.ylabel('log Flux')\n",
"plt.show()\n",
"plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Idenfity additional selected lines"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reload(desiboot)\n",
"desiboot.add_gdarc_lines(id_dict, pixpk, gd_lines)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# IDs\n",
"plt.clf()\n",
"yspec = np.log10(np.maximum(spec,1))\n",
"xplt = np.arange(spec.size)\n",
"plt.plot(xplt,yspec,'b-')\n",
"plt.scatter(xpk,yspec[np.round(xpk).astype(int)],color='red')\n",
"# Guesses\n",
"for jj,xpixpk in enumerate(id_dict['id_pix']):\n",
" plt.text(xpixpk, yspec[int(np.round(xpixpk))], '{:g}'.format(id_dict['id_wave'][jj]),\n",
" ha='center',color='red')\n",
"#\n",
"plt.ylim(0.,np.max(yspec)*1.05)\n",
"plt.xlim(0,spec.size)\n",
"plt.xlabel('pixel')\n",
"plt.ylabel('log Flux')\n",
"plt.show()\n",
"plt.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Residuals\n",
"plt.clf()\n",
"# Fit\n",
"yfit = dufits.func_val(np.array(id_dict['id_wave']),id_dict['fit'])\n",
"# IDs\n",
"plt.scatter(id_dict['id_wave'], np.array(id_dict['id_pix'])-yfit)\n",
"#\n",
"plt.xlabel('ID wave')\n",
"plt.ylabel('Residual (pixels)')\n",
"plt.show()\n",
"plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Identify as many others as possible"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reload(desiboot)\n",
"desiboot.id_remainder(id_dict, pixpk, llist)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# IDs\n",
"plt.clf()\n",
"yspec = np.log10(np.maximum(spec,1))\n",
"xplt = np.arange(spec.size)\n",
"plt.plot(xplt,yspec,'b-')\n",
"plt.scatter(xpk,yspec[np.round(xpk).astype(int)],color='red')\n",
"# Guesses\n",
"for jj,xpixpk in enumerate(id_dict['id_pix']):\n",
" plt.text(xpixpk, yspec[int(np.round(xpixpk))], '{:g}'.format(id_dict['id_wave'][jj]),\n",
" ha='center',color='red')\n",
"#\n",
"plt.ylim(0.,np.max(yspec)*1.05)\n",
"plt.xlim(0,spec.size)\n",
"plt.xlabel('pixel')\n",
"plt.ylabel('log Flux')\n",
"plt.show()\n",
"plt.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Fit with rejection\n",
"final_fit,mask = dufits.iter_fit(np.array(id_dict['id_wave']),np.array(id_dict['id_pix']),'polynomial',3,xmin=0.,xmax=1.)\n",
"final_fit_pix,mask2 = dufits.iter_fit(np.array(id_dict['id_pix']), np.array(id_dict['id_wave']),'polynomial',3,xmin=0.,xmax=1.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Plot residuals\n",
"plt.clf()\n",
"# Fit\n",
"yfit = dufits.func_val(np.array(id_dict['id_wave']),final_fit)\n",
"# IDs\n",
"plt.scatter(id_dict['id_wave'], np.array(id_dict['id_pix'])-yfit)\n",
"#\n",
"plt.xlabel('ID wave')\n",
"plt.ylabel('Residual (pixels)')\n",
"plt.show()\n",
"plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate DESI PSF output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reload(desiboot)\n",
"ny = xfit.shape[0]\n",
"id_dict['final_fit'] = final_fit\n",
"id_dict['wave_min'] = dufits.func_val(0,final_fit_pix)\n",
"id_dict['wave_max'] = dufits.func_val(ny-1,final_fit_pix)\n",
"desiboot.write_psf('tmp.fits',xfit,[id_dict])"
]
},
{
"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
}
| bsd-3-clause |
davisincubator/digblood | .ipynb_checkpoints/Jess's Analysis-checkpoint.ipynb | 1 | 36182 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Jess's DIGBlood IPython notebook"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"//anaconda/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 stuff\n",
"%matplotlib inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"import statsmodels.formula.api as smf\n",
"from scipy.stats import pearsonr\n",
"from __future__ import division\n",
"import matplotlib.pyplot as plt\n",
"from statsmodels.nonparametric.smoothers_lowess import lowess"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.DataFrame.from_csv('/Users/jesskerlin/Documents/GitHub/digblood/data/raw/blood_train.csv')\n",
"df.columns = [c.replace(' ', '_') for c in df.columns]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Months_since_Last_Donation</th>\n",
" <th>Number_of_Donations</th>\n",
