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jtemplon/2015-03-nit-analysis
notebooks/nit-bracketology-analysis.ipynb
1
43670
{ "metadata": { "name": "", "signature": "sha256:07bd9386b41b4e77d9cba4d0072de8ddeb5fc71d02e90d8e765a6cf50abd5b96" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyzing NIT Bracketology\n", "\n", "Analyzing RPI trends in NIT bracketology for the past five tournaments." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load the data:\n", "\n", "I collected this data from: \n", "- Wikipedia (Seed, School, Conference, Record, Wins, Losses, Berth type, Year)\n", "- Basketball State (RPI)\n", "- My own classification (Mid-Major)\n", "\n", "*Note: that Atlantic 10 is not included in the \"Mid-Major\" field.*" ] }, { "cell_type": "code", "collapsed": false, "input": [ "nit_df = pd.read_csv(\"../data/nit-participants-2010-2014.csv\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "nit_df.head(1)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Seed</th>\n", " <th>School</th>\n", " <th>Conference</th>\n", " <th>Record</th>\n", " <th>Wins</th>\n", " <th>Losses</th>\n", " <th>Berth type</th>\n", " <th>Year</th>\n", " <th>Wins-Losses</th>\n", " <th>RPI</th>\n", " <th>Mid-Major</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td> 1</td>\n", " <td> Florida State</td>\n", " <td> ACC</td>\n", " <td> 19\u201313</td>\n", " <td> 19</td>\n", " <td> 13</td>\n", " <td> At-Large</td>\n", " <td> 2014</td>\n", " <td> 6</td>\n", " <td> 51</td>\n", " <td> N</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ " Seed School Conference Record Wins Losses Berth type Year \\\n", "0 1 Florida State ACC 19\u201313 19 13 At-Large 2014 \n", "\n", " Wins-Losses RPI Mid-Major \n", "0 6 51 N " ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initial Analysis:\n", "\n", "- What conferences have had the most NIT participants?\n", "- What is the average RPI of an at-large berth?\n", "- What was the distribution of RPIs for at-large berths?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "nit_df[\"Conference\"].value_counts()[:10]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "SEC 15\n", "ACC 12\n", "Big East 10\n", "Big Ten 9\n", "Pac-12 9\n", "Atlantic 10 9\n", "C-USA 7\n", "MVC 6\n", "Horizon 6\n", "WAC 5\n", "dtype: int64" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "nit_df[\"School\"].value_counts()[:10]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "Mississippi 3\n", "St. John's 3\n", "Stony Brook 3\n", "Dayton 3\n", "Northwestern 3\n", "Illinois 2\n", "Cleveland State 2\n", "Kent State 2\n", "Arizona State 2\n", "Iowa 2\n", "dtype: int64" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "at_large_df = nit_df[nit_df[\"Berth type\"] == \"At-Large\"]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "at_large_df[\"RPI\"].describe()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "count 104.000000\n", "mean 67.913462\n", "std 15.753015\n", "min 31.000000\n", "25% 58.000000\n", "50% 68.000000\n", "75% 76.250000\n", "max 121.000000\n", "dtype: float64" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "at_large_df[\"RPI\"].hist()\n", "pass" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10bf0b0d0>" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The mean RPI for an at-large berth to the NIT is ~68 and 95% of all bids should fall between RPIs of 36 and 100. Let's check out the big outliers." ] }, { "cell_type": "code", "collapsed": false, "input": [ "at_large_df[at_large_df[\"RPI\"] < 36]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Seed</th>\n", " <th>School</th>\n", " <th>Conference</th>\n", " <th>Record</th>\n", " <th>Wins</th>\n", " <th>Losses</th>\n", " <th>Berth type</th>\n", " <th>Year</th>\n", " <th>Wins-Losses</th>\n", " <th>RPI</th>\n", " <th>Mid-Major</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>35 </th>\n", " <td> 1</td>\n", " <td> Southern Mississippi</td>\n", " <td> C-USA</td>\n", " <td> 25\u20139</td>\n", " <td> 25</td>\n", " <td> 9</td>\n", " <td> At-Large</td>\n", " <td> 2013</td>\n", " <td> 16</td>\n", " <td> 31</td>\n", " <td> Y</td>\n", " </tr>\n", " <tr>\n", " <th>119</th>\n", " <td> 6</td>\n", " <td> Harvard</td>\n", " <td> Ivy</td>\n", " <td> 23\u20136</td>\n", " <td> 23</td>\n", " <td> 6</td>\n", " <td> At-Large</td>\n", " <td> 2011</td>\n", " <td> 17</td>\n", " <td> 35</td>\n", " <td> Y</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ " Seed School Conference Record Wins Losses Berth type \\\n", "35 1 Southern Mississippi C-USA 25\u20139 25 9 At-Large \n", "119 6 Harvard Ivy 23\u20136 23 6 At-Large \n", "\n", " Year Wins-Losses RPI Mid-Major \n", "35 2013 16 31 Y \n", "119 2011 17 35 Y " ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "at_large_df[at_large_df[\"RPI\"] > 100]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Seed</th>\n", " <th>School</th>\n", " <th>Conference</th>\n", " <th>Record</th>\n", " <th>Wins</th>\n", " <th>Losses</th>\n", " <th>Berth type</th>\n", " <th>Year</th>\n", " <th>Wins-Losses</th>\n", " <th>RPI</th>\n", " <th>Mid-Major</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>88 </th>\n", " <td> 7</td>\n", " <td> Iowa</td>\n", " <td> Big Ten</td>\n", " <td> 17\u201316</td>\n", " <td> 17</td>\n", " <td> 16</td>\n", " <td> At-Large</td>\n", " <td> 2012</td>\n", " <td> 1</td>\n", " <td> 121</td>\n", " <td> N</td>\n", " </tr>\n", " <tr>\n", " <th>90 </th>\n", " <td> 7</td>\n", " <td> Illinois State</td>\n", " <td> Missouri Valley</td>\n", " <td> 20\u201313</td>\n", " <td> 20</td>\n", " <td> 13</td>\n", " <td> At-Large</td>\n", " <td> 2012</td>\n", " <td> 7</td>\n", " <td> 109</td>\n", " <td> Y</td>\n", " </tr>\n", " <tr>\n", " <th>152</th>\n", " <td> 7</td>\n", " <td> Northwestern</td>\n", " <td> Big Ten</td>\n", " <td> 20-13</td>\n", " <td> 20</td>\n", " <td> 13</td>\n", " <td> At-Large</td>\n", " <td> 2010</td>\n", " <td> 7</td>\n", " <td> 112</td>\n", " <td> N</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ " Seed School Conference Record Wins Losses Berth type \\\n", "88 7 Iowa Big Ten 17\u201316 17 16 At-Large \n", "90 7 Illinois State Missouri Valley 20\u201313 20 13 At-Large \n", "152 7 Northwestern Big Ten 20-13 20 13 At-Large \n", "\n", " Year Wins-Losses RPI Mid-Major \n", "88 2012 1 121 N \n", "90 2012 7 109 Y \n", "152 2010 7 112 N " ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### More On The Ivy League:\n", "\n", "Wow, a six seed for a team with a 35 RPI? Does the **Ivy League** always need that impressive a performance to get an at-large? (Remember the league can't get an automatic bid due to how its NCAA berth is determined.)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "at_large_df[at_large_df[\"Conference\"].str.contains(\"Ivy\")]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Seed</th>\n", " <th>School</th>\n", " <th>Conference</th>\n", " <th>Record</th>\n", " <th>Wins</th>\n", " <th>Losses</th>\n", " <th>Berth type</th>\n", " <th>Year</th>\n", " <th>Wins-Losses</th>\n", " <th>RPI</th>\n", " <th>Mid-Major</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>119</th>\n", " <td> 6</td>\n", " <td> Harvard</td>\n", " <td> Ivy</td>\n", " <td> 23\u20136</td>\n", " <td> 23</td>\n", " <td> 6</td>\n", " <td> At-Large</td>\n", " <td> 2011</td>\n", " <td> 17</td>\n", " <td> 35</td>\n", " <td> Y</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ " Seed School Conference Record Wins Losses Berth type Year \\\n", "119 6 Harvard Ivy 23\u20136 23 6 At-Large 2011 \n", "\n", " Wins-Losses RPI Mid-Major \n", "119 17 35 Y " ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Woah! The Ivy League has only had one at-large bid in the past 5 seasons? Yes, but... Best RPIs of non-winner in the other 4 seasons: \n", "\n", "- 124 - Princeton (13-14)\n", "- 121 - Princeton (12-13)\n", "- 86 - Princeton (11-12)\n", "- 100 - Harvard (09-10)\n", "\n", "Alright, now I feel a little better about Yale or Harvard's chances this season." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Mid-Majors Need To Be (Slightly) Better:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "at_large_df[at_large_df[\"Mid-Major\"] == \"Y\"][\"RPI\"].describe()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "count 28.000000\n", "mean 62.357143\n", "std 17.299570\n", "min 31.000000\n", "25% 50.250000\n", "50% 65.500000\n", "75% 72.000000\n", "max 109.000000\n", "dtype: float64" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "at_large_df[at_large_df[\"Mid-Major\"] == \"Y\"][\"RPI\"].hist()\n", "pass" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10bd47c50>" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "at_large_df[at_large_df[\"Mid-Major\"] == \"N\"][\"RPI\"].describe()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "count 76.000000\n", "mean 69.960526\n", "std 14.740819\n", "min 36.000000\n", "25% 59.000000\n", "50% 69.000000\n", "75% 79.000000\n", "max 121.000000\n", "dtype: float64" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "at_large_df[at_large_df[\"Mid-Major\"] == \"N\"][\"RPI\"].hist()\n", "pass" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10a43ae90>" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bracket Distribution:\n", "\n", "- How many automatic bids are there typically in the NIT?\n", "- How many \"mid-majors\" play in the NIT each season?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "berth_composition = nit_df.groupby([\"Berth type\", \"Year\"]).count()[\"Seed\"].unstack().T\n", "berth_composition" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>Berth type</th>\n", " <th>At-Large</th>\n", " <th>Automatic</th>\n", " </tr>\n", " <tr>\n", " <th>Year</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2010</th>\n", " <td> 24</td>\n", " <td> 8</td>\n", " </tr>\n", " <tr>\n", " <th>2011</th>\n", " <td> 18</td>\n", " <td> 14</td>\n", " </tr>\n", " <tr>\n", " <th>2012</th>\n", " <td> 21</td>\n", " <td> 11</td>\n", " </tr>\n", " <tr>\n", " <th>2013</th>\n", " <td> 22</td>\n", " <td> 10</td>\n", " </tr>\n", " <tr>\n", " <th>2014</th>\n", " <td> 19</td>\n", " <td> 13</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ "Berth type At-Large Automatic\n", "Year \n", "2010 24 8\n", "2011 18 14\n", "2012 21 11\n", "2013 22 10\n", "2014 19 13" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "round(berth_composition[\"Automatic\"].mean(), 1)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "11.2" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "level_composition = nit_df.groupby([\"Mid-Major\", \"Year\"]).count()[\"Seed\"].unstack().T\n", "level_composition" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>Mid-Major</th>\n", " <th>N</th>\n", " <th>Y</th>\n", " </tr>\n", " <tr>\n", " <th>Year</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2010</th>\n", " <td> 16</td>\n", " <td> 16</td>\n", " </tr>\n", " <tr>\n", " <th>2011</th>\n", " <td> 14</td>\n", " <td> 18</td>\n", " </tr>\n", " <tr>\n", " <th>2012</th>\n", " <td> 17</td>\n", " <td> 15</td>\n", " </tr>\n", " <tr>\n", " <th>2013</th>\n", " <td> 16</td>\n", " <td> 16</td>\n", " </tr>\n", " <tr>\n", " <th>2014</th>\n", " <td> 14</td>\n", " <td> 18</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "Mid-Major N Y\n", "Year \n", "2010 16 16\n", "2011 14 18\n", "2012 17 15\n", "2013 16 16\n", "2014 14 18" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "round(level_composition[\"Y\"].mean(), 1)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "16.6" ] } ], "prompt_number": 20 }, { "cell_type": "markdown", "metadata": {}, "source": [ "An average of 11 automatic berths have been given out each season. Around half the overall field is typically filled by mid-majors (but remember that includes the 11 bids)." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
SamLau95/nbinteract
docs/notebooks/examples/examples_intro.ipynb
1
864
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Examples\n", "\n", "This section contains fleshed out example explanations that make use of\n", "`nbinteract`. Many of these examples were originally static pages taken from\n", "[UC Berkeley's Data 8 textbook][data8] augmented with interactivity.\n", "\n", "[data8]: https://www.inferentialthinking.com/\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }
bsd-3-clause
ryanpdwyer/brownian
brownian/ex/Brownian Workup.ipynb
1
129299
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import h5py\n", "import numpy as np\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import brownian\n", "from brownian import u\n", "from brownian import Cantilever\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f = h5py.File('brownian173033.h5', 'r') " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "freq = f['x'][()]\n", "PSD = f['y'][()]\n", "PSD_std = f['y_std'][()]\n", "PSD_err = PSD_std / (f['y'].attrs['n_avg']**0.5) # Standard error" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x11174c690>]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" 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"text/plain": [ "<matplotlib.figure.Figure at 0x10d3b7b50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m = brownian.make_mask(freq, 70000, 71000)\n", "plt.semilogy(freq[m], PSD[m])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cantilever = Cantilever(f_c=70.5*u.kHz,\n", " Q=28000*u.dimensionless,\n", " k_c=3.5*u.N/u.m)\n", "bmf = brownian.BrownianMotionFitter(freq, PSD, PSD_err, 298, cantilever)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Residuals\n", "-------------------------------------\n", " Mean: -8.21e-02\n", " Std. dev.: 2.06e-01\n" ] } ], "source": [ "bmf.calc_fit(70300, 70800)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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g+EswaBoATzzh6iSNaesay9EZY+oR2nLyoIN8Cynl8Ouz4cN/kFuxB8nJru5u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\nq6xU/eQTt7zPPv736BUVpqe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"text/plain": [ "<matplotlib.figure.Figure at 0x111974ed0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = bmf.plot_fit()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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FQDvgXWCJUmqjUsphhnbKbPQiJoKCYNqyU6xP056ZLWZK8nISoxuO5sSNE4zc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\nJU1pksdMSavb+bYdhlyzhuE0UgSMaq2CpNcN+yT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"text/plain": [ "<matplotlib.figure.Figure at 0x1117e4090>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bmf.plot_residuals()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(<matplotlib.figure.Figure at 0x1133e2ed0>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x1135db510>)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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SBOwjOiJ3NfPFFwPDh7ul5a9Dtnzuu68Sdi7YRnRdumQvbIKcCeQt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\nYvToUT9RHjee668HLrhAHfvzrLcbqfK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"text/plain": [ "<matplotlib.figure.Figure at 0x1133e2ed0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bmf.plot_reduced_residuals()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(<matplotlib.figure.Figure at 0x11360f490>,\n", " <matplotlib.axes._subplots.AxesSubplot at 0x1135f0350>)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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E1+ayYcCbsKMlHO1BdbNlbm7fcAMUiTFKdCJxZubM14BtQB+4/jgs7xxxtoKC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\nSEJTohMRkYT2/wE5oZcmh8FG+wAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11360f490>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bmf.plot_cdf()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " Input\n", " -----------------------------------------------\n", " Temperature T: 298 kelvin\n", "\n", " Estimates\n", " -----------------------------------------------\n", " Spring constant k_c: 3.5 newton/meter\n", " Resonance frequency f_c: 70.5 kilohertz\n", " Quality factor Q: 28000 dimensionless\n", " \n", " Fitting\n", " -----------------------------------------------\n", " Fit frequency min f_min: 70300 hertz\n", " Fit frequency max f_max: 70800 hertz\n", "\n", " Results\n", " -----------------------------------------------\n", " Resonance frequency f_c: 70561.35(4) hertz\n", " Spring constant k_c: 22.3(8) newton/meter\n", " Quality Factor Q: 2.9(1)×10⁴ dimensionless\n", " Detector Noise : 7.38(7)×10⁻⁹ nanometer²/hertz\n", " \n" ] } ], "source": [ "print(bmf.report())" ] } ], "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
astroumd/GradMap
notebooks/Lectures2019/Lecture1/GradMap_L1_Student.ipynb
1
60836
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to \"Doing Science\" in Python for REAL Beginners " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python is one of many languages you can use for research and HW purposes. In the next few days, we will work through many of the tool, tips, and tricks that we as graduate students (and PhD researchers) use on a daily basis. We will NOT attempt to teach you all of Python--there isn't time. We will however build up a set of code(s) that will allow you to read and write data, make beautiful publish-worthy plots, fit a line (or any function) to data, and set up algorithms. You will also begin to learn the syntax of Python and can hopefuly apply this knowledge to your current and future work. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Before we begin, a few words on navigating the iPython Notebook:\n", "\n", "* There are two main types of cells : Code and Text \n", "* In \"code\" cells \"#\" at the beginning of a line marks the line as comment\n", "* In \"code\" cells every non commented line is intepreted\n", "* In \"code\" cells, commands that are preceded by % are \"magics\" and are special commands in Ipython to add some functionality to the runtime interactive environment.\n", "* Shift+Return shortcut to execute a cell\n", "* Alt+Return shortcut to execute a cell and create another one below\n", "\n", "Here you can find a complete documentation about the notebook. \n", "http://ipython.org/ipython-doc/1/interactive/notebook.html\n", "In particular have a look at the section about the keyboard shortcuts.\n", "\n", "\n", "## And remember that :\n", "* Indentation has a meaning (we'll talk about this when we cover loops)\n", "* Indexes start from 0 \n", "\n", "We will discuss more about these concepts while doing things. Let's get started now!!!!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A. Numbers, Calculations, and Lists" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Before we start coding, let's play around with the Jupyter environment. Make a new cell below using the Alt+Return shortcut" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Take your newly created cell and write something in it. Switch the type of the cell between a **code** cell and a **text/markdown** cell by using the selection box in the top of the screen. See how it changes?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Insert a comment to yourself (this is always a great idea) by using the # symbol." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "35" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## You can use Python as a calculator:\n", "5*7 #This is a comment and does not affect your code.\n", "#You can have as many as you want. \n", "#Comments help explain your code to others and yourself\n", "#No worries. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "12" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "5+7" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-2" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "5-7" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7142857142857143" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "5/7" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unfortunately, the output of your calculations won't be saved anywhere, so you can't use them later in your calculations. \n", "\n", "There's a way to get around this: by assigning them to variables. A variable is a way of referring to a memory location used by a computer program that can contain values, text, or even more complicated types. Think of variables as containers to store something so you can use or change it later. Variables can be a single letter (like x or y) but they are usually more helpful when they have descriptive names (like age, stars, total_sum).\n", "\n", "Let's assign some variables and `print()` them to the screen." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "a = 10\n", "b = 7" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "print(a)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7\n" ] } ], "source": [ "print(b)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "70 17 1.4285714285714286\n" ] } ], "source": [ "print(a*b , a+b, a/b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also write over variables with new values, but your previous values will be gone." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a = 5\n", "b = 7\n", "print(a*b, a+b, a/b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, let's create a list of numbers" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n" ] } ], "source": [ "numList = [0,1,2,3,4,5,6,7,8,9]\n", "print(numList)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How many elements or numbers does the list numList contain? Yes, this is easy to count now, but you will eventually work with lists that contains MANY numbers. To get the **length** of a list, use `len()`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "L = len(numList)\n", "print(L)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also access particular elements in an array by _indexing_. The syntac for this is the following:\n", "> `numList[index_number]`\n", "\n", "This will return the value in the list that corresponds to the index number. For example, getting the 4th item in the list you would need to type:\n", "> `numList[4]`\n", "\n", "Arrays are numbered starting from 0, such that\n", "\n", "* First position = 0\n", "* Second position = 1\n", "* Third position = 2\n", "* etc.\n", "\n", "It is a bit confusing, but after a bit of time, this becomes quite natural. Try accessing elements of the list you just created:\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numList[4]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How would you access the number 5 in `numList`?" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] } ], "source": [ "x = numList[5]\n", "print(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try making more complicated list:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "fibList = [1, 1, 2, 3, 5, 8, 13, 21, 34, 55]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fibList[5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you know the basics of Python, let's see how it can be used as a graphing calculator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## B. Our first plot! " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python is a fantastic language because it is very powerful and flexible. Also, it is like modular furniture or modular building. You have the Python foundation and choose which modules you want/need and load them before you start working. One of the most loved here at UMD is the _matplotlib_ (https://matplotlib.org/), which provides lots of functionality for making beautiful, publishable plots. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# Run this code\n", "%matplotlib inline \n", "# this \"magic\" command puts the plots right in the jupyter notebook\n", "import matplotlib " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When using modules (also sometimes called _libraries_ or _packages_) you can use a nickname through the __as__ keyword so you don't have to type the long module name every time. For example, ``matplotlib.pyplot`` is typically shortened to ``plt`` like below. " ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# Run this code\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's do a quick simple plot using the list we defined earlier!" ] }, { "cell_type": "code", "execution_count": 29, "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": [ "x = fibList\n", "y = numList\n", "\n", "p = plt.plot(x,y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can change a lot of attributes about plots, like the style of the line, the color, and the thickness of the line. You can add titles, axis labels, and legends. You can also put more than one line on the same plot. This link includes all the ways you can modify plots: https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html. Here is a quick example showing a few of the things you can do with _matplotlib_:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x11693b780>" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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WrF8/tHIzG77cniOQdIKkByWtlrRK0iey8iMl3S9pTbb0ral2gJkzh1ZuZsOX5wNlu4FPRcQcYD5wvaSTgBuAByJiNmkgOw9hYQdYtAhaW/cva21N5WY2unJLBBHxQkT8JFt/BVgNHE+a+rJ3prPFwGV5xWDNq6MDOjuhrQ2ktOzsdEexWR6U7gzN+SBSO/Aw8BZgfURMqXhva0QccHlI0gLSNJnMnDnz1HXr1uUep5lZkUhaGRHzBtsv97GGJE0Cvk4ayvrlWj8XEZ0RMS8i5k2fPj2/AM3MSi7XRCBpHCkJdEXEN7LiFyUdm71/LGnmMzMza5A87xoSaViK1RFxc8Vb95EeUCNb3ptXDGZmNrhaZigbrrOAK4GfSuqd3exPgb8Glkq6BlgPvD/HGMzMbBC5JYKI+CHQ30il5+V1XDMzGxpPTGNmVnJOBGZmJedEYGZWck4EZmYl50RgZlZyTgRmZiXnRGBmVnJOBGZmJedEYGZWck4EZmYl50RgZlZyTgRmZiXnRGBmVnJOBGZmJedEYGZWck4EZmYl50RQEl1d0N4OLS1p2dXV6IjM7GCR51SVdpDo6oIFC6CnJ22vW5e2ATo6GheXmR0cfEZQAgsX7ksCvXp6UrmZmRNBCaxfP7RyMysXJ4ISmDlzaOVmVi5OBCWwaBG0tu5f1tqays3MnAhKoKMDOjuhrQ2ktOzsdEexmSW+a6gkOjrc8JtZdT4jMDMrOScCM7OScyIwMyu53BKBpK9I2ijpqYqyIyXdL2lNtjwir+ObmVlt8jwjuB24oE/ZDcADETEbeCDbNjOzBsotEUTEw8BLfYovBRZn64uBy/I6vpmZ1abefQRHR8QLANnyqP52lLRA0gpJKzZt2lS3AM3Myuag7SyOiM6ImBcR86ZPn97ocMzMCqveieBFSccCZMuNdT6+mZn1Ue8ni+8DrgL+OlveW+fjm5kNSQTs2gU7d+57vfba/tvVyoazT9/tww+Hp5/Ov465JQJJXwPOBqZJ6gb+nJQAlkq6BlgPvD+v4x/MurrSXADr16cRQBct8vAPZtVUNsKj0bAOZ5/XXoOxY2HCBBg/Pi0rX33L+ttn8uShf2bChPr8O+eWCCLiw/28dV5ex2wGni3MmkUE7N49+r9yh7rPmDG1N5r9bU+bNvg+/X3v+PEphiJTRDQ6hkHNmzcvVqxY0egwRkV7e2r8+2prg7Vr6x2NNZNdu2D7dnjllfTqXd++Pf2wyKPB7m2Eh/ILdiT7VNsueiOcJ0krI2LeYPt59NE682xh5RABO3b033APtOzvvd27YdKkdIlh8uR965MmpfklDj30wIb0iCOG3xiPH58uiVjx+T9znc2cWf2MwLOFNdaePamxrbVRrmWfQw7Zv7GuXPZdP+64gfeZNCk1zlKj/6WsiJwI6mzRov37CMCzhQ1VBLz++vAa5/72ee01mDix/4a7snE++mh405sG3mfiRBg3rtH/Uma1cSKos94OYd81lC51bNkCmzen16ZN+y97X7/5zYENOFT/ZV2tcZ4+ffB9Wluh5aB9vNIsX+4stlERkRrqyka8WsNeWfbyy+ka9rRpqbGuXPauT50KU6Yc2HCPH9/oGpsd/NxZbCOya1f6tV5rw755c+pY7K9RP/HEA8uOOMJ3hJgdDJwISmLXLujuhhdfrK1h374djjzywF/o06bB7NlwxhkHNuyHHtroWprZcDgRFMSePbBhQ3oW4Ve/2rfsXX/hBTjmmPTq+6v9zW8+sOzww33N3KwsnAiGoRFDRETAxo37N+6V6889l37Bz5qVXu3t8I53wJVXpvUTTvBdLGZWnRPBEOU1REQEbN16YENfuZw4MTXqvY39KafAe9+b1tva6jcuiZkVi+8aGqKRDhERAc88A8uXw8qV8Oyz+xp/af9f9JXr7e3pjhkzs1r5rqGcDHWIiE2bUqO/fDk8+mhaTpoEb387nHoqnHXWvgZ/yhQ/OWpm9edEMEQDDRHR0wOPP76vwV++HF56CU47DU4/HT76UfiHf4Bjj61/3GZm/XEiGKJqQ0T0Dsw1bRqcfHJq9C+8EG68Md2R47tvzOxg5kQwBK++mjpk586FRx5J1/tbW+F974Prrkvl7rA1s2bj36qkO4Ha29Mv9/b2tN1r61ZYsgTe8550Sefv/x6uuCI9nBWRksOSJTB/vpOAmTWn0p8RVLsd9Npr4bvfTY39o4/CeeelRHDbbelefTOzIil9Ili4cP/r/ZAmFFm6NP3S/+Y30/37ZmZFVfpE0N9tnzt2pGv/ZmZFV7o+gsr+gBkz+h92wTOGmVlZlOqMoG9/wPPPpwe4DjkkzXjVyzOGmVmZlOqMoFp/QEQauqGtLSWFtjbo7CznjGFmVk6FPSO47rrUoO/ZkyY/ueaa6k8EQ3r6d/Pm+sZnZnawKOQZwXXXwZe+lJIApGVnZ/+zYbk/wMzKrJCJoLOzevneven6fyX3B5hZ2RUyEfSeCfQVkZKE+wPMzPZpSCKQdIGkpyU9I+mG0f7+/i4BjRmTGv21a9PZwdq1TgJmZnVPBJLGAF8E/gA4CfiwpJNG8xi9M4bVWm5mVmaNOCM4HXgmIp6NiNeBu4BLR/MAt96axv7vneRlzJi0feuto3kUM7NiaMTto8cDz1VsdwNv77uTpAXAAoCZw7it59Zb0+BxPT1pFjAzM6uuEYmg2mSMB0ycHBGdQCekOYuHc6BTThnOp8zMyqURl4a6gRMqtmcAGxoQh5mZ0ZhE8BgwW9IsSYcAHwLua0AcZmZGAy4NRcRuSR8DvgOMAb4SEavqHYeZmSUNGWsoIr4NfLsRxzYzs/0V8sliMzOrnROBmVnJORGYmZWcE4GZWckpYljPatWVpE1AP9PKvGEaUPTpZYpeR9ev+RW9js1Wv7aImD7YTk2RCGohaUVEzGt0HHkqeh1dv+ZX9DoWtX6+NGRmVnJOBGZmJVekRNDPBJWFUvQ6un7Nr+h1LGT9CtNHYGZmw1OkMwIzMxsGJwIzs5IrRCKQdIGkpyU9I+mGRsczGiR9RdJGSU9VlB0p6X5Ja7LlEY2McSQknSDpQUmrJa2S9ImsvBB1lDRB0nJJ/5HV77NZ+SxJj2b1uzsbir1pSRoj6XFJ38q2i1a/tZJ+KukJSSuyskL8jVZq+kQgaQzwReAPgJOAD0s6qbFRjYrbgQv6lN0APBARs4EHsu1mtRv4VETMAeYD12f/3YpSx9eAcyPibcBc4AJJ84GbgM9l9dsKXNPAGEfDJ4DVFdtFqx/AORExt+L5gaL8jb6h6RMBcDrwTEQ8GxGvA3cBlzY4phGLiIeBl/oUXwosztYXA5fVNahRFBEvRMRPsvVXSI3J8RSkjpFszzbHZa8AzgWWZeVNWz8ASTOAdwNfzrZFgeo3gEL8jVYqQiI4HniuYrs7KyuioyPiBUgNKXBUg+MZFZLagVOARylQHbPLJk8AG4H7gV8C2yJid7ZLs/+t/h3wGWBvtj2VYtUPUvL+N0krJS3IygrzN9qrIRPTjDJVKfM9sU1C0iTg68AnI+Ll9KOyGCJiDzBX0hTgHmBOtd3qG9XokHQRsDEiVko6u7e4yq5NWb8KZ0XEBklHAfdL+nmjA8pDEc4IuoETKrZnABsaFEveXpR0LEC23NjgeEZE0jhSEuiKiG9kxYWqI0BEbAMeIvWFTJHU+wOsmf9WzwIukbSWdDn2XNIZQlHqB0BEbMiWG0nJ/HQK+DdahETwGDA7u1vhEOBDwH0Njikv9wFXZetXAfc2MJYRya4n3wasjoibK94qRB0lTc/OBJB0KPAuUj/Ig8Dl2W5NW7+I+JOImBER7aT/574XER0UpH4AkiZKmty7DpwPPEVB/kYrFeLJYkkXkn6NjAG+EhGLGhzSiEn6GnA2adjbF4E/B74JLAVmAuuB90dE3w7lpiDpHcAPgJ+y7xrzn5L6CZq+jpJ+h9SROIb0g2tpRPyFpN8i/YI+EngcuCIiXmtcpCOXXRr6dERcVKT6ZXW5J9scC9wZEYskTaUAf6OVCpEIzMxs+IpwacjMzEbAicDMrOScCMzMSs6JwMys5JwIzMxKzonAbIQkbR98L7ODlxOBmVnJORFYaUg6TdKT2VwBE7N5At7SZ5+bJF1XsX2jpE9JmiTpAUk/ycanP2CEW0ln947Ln23fIunqbP1USd/PBi/7TsUQBR+X9LMsrrtyq7zZAIow6JxZTSLiMUn3AX8JHArcERFP9dntLtJT6rdm2x8gzQuxE3hPNjDeNODHku6LGp7IzMZU+r/ApRGxSdIHgUXAfyGNZT8rIl7rHZLCrN6cCKxs/oI0PtVO4ON934yIxyUdJek4YDqwNSLWZ435X0l6J2lIjOOBo4Ff13DM3wbeQhq9EtKwEy9k7z0JdEn6JmkIEbO6cyKwsjkSmESaKGYC8GqVfZaRBk47hnSGANBBSgynRsSubNTNCX0+t5v9L7f2vi9gVUScUeVY7wbeCVwC/JmkkyvG8zerC/cRWNl0An8GdJGmVazmLtKImpezb7atw0nj7++SdA7QVuVz64CTJI2XdDhwXlb+NDBd0hmQLhVJOllSC3BCRDxImuBlCilJmdWVzwisNCR9BNgdEXdmc13/u6RzI+J7lftFxKps+OHne2eiIiWOf8omMH8COGCCkoh4TtJS0uWeNaTRN4mI1yVdDnwhSxBjSf0QvwDuyMpEmut3Ww5VNxuQRx81Mys5XxoyMys5JwIzs5JzIjAzKzknAjOzknMiMDMrOScCM7OScyIwMyu5/w+i29z3SjyniQAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Clear the plotting field. \n", "plt.clf() # No need to add anything inside these parentheses. \n", "\n", "# First line\n", "# Second line\n", "z = fibList\n", "# you can shorten the keywords like \"color\" to be just \"c\" for quicker typing\n", "plt.plot(x, z, 'bo')\n", "\n", "# add the labels and titles\n", "plt.xlabel('x values')\n", "plt.ylabel('y values')\n", "plt.title('My First Plot')\n", "plt.legend(loc = 'best')\n", "\n", "#Would you like to save your plot? Uncomment the below line. Here, we use savefig('nameOffigure')\n", "#It should save to the folder you are currently working out of.\n", "#plt.savefig('MyFirstFigure.jpg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### EXERCISE 1: \n", "\n", "Create two lists of numbers: `list1` will be the integers from 0 to 9 and `list2` will be the elements of `list1` squared. \n", "\n", "Plot the two lists with _matplotlib_ and make some changes to the color, linestyle, or linewidth. \n", "\n", "Add labels, a title, and a legend to your plot. \n", "\n", "Save the plot once you are done. \n", "\n", "Be creative and feel free to look up the different linestyles using the link above. " ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x116845c18>]" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "randList = [5, 7, 9, 14, 0, 11, 3, 2]\n", "aList = [0, 1, 2, 3, 5, 8, 14, 17]\n", "x = randList\n", "y = aList\n", "\n", "plt.plot (x,y, 'm-')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## C. Logic, If/Else, and Loops\n", "\n", "Let's now switch gears a bit and discuss logic in Python. Conditional (logic) statements form the backbone of programming. These statements in Python return either **True** or **False** and have a special name in programming: _Booleans_. Sometimes this type of logic is also called _Boolean logic_. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Example conditional statements\n", "x = 1\n", "y = 2\n", "x<y #x is less than y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Think of the statement $x<y$ as asking the question \"is x less than y?\" If it is, then it returns **True** and if x is not less than y it returns **False**. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#x is greater than y\n", "x>y" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#x is less-than or equal to y\n", "x<=y" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#x is greater-than or equal to y\n", "x>=y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you let a and b be conditional statements (like the above statements, e.g. a = x < y), then you can combine the two together using logical operators, which can be thought of as functions for conditional statements.\n", "\n", "There are three logical operators that are handy to know:\n", "\n", "* **And** operator: ** `a and b`**\n", " - outputs **True** only if both a and b are **True**\n", "* **Or** operator: **`a or b`**\n", " - outputs **True** if at least one of a and b are **True**\n", "* **Not** operator: **`not(a)`**\n", " - outputs the negation of a " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Example of and operator\n", "(1<2) and (2<3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Example of or operator\n", "(1<2) or (2>3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Example of not operator\n", "not(1<2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, these might not seem especially useful at first, but they're the bread and butter of programming. Even more importantly, they are used when we are doing if/else statements or loops, which we will now cover.\n", "\n", "An `if/else` statement (or simply an if statement) are segments of code that have a conditional statement built into it, such that the code within that segment doesn't activate unless the conditional statement is true.\n", "\n", "Here's an example. Play around with the variables x and y to see what happens." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = 1\n", "y = 2\n", "if (x < y):\n", " print(\"Yup, totally true!\")\n", "else:\n", " print(\"Nope, completely wrong!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The idea here is that Python checks to see if the statement (in this case \"x < y\") is **True**. If it is, then it will do what is below the if statement. The else statement tells Python what to do if the condition is **False**.