" <th>Total_Volume_Donated_(c.c.)</th>\n",
" <th>Months_since_First_Donation</th>\n",
" <th>Made_Donation_in_March_2007</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>619</th>\n",
" <td>2</td>\n",
" <td>50</td>\n",
" <td>12500</td>\n",
" <td>98</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>664</th>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>3250</td>\n",
" <td>28</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>441</th>\n",
" <td>1</td>\n",
" <td>16</td>\n",
" <td>4000</td>\n",
" <td>35</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>160</th>\n",
" <td>2</td>\n",
" <td>20</td>\n",
" <td>5000</td>\n",
" <td>45</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>358</th>\n",
" <td>1</td>\n",
" <td>24</td>\n",
" <td>6000</td>\n",
" <td>77</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Months_since_Last_Donation Number_of_Donations \\\n",
"619 2 50 \n",
"664 0 13 \n",
"441 1 16 \n",
"160 2 20 \n",
"358 1 24 \n",
"\n",
" Total_Volume_Donated_(c.c.) Months_since_First_Donation \\\n",
"619 12500 98 \n",
"664 3250 28 \n",
"441 4000 35 \n",
"160 5000 45 \n",
"358 6000 77 \n",
"\n",
" Made_Donation_in_March_2007 \n",
"619 1 \n",
"664 1 \n",
"441 1 \n",
"160 1 \n",
"358 0 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Show the first few lines of the database\n",
"df[:5]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1.0, 0.0)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"pearsonr(df['Number_of_Donations'],df['Total_Volume_Donated_(c.c.)'])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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ZMr/O8cPeRPsIMAF4g4DPe8D+Ft/4io/1PwnmBe2b64Hm/uu3avpf1DH2mrjx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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x116904a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = df[['Months_since_Last_Donation','Made_Donation_in_March_2007']].groupby(['Months_since_Last_Donation']).mean().plot(kind = 'bar')\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'scipy' 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-8-3fa1a30b6914>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Months_since_Last_Donation'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[0;31m#.apply(lambda x: np.log(x)).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Made_Donation_in_March_2007'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhalfnorm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0mx_out\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweighted_moving_average\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstep_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0msmoothed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'scipy' is not defined"
]
}
],
"source": [
"# From http://stackoverflow.com/questions/18517722/weighted-moving-average-in-python \n",
"def weighted_moving_average(x,y,step_size=0.05,width=1):\n",
" bin_centers = np.arange(np.min(x),np.max(x)-0.5*step_size,step_size)+0.5*step_size\n",
" bin_avg = np.zeros(len(bin_centers))\n",
"\n",
" #We're going to weight with a Gaussian function\n",
" def gaussian(x,amp=1,mean=0,sigma=1):\n",
" return amp*np.exp(-(x-mean)**2/(2*sigma**2))\n",
"\n",
" for index in range(0,len(bin_centers)):\n",
" bin_center = bin_centers[index]\n",
" weights = gaussian(x,mean=bin_center,sigma=width)\n",
" bin_avg[index] = np.average(y,weights=weights)\n",
"\n",
" return (bin_centers,bin_avg)\n",
"\n",
"data = df[['Months_since_First_Donation','Made_Donation_in_March_2007']].groupby(['Months_since_First_Donation']).mean()\n",
"count = df[['Months_since_First_Donation','Made_Donation_in_March_2007']].groupby(['Months_since_First_Donation']).mean()\n",
"df = df.sort_values('Months_since_Last_Donation')\n",
"x = df['Months_since_Last_Donation'].values #.apply(lambda x: np.log(x)).\n",
"y = df['Made_Donation_in_March_2007'].values\n",
"scipy.stats.halfnorm\n",
"x_out,y_out = weighted_moving_average(x,y,step_size = 1,width = 5)\n",
"print smoothed\n",
"plt.plot(x_out,y_out)\n",
"print x\n",
"\n",
"#plt.bar(data.index,data.Made_Donation_in_March_2007)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0 0 0 0 1 1 1 1 1 1 1 1 1 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 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 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 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 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 2 2 2 2 2 2 2 2 2 2 2\n",
" 2 2 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4\n",
" 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4\n",
" 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4\n",
" 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4\n",
" 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4\n",
" 4 4 4 5 5 6 6 6 6 7 7 7 7 7 8 8 8 8 8 9 9 9 9 9 9\n",
" 9 9 9 9 9 9 9 9 9 9 9 10 10 10 11 11 11 11 11 11 11 11 11 11 11\n",
" 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11\n",
" 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12\n",
" 12 12 12 12 13 13 13 13 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14\n",
" 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14\n",
" 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 15 16 16 16 16 16 16\n",
" 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16\n",
" 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 17 18 18 20 21 21 21 21\n",
" 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21 21\n",
" 21 21 21 21 21 21 21 21 22 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23\n",
" 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 25 26 35 39 72\n",
" 74]\n"
]
}
],
"source": [
"print x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since Total Volume Donated adds no information, I won't include it as a feature."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"436 0.366096\n",
"214 0.343896\n",
"676 0.310818\n",
"664 0.515535\n",
"358 0.602107\n",
"607 0.347716\n",
"182 0.285178\n",
"164 0.462686\n",
"106 0.325693\n",
"285 0.448608\n",
"330 0.437552\n",
"441 0.551485\n",
"258 0.394102\n",
"291 0.308770\n",
"392 0.308770\n",
"589 0.308770\n",
"220 0.308770\n",
"410 0.308770\n",
"88 0.308770\n",
"619 1.106322\n",
"631 0.292705\n",
"618 0.275603\n",
"238 0.294691\n",
"587 0.308770\n",
"655 0.261525\n",
"700 0.126429\n",
"19 0.288645\n",
"434 0.218251\n",
"195 0.251418\n",
"519 0.199074\n",
" ... \n",
"645 0.029055\n",
"538 0.059198\n",
"451 0.640713\n",
"325 0.254074\n",
"193 0.104635\n",
"378 0.068268\n",
"597 -0.109064\n",
"295 -0.080818\n",
"475 0.021971\n",
"141 0.046068\n",
"576 0.046068\n",
"78 0.059198\n",
"110 0.046068\n",
"588 0.046068\n",
"388 0.046068\n",
"117 0.046068\n",
"514 0.014976\n",
"739 0.036050\n",
"604 0.039073\n",
"22 0.327756\n",
"595 0.046157\n",
"210 0.053152\n",
"180 0.060236\n",
"554 0.046068\n",
"183 0.056467\n",
"281 0.027804\n",
"673 -0.147321\n",
"541 -0.154085\n",
"74 -0.566903\n",
"350 -0.591922\n",
"dtype: float64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.drop('Total_Volume_Donated_(c.c.)', axis = 1)\n",
"model = smf.ols('Made_Donation_in_March_2007 ~ Months_since_Last_Donation + Number_of_Donations + Months_since_First_Donation', data = df)\n",
"result = model.fit()\n",
"result.summary()\n",
"result.fittedvalues"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'Months_since_Last_Donation', u'Number_of_Donations',\n",
" u'Total_Volume_Donated_(c.c.)', u'Months_since_First_Donation',\n",
" u'Made_Donation_in_March_2007'],\n",
" dtype='object')"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.keys()\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"436 0.239583\n",
"214 0.239583\n",
"676 0.239583\n",
"664 0.239583\n",
"358 0.239583\n",
"607 0.239583\n",
"182 0.239583\n",
"164 0.239583\n",
"106 0.239583\n",
"285 0.239583\n",
"330 0.239583\n",
"441 0.239583\n",
"258 0.239583\n",
"291 0.239583\n",
"392 0.239583\n",
"589 0.239583\n",
"220 0.239583\n",
"410 0.239583\n",
"88 0.239583\n",
"619 0.239583\n",
"631 0.239583\n",
"618 0.239583\n",
"238 0.239583\n",
"587 0.239583\n",