\n", "\n", "Note that Python requires you to indent these segments of code, and WILL NOT like it if you don't. Some languages don't require it, but Python is very particular when it comes to this point. (The parentheses around the conditional statement, however, are optional.)\n", "\n", "You also do not always need an \"else\" segment, which effectively means that if the condition isn't **True**, then that segment of code doesn't do anything, and Python will just continue on past the if statement.\n", "\n", "Here is an example of such a case. Play around with it to see what happens when you change the values of x and y." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = 2\n", "y = 1\n", "if (x > y):\n", " print(\"x is greater than y\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's a more complicated case. Here, we introduce some logic that helps you figure out if two objects are equal or not. \n", "\n", "There's the `==` operator and the `!=` operator. Can you figure out what they mean?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = 2\n", "y = 2\n", "if (x == y):\n", " print(\"x and y are equal\")\n", "if (x != y):\n", " print(\"x and y are not equal\")\n", "if (x > y or x < y):\n", " print(\"x and y are not equal (again!)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While-loops are similar to if statements, in the sense that they also have a conditional statement built into them. The code inside the loop will execute when the conditional is **True**. And then it will check the conditional and, if it evaluates to **True**, the code will execute again. And so on and so forth...\n", "\n", "The funny thing about while-loops is that they will KEEP executing that segment of code until the conditional statement evaluates to **False**...which hopefully will happen...right?\n", "\n", "Although this seems a bit strange, you can get the hang of it!\n", "\n", "For example, let's say we want Python to count from 1 to 10." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = 0\n", "while (x <= 10):\n", " print(x)\n", " x = x+1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note here that we tell Python to print the number x (x starts at 1) and then redefining x as itself +1 (so, x=1 gets redefined to x = x+1 = 1+1 = 2). Python then executes the loop again, but now x has been incremented by 1. We continue this process from x = 1 to x = 10, printing out x every time. Thus, with a fairly compact bit of code, you get 10 lines of output.\n", "\n", "It is sometimes handy to define what is known as a DUMMY VARIABLE, whose only job is to count the number of times the loop has been executed. Let's call this dummy variable i." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n", "8\n", "16\n", "32\n", "64\n", "128\n", "256\n", "512\n", "1024\n", "2048\n" ] } ], "source": [ "x = 2\n", "i = 0 #dummy variable\n", "while (i<10):\n", " x = 2*x\n", " print(x)\n", " i = i+1 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we want to combine lists with loops! You can use the dummy variable as a way to access a value in the list through its index. In exercise 1 we asked you to square the elements in a given list by hand, let's now do it by using a loop. \n", "\n", "In Python, the command to square something is ``**``. So `3**2` will give you 9." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "myList = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n", "# we want to end the loop at the end of the list i.e., the length of the list\n", "end = len(myList)\n", "\n", "i = 0\n", "while i < end:\n", " num = myList[i]\n", " print(num**2)\n", " i = i + 1\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Isn't that much easier than squaring everything by hand? Loops are your friends in programming and will make menial, reptitive tasks go by very quickly. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All this information with logic, loops, and lists may be confusing, but you will get the hang of it with practice! And by combining these concepts, your programming in Python can be very powerful. Let's try an example where we use an ``if/then`` nested inside of a loop by finding how many times the number 2 shows up in the following list. Remember that indentation is very important in Python!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "twoList = [2, 5, 6, 2, 4, 1, 5, 7, 3, 2, 5, 2]\n", "# this variable will count up how many times the number 2 appears in the above list\n", "count = 0\n", "end = len(twoList)\n", "i = 0\n", "\n", "while i < end:\n", " if twoList[i] == 2:\n", " count = count + 1\n", " \n", " i = i + 1\n", " \n", "print(count)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice how the indentation is set up. What happens if you indent the print statement? How about removing the indentation on the if statement? Play around with it so you get the hang of indentation in nested code. \n", "\n", "Before you continue on to your second exercise, there is one more type of loop we should introduce: the _for-loop_. Instead of using a conditional to end the loop, the for-loop will iterate over the elements in a sequence or list. See the below example:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "grades = [94, 83, 71, 78, 88, 90]\n", "\n", "for i in grades:\n", " print(i)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See how the for-loop prints the elements of the list without you having to specifically access them or even add 1 to your dummy variable? This is the magic of a for-loop! Python will automatically \"assign\" the first element in the list to the variable __i__, print it, then go to the next element until the list ends. You can produce the same output with the below while-loop:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "grades = [94, 83, 71, 78, 88, 90]\n", "end = len(grades)\n", "i = 0\n", "\n", "while i < end:\n", " print(grades[i])\n", " i = i + 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's a bit more effort than the for-loop. However, we will encourage you to use while-loops when you're beginning programming so you can keep track of your dummy variables and clearly see what ends the loop. It's essential to practice good programming habits right when you start! " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### EXERCISE 2: Truth Table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A truth table is a way of showing the **True** and **False** values of different operations. They typically start with values for two variables and then find the result when they are combined with different operators. Using two separate while-loops, generate two columns of a truth table for the lists `x` and `y`, defined below. That is, find the values for each element in the list for `x and y` in one loop and then the values of `x or y` in another. Note: you can always check your answer by doing it in your head! Checking your work is a good habit to have for the future :)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = [True, True, False, False]\n", "y = [True, False, True, False]\n", "\n", "# your code here\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
etotheipluspi/ParticleFilters
src/Particle-Filter-Tutorial.ipynb
1
174432
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO: Loading help data...\n" ] } ], "source": [ "using Colladas\n", "using UrbanMaps\n", "using RayCasters\n", "using MapPlot\n", "using Distributions" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x10c241ed0>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "1-element Array{Any,1}:\n", " PyObject <matplotlib.lines.Line2D object at 0x1172fee50>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coll = ColladaObjects(\"demo_map_border2D.dae\")\n", "map = UrbanMap(coll, 10, 10);\n", "plot(map)\n", "plot([3.5],[6.5],\"*\",color=\"r\",markersize=5.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Robot Model\n", "\n", "Dynamics are linear with Gaussian noise:\n", "\n", "$x_{k} = A x_{k-1} + B u_{k-1} + v_{k-1}$\n", "\n", "Robot can only measure the straighline distance between itself and the nearest object (or boundary) in the -y direction. Measurements are noisy and Gaussian. " ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "observe (generic function with 1 method)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type RobotModel\n", " A::Matrix{Float64}\n", " B::Matrix{Float64}\n", " # can be sigma values\n", " process_noise::Normal\n", " observation_noise::Normal\n", " map::UrbanMap\n", "end\n", "\n", "typealias State Vector{Float64} # x,y position\n", "typealias Action Vector{Float64} # x,y control\n", "typealias Observation Float64 # distance to nearest wall in -y direction\n", "\n", "# generative model\n", "function move(m::RobotModel, x::State, u::Action)\n", " pn = m.process_noise\n", " sp = m.A*x + m.B*u + rand(pn, 2)\n", "end\n", "\n", "# observation function\n", "function observation(m::RobotModel, o::Observation, x::State, u::Action)\n", " map = m.map\n", " if inBuilding(map, x)\n", " return 0.0\n", " end\n", " if 0.0 <= x[1] <= 9.0 && 0.0 <= x[2] <= 7.0\n", " return 0.0\n", " end\n", " on = params(m.observation_noise)[2]\n", " d = nearest_wall(map, x, :down)\n", " n = Normal(d, on)\n", " return pdf(n, o)\n", "end\n", "\n", "function observe(m::RobotModel, x::State)\n", " on = m.observation_noise\n", " d = nearest_wall(m.map, x, :down) + rand(on)\n", "end\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "plot (generic function with 3 methods)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type Belief\n", " particles::Matrix{Float64}\n", " temp_particles::Matrix{Float64}\n", " weights::Vector{Float64}\n", " temp_weights::Vector{Float64}\n", " n::Int64\n", "end\n", "\n", "function rand!(x::Vector{Float64}, b::Belief)\n", " i = rand(1:b.n)\n", " x[1:end] = b.particles[:,i]\n", "end\n", "\n", "function update_belief!(b::Belief, m::RobotModel, u::Action, o::Observation)\n", " n = b.n\n", " x = zeros(2)\n", " weights = b.temp_weights\n", " particles = b.temp_particles\n", " for i = 1:n\n", " rand!(x, b)\n", " xp = move(m, x, u)\n", " weights[i] = observation(m, o, xp, u)\n", " particles[:,i] = xp\n", " end\n", " norm = sum(weights)\n", " for i = 1:n; weights[i] /= norm; end;\n", " dist = Categorical(weights)\n", " updated_weights = b.weights\n", " updated_particles = b.particles\n", " for i = 1:n\n", " k = rand(dist)\n", " updated_weights[i] = weights[k]\n", " updated_particles[:,i] = particles[:,k]\n", " end\n", " b\n", "end\n", "\n", "\n", "function update_positions!(b::Belief, m::RobotModel, u::Action)\n", " for i = 1:b.n\n", " x = particles[]\n", " xp = move(m, ) \n", " end\n", "end\n", "\n", "function add_noise!(b::Belief)\n", " particles = b.paticles\n", " for i = 1:b.n\n", " particles[1,i] += rand() \n", " end\n", "end\n", "\n", "function PyPlot.plot(b::Belief)\n", " n = b.n\n", " par = b.particles\n", " weights = b.weights\n", " norm = maximum(weights)\n", " for i = 1:n\n", " ms = 10.0 * weights[i]/norm\n", " plot(par[1,i], par[2,i], \".\", color=\"r\",markersize=5.0) \n", " end\n", "end" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [], "source": [ "A = eye(2)\n", "B = eye(2)\n", "pn = Normal(0.0, 0.1)\n", "on = Normal(0.0, 0.2)\n", "robot = RobotModel(A, B, pn, on, map);\n" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x12090d190>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject <matplotlib.text.Text object at 0x1232b5f50>" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "us = {[0.0,-0.5], [0.5,0.0], [0.0,-0.5,], [0.0,-0.5], [-1.0,0.0], [-0.5,-0.5]}\n", "x0 = [4.0,6.5]\n", "trajectory = [[4.0, 4.0, 4.5, 4.5, 4.5, 3.5, 3.0] [6.5, 6.0, 6.0, 5.5, 5.0, 5.0, 4.5]]\n", "ns = 200\n", "xf = zeros(2,ns)\n", "for i = 1:ns\n", " x = deepcopy(x0)\n", " for j = 1:length(us)\n", " u = us[j]\n", " x = move(robot, x, u)\n", " end\n", " xf[:,i] = x\n", "end\n", "plot(map)\n", "plot(vec(trajectory[:,1]),vec(trajectory[:,2]),\"--\",lw=2.0,color=\"b\")\n", "plot(vec(xf[1,:]),vec(xf[2,:]),\".\",color=\"k\")\n", "title(\"Process Noise\")" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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YsECnTp3SzJkzVb9+/YvWmDt3rmbNmqUbbrhBjRo1Unh4uL7++mstW7ZMwcHB+tOf/pQ/d8iQIUpLS9Mrr7yixo0bq3v37rriiit07NgxffPNN9q0aZPuvffeAn/nAJQxRb0/1Oeff2769etnGjVqZEJDQ01ERITp0KGDSUlJ8Zj75Zdfmu7du5uqVauaiIgIc/fdd5sjR45472ZVALxm69atZtCgQaZhw4YmJCTEVKtWzbRs2dKMHTvWHDx4sNBt5syZY1wul0lOTjbLly83HTp0MFWqVDHh4eGmX79+Zvfu3QXmp6enm9GjR5trrrnG1KpVywQFBZmGDRuahIQEs3nz5kJrfPzxx6Z///4mOjraVK5c2URFRZm2bduaRx55xKSlpRWY26BBA9OwYcMi7W/Tpk2Ny+UyNWvWNGfPni10TnZ2tnniiSdMkyZNTHBwsLnyyivN8OHDzQ8//GA6duxoXC6Xxzbbtm0zXbp0MWFhYcblchmXy2U+/PBDY8zP99HMe78K265Pnz6mVq1apnLlyqZ+/frmwQcfNBkZGR5zBw8ebFwul9m/f7/Hc5e6F+r/ylvDvPuQ/q/Vq1cbx3GMy+XyuA+pMcYcP37cjBs3zjRt2tQEBQWZ8PBw061bN7NmzZoi9bZlyxbzwAMPmNatW5uIiAgTEhJimjZtau69917zxRdfFNrT8uXLTa9evUxUVJSpXLmyqVOnjrn22mvNhAkTzK5du4q03wBKJ8eYC3z58v9YuXKlZsyYoeuuu07R0dHKzs7WokWLtGnTJk2ePFlPPPGEpJ/PsWrbtq3Cw8P18MMPKysrS9OmTcu/orKwe/sBAACg4ipyIC3M+fPnFR8fr2PHjuWfO/Xggw9q7ty5crvd+ec+rVu3Tl27dtVrr72moUOHeqdzAAAAlAsluqjJ5XKpXr16BY56Ll68WL169SpwIn6XLl3UrFkzLViwoCTlAAAAUA4V+6Km7OxsZWdnKzMzM/8G1zNnzpQkHTx4UEeOHCn0W0TatWunlStXlrxjAAAAlCvFDqSjR4/WrFmzft44MFAzZszQsGHDJP1yQ+TCvsGjTp06OnbsmM6ePct5pAAAAMhX7EA6atQo9e/fX4cOHdL8+fM1fPhwhYSEaNCgQcrJyZFU+Pc8BwcHS5JycnI8AunRo0e1evVqNWjQIP/r+QAAAFB65OTkaN++ferevbvHPZxLrKSX6Xfr1s3UqFHD5OTkmG3bthnHcQq9FdTYsWON4zjmzJkzHs+lpKQYSTx48ODBgwcPHjxK+aOwnFdSJb4xft++fbVmzRq53e78j+oL+3aVjIwMRUZGFvpxfYMGDSRJKSkpio2NLWlL8IFRo0Zp+vTptttAIVib0ou1Kd1Yn9KLtSmd0tPTNXDgwPzc5k0lDqR5H9O7XC7VrVtXtWrV0rZt2zzmbd26VW3atCn0NfI+po+NjfX79zajaMLCwlibUoq1Kb1Ym9KN9Sm9WJvSzRenVxb5tk9HjhzxGDt79qzmzp2ryMhItWjRQtLPR0yXL1+uAwcO5M9bt26ddu/erYSEBC+0DAAAgPKkyEdIhw0bpqysLN10002Kjo7W4cOHNX/+fH311VeaPXu2AgICJEnjx4/XwoUL1alTJ40cOVJZWVl6/vnn1apVKw0ZMsRnOwIAAICyqciB9K677tKbb76pV199VT/88IOqV6+ua6+9VjNnzlSXLl3y59WrV08ffvihRo8erccee0xBQUHq1auXXnjhBW73BAAAAA9FDqSJiYlKTEws0tzmzZtr1apVl90USp+kpCTbLeACWJvSi7Up3Vif0ou1qXhK9F323rJ9+3bFx8crLS2Nk5gBAABKIV/mtRJ9lz0AAABQUgRSAAAAWEUgBQAAgFUEUgAAAFhFIAUAAIBVBFIAAABYRSAFAACAVQRSAAAAWEUgBQAAgFUEUgAAAFhFIAUAAIBVBFIAAABYRSAFAACAVQRSAAAAWEUgBQAAgFUEUgAAAFhFIAUAAIBVBFIAAABYRSAFAACAVQRSAAAAWEUgBQAAgFUEUgAAAFhFIAUAAIBVgbYbAMq67Oxsud1uv9eNiYlRaGio3+sCAOBtBFKghNxut+Lj4/1eNy0tTXFxcX6vCwCAtxFIAa9JkRTrhzrpkgb6oQ4AAP5BIAW8JlYSRywBACguLmoCAACAVQRSAAAAWEUgBQAAgFUEUgAAAFhFIAUAAIBVBFIAAABYRSAFAACAVQRSAAAAWEUgBQAAgFUEUgAAAFhFIAUAAIBVBFIAAABYRSAFAACAVQRSAAAAWEUgBQAAgFUEUgAAAFhFIAUAAIBVBFIAAABYRSAFAACAVQRSAAAAWEUgBQAAgFUEUgAAAFhFIAUAAIBVBFIAAABYRSAFAACAVQRSAAAAWEUgBQAAgFUEUgAAAFhFIAUAAIBVBFIAAABYRSAFAACAVQRSAAAAWEUgBQAAgFUEUgAAAFhFIAUAAIBVBFIAAABYRSAFAACAVQRSAAAAWFXkQLpt2zYNHz5cLVq0UNWqVVW/fn0lJiZq9+7dBeYNHjxYLpfL4xEbG+v15gEAAFD2BRZ14nPPPafNmzcrISFBrVq1UkZGhmbOnKm4uDj961//UosWLfLnBgUF6c033yywfVhYmPe6BgAAQLlR5ED6yCOPqF27dgoM/GWTxMREtWzZUlOnTtW8efPyxytVqqQBAwZ4t1MAAACUS0X+yL5Dhw4FwqgkNWnSRM2bN5fb7S4wbozR+fPndeLECe90CQAAgHKrRBc1GWP03//+VzVr1iwwnp2drerVq6tGjRqKjIzU8OHDderUqRI1CgAAgPKpyB/ZF2b+/Pk6dOiQnnnmmfyx6OhojRs3TnFxcTp//rxWrlypV155RTt37tTGjRsVEBBQ4qYBAABQflx2IHW73XrooYd03XXXadCgQfnjzz77bIF5/fv3V7NmzfTEE09o0aJFSkxMvPxuAQAAUO5cViA9fPiwbrvtNoWHh2vRokVyHOei80eNGqUJEyZo3bp1Fw2ko0aN8rgaPykpSUlJSZfTJgAAAC5DamqqUlNTC4xlZmb6rF6xA2lmZqZuvfVWnThxQps2bVLt2rUvuU1wcLAiIiJ07Nixi86bPn264uLiitsSAAAAvKiwA4Lbt29XfHy8T+oVK5CePn1avXv31p49e7R27VrFxMQUabusrCwdPXpUtWrVuqwmAQAAUH4V+Sr73NxcJSYmasuWLVq4cKGuvfZajzk//fSTsrKyPMYnT54sSerRo0cJWgUAAEB5VKwb4y9btky9e/fW0aNHlZKSUuD5gQMHKiMjQ23bttWAAQN01VVXSZJWr16tlStX6tZbb9Udd9zh3e4BAABQ5hU5kO7cuVOO42jZsmVatmxZgeccx9HAgQMVHh6u3r17a82aNUpOTlZubq6aNm2qKVOmaMyYMV5vHgAAAGVfkQPphg0bLjknLCxMc+fOLVFDAAAAqFhK9E1NAAAAQEkRSAEAAGAVgRQAAABWEUgBAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWEUgBAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWEUgBAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWEUgBAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWEUgBAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWEUgBAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWEUgBAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWEUgBAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWEUgBAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGBVoO0GAJRu2dnZcrvdfq0ZExOj0NBQv9YEANhDIAVwUW63W/Hx8X6tmZaWpri4OL/WBADYQyAFUEQpkmJ9XCNd0kAf1wAAlDYEUgBFFCuJo5YAAO/joiYAAABYRSAFAACAVQRSAAAAWEUgBQAAgFUEUgAAAFhFIAUAAIBVRQ6k27Zt0/Dhw9WiRQtVrVpV9evXV2Jionbv3u0xNz09XT169FC1atUUGRmpe+65R0ePHvVq4wAAACgfinwf0ueee06bN29WQkKCWrVqpYyMDM2cOVNxcXH617/+pRYtWkiSDhw4oJtuuknh4eGaMmWKsrKyNG3aNP3nP//R1q1bValSJZ/tDAAAAMqeIgfSRx55RO3atVNg4C+bJCYmqmXLlpo6darmzZsnSXr22WeVk5OjHTt2qF69epKk9u3bq2vXrpozZ46GDh3q5V0AAABAWVbkj+w7dOhQIIxKUpMmTdS8eXO53e78scWLF6tXr175YVSSunTpombNmmnBggVeaBkAAADlSYkuajLG6L///a9q1qwpSTp48KCOHDmia665xmNuu3bttGPHjpKUAwAAQDlUokA6f/58HTp0SImJiZKkjIwMSVKdOnU85tapU0fHjh3T2bNnS1ISAAAA5UyRzyH9X263Ww899JCuu+46DRo0SJKUk5MjSQoKCvKYHxwcnD+HC5sAFO7n3yHp6el+rRoTE6PQ0FC/1gQA/OKyAunhw4d12223KTw8XIsWLZLjOJKkkJAQSdJPP/3ksc3p06cLzCnMqFGjFBYWVmAsKSlJSUlJl9MmgDJnnyRp4MCBfq2alpamuLg4v9YEgNIsNTVVqampBcYyMzN9Vq/YgTQzM1O33nqrTpw4oU2bNql27dr5z+V9VJ/30f2vZWRkKDIy8qJHR6dPn87/FABISpEU64c66ZL8G34BoCwo7IDg9u3bFR8f75N6xQqkp0+fVu/evbVnzx6tXbtWMTExBZ6vW7euatWqpW3btnlsu3XrVrVp06Zk3QKoIGIl8Y9TAKgoinxRU25urhITE7VlyxYtXLhQ1157baHz+vbtq+XLl+vAgQP5Y+vWrdPu3buVkJBQ8o4BAABQrhTrxvjLli1T7969dfToUaWkpBR4Pu+cr/Hjx2vhwoXq1KmTRo4cqaysLD3//PNq1aqVhgwZ4t3uAQAAUOYVOZDu3LlTjuNo2bJlWrZsWYHnHMfJD6T16tXThx9+qNGjR+uxxx5TUFCQevXqpRdeeIGr6wEAAOChyIF0w4YNRX7R5s2ba9WqVZfVEAAAACqWEt0YHwAAACgpAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsCrTdAIDiypEkpaen+6Wav+oAACouAilQ5uyTJA0cONBuGwAAeAmBFCizUiTF+qHOCkkT/FAHAFBREUiBMitWUpwf6vCRPQDAt7ioCQAAAFYRSAEAAGAVgRQAAABWEUgBAABgFYEUAAAAVhFIAQAAYFWxAumpU6f09NNPq0ePHoqIiJDL5VJycrLHvMGDB8vlcnk8YmP9cc9EAAAAlCXFug/pkSNHNHnyZNWvX19t2rTRxo0b5ThOoXODgoL05ptvFhgLCwu7/E4BAABQLhUrkEZHR+vw4cOKiorFRCyCAAAb0klEQVRSWlqa2rVrd8G5lSpV0oABA0rcIAAAAMq3Yn1kX7lyZUVFRUmSjDEXnWuM0fnz53XixInL7w4AAADlns8uasrOzlb16tVVo0YNRUZGavjw4Tp16pSvygEAAKCM8sl32UdHR2vcuHGKi4vT+fPntXLlSr3yyivauXOnNm7cqICAAF+UBQAAQBnkk0D67LPPFvhz//791axZMz3xxBNatGiREhMTfVEWAIopR5KUnp7u+0o5Odq3b58aNGigkJAQn9fLExMTo9DQUL/VA4DL4ZNAWphRo0ZpwoQJWrdu3QUD6ahRozyuxE9KSlJSUpI/WgRQ4eyTJA0cONBuGz6UlpamuLg4220AKGNSU1OVmppaYCwzM9Nn9fwWSIODgxUREaFjx45dcM706dP5xQnAghRJvr5P8gpJE/xUS5LSJZXfoA3Atwo7ILh9+3bFx8f7pJ7fAmlWVpaOHj2qWrVq+askABRRrCRf/2M477QAf9QCgLLF61fZ//TTT8rKyvIYnzx5siSpR48e3i4JAACAMqzYR0hnzpyp48eP69ChQ5KkpUuX6ttvv5UkPfzwwzp27Jjatm2rAQMG6KqrrpIkrV69WitXrtStt96qO+64w4vtAwAAoKwrdiB94YUXtH//fkmS4zh65513tGTJEjmOo3vuuUfh4eHq3bu31qxZo+TkZOXm5qpp06aaMmWKxowZ4/UdAAAAQNlW7ED6zTffXHLO3LlzL6sZAAAAVDw++6YmAAAAoCgIpAAAALCKQAoAAACrCKQAAACwikAKAAAAqwikAAAAsIpACgAAAKsIpAAAALCKQAoAAACrCKQAAACwikAKAAAAqwikAAAAsIpACgAAAKsIpAAAALCKQAoAAACrCKQAAACwikAKAAAAqwikAAAAsIpACgAAAKsIpAAAALCKQAoAAACrCKQAAACwikAKAAAAqwikAAAAsCrQdgOAL2RnZ8vtdvulVnp6ul/qAABQXhFIUS653W7Fx8fbbgMAABQBgRTlXIqkWB/XWCFpgo9rAABQfhFIUc7FSorzcQ0+sgcAoCS4qAkAAABWEUgBAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABWEUgBAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGBVoO0GYE92drbcbrdfa8bExCg0NNSvNQEAQOlGIK3A3G634uPj/VozLS1NcXFxfq0JAABKNwIpJKVIivVxjXRJA31cAwAAlEUEUujnMMpRSwAAYAcXNQEAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwKtN0AAKBsy87Oltvt9nvdmJgYhYaG+r0uAO8jkAIASsTtdis+Pt7vddPS0hQXF+f3ugC8j0AKAPCSFEmxfqiTLmmgH+oA8BcCKQDAS2IlccQSQPFxURMAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMCqYgXSU6dO6emnn1aPHj0UEREhl8ul5OTkQuemp6erR48eqlatmiIjI3XPPffo6NGjXmkaAAAA5UexAumRI0c0efJk7dq1S23atJEkOY7jMe/AgQO66aabtHfvXk2ZMkVjxozR+++/r65du+rs2bPe6RwAAADlQrFujB8dHa3Dhw8rKipKaWlpateuXaHznn32WeXk5GjHjh2qV6+eJKl9+/bq2rWr5syZo6FDh5a8cwAAAJQLxTpCWrlyZUVFRUmSjDEXnLd48WL16tUrP4xKUpcuXdSsWTMtWLDgMlsFAABAeeT1i5oOHjyoI0eO6JprrvF4rl27dtqxY4e3SwIAAKAM83ogzcjIkCTVqVPH47k6dero2LFjnEcKAACAfMU6h7QocnJyJElBQUEezwUHB+fPqVSpkrdLAwDy/fy7OD093eeV/FEDQPnm9UAaEhIiSfrpp588njt9+nSBOf9r1KhRCgsLKzCWlJSkpKQkL3cJAOXdPknSwIED7bYBoExKTU1VampqgbHMzEyf1fN6IM37qD7vo/tfy8jIUGRk5AWPjk6fPl1xcXHebgkAKrAUSbE+rrFC0gQf1wDgT4UdENy+fbvi4+N9Us/rgbRu3bqqVauWtm3b5vHc1q1b8+9fCgDwh1hJvv6HPh/ZAygZn3x1aN++fbV8+XIdOHAgf2zdunXavXu3EhISfFESAAAAZVSxj5DOnDlTx48f16FDhyRJS5cu1bfffitJevjhh1W9enWNHz9eCxcuVKdOnTRy5EhlZWXp+eefV6tWrTRkyBDv7gEAAADKtGIH0hdeeEH79++X9PPXhr7zzjtasmSJHMfRPffco+rVq6tevXr68MMPNXr0aD322GMKCgpSr1699MILL3B1PQAAAAoodiD95ptvijSvefPmWrVqVbEbAgAAQMXik3NIAQAAgKIikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAqkDbDaCiyJEkpaen+6Wav+oAAICSI5DCT/ZJkgYOHGi3DQAAUOoQSOFnKZJi/VBnhaQJfqgDAABKikAKP4uVFOeHOnxkDwBAWcFFTQAAALCKQAoAAACrCKQAAACwikAKAAAAqwikAAAAsIpACgAAAKsIpAAAALCKQAoAAACrCKQAAACwikAKAAAAqwikAAAAsIpACgAAAKsCbTdQXDt27FBmZqZfa8bFxal69ep+rQkAKB2ys7Pldrv9WjMmJkahoaF+rQnYVOYC6cMP/0kff/yRX2tu2bJF7du392tNAEDp4Ha7FR8f79eaaWlpiouL82tNwKYyF0jPn5ekOyRN80O1XZJ6+aEOAKD0S5EU6+Ma6ZIG+rgGUPqUuUD6s+qSmvihTo4fagAAyoZYSRy1BHyBi5oAAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABW+SSQbty4US6Xq9DH1q1bfVESAAAAZZRP70M6cuRItWvXrsBY48aNfVkSAAAAZYxPA+mNN96oO++805clAAAAUMb59BxSY4yysrJ07tw5X5YBAABAGebTQDpkyBCFhYUpJCREnTt3Vlpami/LAQAAoAzyyUf2QUFB6tevn3r27KmaNWvqiy++0LRp03TjjTfqk08+UZs2bXxRFgAAAGWQTwJphw4d1KFDh/w/9+rVS/369VOrVq30+OOPa+XKlb4oW+ZlZ2fL7Xb7rV56errfagEAAFyITy9q+rXGjRvrjjvu0JIlS2SMkeM4HnNGjRqlsLCwAmNJSUlKSkryV5tWud1uxcfH224DAABUcKmpqUpNTS0wlpmZ6bN6fgukklSvXj2dOXNGp06dUtWqVT2enz59uuLi4vzZUimVIinWD3VWSJrghzoAAKAsKeyA4Pbt23124MyvgXTv3r0KCQkpNIzi12Il+SOY85E9AACwzydX2R85csRjbOfOnVq6dKm6devmi5IAAAAoo3xyhDQxMVGhoaHq0KGDoqKi9OWXX2rWrFmqWrWqpk6d6ouSAAAAKKN8Ekj79Omj+fPna/r06Tpx4oSioqLUr18/Pf3002rUqJEvSgIAAKCM8kkgHTFihEaMGOGLlwYAAEA549NvagIAAAAuhUAKAAAAqwikAAAAsIpACgAAAKsIpAAAALCKQAoAAACrCKQAAACwikAKAAAAq3xyY/zyxu12KzDQ929Venq6z2sAQNmXI8l/vzP9+7vZv/uWJyYmRqGhoX6tCfwagbQIBg0aZLsFAEC+fZKkgQMH2m3DJ/ZJ8v++paWlKS4uzq81gV8jkBZZiqRYH9dYIWmCj2sAQHnhj9/Lkp3fzf7at3RJ5THYo6whkBZZrCRf/+uRj+wBoOj88XtZsvO72V/7BpQOXNQEAAAAqwikAAAAsIpACgAAAKsIpAAAALCKQAoAAACrCKQAAACwikAKAAAAqwikAAAAsIpACgAAAKsIpAAAALCKQAoAAACrCKQAAACwikAKAAAAqwikAAAAsIpACgAAAKsIpAAAALCKQAoAAACrCKQAAACwikAKAAAAqwikAAAAsIpACgAAAKsIpAAAALCKQAoAAACrCKQAAACwikAKAAAAqwikAAAAsIpACgAAAKsIpAAAALAq0HYDAADAlhxJUnp6ut8qxsTEKDQ01G/1UDYQSAEAqLD2SZIGDhzot4ppaWmKi4vzWz2UDQRSAAAqvBRJsT6ukS7Jf8EXZQuBFACACi9WEkctYQ8XNQEAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwKtN0AAACAt2VnZ8vtdvu9bkxMjEJDQ/1et6wjkAIAgHLH7XYrPj7e73XT0tIUFxfn97plHYEUAACUYymSYv1QJ13SQD/UKZ8IpAAAoByLlcQRy9KOi5oAAABgFYEUAAAAVhFIAQAAYBWBFAAAAFYRSAEAAGAVgRQAAABW+SSQ/vTTTxo3bpyio6MVGhqq3/72t1q7dq0vSgEAAKCM80kgHTx4sKZPn667775bM2bMUEBAgHr27Kn/9//+ny/KwS9SbTeAC2JtSi/WpnRjfUqr1FTWpqLxeiDdunWr/vnPf2rq1Kl67rnndN9992n9+vWqX7++Hn30UW+Xg9/wy6H0Ym1KL9amdGN9SisCacXj9UC6aNEiBQYGatiwYfljQUFB+sMf/qDNmzfr4MGD3i4JAACAMszrgXTHjh1q1qyZqlatWmC8Xbt2kqTPPvvM2yUBAABQhnk9kGZkZKhOnToe43ljhw4d8nZJAAAAlGGB3n7BnJwcBQUFeYwHBwfnP1/YNpKUnp5+ydc/eTJL0leSZpWoz6L59ekFKyRdur+Sybvoyx+1ilvvgKT5fqrlDf6sZ3vfSro2xa3nS7bfS2/79dqUt32zVcub9Yr6s8N76R3f/FxpxYpL/v/+wIEDmj+/ZL/Xvvnmm///v/z1Xv5cryhZpqzK27fCslyJGS9r0aKFueWWWzzGv/jiC+M4jpk1a5bHcykpKUYSDx48ePDgwYMHj1L+SElJ8XZ8NF4/QlqnTp1CP5bPyMiQJEVHR3s81717d6WkpKhBgwYKCQnxdksAAAAooZycHO3bt0/du3f3+mt7PZC2bdtWGzduVFZWlqpVq5Y/vmXLFklSmzZtPLapWbOmfv/733u7FQAAAHjR9ddf75PX9fpFTf369VNubq5mzfrlHM+ffvpJs2fP1m9/+1vVrVvX2yUBAABQhnn9CGn79u2VkJCgxx9/XN9//70aN26s5ORkffvtt5o9e7a3ywEAAKCMc4wxxtsv+tNPP2nChAlKSUnRjz/+qNatW2vy5Mnq2rWrt0sBAACgjPNJIAUAAACKyuvnkAIAAADF4bNA+sUXXyghIUGNGzdWlSpVFBkZqeuuu67IN7pdu3atunTpoqioKFWrVk2tW7fW//3f/+n8+fO+arlC+8tf/iKXy6WWLVsWaf7x48c1bNgw1apVS1WrVlXnzp21Y8cOH3dZcRVnfdatW6d7771XzZo1U5UqVdS4cWMNHTpUhw8f9kOnFU9xf3Z+bejQoXK5XOrdu7cPOsPlrM3atWvVuXNn1ahRQ9WrV9c111yjBQsW+LDLiqu460Mu8I2NGzfK5XIV+ti6deslt/dWHvD6RU15vv32W508eVKDBw9WdHS0srOztWjRIt19993at2+fnnjiiQtuu2rVKvXs2VNXX321nnjiCYWGhmrFihUaOXKkvv76a7300ku+artCOnDggJ599llVqVJFjuNccv758+d122236d///rceffRRRUZG6pVXXlHHjh2VlpamJk2a+KHriqO46zNu3DgdP35cCQkJatq0qb7++mvNnDlTy5cv12effabf/OY3fui6Yiju2vzap59+quTkZAUHBxd7W1za5azN7Nmzdd9996lbt26aMmWKAgIC5Ha7deDAAR93W/EUd33IBb43cuRItWvXrsBY48aNL7qNV/OA12+1fxG5ubmmTZs25sorr7zovAEDBpjg4GDz448/Fhi/+eabTVhYmC9brJASExPNLbfcYjp27GiuvvrqS87/5z//aRzHMYsXL84fO3LkiAkPDzcDBgzwZasVUnHXZ9OmTR5jH330kXEcxzz55JO+aLHCKu7a5Dl//rzp0KGDue+++0yDBg1M7969fdhlxVTctfnmm29MSEiI+dOf/uSH7lDc9SEX+M6GDRs8/p9eVN7MA349h9TlcqlevXqqVKnSReeFhIQoKChIYWFhBcZr166t0NBQX7ZY4Xz00UdavHixXnrpJRljivQv1UWLFql27dq6884788dq1qyp/v3767333tPZs2d92XKFcjnrc8MNN3iM3XjjjYqIiJDb7fZFmxXS5axNnnnz5unLL7/UM888I8N1pV53OWvz97//XcYYTZo0SZJ08uRJ1sZHLmd9yAW+Z4xRVlaWzp07V+RtvJkHfB5Is7OzdfToUX399deaPn26Vq9erUcfffSi24wYMULnz5/X/fffL7fbrf379+vvf/+73nnnHT3++OO+brnCyM3N1YgRIzR06FC1aNGiyNvt2LFDcXFxHuPt2rVTdna2vvrqK2+2WWFd7voU5uTJk8rKylLNmjW91F3FVpK1ycrK0rhx4zR+/HhOn/CBy12btWvXKiYmRsuXL1e9evVUvXp11axZU0899RTB1Isud33IBb43ZMgQhYWFKSQkRJ07d1ZaWtolt/FmHvDZOaR5Ro8enf+tTYGBgZoxY4aGDRt20W1at26t9evXq3fv3nrjjTckSQEBAfrb3/52yW1RdH//+9/17bffav369cXaLiMjQx07dvQYr1OnjiTp0KFDJQ5QuPz1KcxLL72ks2fPKjEx0QudoSRrM2nSJFWpUkWjRo3yQWe43LXZvXu3AgMDde+992rcuHFq3bq1Fi9erGeeeUbnzp3Ts88+66OOK5bLXR9yge8EBQWpX79+6tmzp2rWrKkvvvhC06ZN04033qhPPvmk0K98z+PVPFDsEwaKye12m3Xr1pl58+aZHj16mICAADNnzpyLbpOenm6ioqJMu3btzLx588zChQtNnz59TKVKlcy7777r65YrhKNHj5qIiAjz4osv5o/dfPPNpmXLlpfcNiAgwDz00EMe4+vWrTOO45j33nvPq71WRCVZn//14YcfmsDAQHPXXXd5s8UKqyRrs2vXLlO5cmWzZMmS/LH69etzDqmXlGRtXC6XcRzH/PWvfy0wfuutt5rQ0FCTlZXl9X4rmpKsD7nAv/bs2WNCQ0NNjx49LjrPm3nArxc1GWNMt27dTI0aNUx2dvYF59x2220mOjranDp1qsB4p06dTN26dc25c+d83Wa598c//tE0a9bMnD17Nn+sqL8Yqlatau677z6P8ffff984jmM++OADr/ZaEZVkfX4tPT3dREREmLi4OHPy5Elvt1khlWRtevToYTp16lRgjEDqPSVZmypVqhiXy2W+++67AuPJycnGcZxCLxZE8ZRkfcgF/peUlGSCgoLM+fPnLzjHm3nA7zfG79u3rzIzM7Vr164Lzvn444/VuXNnjxOVe/furUOHDmn//v2+brNc2717t15//XWNGDFCBw4c0L59+7Rv3z6dPn1aZ86c0f79+/Xjjz9ecPs6dero0KFDHuMZGRmSpOjoaJ/1XhGUdH3yfPfdd+rWrZvCw8O1YsUKValSxQ/dl28lWZv169dr9erVevjhh/O327dvn86dO6fs7Gzt379fWVlZft6j8qOkPzd5v7f+97zeqKgoSSrSzxwurKTrQy7wv3r16unMmTM6derUBed4NQ9cVmwugZdeesk4jmN27tx5wTlVqlQxSUlJHuPPPfeccRzH7Nq1y5ctlnt5t3i42GPUqFEX3D4hIcHUrl3b419NQ4cONVWrVjVnzpzx9S6UayVdH2N+/mgsJibG1K5d2+zZs8dPnZd/JVmb2bNnX3Lbl19+2c97VH6U9OcmKSnJOI5j9u7dW2D8zTffNI7jmM2bN/t6F8q1kq4PucD/+vbta0JDQy86x5t5wGcXNR05ckS1atUqMHb27FnNnTtXkZGR+Se5ZmRkKDMzU02aNFFg4M/ttG3bVh988IGOHTumiIgIST9fmbdgwQJVr179kjdqxcW1bNlS77zzToFbbRhj9OSTT+rkyZN6+eWX89/jwtanX79+WrRokZYsWaK+fftKko4ePaqFCxeqd+/el7ytFy6upOtz6tQp9ezZUxkZGdqwYQM/L15UkrXp0qWL3n333QKvZ4zRsGHD1KBBAz3xxBO6+uqr/bo/5UlJf24SExP19ttv680339Qzzzwj6eebfs+ePVuRkZGKj4/3/06VIyVdH3KB7xSW13bu3KmlS5fqtttuyx87fPiwjh8/7rs8UMzAXGS/+93vTJcuXczEiRPN66+/biZPnmxiYmKMy+UyycnJ+fMGDRpkHMcx+/fvzx9buXKlcblcpkmTJuavf/2rmTFjhunQoYNxHMc8++yzvmq5wrv55ps9blBc2Prk5uaaDh06mGrVqplJkyaZv/3tb6ZFixYmLCzMfPXVV/5uu8Io6vrccccdxnEc84c//MHMmzevwIOT/32jqGtTGM4h9a3irM0tt9xiXC6Xuf/++83f/vY307VrV+M4jnn99df92XKFUtT1IRf4TqdOncxtt91mnnnmGTNr1izzpz/9yYSGhprw8HDjdrvz5/k6D/jsCOldd92lN998U6+++qp++OEHVa9eXddee61mzpypLl265M9zHMfjprg9evTQihUr9Je//EUTJ07UuXPnFBMTo9dee01Dhw71VcsVXmFrUdiYy+XSihUrNHbsWM2YMUM5OTlq37695s6dq6ZNm/qz5QqlqOuzc+dOOY6jt956S2+99VaB5xo0aKA77rjD571WNEVdmwttC98pztq8++67evLJJ/XPf/5Tc+bMUUxMjObPn6+kpCR/tVvhFHV9yAW+06dPH82fP1/Tp0/XiRMnFBUVpX79+unpp59Wo0aN8uf5Og84xnDHXwAAANjj96vsAQAAgF8jkAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwCoCKQAAAKwikAIAAMAqAikAAACsIpACAADAKgIpAAAArCKQAgAAwKr/D2TDWfQ3KA8NAAAAAElFTkSuQmCC", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x1242fbfd0>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "PyObject <matplotlib.text.Text object at 0x128891ad0>" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "of = zeros(ns)\n", "for i = 1:ns\n", " of[i] = observe(robot, x0)\n", "end\n", "fig = figure(facecolor=\"white\")\n", "ax = fig[:add_subplot](111)\n", "ax[:hist](of,bins=20)\n", "title(\"Observation Noise\")\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x11acc9fd0>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ns = 300\n", "p0 = zeros(2,ns)\n", "x_d = Uniform(0,5)\n", "y_d = Uniform(4,7)\n", "for i = 1:ns\n", " p0[1,i] = rand(x_d)\n", " p0[2,i] = rand(y_d)\n", "end\n", "w0 = zeros(ns) + 1.0/ns\n", "b = Belief(p0, p0, w0, w0, ns);\n", "plot(map)\n", "plot(b)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [], "text/plain": [ "Slider{Int64}([Input{Int64}] 6,\"l\",6,1:11)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x11e4b6a10>)" ] }, "execution_count": 41, "metadata": { "comm_id": "ee9010d3-59d2-4553-9421-779b6d231723", "reactive": true }, "output_type": "execute_result" } ], "source": [ "using Interact\n", "using PyPlot\n", "\n", "A = eye(2)\n", "B = eye(2)\n", "pn = Normal(0.0, 0.1)\n", "on = Normal(0.0, 0.2)\n", "robot = RobotModel(A, B, pn, on, map);\n", "\n", "\n", "x = [4.0,6.5]\n", "us = {[0.0, 0.0], [0.0,0.0], [0.0,-0.5], [0.5,0.0], [0.0,-0.5,], [0.0,-0.5], [-1.0,0.0], [-0.5,-0.5], [-0.5, 0.0], [0.0, 0.0], [0.0, 0.0]}\n", "n_steps = length(us)\n", "trajectory = zeros(2,n_steps)\n", "\n", "ns = 500\n", "p0 = zeros(2,ns)\n", "x_d = Uniform(0,5)\n", "y_d = Uniform(4,7)\n", "for i = 1:ns\n", " p0[1,i] = rand(x_d)\n", " p0[2,i] = rand(y_d)\n", "end\n", "w0 = zeros(ns) + 1.0/ns\n", "b = Belief(p0, p0, w0, w0, ns);\n", "beliefs = Belief[]\n", "for i = 1:n_steps\n", " push!(beliefs, deepcopy(b))\n", " trajectory[:,i] = x\n", " u = us[i]\n", " x = move(robot, x, u)\n", " obs = observe(robot,x)\n", " update_belief!(b, robot, u, obs)\n", "end\n", "\n", "fig = figure(facecolor=\"white\")\n", "\n", "@manipulate for l = 1:n_steps; withfig(fig) do\n", " b = beliefs[l]\n", " ax = fig[:add_subplot](111)\n", " plot(map)\n", " plot(b)\n", " #plot(trajectory[1,l], trajectory[1,l], \"o\", color=\"b\", markersize=5.0)\n", " plot(vec(trajectory[1,1:l]),vec(trajectory[2,1:l]),\"--\",lw=2.0,color=\"b\")\n", " #plot(map,b,trajectory)->(plot(map), plot(b),plot(vec(trajectory[1,1:l]),vec(trajectory[2,1:l]),\"--\",lw=2.0,color=\"b\"))\n", "end\n", "end\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.3.4", "language": "julia", "name": "julia-0.3" }, "language_info": { "name": "julia", "version": "0.3.4" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
4dsolutions/Python5
GrapheneWithQrays.ipynb
1
17642
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[Oregon Curriculum Network](http://www.4dsolutions.net/ocn) <br />\n", "[Discovering Math with Python](Introduction.ipynb)\n", "\n", "# Quadrays and Graphene\n", "\n", "<p><a href=\"https://commons.wikimedia.org/wiki/File:Graphen.jpg#/media/File:Graphen.jpg\"><img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/9/9e/Graphen.jpg/1200px-Graphen.jpg\" alt=\"Graphen.jpg\"></a><br>By <a href=\"//commons.wikimedia.org/w/index.php?title=User:AlexanderAlUS&amp;action=edit&amp;redlink=1\" class=\"new\" title=\"User:AlexanderAlUS (page does not exist)\">AlexanderAlUS</a> - <span class=\"int-own-work\" lang=\"en\">Own work</span>, <a href=\"https://creativecommons.org/licenses/by-sa/3.0\" title=\"Creative Commons Attribution-Share Alike 3.0\">CC BY-SA 3.0</a>, <a href=\"https://commons.wikimedia.org/w/index.php?curid=11294534\">Link</a></p>\n", "\n", "\"Graphene\" refers to an hexagonal grid of cells, the vertexes being carbon atoms. However any hexagonal mesh, such as for game boards, might be referred to as a \"graphene pattern\".\n", "\n", "Quadrays are explained [in other Notebooks](QuadraysJN.ipynb). Four basis vectors emanate to the corners of a volume 1 tetrahedron of edges 2R or 1D, in the canonical version, where R and D refer respectively to the Radius and Diameter of imaginary spheres packed together, giving this home base tetrahedron.\n", "\n", "![quadrays](https://upload.wikimedia.org/wikipedia/commons/9/99/Quadray.gif)\n", "\n", "<a data-flickr-embed=\"true\" href=\"https://farm5.staticflickr.com/4143/4949799682_327b33e8d5.jpg\" title=\"Tetrahedron\"><img src=\"https://farm5.staticflickr.com/4143/4949799682_327b33e8d5.jpg\" width=\"375\" height=\"500\" alt=\"Tetrahedron\"></a><script async src=\"//embedr.flickr.com/assets/client-code.js\" charset=\"utf-8\"></script>\n", "\n", "The Quadrays {2, 1, 1, 0}, meaning all 12 permutations of those numbers, fan out from (0,0,0,0) to the corners of a cuboctahedron. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{(0, 1, 1, 2), (1, 0, 1, 2), (2, 0, 1, 1), (0, 2, 1, 1), (0, 1, 2, 1), (1, 2, 1, 0), (1, 1, 2, 0), (2, 1, 1, 0), (1, 0, 2, 1), (1, 2, 0, 1), (2, 1, 0, 1), (1, 1, 0, 2)}\n" ] } ], "source": [ "from itertools import permutations\n", "g = permutations((2,1,1,0))\n", "unique = {p for p in g} # set comprehension\n", "print(unique)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I have [elsewhere](Generating%20the%20FCC.ipynb) used this fact to algorithmically generate consecutive shells of 12, 42, 92, 162... spheres (balls) respectively; a growing cuboctahedron of $10 S^{2} + 2$ balls per shell S = 1,2,3... (1 when S=0).\n", "\n", "![Image of Cubocta](http://www.4dsolutions.net/ocn/graphics/cubanim.gif)\n", "\n", "However suppose we don't want to grow the grid omni-directionally, but only in a plane. Each ball will be surrounded by six neighbors meaning at the center of a hexagon.\n", "\n", "The cuboctahedron supplies four such hexagons i.e. its 24 edges comprise four hexagons orbiting the center. We may use any one of them.\n", "\n", "## The Algorithm\n", "The algorithm begins with a planar subset of the vectors {2, 1, 1, 0} used to compute the six vertexes surrounding (0,0,0,0). We may call these six vertexes \"carbons\".\n", "\n", "Then hop to neighboring hexagon centers (where no carbon is located) using an additional set of vectors. \n", "\n", "From these new centers, compute the six surrounding carbons again, some of which will have already been found, as neighbors share fences, with three faces (centers) sharing each fence post (carbon). \n", "\n", "Using the Python set object, the algorithm filters to keep only unique carbons. \n", "\n", "Keep track of hexagon centers, a dual mesh, in a separate set. \n", "\n", "(0,0,0,0) will be the first center (ring0).\n", "\n", "If qrays r, s are 60 degrees apart on the same hexagon, pointing to neighboring carbons, then r + s will be the \"hop\" vector over the fence (edge) to the neighboring \"yard\" (face), or center. \n", "\n", "Once we have six vertex vectors from a center, computing the six hop vectors (for jumping over the fences) will be a matter of summing pairs of adjacent (60 degree separated) vectors. We only keep new centers i.e. those of the next ring (see below).\n", "\n", "What about edges?\n", "\n", "As we go around a hexagon in 60 degree increments, say in a clockwise direction, we will be finding edges in terms of adjacent ball pairs. \n", "\n", "To avoid redundancy, (ball_a, ball_b) -- any edge -- [will be sorted](https://github.com/4dsolutions/SAISOFT/blob/master/OrderingPolys.ipynb). Any two quadrays may be ordered as 4-tuples e.g. (2, 1, 1, 0) is \"greater than\" (2, 1, 0, 1). \n", "\n", "With unique representations of any edge, in the form of sorted tuples of qray namedtuples, we will be able to employ the same general technique employed with vertexes (carbons) and face centers: check the existing database for uniqueness and throw away (filter) anything already in the database. Sets will not allow duplicates.\n", "\n", "The first step is to isolate six of the twelve from {2, 1, 1, 0} that define a hexagon." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "<table class=\"multicol\" role=\"presentation\" style=\"border-collapse: collapse; padding: 0; border: 0; background:transparent; width:100%;\"><tbody><tr>\n", "<td style=\"text-align: left; vertical-align: top;\">\n", "<table class=\"wikitable\">\n", "\n", "<tbody><tr>\n", "<th>Shape\n", "</th>\n", "<th>Volume\n", "</th>\n", "<th>Vertex Inventory (sum of Quadrays)\n", "</th></tr>\n", "<tr>\n", "<td>Tetrahedron\n", "</td>\n", "<td>1\n", "</td>\n", "<td>A,B,C,D\n", "</td></tr>\n", "<tr>\n", "<td>Inverse Tetrahedron\n", "</td>\n", "<td>1\n", "</td>\n", "<td>E,F,G,H = B+C+D, A+C+D, A+B+D, A+B+C\n", "</td></tr>\n", "<tr>\n", "<td>Duo-Tet Cube\n", "</td>\n", "<td>3\n", "</td>\n", "<td>A through H\n", "</td></tr>\n", "<tr>\n", "<td>Octahedron\n", "</td>\n", "<td>4\n", "</td>\n", "<td>I,J,K,L,M,N = A+B, A+C, A+D, B+C, B+D, C+D\n", "</td></tr>\n", "<tr>\n", "<td>Rhombic Dodecahedron\n", "</td>\n", "<td>6\n", "</td>\n", "<td>A through N\n", "</td></tr>\n", "<tr>\n", "<td>Cuboctahedron\n", "</td>\n", "<td>20\n", "</td>\n", "<td>O,P,Q,R,S,T = I+J, I+K, I+L, I+M, N+J, N+K; U,V,W,X,Y,Z = N+L, N+M, J+L, L+M, M+K, K+J\n", "</td></tr></tbody></table>\n", "<p>&#32;\n", "</p>\n", "</td>\n", "<td style=\"text-align: left; vertical-align: top;\">\n", "<p><a href=\"https://upload.wikimedia.org/wikipedia/commons/d/dc/Povlabels.gif\" class=\"image\" title=\"Points A-Z\"><img alt=\"Points A-Z\" src=\"https://upload.wikimedia.org/wikipedia/commons/d/dc/Povlabels.gif\" width=\"320\" height=\"240\" data-file-width=\"320\" data-file-height=\"240\" /></a>\n", "&#32;\n", "</p>\n", "</td></tr></tbody></table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One of the hexagons is TZOQXV. Do you see it in the above graphic? Another one is TYRQWS. If we regenerate all of the vectors A-Z mentioned above, we'll have a vocabulary suitable for graphene grid development, and then some." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from qrays import Qvector, IVM\n", "\n", "A, B, C, D = Qvector((1,0,0,0)), Qvector((0,1,0,0)), Qvector((0,0,1,0)), Qvector((0,0,0,1))\n", "E,F,G,H = B+C+D, A+C+D, A+B+D, A+B+C\n", "I,J,K,L,M,N = A+B, A+C, A+D, B+C, B+D, C+D\n", "O,P,Q,R,S,T = I+J, I+K, I+L, I+M, N+J, N+K; U,V,W,X,Y,Z = N+L, N+M, J+L, L+M, M+K, K+J" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# two \"beacons\" of six spokes\n", "hexrays = [T, Z, O, Q, X, V] # to surrounding carbon atoms\n", "hoprays = [T+Z, Z+O, O+Q, Q+X, X+V, V+T] # to neighboring (vacant) hex centers" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(60.0, 60.0, 60.0, 60.0, 60.0, 60.0)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(T.angle(Z), Z.angle(O), O.angle(Q), Q.angle(X), X.angle(V), V.angle(T))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets verify that, going around the hexagon, each pair of consecutive hexrays is 60 degree apart. And ditto for hoprays, the vectors we'll use to jump over the fence to neighboring hexagon centers." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(60.0, 60.0, 60.0, 60.0, 60.0, 60.0)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(hoprays[0].angle(hoprays[1]),\n", " hoprays[1].angle(hoprays[2]),\n", " hoprays[2].angle(hoprays[3]),\n", " hoprays[3].angle(hoprays[4]),\n", " hoprays[4].angle(hoprays[5]),\n", " hoprays[5].angle(hoprays[0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like we're in business!\n", "\n", "As with the growing cuboctahedron and the CCP packing, it makes sense to think in terms of consecutive rings.\n", "\n", "The [hexagonal coordination sequence](https://oeis.org/A008458) is generated by:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 6, 12, 18, 24, 30, 36, 42, 48, 54]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def A008458(n):\n", " # OEIS number\n", " if n == 0:\n", " return 1\n", " return 6 * n\n", " \n", "[A008458(x) for x in range(10)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I will use this as a check as the algorithm generates multiple rings." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "centers = {IVM(0,0,0,0)} # center face\n", "edges = set() # no duplicates permitted\n", "carbons = set()\n", "\n", "ring0 = [Qvector((0,0,0,0))]\n", "\n", "def next_ring(ring):\n", " \"\"\"\n", " Use only the most recently added hexagonal ring \n", " of face centers to compute the next ring, moving \n", " outward: 1, 6, 12, 18, 24...\n", " \"\"\"\n", " new_faces = []\n", " for face in ring:\n", " verts = []\n", " \n", " # CARBONS\n", " for spoke in hexrays:\n", " v = face + spoke\n", " carbons.add(v.coords) # just the namedtuple is added to the set\n", " verts.append(v)\n", " \n", " # EDGES\n", " for bond in zip(verts, verts[1:] + [verts[0]]):\n", " # adding carbon-to-carbon bonds if not already in the set\n", " edge = tuple(sorted([bond[0].coords, bond[1].coords]))\n", " edges.add(edge)\n", " \n", " # CENTERS\n", " for jump in hoprays:\n", " neighbor = face + jump\n", " previous = len(centers)\n", " centers.add(neighbor.coords)\n", " if len(centers) > previous: # if True, face is new\n", " new_faces.append(neighbor)\n", " \n", " return new_faces" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ring: 0 Number: 1\n", "Ring: 1 Number: 6\n", "Ring: 2 Number: 12\n", "Ring: 3 Number: 18\n", "Ring: 4 Number: 24\n", "Ring: 5 Number: 30\n", "Ring: 6 Number: 36\n", "Ring: 7 Number: 42\n", "Ring: 8 Number: 48\n", "Ring: 9 Number: 54\n", "Ring: 10 Number: 60\n", "Ring: 11 Number: 66\n" ] } ], "source": [ "def rings(n):\n", " prev = ring0\n", " for ring in range(n):\n", " print(\"Ring: {:3} Number: {:4}\".format(ring, len(prev)))\n", " nxt = next_ring(prev)\n", " prev = nxt\n", "\n", " \n", "rings(12)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a data-flickr-embed=\"true\" href=\"https://www.flickr.com/photos/kirbyurner/2949087863/in/album-72157612943105800/\" title=\"Global Matrix\"><img src=\"https://farm4.staticflickr.com/3249/2949087863_aea4cecb7f.jpg\" width=\"500\" height=\"375\" alt=\"Global Matrix\"></a><script async src=\"//embedr.flickr.com/assets/client-code.js\" charset=\"utf-8\"></script>\n", "\n", "Note these are the expected numbers for consecutive rings.\n", "\n", "Now that we have our database, it's time to generate some graphical output. As with the FCC, I'll use [POV-Ray's scene description language](http://www.4dsolutions.net/ocn/numeracy0.html) and then render in [POV-Ray](http://www.povray.org). We just want to look at the edges and carbon atom vertexes." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sph = \"\"\"sphere { %s 0.1 texture { pigment { color rgb <1,0,0> } } }\"\"\"\n", "cyl = \"\"\"cylinder { %s %s 0.05 texture { pigment { color rgb <1.0, 0.65, 0.0> } } }\"\"\"\n", "\n", "def make_graphene(fname=\"../c6xty/graphene.pov\", append=True):\n", " \"\"\"\n", " Scan through carbons, edges, converting to XYZ and embedding\n", " in POV-Ray Scene Description Language\n", " \"\"\"\n", " if append:\n", " pov = open(fname, \"a\")\n", " else:\n", " pov = open(fname, \"w\")\n", "\n", " # graphene will be included as a single object in the \n", " # parent povray script, where lighting, camera position,\n", " # and background are defined\n", " \n", " print(\"#declare graphene = union{\", file=pov)\n", " for atom in carbons:\n", " v = Qvector(atom).xyz()\n", " s = sph % \"<{0.x}, {0.y}, {0.z}>\".format(v)\n", " print(s, file=pov)\n", " for bond in edges:\n", " v0, v1 = bond\n", " v0 = Qvector(v0).xyz()\n", " v1 = Qvector(v1).xyz()\n", " c = cyl % (\"<{0.x}, {0.y}, {0.z}>\".format(v0), \"<{0.x}, {0.y}, {0.z}>\".format(v1))\n", " print(c, file=pov)\n", " print(\"}\\n\", file=pov)\n", " \n", "make_graphene(append=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Success!\n", "\n", "<a data-flickr-embed=\"true\" href=\"https://www.flickr.com/photos/kirbyurner/44743063302/in/dateposted-public/\" title=\"Another Test\"><img src=\"https://farm2.staticflickr.com/1877/44743063302_e7db33cdea_b.jpg\" width=\"1024\" height=\"768\" alt=\"Another Test\"></a><script async src=\"//embedr.flickr.com/assets/client-code.js\" charset=\"utf-8\"></script>\n", "\n", "## ADDENDUM\n", "\n", "The graphrene grid needs twelve of its hexagons to become pentagons in order to lose the 720 degrees necessary to turn it into a concave / convex \"global matrix\" or \"hexapent\" grid. \n", "\n", "This 720 degrees is characteristic of polyhedrons more generally and is sometimes referred to as \"Descartes' Deficit\". We may visualize the subtration of 720 degrees as the removal of one tetrahedron, thereby likewise begetting a tetrahedron (something with an inside and outside, not flat).\n", "\n", "<a data-flickr-embed=\"true\" href=\"https://www.flickr.com/photos/kirbyurner/44174173534/in/dateposted-public/\" title=\"Better Sunlight\"><img src=\"https://farm2.staticflickr.com/1958/44174173534_2773c5f9cf_z.jpg\" width=\"640\" height=\"480\" alt=\"Better Sunlight\"></a><script async src=\"//embedr.flickr.com/assets/client-code.js\" charset=\"utf-8\"></script>" ] } ], "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
aeroaks/PyFME
examples/examples_as_notebooks/example_003_stationary_ascent.ipynb
2
14287780
null
mit
ES-DOC/esdoc-jupyterhub
notebooks/niwa/cmip6/models/ukesm1-0-ll/aerosol.ipynb
1
84294
{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Aerosol \n", "**MIP Era**: CMIP6 \n", "**Institute**: NIWA \n", "**Source ID**: UKESM1-0-LL \n", "**Topic**: Aerosol \n", "**Sub-Topics**: Transport, Emissions, Concentrations, Optical Radiative Properties, Model. \n", "**Properties**: 69 (37 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/aerosol?client=jupyter-notebook) \n", "**Initialized From**: -- \n", "\n", "**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "**Notebook Initialised**: 2018-02-15 16:54:30" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "**IMPORTANT: to be executed each time you run the notebook** " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'niwa', 'ukesm1-0-ll', 'aerosol')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "*Set document authors*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "*Specify document contributors* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "*Specify document publication status* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties](#1.-Key-Properties) \n", "[2. Key Properties --&gt; Software Properties](#2.-Key-Properties---&gt;-Software-Properties) \n", "[3. Key Properties --&gt; Timestep Framework](#3.-Key-Properties---&gt;-Timestep-Framework) \n", "[4. Key Properties --&gt; Meteorological Forcings](#4.-Key-Properties---&gt;-Meteorological-Forcings) \n", "[5. Key Properties --&gt; Resolution](#5.-Key-Properties---&gt;-Resolution) \n", "[6. Key Properties --&gt; Tuning Applied](#6.-Key-Properties---&gt;-Tuning-Applied) \n", "[7. Transport](#7.-Transport) \n", "[8. Emissions](#8.-Emissions) \n", "[9. Concentrations](#9.-Concentrations) \n", "[10. Optical Radiative Properties](#10.-Optical-Radiative-Properties) \n", "[11. Optical Radiative Properties --&gt; Absorption](#11.-Optical-Radiative-Properties---&gt;-Absorption) \n", "[12. Optical Radiative Properties --&gt; Mixtures](#12.-Optical-Radiative-Properties---&gt;-Mixtures) \n", "[13. Optical Radiative Properties --&gt; Impact Of H2o](#13.-Optical-Radiative-Properties---&gt;-Impact-Of-H2o) \n", "[14. Optical Radiative Properties --&gt; Radiative Scheme](#14.-Optical-Radiative-Properties---&gt;-Radiative-Scheme) \n", "[15. Optical Radiative Properties --&gt; Cloud Interactions](#15.-Optical-Radiative-Properties---&gt;-Cloud-Interactions) \n", "[16. Model](#16.-Model) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "*Key properties of the aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of aerosol model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of aerosol model code*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Scheme Scope\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Atmospheric domains covered by the aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.scheme_scope') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"troposhere\" \n", "# \"stratosphere\" \n", "# \"mesosphere\" \n", "# \"mesosphere\" \n", "# \"whole atmosphere\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Basic Approximations\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Basic approximations made in the aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.basic_approximations') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Prognostic Variables Form\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Prognostic variables in the aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.prognostic_variables_form') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"3D mass/volume ratio for aerosols\" \n", "# \"3D number concenttration for aerosols\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.6. Number Of Tracers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of tracers in the aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.number_of_tracers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.7. Family Approach\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Are aerosol calculations generalized into families of species?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.family_approach') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&gt; Software Properties \n", "*Software properties of aerosol code*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Repository\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Location of code for this component.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.software_properties.repository') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Code Version\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Code version identifier.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.software_properties.code_version') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Code Languages\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Code language(s).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.software_properties.code_languages') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --&gt; Timestep Framework \n", "*Physical properties of seawater in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Mathematical method deployed to solve the time evolution of the prognostic variables*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses atmospheric chemistry time stepping\" \n", "# \"Specific timestepping (operator splitting)\" \n", "# \"Specific timestepping (integrated)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Split Operator Advection Timestep\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Timestep for aerosol advection (in seconds)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.split_operator_advection_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Split Operator Physical Timestep\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Timestep for aerosol physics (in seconds).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.split_operator_physical_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.4. Integrated Timestep\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Timestep for the aerosol model (in seconds)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.integrated_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.5. Integrated Scheme Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify the type of timestep scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.integrated_scheme_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Implicit\" \n", "# \"Semi-implicit\" \n", "# \"Semi-analytic\" \n", "# \"Impact solver\" \n", "# \"Back Euler\" \n", "# \"Newton Raphson\" \n", "# \"Rosenbrock\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --&gt; Meteorological Forcings \n", "**" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Variables 3D\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Three dimensionsal forcing variables, e.g. U, V, W, T, Q, P, conventive mass flux*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.variables_3D') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Variables 2D\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Two dimensionsal forcing variables, e.g. land-sea mask definition*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.variables_2D') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Frequency\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Frequency with which meteological forcings are applied (in seconds).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.frequency') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --&gt; Resolution \n", "*Resolution in the aersosol model grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *This is a string usually used by the modelling group to describe the resolution of this grid, e.g. ORCA025, N512L180, T512L70 etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.resolution.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Canonical Horizontal Resolution\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Number Of Horizontal Gridpoints\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Total number of horizontal (XY) points (or degrees of freedom) on computational grid.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Number Of Vertical Levels\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Number of vertical levels resolved on computational grid.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.resolution.number_of_vertical_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.5. Is Adaptive Grid\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Default is False. Set true if grid resolution changes during execution.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.resolution.is_adaptive_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Key Properties --&gt; Tuning Applied \n", "*Tuning methodology for aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General overview description of tuning: explain and motivate the main targets and metrics retained. &amp;Document the relative weight given to climate performance metrics versus process oriented metrics, &amp;and on the possible conflicts with parameterization level tuning. In particular describe any struggle &amp;with a parameter value that required pushing it to its limits to solve a particular model deficiency.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Global Mean Metrics Used\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List set of metrics of the global mean state used in tuning model/component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.global_mean_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.3. Regional Metrics Used\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List of regional metrics of mean state used in tuning model/component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.regional_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.4. Trend Metrics Used\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List observed trend metrics used in tuning model/component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.trend_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Transport \n", "*Aerosol transport*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of transport in atmosperic aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Method for aerosol transport modeling*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses Atmospheric chemistry transport scheme\" \n", "# \"Specific transport scheme (eulerian)\" \n", "# \"Specific transport scheme (semi-lagrangian)\" \n", "# \"Specific transport scheme (eulerian and semi-lagrangian)\" \n", "# \"Specific transport scheme (lagrangian)\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Mass Conservation Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Method used to ensure mass conservation.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.mass_conservation_scheme') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses Atmospheric chemistry transport scheme\" \n", "# \"Mass adjustment\" \n", "# \"Concentrations positivity\" \n", "# \"Gradients monotonicity\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.4. Convention\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Transport by convention*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.convention') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses Atmospheric chemistry transport scheme\" \n", "# \"Convective fluxes connected to tracers\" \n", "# \"Vertical velocities connected to tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Emissions \n", "*Atmospheric aerosol emissions*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of emissions in atmosperic aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Method used to define aerosol species (several methods allowed because the different species may not use the same method).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.method') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Prescribed (climatology)\" \n", "# \"Prescribed CMIP6\" \n", "# \"Prescribed above surface\" \n", "# \"Interactive\" \n", "# \"Interactive above surface\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Sources\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Sources of the aerosol species are taken into account in the emissions scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.sources') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Vegetation\" \n", "# \"Volcanos\" \n", "# \"Bare ground\" \n", "# \"Sea surface\" \n", "# \"Lightning\" \n", "# \"Fires\" \n", "# \"Aircraft\" \n", "# \"Anthropogenic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Prescribed Climatology\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify the climatology type for aerosol emissions*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.prescribed_climatology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Interannual\" \n", "# \"Annual\" \n", "# \"Monthly\" \n", "# \"Daily\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Prescribed Climatology Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of aerosol species emitted and prescribed via a climatology*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.prescribed_climatology_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.6. Prescribed Spatially Uniform Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of aerosol species emitted and prescribed as spatially uniform*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.prescribed_spatially_uniform_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.7. Interactive Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of aerosol species emitted and specified via an interactive method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.interactive_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.8. Other Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of aerosol species emitted and specified via an &quot;other method&quot;*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.other_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.9. Other Method Characteristics\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Characteristics of the &quot;other method&quot; used for aerosol emissions*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.other_method_characteristics') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Concentrations \n", "*Atmospheric aerosol concentrations*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of concentrations in atmosperic aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Prescribed Lower Boundary\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of species prescribed at the lower boundary.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_lower_boundary') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Prescribed Upper Boundary\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of species prescribed at the upper boundary.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_upper_boundary') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Prescribed Fields Mmr\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of species prescribed as mass mixing ratios.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_fields_mmr') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.5. Prescribed Fields Mmr\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of species prescribed as AOD plus CCNs.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_fields_mmr') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Optical Radiative Properties \n", "*Aerosol optical and radiative properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of optical and radiative properties*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Optical Radiative Properties --&gt; Absorption \n", "*Absortion properties in aerosol scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Black Carbon\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Absorption mass coefficient of black carbon at 550nm (if non-absorbing enter 0)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.black_carbon') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Dust\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Absorption mass coefficient of dust at 550nm (if non-absorbing enter 0)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.dust') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Organics\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Absorption mass coefficient of organics at 550nm (if non-absorbing enter 0)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.organics') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Optical Radiative Properties --&gt; Mixtures \n", "**" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. External\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there external mixing with respect to chemical composition?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.external') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Internal\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there internal mixing with respect to chemical composition?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.internal') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Mixing Rule\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If there is internal mixing with respect to chemical composition then indicate the mixinrg rule*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.mixing_rule') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Optical Radiative Properties --&gt; Impact Of H2o \n", "**" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Size\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does H2O impact size?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.impact_of_h2o.size') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Internal Mixture\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does H2O impact internal mixture?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.impact_of_h2o.internal_mixture') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Optical Radiative Properties --&gt; Radiative Scheme \n", "*Radiative scheme for aerosol*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of radiative scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Shortwave Bands\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of shortwave bands*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.shortwave_bands') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Longwave Bands\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of longwave bands*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.longwave_bands') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Optical Radiative Properties --&gt; Cloud Interactions \n", "*Aerosol-cloud interactions*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of aerosol-cloud interactions*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Twomey\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the Twomey effect included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.twomey') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Twomey Minimum Ccn\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If the Twomey effect is included, then what is the minimum CCN number?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.twomey_minimum_ccn') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Drizzle\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the scheme affect drizzle?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.drizzle') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Cloud Lifetime\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the scheme affect cloud lifetime?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.cloud_lifetime') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.6. Longwave Bands\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of longwave bands*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.longwave_bands') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Model \n", "*Aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of atmosperic aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Processes included in the Aerosol model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Dry deposition\" \n", "# \"Sedimentation\" \n", "# \"Wet deposition (impaction scavenging)\" \n", "# \"Wet deposition (nucleation scavenging)\" \n", "# \"Coagulation\" \n", "# \"Oxidation (gas phase)\" \n", "# \"Oxidation (in cloud)\" \n", "# \"Condensation\" \n", "# \"Ageing\" \n", "# \"Advection (horizontal)\" \n", "# \"Advection (vertical)\" \n", "# \"Heterogeneous chemistry\" \n", "# \"Nucleation\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.3. Coupling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Other model components coupled to the Aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.coupling') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Radiation\" \n", "# \"Land surface\" \n", "# \"Heterogeneous chemistry\" \n", "# \"Clouds\" \n", "# \"Ocean\" \n", "# \"Cryosphere\" \n", "# \"Gas phase chemistry\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.4. Gas Phase Precursors\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List of gas phase aerosol precursors.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.gas_phase_precursors') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"DMS\" \n", "# \"SO2\" \n", "# \"Ammonia\" \n", "# \"Iodine\" \n", "# \"Terpene\" \n", "# \"Isoprene\" \n", "# \"VOC\" \n", "# \"NOx\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.5. Scheme Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Type(s) of aerosol scheme used by the aerosols model (potentially multiple: some species may be covered by one type of aerosol scheme and other species covered by another type).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.scheme_type') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Bulk\" \n", "# \"Modal\" \n", "# \"Bin\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.6. Bulk Scheme Species\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List of species covered by the bulk scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.bulk_scheme_species') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Sulphate\" \n", "# \"Nitrate\" \n", "# \"Sea salt\" \n", "# \"Dust\" \n", "# \"Ice\" \n", "# \"Organic\" \n", "# \"Black carbon / soot\" \n", "# \"SOA (secondary organic aerosols)\" \n", "# \"POM (particulate organic matter)\" \n", "# \"Polar stratospheric ice\" \n", "# \"NAT (Nitric acid trihydrate)\" \n", "# \"NAD (Nitric acid dihydrate)\" \n", "# \"STS (supercooled ternary solution aerosol particule)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }
gpl-3.0
mathnathan/notebooks
cmb_phase_plane/isc4220/Lab1.ipynb
1
35747