"655 0.239583\n",
"700 0.239583\n",
"19 0.239583\n",
"434 0.239583\n",
"195 0.239583\n",
"519 0.239583\n",
" ... \n",
"645 0.239583\n",
"538 0.239583\n",
"451 0.239583\n",
"325 0.239583\n",
"193 0.239583\n",
"378 0.239583\n",
"597 0.239583\n",
"295 0.239583\n",
"475 0.239583\n",
"141 0.239583\n",
"576 0.239583\n",
"78 0.239583\n",
"110 0.239583\n",
"588 0.239583\n",
"388 0.239583\n",
"117 0.239583\n",
"514 0.239583\n",
"739 0.239583\n",
"604 0.239583\n",
"22 0.239583\n",
"595 0.239583\n",
"210 0.239583\n",
"180 0.239583\n",
"554 0.239583\n",
"183 0.239583\n",
"281 0.239583\n",
"673 0.239583\n",
"541 0.239583\n",
"74 0.239583\n",
"350 0.239583\n",
"Name: Means, dtype: float64\n"
]
}
],
"source": [
"mean = df['Made_Donation_in_March_2007'].mean()\n",
"df['Means'] = np.ones([576,1])*mean\n",
"print df['Means']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training log-loss score 0.550599168862\n"
]
}
],
"source": [
"# Training evaluation\n",
"from sklearn.metrics import log_loss\n",
"pred = np.array(df.Means)\n",
"actual = df['Made_Donation_in_March_2007']\n",
"print 'Training log-loss score ' + str(log_loss(actual,pred))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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>Months_since_Last_Donation</th>\n",
" <th>Number_of_Donations</th>\n",
" <th>Total_Volume_Donated_(c.c.)</th>\n",
" <th>Months_since_First_Donation</th>\n",
" <th>Made_Donation_in_March_2007</th>\n",
" <th>Means</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>576.000000</td>\n",
" <td>576.000000</td>\n",
" <td>576.000000</td>\n",
" <td>576.000000</td>\n",
" <td>576.000000</td>\n",
" <td>5.760000e+02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>9.439236</td>\n",
" <td>5.427083</td>\n",
" <td>1356.770833</td>\n",
" <td>34.050347</td>\n",
" <td>0.239583</td>\n",
" <td>2.395833e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>8.175454</td>\n",
" <td>5.740010</td>\n",
" <td>1435.002556</td>\n",
" <td>24.227672</td>\n",
" <td>0.427200</td>\n",
" <td>1.083408e-15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>250.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.395833e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>500.000000</td>\n",
" <td>16.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.395833e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>7.000000</td>\n",
" <td>4.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>28.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.395833e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>14.000000</td>\n",
" <td>7.000000</td>\n",
" <td>1750.000000</td>\n",
" <td>49.250000</td>\n",
" <td>0.000000</td>\n",
" <td>2.395833e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>74.000000</td>\n",
" <td>50.000000</td>\n",
" <td>12500.000000</td>\n",
" <td>98.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.395833e-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Months_since_Last_Donation Number_of_Donations \\\n",
"count 576.000000 576.000000 \n",
"mean 9.439236 5.427083 \n",
"std 8.175454 5.740010 \n",
"min 0.000000 1.000000 \n",
"25% 2.000000 2.000000 \n",
"50% 7.000000 4.000000 \n",
"75% 14.000000 7.000000 \n",
"max 74.000000 50.000000 \n",
"\n",
" Total_Volume_Donated_(c.c.) Months_since_First_Donation \\\n",
"count 576.000000 576.000000 \n",
"mean 1356.770833 34.050347 \n",
"std 1435.002556 24.227672 \n",
"min 250.000000 2.000000 \n",
"25% 500.000000 16.000000 \n",
"50% 1000.000000 28.000000 \n",
"75% 1750.000000 49.250000 \n",
"max 12500.000000 98.000000 \n",
"\n",
" Made_Donation_in_March_2007 Means \n",
"count 576.000000 5.760000e+02 \n",
"mean 0.239583 2.395833e-01 \n",
"std 0.427200 1.083408e-15 \n",
"min 0.000000 2.395833e-01 \n",
"25% 0.000000 2.395833e-01 \n",
"50% 0.000000 2.395833e-01 \n",
"75% 0.000000 2.395833e-01 \n",
"max 1.000000 2.395833e-01 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()\n",
"\n"
]
}
],
"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": 0
}
| mit |
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