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ISC 4220\n", "## Lab 1 - Nonlinear Equations\n", "\n", "We import 3 handy libraries: python's standard numeric library *numpy*, a 'matlab like' plotting tool *matplotlib*, and a system tool for executive functions *sys*" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import sys\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Part 1\n", "\n", "We are first tasked with finding a zero of the function\n", "\n", "$$\\tan(x)-\\frac{1}{1+x^2}$$\n", "\n", "up to 3 decimal places using the Bisection Method over the interval $[0,1]$ ." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need a Bisection Method algorithm to both of these problems. A simple implementation is given below, followed by an explanation." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "def bisection(f, a, b, tol):\n", " if f(a)*f(b) > 0:\n", " print(\"ERROR: There may not be a zero in the interval [%f,%f]\" % (a,b))\n", " sys.exit()\n", " elif f(a)*f(b) < 0:\n", " mid = (a+b)/2.0\n", " err = sys.maxsize\n", " itr = 1\n", " while err > tol:\n", " if f(a)*f(mid) < 0:\n", " b = mid\n", " elif f(b)*f(mid) < 0:\n", " a = mid\n", " else:\n", " return (mid,0.0,itr)\n", " oldMid = mid\n", " mid = (a+b)/2.0\n", " err = np.abs(oldMid-mid)/np.abs(mid)\n", " itr += 1\n", " else:\n", " if f(a) == 0:\n", " return (a,0.0,0)\n", " else:\n", " return (b,0.0,0)\n", "\n", " return (mid,err,itr)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.62353515625, 0.00078308535630383712, 11)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def g(x,P):\n", " return (1e5*((x/12.0)*(1+(x/12.0))**180)/(((1+x/12.0)**180)-1))-P\n", "\n", "def f(x):\n", " return np.tan(x)-1/(1+x*x)\n", "\n", "bisection(f, 0, 1, 0.001)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def p(x):\n", " return 1e5*((x/12.0)*(1+(x/12.0))**180)/((1+(x/12.0))**180-1)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.051761532355397925, 5.7576140483093871e-07, 25)" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z,e,it = bisection(lambda x: g(x,800),0.000001,1,1e-6); z,e,it" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "800.000151896154" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p(z)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.010342045350186525, 7.2041582627097287e-07, 27)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z,e,it = bisection(lambda x: g(x,600),0.000001,1,1e-6); z,e,it" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "599.9999949473979" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p(z)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.031950279545515776, 9.3277094939623932e-07, 25)" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z,e,it = bisection(lambda x: g(x,700),0.000001,1,1e-6); z,e,it" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "700.0000727848583" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p(z)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.07020953889554739, 8.4895280766574085e-07, 24)" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z,e,it = bisection(lambda x: g(x,900),0.000001,1,1e-6); z,e,it" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "900.0001560979272" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p(z)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.08759333536976575, 6.804694092156407e-07, 24)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z,e,it = bisection(lambda x: g(x,1000),0.000001,1,1e-6); z,e,it" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "999.9999162748297" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p(z)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-0.013728153714224109, 8.6835631376167473e-07, 23)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z,e,it = bisection(lambda x: g(x,500),-0.1,-0.00000000001,1e-6); z,e,it" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-55.56004353185165" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g(z,555.56)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "499.9999564681483" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p(z)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f0cbfaf3a58>]" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0c985f9668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = np.arange(-.5,-0.0001,.001)\n", "plt.plot(t,g(t,500))\n", "#g(0,500)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ndc08/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: divide by zero encountered in true_divide\n", " \n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f0c984ab5f8>]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0cbfab7828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t = np.arange(-0.0001,0.0001,0.00000001)\n", "plt.plot(t,p(t))" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "555.5555555555555" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "100000/180.0" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(3.14141845703125, 5.8287512871825758e-05, 14)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bisection(lambda x: np.sin(x),1,4,0.0001)" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
sbussmann/sensor-fusion
Code/Rotate Sensor Data to Vehicle Reference Frame.ipynb
1
144476
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Goal: rotate XYZ signals to vehicle reference frame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Experiment: I drove my car from home to Censio and back. My phone rested on my seat facing forwards for the trip to Censio. Nick was in the passenger seat with his phone in his pocket. For the return trip, we swapped phones. The total time for the trip was about 15 minutes." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# load the raw data\n", "df = pd.read_csv('../Data/shaneiphone_exp2.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The XYZ axes from SensorLog are in the frame of the iPhone. On the way to Censio, my phone was placed flat on the driver seat. On the return trip Nick had my phone in his pocket at a slight angle. This is reflected in the step function like behavior of the gravityXYZ time series. " ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10cfae2d0>" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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fa3EnhPQjhBwjhJwkhEyys88HlY8fIIR0dHS8LgldQEAAYlEtdyt+/dW9QFRt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\nAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10cfade50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot gravity signal\n", "df[['motionGravityX', 'motionGravityY', 'motionGravityZ']].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### This step function like behavior is present in the accelerometer and gyroscope XYZ signals. My phone was on the driver side for the trip out and on the passenger side for the return trip. Machine learning classifiers will pick up on this signal and classify driver vs. passenger side simply based on phone orientation. This will generalize poorly, since there is no reason to expect that phones placed on a particular side of a vehicle will have a given orientation. Therefore, the reference frame needs to be adjusted to match that of the vehicle. This can be done using quaternions." ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10d163790>" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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hZ4vWf3Pqbd1WZi+eE0QwfPJJA6P2jzkGxlhraBXEatkFfvqpAQdseVR7q3nm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\nUTsR4WDDB09wzSUwizD+/HDnQsSL996LHWkWweOPO2oRba/bHnod2N/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"text/plain": [ "<matplotlib.figure.Figure at 0x10d237cd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot gyroscope signal [indices 7000 to 10000 include a series of sharp turns in the Censio parking lot]\n", "df[7000:10000][['gyroRotationX', 'gyroRotationY', 'gyroRotationZ']].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### If the reference frame was that of the vehicle instead of the phone, only gyroRotationZ should trace turns." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### SensorLog provides a quaternion for every point in time, so rotating the XYZ signals to the vehicle reference frame is straightforward (see quatrotate.py)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from quatrotate import qv_mult # this routine implements rotation via quaternion multiplication\n", "import numpy as np\n", "\n", "def getrot(quatern, vector):\n", " rotatedvector = []\n", " for i in range(vector.shape[0]):\n", " rotatedvector.append(qv_mult(tuple(quatern[i, :]), \n", " tuple(vector[i, :])))\n", " return np.array(rotatedvector)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def rotate(df):\n", "\n", " \"\"\" Generate rdf, a rotated version of df where the z-axis is aligned\n", " with gravity. \"\"\"\n", "\n", " varlist = ['accelerometerAcceleration', 'motionUserAcceleration',\n", " 'motionGravity', 'motionMagneticField', 'gyroRotation']\n", "\n", " quaternion = df[['motionQuaternionW', 'motionQuaternionX', \n", " 'motionQuaternionY', 'motionQuaternionZ']].values\n", "\n", " for ivar in varlist:\n", " print(\"...\" + ivar)\n", " xyzlist = [ivar + 'X', ivar + 'Y', ivar + 'Z']\n", " xyz = df[xyzlist].values\n", " xyz_rotated = getrot(quaternion, xyz)\n", " df[ivar + 'X'] = xyz_rotated[:, 0]\n", " df[ivar + 'Y'] = xyz_rotated[:, 1]\n", " df[ivar + 'Z'] = xyz_rotated[:, 2]\n" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "...accelerometerAcceleration\n", "...motionUserAcceleration\n", "...motionGravity\n", "...motionMagneticField\n", "...gyroRotation\n" ] } ], "source": [ "# rotate does an in-place rotation\n", "rdf = df.copy()\n", "rotate(rdf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Now let's investigate the rotated XYZ signals" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10d3c9a10>" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10d13df90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rdf[['motionGravityX', 'motionGravityY', 'motionGravityZ']].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Nice, GravityZ = -1 and GravityX = GravityY = 0" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10d535cd0>" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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QpnXFPWb5GA59OVe+efll7xKjpzCPfedDV6RjJxjWr4cpk0PrgFFeVGnB4CIt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\nQwWDoiiK4kMFg6IoiuJDBYOiKIri4/8BXWEANKqPaU8AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10d554690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# indices 7000 to 10000 include a series of sharp turns in the Censio parking lot\n", "rdf[7000:10000][['gyroRotationX', 'gyroRotationY', 'gyroRotationZ']].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Also nice: gyroRotationY and gyroRotationX show no significant change during a left or right turn" ] }, { "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
ioshchepkov/SHTOOLS
examples/notebooks/tutorial_3.ipynb
1
1058914
null
bsd-3-clause
wrightaprilm/squamates
ExploratoryNotebooks/.ipynb_checkpoints/Change_counter-checkpoint.ipynb
1
4479
{ "metadata": { "name": "", "signature": "sha256:ba8c2f87fafbd8c0950c867f61ab0a4a9a7c2b5e64f334cb191c52a80b4cf6d4" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import dendropy\n", "import pandas as pd" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "data = pd.read_csv('PyronParityData.csv', index_col=0, header=False)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "taxa = dendropy.TaxonSet()\n", "mle = dendropy.Tree.get_from_path('./Trees/garli_opt/pbnoterm', 'newick', taxon_set=taxa, preserve_underscores=True) " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 99 }, { "cell_type": "code", "collapsed": false, "input": [ "for idx, nd in enumerate(mle.postorder_node_iter()):\n", " if nd.label is None:\n", " lookup = '{}'.format(nd.taxon)\n", " nd.label = int(data.ix[lookup])\n", " else: \n", " pass" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 100 }, { "cell_type": "code", "collapsed": false, "input": [ "putative_c = []\n", "putative_co = []\n", "total = []\n", "childs = []\n", "for index, node in enumerate(mle.postorder_node_iter()):\n", " total.append(index)\n", " if node.parent_node is None:\n", " pass\n", " elif .5 < float(node.label) < 1 or float(node.label) == 0: #Is likely oviparous \n", " if float(node.parent_node.label) < .5 : #List of nodes that demonstrate change away from oviparity. \n", " if node.taxon is not None :\n", " putative_co.append([node.parent_node.label, node.taxon])\n", " else:\n", " putative_co.append(node.parent_node.label)\n", " for nd in node.child_nodes():\n", "# print nd.taxon\n", " pass\n", " elif 0 < float(node.label) < .5 or float(node.label) == 1: \n", " if float(node.parent_node.label) > .5: \n", " putative_c.append([node.parent_node.label,node.taxon]) \n", "print len(putative_c), 'changes to viviparity' \n", "print len(putative_co), 'reversions to oviparity' " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "61 changes to viviparity\n", "61 reversions to oviparity\n" ] } ], "prompt_number": 101 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright (c) <2014> <April Wright, wright.aprilm@gmail.com>\n", "\n", "\n", "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n", "\n", "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", "\n", "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
NlGG/Econometrics
TimeSeries/KalmanFilter.ipynb
1
128221
{ "cells": [ { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.read_csv('http://indexes.nikkei.co.jp/nkave/historical/nikkei_stock_average_daily_jp.csv')\n", "df.columns = ['date', 'close', 'open', 'high', 'low']\n", "df = df.dropna()" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": true }, "outputs": [], "source": [ "prices = df['close']" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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l72bkgHffha9/Hc4/X9tSA5x5Jpx3Hhx6qFYLf/IJHHxwfu3MN59/rus5/O1v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\nG/XTe3LZLK6wUJf5zJa7x4t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HQMpU6zjYGXC0c+5Q4BTghyLyjQQj4mFnO+Aw4B7n3GHA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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10df3cf50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(prices)\n", "prices = np.array(prices)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def noise(d):\n", " d_noise = np.zeros(len(d))\n", " for i in range(len(d)):\n", " d_noise[i] = d[i] + np.random.normal(0, 1000)\n", " return d_noise" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [], "source": [ "prices_noise = noise(prices)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x10d2da6d0>]" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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DgaivD7f98/rb7hzvfW/uSKdiWbHCznt44gl4NOUR9oIL7FKo3/oW7L23vffm5uTveuFC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\nEqkpgRCBjg7NQSiKonQFNWdq3UgmRVEUpXOpOYF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Lu/ZqPx56SOsNfM898Npr1s9NiU5pagtqiaKqIm778jaW/3E5JSfDWLNJ2wFs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\nFbZTKBTeghIEN6HPVq6srSTUX+0QFAqF56MEwU3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"text/plain": [ "<matplotlib.figure.Figure at 0x109c83d10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p1 = plt.plot(prices[:50])\n", "p2 = plt.plot(prices_noise[:50])\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10d96e190>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "def lkf(T, Y, U, mu0, Sigma0, A, B, C, Q, R):\n", " '''Linear Kalman Filter\n", " \n", " - 状態方程式\n", " x = A * x_ + B * u + w, w ~ N(0,Q)\n", " - 観測方程式\n", " y = C * x + v, v ~ N(0,R)\n", " \n", " Parameters\n", " ==========\n", " - T : ステップ数\n", " - Y : 観測列\n", " - U : 入力列\n", " - mu0 : 初期状態推定値\n", " - Sigma0 : 初期誤差共分散行列\n", " - A, B, C, Q, R : カルマンフィルタの係数 \n", " \n", " Returns\n", " =======\n", " - M : 状態推定値列\n", " '''\n", "\n", " mu = mu0 # 初期状態推定値\n", " Sigma = Sigma0 # 初期誤差共分散行列\n", "\n", " M = [mu] # 状態推定値列\n", "\n", " for i in range(T):\n", " # 推定\n", " mu_ = A * mu + B * U[i]\n", " Sigma_ = Q + A * Sigma * A.T\n", "\n", " # 更新\n", " yi = Y[i+1] - C * mu_\n", " S = C * Sigma_ * C.T + R\n", " K = Sigma_ * C.T * S.I\n", " mu = mu_ + K * yi\n", " Sigma = Sigma_ - K * C * Sigma_\n", " M.append(mu)\n", "\n", " return M\n", "\n", "def main():\n", " # 状態方程式\n", " # x = A * x_ + B * u + w, w ~ N(0,Q)\n", " A = np.mat([[1,0], [0,1]])\n", " B = np.mat([[1,0], [0,1]])\n", " Q = np.mat([[1,0], [0,1]])\n", " # 観測方程式\n", " # y = C * x + v, v ~ N(0,R)\n", " C = np.mat([[1,0], [0,1]])\n", " R = np.mat([[2,0], [0,2]])\n", "\n", " # 観測のテストデータの生成\n", " T = 10 # 観測数\n", " x = np.mat([[0],[0]]) # 初期位置\n", " X = [x] # 状態列\n", " Y = [x] # 観測列\n", " u = np.mat([[2],[2]]) # 入力（一定）\n", " U = [u] # 入力列\n", " for i in range(T):\n", " x = A * x + B * u + np.random.multivariate_normal([0, 0], Q, 1).T\n", " X.append(x)\n", " y = C * x + np.random.multivariate_normal([0, 0], R, 1).T\n", " Y.append(y)\n", " U.append(u)\n", "\n", " # LKF\n", " mu0 = np.mat([[0],[0]]) # 初期状態推定値\n", " Sigma0 = np.mat([[0,0],[0,0]]) # 初期誤差共分散行列\n", " M = lkf(T, Y, U, mu0, Sigma0, A, B, C, Q, R)\n", "\n", " # 描画\n", " a, b = np.array(np.concatenate(X,axis=1))\n", " plt.plot(a,b,'rs-')\n", " a, b = np.array(np.concatenate(Y,axis=1))\n", " plt.plot(a,b,'g^-')\n", " a, b = np.array(np.concatenate(M,axis=1))\n", " plt.plot(a,b,'bo-')\n", " plt.axis('equal')\n", " plt.show()\n", "\n", "if __name__ == '__main__':\n", " main()" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def update(P, C, R, x_hat, obs, I):\n", " \"\"\"\n", " P: 誤差共分散行列\n", " C: 観測系数行列\n", " R: 観測ノイズ分散行列\n", " \"\"\"\n", " #カルマンゲイン\n", " G = P * C / (C.T * P * C + R)\n", " x_hat = x_hat + G * (obs - C.T * x_hat)\n", " P = (I - G * C.T) * P\n", " return x_hat, P\n", "\n", "y = prices_noise\n", "A = np.mat([1])\n", "P = np.mat([[1, 0], [0, 1]])\n", "R = np.mat([1])\n", "I = np.identity(2)\n", "x_hat = np.mat([[0], [0]])\n", "\n", "X = np.array([])\n", "for i in range(365):\n", " C = np.mat([[x[i]], [1]])\n", " obs = np.mat([y[i]])\n", " x_hat, P = update(P, C, R, x_hat, obs, I)\n", " X = np.append(X, x_hat)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x10dd30c10>]" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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mit
abraia/abraia-python
notebooks/visual-similarity.ipynb
1
1715867
null
mit
PMEAL/OpenPNM
examples/reference/settings/understanding_reactive_transport_settings.ipynb
1
633
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Understanding Reactive Transport Settings" ] } ], "metadata": { "@webio": { "lastCommId": null, "lastKernelId": null }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
WillenZh/deep-learning-project
tutorials/embeddings/Skip-Grams-Solution.ipynb
71
813006
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Skip-gram word2vec\n", "\n", "In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language processing. This will come in handy when dealing with things like machine translation.\n", "\n", "## Readings\n", "\n", "Here are the resources I used to build this notebook. I suggest reading these either beforehand or while you're working on this material.\n", "\n", "* A really good [conceptual overview](http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/) of word2vec from Chris McCormick \n", "* [First word2vec paper](https://arxiv.org/pdf/1301.3781.pdf) from Mikolov et al.\n", "* [NIPS paper](http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) with improvements for word2vec also from Mikolov et al.\n", "* An [implementation of word2vec](http://www.thushv.com/natural_language_processing/word2vec-part-1-nlp-with-deep-learning-with-tensorflow-skip-gram/) from Thushan Ganegedara\n", "* TensorFlow [word2vec tutorial](https://www.tensorflow.org/tutorials/word2vec)\n", "\n", "## Word embeddings\n", "\n", "When you're dealing with words in text, you end up with tens of thousands of classes to predict, one for each word. Trying to one-hot encode these words is massively inefficient, you'll have one element set to 1 and the other 50,000 set to 0. The matrix multiplication going into the first hidden layer will have almost all of the resulting values be zero. This a huge waste of computation. \n", "\n", "![one-hot encodings](assets/one_hot_encoding.png)\n", "\n", "To solve this problem and greatly increase the efficiency of our networks, we use what are called embeddings. Embeddings are just a fully connected layer like you've seen before. We call this layer the embedding layer and the weights are embedding weights. We skip the multiplication into the embedding layer by instead directly grabbing the hidden layer values from the weight matrix. We can do this because the multiplication of a one-hot encoded vector with a matrix returns the row of the matrix corresponding the index of the \"on\" input unit.\n", "\n", "![lookup](assets/lookup_matrix.png)\n", "\n", "Instead of doing the matrix multiplication, we use the weight matrix as a lookup table. We encode the words as integers, for example \"heart\" is encoded as 958, \"mind\" as 18094. Then to get hidden layer values for \"heart\", you just take the 958th row of the embedding matrix. This process is called an **embedding lookup** and the number of hidden units is the **embedding dimension**.\n", "\n", "<img src='assets/tokenize_lookup.png' width=500>\n", " \n", "There is nothing magical going on here. The embedding lookup table is just a weight matrix. The embedding layer is just a hidden layer. The lookup is just a shortcut for the matrix multiplication. The lookup table is trained just like any weight matrix as well.\n", "\n", "Embeddings aren't only used for words of course. You can use them for any model where you have a massive number of classes. A particular type of model called **Word2Vec** uses the embedding layer to find vector representations of words that contain semantic meaning.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Word2Vec\n", "\n", "The word2vec algorithm finds much more efficient representations by finding vectors that represent the words. These vectors also contain semantic information about the words. Words that show up in similar contexts, such as \"black\", \"white\", and \"red\" will have vectors near each other. There are two architectures for implementing word2vec, CBOW (Continuous Bag-Of-Words) and Skip-gram.\n", "\n", "<img src=\"assets/word2vec_architectures.png\" width=\"500\">\n", "\n", "In this implementation, we'll be using the skip-gram architecture because it performs better than CBOW. Here, we pass in a word and try to predict the words surrounding it in the text. In this way, we can train the network to learn representations for words that show up in similar contexts.\n", "\n", "First up, importing packages." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import time\n", "\n", "import numpy as np\n", "import tensorflow as tf\n", "\n", "import utils" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the [text8 dataset](http://mattmahoney.net/dc/textdata.html), a file of cleaned up Wikipedia articles from Matt Mahoney. The next cell will download the data set to the `data` folder. Then you can extract it and delete the archive file to save storage space." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Text8 Dataset: 31.4MB [00:16, 1.88MB/s] \n" ] } ], "source": [ "from urllib.request import urlretrieve\n", "from os.path import isfile, isdir\n", "from tqdm import tqdm\n", "import zipfile\n", "\n", "dataset_folder_path = 'data'\n", "dataset_filename = 'text8.zip'\n", "dataset_name = 'Text8 Dataset'\n", "\n", "class DLProgress(tqdm):\n", " last_block = 0\n", "\n", " def hook(self, block_num=1, block_size=1, total_size=None):\n", " self.total = total_size\n", " self.update((block_num - self.last_block) * block_size)\n", " self.last_block = block_num\n", "\n", "if not isfile(dataset_filename):\n", " with DLProgress(unit='B', unit_scale=True, miniters=1, desc=dataset_name) as pbar:\n", " urlretrieve(\n", " 'http://mattmahoney.net/dc/text8.zip',\n", " dataset_filename,\n", " pbar.hook)\n", "\n", "if not isdir(dataset_folder_path):\n", " with zipfile.ZipFile(dataset_filename) as zip_ref:\n", " zip_ref.extractall(dataset_folder_path)\n", " \n", "with open('data/text8') as f:\n", " text = f.read()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preprocessing\n", "\n", "Here I'm fixing up the text to make training easier. This comes from the `utils` module I wrote. The `preprocess` function coverts any punctuation into tokens, so a period is changed to ` <PERIOD> `. In this data set, there aren't any periods, but it will help in other NLP problems. I'm also removing all words that show up five or fewer times in the dataset. This will greatly reduce issues due to noise in the data and improve the quality of the vector representations. If you want to write your own functions for this stuff, go for it." ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first', 'used', 'against', 'early', 'working', 'class', 'radicals', 'including', 'the', 'diggers', 'of', 'the', 'english', 'revolution', 'and', 'the', 'sans', 'culottes', 'of', 'the', 'french', 'revolution', 'whilst']\n" ] } ], "source": [ "words = utils.preprocess(text)\n", "print(words[:30])" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total words: 16680599\n", "Unique words: 63641\n" ] } ], "source": [ "print(\"Total words: {}\".format(len(words)))\n", "print(\"Unique words: {}\".format(len(set(words))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here I'm creating dictionaries to covert words to integers and backwards, integers to words. The integers are assigned in descending frequency order, so the most frequent word (\"the\") is given the integer 0 and the next most frequent is 1 and so on. The words are converted to integers and stored in the list `int_words`." ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "vocab_to_int, int_to_vocab = utils.create_lookup_tables(words)\n", "int_words = [vocab_to_int[word] for word in words]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Subsampling\n", "\n", "Words that show up often such as \"the\", \"of\", and \"for\" don't provide much context to the nearby words. If we discard some of them, we can remove some of the noise from our data and in return get faster training and better representations. This process is called subsampling by Mikolov. For each word $w_i$ in the training set, we'll discard it with probability given by \n", "\n", "$$ P(w_i) = 1 - \\sqrt{\\frac{t}{f(w_i)}} $$\n", "\n", "where $t$ is a threshold parameter and $f(w_i)$ is the frequency of word $w_i$ in the total dataset.\n", "\n", "I'm going to leave this up to you as an exercise. Check out my solution to see how I did it.\n", "\n", "> **Exercise:** Implement subsampling for the words in `int_words`. That is, go through `int_words` and discard each word given the probablility $P(w_i)$ shown above. Note that $P(w_i)$ is that probability that a word is discarded. Assign the subsampled data to `train_words`." ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from collections import Counter\n", "import random\n", "\n", "threshold = 1e-5\n", "word_counts = Counter(int_words)\n", "total_count = len(int_words)\n", "freqs = {word: count/total_count for word, count in word_counts.items()}\n", "p_drop = {word: 1 - np.sqrt(threshold/freqs[word]) for word in word_counts}\n", "train_words = [word for word in int_words if random.random() < (1 - p_drop[word])]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Making batches" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that our data is in good shape, we need to get it into the proper form to pass it into our network. With the skip-gram architecture, for each word in the text, we want to grab all the words in a window around that word, with size $C$. \n", "\n", "From [Mikolov et al.](https://arxiv.org/pdf/1301.3781.pdf): \n", "\n", "\"Since the more distant words are usually less related to the current word than those close to it, we give less weight to the distant words by sampling less from those words in our training examples... If we choose $C = 5$, for each training word we will select randomly a number $R$ in range $< 1; C >$, and then use $R$ words from history and $R$ words from the future of the current word as correct labels.\"\n", "\n", "> **Exercise:** Implement a function `get_target` that receives a list of words, an index, and a window size, then returns a list of words in the window around the index. Make sure to use the algorithm described above, where you chose a random number of words to from the window." ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_target(words, idx, window_size=5):\n", " ''' Get a list of words in a window around an index. '''\n", " \n", " R = np.random.randint(1, window_size+1)\n", " start = idx - R if (idx - R) > 0 else 0\n", " stop = idx + R\n", " target_words = set(words[start:idx] + words[idx+1:stop+1])\n", " \n", " return list(target_words)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's a function that returns batches for our network. The idea is that it grabs `batch_size` words from a words list. Then for each of those words, it gets the target words in the window. I haven't found a way to pass in a random number of target words and get it to work with the architecture, so I make one row per input-target pair. This is a generator function by the way, helps save memory." ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_batches(words, batch_size, window_size=5):\n", " ''' Create a generator of word batches as a tuple (inputs, targets) '''\n", " \n", " n_batches = len(words)//batch_size\n", " \n", " # only full batches\n", " words = words[:n_batches*batch_size]\n", " \n", " for idx in range(0, len(words), batch_size):\n", " x, y = [], []\n", " batch = words[idx:idx+batch_size]\n", " for ii in range(len(batch)):\n", " batch_x = batch[ii]\n", " batch_y = get_target(batch, ii, window_size)\n", " y.extend(batch_y)\n", " x.extend([batch_x]*len(batch_y))\n", " yield x, y\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building the graph\n", "\n", "From [Chris McCormick's blog](http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/), we can see the general structure of our network.\n", "![embedding_network](./assets/skip_gram_net_arch.png)\n", "\n", "The input words are passed in as one-hot encoded vectors. This will go into a hidden layer of linear units, then into a softmax layer. We'll use the softmax layer to make a prediction like normal.\n", "\n", "The idea here is to train the hidden layer weight matrix to find efficient representations for our words. We can discard the softmax layer becuase we don't really care about making predictions with this network. We just want the embedding matrix so we can use it in other networks we build from the dataset.\n", "\n", "I'm going to have you build the graph in stages now. First off, creating the `inputs` and `labels` placeholders like normal.\n", "\n", "> **Exercise:** Assign `inputs` and `labels` using `tf.placeholder`. We're going to be passing in integers, so set the data types to `tf.int32`. The batches we're passing in will have varying sizes, so set the batch sizes to [`None`]. To make things work later, you'll need to set the second dimension of `labels` to `None` or `1`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train_graph = tf.Graph()\n", "with train_graph.as_default():\n", " inputs = tf.placeholder(tf.int32, [None], name='inputs')\n", " labels = tf.placeholder(tf.int32, [None, None], name='labels')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Embedding\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "The embedding matrix has a size of the number of words by the number of units in the hidden layer. So, if you have 10,000 words and 300 hidden units, the matrix will have size $10,000 \\times 300$. Remember that we're using tokenized data for our inputs, usually as integers, where the number of tokens is the number of words in our vocabulary.\n", "\n", "\n", "> **Exercise:** Tensorflow provides a convenient function [`tf.nn.embedding_lookup`](https://www.tensorflow.org/api_docs/python/tf/nn/embedding_lookup) that does this lookup for us. You pass in the embedding matrix and a tensor of integers, then it returns rows in the matrix corresponding to those integers. Below, set the number of embedding features you'll use (200 is a good start), create the embedding matrix variable, and use `tf.nn.embedding_lookup` to get the embedding tensors. For the embedding matrix, I suggest you initialize it with a uniform random numbers between -1 and 1 using [tf.random_uniform](https://www.tensorflow.org/api_docs/python/tf/random_uniform)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n_vocab = len(int_to_vocab)\n", "n_embedding = 200 # Number of embedding features \n", "with train_graph.as_default():\n", " embedding = tf.Variable(tf.random_uniform((n_vocab, n_embedding), -1, 1))\n", " embed = tf.nn.embedding_lookup(embedding, inputs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Negative sampling\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For every example we give the network, we train it using the output from the softmax layer. That means for each input, we're making very small changes to millions of weights even though we only have one true example. This makes training the network very inefficient. We can approximate the loss from the softmax layer by only updating a small subset of all the weights at once. We'll update the weights for the correct label, but only a small number of incorrect labels. This is called [\"negative sampling\"](http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf). Tensorflow has a convenient function to do this, [`tf.nn.sampled_softmax_loss`](https://www.tensorflow.org/api_docs/python/tf/nn/sampled_softmax_loss).\n", "\n", "> **Exercise:** Below, create weights and biases for the softmax layer. Then, use [`tf.nn.sampled_softmax_loss`](https://www.tensorflow.org/api_docs/python/tf/nn/sampled_softmax_loss) to calculate the loss. Be sure to read the documentation to figure out how it works." ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Number of negative labels to sample\n", "n_sampled = 100\n", "with train_graph.as_default():\n", " softmax_w = tf.Variable(tf.truncated_normal((n_vocab, n_embedding), stddev=0.1))\n", " softmax_b = tf.Variable(tf.zeros(n_vocab))\n", " \n", " # Calculate the loss using negative sampling\n", " loss = tf.nn.sampled_softmax_loss(softmax_w, softmax_b, \n", " labels, embed,\n", " n_sampled, n_vocab)\n", " \n", " cost = tf.reduce_mean(loss)\n", " optimizer = tf.train.AdamOptimizer().minimize(cost)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Validation\n", "\n", "This code is from Thushan Ganegedara's implementation. Here we're going to choose a few common words and few uncommon words. Then, we'll print out the closest words to them. It's a nice way to check that our embedding table is grouping together words with similar semantic meanings." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with train_graph.as_default():\n", " ## From Thushan Ganegedara's implementation\n", " valid_size = 16 # Random set of words to evaluate similarity on.\n", " valid_window = 100\n", " # pick 8 samples from (0,100) and (1000,1100) each ranges. lower id implies more frequent \n", " valid_examples = np.array(random.sample(range(valid_window), valid_size//2))\n", " valid_examples = np.append(valid_examples, \n", " random.sample(range(1000,1000+valid_window), valid_size//2))\n", "\n", " valid_dataset = tf.constant(valid_examples, dtype=tf.int32)\n", " \n", " # We use the cosine distance:\n", " norm = tf.sqrt(tf.reduce_sum(tf.square(embedding), 1, keep_dims=True))\n", " normalized_embedding = embedding / norm\n", " valid_embedding = tf.nn.embedding_lookup(normalized_embedding, valid_dataset)\n", " similarity = tf.matmul(valid_embedding, tf.transpose(normalized_embedding))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# If the checkpoints directory doesn't exist:\n", "!mkdir checkpoints" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "epochs = 10\n", "batch_size = 1000\n", "window_size = 10\n", "\n", "with train_graph.as_default():\n", " saver = tf.train.Saver()\n", "\n", "with tf.Session(graph=train_graph) as sess:\n", " iteration = 1\n", " loss = 0\n", " sess.run(tf.global_variables_initializer())\n", "\n", " for e in range(1, epochs+1):\n", " batches = get_batches(train_words, batch_size, window_size)\n", " start = time.time()\n", " for x, y in batches:\n", " \n", " feed = {inputs: x,\n", " labels: np.array(y)[:, None]}\n", " train_loss, _ = sess.run([cost, optimizer], feed_dict=feed)\n", " \n", " loss += train_loss\n", " \n", " if iteration % 100 == 0: \n", " end = time.time()\n", " print(\"Epoch {}/{}\".format(e, epochs),\n", " \"Iteration: {}\".format(iteration),\n", " \"Avg. Training loss: {:.4f}\".format(loss/100),\n", " \"{:.4f} sec/batch\".format((end-start)/100))\n", " loss = 0\n", " start = time.time()\n", " \n", " if iteration % 1000 == 0:\n", " # note that this is expensive (~20% slowdown if computed every 500 steps)\n", " sim = similarity.eval()\n", " for i in range(valid_size):\n", " valid_word = int_to_vocab[valid_examples[i]]\n", " top_k = 8 # number of nearest neighbors\n", " nearest = (-sim[i, :]).argsort()[1:top_k+1]\n", " log = 'Nearest to %s:' % valid_word\n", " for k in range(top_k):\n", " close_word = int_to_vocab[nearest[k]]\n", " log = '%s %s,' % (log, close_word)\n", " print(log)\n", " \n", " iteration += 1\n", " save_path = saver.save(sess, \"checkpoints/text8.ckpt\")\n", " embed_mat = sess.run(normalized_embedding)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Restore the trained network if you need to:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with train_graph.as_default():\n", " saver = tf.train.Saver()\n", "\n", "with tf.Session(graph=train_graph) as sess:\n", " saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\n", " embed_mat = sess.run(embedding)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing the word vectors\n", "\n", "Below we'll use T-SNE to visualize how our high-dimensional word vectors cluster together. T-SNE is used to project these vectors into two dimensions while preserving local stucture. Check out [this post from Christopher Olah](http://colah.github.io/posts/2014-10-Visualizing-MNIST/) to learn more about T-SNE and other ways to visualize high-dimensional data." ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'retina'\n", "\n", "import matplotlib.pyplot as plt\n", "from sklearn.manifold import TSNE" ] }, { "cell_type": "code", "execution_count": 138, "metadata": { "collapsed": true }, "outputs": [], "source": [ "viz_words = 500\n", "tsne = TSNE()\n", "embed_tsne = tsne.fit_transform(embed_mat[:viz_words, :])" ] }, { "cell_type": "code", "execution_count": 139, "metadata": {}, "outputs": [ { "data": { "image/png": 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D6wVA0uklrmxtbVtkuSpfX98GAVDdM0wmk7Zv397ofeHh4Q0CIEkKDg5WcHCw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\ncWFhYZibmxMTE0NMTAz29vYEBQURFhbG9OnTm72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Ir+/XsWPHmD9/PmvWrGHevHkG17h4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VcD1q9fz7asLBoaGnBzcyM8PBxfX1+l4xsaGti5cyexsbGUlJQgkUhwdXUlNDSU4cOHK47b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\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PPlERXhQKBaWlpYI30/3gt99+IzY2Fh8fH/r06SOUTcnLy1OJXoWmTKcPP/wQmUxG7969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xIhUV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rHbp3QkNDiYyM5OeffyYkJAQTExMi4q/xyf4EsqL2qK17O9aGfxZOnL6IvedDOPo8LIwVJJ+g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+fj5eWlsbF3ZmYmycnJjBw5UkUAgoYgy9y5c/nwww8FixRoCNDExcUREhLCggULhO3p6elk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uyiZvRwd0dHQUQaCKigquX7/OtGnTFDMToqOjcXBwICYmBlA/S0HH1I6D4TeorJZRU3GL\nymoZrq6ulJeXK73nXLt2DRMTE6RSKbt27SIzM5PQ0FD09fV56623yM3NJSsriy1btigqezQ1Namq\nqmp19eSDIm9d2OLMnLs8DvOjWjvzyGXIs2RGHKcs5zp1aXGcyjZkwkAvlcqw1tDS0mLFihWcOXOG\nkydPcvnyZaqqqjAxMcHW1pZZs2apDG4X2qal93BL994UpfxObtw5TBy7oq1nSExaMam5pezcsoX6\n+nrGjh3b7DVGjx7Njz/+yA8//MCyZcsU1cR5eXlqZ1wJj5a8+nbdkdYH6OrrIVuvCwsXLmTz5s0s\nW7aMNWvWNNlSrb1GjRpFTEwM27dvZ9WqVYrq94qKCnbv3q1yvJaWFv7+/hw/fpxdu3Yxb948xb7k\n5GTOnDmj8hhPT08cHBxISEjgwoULDBkyRLHvwoULxMfH4+DgQI8ePe7rcxMEQRCEjkIEgQRBEISn\nxoNubyUPwNyvChg3NzcA4uPj1bbIkC/MNs5alAeECgtVs7sbZ+XffY1r165RX1+v0hJOXQam8ORr\nqTpA39yWLqNfIif6NGVZydTX30HfzJYxs+YxYYBHm4JAcnbmBgSoaSsk3Bt1Q+HlympNKLyaTGlp\nKdeuXePOnTv07NkTJycnLCwsiI6OZuLEiURHRyORSJQqgfJuVZKQUcLr2yMV26rLbxGfVoR9jQ5m\nmjVK74VSqRRtbW0KCgr47bffyMjIUFQaNX5vOn78OJGRDeesrKykrq6u1dWTD4p88bSpzw91Hpf5\nUa2deaRrbIH7yJmK/14wzkvx+9rcgv+WLVvUbpdIJIwcOVJp7lRTbGxsmr3G2rVrWzzH06al93Aj\naydsewwhL/4C145sxszZCw0tbf726h40q0rw8vJSmUV4t2effZZLly5x8eJFFi9eTJ8+faioqCA0\nNBRvb29RzdUBdXdQX1nenJi0YhaNH87ixTp88cUXLFu2jNWrVytVZ96rUaNGERoaSmRkJIsWLcLP\nzw+ZTMbFixfx8PAgKytL8R1bbvbs2URHR3Po0CGSk5Px8vJSzJ/r16+fyutPIpHw+uuv8+677/Lx\nxx8zcOBAHB0dycrK4rfffkNfX5/XX39dbWtkQRAEQXgSiCCQIAiC8NRobXsrU8duitYo9SXprW5v\nZWJiAkBBQcF9uV8rKyt69erFlStXOHz4MM8++6xiX2JiImfPnsXIyIhBgwYptssz6E+ePMnIkSMV\nC6WFhYXs2rVL5RrW1tb4+PgQGxvL0aNHmThxomJfZGRkk/OAhCdba6oDjKyd8Bg9W2mbU9eu+Ph4\nqCzYNrdIGxAQoGi5KNxfTVU+ylUa2HE9KZ5v953ApK4YHR0dPD09gYaqn8jISGpra4mPj8fZ2RlT\nU1PFec/EZyO9XaP2vAXFpeTWVHI5JR/5j9bAwIDa2lq8vb0JDAzkxRdfZPr06bi5ufHFF1+oPc/6\n9esfSRWmOuN7O2NrZsDO0GRi0lQDap3MDejhbEEXO5PHqoqtvTOP7sesJOHBac17uEPv0eib21GY\nGE7xjWjq79zBvpsrc158kalTp6qdRdiYtrY2q1atYufOnYSGhnL48GFsbGyYMWMGgwYNEkGgDqg1\n7R+betzUgAC0tbX5/PPPFYGg+0UikbB8+XL27t3LqVOn+Pnnn7GwsCAgIICJEydy6dIl9PX1lR5j\nYmLCJ598wrZt2wgPDyclJQUHBwcWLlyIjY2N2tdft27dWLduHXv27OHKlSuEh4djYmLCiBEjCAwM\nxMHB4b49J0EQBEHoaEQQSBAEQXgqPIz2Vg4ODlhaWnLu3Dk0NTWxsbFRZDu3p2UOwKJFi3jrrbf4\n/vvviYqKwsPDg8LCQs6fP4+GhgZLlixR+sO4W7dueHt7ExcXx9KlS+nZsye3bt0iPDyc3r17c/78\neZVrLFiwgL///e9s3ryZiIgIXF1dyc3N5eLFi/j5+REWFiYyI58yra0OuF+PE+6/5iof5YztXMmu\nhy0/ncTHvIbu3bujo9Mwn6lnz56cOXOGX375haqqKkUVkPy8zamWFqGprcv+S6mMG1tIb1crunfv\nzvbt26mrq8PNzQ09PT2cnZ1JT09HKpU+sIHj91NvVyt6u1qptNZ7nII+d2vPwHjfzhaP7fN9WrT2\nvdjCxRsLF2/Ffy8Y58VUNRWZTVV0GRgYMG/ePKV2XHKiJVzH05rgoK6RGX1mrVT7uOHDhzN8+HDF\n9qCgIIKCglTO0dzPfsmSJSxZskRlu46ODi+88AIvvPCC0nZ5MpKTk+psQHNzcxYvXqz2Ok3dg4OD\nA0uXLm3y/hpr6vlB8wksPj4+4vUvCIIgdDgaLR8iCIIgCI+/1ra3MrR2oiwrmcLkCO7UVjPm+XlM\nmDChVdfQ0NBgxYoVeHl5ceHCBXbu3Mn27dvJy8tr933b2dmxbt06JkyYQFZWFj/99BMRERH06dOH\nTz75BD8/P5XHvPPOO4wdO5aioiJ+/vlnrl+/zpw5c5g7d67aazg5OfHpp58yaNAgEhISOHToEHl5\neSxfvlzRG10+O0h4OrQmyz8n5gxR2z9AmnezTY8THo7mKh/lDMzt0dLRozQjkci4JKV2b/L5P3v3\n7lX679act05WS1VZkdKweRcXF0pLS8nPz1fM85k6dSoymYwvvviC8vJyldab1dXVaod+P2ouNsZM\nHeBK0DAPpg5wfewDIm0ZGC+RQNAwjwd7Q8I9ExVegjodOcGjuFg1EC2VSvnhhx8AlKreBUEQBEFo\nO5GuKQiCIDwVHlZ7Kw8PjyZbZLTU9qqprEFLS0sWLlzY3K0rMTQ05NVXX+XVV19t9TUcHR1Zvny5\nyvazZ88CqhmYTWUFQ/OZk0LHkZ+fT3BwMAEBASpZuaI64PHWUuWjnERDAyObztzKTKQWsHTsothn\nY2ODvb09OTk5aGho4O3t3erzGpjZUJIWT2bkcY6WFaGfdpb4K5dxd3dHU1OTd999l549eypazO3Y\nsYMtW7ZgZGTEm2++iVQqJS8vj4MHD97LP4PQSq2deSSRwOuTfTv8nCNBvIcL6nXk4OB3333HjRs3\n8PT0xNTUlMLCQiIjI5FKpYwfP17R7lgQBEEQhPYRlUCCIAhCu7399ttMmTLlUd9Gq3Tk7MdHrb6+\nnpKSEpXt0dHRhIaG4uTk9ND6pOfn5zNlyhTWr1//UK4HEBwcTHBw8AO9RkhICFOmTOkw801ao6Xq\nAOuuA/CasghDS4cOXR2wfv16pkyZQn5+/lNzD22Z+2Bk19D6SVNHj1INU6V98sqgLl26YGho2Orz\naukbY2jthKaWDkXJEZw4dQZ3d3fWrVvH7t27mThxInl5eRw9epSamho8PT3p1q0bDg4OHDx4kLCw\nMCoqKvD19cXOzq7Vz0Vov/G9nVn7gh++nS3U7vftbMHaF/wY10u1JZPQMYkKL+Fu8uBgWzys4ODg\nwYMxNzcnPDxc8TnQqVMnXn311TYlQgmCIAiCoN6Tv7IlCIIgCHTs7MdHrba2lrlz5+Lj44OTkxMa\nGhqkp6dz5coVtLS0WLBgwaO+ReEBsLCwYPPmzU22+mupOkBLzwAtPQNRHdABtabyUc6mux823Rva\nSlbV3lHat2jRIhYtWqT2vB5j5qicS90sCYCX/LsqLTD/9a9/bfX9NUXMXLj/nsSZR08zUeElqPPC\ncA/e3hHWYltPeLjBwaFDhzJ06NCHci1BEARBeBqJIJAgCILwVBCtUZqmpaXFhAkTiI6OJikpierq\nakxMTBgyZAjTp0/Hzc3tod1LS4EJoUFiYiJvvvkmAwcOZMWKFWqPWbBgAbm5uWzbtg19fX2OHTtG\nREQE6enplJSUoKenh7u7O88++ywWFqqZwfLqqPfeWMkHn24mPjqSmkopdt5Dsff1JyfmDBUpv7F2\nzRqV6oDo6GgOHDhAUlISVVVV2NjYMHjwYP785z9jaGio9jrqWgzu3LmTXbt2sWbNGnx8fBTb4+Pj\n2b9/P6mpqZSWlmJkZIStrS19+/Zl5syZbfvHfAI9qMpHUVH5dHCxMX4qPvueBuN7O2NrZsDO0GRi\n0lS///h2tiBomIcIAD1FRHBQEARBEJ5O4i8yQRAE4anRUbMf5Zpa8H7QNDQ0mD9//kO7XnO0tLRw\ndHR81LfR4clbZ0VERCCVSjE2Vl6wTUpKIjMzk8GDB2NsbExJSQnffPMNnp6e9OrVC1NTU9LT0/nq\nq6/46aefWLduHWPHjmX9+vWEhITw3XffkZGRQVpaGmfPjkJTU5NBfQZg6x6AiZUdRgYVnCiOpiD3\nOls3fkxxWgIvv/wyOjo6HDt2jC+//BJdXV0SEhJwdXVFW1ubTz75hNWrV+Pp6YmrqyvPPvssI0aM\nUHlu9fX1HDt2jBMnTnDhwgXS09P55z//SWBgIBMmTCAqKooPPvgAAwMD/Pz82LZtG9bW1lhZWfHZ\nZ59x7NgxSkpKWLx4sVJbwcYt/2xsbBRBp5SUFE6dOkVsbCyFhYVUV1djZWWFn58fM2bMwMjISOn+\nQkJCWL9+PUuWLMHa2ppdu3aRkpKCRCKhR48evPzyy0oztBq3zGzqHu63B1X5KCoqBeHxIyq8hLuJ\n4KAgCIIgPH1EEEgQBOEJkJiYyIEDB0hISKC8vBwzMzP69evHzJkzlTL83377beLi4jh48CD79+/n\n5MmTFBQUYGZmxogRI5g1axZaWqofDefOnePAgQNkZGSgr69Pnz59mDNnzkN8hvdHe7Ifp0yZgre3\nN2vXrn14N/oUy8/PJzg4mICAAJYsWQKgCExs2bKFqKgojhw5QnZ2NgYGBgwcOJC5c+eqVJcAFBYW\ncuDAASIiIigqKkJHRwd7e3sGDBhAYGBgs/fRXEBO3T3K5eTksHXrVq5cuYJMJsPV1ZXnn3++2WsV\nFhayb98+xX3q6+vj6elJYGAgHh5NByIDAgLYtm0bZ8+eZfLkyUr75LOHAgICADAyMuL777/HyuqP\nBZ38/HwuXryIVCrl3//+N/7+/op933//PdevX/9/9s48oKpqff+fwzzIIDKIOACKooCIQyiGmnNe\n1HJKLJVu2f1lpubQNy2HBi3LbupNTaurpqKWWiJOOYIGgojMKiAgpOgRmQ5Hmfn9wT07jucwqom1\nPn/pHtZae7PPOXut532fFyMjIzp16oSNjQ13797Fp1s7zMzM2Lp1K/Z21uTm2GJubs6hQ4eorKxk\nwoQJbNq0CSMjI/7973/z5ptv4ujoyL1797C1tSUvLw9DQ0Nu3brF6tWruXv3rsZ1ffnll4SEhGBt\nbY2HhwfFxcUoFAo2btxIUlISZWVlVFVV8emnn+Lk5MSpU6dwdHSksLAQLy8vevfujUwmw9LSEn9/\nf86fP096ejpjxoyRnpOaz8uxY8cIDw/Hw8ODHj16UFVVRWpqKr/88gsXL17kyy+/xNjYWGOckZGR\nRERE0KtXL55//nmysrKIiooiJSWFDRs2YG5uDtCgMTxqHlfmo8iobD7U9j108+ZNtmzZwpUrV8jP\nz8fU1JTdu3c/wZEKmgsiw0tQEyEOCgQCgUDw90KIQAKBQPCUc/z4cb7++mv09fXx9vbG2tqamzdv\ncuzYMSIjI1m9ejU2NjZq56xevZrExER69eqFiYkJUVFR7Nu3j/z8fI1F7QMHDvDdd99hamrK4MGD\nMTU1JTo6moULFz6Vll0i+vHpZcuWLURHR/PMM8/g5eVFXFwcx44dIzs7mxUrVqgdm5KSwrJly1Ao\nFLi7u+Pj40NJSQmZmZkEBgbWKwI1hZs3b7JgwQIUCgW9evXC2dlZGluvXr20nnPt2jWWLFlCUVER\nPXv2xMfHh8LCQs6fP8+7777L+++/T+/evQE0Fmo6uvdBJtvOqVOn1ESg8vJyzp49i4WFhdSvvr6+\nmgCkQk9Pjy5duiCXy0lOTpa2p6am0q9fPwoKCli3bh22trbMmDGD/fv3Y2hoyJo1azh79iwKhYKF\nCxfy3Xffcfz4cUxMTCgvL+fFF1+UMroyMjJ49tlnWbNmDa+99hoVFRX8+9//ZuHChWzfvh1jY2NJ\nZAkNDSUkJARnZ2dWrVrF/v37uX37Nu+//z47d+4kJCQEe3t7AAwMDKTxZmRk8NxzzzFnzhx0dXWl\n7b169UIul5Oens7YsWOxtbXVuAcTJ07kzTffREdHR2378ePHWbduHYcOHWLChAka550/f56PPvoI\nT09Padu2bdvYu3cvx48fZ/z48QBMmTKl3jE8Dh5X5mNzz6j8O1NZWcknn3xCdnY2zz33HNbW1mqf\nE4FAIHgQIQ4KBAKBQPD3QIhAAoFA8BRz48YNNmzYgJ2dHZ9++imtWrWS9sXGxrJkyRI2b96sUTMk\nOzub9evXSxZSU6dOZfbs2Zw6dYrp06fTsmVLoDrSeOvWrbRo0YK1a9dKi5fTp0/ns88+Iyws7E+6\n0keLiH58Orly5Qpff/21JGpWVFTw/vvvExcXR3JyMp07dwaqRZDPPvsMhULBggULNCzHcnJyHsv4\nNm7ciEKhYMaMGYwZM0baHhERwSeffKJxfEVFBatWraK4uJiVK1fi7u4u7cvNzeWdd95h3bp1zPpg\nFT+GZ2jNviikFfmxiWRlZUkWZJGRkSgUCsaOHasmiGRmZrJ//34SEhLIy8tDoVAQGxuLtbU1zs7O\n5Ob+0f7kyZPZvXs3BgYGODo6IpPJ8Pb25sSJE7z44otqdmf6+vr4+voSGBhITEwMAN27d5f26+jo\nEBAQgJmZGR07diQhIYGysjJGjx7Nrl27yM7OlupOHT9+HICAgACMjIykNgwNDQkICOCDDz6gpKQE\ngPnz5+Pr68vdu3extLTktddeU7vehlKbKDN06FC+++47Ll26pFUEGjBggJoABDBy5Ej27t2rJqg9\nKR5X3QdRT6J5oK1+2u3bt8nKymLEiBHMmjXrCY5OIBAIBAKBQCAQNCeECCQQCARPMUeOHKG8vJwZ\nM2aoCUAAnp6eeHt7ExkZyf3799XsjFQLsiqMjIwYOHAgu3fvJjU1lT59+gBw5swZysvL8fPzU1so\nlclkvPrqq4SHh1PVkHBwLSQnJ/Pzzz+TlJREYWEhZmZmdOjQgREjRvDss8+qWd1MnDiRHTt2EB8f\nT2FhIStWrJAsuhQKBfv37+f8+fPI5XL09PTo1KkTEyZMwMvLS61PpVLJsWPHuHjxIjdu3KCgoAAT\nExNcXV2ZOHGimgCkqvsBkJCQoFbXw9/fnylTpkj/b6gdn4rU1FS2b99OUlISMpmMzp0788orrzTp\nPj7tPCjEtTWt/Xny9/dXy2rT1dVl6NChJCYmqolAkZGRyOVyvL29tdac0ZYR87Dk5OQQExODnZ2d\nhjWbt7c37u7uJCQkqG2PiooiOzubF198UU0AguoF3vHjx7Ni9TrmfLUH8zbasynKrbtwLTmRNVt+\n4sul8wBNKziofkbnzn+XvKJiHJw6Y9elIz0sjMjJyaF169YAlJWVScd36tQJAAsLC2QymTSmmvtq\novr+Udm71XzubWxssLOzA5AEZqVSiYeHB7t27aKwsFA69tq1a8hkMq01sdzd3dHR0aGkpISlS5fy\nyy+/cOLECcm2bvny5UyfPp0ePXpovVe1UV5eztGjRwkNDSUrKwulUqn2vabNsq62+6B6toqKiho1\nhsfF48p8FBmVTx5t9dO0ff4EAoFAIBAIBAKBQIhAAoFA8JRRc9E8+HQE90rKSUhIICUlRePYgoIC\nKisruXHjhtqCpbY6I6rF9ZqLl9euXQPQuiDbunVrbGxskMvljb6GY8eOsWHDBnR0dPD29qZNmzbk\n5+eTmprKoUOHePbZZ6Vjs7OzmT9/Pg4ODgwaNIiSkhIp8lkul7No0SLkcjlubm706tWL4uJiLly4\nwLJly3jrrbcYMWKE1Nbvv//O9u3bcXNzo0+fPrRo0QK5XE5kZCQXL15kyZIlkn2Wk5MT/v7+7Nq1\nC1tbW7UF9Zr3o7F2fJcvX+aDDz6gvLwcHx8f7O3tSUtLY9GiRRpZBX9lLqXnsDM0RSO7paQon6ys\nPLrc1VxEb+ii+5UrVwBqtWB7HKSlpQHQrVs3DVsxqH5mHhSBVOO8c+cOgYGBGudcSEghXV5IG4c7\ntYpAlu1cydI3YvfPh5jy8it0sjbk4sWLODk54eTkBFTf61mLVpOcko3LsOkU2TlSBCTl5lNQaYKD\nsTmUKtXaVdWrUQlAgJRlo80GUrVPZT2Vl5dH+/btq8doaSkdl5eXJ7WhOraiooKKigqgWhwyMzOT\napMplX+MS1dXF3NzcwoKCujTpw99+vShuLiYESNGYGpqSmZmJh9++CHr1q1Ty1Sqj88//5zw8HBa\nt26Nt7c3LVu2RF9fH4CgoCA1cawmLVq0qPU+VFZWNrj/x83jynwUGZVPlgdrAtUMVNi1axe7du0C\nNIMWBAKBQCAQCAQCwd8PIQIJBALBU4K2RfPEq1mUKHL5ZO33OLQyxcJEu/d/cXGx2v+1FSTXtnip\nWoCtuYhbk5YtWzZaBMrKypIsbFatWiUtFKt40KorKSmJiRMnMm3aNI22vvrqK+7cucPChQsZMGCA\n2rgXLVrE5s2b8fb2lsbftm1btm3bJhVsr9nn/Pnz+e677yThwNnZGWdnZ0kE0raI1lg7vqqqKtau\nXUtpaSkffPAB3t7e0vFBQUF8++23DbqHTztHL2XWaSVVeL+U4IuZDIvJYkSPPxbzG7rornpuH8yO\ne5w05LPyIKoMmHPnzmk9Jykrj6oqqCzXLkIA6Ojp07J9N3JSo1m78zDjPVtSUVEhiZaqe52Z9Tt6\nhiaY2Tmqj+F+KeFxKXRqozm+pmBvb49cLic+Pl4SNfPz84Hqe5SWloaBgQHt2rWTRDATExPy8/Mp\nLy/H1NQUhUJBeXk5enp6auJ2RUUFhYWFaiKUkZER5ubmuLu74+npyc6dO4mKimqwCJSSkkJ4eDg9\nevRg+fLlanZyVVVV7Nu376HvSXPhcdV9EPUkmgf+/v7I5XJOk0F3EAAAIABJREFUnjyJu7u7FKyg\nLYhDIBAIBAKBQCAQ/L0QIpBAIBA8BdS2aK5rUF0zw2n0O+gZGjHLr7vaovnDohKL8vPzNcQa+COq\nvzEcPnyYiooKJk+erLXNB626LC0t8ff31zguPT2dhIQE+vfvryYAqcb98ssv88knnxAWFsaoUaPU\nrkdbn/379+fgwYPcuXNHLXOnLhprx3flyhVu3LiBu7u7mgAE4OfnR3BwMNnZ2Q3q+2nlUnpOvbVE\nAKiCr4LjsLUwrudATVR/59psvBqCKptHlaFSE21WXzU/K9rQ9llRnfOgIAjVGX//2hTaoLFadexB\nTmo0Eb+dRTfLAF1dXQYNGqR2rw1atKS48C73825j3NJOOrdUWUBFaTFpurqkZBc0qL+66NGjB4mJ\niQQHB0tC1J07d5DL5fz888/cu3eP4cOHo6+vT3x8PACurq7cuXOHEydO4OzsTGxsLImJieTk5HD5\n8mWp7cTERCorKzEzM6OiokKj/o/q3hsaGqptr+tvqfq8PfPMMxrtJScnU1pa+jC3o0FjEAgeBVOm\nTCE+Pp6TJ0/i4eEhsn8EAsETYc2aNZw8eZLvv/++1pp7f4cxCAQCgUDQ3BAikEAgEDRz6lo0N7V2\n4N7dmxTdycTCobO0aP6o6jB07NiRsLAw4uPj1Qq9A9y6dYs7d+40qJ2adkGHQiK5V1LeYKsuJycn\nyZqpJqosAqVSqdVKq6CgekE7KytLbfvly5cJCgriypUrUvZBTe7evdtgEUg1hoba8aWmpgJo1H+B\n6kXibt26/eVFoJ2hKfULQP+jqgoCz6bg0Mg+XF1dAbh48SLPP/98I8+uRiXQPJiZBkh/x5o4OzsD\n1ZlrlZWVGpZwKsGjJl26dAGqxY0HRaCYDM1+a6OFTTsMzazIz0oi4Z4JY4YPwsLCgp2/hEv32tbV\nm8KbqST/ugXLDt3Q1TdCcSud4nw55g4uVFXBqfgb+Ng3uFuttGzZkhkzZrBx40bmzJlDeno6enp6\nTJw4EXNzc9q2bUtAQAC3b9/m4MGD6OrqMnPmTFauXMmGDRuwt7cnMzOTN954AwcHB/r06cOFCxco\nLS1l586dQPVndNq0aXTt2hU7OzuysrK4e/curVq1wtbWVkMUVtU/u3PnDvb26heoqlX0YN2vgoIC\nNm7c+HA3o4FjEAgaQmPqpwkengft9poro0ePxt3dnU8//fRJD0UgqJWn5fMkEAgEAsFfGSECCQQC\nQTOnrkVzm87PcDc1mhsXf8XQzAojc2sCz6ZIIlB5eTlXr17Fzc2tSX0PGjSIXbt2ERwczLBhw6Ro\nuqqqKrZs2aJWPF0bWi3skm9QosjliyOpBAw1qlew0majBaBQKACIiYkhJiam1vPv378v/Ts8PJxP\nP/0UAwMDevTogb29PUZGRshkMuLj40lISKi1/oc2VHZe+/fvr/M4lR3fvXv3gMZZhv2VyJArNGoA\n1Ufc9VyMKa7/wBo888wz2NraEhERQWhoqIYokJOTo5Fx9iCdO3cG4MSJEzz33HNSlkhOTo5Ua6Mm\n1tbW9OjRg5iYGIKDgxkzZoy0LyIiQqMeEIC3tzf29vYcOnSI7t2707t3b2nfvZJqcVJ5Jwvjlq3R\n0dMUQmvSytmTm7GnqaisYsiQIRr32rxNJzo+58+t+LPkX09EJtPBwMwKE2sHjCxsKC64Q9rtQlxb\n6NbRS8MYNWoU9vb27N+/n99++w0jIyPu378vfe5++OEHzp49i1Kp5NVXX6V379588skn/PDDD6Sk\npFBVVUV+fj4dOnQgKyuLzMxMVqxYQVlZGb6+vvj4+BAeHk5KSgqxsbHI5XLs7e2ZNGkSY8aM0bAN\n9PT0ZP/+/Xz99df4+PhgbGyMqakpfn5+uLi40LVrV8LCwli4cCHdunUjPz+fixcv4uDggJWV1UPf\nj/rGIBDURVPqpwkEAsGTYtq0aUyYMOGR/X4KBAKBQCB4NAgRSCAQCJox9S2aG1lY0957DJkRQVwO\n/gZz+478bt6KlvILVBYXkpSUhLm5Od98802T+re1tWX69Ol8//33zJ49G19fX0xNTYmOjkapVOLo\n6EhGRobWc2uzsNMzMKIEiLl6nUW3lbxTj4VdzcL0NVHVBXnjjTfUIvjrYseOHejr6/PVV19p1AxZ\nv3691oX6ulBli+zZs0etTkltqI5pjGXYX4nGZLfU5EauslHH6+np8d5777F06VK++OILjhw5gqur\nK6WlpWRlZREbG8uBAwfqbKNLly64u7uTkJDAvHnz8PT0JD8/n8jISLy8vLTW8XnzzTdZsGAB3377\nLZcuXcLJyYns7GzCw8N55plniIyM1Bjn4sWLWbp0KR9++CFdu3bFyckJQ0NDQi4lkxh+iRJFHh7j\n59crArX2GEBrjwG8OaIbPs848UtkusYxFg6dsXDoLP2/pCifxF/WYtrKgW6jZwJwI/e8tP/777+v\ns88pU6ZIllMnT55U2+fl5YWXlxcxMTG4u7uzcOFCtmzZwtmzZ7l37x7t2rVj3LhxDBw4EIBu3brx\n2WefAdUi85EjRzh+/DiZmZl06tSJdu3aMWzYMEaNGoVMJuPZZ5+V+lJFwk+dOlXrOHv27Mlrr73G\nsWPHOHDgAOXl5dja2uLn54eOjg5Llixhx44dREVFcfDgQVq1asXw4cN56aWXmDlzZp33oKHUNQaB\noDaaWj9NIBAInhRWVlZCABIIBAKBoBkiRCCBQCBoxjRk0dzKuTvGLe2QXz6P4nY6ilvXOHIvje4u\n7enfvz++vr4PNYYXXngBKysr9u3bx8mTJzE2NqZnz568+uqrfPHFF1rPqcvCzsS6Lcq7Nym8mYqR\nhXWTLexqWmk1VATKzs6mffv2GgJQVVUViYmJWs+RyWRUVlbWOobU1FQSExPp06dPvf136tQJQKvY\nVFlZSVJSUr1tPM2oslsaS2m59vtfFy4uLqxbt469e/cSFRXFlStXMDY2xt7enpdffrlBbXzwwQf8\n97//JSIigoMHD9KmTRsCAgLo2bOnVhGoTZs2fPnll2zdupXY2Fji4+NxdHTk/fffp7CwUEMEAnB0\ndOQ///kPv/zyC5GRkZw4cQIdHR30jFpg3LI19h6D0DNseF2kHo7Vn6OG3GvDFpb0fGWZ2rYh46Yx\nxfdjrcfXFH0eZMiQIVINIG1YWVkxf/584A9bmEuXLkkiUE1kMhmjRo2SannVx8GDB+s95oUXXuCF\nF17Qus/MzIw333xT6z5tQlh911rbeOoag0DwIE2pnyYmdgKBoC7i4+NZvHgx/v7+Wn/PX3vtNeCP\n376TJ0+yZs0a5s6di42NDbt27SI1NRWZTIabmxv//Oc/Nd6pH6zHExgYKGVQnzx5Ui1oZO7cuWq/\np9HR0QQFBZGcnMz9+/extramX79+vPTSS1rresbExLBr1y6uXbuGvr4+bm5uBAQEPPR9EggEAoHg\nr4iYKwgEAkEzpqGL5sYt7ejgM1b6//RBnZni66JxXF2e8XUtbA4YMEDDUquu9uq2sOtNTspFbiWE\nYt6mI0YWNmoWdg2x6oLqRX43NzfCwsI4fvw4w4YN0zgmIyODli1bYmFhAVRnNt28eZPc3FwpSrGq\nqorAwECN2kEqzM3NtdaFAfDz8+PYsWN89913tGnTBgcH9eo1D9rxubq64uDgQEJCAhEREWp1YIKD\ng//y9YBMDOt/7dAmTIyf+jovPOOk9XgPD49aF91tbGxqXdyvSW0ZL6amprz99tu8/fbbGvtq69Pe\n3p5FixZp3Vfb58vCwoLp06czffp0te0LtoU3yj6vewcrHG2ra8805F5ro6nnCQSCR0tT6qdN82pR\n/8GCh6KkpISgoCDOnj3LzZs3kclkdOjQgTFjxqi9J4WGhvLFF18wduxYXn/9dY12ysrKmDp1KgYG\nBmzZskWyHFWde/ToUdLS0igtLcXOzo5BgwYxbtw4rTUSBYLHTWRkJBEREfTq1Yvnn3+erKwsoqKi\nSElJYcOGDZibm9d6roeHB0qlkqCgIJycnOjbt6+0z8npj3e7Xbt2ERgYiJmZGX369MHCwoKMjAx+\n/vlnoqKiWL16tVrW/W+//caqVavQ19fH19eXli1bkpSUxIIFC9TaFQgEjedBQbg+aqv79aAoLBAI\nnixipi8QCATNmKdxIbd+Czsb2vV5nqzIQ1w5vAmLtq7cjLHC7GYYubeyMDExYeXKlQ3qa8GCBbz/\n/vusW7eOgwcP0qVLF0xNTcnJySEjI4Pr16+zevVqSQR64YUXWL9+PbNnz6Z///7o6upy+fJlMjMz\ntdp1QXUtj9DQUD766CM6duyInp4ebm5uuLu707ZtW2bPns26det466236NmzJw4ODlRUVCCXyzXs\n+GQyGXPmzOGDDz5g5cqV+Pj4YG9vT1paGrGxsfTq1YuLFy824a4/HaiyVP6s8552Xh7gwqKdEQ1a\nCJbJUBN+xb0WCJ5emlo/7UZ77fapgkeDUqlk8eLFpKWl0bFjR4YNG0ZlZSWXLl3iiy++4Pr165It\nZd++fTE1NeXMmTO8+uqraiIPVNeKUyqVDB8+XG3f2rVrOXHiBNbW1vj4+GBqasrVq1fZsWMHsbGx\nfPzxxxptCQSPm/Pnz/PRRx/h6ekpbdu2bRt79+7l+PHjjB8/vtZzPTw8sLOzIygoCGdnZ60ZSHFx\ncQQGBuLq6sry5cvVsn5U2UiBgYGSoFpcXMz69evR0dHhs88+w8Xlj/ef7777rl7LX4FAIBAI/o4I\nEUggEAiaMU/jQm5DLOysXXphbGnL7cvhFN3OoOD3K5wubsOA3u4MHz68wX1ZW1uzZs0aDh48SFhY\nGGfOnKGyshJLS0vat2+Pn58fHTp0kI4fOXIk+vr6HDhwgJMnT2JgYICbmxtz5swhLCxMqwj0xhtv\nABAbG0tUVBRVVVX4+/vj7u4OwHPPPYeTkxO//PILcXFxXLp0CSMjI6ysrLTa8XXt2pVVq1axfft2\noqKigGpbuU8//ZTo6Oi/tAjkaGuGR3urJme3/N3wcrJm7j886rWEksngHb/uapaKzfVeN8QWpqqq\niqNHj3L8+HGysrKoqqqiffv2DB06lOeff77WOmECwV+FptZPu3rzr11X7knz7bffkpaWRkBAgNqi\nd2lpKStWrOCnn36if//+ODs7Y2BggK+vL0ePHiU6OlrDMlb13Td48GC1bSdOnKBfv34sWLAAAwMD\naZ/qu/PQoUOMGTPmMV+pQKDOgAED1AQgqH6n3rt3L8nJyQ/dviq7+u2339awfRsyZAhBQUGcOXNG\nEoHOnz+PQqFg8ODBagIQgL+/PydOnECpbFw9SYFA8OiZNm0aEyZMEHXCBIJmghCBBAKBoBnTXBdy\n66KhFnamNu1wtvnDR/xBCztbW9sG1fowNjZm0qRJTJo0qUH91mZ75+joqDU60cLCgoULF9bZpqOj\no1rqe3106tSJDz/8UGO7q6trrTVX/io8THbL35GRXu2xszQh8GwKcdc1vwe6d7Biiq+L1ppazeFe\nP/gZbogtzJdffklISAjW1tYMHz4cmUxGeHg4GzdulKxeBIK/Mk2tn3a/tOIRj+TvQ4ZcQUxGDvdK\nyjEx1KOtqfoXp0Kh4PTp07i4uGhkPRgYGBAQEEB0dDQhISE4OzsD1QLP0aNHOXnypJoIlJeXR3R0\nNM7Ozjg6Okrbg4KC0NXVZc6cOWoCEMDkyZMJDg7mzJkzQgQSNIqaz/adrN+b9P2iqmlZE5V1c1FR\n0UOP8cqVK+jp6WmttwjV9okFBQUoFArMzMy4du0agBSQVRNTU1OcnJy01t8UCAR/LlZWVkIAEgia\nEUIEEggEgmZOc1jIbQxPo4Wd4M/jYbJb/q54OVnj5WStsUjZw9G6TsG3Od7r+mxhQkNDpUXUVatW\nYWRkBMArr7zCokWLCAkJoU+fPgwcOPCxj1UgeFI0tX6aazc33guoP3hC8AeX0nPYGZqiEWxTUpRP\nVlYeXe5WL3AnJydTWVkJVGflPEhFRbUAV7O+YNeuXXFwcCAyMpKioiJatKiu2aTKWh46dOgf/ZWU\nkJ6ejrm5ea1WVvr6+rXWLxQIHkTbs624nUHK9bts/Pkc33y/jf/32nStwUeqeiCqfapntyYqW0LV\n5+JhUCgUVFRUSJnCtXH//n3MzMykLB9LS0utx7Vs2fKhxyRoGAcPHuTIkSPcvn2b0tJSXn/9dcaO\nHVv/iY+Z0aNH4+7uXmc93D+DmrVyJk6cyI4dO4iPj6ewsJAVK1bg4eGBQqFg//79nD9/Hrlcjp6e\nHp06dWLChAl4eXmptaeyR5w7dy7m5ub8+OOPpKeno6enh6enJ9OnT6dNmzZq5yxatIiEhAStwZU1\n29MWJKlUKtm+fTvh4eEoFApat27N888/j5+fX4My8+uqCZScnMzPP/9MUlIShYWFmJmZ0aFDB0aM\nGMGzzz7bkNsrEAgaiVhxEwgEgmZOc1zIrYun0cLur0BtBTmbIw9mt5QU5ZP4y1paOfegg8/YOrNb\n/s442po1OsvvYTKJHhX1RdjX5Pjx4wAEBARIAhCAkZERAQEBfPDBB/z6669CBBL8pRG/o38ORy9l\n1vluVXi/lOCLmQyLycJQoQAgJSWFlJSUWtssLi5W+//gwYPZvn07oaGhjBo1CoBTp06hp6en9j1W\nVFREVVUVBQUF9S6ECwT1Ud+zfSuviIIbecRdv0tNCUipVGrYsf0ZmJiYUFVV1eBnXzXG/Px8rfvz\n8pqvNWZT3tfrW6h/UoSGhrJ582acnZ0ZM2YM+vr6uLq6PulhNUuys7OZP38+Dg4ODBo0iJKSEkxM\nTJDL5SxatAi5XI6bmxu9evWiuLiYCxcusGzZMt566y1GjBih0V5YWBgXL16kX79+eHh4kJaWRlhY\nGPHx8XzxxRc4ODg89JjLy8tZsmQJRUVFDBgwgPLycsLCwti8eTO///47b775ZpPbPnbsGBs2bEBH\nRwdvb2/atGlDfn4+qampHDp0SIhAAsFjQohAAoFA8BTQHBZyG8rTaGH3JGiuE7o/i5rZLWeir7Du\nXAu8XO1Y8q8Bf7tn4XHT1Eyih6WhEfY1uXbtGjKZDA8PD4197u7u6OjoSDYw8Ee08vfff/+IRy8Q\nPD7qe27F7+jj51J6Tr3BNQBUwVfBcbziXm3PNnbsWKkuSUMYPHgwO3bs4NSpU4waNYq0tDQyMjLw\n9vbG3NxcOk61qO3s7MzatWsbfT0CgYq6nm09A2MAyouVVFXB4ehMXkrPwcvJmuzs7McmAuno6AC1\nZw25urpy4cIFMjMzad++fb3tdezYEYCEhASGDRumtk+pVJKenv6QIxY0hAsXLgCwbNmyZmf5tXHj\nRgwNDZ/0MCSSkpKYOHEi06ZNU9u+aNEi7ty5w8KFCxkwYIC0XalUsmjRIjZv3oy3t7dG1ltkZCRL\nly5VsxoNCgri22+/ZcOGDaxYseKhx5ybm4udnR3r169HX18fqM4OnDdvHocPH8bX11erJWN9ZGVl\nsXHjRkxMTFi1apXGZz4np2l1EQUCQf0IEUggEAieEp7UQm5TeNos7P4KWFlZSS/UTxOOtma8MtSL\noe4/YGJigpVV83qW/0o0JZOoqTQmwn5Ejz9qgymVSszMzNDT03xF1dXVxdzcnIKCgsc1bIFAK08i\n01L8jj5edoamNOjeAlRVQeQtkMlkJCUlNaofa2trPD09iYmJ4caNG5w8eRJAI/jDyMiI9u3bk5mZ\nKdU9EQiaQl3PtqG5NboGRihuZ0jbAs+m4OZgzqZNmx7bmFq0aIFMJuPOnTta948dO5YLFy7wn//8\nh0WLFmkICsXFxVy/fp0uXboA0LdvX1q0aEFISAh+fn64uPzx/bdr1y7JLu6vQt++fdm4cWOzs7nL\nza0OVGhuAhBA27Ztn0i/tWW/W1pa4u/vr3Zseno6CQkJ9O/fX00AgurAgJdffplPPvmEsLAwKZNU\nRffu3dUEIAA/Pz+Cg4OJi4tDLpdr2K81henTp0sCEICZmRmTJ09mzZo1nDhxokki0OHDh6moqGDy\n5MlaRV9VvTGBQPDoESKQQCAQPGX8mQu59REfH8/ixYvx9/dX8xR/2izs/gro6ek9sQnPw/I0j12g\nSWMj7G0tjKXvAFNTUxQKBeXl5RpCUEVFBYWFhU+d0CkQNAXxO/r4yJArGpVlBXD1Til9evcj9kIY\nu3fvZtKkSVJ2g4rs7Gx0dHSws7NT2z5kyBBiYmL49ddfCQkJwdzcXGPxDuCFF15g3bp1rF27lnfe\neUcjI6OoqIjbt29LWRACwYPU92zr6Opi2+UZsqKOcj/vFnevxRD8YxFZv26ig4PdY1vMNzIyonPn\nziQmJrJ69WocHBwkGyhHR0eplskPP/zAG2+8Qe/evbGzs6O4uBi5XE5CQgLdunXjww8/lNqbNWsW\nq1at4r333sPX15eWLVuSlJTE9evXcXd3JyEh4bFcy5PA1NT0idj01UZgYKCadd/o0aOlf6vqzsTG\nxrJ//36Sk5MpLi7G1tYWHx8fJkyYoHEtqpo1P//8M3v37uXMmTPcvn2bgQMHqgVehIaGcvToUdLS\n0igtLcXOzo5BgwYxbtw4NaFCNSZtNYFyc3P54YcfiIqK4v79+zg4ODB27FhsbW21zmlVY/vll1/Y\nt28fJ06c4M6dO1haWjJw4EBeeeUV9PT06s1+H+LURWOMV65cAaoDoLTVmlMFPWmrBactY15HR4du\n3bqRnZ1NWlraQ4tAurq6dO3atda+09LSmtTu1atXAejVq1fTBycQCJqEEIEEAoFA8Fh4mizsGlqY\n8urVq+zfv5+kpCSKioqwtLSkd+/e+Pv7a0yc65rQ3L59W5qcrlmzhjVr1kjnqQpn5ubm8uuvvxId\nHU12djZFRUWYm5vj7u7O5MmTadeunVp/tUWq1yzIGR0dTXBwMDdv3sTExIS+ffvy6quvYmpqqnb+\nSy+9xNatW4mPj6esrAxXV1def/11OnToQEFBAdu3b5cKXTs6OhIQEED37t2lPh/V2FVt7dmzh6io\nKHJzczExMcHNzY1JkybRqVMntWNrWuxZWlqyd+9e0tLSuHfvntZiqILHQ30R9qpCslVVlVRVVUch\nq74HnJ2diY2NJTExEU9PT7XzEhMTqaysFAuggr8NT9Pv6NNETEbTrGY8Bo2luPAuO3fu5PTp03Tr\n1g1LS0tyc3PJysoiJSWFhQsXaohA/fr1w8TEhKCgIMrLyxk9erTWbMdhw4aRmprK4cOHmTFjBl5e\nXtja2qJQKKT3hqFDh/LWW281afyCvz4NebZbdx9EcVEeWeeDKPpfRpCN1zA++mAOM2fOfGxjmz9/\nPt9++y3R0dGEhoZSVVWFtbU1jo6OAEyYMIFu3bpx8OBBkpKSiIiIwMTEhFatWjFixAiNWoD9+/fn\no48+IjAwkLNnz6Kvr4+7uzurV69m7969T0wEakyxe7lcztatW4mJiaG4uJgOHTowZcoUDZG4Ngtp\nlbXo+vXrpfuQn5+PjY0Nw4cPZ/z48dI7V00aM5+5desWe/fuJS4ujrt372JgUG2N2apVKyorK8nL\ny1PLcAkNDWXDhg2cPXsWQBL5DA0N2bt3LxEREXzxxRdaRa2VK1eSkpJCr1696Nu3LxYWFtK+tWvX\ncuLECaytrfHx8cHU1JSrV6+yY8cOYmNj+fjjj9HV1a3zb1NQUMDChQuRy+W4u7vj6upKXl4eGzdu\nxMvLq85zV69eTWJiIr169cLExISoqCj27dtHfn4+rgPH1Zv9HppawLEHst8V/6s1FxMTQ0xMTK19\n379/X2Pbg/ZwKlTZYnVlw6lEvJUrV9Z6DIC5ublGsEPNvrOyshg9ejT+/v4MHTq0zrZqUlRUbQfd\nqlWrBp8jEAgeDUIEEggEAsFj42mwsGtoYcrjx4/z9ddfo6+vj7e3N9bW1ty8eZNjx44RGRnJ6tWr\nsbGx0Whf24TGw8MDU1NTIiIi8Pb2xtnZWTpeNSlKSEjgp59+onv37vj4+GBsbMzNmzcJCwsjMjKS\nzz//HCcnpwZf55YtW4iOjuaZZ57By8uLuLg4jh07RnZ2tppv9O3bt5k/fz7t2rVjyJAhyOVywsPD\nWbRoEatXr2bZsmWYmJjg6+uLQqHg7NmzLF++nE2bNknX/6jGfvv2bd59911yc3Pp3r07AwYMICcn\nh3PnznHhwgUWL16sNZr6t99+4+LFi/Tq1Yvnn38euVze4PskeDgaEmGva2CMTCaj7F51hGPc9Vwy\n5Aocbc0YNmwYsbGxbNu2jU8//RRDQ0Oqqqr45ZdfWL58OXK5nNLSUr755humTp2qtf2ysjIOHDjA\nmTNnyM7ORldXFycnJ0aPHq22AFNcXIy/vz8uLi58/vnn0vbS0lImT55MWVkZ8+bN47nnnpP2HT58\nmI0bNzJ79mypDkFDo0QFTyc1I55Pnjwp2XkBzJ07l4EDB3L06FGioqLIzMwkLy8PIyMjOnbsyIsv\nvtioSNeQkBDWrFlD69at+fDDD7G1tZV+R4NPnGXHnr1kZqRRWVaKY1t7ujoMoLNt9/obFqhxr6S8\nSedVyPT57LPPOHr0KCEhIYSFhVFaWoqlpSVt2rTh9ddf17qQaGhoSP/+/Tl+/DhQXSeoNt588016\n9+7NkSNHiI2NRalU0qJFC2xsbBg3bpza95FA8CANebZlMhnWnXqSlx5Pa3df2vQYTL9BnTE0NJTq\nlE2ePBlTU1OGDBlSZ91KbQE2c+fO1WqbaW9vz9KlS+scW7du3ejWrVu916CiR48e9OjRo8FjeNw0\npti9XC5n3rx5tG7dmsGDB0vv1B9//DGffPKJWnBVXZSXl7N06VJyc3Pp3bs3Ojo6nD9/nm3btlFW\nVqZhQdaY+Uxubi7z5s3j3r179O7dGx8fH0pLS7l9+zaxsbHY2tqSl5cnZc6sXbuWrVu3cvXqVWxt\nbXn55ZeRy+VcvnwZDw8PRo4cydGjR9myZQuzZs3SuJYF+hCOAAAgAElEQVQ7d+6wfv16tXppUP3b\ne+LECfr168eCBQskIQr++I0+dOgQY8aMqfNebdu2Dblczvjx4wkICJC2jx07lnnz5tV5bnZ2NuvX\nr5esOqdOncrs2bP5Ofgoenfs0TNqUef5VMk0st9Vme1vvPGGWjZVQ8jPz9e6PS8vD0BNZFMJORUV\nFRpCmUqQ0UZhYSGVlZUaQpCqb2NjY0nIagwtWlTfqwMHDrBz586/bX1cgeBJIGakAoHgqeJRZitA\ndZTM3r17CQ8PRy6XY2BgQOfOnRk3bpzapCI0NJQvvvii1oLAZWVlTJ06FQMDA7Zs2aL2gtWU1PX/\n+7//Y9u2bVy4cIHi4mKcnJwICAjAzc2N4uJiAgMDOXfuHHl5edjb2zNlyhSN6LKH6X/RokX88MMP\nREZGolAosLe3Z9y4cWpRPqoME6j2365pC7By5Uq1NPXmZGFXk4YWprxx4wYbNmzAzs6OTz/9VC1y\nKTY2liVLlrB582bef/99jT5qm9AARERE0K9fP60vvp6enuzYsQNjY2O17enp6bz77rts27aN5cuX\nN/har1y5wtdffy1N7CoqKnj//feJi4sjOTlZiupKSEhg6tSpTJo0STp39+7d7Ny5k/nz5/Pss88y\nc+ZMKbLQy8uLf//73xw4cED6bDyqsa9fv57c3FyN8YwaNYr33nuPr776iv/+978YGRmpnRcVFcWy\nZcuEzcAToCFRyLr6Bpi0cqBInknGuf0Ymrfi62/TmPXyaAYOHMj58+c5d+4cM2fOpF+/foSGhnLm\nzBmqqqrw8fFh1KhRREREkJycrGEbp1oMSUhIoG3btvzjH/+gpKSE3377jVWrVpGWliYV5TUyMsLF\nxYXk5GTu378vPa9JSUmUlZUB1Z/vmouusbGxABpZSlB3lOiTWIwSPBo8PDxQKpUEBQXh5ORE3759\npX1OTk4oFAo2b95M165d6dGjBxYWFuTl5REZGcny5ct5++23GT58eL397Nu3j23btuHq6sqSJUvU\nasLs2rWLwMBAzMzMmDhqMBYWFmRkZPDzzz8TFRXF6tWrhU1iIzAxrH8KbNjCkp6vLNM4T09PDz8/\nP/z8/BrV5+zZs5k9e3aDju3Tp4/WAAdtiCxXQU0a8mwD6BlU/96V3SsEILeoWNqXnZ2NUqlsVvZj\nTwONLXYfHx/PlClT1ESagQMHsmzZMvbv399gESg3NxcnJyc++eQTSRyZMmUK//rXvzhw4AATJ06U\n3pMaO5/57bffUCgUzJgxg+59n5MC+mx76DFp+v/j+6+/kM5XCTWtWrXC3d2dl156SXrfUgk1np6e\nGBsbc/r0af71r39pzENfeeUVrfOloKAgdHV1mTNnjpoABNWCZXBwMGfOnKlTBCovLyckJARTU1Ne\neukltX1OTk4MHjyYX3/9tdbzAwIC1H6XjYyMGDhwICcjN2Jz9yYWDp1rPVfFg9nvqhpXiYmJjRaB\n4uPjmTx5stq2yspKqW5dzQBDleiSk5ODnZ0dfn5+DBgwABsbmzqvuaKigsuXL+Pm5qbRN0DPnj2Z\nPHky5ubmFBcXa2tCK126dCElJYXk5OQGnyMQCB4NQgQSCARPJY8iW0GpVLJw4UKysrJwcXFh7Nix\nFBQUcO7cOZYuXcrMmTMZOXIkUF2Q09TUlDNnzvDqq69qRNFERESgVCoZPny42r6mpK4rlUreffdd\njI2NGThwoDT+pUuXsnr1atavX49CoaBPnz5UVFQQEhLC559/jo2NjfQy+Sj619PTo3///pSVlXHu\n3DnWrl2LTCaTBAvVQtjJkydxd3dXE30etEJpTtTMSjp76EcU90p49dVX6yxMeeTIEcrLy5kxY4ZG\n6rqnpyfe3t5ERkaqLSSrqG1CUx81LRBq4uTkRPfu3bl06ZLWuim14e/vr5appKury9ChQ0lMTCQ5\nOZlnnnkGAFtbWyZMmKB27pAhQ9i5cydlZWX885//VLOWGDhwIGvXrlXzhX4UY8/JyeHSpUtS9HNN\nunbtysCBAzl9+jRhYWEaUdXe3t5CAHpCNDTC3rH/i/wedYzC7GtUXE/g1E1Tnu/bDUdHR9599108\nPDw4fvw4e/bsISEhAWtra5YuXcqECROQyWRMnTqVxYsXk5ubq+Z5/vPPP5OQkECvXr1YsmSJ9P02\nZcoU5s2bx08//USfPn0kj3NPT08uX75MQkKCtOgaGxuLjo4O7u7ukugDUFVVRXx8PK1bt9bqs15b\nlOipU6eYPn16syvoLGgYHh4e2NnZERQUhLOzs1qtAKgOAvnvf/+rUchY9Vu6ZcsWBg0apLFwpaKq\nqorNmzcTHByMj48P8+fPVzs2Li6OwMBAXF1dWb58udrCrMoiKDAwUGuAikA7PRybZp/X1PMEgj+L\nhj6jhubW6BoYUfD7VcqKlVy9UZ2ZW1payqZNmx7nEP9SNGVOocLW1lZDjOjZsyc2NjaNXhz/17/+\npfa7YWFhgbe3N6dOneLGjRt06NABaNp8puBeKdtCrlEUq2m1lp90C8P7pcAfQo2rqytRUVFqIpZK\nqImIiKBjx44kJCTw+++/a7gCuLi4aPRRUlJCeno65ubmHDhwQOv16+vra62bU5Pff/+d0tJSXFxc\nNOZqUJ2FVpcgom1sFfqmFN4vxaqk4QJIzex3FxcX3NzcCAsL4/jx41KGeU0yMjJo2bKlxtwqLi6O\nCxcuqAUMBAcHk52dTffu3dXeU11cXAgLC+PYsWNMmzYNc3NzzM3NiY2NJSQkpM7xbtu2jRUrVkiC\nnUKhYM+ePQCMHDlSqunaGBFo1KhRHDlyhNOnT2u18MvJydH4vAgEgkeDEIEEAsFTyaPIVti6dStZ\nWVmMHDlS7dgJEybwzjvvsGnTJnr27ImtrS0GBgb4+vpy9OhRoqOjtXo1g7rFR1NT19PT0zXGpBr/\n4sWL6dq1KytXrpTae+6553jvvffYu3evWibKw/Q/bNgwZs2aJaV/jx07llmzZrFv3z41EcjU1JST\nJ0/i4eGhsTDW3NBWsPNq6AWUd+9yOA3ap+fUWldBVbgzISGBlJQUjf0FBQVUVlZy48YNjTo12iYN\nDeXChQscOXKE1NRUCgsLqaioUNtfWFjY4CK+D44rQ64g/lYJN3KVnI5Jo7VTtf2Gs7OzRtq/qg8H\nBweNiZOOjg6WlpYaEY4PO3aVqOTm5qZVLOrevTunT58mLS1NQwTq3Ln+aLwngcq7XWW38lekoVHI\nhmZWdHzuj+jXN0d0Y8gz1YsBMpmMUaNGMWrUKP7zn/9gZGTEnDlz1DIRDQwMmD59OosXL1Zr9/jx\n48hkMl5//XW1iaWFhQWTJ09m3bp1/Prrr2oi0O7du4mNjVUTgTp16oSPjw/ffPMNN27cwMHBgbS0\nNBQKBT4+PlqvqbYo0d27d5OamtrgyH7B04W+vr7WxQpTU1OGDRvG999/T3JyMu7u7hrHlJaWsnr1\nasLDwxk9ejQzZszQqN+gyvR4++23NSLzhwwZQlBQEGfOnBEiUCNwtDXDo71VvdaVNenewapZZjQL\nBDVxtDXDpbU5KbcK6zxOR1cX2y7PkB0fypXDm7jVzpWPciLIunYFKyurBr9b/l15mDmFCicnJ631\nVqytraV5R0MwNTXF3t5eazugbvfV2PmM0sSB5NtKrhz+EYu2nTG374ipTTuMLGyQyWTcKbxPkTyP\ng5GpklBz6dIlbty4QWhoqJSVAn8INap3IW01a7QFyxQVFVFVVUVBQYGa60RjuXfvHlB7LZ3atqvQ\nlhmXcaf63lZVVTZqLDEZOdLvyYIFC3j//fdZt24dBw8epEuXLpiampKTk0NGRgbXr19n9erVlJSU\n8Nprr9GuXTtJcHnxxRexsrLCyckJJycnrl27hqGhIcbGxkybNk1yQxk/fjxmZmb89NNPpKenS3+f\nNm3aMGzYMMLCwgB1R5CkpCTJyi84OJjnn3+eLl268Ntvv5Gbm8uoUaOoqqrSWhOoZh2piIgIcnJy\nWLRoET179mTatGm0a9cOQ0NDLl++DMCsWbMwMjKivLwcpVLJ6NGjWbt2LVCdjXTs2DFOnTpFZmYm\nFRUVtG3blmHDhvGPf/xD7Z2ppmPMxIkT2bFjB/Hx8RQWFrJixQq1gFWB4O+KEIEEAsFTycNmK5SX\nl3P69GmMjIyYNm2a2rFt2rRh9OjR7Nmzh1OnTkmp1oMHD+bo0aOcPHlSbTEvLy+P6OhonJ2dpeKm\n0PTUdUNDw1rHX1RUxBtvvKHWnpubG7a2tmqZGA/b/+uvv642MWnXrh3dunUjISGB4uJiDfut5s7R\nS5laC3aWl1a/RF/Lq2DRzgje8euuVrBTRWFh9WR6//79dfajLQqqqdH/QUFBfPvtt7Ro0YIePXpg\nY2ODoaEhMpmM8+fPk56eTnl5w+saqKwAak5cFbczyMop4nhsFhcV4WRl5dGlh2ZVU9Viem12Q7q6\numoiz6MYu2pyWNv9U23X5mUtMi4aTs0J06OwLHsUEfY1I2t//S2aeyXlWhfQu3XrpvY9df/+fbKz\ns2nVqpUUmVgTVVRqze9KV1dXDAwMpIwfpVLJtWvXGD9+vHR8bGwsDg4OxMXFqbWjQqlUEhkZybFj\nxzRqE6iy7+ryXBc0Px6sY9fWtJZqz/8jMzOT/fv3k5CQQF5eHqWlpWr7c3M1xYaSkhI++OADrly5\nQkBAAOPHj9fa9pUrV9DT0+PcuXNa95eVlVFQUIBCoVATIQV18/IAFxbtjKi1kHdNZDKY4tv0gA6B\n4M/EtW3LekUggNbdByHT0+duajR3U6MJqbzNK+P/wZQpU5g5c+afMNKnk4edU6hQvZc/iK6uLlUN\n+WL6H9rECblczldffUVJSQmVlX8IFHXNZ3JyckhLS8PZ2Zni4mIupeewNTybLiNfJzsuhMLsa+Rn\nVi/aG5haYNu1H1BtcfbVLxcoV5ZQVVVAamoqubm57Nu3T6sTgqpmjbY5xYNBEDWvz9nZWRIGmoKq\nv9pq6dS2vS6KyyrqP0gLNbPmra2tWbNmDQcPHiQsLIwzZ85QWVmJpaUl7du3x8/Pjw4dOkh/u7y8\nPJKSkvD19SUgIICQkBAuXLhATEwMU6ZM4fr169y5c0fNDeXLL79kyZIlHDhwgISEBDIzMykvL2fW\nrFlYWFhIIhD8kcVcUFBA69atGTduHLt27eLHH3+kQ4cOeHh4MGHCBPz8/EhISNC4NqVSqVZHqqio\niISEBGxsbDh9+jR+fn6YmZnxxhtv0Lp1a3799VcMDAzQ1dWlRYsWuLi4SNZ45eXlfPzxx0RHR+Pg\n4MDAgQMxMDAgLi6OTZs2kZycrLWWU3Z2NvPnz8fBwYFBgwZRUlIiLHMFgv8hRCCBQNCsqW0h5mGz\nFX7//XdKSkro2rWr1kWT7t27s2fPHq5duyZt69q1Kw4ODlKdIdXLu+plrWYEzMOkrtc1/uLiYlq3\nbq1xTqtWrdSsAx6m/zZt2mh9UaoZUfY0iUCX0nO0TtYA9AyMKAHK7inQ1TfUKNipQjUB2bNnT6Nf\nIrVNaOqjoqKCwMBAWrZsyZo1azQiMhsTIViT2iauKgrvlxJ8MZNhMVl1Tlzr4lGNXXXPG1P4VEVT\n7rng0fAwEfbaImsTU7MpUeTy2cErTB+qp/bZ1NXVVVtgUAmHtUUwaxMO9fT06NatG7GxsRQUFHDl\nyhUqKyvx9PSkXbt2WFlZERsby6hRo4iNjUUmk2mtBwTVAvqDqATUmoswTUFVg+3777/XakUneDRo\newYBSoryq0Xyu5pi3tWrV1m8eLH03Hh7e2NiYoJMJiMtLY2IiAipxlRN7t+/z7Vr1zAxMaFnz561\njkmhUFBRUVFvBPT9+/eFCNQIvJysmfsPjzp/E6FaAHrHr3u9Uf2CP5/4+HgWL16Mv79/s89G/zOx\naqH5W6QNmUxGa7dnae1WXVN0+qDOktj5V85YfhgexZziSVLXfEZlLzp37lzc3d1ZsC2cqiowsrDB\nyXcCVZUV3M+7TeGtNHKuXuD3qKPoGVTPB3X0jLiRq8TL3ZVXXnmFHTt28NJLL/HKK6+o9aFUKvnn\nP/+JgYEB7do1bJ5hZGRE+/btyczMfKhgh7Zt22JgYEBGRoZW++6aWUsNxUhf08rsQWqrL1cTY2Nj\nJk2apOZw8iAqESgjI4PWrVszY8YMyZ1D5YaSkZFRqxvKxYsXpXqsKkcQFxcXPDw8pHbWrFkjOYJs\n2LBBWmuZNGkSs2bNwsHBgQ0bNmgdn62tLQcPHuTgwYOcP3+eGTNmaASaFhcXS22q+kxLS2Pu3Lla\n6+P++OOPREdH4+fnx4wZM6RzKysr+frrrzl+/Dj9+/fH29tb7bykpCQmTpwo1aQSCAR/IEQggUDQ\nLKl3IeYhsxVUKeG1LRZaWVlRUlLC1q1bMTU1lSLkBw8ezPbt2wkNDWXUqFEAnDp1Cj09PQYOHCid\n/zCp63WNv7YirQ9mYjxM/3X1AQ+/mPlnszM0pdYFHhPrtijv3qTwZipGFtYaBTtVdOnShdTUVBIT\nEx+ZpVPNF9kHKSwsRKlU4unpqfGMFhcXq4mTDSX++l3WHEurP+q5ioeauD6qsasKmiYmJlJRUaHh\nGa3KyujYsaOUzaKySAgMDOT777+nrKwMV1dXXn/9dTp06EBBQQHbt2+XhFxHR0cCAgK0Znbs3buX\n8PBw5HI5BgYGdO7cmXHjxmlkelRVVXHq1CmOHj3KzZs3uX//PhYWFrRr145hw4bh6+srLVSpqFn8\n9VFl4DQVKysrqZDxo6IpEfa1CZS6+tWLWTEpv3PltlItsraiooLCwkJJoFZ9d6kEwgepTTj09PQk\nJiaG2NhYrly5goGBgWQX1717dy5evEhZWRmJiYm0b9++1ppXgqebporke/bsobS0lJUrV2pYjfz0\n009ERERobc/S0pLZs2fz8ccfs3jxYj766COt9qEmJiZUVVU9lA2OQDsjvdpjZ2lC4NkU4q5rCtfd\nO1gxxdelWS3i/t141Nmqfwcaasv6qM77O/Eo5hRPkrrmM3379mXjxo20bNmy2jL6gTm4TEcXk1Zt\nMGnVhhY27Uj+dSvFhXfRMzJFV9+AMgNLrqSkMWfOHHbv3k1wcDBDhgxRs6rbsWMH9+7dY/jw4VKN\nmYbwwgsvsG7dOtauXcs777yj8R5XVFTE7du36dixY61t6Onp4evry8mTJ9mzZw8BAQHSvvT0dE6d\nOtXg8ajoaNe098GHqS9naWmpMSdqbO3WunhUjiDa6iA2Joi0qqqK4OBgWrZsqTEeHR0dXnvtNU6c\nOMGZM2c0RCBLS0v8/f0fbFIgECBEIIFA0Az5M7IVVAuetS0WqqxbHnzJSk5O5sKFC7Rr145Ro0aR\nlpZGRkYG3t7eahHpjyp1vak86f6bC9omMTWx6dybnJSL3EoIxbxNR4wsbNQKdqoKU/r5+XHs2DG+\n++472rRpg4ODg1o75eXlXL16FTc3twaPTRXJJpfLNfZZWlpiaGhIamqq2st2eXk5mzdvlqLBGsO+\n8w0QgP7Hw0xcH9XYra2t6dGjBzExMQQFBfHiiy9K+65evUpISAgtWrSgX79+KBQK4A+LhFatWjFs\n2DDkcjnh4eEsWrSI1atXs2zZMkxMTNQsEpYvX86mTZsk2y6lUsnChQvJysrCxcWFsWPHUlBQwLlz\n51i6dCkzZ85k5MiR0li2b9/OTz/9hJ2dHc8++yympqbk5uaSkpLCuXPn8PX1xc7ODn9/f4KCggDU\nIuNUYteTQk9PT6t12sPQ2Ah7oNZjTazsuZebTZH8OoZmLdUEyqSkJDUR1djYGHt7e27dusXNmzdp\n06aNWls1hcOaqDJ7VCKQyiJOte/MmTMcPnyY4uLiWrOA/qo09wXYmuObMmUKW7duJSYmhuLiYjp0\n6MCUKVM0FrrKyso4cOAAZ86cITs7G11dXUxatuZyZVss22v/DlctqFRVVmqI5Ddv3sTMzEyr17w2\nq5SaeHp68uGHH/Lhhx+yZMkSli9fjqurq9oxrq6uXLhwgczMTK3FxgUPh5eTNV5O1hqZ5z0crUUN\nIMFTyaOwZRVo8qjmFE+SuuYzpqamGBoacvXqVVKU1fPke3dvYmBmJWX8qCi7X515LauxMG/r2pfc\ntNMEBgYydepUtmzZwpw5c3j22WexsLAgOjqa+Ph4unTpoibANIRhw4aRmprK4cOHmTFjBl5eXtja\n2qJQKLh9+zYJCQkMHTqUt956q852AgICiIuLY9++fVy9epWuXbuSm5vLuXPn6N27N+fPn9dap6k2\nWrc0wdxYU+yoi4bWl6vNDcXe3p47d+6oHduU2q218bCOIN7e3vzwww988803XLp0CS8vL7p160a7\ndu0a5dRw48YNFAoFbdq0Yc+ePVqPMTAw0Opq4uTk1CiRUSD4OyFEIIFA0KyoK81ejYfMVmjbti2G\nhoakp6ejVCo1Iori4+MxMDDg7bffVoskadGiBebm5qSmpnLjxg1OnjwJoJHC/KhS15vKn9V/Xdks\nzYGYjLpfeI0sbGjX53myIg9x5fAmLNq6YmhmxcovojApy8PExISVK1fStm1bZs+ezbp163jrrbfo\n2bMnDg4OVFRUIJfLSUpKwtzcnG+++abBY3N1dcXQ0JCgoCAUCoVkVeXn54epqSmjR49m7969vPXW\nW/Tt25fy8nLi4uJQKBR0795dWtBuCPdKykn6PR/DFnUXPa1JzYlrY5DJZI9s7G+99Rbvvvsu//3v\nf4mOjsbFxYWcnBzOnTuHjo4Oc+fOxdjYWBKB6rJImD9/vmSRsHjxYhISEpg3bx7//ve/OXDggFRU\nfevWrWRlZTFy5Eg1O4UJEybwzjvvsGnTJnr27ClZch09epRWrVqxfv16DTswleBla2vLlClTpO+L\n5mRbo22RPz8/n/379xMZGUlOTg56enpYWlri6urK5MmTtVpSPkhjIuxVliPasOrYg5zUaG4lnMWi\nbef/z96bB0RV7///j2GGHdkX2WQRURR3lFxR0dx3ryZfU0vqlvfWTbvZ1UxvWVaf/Hltv1l2LdMs\nwb0CARdwAwFlcQNkcWGTRUCQfX5/0EwMM+yoqO/HP9k573POe4YzZ3k9X6/nC5lu3T772Bvz/fff\nq40fP348O3bs4LvvvmPNmjXKa1RxcTG7d+8G6oIJ9enevTuGhoZERkZSVFSkUtWpqBLbs2ePyv83\nxs2bN9m+fTsXL16kqqoKqVRKUVGRyhiFDYemyhFNf4/6lWPLli1T/tva2vqhWPZ0Rmu63NxcVq5c\nSdeuXRk3bpxS5N2wYQPvvfee8u9WXV3NunXrSExMxMHBgalTp1JRUcFnO/ZzO/8sXT2zsRugbkki\n1dFHIpFQVVakJpLb2Nhw69Yt0tPTVfoChoSEEBsb2+zc+/Tpw4YNG1i/fj1vv/0269evV+mBNXPm\nTM6dO8dnn33G6tWrNVZYZmRk0LNnz7Z8dYI/cLbuIkQfwWNBe2xZBY3TUe8UHUFSUhL79u0jPDyc\nyspKFi9ejJOTExMnTmTkyJEqY/Pz8/m///s/ZYKEVCrl6tWrau8zZ86c4ciRIwwcOBC/N/8DQEFa\nPKnhv6CtZ4jtAF/u5mRQkpNGRUkBWlIZBuZdlf2LLNwGYm9ZQ2RkJEePHqWiooKbN28SEhJCdXU1\nWlpaDBkyhE2bNjXqOtEUL7/8Ml5eXvz+++/ExcVRWlqKkZERVlZWzJkzh7Fjxza7D1NTUz7++GN+\n+OEHoqOjSUpKwt7enpdffhk9PT3Onj2rJqI0h72FIfdaqG20pL9cc24og0ys1LZpbe/WpmivI4i1\ntTWbN29m165dxMbGKvsNWVpaMmfOHJVn2qZQvNtlZmY2WQl97949tWWiN6xA0DhCBBIIBJ2Kpsrs\nG9KeagWZTMaYMWMIDg7mxx9/5K9//atyXVZWFocOHUJbW5t58+apBVwsLCwAOHLkCCdOnMDY2Fij\nRVhHlK63hwdxfEX1U8OMpM5C/cabjWHZYzD6ptbkXD7D3Zx0im5e4UppV8Z69+Ppp59Wjhs7diwu\nLi7s37+f+Ph4zp8/j56eHubm5owYMYJRo0a1am5GRkasXr2an376ibCwMMrLy5XHMTQ0ZNGiRZiY\nmHDkyBGCgoIwMDBg4MCBLFq0iF27drXqWMX3Kmn961bdC29bggIdNfeuXbvyn//8h59//pno6GgS\nExPR19dn0KBBLFiwQM06qb0WCdXV1Rw7dgw9PT0WL16sMtbOzo7p06fz888/c/ToUZ555hnlOqlU\nqjFzUFND3M5ORUUFq1atIisriwEDBjB06FDkcjm5ubmcPXuWESNGtEgEgpZl2DeXWWtk5Yh1L29y\nr0Ry+df/YtatNzdjtLgZshVbKzO16/OcOXOIiYkhMjKSV155BS8vLyoqKjh58iRFRUXMnTuX3r17\nq2yjpaWFp6en0rarfrWPtbU1tra2ZGVlKcc1Rk5ODv/85z9xdnZm0qRJFBYWEhgYSHJyMgkJCRr9\nzlvCwoULOXv2LGlpacyYMUN5LW9LEKW13A+7wPtBQkICfn5+KkkbPj4+rF+/nr179ypFoH379pGY\nmMjgwYN5++23kUqlpOeWsOemBcVB35KdeBJje3eMrFSrjKXaOhhY2HM39zrpJ/eSFW+BU/lVpj09\nhhkzZhAbG8uqVauU1YAKu50RI0Zw6tSpZuffs2dPNm7cyNq1a/n3v//N2rVrldaT/fv3Z8mSJfzw\nww+8+OKLeHl5YWNjQ3l5Obm5uSQmJtK7d2/eeeedDvxGBYKHj0Iwh7p+JYpECoDXXntNRYROTU1l\nx44dXL58maqqKtzd3Vm8eLHS2rM+NTU1BAcHc/ToUa5fv05NTQ0ODg5MmDCBqVOnasxWP3nyJIcP\nHyYtLY3q6mpsbW3x8fFh1qxZahnnCrH+s88+Y9euXZw5c4b8/Hzmz59PVVUVAQEBjfbASElJYcWK\nFQwZMoR169a17YujbbasgqbpyHeK9hAcHKzs1zvSDSkAACAASURBVGJiYoKBgQFeXl6kpKTw66+/\nqohAlZWVbNq0iV69eqkkSMhkMtzd3UlPT1e+z5SVlWFubs7MmTOV1oBmzp7ong+l6t5dbkUHUVNd\niW4XC0wdPQA5tdVV2PYbozzejAWLsZdMYcmSJcjlcoyNjbG3t0dPT4+qqiokEgn79u1T6xX0wQcf\ntOizDxkypMW23IcOHdK43MLCghUrVqgt37FjB4Bar6Km5ubr64uvr2+zLibQsv5yLXFDSSiQ8cnG\nL/FtoxvKg8DR0ZE333yTmpoa0tLSuHDhAocPH2br1q3o6empJWNpQvHcOWzYMBVL7ZYgesMKBI0j\nRCCBQNBpaC4YqIm2VisALFmyhIsXL3L48GGSk5Pp27cvxcXFnDx5knv37vHMM8/g7++vzMhWZK6Y\nmZlx4cIFVq9ejVwux83NDZms7nKanZ1NQEAA8fHx5Ofnk5WVxbZt29i/fz+zZs3C0dGx1aXrbaWj\nSuebwt7eHgsLC8LDw5FKpVhbWyORSBg7dmynyBBvqb+5oZUjrvWCfi9P7M2soS5q45ydnVtsidSS\nF5rBgwczePBgjeukUimzZs1i1qxZautee+01tXkoGnJqGms9eCrfH09SW9fFxlmlWWnDxqX1X3gb\ne5kC9QbCrZ17ZWUlgMbSfQsLC5YvX66yTCEqnItIVrFIGDlyJG+99ZbK2NZYJNy8eZOKigo8PDw0\nVs/169ePn3/+WaWv0ZgxYzh06BDLly9n5MiReHp60qtXrwcSoL8fxMXFkZWVxcyZM5XVUQqqq6s1\nNrhvjqYy7JvLrAWwHzwR3S7m3E46R15yNFJdA0yeHsOGdSt59dVXVcbKZDI2bNjA/v37OXHiBIcP\nH0ZLSwsXFxdefPFFRo8erfEY/fv3JzIyEgMDAzVxsX///mRlZeHm5tbk3zUxMZHZs2fz/PPPK5eZ\nmZnx5ptvcuDAAV588cU2iSl+fn7k5uaSlpbGzJkzH+i19X7YBd4PrK2tWbBggcqyQYMGYWVlRVLS\nn9e+kJAQJBIJ/v7+SsH4Qnoe2nqGdPUcTcbZg+SnxKqJQADOI2ZzMzqY4qxr1GQk8n12DB7dHfH1\n9WXdunX8/PPPREREIJVK6dGjBxs3biQnJ6dFIhDUWUN+8MEHrF27lnfffZfVq1crg13z5s2jd+/e\nHDp0iEuXLinPVQsLCyZOnKhSvSYQPC707duX0tJSDh48iIuLC0899ZRynYuLC6WldZZUKSkpBAYG\n0qtXL55++mlu377NqVOnWLt2LZ9++qmK5VV1dTUbNmwgNjYWe3t7fHx80NHRIT4+nq+//pqkpCRW\nrlypMo8ffviBPXv2YGxsjI+PD3p6esTExPDDDz8QGxvLhg0blO8B9Y/z1ltvUVJSwsCBAzEwMMDG\nxoa+ffsSGBhIcHCwRhEoKCgIgMmTJ7fru2utLWtn6lvTWemod4rGntcVaHp/UIgNN27c4JVXXsHA\nwICPPvpIzSK0vuWX4rxvLEHCwMCA7777Trk8LCyMLVu24OrqSvc/rAENLR0wsnKk4u4djO3ccB09\nHy1Z3bN6VXkplw9+zu0rZ7HpMxItqfSPJB8XwsLCVHoBQd1vYv369QQEBDB58mRlUuWDpqCgQC2B\nKD09nYMHD9KlS5cmk30aoyP6yz0oN5QHiVQqxc3NDTc3Nzw8PPjXv/7FmTNnlCJQU44iDg4OGBoa\ncvXqVaqrq9WusQKBoG2IX5JAIOg0tCQY2Nh2bRGBunTpwqZNm9izZw+nT59m//796OrqKhvA29vb\nq5Qf18/GHj16tDKw9OyzzwJ1D5UrV66krKwMLy8vhg8fTmVlJbGxsURERBATE0NMTEyrS9fbQ0eU\nzjeFlpYWb731Ftu3b+fUqVPcu3cPuVxO7969O4UIJHzR6+jsTYJv3boF0OwLYXMWCT0HqL85tcYi\noaysDEDt5VCBYrki8ATg7++PjY0NoaGhBAQEEBAQgFQqxcvLi2XLlqm9BD9sGvMYb4imhq4ymazD\nX8JaklkrkUiw6jkUq55DlctGj3HH0NBQox2ajo4O8+fPZ/78+S2ex/Tp0xu1qPjb3/7WpFi+du1a\n0tLSMDQ0VGtEu2TJEgoLCwkLC+PMmTNtrgZ6WDS0p3vY1nSNnb8uLi4aq/EsLS25cuUKUGcZkpWV\nhYWFhYqwpTgHjbo6140rzNZ4bN0u5nQf++ffd8kYd3z/yJ5vLDvZ09NT49+8se/KyclJmY3ckN69\ne6tVsQnuD4pgaGPVGoIHQ9++fbGxseHgwYO4urqqWakmJCQAcO7cObW/VVBQEF988QUHDx7k5Zdf\nVi7/5ZdfiI2NZdq0abzwwgsqQcjPP/+ckJAQRowYoWw0fuXKFfbs2YOlpSWbN29W2gwtWbKE999/\nn3PnzrF37161+01BQQGOjo588MEHav0zvLy8OHfuHBkZGTg5OSmX37t3jxMnTmBpadloglBr6IjA\ntOBPHtY7Rf37XsSvv1BSVsFzzz2nsUdcw55DLU2QaIgmS0HHIZOVAhCAtp4hJg49yU+No6IkH+/+\nvZTv45qefWUyGVOnTiU+Pp64uDjGjRvXsi+gg1mxYgW2trY4OTmhq6tLZmYm0dHR1NbW8ve//13j\n829LaG9/uQflhnK/SUlJwdbWVi1p6s6dOwAq1tlN9ceVSqVMnz6d3bt3s3XrVvz9/dX+NgUFBZSW\nlqpVbwkEgsYRIpBAIOg0tCQYqGtk2mHVClBnp7N06VKNDSobPpDUz8b+9NNP1USOU6dOUVJSwgsv\nvKDS+B3qPPu1tLSafbBs7fwVNFV10hGl86C5ggOgR48evP/++y3a/4NG+KLX0RnFsLCwMIKDgzl7\n9iwZGRlUVVVhZ2eHjY2Nmji5evVqjp46h+n4V8m+eJr8axeoKitCpmeEmbMnFt0HUnyvksMx15lw\n4QYT/7BICA8PZ+/evURHR5OamoqVlVWTzWgVQlFhYaHG9QUFBSrjoE4InTlzJjNnzqSoqIiLFy8S\nERHByZMnuX79Ol988UWnaE7arICWfxeoC1pbWFgQEBDAtWvX8PLywsPDA1dX11Y1y20pnV2g1ERj\nQkT37t01esn37duXsLAwUlNTH/mA8sOypmteANa8nVQqVfYrUIi3DUVexbmkrWcEQE1leYvm9DDP\nQYFA8CceHh5q19bx48fz3//+VyXQLZfLOXz4MGZmZvj7+6vc07S0tFi2bBmhoaEcP35cKQKFhIQA\nsGDBApU+E1KplGXLlhEdHc2RI0c0Jh0sW7ZMYwP1yZMnc+7cOYKCglTsqE+cOEF5eTlz587tsPtt\newPTgj950O8Umu57V8PPUZqfz2+p0C0tr1kRoCUJEo2hsBQEkOnoodtFPUFK26DO9ri28p6KpeDt\n27cJCAggLi6O27dvKyv+FeTn5zd57PvJpEmTOHv2LCdOnODevXsYGhoyaNAgZs+erdansS20pb/c\ng3ZDuZ8cO3aMoKAgevfuTdeuXTEyMiI7O5uoqCi0tbWZOXOmcmxz/XEXLFhAWloav//+O1FRUfTr\n1w8LCwuKiorIzMzk0qVLLF68WIhAAkErEG8vAoGg0/Cwg4EtzZBvDk1Cj6aXQMGDQfiid04x7Msv\nvwSgqKiInj170r9/f27fvs3mzZu5deuWil94zp0y0nKLMT25j7u5GRjbuSHV1qU4M4Wci6cov/NH\nFWE9i4Tr8af49ttvMTQ0xNLSkm7dupGens4bb7zRaFWQg4MDurq6pKWlUVpaqhbYVmQdu7m5adze\nxMSE4cOHM3z4cIqLi4mPjycjI0M5XktLi+rq5sXujqYlHuP1BbRNmzaxa9cuIiMjlY3tjY2NmTJl\nCgsWLOjQaqDOKFA2RnNCRHdPzWKfqakpoFpB9qjyMKzpWnv+Nobi99xQ5FWcS1XldUKoVLtl9+vH\nrWJUIOgMtOVZvKGFJ9RVHZiamnL37l3lslu3blFSUoKdnR0///yzxn3p6Ohw48YN5f8r7F/r94pT\nYG9vj6WlJTk5OWrPDDo6Ojg7O2s8hqKv17Fjx1i6dKkyMz4oKAipVNphvWPq05bAtECdB/VO0dh9\nr/qPJIVrhTWs3hnJimn9mrzvGRkZaVxeP0GiMRSWgv77QKqj+b4o0dJCIoFFo/+sKMvOzmblypXc\nvXuXPn36MGjQIAwMDNDS0iI3N5ewsLA2WQt3FAsXLlSr2n7YPGg3lPvJ6NGjqaqq4vLly6SkpFBZ\nWYmFhQWjRo1i9uzZKtWPzfXHlclkvPXWWxw/fpzQ0FDOnTtHeXk5xsbG2NjYsGjRIsaMGfOQPqlA\n8GgiRCCBQNBpeFjBwJZmyDeHt7c3P/zwA//97385f/48AwcOpHfv3jg6OooGhQ8R4YteR2cTwz7/\n/PMW+4VfvFGIXA4VJQV4THsZmW6diFNTVcmV377mzvWLdZOmziLhm18jyQrbjpGREZ988gnLli3D\n09OTjRs38uGHH3L69GmNc5LJZIwZM4bg4GB+/PFHlQzdrKwsDh06hEwmU1YqVVVVkZKSotZ4urq6\nWhl4amh7kJ6eTmVlZZvtJlpLWz3GX331VeRyOTdu3CAuLo5ff/2V3bt3I5fL1Rr6tofOKFBqoiVC\nxKHTl5msQYhQWGAoAoSKrFyFDWF96gcsHwYdlQzRUXSkR76+vj62trZkZ2eTmZmJnZ0d8Oc5ePzo\n+bpx5l2bndfjWDH6IKhvL7hgwQK2b99OQkICVVVV9OrVC39/f5ycnCgqKmLHjh1ERUVx9+5dnJ2d\nWbp0Kf369VPua8uWLYSFhbFt2zY1MTIhIYE1a9awcOFCFQuxhn0bdXR0sLCwwMPDg8WLF9OlSxdW\nr15NYmKi8hhbtmxRbq/pWIKOoT3P4o1VIkqlUpVeEyUlJQBkZmaq2D035N69e8p/K2xi61cB1cfc\n3Jzbt2+riUAmJiaNPvtLJBImTZrE999/T0REBOPHjyclJYVr167x1FNPNWpJK3j4PIh3iqbuezId\nPSqAqrISpNq69703zKSB3fDqbsW1nGKN67tZGmFqb8aIXn/eN/fv309JSYlGO83w8HDCwsLuy1wf\nZR60G4qfn5+avWZz+9DkCNK3b1+1bXr27EnPnj0b3U9DmuqPCyh7DbfEwr65flsCgUCIQAKBoBPx\nMIKBHZVhDHUPHps3b2bXrl3ExsYqA82WlpbMmTOn0X4TgvuP8EXvfGJYS/3C03NLuF1cF5CxGzhe\nKQABSLV1MHf25Nb5MGqrKpTLT0VEYFlazrx581QCdhKJhOeee44zZ840mgG5ZMkSLl68yOHDh0lO\nTqZv374UFxdz8uRJ7t27x0svvYSNjQ0AlZWVrFq1CltbW9zc3LC2tqayspILFy5w48YNvL29VSwK\n+vfvT3JyMuvXr6dPnz5oa2vj4uLC0KFDNc6lI2iPx7hEIqFbt25069aNYcOG8dxzz3H27NkOFYGg\n8wmUDWmpEFFWkMWmfefUAjKKCjJXV1fgz4Bl/QbOClJSUjTuuynhqCNoLABbkpNBXPgp7lbUaLQD\nvd90tEf++PHj2bFjB9999x1r1qxRfq8zB3Zl96fhAFh0H9jkcR7XitEHSU5ODq+//jqOjo74+vqS\nm5vLmTNnWL16NZs2bVI2LR81ahQlJSVERETw73//m6+//horK6s2HbOxvo05OTkcO3aMadOm0aVL\nF8aPH4+hoSGRkZF4e3srf7dw/20Pn1Q68lm8KRRVwMOGDWPNmjWt2qawsFDjc4vCJrbhuVFfANIk\nWE6YMIFdu3YRFBTE+PHjCQoKAuqsqgSdm/v9TtHUfc/A0oHS/EyKM1PQM7F8IL1hLLroYdFFj7f/\nOlrNUvB0aBE/3YxWGZ+VlQXA8OHD1faleB4SqPKw3VAEAsGTg7hqCASCTsWDDAa2JcO4ORwdHXnz\nzTepqakhLS2NCxcucPjwYbZu3Yqenh4TJkxo83wF7UP4oj9cMazh9+5oBFEngpr1C69vkWBgYae2\nX21DEwDk9bJ9ywqyKC6v1Ojt3bVrV6ysrDQ2IYW6ap1NmzaxZ88eTp8+zf79+9HV1cXd3Z05c+Yw\ncOCfAWJdXV2WLl1KQkICly9f5uzZs8pqg+XLl6v93hcsWEBpaSlRUVFcunSJ2tpafH1975sI1BaP\n8ci4K8QnO9Cvh2qgTWGhVb+yqaPobAJlQ1oqRFRXlpMVf4JdEbbKOSYnJ3P8+HEMDQ0ZNmwYAO7u\n7gCEhoYyduxYpFIpUCcKNZadrmiee/v2bY2ByPbQXAC2qqaWizcKCW5nALa13A+P/Dlz5hATE0Nk\nZCSvvPIKXl5eVFRUcPLkSewNaylzGoGRtXqzbQWPe8XogyIxMZFnn31WpY/K7t272blzJ6+//joj\nR45k+fLlykD6wIED2bx5MwcOHMDf379Nx2xJ30ZAmbkeGRnJsGHDHvk+Xp2d5p7FFeeAvLZWY9VD\nVFQUEolEY1Z7QxwcHDA0NOTq1atUV1e3yNrU1dWVa9eukZiYqHbtzcrKIi8vDxsbm1YLhCYmJowY\nMYLjx49z+fJlTpw4gY2NDYMGDWrVfgQPh/v1TtHcfc/K3Yu85BiyE8MxtuuOnomVyn0vLy8PS8v7\nc3/SZCmoqa5eIXQmJCSoPN/GxsZy5MiR+zK3R51HyRpZIBA82ggRSCAQdCoeZDCwLRnGji3MxpZK\npbi5ueHm5oaHhwf/+te/OHPmjBCBOgFPui/6gxbDNFUYVJQUcjXoWwykNfg8NYiJEyc26hde3+pA\npsGTXCLRQkumjcuoeVh0r+sOX1NZQU2tXNmLpaE1gJmZmVIEamiRAHUZvUuXLmXp0qVNfjaZTMbc\nuXOZO3duC76Jut5gy5cvZ/ny5S0a317a4jFekpXKC/7LGDtsEHZ2dpiampKXl0dkZCQSiYQ5c+bc\nh5l23mq91ggRXWycyE85T8A3mXQt8kVaU05ERAS1tbX87W9/U2aU9+zZE09PTxITE1m5ciX9+/fn\nzp07REVFMXDgQE6ePKm27/79+7N3714+//xzhg8fjr6+PoaGhkybNq1dn6/FyRC0PBmio7gfHvky\nmYwNGzawf/9+Tpw4weHDh9HS0sLFxYUXX3yRLo69O905+CjTmL2gtbU18+bNUxnr6+vLzp07qaqq\n4vnnn1eppPDx8eGTTz4hNTW13XMSfRs7F809i0t19JFIJFSVFbW76kEqlTJ9+nR2797N1q1b8ff3\nVzsfCgoKKC0tVVbxTpgwgZCQEHbv3s3QoUMxMalLPKmtrWXbtm3I5fI29/CZMmUKx48f56OPPqK8\nvJz58+cL++hHjI5+p2juvqdnYoXjkMnciPqVK799jYlDL3S7mLPx42gMqgoxMDBg48aNHTaftjB1\n6lRCQ0P58MMPGTFiBObm5mRkZBAbG8vIkSOJiIh4qPPrjDwq1sgCgeDRR4hAAoGg0/EggoFtzTC2\nNq5r/K0pGzslJQVbW1u1bEBFP4j7kUEvELSVByGGNVZhkHvlDNUVZZgNm0mm/QCchv7Z2LahX3hb\nrA6kOrpIayXcuXOHbt3Us/obNoZ/XGmJx3hDjO264+6oR0VFnfBTVlaGubk5AwYMYNasWWr9jzqS\nzlit1xohQsfQDMehU8k8H8b+g79ibaxD9+7deeaZZ9Syu9euXct3331HZGQkhw4dws7OjqVLlzJo\n0CCNItCgQYNYtmwZwcHBHDhwgOrqaqytrRk6dKiyx4qfnx/bt2/nwoULlJeX4+TkhJ+fH0OGDFHZ\nV1VVFQcOHOD48eMcOXuR4vJq9M1ssOo5FDOnPspxWfHHuXW+7rdYlp9JzI53mBeog37VHbp06XLf\nrOkUdIRH/gcffKC2jY6ODvPnz1epQqlPZzsHH0Wa6+/SrWdfZeWNAkUfFHt7e/T1VcVGLS0tpSDd\nVkTfxs5HS57Fpdo6GFjYczf3Oukn95IVb4FT+VWmPT2mTcdcsGABaWlp/P7770RFRdGvXz8sLCwo\nKioiMzOTS5cusXjxYqUI5OHhwdy5cwkMDORvf/sbI0aMQE9Pj5iYGDIyMujdu3ebkyM8PDxwcXEh\nLS0NmUwmEsUELbrvWfYYjL6pNTmXz3A3J52im1e4UtqVsd792ixIdiTOzs5s3LiRH3/8kXP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uwoUIg6Ojo6jB8/nn379hEZGamSYRYUFATApEmT1LZvb5ZrSUkJx44do0ePHioCkGLfS5cuJTY2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\nge4D4/s5YWtm2K7JuJezBUHDu4vvkYeMtuPKUE8bK4duQKvw01YE6tatG35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1a6xYsULK\nvK+rq5Ouzauvvoquri5r166V2hIqFAq1e+3ixYt8/fXXbN26FYAlS5awefNmTp48SUxMjNp5KIO+\nmozaof0Kv7vBUO/3vUL93vUEgseBuxkfNr18senVmiRhbKCrMu+tt97irbfeanfduxFeg4KC1Cps\nb0UmkzFp0iS19pOgXiVhZGREYGAggYGBHdq/QPCgmDdvHvX19X/2YdwV4eHhALz44osqvlm6urrM\nmDGDxYsX/1mHJhAIBI8E4k1EIBAIBH86dXV1TJ06le7du/PFF19I0xsaGggMDKSxsZF58+YxcuRI\naV5oaCjr16/nnXfeYezYsQBUVlayZ88ezp07R2FhIdra2nTr1o3nn39eY5a+4K/HX8GrQ1tbm8WL\nF7N06VL++c9/4u7ujqurKzcqGjgWk0ZWZgb1laX0eW4+OgbG1FeVkZtbSs+b6mKOEk1+RNDqz3Kr\ncXVwcDAbNmzA2NiYvn37Ym1tjZ6eHjKZjHPnzpGdna3iYaTE2Ni43X0oblHl5HK5igCiRFNrsY4S\nERFBTU0NkydPVhOA4Devmrbo6enx2muvqYhXjo6OeHh4kJiYSF1dHfr6+tK8xMREnnnmGV599VVp\n2sSJE3n//fdZt24dAwYMwNDQkIiICMLDwxkyZAgLFiyQTMvhtzZnBw4cYMqUVi+LuxWwli1bhp2d\nnco0hULBqlWrOHr0KBMnTpQqyjpKVFQUTU1NjJjwAp9v3k9LUyNu/oGYObZuRyaTUX49DYceT1Ka\nk4hcR49T0fEs/vhLLsfEMGnSJGbPns0PP/yAk5MTo0aNIiwsjEOHDuHo6Eh+fj7PPPMMnp6e5Ofn\nU1xcjK2tLY6Ojjz33HNs2LCBpqYmtXOdPHkyTk5OZGdnS9PGjRvHyZMnyczMVDsPNzc34DdT9luF\nyfYq/O6Gvi6/zw/h964nEDwOiHElEPxxWFtb/9mH0CHatqU7cjqGmvomjc947bWAFQgEAsFvCBFI\nIBA8ctzOC0LwaKKvr0/37t1JS0ujtrZWapeTnJxMY2MjAHFxcSoiUFxcHIDkN1FYWMiiRYsoLCyk\nd+/eDBgwgLq6Oi5cuMCyZct46623GDdu3B98ZoI/mr+KV4eLiwtr167l119/JSoqip93BZNRUIm2\nvjEG5p2x6xOAtt5vbaUqahvYf/EqYy/lMq6v4+/eb3NzM9u2bcPc3JxVq1apVfu0rejpKOXVDdwo\nq2FbZDqGetp49PUlMzOTOXPmMGLECDw9PXF3d2+3DV1HSU1t9YcYMGBAh9fp0qWLRm8nLX0TbpTV\nsDksDltbG6naysjISMXzCFqrrAICAoiIiODs2bOMHj2a4OBg5HI5c+fOVRGAAAIDA9m/fz/Hjx+X\nRKC7FbBuFYCgVaSZMmUKR48eJTY2tkMiUE5hJadT8rleUk3JpcvoyxVs3X+cstxU6ipuUpJ1idrS\nfACa6qpRtLRg5tCTsquXaaypQAH8tHUrY4cO4LXXXqOlpYWwsDA6derErFmzSE1NJTExkf379wNQ\nVFTE1q1b2bdvH9euXcPFxYVt27aRl5cHtLYiLCoqIjY2VuXz/eWXXygsLJSmKYNXytZvt35Wffv2\n5dKlS+zfv1/6jKHVi+pe/YCgtU1OHyeLDpnYK/FytnjoWusIBA8TYlwJ/irc6q1nZmaGj48PU6dO\nVXmuUrZq/fXXX9m9ezfh4eEUFRVhZmaGv78/L7/8skaPm+PHj7N3716uXbuGgYEB/fv3Z+bMmXz5\n5Zfttn69FU1tYhUKBUePHuXQoUPk5eVRW1tLp06dcHR0ZOzYsRrbFdbV1bFt2zYiIyMpKyvD2tqa\nJ554gueee+6ekqVis4v5+WS6yvdBUkZrNfLnISnMGKNNP9ffno3kcvk9JRIJBALB44AQgQQCgUDw\nUODt7c3ly5dJTExk4MCBQKvQozT2VYo+0PqSkpCQQOfOnaXez9988w1FRUW8//77jBgxQlq2urqa\nRYsW8cMPP+Dr69uuT4Tgr8HdeHWkh22isuAKXi8s7LBXx+TJkykuLuazzz5TmX67ljGjR4/W6C0D\nrSb17b2sd+rUiRkzZuA1YiKLfj6Pdztd36TWdi0tfLM/HptOBiovxvX19axcuZLCwkLeffddzRv5\nPyoqKqiursbb21tNAKqrq9NYedEeyhf4Q5euUllYyebjSl8gUzp5PAElKQQHB7Nv3z5kMhmenp68\n8sorGlvXdQSlIGBpadnhdW6tkFIe8/5zOdwsrGT7qQz0jIulaquAod00mnb36dOHiIgIsrKyGDZs\nGNnZ2ZiamrJv3z6N+9XR0SE3N1f6+24FLGXVY3R0NDdu3KCurk5lvtInqD3aBlduZl4ht7gKFJUg\nk8GVMJob6mioLOHq+f3oGBijrWeIXM8ALbkO2gbGOA58kqvn91NxPYOKpnrSMi2ZNWsWKSkp5OTk\nMHnyZOLj43F0dCQ2NpbTp09ja2tLbm4uZWVl1NbWYmJiQmZmpkqFT79+/Thz5gzLly/H0NCQq1ev\nsn79epqbm+nTp49UxaOsIru1ik3Jm2++yYIFC9iwYQOxsbG4urqSn5/P2bNnGTRoEFFRUR36nG/H\n30Z0Z9HP5+/oPQatH2vQ8N93X99vFAoFISEhHDp0iBs3bmBiYsKQIUOYNm2a5F/StoVVY2Mj+/bt\n4/jx4+Tn5yOXy3F1dWXy5MkMGzZM4z5OnTrF/v37papBOzs7/P39efrppzV6YV26dInt27eTmZmJ\njo4OvXv3ZubMmQ/k/AUPN4/quBIIlISFhUneer6+vlhZWZGXl8fhw4eJiopi5cqValU4K1euJCkp\nSaomjo6OZvfu3ZSVlak9t+3evZtNmzZhbGzMqFGjMDIyIjY2lvfff7/dqu+O8tNPP7Fz505sbW0Z\nNmwYRkZGlJSUkJ6ezqlTp9REoKamJpYuXUpJSQk+Pj5oaWlx7tw5Nm/eTGNjo1rSTEc5FHtVo7en\nXEcPgEvp10gpqOa9SV5S4lNzczMVFRUaq74FAoFA0IoQgQQCgUDwUODt7c2OHTuIi4tTEYG6deuG\nn58f33//PdevX8fe3p6srCwqKyvx8/MDWn09EhMTGTp0qIoABK1B3r/97W8sX76cM2fO3JPp/OOM\nskLhVvHjYeNevDoe1nO8nccRgFzXAJlMRmNNueRx1FYEuhuUIun69evR1tbm5ZdfZtOmTcTFxZGS\nkkJNTQ1OTk4AlJeX89NPP3HixAmio6OprKxk1KhReHl5qbzAtzQ3U1dRTHLwtzRUl6Ml18bQ0h7b\n3n78Y9E72OtUcvbsWcLCwpg7dy4tLS1Mnz6diooK0tPTmTp1KlVVVSrm8AUFBVy5coVly5ahUCgw\nMDCgqKiI5uZmbt68iYuLy12fe3tBByUVtQ2cyiznsIZqK+XnVl1dTVVVFQqFgvLycrZv396hfd+N\ngFVdXc17771HQUEBPXr0YNSoURgbGyOXy6muriY4OFiqoLyb89TS1qWluRHvF/+BXFef6qJcbiSe\norIgm5am1u3pm1rSWFuFVfcBGJjZkBzyHWW5KSQlJXD9aja1tbU0NzeTmZnJjRs3gFavqKtXr3Lz\n5k169OiBn58fAQEBPP3002pVUtBarbNjxw5OnTpFcXExFhYWLFq0iG3btnXos4TWCq+vvvpKuncT\nEhJwcXHhgw8+oKKi4r6IQP1crXh3Yp/b3jPQGqh+b5LX7x6T95vvv/+e0NBQLCwsGD9+PNra2pw/\nf560tDSamppUss6VAb7ExEQcHByYOHEi9fX1nD59mhUrVpCVlcX06dNVtr9lyxZ27twpVU7q6+tz\n8eJFtmzZQkxMDB9//LHKPpTb0tHRYfjw4Zibm5OcnMyCBQtwdXX9wz4XwcPBvYyrwsJCZs2axejR\nowkKCmLTpk1cunSJuro6nJ2dCQoKkp4vofW79PDhw1y8eJHr169TXl6OoaEhvXr14oUXXqBXr15q\n+1U+J/zjH/9g8+bNXLhwgbq6OlxdXZk5cya9e/eWKiNOnTpFaWkpdnZ2BAUFtSuanjx5kkOHDpGV\nlUVDQwO2trYEBATw7LPPahRNBQ8v169f57vvvsPW1pbPPvtM5Tc9Li6OJUuW8MMPP/DBBx+orJef\nn8+6desknxulKH/06FFmzJiBubk5ADdu3OCnn37C1NSU1atXS4LHjBkzWLlyJSdPnryn4z906BCW\nlpasW7cOPT09lXmaPBtLSkpwdXVl+fLl0u95UFAQb7zxBvv27eOFF17QWMl0O2Kzi9sd/4YWdtSU\n5FNVeAU9E3OVxCdlC1iBQCAQtI8QgQQCwe/i/PnzBAcHk5ubS2VlJaampnTp0oXhw4czYcIEFixY\nQFpaGj/++KMUtGvL3r17+fe//82rr77KM888A0BOTg47d+4kJSWFkpISDA0NsbKykrLDtbW1mTVr\nltQS5lbzx7bZ9PX19QQHBxMZGUleXh4ymQxnZ2emTJmiJhIkJCSwePFipk6dysCBA9m6dSspKSnI\nZDK8vb2ZPXs2VlZW3Lhxgy1bthAXF0ddXR09e/Zk9uzZakGKsrIy9uzZQ1RUFMXFxZL5eq9evQgM\nDKRz58735Ro86rTt8Wyop42ngz26urpSxU91dTWZmZk899xzeHl5Aa0vUPb29sTHxwNI05Utqqqr\nqzUGCsvLywFUMu8Ff01u5w2Q9OtqAHo/PRcAZ79npOD2w+opcCePIwC5ji6GlvZUFV4l59Qe8uMt\nca5LZdITAXe9P5lMxhNPPEFUVBRbt25l586dmJmZ0dDQgEwmQ6FQcPnyZW7cuMHatWsxNDRk8ODB\nJCcnc/PmTT766CPeXvwpqw5lolBAU0MdJVlx1FeWINfRw7rXYJrqqym7kkxGxFb+t6qUHz+aw9/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U1khG+h+mYehuadGRIwnJ62huzYseOOQdT2Amlubm5qLUGU95m9vb1KKxNoreBpkuvTUNOa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\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UFxfj6+vLyJEjKSoq4vz588TFxbFq1Sq5h1jfvn3R1tbm6tWrCkGgq1evKvy/7Bo0NDSQ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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5f8c12e358>" ] }, "metadata": { "image/png": { "height": 793, "width": 832 } }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(14, 14))\n", "for idx in range(viz_words):\n", " plt.scatter(*embed_tsne[idx, :], color='steelblue')\n", " plt.annotate(int_to_vocab[idx], (embed_tsne[idx, 0], embed_tsne[idx, 1]), alpha=0.7)" ] } ], "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
ES-DOC/esdoc-jupyterhub
notebooks/cas/cmip6/models/fgoals-g3/ocean.ipynb
1
164407
{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Ocean \n", "**MIP Era**: CMIP6 \n", "**Institute**: CAS \n", "**Source ID**: FGOALS-G3 \n", "**Topic**: Ocean \n", "**Sub-Topics**: Timestepping Framework, Advection, Lateral Physics, Vertical Physics, Uplow Boundaries, Boundary Forcing. \n", "**Properties**: 133 (101 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/ocean?client=jupyter-notebook) \n", "**Initialized From**: -- \n", "\n", "**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "**Notebook Initialised**: 2018-02-15 16:53:44" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "**IMPORTANT: to be executed each time you run the notebook** " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'cas', 'fgoals-g3', 'ocean')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "*Set document authors*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "*Specify document contributors* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "*Specify document publication status* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties](#1.-Key-Properties) \n", "[2. Key Properties --&gt; Seawater Properties](#2.-Key-Properties---&gt;-Seawater-Properties) \n", "[3. Key Properties --&gt; Bathymetry](#3.-Key-Properties---&gt;-Bathymetry) \n", "[4. Key Properties --&gt; Nonoceanic Waters](#4.-Key-Properties---&gt;-Nonoceanic-Waters) \n", "[5. Key Properties --&gt; Software Properties](#5.-Key-Properties---&gt;-Software-Properties) \n", "[6. Key Properties --&gt; Resolution](#6.-Key-Properties---&gt;-Resolution) \n", "[7. Key Properties --&gt; Tuning Applied](#7.-Key-Properties---&gt;-Tuning-Applied) \n", "[8. Key Properties --&gt; Conservation](#8.-Key-Properties---&gt;-Conservation) \n", "[9. Grid](#9.-Grid) \n", "[10. Grid --&gt; Discretisation --&gt; Vertical](#10.-Grid---&gt;-Discretisation---&gt;-Vertical) \n", "[11. Grid --&gt; Discretisation --&gt; Horizontal](#11.-Grid---&gt;-Discretisation---&gt;-Horizontal) \n", "[12. Timestepping Framework](#12.-Timestepping-Framework) \n", "[13. Timestepping Framework --&gt; Tracers](#13.-Timestepping-Framework---&gt;-Tracers) \n", "[14. Timestepping Framework --&gt; Baroclinic Dynamics](#14.-Timestepping-Framework---&gt;-Baroclinic-Dynamics) \n", "[15. Timestepping Framework --&gt; Barotropic](#15.-Timestepping-Framework---&gt;-Barotropic) \n", "[16. Timestepping Framework --&gt; Vertical Physics](#16.-Timestepping-Framework---&gt;-Vertical-Physics) \n", "[17. Advection](#17.-Advection) \n", "[18. Advection --&gt; Momentum](#18.-Advection---&gt;-Momentum) \n", "[19. Advection --&gt; Lateral Tracers](#19.-Advection---&gt;-Lateral-Tracers) \n", "[20. Advection --&gt; Vertical Tracers](#20.-Advection---&gt;-Vertical-Tracers) \n", "[21. Lateral Physics](#21.-Lateral-Physics) \n", "[22. Lateral Physics --&gt; Momentum --&gt; Operator](#22.-Lateral-Physics---&gt;-Momentum---&gt;-Operator) \n", "[23. Lateral Physics --&gt; Momentum --&gt; Eddy Viscosity Coeff](#23.-Lateral-Physics---&gt;-Momentum---&gt;-Eddy-Viscosity-Coeff) \n", "[24. Lateral Physics --&gt; Tracers](#24.-Lateral-Physics---&gt;-Tracers) \n", "[25. Lateral Physics --&gt; Tracers --&gt; Operator](#25.-Lateral-Physics---&gt;-Tracers---&gt;-Operator) \n", "[26. Lateral Physics --&gt; Tracers --&gt; Eddy Diffusity Coeff](#26.-Lateral-Physics---&gt;-Tracers---&gt;-Eddy-Diffusity-Coeff) \n", "[27. Lateral Physics --&gt; Tracers --&gt; Eddy Induced Velocity](#27.-Lateral-Physics---&gt;-Tracers---&gt;-Eddy-Induced-Velocity) \n", "[28. Vertical Physics](#28.-Vertical-Physics) \n", "[29. Vertical Physics --&gt; Boundary Layer Mixing --&gt; Details](#29.-Vertical-Physics---&gt;-Boundary-Layer-Mixing---&gt;-Details) \n", "[30. Vertical Physics --&gt; Boundary Layer Mixing --&gt; Tracers](#30.-Vertical-Physics---&gt;-Boundary-Layer-Mixing---&gt;-Tracers) \n", "[31. Vertical Physics --&gt; Boundary Layer Mixing --&gt; Momentum](#31.-Vertical-Physics---&gt;-Boundary-Layer-Mixing---&gt;-Momentum) \n", "[32. Vertical Physics --&gt; Interior Mixing --&gt; Details](#32.-Vertical-Physics---&gt;-Interior-Mixing---&gt;-Details) \n", "[33. Vertical Physics --&gt; Interior Mixing --&gt; Tracers](#33.-Vertical-Physics---&gt;-Interior-Mixing---&gt;-Tracers) \n", "[34. Vertical Physics --&gt; Interior Mixing --&gt; Momentum](#34.-Vertical-Physics---&gt;-Interior-Mixing---&gt;-Momentum) \n", "[35. Uplow Boundaries --&gt; Free Surface](#35.-Uplow-Boundaries---&gt;-Free-Surface) \n", "[36. Uplow Boundaries --&gt; Bottom Boundary Layer](#36.-Uplow-Boundaries---&gt;-Bottom-Boundary-Layer) \n", "[37. Boundary Forcing](#37.-Boundary-Forcing) \n", "[38. Boundary Forcing --&gt; Momentum --&gt; Bottom Friction](#38.-Boundary-Forcing---&gt;-Momentum---&gt;-Bottom-Friction) \n", "[39. Boundary Forcing --&gt; Momentum --&gt; Lateral Friction](#39.-Boundary-Forcing---&gt;-Momentum---&gt;-Lateral-Friction) \n", "[40. Boundary Forcing --&gt; Tracers --&gt; Sunlight Penetration](#40.-Boundary-Forcing---&gt;-Tracers---&gt;-Sunlight-Penetration) \n", "[41. Boundary Forcing --&gt; Tracers --&gt; Fresh Water Forcing](#41.-Boundary-Forcing---&gt;-Tracers---&gt;-Fresh-Water-Forcing) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "*Ocean key properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of ocean model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of ocean model code (NEMO 3.6, MOM 5.0,...)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Model Family\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of ocean model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.model_family') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"OGCM\" \n", "# \"slab ocean\" \n", "# \"mixed layer ocean\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Basic Approximations\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Basic approximations made in the ocean.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.basic_approximations') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Primitive equations\" \n", "# \"Non-hydrostatic\" \n", "# \"Boussinesq\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List of prognostic variables in the ocean component.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.prognostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Potential temperature\" \n", "# \"Conservative temperature\" \n", "# \"Salinity\" \n", "# \"U-velocity\" \n", "# \"V-velocity\" \n", "# \"W-velocity\" \n", "# \"SSH\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&gt; Seawater Properties \n", "*Physical properties of seawater in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Eos Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of EOS for sea water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Linear\" \n", "# \"Wright, 1997\" \n", "# \"Mc Dougall et al.\" \n", "# \"Jackett et al. 2006\" \n", "# \"TEOS 2010\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Eos Functional Temp\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Temperature used in EOS for sea water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_temp') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Potential temperature\" \n", "# \"Conservative temperature\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Eos Functional Salt\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Salinity used in EOS for sea water*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_salt') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Practical salinity Sp\" \n", "# \"Absolute salinity Sa\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.4. Eos Functional Depth\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Depth or pressure used in EOS for sea water ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Pressure (dbars)\" \n", "# \"Depth (meters)\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.5. Ocean Freezing Point\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Equation used to compute the freezing point (in deg C) of seawater, as a function of salinity and pressure*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_freezing_point') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"TEOS 2010\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.6. Ocean Specific Heat\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specific heat in ocean (cpocean) in J/(kg K)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_specific_heat') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.7. Ocean Reference Density\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Boussinesq reference density (rhozero) in kg / m3*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_reference_density') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --&gt; Bathymetry \n", "*Properties of bathymetry in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Reference Dates\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Reference date of bathymetry*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.bathymetry.reference_dates') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Present day\" \n", "# \"21000 years BP\" \n", "# \"6000 years BP\" \n", "# \"LGM\" \n", "# \"Pliocene\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the bathymetry fixed in time in the ocean ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.bathymetry.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Ocean Smoothing\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe any smoothing or hand editing of bathymetry in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.bathymetry.ocean_smoothing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.4. Source\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe source of bathymetry in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.bathymetry.source') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --&gt; Nonoceanic Waters \n", "*Non oceanic waters treatement in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Isolated Seas\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how isolated seas is performed*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.nonoceanic_waters.isolated_seas') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. River Mouth\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe if/how river mouth mixing or estuaries specific treatment is performed*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.nonoceanic_waters.river_mouth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --&gt; Software Properties \n", "*Software properties of ocean code*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Repository\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Location of code for this component.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.software_properties.repository') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Code Version\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Code version identifier.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.software_properties.code_version') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Code Languages\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Code language(s).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.software_properties.code_languages') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Key Properties --&gt; Resolution \n", "*Resolution in the ocean grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *This is a string usually used by the modelling group to describe the resolution of this grid, e.g. ORCA025, N512L180, T512L70 etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Canonical Horizontal Resolution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.3. Range Horizontal Resolution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Range of horizontal resolution with spatial details, eg. 50(Equator)-100km or 0.1-0.5 degrees etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.range_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.4. Number Of Horizontal Gridpoints\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Total number of horizontal (XY) points (or degrees of freedom) on computational grid.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.5. Number Of Vertical Levels\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of vertical levels resolved on computational grid.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.number_of_vertical_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.6. Is Adaptive Grid\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Default is False. Set true if grid resolution changes during execution.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.is_adaptive_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.7. Thickness Level 1\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Thickness of first surface ocean level (in meters)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.resolution.thickness_level_1') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Key Properties --&gt; Tuning Applied \n", "*Tuning methodology for ocean component*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General overview description of tuning: explain and motivate the main targets and metrics retained. &amp;Document the relative weight given to climate performance metrics versus process oriented metrics, &amp;and on the possible conflicts with parameterization level tuning. In particular describe any struggle &amp;with a parameter value that required pushing it to its limits to solve a particular model deficiency.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.tuning_applied.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. Global Mean Metrics Used\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List set of metrics of the global mean state used in tuning model/component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.tuning_applied.global_mean_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Regional Metrics Used\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List of regional metrics of mean state (e.g THC, AABW, regional means etc) used in tuning model/component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.tuning_applied.regional_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.4. Trend Metrics Used\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List observed trend metrics used in tuning model/component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.tuning_applied.trend_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Key Properties --&gt; Conservation \n", "*Conservation in the ocean component*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Brief description of conservation methodology*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Properties conserved in the ocean by the numerical schemes*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.scheme') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Energy\" \n", "# \"Enstrophy\" \n", "# \"Salt\" \n", "# \"Volume of ocean\" \n", "# \"Momentum\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Consistency Properties\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Any additional consistency properties (energy conversion, pressure gradient discretisation, ...)?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.consistency_properties') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Corrected Conserved Prognostic Variables\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Set of variables which are conserved by *more* than the numerical scheme alone.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.corrected_conserved_prognostic_variables') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Was Flux Correction Used\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Does conservation involve flux correction ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.key_properties.conservation.was_flux_correction_used') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Grid \n", "*Ocean grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of grid in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Grid --&gt; Discretisation --&gt; Vertical \n", "*Properties of vertical discretisation in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Coordinates\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of vertical coordinates in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.vertical.coordinates') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Z-coordinate\" \n", "# \"Z*-coordinate\" \n", "# \"S-coordinate\" \n", "# \"Isopycnic - sigma 0\" \n", "# \"Isopycnic - sigma 2\" \n", "# \"Isopycnic - sigma 4\" \n", "# \"Isopycnic - other\" \n", "# \"Hybrid / Z+S\" \n", "# \"Hybrid / Z+isopycnic\" \n", "# \"Hybrid / other\" \n", "# \"Pressure referenced (P)\" \n", "# \"P*\" \n", "# \"Z**\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 10.2. Partial Steps\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Using partial steps with Z or Z* vertical coordinate in ocean ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.vertical.partial_steps') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Grid --&gt; Discretisation --&gt; Horizontal \n", "*Type of horizontal discretisation scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Horizontal grid type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Lat-lon\" \n", "# \"Rotated north pole\" \n", "# \"Two north poles (ORCA-style)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Staggering\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Horizontal grid staggering type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.staggering') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Arakawa B-grid\" \n", "# \"Arakawa C-grid\" \n", "# \"Arakawa E-grid\" \n", "# \"N/a\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Horizontal discretisation scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Finite difference\" \n", "# \"Finite volumes\" \n", "# \"Finite elements\" \n", "# \"Unstructured grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Timestepping Framework \n", "*Ocean Timestepping Framework*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of time stepping in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Diurnal Cycle\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Diurnal cycle type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.diurnal_cycle') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Via coupling\" \n", "# \"Specific treatment\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Timestepping Framework --&gt; Tracers \n", "*Properties of tracers time stepping in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Tracers time stepping scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.tracers.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Leap-frog + Asselin filter\" \n", "# \"Leap-frog + Periodic Euler\" \n", "# \"Predictor-corrector\" \n", "# \"Runge-Kutta 2\" \n", "# \"AM3-LF\" \n", "# \"Forward-backward\" \n", "# \"Forward operator\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Time Step\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Tracers time step (in seconds)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.tracers.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Timestepping Framework --&gt; Baroclinic Dynamics \n", "*Baroclinic dynamics in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Baroclinic dynamics type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Preconditioned conjugate gradient\" \n", "# \"Sub cyling\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Baroclinic dynamics scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Leap-frog + Asselin filter\" \n", "# \"Leap-frog + Periodic Euler\" \n", "# \"Predictor-corrector\" \n", "# \"Runge-Kutta 2\" \n", "# \"AM3-LF\" \n", "# \"Forward-backward\" \n", "# \"Forward operator\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Time Step\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Baroclinic time step (in seconds)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Timestepping Framework --&gt; Barotropic \n", "*Barotropic time stepping in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Splitting\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time splitting method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.barotropic.splitting') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"split explicit\" \n", "# \"implicit\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Time Step\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Barotropic time step (in seconds)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.barotropic.time_step') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Timestepping Framework --&gt; Vertical Physics \n", "*Vertical physics time stepping in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Details of vertical time stepping in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.timestepping_framework.vertical_physics.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Advection \n", "*Ocean advection*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of advection in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Advection --&gt; Momentum \n", "*Properties of lateral momemtum advection scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of lateral momemtum advection scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.momentum.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Flux form\" \n", "# \"Vector form\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Scheme Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of ocean momemtum advection scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.momentum.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. ALE\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Using ALE for vertical advection ? (if vertical coordinates are sigma)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.momentum.ALE') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Advection --&gt; Lateral Tracers \n", "*Properties of lateral tracer advection scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Order\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Order of lateral tracer advection scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.2. Flux Limiter\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Monotonic flux limiter for lateral tracer advection scheme in ocean ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.flux_limiter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.3. Effective Order\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Effective order of limited lateral tracer advection scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.effective_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.4. Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Descriptive text for lateral tracer advection scheme in ocean (e.g. MUSCL, PPM-H5, PRATHER,...)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.5. Passive Tracers\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Passive tracers advected*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.passive_tracers') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Ideal age\" \n", "# \"CFC 11\" \n", "# \"CFC 12\" \n", "# \"SF6\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 19.6. Passive Tracers Advection\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Is advection of passive tracers different than active ? if so, describe.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.lateral_tracers.passive_tracers_advection') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Advection --&gt; Vertical Tracers \n", "*Properties of vertical tracer advection scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Descriptive text for vertical tracer advection scheme in ocean (e.g. MUSCL, PPM-H5, PRATHER,...)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.vertical_tracers.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Flux Limiter\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Monotonic flux limiter for vertical tracer advection scheme in ocean ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.advection.vertical_tracers.flux_limiter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Lateral Physics \n", "*Ocean lateral physics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of lateral physics in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 21.2. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of transient eddy representation in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Eddy active\" \n", "# \"Eddy admitting\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Lateral Physics --&gt; Momentum --&gt; Operator \n", "*Properties of lateral physics operator for momentum in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Direction\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Direction of lateral physics momemtum scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.direction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Horizontal\" \n", "# \"Isopycnal\" \n", "# \"Isoneutral\" \n", "# \"Geopotential\" \n", "# \"Iso-level\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Order\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Order of lateral physics momemtum scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Harmonic\" \n", "# \"Bi-harmonic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.3. Discretisation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Discretisation of lateral physics momemtum scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Second order\" \n", "# \"Higher order\" \n", "# \"Flux limiter\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Lateral Physics --&gt; Momentum --&gt; Eddy Viscosity Coeff \n", "*Properties of eddy viscosity coeff in lateral physics momemtum scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Lateral physics momemtum eddy viscosity coeff type in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Space varying\" \n", "# \"Time + space varying (Smagorinsky)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. Constant Coefficient\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If constant, value of eddy viscosity coeff in lateral physics momemtum scheme (in m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.constant_coefficient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.3. Variable Coefficient\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If space-varying, describe variations of eddy viscosity coeff in lateral physics momemtum scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.variable_coefficient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.4. Coeff Background\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe background eddy viscosity coeff in lateral physics momemtum scheme (give values in m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.coeff_background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.5. Coeff Backscatter\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there backscatter in eddy viscosity coeff in lateral physics momemtum scheme ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.coeff_backscatter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 24. Lateral Physics --&gt; Tracers \n", "*Properties of lateral physics for tracers in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 24.1. Mesoscale Closure\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there a mesoscale closure in the lateral physics tracers scheme ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.mesoscale_closure') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 24.2. Submesoscale Mixing\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there a submesoscale mixing parameterisation (i.e Fox-Kemper) in the lateral physics tracers scheme ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.submesoscale_mixing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 25. Lateral Physics --&gt; Tracers --&gt; Operator \n", "*Properties of lateral physics operator for tracers in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 25.1. Direction\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Direction of lateral physics tracers scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.direction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Horizontal\" \n", "# \"Isopycnal\" \n", "# \"Isoneutral\" \n", "# \"Geopotential\" \n", "# \"Iso-level\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 25.2. Order\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Order of lateral physics tracers scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Harmonic\" \n", "# \"Bi-harmonic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 25.3. Discretisation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Discretisation of lateral physics tracers scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.discretisation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Second order\" \n", "# \"Higher order\" \n", "# \"Flux limiter\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 26. Lateral Physics --&gt; Tracers --&gt; Eddy Diffusity Coeff \n", "*Properties of eddy diffusity coeff in lateral physics tracers scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 26.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Lateral physics tracers eddy diffusity coeff type in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Space varying\" \n", "# \"Time + space varying (Smagorinsky)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.2. Constant Coefficient\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If constant, value of eddy diffusity coeff in lateral physics tracers scheme (in m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.constant_coefficient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.3. Variable Coefficient\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If space-varying, describe variations of eddy diffusity coeff in lateral physics tracers scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.variable_coefficient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.4. Coeff Background\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe background eddy diffusity coeff in lateral physics tracers scheme (give values in m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.coeff_background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 26.5. Coeff Backscatter\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there backscatter in eddy diffusity coeff in lateral physics tracers scheme ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.coeff_backscatter') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 27. Lateral Physics --&gt; Tracers --&gt; Eddy Induced Velocity \n", "*Properties of eddy induced velocity (EIV) in lateral physics tracers scheme in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 27.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of EIV in lateral physics tracers in the ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"GM\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.2. Constant Val\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If EIV scheme for tracers is constant, specify coefficient value (M2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.constant_val') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.3. Flux Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of EIV flux (advective or skew)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.flux_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.4. Added Diffusivity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of EIV added diffusivity (constant, flow dependent or none)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.added_diffusivity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 28. Vertical Physics \n", "*Ocean Vertical Physics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 28.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of vertical physics in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 29. Vertical Physics --&gt; Boundary Layer Mixing --&gt; Details \n", "*Properties of vertical physics in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 29.1. Langmuir Cells Mixing\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there Langmuir cells mixing in upper ocean ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.details.langmuir_cells_mixing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 30. Vertical Physics --&gt; Boundary Layer Mixing --&gt; Tracers \n", "*Properties of boundary layer (BL) mixing on tracers in the ocean *" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 30.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of boundary layer mixing for tracers in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant value\" \n", "# \"Turbulent closure - TKE\" \n", "# \"Turbulent closure - KPP\" \n", "# \"Turbulent closure - Mellor-Yamada\" \n", "# \"Turbulent closure - Bulk Mixed Layer\" \n", "# \"Richardson number dependent - PP\" \n", "# \"Richardson number dependent - KT\" \n", "# \"Imbeded as isopycnic vertical coordinate\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.2. Closure Order\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If turbulent BL mixing of tracers, specific order of closure (0, 1, 2.5, 3)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.closure_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.3. Constant\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If constant BL mixing of tracers, specific coefficient (m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.constant') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.4. Background\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Background BL mixing of tracers coefficient, (schema and value in m2/s - may by none)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 31. Vertical Physics --&gt; Boundary Layer Mixing --&gt; Momentum \n", "*Properties of boundary layer (BL) mixing on momentum in the ocean *" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 31.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of boundary layer mixing for momentum in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant value\" \n", "# \"Turbulent closure - TKE\" \n", "# \"Turbulent closure - KPP\" \n", "# \"Turbulent closure - Mellor-Yamada\" \n", "# \"Turbulent closure - Bulk Mixed Layer\" \n", "# \"Richardson number dependent - PP\" \n", "# \"Richardson number dependent - KT\" \n", "# \"Imbeded as isopycnic vertical coordinate\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.2. Closure Order\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If turbulent BL mixing of momentum, specific order of closure (0, 1, 2.5, 3)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.closure_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.3. Constant\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If constant BL mixing of momentum, specific coefficient (m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.constant') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.4. Background\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Background BL mixing of momentum coefficient, (schema and value in m2/s - may by none)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 32. Vertical Physics --&gt; Interior Mixing --&gt; Details \n", "*Properties of interior mixing in the ocean *" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 32.1. Convection Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of vertical convection in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.convection_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Non-penetrative convective adjustment\" \n", "# \"Enhanced vertical diffusion\" \n", "# \"Included in turbulence closure\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.2. Tide Induced Mixing\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how tide induced mixing is modelled (barotropic, baroclinic, none)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.tide_induced_mixing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.3. Double Diffusion\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there double diffusion*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.double_diffusion') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.4. Shear Mixing\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there interior shear mixing*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.shear_mixing') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 33. Vertical Physics --&gt; Interior Mixing --&gt; Tracers \n", "*Properties of interior mixing on tracers in the ocean *" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 33.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of interior mixing for tracers in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant value\" \n", "# \"Turbulent closure / TKE\" \n", "# \"Turbulent closure - Mellor-Yamada\" \n", "# \"Richardson number dependent - PP\" \n", "# \"Richardson number dependent - KT\" \n", "# \"Imbeded as isopycnic vertical coordinate\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.2. Constant\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If constant interior mixing of tracers, specific coefficient (m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.constant') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.3. Profile\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the background interior mixing using a vertical profile for tracers (i.e is NOT constant) ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.profile') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 33.4. Background\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Background interior mixing of tracers coefficient, (schema and value in m2/s - may by none)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 34. Vertical Physics --&gt; Interior Mixing --&gt; Momentum \n", "*Properties of interior mixing on momentum in the ocean *" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 34.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of interior mixing for momentum in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant value\" \n", "# \"Turbulent closure / TKE\" \n", "# \"Turbulent closure - Mellor-Yamada\" \n", "# \"Richardson number dependent - PP\" \n", "# \"Richardson number dependent - KT\" \n", "# \"Imbeded as isopycnic vertical coordinate\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 34.2. Constant\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If constant interior mixing of momentum, specific coefficient (m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.constant') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 34.3. Profile\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the background interior mixing using a vertical profile for momentum (i.e is NOT constant) ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.profile') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 34.4. Background\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Background interior mixing of momentum coefficient, (schema and value in m2/s - may by none)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 35. Uplow Boundaries --&gt; Free Surface \n", "*Properties of free surface in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 35.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of free surface in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 35.2. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Free surface scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Linear implicit\" \n", "# \"Linear filtered\" \n", "# \"Linear semi-explicit\" \n", "# \"Non-linear implicit\" \n", "# \"Non-linear filtered\" \n", "# \"Non-linear semi-explicit\" \n", "# \"Fully explicit\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 35.3. Embeded Seaice\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the sea-ice embeded in the ocean model (instead of levitating) ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.embeded_seaice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 36. Uplow Boundaries --&gt; Bottom Boundary Layer \n", "*Properties of bottom boundary layer in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 36.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of bottom boundary layer in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.2. Type Of Bbl\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of bottom boundary layer in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.type_of_bbl') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Diffusive\" \n", "# \"Acvective\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.3. Lateral Mixing Coef\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If bottom BL is diffusive, specify value of lateral mixing coefficient (in m2/s)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.lateral_mixing_coef') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.4. Sill Overflow\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe any specific treatment of sill overflows*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.sill_overflow') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 37. Boundary Forcing \n", "*Ocean boundary forcing*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 37.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of boundary forcing in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.2. Surface Pressure\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how surface pressure is transmitted to ocean (via sea-ice, nothing specific,...)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.surface_pressure') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.3. Momentum Flux Correction\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe any type of ocean surface momentum flux correction and, if applicable, how it is applied and where.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.momentum_flux_correction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.4. Tracers Flux Correction\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe any type of ocean surface tracers flux correction and, if applicable, how it is applied and where.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers_flux_correction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.5. Wave Effects\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe if/how wave effects are modelled at ocean surface.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.wave_effects') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.6. River Runoff Budget\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe how river runoff from land surface is routed to ocean and any global adjustment done.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.river_runoff_budget') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.7. Geothermal Heating\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Describe if/how geothermal heating is present at ocean bottom.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.geothermal_heating') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 38. Boundary Forcing --&gt; Momentum --&gt; Bottom Friction \n", "*Properties of momentum bottom friction in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 38.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of momentum bottom friction in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.momentum.bottom_friction.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Linear\" \n", "# \"Non-linear\" \n", "# \"Non-linear (drag function of speed of tides)\" \n", "# \"Constant drag coefficient\" \n", "# \"None\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 39. Boundary Forcing --&gt; Momentum --&gt; Lateral Friction \n", "*Properties of momentum lateral friction in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 39.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of momentum lateral friction in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.momentum.lateral_friction.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Free-slip\" \n", "# \"No-slip\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 40. Boundary Forcing --&gt; Tracers --&gt; Sunlight Penetration \n", "*Properties of sunlight penetration scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 40.1. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of sunlight penetration scheme in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"1 extinction depth\" \n", "# \"2 extinction depth\" \n", "# \"3 extinction depth\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 40.2. Ocean Colour\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the ocean sunlight penetration scheme ocean colour dependent ?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.ocean_colour') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 40.3. Extinction Depth\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Describe and list extinctions depths for sunlight penetration scheme (if applicable).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.extinction_depth') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 41. Boundary Forcing --&gt; Tracers --&gt; Fresh Water Forcing \n", "*Properties of surface fresh water forcing in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 41.1. From Atmopshere\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of surface fresh water forcing from atmos in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.from_atmopshere') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Freshwater flux\" \n", "# \"Virtual salt flux\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 41.2. From Sea Ice\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of surface fresh water forcing from sea-ice in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.from_sea_ice') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Freshwater flux\" \n", "# \"Virtual salt flux\" \n", "# \"Real salt flux\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 41.3. Forced Mode Restoring\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of surface salinity restoring in forced mode (OMIP)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.forced_mode_restoring') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }
gpl-3.0
WaltGurley/jupyter-notebooks-intro
Jupyter - interface.ipynb
1
7574
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using a notebook\n", "The purpose of this notebook is to introduce the Jupyter interface. This notebook is a guide to the Jupyter interface and writing code and text in Jupyter notebooks with the _Python_ programming language and _Markdown_, the lightweight markup language.\n", "\n", "_This notebook was originally created for a Digital Mixer session at the [2016 STELLA Unconference](https://stellagroup.wordpress.com/)_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cells\n", "---\n", "The basic structure of a Jupyter notebook consists of linear sequence of cells from the top to the bottom of the page. A cell's content can consist of either:\n", "\n", "#### 1. code and code output" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "3\n", "4\n" ] } ], "source": [ "# create a range of numbers\n", "numbers = range(0, 5)\n", "# print out each of the numbers in the range\n", "for number in numbers:\n", " print(number)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2. Markdown/html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### This is a big header written in markdown\n", "#### This is a medium header written in markdown\n", "##### This is a small header written in markdown\n", "This is a paragraph written in markdown\n", "<div class=\"alert alert-warning\" role=\"alert\"><p>You can also write with <strong>html tags</strong></p></div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3. raw text " ] }, { "cell_type": "raw", "metadata": {}, "source": [ "This is ugly. You probably don't need to use raw text." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Click on any text above to see what cell it belongs to. The active cell will be surrounded by a green or blue outline. A green outline indicates you are in edit mode for that cell and you can type in the cell. A blue outline indicates that you are in command mode and you cannot type in the active cell. **To enter edit mode in a cell click on any code input area or double-click on any rendered Markdown text.**\n", "\n", "Notice that you can see the content type of the active cell in the multi-choice button in the notebook toolbar at the top of the page:\n", "\n", "![alt text](./cellType.png)\n", "\n", "**You can also use this button to change the cell type.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adding, removing, and moving cells\n", "---\n", "You can manage cells using the notebook toolbar.\n", "* **Adding a cell:** To add a new cell below the active cell, click <i class=\"fa-plus fa\"></i>\n", "* **Cut/copy/paste a cell:** Use <i class=\"fa-cut fa\"></i> to cut or <i class=\"fa-copy fa\"></i> to copy a cell and <i class=\"fa-paste fa\"></i> to paste the cut/copied cell below the active cell\n", "* **Move a cell:** To move the active cell up or down, click <i class=\"fa-arrow-up fa\"></i> or <i class=\"fa-arrow-down fa\"></i>\n", "* **Delete a cell:** To delete the active cell, click `Edit > Delete Cells`\n", "\n", "**Try adding a new Markdown cell and a Code cell, moving them around, and deleting them. _If you accidentally delete something you shouldn't have, you can undo it by going to:_** `Edit > Undo Delete Cells`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Running a cell\n", "---\n", "To run code in a cell or to render markdown as html in a cell you must _run_ the cell. \n", "\n", "**To run the contents of a cell:**\n", "1. activate it\n", "2. press `shift`+`return` _or_ click <i class=\"fa-step-forward fa\"></i> in the notebook toolbar at the top of the page.\n", "\n", "**Try running the three Python code cells below.** _You can edit and re-run a cell as many times as you want._" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# this is Python code -> RUN IT\n", "x = 2\n", "\n", "# the output of the last line of code is shown below the cell\n", "x * x" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# this is Python code -> RUN IT\n", "x = 2\n", "\n", "# you can also use the 'print' statement to print information to the output below the cell\n", "print(x)\n", "\n", "# the output of the last line of code is still shown below the cell\n", "x * x" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# this is Python code -> RUN IT\n", "x = 2\n", "\n", "# if there is an error in your code an error message will display in the output below the cell\n", "x + \"two\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Try editing and adding some text this Markdown cell**\n", "\n", "Double-click on **_this text_** to start\n", "\n", "_when you are done press `shift+return` to run_\n", "\n", "For an overview of Markdown format check out [Markdown Cheatsheet](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "More information on the UI\n", "---\n", "* In the notebook Menubar click `Help > User Interface Tour` for a quick, guided overview of the user interface.\n", "* Another overview of the interface from the Jupyter website: [Overview of the Notebook UI](http://nbviewer.jupyter.org/github/ipython/ipython/blob/3.x/examples/Notebook/Notebook%20Basics.ipynb#Overview-of-the-Notebook-UI)\n", "* More information on running code from the Jupyter website: [Running Code](http://nbviewer.jupyter.org/github/ipython/ipython/blob/3.x/examples/Notebook/Running%20Code.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wrap up\n", "---\n", "You should have a basic understanding of how to navigate an interactive notebook, the cell types, how to manipulate cells (adding, moving, removing, etc.), and how to run cells.\n", "\n", "**Next:** For a basic overview of data analysis and visualization with pandas and matplotlib, download the following notebook and add it to your notebooks folder by uploading it in the Notebook dashboard: http://nbviewer.jupyter.org/github/WaltGurley/jupyter-notebooks-intro/blob/master/Jupyter%20-%20coding%20with%20Python.ipynb" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
pdwyys20/deep-learning
UdacityNotes/3_CNN/cnn/ConvNets in TF.ipynb
1
6361
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", "Extracting ./train-images-idx3-ubyte.gz\n", "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n", "Extracting ./train-labels-idx1-ubyte.gz\n", "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n", "Extracting ./t10k-images-idx3-ubyte.gz\n", "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n", "Extracting ./t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets(\".\", one_hot=True, reshape=False)\n", "import tensorflow as tf" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Parameters\n", "learning_rate = 0.001\n", "training_iters = 200000\n", "batch_size = 128\n", "display_step = 10\n", "\n", "# Network Parameters\n", "n_input = 784\n", "n_classes = 10\n", "dropout = 0.75\n", "\n", "# TF Graph input\n", "x = tf.placeholder(tf.float32, [None, n_input])\n", "y = tf.placeholder(tf.float32, [None, n_classes])\n", "keep_prob = tf.placeholder(tf.float32)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "weights = {\n", " # 5x5 conv, 1 input depth, 32 output depth\n", " 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),\n", " # 5x5 conv, 32 inputs, 64 outputs\n", " 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),\n", " # fully connected, 7*7*64 inputs, 1024 outputs\n", " 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),\n", " # 1024 inputs, 10 outputs (class prediction)\n", " 'out': tf.Variable(tf.random_normal([1024, n_classes]))\n", "}\n", "\n", "biases = {\n", " 'bc1': tf.Variable(tf.random_normal([32])),\n", " 'bc2': tf.Variable(tf.random_normal([64])),\n", " 'bd1': tf.Variable(tf.random_normal([1024])),\n", " 'out': tf.Variable(tf.random_normal([n_classes]))\n", "}" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def conv2d(x, W, b, strides=1):\n", " x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')\n", " x = tf.nn.bias_add(x, b)\n", " return tf.nn.relu(x)\n", "\n", "def maxpool2d(x, k=2):\n", " return tf.nn.max_pool(x, ksize=[1,k,k,1], strides=[1,k,k,1], padding='SAME')\n", "\n", "# Create model\n", "def conv_net(x, weights, biases, dropout):\n", " x = tf.reshape(x, shape=[-1, 28, 28, 1])\n", " conv1 = conv2d(x, weights['wc1'], biases['bc1'])\n", " conv1 = maxpool2d(conv1, k=2)\n", " conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])\n", " conv2 = maxpool2d(conv2, k=2)\n", " \n", " fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])\n", " fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])\n", " fc1 = tf.nn.relu(fc1)\n", " fc1 = tf.nn.dropout(fc1, dropout)\n", " out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])\n", " return out" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Construct model\n", "pred = conv_net(x, weights, biases, keep_prob)\n", "\n", "# Define loss and optimizer\n", "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n", "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", "\n", "# Evaluate model\n", "correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", "\n", "# Initializing the variables\n", "init = tf.global_variables_initializer()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Launch the graph\n", "with tf.Session() as sess:\n", " sess.run(init)\n", " step = 1\n", " # Keep training until reach max iterations\n", " while step * batch_size < training_iters:\n", " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", " # Run optimization op (backprop)\n", " sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,\n", " keep_prob: dropout})\n", " if step % display_step == 0:\n", " # Calculate batch loss and accuracy\n", " loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,\n", " y: batch_y,\n", " keep_prob: 1.})\n", " print(\"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \\\n", " \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \\\n", " \"{:.5f}\".format(acc))\n", " step += 1\n", " print(\"Optimization Finished!\")\n", "\n", " # Calculate accuracy for 256 mnist test images\n", " print(\"Testing Accuracy:\", \\\n", " sess.run(accuracy, feed_dict={x: mnist.test.images[:256],\n", " y: mnist.test.labels[:256],\n", " keep_prob: 1.}))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
Sevenan/liupengyuan.github.io
chapter2/homework/computer/4-19/201611680484.ipynb
15
3795
{ "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "输入字符串abcde\n", "edcba\n" ] } ], "source": [ "def reverse(s):\n", " print(s[len(s)::-1])\n", " \n", "s=str(input('输入字符串'))\n", "reverse(s)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "输入符号：*\n", "输入行数：3\n", "* \n", "* * \n", "* * * \n", "* * * * \n", "* * * * * \n", "* * * * * * \n", "输入符号：*\n", "输入行数：3\n", " * \n", " * * \n", " * * * \n", " * * * * \n", " * * * * * \n", "* * * * * * \n", "输入符号：*\n", "输入行数：3\n", " * \n", " * * \n", " * * * \n", " * * * * \n", " * * * * * \n", "* * * * * * \n" ] } ], "source": [ "def tran():\n", " m=str(input('输入符号：'))\n", " n=int(input('输入行数：'))\n", " for i in range(1,n*2+1):\n", " for j in range(0,i):\n", " print(m , end=' ')\n", " print()\n", " \n", "def tran2():\n", " m=str(input('输入符号：'))\n", " n=int(input('输入行数：'))\n", " for i in range(1,n*2+1):\n", " for k in range(n*2-i,0,-1):\n", " print(' ',end='')\n", " for j in range(0,i):\n", " print(m , end=' ')\n", " print()\n", " \n", "def tran3():\n", " m=str(input('输入符号：'))\n", " n=int(input('输入行数：'))\n", " for i in range(1,2*n+1):\n", " for k in range(4*n-2*i,0,-1):\n", " print(' ',end='')\n", " for j in range(0,i):\n", " print(m, end=' ')\n", " print() \n", " \n", "tran()\n", "tran2()\n", "tran3()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请输入一个单词watch\n", "加es\n" ] } ], "source": [ "word=str(input('请输入一个单词'))\n", "if word[len(word)-1]=='f':\n", " print('把f变为ve加s')\n", "elif word[len(word)-1]=='y':\n", " print('把y变i加es')\n", "elif word[len(word)-1]=='s':\n", " print('加es')\n", "elif word[len(word)-1]=='h' and word[len(word)-2]=='s':\n", " print('加es')\n", "elif word[len(word)-1]=='h' and word[len(word)-2]=='c':\n", " print('加es')\n", "elif word[len(word)-1]=='x':\n", " print('加es')\n", "else :print('加s')" ] }, { "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 }
mit
empet/Math
Gamma-iterated-factorial.ipynb
1
417569
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##<center> Iterated complex factorial. Gamma function fractals </center>##" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recently we came across a discussion on [*Gamma Function and Fractal Factorials*](https://plus.google.com/collection/8zrhX), having as starting point this [blogpost](http://www.mathistopheles.co.uk/2015/05/14/fractal-factorials/). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this IPython Notebook we give the Python code to reproduce the [Mathistopheles' results](http://www.mathistopheles.co.uk/2015/05/14/fractal-factorials/), and experiment further with this kind of fractals." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The general properties of Gamma function can be found\n", "[here](http://www.tau.ac.il/~tsirel/dump/Static/knowino.org/wiki/Gamma_function.html).\n", "\n", "The basic idea is to study through numerical experiments how behaves the discrete dynamical system defined by the complex function $f(z)=\\Gamma(z+1)$.\n", "\n", "\n", " To study the dynamics of this map, one computes iterates $f^n=f\\circ f \\circ \\cdots \\circ f$, and the orbit of a point $z$ through $f$ is the sequence $(z_n)$ with $z_n=f^n(z)$.\n", "\n", "Since for positive integers we have $\\Gamma(n+1)=n!$, one extends the factorial to a complex number $z$, $z\\neq k$, with $k$ an integer, $k\\leq 0$. Namely $z!=\\Gamma(z+1)=f(z)$.\n", "The iterated factorial of $z$ is $f^n(z)$. For example $((z!)!)!=f^3(z)$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start with a numerical experiment illustrating dynamical behaviour of a few orbits." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from scipy.special import gamma\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.axes_grid1 import make_axes_locatable # we need this function to locate colorbars\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us compute a segment of orbit of a point $z_0=z$, i.e. a few terms of the associated sequence $z_n=\\Gamma(z_{n-1}+1)$:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0.112294242346326 + 0.323612885501927j)\n", "(0.874251610757277 - 0.106907952790729j)\n", "(0.948777931190684 - 0.0344226143472293j)\n", "(0.978939780955043 - 0.0131190526063302j)\n", "(0.991207960208101 - 0.00532017265947946j)\n", "(0.996303046707114 - 0.00221084553826231j)\n", "(0.998440602150385 - 0.000927985087495601j)\n", "(0.999341357858007 - 0.000391146097995793j)\n", "(0.999721652071882 - 0.000165158278610297j)\n", "(0.999882339530896 - 6.97884699652631e-5j)\n", "(0.999950258692482 - 2.94987085551536e-5j)\n", "(0.999978970814976 - 1.2470383308318e-5j)\n", "(0.999991109308073 - 5.27206671295952e-6j)\n", "(0.999996241175832 - 2.22890861209908e-6j)\n", "(0.999998410831796 - 9.42340744709546e-7j)\n", "(0.999999328125252 - 3.98405671695687e-7j)\n", "(0.999999715942002 - 1.68439456525581e-7j)\n", "(0.99999987990475 - 7.12135242212195e-8j)\n", "(0.999999949225613 - 3.01079554434357e-8j)\n", "(0.999999978533385 - 1.27291706641559e-8j)\n", "(0.999999990924252 - 5.38169373052753e-9j)\n", "(0.999999996162916 - 2.27529576533372e-9j)\n", "(0.999999998377741 - 9.6195940010783e-10j)\n", "(0.999999999314134 - 4.06701364080917e-10j)\n", "(0.999999999710027 - 1.7194696556683e-10j)\n" ] } ], "source": [ "z=1+2*1j\n", "for k in range(25):\n", " z=gamma(z+1)\n", " print z " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We notice that the sequence $z_n$ approaches 1." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(5.77861687872-1.48371352226j)\n", "(-369.648653382-156.601091536j)\n", "(-0-0j)\n", "(1+0j)\n", "(1+0j)\n", "(1+0j)\n", "(1+0j)\n", "(1+0j)\n", "(1+0j)\n", "(1+0j)\n", "(1+0j)\n", "(1+0j)\n", "(1+0j)\n", "(1+0j)\n", "(1+0j)\n" ] } ], "source": [ "z=3-0.2*1j\n", "for k in range(10):\n", " z=gamma(z+1)\n", " print z " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(5.73689190891-1.36064649141j)\n", "(-306.495252172-227.687734931j)\n", "(nan+nan*j)\n", "(nan+nan*j)\n", "(nan+nan*j)\n", "(nan+nan*j)\n", "(nan+nan*j)\n", "(nan+nan*j)\n", "(nan+nan*j)\n", "(nan+nan*j)\n" ] } ], "source": [ "z=2.9899809160305342-0.18574297188755021*1j\n", "for k in range(10):\n", " z=gamma(z+1)\n", " print z " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Choosing randomly points in the complex plane, their orbits either approach 1 or after a number of iterations\n", "the function `gamma` returns a `nan` (not a number), except for fixed points of the function $f(z)=\\Gamma(z+1)$, i.e. points $z$, sucha that $f(z)=z$.\n", "\n", "$z=1$ and $z=2$ are fixed points:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us check if $z_0=1$ and $z_0=2$ are attracting, repelling or neutral fixed points, i.e. if $|f'(z_0)|$ is less than 1, greater than 1 or equal to 1. For we import `mpmath` library: " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import mpmath as mpm " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`mpmath` is a Python library for real and complex floating-point arithmetic with arbitrary precision. It provides functions to evaluate special mathematical functions such as `gamma`, `zeta`, `hurwitz`, and more.\n", "The function of interest for gamma fractals is `mpmath.factorial(z)`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The derivative of `mpm.factorial` at 1 is:" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0\n" ] } ], "source": [ "\n", "deriv = mpm.diffs(mpm.factorial, 1) # deriv is a generator \n", "print next(deriv)\n" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.0\n" ] } ], "source": [ "deriv = mpm.diffs(mpm.factorial, 2) \n", "print next(deriv)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Hence $1$ is a neutral fixed point and 2 is repelling. Since $f'(1)=1$ is a root of unity, $1$ is a rationally neutral fixed point. \n", " \n", " *Open question*: Does this function exhibit more fixed points or periodic points (points $z$ such that there exists a positive integer $q$, and $f^q(z)=z$)?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to illustrate how fast the orbits are attracted by 1 or they reach a not a number, `nan`, we adapt to this case the Escape Time Algorithm used to generate\n", "[Julia sets](http://nbviewer.ipython.org/github/empet/Math/blob/master/Julia-set.ipynb).\n", "\n", "To each point $z_0$ one associates\n", "an integer $n$, which is either the first iterate that is at a distance less than $0.005$ from $1$\n", "or the modulus $|z_n|$ is a `nan`. If in a prescribed number of iterations, `Miter`, no one of the two conditions\n", "is met than the `Miter` is associated to that point:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def iterGamma(z,Miter):\n", " \n", " for n in range(Miter):\n", " if np.abs(z-1)<0.005 or np.isnan(np.abs(z)):\n", " return n \n", " z=gamma(z+1) \n", " \n", " return Miter \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next Python function generates the visual representation of the patterns associated to the function $f(z)=\\Gamma(z+1)$, through the above algorithm. More precisely, to a narrow grid of a rectangular region in the complex plane, one associates an array `w` of integer numbers, `n`, returned by the `iteratedGamma` function. This array is transformed into an image, mapping the n values to a colormap. \n", "\n", "After many experiments we decided to use the `cubehelix` colormap for most of our plots in this IPython Notebook. The list of all colormaps provided by `matplotlib` can be found [here](http://matplotlib.org/users/colormaps.html)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plotGammaiterated(re, im, cmap, filename=False, N=100, Miter=50):\n", " \n", " # re is a tuple giving the interval on the real axis for the rectangular region\n", " # im is a tuple giving the interval on the imaginary axis\n", " # cmap is the name of the colormap used to plot the fractal set\n", " # filename is a string such as 'imagefile.png'\n", " # N is the number of points in an unit interval\n", " \n", " Nx=int((re[1]-re[0])*N)# horizontal resolution\n", " Ny=int((im[1]-im[0])*N)# vertical resolution\n", " x=np.linspace(re[0], re[1], Nx)\n", " y=np.linspace(im[0], im[1], Ny)\n", " w=np.zeros((Ny,Nx), dtype=int)\n", "\n", " for n in xrange(Ny):\n", " for m in xrange(Nx):\n", " z =x[m]+1j*y[n]\n", " w[n][m] = iterGamma(z, Miter)\n", " \n", " fig=plt.figure() \n", " ax = fig.add_subplot(111) \n", "\n", " im=plt.imshow(w, cmap=cmap, extent=(re[0], re[1], im[0], im[1]), interpolation='nearest', origin='lower') \n", " divider = make_axes_locatable(ax)\n", " cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", " plt.colorbar(im, cax=cax)\n", " \n", " if (filename):\n", " plt.savefig(filename) " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.rcParams['figure.figsize']=11, 12" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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bLpcbGUgyrUPCGU2SIp9RTLUTwTHNlpEvljC9+RgG+HZ84+PH8Nq5h3DsrTYA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"text/plain": [ "<matplotlib.figure.Figure at 0x11f74908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotGammaiterated((-5.5, 0),(-2,2), 'cubehelix' )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Enlarging the above rectangle we get:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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RLRrpRRxbdUnkCUYoCSFpgCIZDkmQyaiIY5zubiWKboa8yGTSRNKNZtkz/1+7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\nDTg2ULTuxnUCqUYqnaJLTulLXbTHHtFQt6nbjp+IVubSG03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Y02o94WExnOdVTPLIHhZk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\nVT7Xtdyu8jlzH3bkLmHvVBOn5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"text/plain": [ "<matplotlib.figure.Figure at 0x152c1da0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotGammaiterated((-5, 5),(-5,5), 'cubehelix' )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We change the colormap:" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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SzDxJHYsyqoikQG7SIrd9tupE55b+BsYbv9UuNa5e5yiY\nJCwiSXnPmkxy6B6istt7gMtnGgCMi4cqk2nqkKOSRGEh8SXq75M6eoP6etzhvuzel1jntI2XtLh6\nDcw/Mx+La/Msok/5X1hMNUIZly/rtGSSEhkdUV9k3Kh1O7hWO2deFK7VzuHvf/Qu3po7CKVjTtxg\n6ptMStyGAhOd7XZ7D1xHafAaifTyHtVt5DKtrkFO0Us7GL0kXpiKUMZBJCmRs0NcZVJOe++1zgIY\nPvn/zsOGud0sQKkk4xJHmVzOHF1OVakMY/xYO5F0k1Wna1S7MWfdqcdhXnHK5uRw2KAEQZkk0+Sw\nW7C9CNW6HRx2C5Yy9cZbFQBAYbWBTK5pWUbSBjUnJEjierM4LuPcVKmfgdV5Ypy2sL1O0XmmnpLm\na2pIAYVzdki9UFImSZw47BbQ6xTRul3CFy79CK++vTGyTa9TxKnCLct9CZlV4i6TL7/fxxefDrdb\ngpNM+omCyuXIkU1VbtVop911zi6y2a4/tsxQUjKHSUmAMr1CSZGcTeJy0XGKUgpeufoxy+WLxR1c\nWlrCbu9B6tPfk/RqJbNBlL9prx1qdnsPcNA8Bzxd890G2muU0kom17I5s+3m1f39oW3depKr780q\nqileOw131OsUkVkd3d+IeGrWb6ZyzHaQdsA62kkJjT+pE0qKJIkLqlSuZXP4XDmDH/ykgVdulxz3\n/fgTuaELE6OTZFrEaeiguNTDCtEmeiVzAlfeNX7P3/yjNXz5EzXcOGj7uhn0Mve3Hbu9B9i6U8ap\nwv5QlFKVQLkMIaPnF/N47d7eSDMcWTovLS3h9Q/mRtYfrWsOHUOc8/akZZWTXVTvZ839N5fr2MqV\nLd97JtdEb3U0UirWW4mrHaq0xlVU+3o6YpSpEsq0y6TdSYXRHYM4X3wEX72xii9/oma+btcfmw3e\nL1/+Ia7VzmEh28J3mkcN/imThMSby2caePXtIr78iRru9B5NXJ4sV07nNSGtVk1k3LhWO4c3GnNY\nr+yZy6wika9c/RhOb2yb6+3WqcjbvvFWBac3ts1l4rVVin0h2xqRRiGpC9kW9jAsvOK13FTosFvA\nF5+ew8vvG9fqysmu0X59o2Ar2fJytV6ymMpSGgcZjROpEcq0yOQ4UuS0D2UzWtQo5fNrN/HavQ6+\n+nPAN/7VuimT+fK82SnnsFhAtSv2yE6/0hHAHt9EJQk3iNX7WWwN2kQDxm8aAE5vbGMh67+5iplC\nllLNTsg9ya2GKpJFU70xLZe3sNO4COAo6reQbQ1FE3udItYr11Gvb5py2+sUsblcxxtvXcR65Tqq\n3z2PymduAADubm+YwrVysYXW7RLypT7a9ceo1c8iX55HYbWBdv0x7kJsa9TLOBfKdRyu73BPc/Um\nu4B8eR6714+W/7fS2pr0XD7nyscTy9vvPUb+GX3w/7z5P3AkkOK13eDw6n6CvRtINYkWymkPTh4G\nYZ80rcpP44U7zhcfcSKvdo8++5e7Tbx4oWpKZLv+GH/r83+KK++WcHfb6KgjvnNeLy6EpIE4fc/d\n2kIL6QoSNdrolKFYyZxArdtBvb6JtbWb5nBkcjmXlpawnDlmpuHr9U3cbczh9Ma2KYQAUKoUcbcx\nhx0YQizkbBfnYXQbKSBfNsT5WqeIdv0xqnVjX1EGcBSxM/Z/jPXKDTSqxvp2/bEpcXJ2RrxWkaVO\nfi2eAxhZLyOvGyl7II/ysbzgNAyiOJ6VTDqRlgZ0iRTKpE+ZGPUJ02v7k6QQ9efphtz2p92YM+/K\njbvmo5PYb736kcEzsWzwPS8VRyIXaYRRyvgQ1Xctad/vU4VbOMw1kckNp1s/+1Qfuz2j2YrXtpRC\nXNeyOXyuuIR7jx4aEoijecHlc8kBgCutJjK5OSwWd3Ctdg6NqtEWL1+ex6nCLexUL2JnUL6Rms4O\ntjEihqWKBtHHuFHVkS/DjB7K5yYVIwWsyJvNWJU71Yu2ZclSJ4ui37Gr5e2dJFJd57TtSF0ttlPL\nslrv5xhJJ5KpFyeBMhksbm10iMG4ojPSFsdmrDYrrLalcBESD9ayOVxaWkLlZBenCrfwy+XT5mt1\nOzfUKOiv1nr41jsnLbcxb0wbc2aEVCwTtOuPsdc6O3T9uru9MXIcIaBecTp/2Y5DOZBGNRqoiuQ4\n+NnXy7Z+ylMlcRJp1PXpPsIiMRFKimS4cPiWcDk64U92pxr379GkMEo5myTpey0L4sefOIpC3nv0\nEMuZY7i6v28O5SPPjiWw6xQic9A02mJW7zfR6xi9oTeX69gDzAgjALTrAFDAysXWiNCJdp1yalmk\nqO2Qy7CLODqlpuXn40QaHetmU65dFFCNWtqhptDl52rZVtta1dGuvmxDGTHTFkkgWJlM0okSSJ5Y\nJu3znYRZea+Uytki7t9ru3aUK5kT+OqNVZTLW1jL5swOOcA6ULmOtWzO7CijpquB4Q44snS+8VbF\nvAmnpU4AACAASURBVAaJ9tQAUDV/F6MhJrkzimCoN7LSXtEJP9dcK7FybLcYsGRaHdvLMqd9ZQm0\nS487lWnVMcctetlPydDmsRTKKCTSPPYMy6RM2tpZBsE47cqG2j6WigCsT+pWJ/t8eR75Uj+QZgn8\nGxIyPlZS+en+x4Bnf4TdniGN4trx/NpN1LpHbSeFTIrfoEhNt1Ey96llb2Etm0P1fnZoG0G+1Dej\nln7wKpF227v1WvZcro2cWUX01OdW+7sJml2Z8jq1HFUY7SKiblHKWY5Qxq4NZZQyGSRJlsmkkJTP\nWK2n1Xe8sNoY2cbq5sbrzB2EJIGk/IaB0d7W/3ruR0OTD5TLW0Pv57BbGJJJ0e5RRrw+7BZQ63Zs\nb/zEvpN8XkIUna6xIze1NsPihInXY9qJoVvnGTvctrGSU1EPu7rI9XQqvz/lR1jEIkIZB4kMKjKZ\npBOkV+IYrUza5yyijJlccWRKMrFuxRgSbvAZH0UmxawWIuKhSmUaBz5n2jt60j6qwDgcdgtmr2ur\n3ttfe+4n+NVadmgMyuHRHawoobB6tP+XlbaP4jkAvHihjt2RMRjtWa9cxw4uWpal4hSB9CpkVjLo\ntTOMXcTSKYXs1F7Taighq0ipVV3Vcp3aUnrt6T0LRCaUcZBIQVxkcpz9p33R5YXeHVX4RPsocQGS\n0+Bi253qRbx4oWq0t1K+B2vZHP7a8RJ+B42hcuTjOUkl/14kbiRZVJ1+a79Zv4uD5iYWylsAjn57\n7r2jDan8+g+PlskDcAuMcWu9RwmN8SFHx3nMl+fR+L4RXbWTKi/YtQ/0044ySOFyklC7OqptJ9V1\nVv87taV0iqgy5R0CcZLJODBJG7k0jBVnlQqa5vH9MK6crWRODDW8l+fCfenJU8iX+vhccQmXlpaG\nhFQ8//q/O5ys4oQQz8jD8agPN55fu4m1bM787Xo5t7Xrj9HrFHH5jHHT6NT2ceWiv2YvxliTo8cz\nn3tsjzgudvI16XBBbpFBq9lshBSqy6yGNZK3t0pjq1FKOcJqFel0+pzTMmzQ1IQyX543H3Eiyuhk\nUGNAHqVTpydaQRxPFUm/YhlH7No4vvr2hrle3ma39wDfeuckMrkmvv7DJ8w2WfJ2tW4HC9kWPldc\nsh0kOY1tK5McxSL2xPnv6kUa3WRzJXMC1ftZM5LptVNMo6rj9Q/cz3+71wtYe8H73N12Y036aYMo\n47Ter5zadXZxS3vL+6jyp5Yl/rcaB1PIn7yf+nBKp8v7WL0/p/eeRqaS8o6bRALRi2RYTHvYn3GP\nN644xvliZCd1K5kTyOSaQ3Punn9yDt/4VxeByvWhtmrXaufQbszhqz+3g2+9c9Jsf7VeuY6v//AJ\ntG5fxOmNbXPwZFkwrVLfTHdPh0lvhMKa2pV4x+m34tRZBjj6++0BuNI6atZiN8yPHWKYILshf8Rz\nYzvvHU3sZMbLkDZW29mliuV1drLn9NpJHp16gbu1f5Q/A7vONepru5S4VXtPuXy7z8wJDhuUYKI8\neU9LiKbd1nEaIhsXmfTSWUF0otmpXsTeagOXzzSw28vhjbcq2FttYCXTwPpgrLpLz27jyrslZHJN\nXD7TwNX9fbz8fsGc1m2xCLz05Cl8s258tpWTXXPAZLmzDpkOYUTR7cpMm2jG5TfslZGZrpTZaIYH\nGIc513W+NF6Hpnb9MdZeuDWY+nC0LaU877XfIYF81cMiKuckSH7kySnKaHV8pyill046qjzaSand\nce229Ypd2jyNJDu/GDHjjEk4TZJy8hZ33k4k5b0AR51wXnryFADjorTbe4CVzAnkS32cKtwaSm0t\nZ44hk2sO5v99MBRlXMi2UDnZxWv39syL2RtvVfDSk6fM2Ti8TO1GJieKJhlpaAaSFkZl0mKbwblM\nTIs47nlLjkbKy9TXnstzkJlJRMkNp7LtooN2UT8/QmuV8pa3c0vb20UinXp6W6XU5X2cPou+Pt1H\nWMxUhDIJkUmntnDjDA8T1xR4vtQfPkG/9ziWTSPssLtYrGVzpuiJuXjbjTlcwzlkck08v3YTQA7V\nLrDXOos9AFt3jM/tyrulobLEcEHXaufw/NpN7K02kMk1zQGOhaTKY+GpdUw6UY8qEBeZk+sxzfPY\nLA8dJH/vzMHIPUqcIYQAfAzxI6i9eXZoiB+574E8lmSco5RWEiYLliprVvLmFCG06n1tt9xrqlwu\nW6xzKlPeRt5PLWOWIpQzI5RRzoDjZXsvnSrENkkYd9CPyLp11orrBc3pYnuhvwicaQBnGmY0snKy\ni/OLedw4aJvtq0SD+Xy5NHRhKFU0HOaaqKE1mHmjg1MFoyF+Ze0m7vROmDIpz8gh182uzk7E9bOe\nNnERSSvUdntJIanfLb8yGcgxLcaNtIpODrWvtOiJPFSmQ5TMTnjcImt2oqeucxIqJ3G1ihS6iZ6T\nANpFGOXjqvXwgl2bTK/7J+uXbE98z5opISiZVLf3u09UJ3On41pdENVlSb0IvTV3AMCIIoq/V63b\nwY2DtimATik0dQYNld3eA9+RSS9yH8eo5jS/A0lKMSeprkn9HUfJ6Y1tAEc33GrKW2C73KV3sopf\neRoXu45Cdm0dx6mT1b5OqXQ1SipLsVN00ioSKT/k9zzp55YEZiJCGdc7+UmHenEb0FolqvShU7TS\n7m8TxQXIy2ej1kuOUsqzaAhUGTTm6s0avbddIh6ibHUeYSvBDHJg81lMcyZFzKxIasQyblidH6OI\nTgpqb549qocUkbSiXbdOq1qlW/1GKt1S36oA+olSuqW+nVLWbtFIuXyrz8DquSqBXj8juwjlrJF6\noQz6JOvnQjvORdmug4XT+INJkEpxbIFdHaISGa+fidhOfS9OUimQ/06ZXHMkzb1ysYXd68Y26vdW\n7aijLrN6H3GMNsaNJIukSlhiGecbjHFvypPQbEjGbdpEczsLsfGbyh5HKtVtvLSntFvm1J7STjCt\n2kvK792tPeU4IujWPtNumRX9MEcbnyKpFso437FbnQideuuq0/dNQtQdHUQdgto/iveiiqUqlW5k\nck2c3mhiodLCL5dP42++/GG0bpdQqvTx/NrNQSTTGjeRtHrtlzhLRFCkSSZl2o25WJ37gvweBTWA\nv1yOeqMX9blRxaoNZST1CEEqxWs3qbSLWjqlpEXZat2syvGajrba3ql95iykuWXSeUZFODIZ5gVW\nlcmVzAmzt7AbaZwlRUWeDUj9OzitmyZ+L0Qiuvzl7z5zFIFozOH8Yt7X39SrTIo2d/JjVkn7e0/j\n+wvrPJfG86cXkZGlzuv+XiJ5XlLIbh191G2s2lc6RSrlbezWq1Jq1W7STjyt3sskEc/+lB9hkbqz\nTr7Uj8XduZPYOJ3AVJFUpdIuipmUTjpuqHI4jiT63dePgKkXanWqNj9z/srkS32sV64DAF67t+ep\nznbHtnsPVsyaWM7S+03Lex2nE+Ikx5DPGXG4lgj8tuP0E3Wz29+qDKv2g06df9ykUo1UqsvsemX7\nSX/bRTDV+tlhF/m0SrMnsT2lpmkva5r2gaZpfywtK2qa9nuapm1rmvaGpmkn3cpJ/tlGIk4/fj+o\nkricOYaPP5HDx58wlnuNVCYBO2EMM7rot2y3C7GVpFnJnBe5rJzs4sULVQBAvb5pzp5jhVN56ph5\nfmVikqkwp/H3DKKsNMjVOET5vif9u4UtkmK2KfV46vc36nQzMH47P6/RSqcyrLZ3EzO/Uukmjmqq\nWl5nlQpXU+tymXZy6YZdJFMtz4uIyzzW9ak+LHgFwGeUZX8XwO/pur4B4P8evHYkNW0okyqTTtx7\n9BDLmWO403sUSvnTaC8Up0iol7ExrS6+amRgaDw4pb2aXdtD+ZjqejHXN2DMrnPjoG27r1O5VvV3\nbcTv8F6smGQM1qjaps2qSMrErV2lG9MQSYHICsljusqokzBEgVVbRD94kScnGbKLurlF5+yij07L\n7SKV8vGspNLufdqJptfPx+pzsYuM2hH3aKWu61c1TTujLP4FAD87eP5PALwJF6lMxZl2GifKaYvR\nvUcP8fL7/dBkMmzGiVCJtJP6CKNuXrCbEtJqzEg1WumEvH4lcwLLmWP40kfv42vP/QR3eo+GItKT\nSJiXFJmfNFqQHanI9Jm2FI37946iTeNy5pj5u5OPH8fv7LhyEpcOInbRSqdlVuutRFZ9Lra3k0q7\njj2qpFq1tbSqq9Nn7LSuD32qD488pev6B4PnHwB4ym2HqUQohfAFfUKL6x33pCehO71H5jR8H3/i\nJ/jBT7z17PY7hBAQfJTSz3v3euFQtwtiyA+nMecAC2lU75Sl9XKHGvGddOolLY67kG1hOWP8BF9+\nv49e5wlcPtMZ2q51u4TCasO2DD91V5Hfi1tKL6gL67SnAo06shQ3xo1UTqvXfxQyudt7YP4O17I5\nM0opnyOMzyy8aQ794hRNdMJL+z6/kUovnWisej+rUVev6W/1tV0UU66LVRTR7bOzi0havW+3z3Oc\n9HqQ/NmfNLD79uh1xCu6ruuaprma6FRT3l5OZE4XgLgK5KTUuh0zBSPPftLrFPH1HwK9zioWizuo\nnOxGVUVPhCGTdvuGOY6cOYiwy92zlZB5kUp5mUhvHzTPAQCu7t8yvwuZXBOFVQ/1tZFJP3f6TlIZ\nhkhMQywpk8kiCpmsdTuo1zeBtZsjdTnsFoa/+6UiopJKOyGZRCyDToHbdVqR14WRAlffj51Yqu/d\nSm7l9+L1PTq9joNMAsCH/3wJH/7zJfP1W7/zjpfdPtA07bSu63c1TVsGcM9th9idcUUvbatHVEw7\n7XGtds485uUzR3cVYXXOCeL9eS3DLo0tGserD7/leDm2VW9OFS8ngKETjc+LjDi22mbLbqpFv/g5\ngQ29j4GEifrFMeXnBcqkPfxsDFYyJ/DSk6fw+We3zd+c1W9P7mSWpqBGWB12vGznt8OO3TIrebSL\nHNqlup1S4Wr9vH5mfonpsEH/EsDfGDz/GwCuuO3AM0vMWMmcwGJxBwfNdWRyTbz69gYA4KC5jt3e\nA1TvZ3HYLUxtpgcvQ+D4kUkr3AZ0dxNLP8cW5an7yVOsjS1jIrLpsT2l+BvWuh2zHebd7Y2hdb56\npztEVv3sLxDHDrNNaxi9+ylMyWMa0Umrc+ad3iPzHGt3w76QbaFysotThVvI5JooVTSUKlqodbXC\nSWTUtn1+y3WTJKfyrfa32l7dzioyaSV6bsus2jyK8q1S32qd1Hqr6+Toqiqg8murcuIQnfSCpmnf\nBvAHAD6qadptTdO+AOBXAfwlTdO2AXx68NqR1PTyTjrynXGvUwYAtG4bIWpxV7x1p4zW7RLypT4W\nizsAhtv8BIndYNlWg4p7wW1mILuTuWgC4PQ+/aTAr9XODZoPAFWlBcG4qSw/aSf18xL1Pr2xjcrJ\nLq7Vzo28F6d2rupcw2PLpE16CBidpSnsJgckfMLu9T2t5i9ez3/i+yr+r3aB6uDYX/5EDS+/X8CV\nVhFACacKRrOTatcY1qvWPTrW4eAGaCHbQiZ3Fq3bpammwP30Jg6zfaVV+VaRRr9pcHW91bnVT/tK\nt/djhVW0UkaVSjv8HDfqqRd1Xf9Fm1U/56ccCmUEHHYLIyfRvdZZ9DpF7DTmsF65jrqFQAjBbDfm\nkMkZkcO15bp5UpUv8n4b0cvS4jZMTdDpUCGTy5ljePLYcdx79NCyd7tXqbQaoufS0hKu7u+j3ZhD\nudwaXCRGpzec9G5StEUUF2z5M7IbTqjXKaJR1VEDAOjIXBz920U9JZwQ8bAiSUG9P0YnvZO0oYTs\nMhVWy63OE/L5Tbz3Xq6J13ALd7cvmttlck1stQaZmZM3sVO9iJ3BOvF5ffbZbVzFLXzvy/8LPv0P\nfwm9ThG714O/ybI6H3kVpXHl0i4FbVf+uG0s7aKV6nqnNpZ29fXSxlI+lpUg2rWjdIt62u03C/Ds\nGyPExXCnapzcRNvRdmPOjEgKMrkmNpfr2LpTHinHixhOE6/zlj957DgyPz6OJ48dN3tdTtpuVHwG\nu70HOOwW8IVLP5qovKARKV+RRpsklRbWyUuk5AHn5gmExAWnyQHMqH5jzrxJlzlorpvPrc4/7cYc\nXrn6MdTrm/jUN/8OKie72Fyuo/KZG4ENgO6UYrZLs7qVNw5e21j6SYdb1ccpZe4ml1aRTKt0uFWq\n3KkNpSqLk7Y3dSKmbSh9wwhlhIiT3krmBF7vNgEcndzESU9IpWhbJ7i7vYHKhSo2l+vm3bjdDCpy\nWzgr7NKXVtEeNaIhl2lVjp+I1r1HD/Hkh4z/7fATpTyqfwm91QYun2ng6v6+ZUq5jdELi1+cTvJq\nxFH+XD77VB9X928BGE0vA8PtKaO6SRDfwcqFKg67BceB2gVu3w2VSd/frEUn7VKtfqQm6iill/PD\nuDcx6m9p+JxwhLiBF7Qbc0DJ2Fa0rxxaJz1vo4Q3FCnNl+fNZkkHzXU0qv7SmV6lxK+8TCtiaVW+\nl3S4V6l0OpZbututeZJVhxynTjtehXlWoFB6IIwx2A67BSDbxfnFPF67t4de56zvMsSJUlzg5ROm\nuEg41ds84WK0TZzaNu+IeaBUNNsRiVSywK6tpRfu9B7h5feNeouynRBNB4bF/OjCMTSETr2AVwep\nZQCofObGUH3zpT7add9V9oT8WdhdQMUMOWLGDhmxjxjGZGQMygDbcKlpesCYEjKTa+Lzlxp4/YOs\neXNjta3d+/Mrl36ZFZn0O1h9HKYMnIRJIuJydFFtL+2GEG11SK58+WjYIPHcalm7DjRwFvnyHPJS\nEmm9ch3V754f+z0FhVuE0A6nTixeyx+3A49TXZw601gdU15ulxZ3k0qVpHXECYPZOAvHCLnXdK3b\nwY2DNtayOcvUCwDbOZnF/M9WMin2k48pixdgnKjFlGNq/cwypI4eTj+QvdaoDI8TaRKDuYsyL/QX\nbbeVpWQlc2Ik9W914VUjBUKUwpxHXMZOtq7u7+O1e3uGEH8w56mTQRjzDOfL80OfR+VkF5WTXTSq\nOnavF/D6B3M4aK6bMwi1G3NDf+dxB6on3ojLoNrTIojmFWIGnHG+c3bju07ye6t+97y5v7ipVQlj\nWBo33M7xdngdRsepbLsIn5cIptV6Nd1t1yPcLvWtlu2UeXJK9fshBnN5BwIjlFNEbtvY6xTRyzVx\nOBADdTYhdaBp9fUbb1UAGHe8B831kYicwVE0UVCvb2KxuIM3qhu27QlFWVbpgvZ7j1H65AmgVMRh\nromraOFUYR+XlpbwSnXDrGe+1Mepwi1cWloy58iVI4ry3Lm1bseMRsqCcjvXxuvvz6HXKWNzuY7z\ni3k8eew4frPeMd9zvtQHlraxuVzH1f0OAG/RLyNKoKOBs6h85gZ6naIRXQjoztKqaYB6gRSDKptt\nZ6V165Xro9vj6KI4FFkNKDonIo4iTVdT1tfePAtI03a164+RLxltPo1B972POGDXUzyMtL6f+czj\nytgjEEiRs7gxyY2FfCOsRvQFF/qLeKt3YL5eLO4gkyva3rw7cRR5HJzfHKKT6nlbLkOsy5fnhyKV\nI+f6iNKobpFAO7zU16lsL1JpdyynaKjXNL+TdHqJYqrlRHFTEAemIpRR91ANAre0t5zqFdtapfl6\nUtoVKCBfLmG9ch13Le6G3aRSlGuUZxGCly74IsX+/NpN/LXjJXxpG0Op6pF9PZy8hBjvXi/AiJce\n1RuYx2GxACz1zYHaW7dLOL2xjRpa2LpTHsiLMUMMlrbNMgUvv980G8jXurfMGYUOu4WhhvWvvr2B\n9qB3/EK2hYXyFoBNx7ZL5sw29cfmiX3lYgPteiGwE7eI9InvgZi3+07vEa7u72Mtm8OeTftN0ZRg\nLZvD+cW82etdFragf1fPr93ElSvPQUijfLF0YiHbwrXaJp5XZhvxsl/Yww/5SRHHUbqA8COTUbej\ntMIqOmnXQU8sF2JZvZ81fhfLdfP13107jR/8pIPdXh1bgCepVM+3qlTKy8X26n5W52+naKfT3zpO\nkgm4i6aXqJ7fzjx2+wTx2VhJpSjHqU2oXTl+iHrYoKBghDJARGNuIRGiA8laNocaWmZUTZa/dv0x\n9lbPDkWa3C7k8gXQ7S5frpOoy5e+Vx66gKidLLywWNwZRKysKaw2UDnZxStXjUiqkCaRfj9VuGVM\neTbg9Q/mRtpfynUS2+7lmrh8poFXtpX3ONhGvjg6fY5Wy3evF7BysYU/fc8+1e5E/pl5Mzprx2v3\n9gZDRG1ANNkUf3t5P9EZYC/XxNYdY8Ykt+jfJG13nv4rx/DGWxW033swfBfu8Bnmy/Mol68DAMrl\nLdS6RlMFkS63wms638/30SpCm5b0sJ/34dThwMt87dPE6bzlRyZlxI3X1uBG91rt6Dv09U4Tvc6q\neW4prDaQyTVHOjvKqNFHeZlVdFKNWsqiKUcn1XImYUR0pDqFLZt+09he1ll12Blnf7/7yJFSpx7m\nVmVZ9SafVSYWSk3TPgPgmwDmAfwjXdd/beJaJQBVekSaWEQEAWPsPuNCe3QRtRUcm1S3E0JGvdZ1\nLZtD9X4W65XrgwjYeB0kZPGxEg7RFk+k5dW6XFpaGmovCVhHgO16rYse0Spy1BIw2pkaETeLbW16\n7BljyYVzIt7tPcAvl0/j69K1VBVJtVMVAGwu17Hbcy7bazTRjnZjDo3vj6YOnWTyxQtVXKttmvsL\n1iu3ANiPGSj/HuI0SHrcpMsPXtp8JfX9WcmkGFZMHa9WLFdRh1IzfndFvHihOnKesvrOq1FHNVKp\npr1l1M88zL/BUPQzwt7HQXX6sSrLb4cfL/Xxu96JcT7jeOUHxmciodQ0bR7Ab8IYTX0XwJamaf9S\n1/W3g6hcXFEHq+51iubFuIF5lCpG+8JyeQvAUbtFJ4Z7JI+m4KzujPPledfBdEW7OJGWBAAUd/Bq\nfd2s/8jg2yXn6FCjquP9987ASrxWLrawe72AWv3sUO9GwUFzHa/atBxQI5R2kSoRrXTrhSnL5NoL\nt1B786xtmxfg6EJ87m+8i5v/5Ix1JW1wik6KtqO1bgfrc/8Rep13jOMpkTVZLsXz1u0SrnWKjulk\nM8JZHr6T9oP4/lr1ZLSLgoj0uNUFciVzAlf390d66u/2HoQ2u5Md417ckoJbj1NPZXhIe4cx2oVf\nVGEUr7/1zknH/dRe2+L5NZwzX5vb2dycqdFIdZl4bddMyUpAp8043/0gJHTcDj/y/9M4ltjfr2Ba\npclnkUkjlBcA7Oi6/i4AaJr2zwD8VQBDQpnkNpTyRT+ziqE2cXuts5LQHZ1YMrkGgCPxKZe3jOgg\ndoDK6Lhkogy7k5LArg2PHaIuC9mW2RP7+bWbRkeYQR3WXmiac9VW7xszx5wq3EL7kxdd57UeGotr\nUDejbZLcltKgVNFGIojA0YlcXKzUucPlSLC6n12q0+qkLdLzTkNRiPfjVyZLnzxh1skqyprJNc0o\n3M9/50+RL1nLo1kv5eL39z96F1/6XgUvXqiafyOBKd+rQsjHP6GpaR871Iuh+n2s1zfxat343l95\nt4QvffQ+gNFokiDoKOXQzZm4Waj1kV9L96AW7ZqUOUjhexXyKCZA6H3oIX61ZoTu1Zm+5LbsclQS\nODrnyE1N1HOJ3fnVLU1tlSa3W5eUZhlh3oA5nmcikrJxUuryunE+L7ahNFgBcFt6/WcALqobLWRb\nOFAXJgjzJDBoi3hpaQmvfzBnypP8JSusNrC5XMfnikv4zsmbuFY7h0tLS3j17Y1BL8Mm7Hoie+kc\n4CadMqKdkCCTa5ppeMHd7Q3kS33s5ZrYvV7A5cs/RK2LwYDqGt5/Tzm+RZpYrruo42j9+sOpciUa\n4jRFodyjGRi9MMhtT+W62PW2lN8LMHyS8HuHmX/mKCrpFL05aK6bn7sqkOpnoV7wfuWd0wCM9oe1\nN5/D2gujKX/z2CX/UqlGacWyke1sOhCMLB+8v6PI/BxWO3kg1/Y0N3vQtGt9dJq/DuAryK/Npbqd\nU6f568gVvxJ1NUJnAWXgx3UsZO9a3oyoKW67ZiV+OyM5iabVMruUeFJkMmyi+h2GKauzGp0EJhdK\nT1p981/sotcxLh4LZ57Bh37qmQkPO13ESUFEHq/u72Mhawhbu14YuiC3bpewBaB6/6gH89X9fTy/\ndvOo96HNx+ZFFv30SG3dLqGwetQAvjdIndak/hKnN7bNDkSZF5qo3s9iIduxGYbIvu3hUGNw5X8A\nyJe0kaicsXw4QimeC0RK3mqWi5HXFuknJ5xShV5pv/cY7fKgjVfJPiW4WNwxL3xqJxxPEcpG2ejh\nbiGTwFGPe7shn9zeg8BRsh06OA01zxi8l3J5axAZv4/bufZQhHKaKW8jWvcVM2qXVpkEMBMyCQCH\nqKP3oYc4/HfWN+dqhNIqKwJ4S/XLOLUpVreRb6y9tLWcReIYhXTCS31lH3h48D4e/vj9sY6VRCYV\nyl0Aq9LrVRhRyiGW/+LPJDblrZ5sVNlZe8E4YYmewUbHHONEJrehPHSYhku0O1TbUTpFhLxEKEVd\nDnNNo1d1p4hrtXNYLO6Yc0aL97PVOnpfB811yw4aI+VbRCiFZIt6mts2jvazOoHLqSlZyuTP20m6\nzG1sTtRWbShVeRLvZ+2FW77S3o3vPxiUNT/U/lRunyqk/v/63Efw0lvvWLbnEs/VVNyvvHMaL16o\notbtYCHbGYrIqOk8vzIpY3WzYIXb0Cfl8pbZ6evymQbuuHQoCrpTzlBqUbyfFKaAVdL+Hu/0HmE5\nc8yYmjX3EHgEfPFp4z3LbSjV9LdA/J7k35bVzR3gfDPqlPlwCwZE3YZyHNIofX73V9tzeh266Pji\n0zi++LT5unPne5bHTM63wZlJhfIGgI9omnYGQB3A5wH84oRlxh6raFrpkyfQrj9GqfL/t/fuQZJd\n933f984++sHh7PZMa7HbwwWWs6sBYaBatLOLtSSgBNsUC2BJFlSVAHGi2Caq4hRiJaIiU6GoCycj\n2gAAIABJREFUqqhUKatkqWzTNh1WHJssy7ITIJK5FaUIGlQpdC1kZTEri+gsBXI40wKwnN7lanZm\nZzToxz7m5o+ec+f0nXNf3fdxzr3fT1XXTN/n6e77+N7f03LiLDudC2g0lhxh6ef2d1snw2QRdjsP\nHDHqRXd9CqXKcIzCSjpd3sRzjy0r+1p79buVqTctzDzzDtpfP3tAXKxdqaHaOISTi8vKkhwzsyt4\n7qFdXHqnfuAhI0yWNzAULCvrFz3jnsRfOcvbHUPpFpGyOI4aQylvDziYmCPE5EK5gpXd30Wp8oFh\nIXWXIHYLS2A/hMJ33zGIyfoPHsX6H9xVfh8H9rd3XA6zvJ8AcFDYi+QbVdHpNK2TQL4tkoA65jXq\nZw5jpcs6IQfYj8N1Z3k/f2bdN0FHDiMB9h9In1q4htfXm55dcUa24WGFdL9XJe+418/KQmmaOASC\nx+xXIzLs542ShONXSqjI7m5gQkFp2/Z9y7J+GsC/wzBw61/kPcNb4BY7wwvVIcgFAIR7WY4X87qQ\nuDN1w1BtHMJcbRVr8O8NKwRwu99D8zjw+psX0anvotFYGishors+hZm9j+8VOzQ4rS7JIcr+zNW2\n0HFZe0URc78sbxHDqsqZd1sbhvv2uDm4njjF30nqUAYxXzqKz3duAtgv9eQVPymzdKMRWIdy4np2\n9V1HVI5M98l4ff3NJs41h3UosZfRf3vzrFNEX4X7M+hSMggIF0aiK35Z+c4yhn6+tcHdA6WDvBK8\nvKbLLnBgP8FSVdrMTzi6p3lleft5m5Is35RmHUrfcSTsUo66ftTuOu5zKUqNXxY2nwDbtl8D8FoM\nYzEeOct3p19zWuWJtoOiJE9XMjgJUbgidW4IEgfyxSjohiyPSRQM/sIPd/Dy7zccESDHWIZle+Mc\n5i8Ot6uykG5er6NV2cAnn/4WXnl70emUI8Zye/MsZvbCAAAMrZa90SzvmdkV539h5V0oV/Da90Yz\nnYWQbDSWnM/U6VwI5baSl9m39o7vMh7iXdz8hRNzuHFsHZe3VrFQrmDpRsPp2iFbST759LdcnXKC\nE1kmuYG89zv38V/8zLdwqTO06LrjYpX76zxApz60wnc6e51yasMYz7ZaT4YianiMX5auSa5FFVEs\nWb7FpDUTk6KUlgrRgEFGJSrdXN3uAth/IL0w0imnhLfeX8fa4K50zvl3y4kadiR7jlSWSS+Pk2r7\nYQmq0ZgkOolGVYmhMLVZvdZxT/PrnOPXUadopNIpx9T4SZkgd48qS9mrV3G9uR8rKErKqOqghSmI\nO13edEryqAvq7jrLARW80X4Crb2amKJGoKoFY5inLOHan3t2uK0vXf7IfhB6fXdvn8ecUkVyB6Gn\nzwwv7u2yyDg/NvLdAcP4qC9iKCqFsDpx+Aja/Zsj1sgXH1ve6ws+/M69YlVl5O+q+exV3N48i83r\ndXTfDY4dDYt83E+XN7E2uHsgy9nr3BBiEti/UbrFZNzn1RvtJ7DwzH6sb9ib206/FrntolgvacII\nMt3Elpuk3aO6tV0EvEWlwK+Xd/N4HzjewU8eqePNqW3MP7SLz3du7nUEa4Tu5R26qkEIkSmvJ4tK\n9zb9fmvT3NVpWhtVsfFRLYVe7muV9d83JCggBl2FfmfgeLD1YorIbhdZXAHA68v+ddDc70WShmz5\nFOVinHUUdRGFpe/jT7YAqJ/49933Q+STSYhFMX7R9eYyVnFycRmD0/siZ6dfw2XsWyGERUIWRgvl\nysjNQY5NPd2r4rmHtrE2GFoahLAChtbLmVk4olW4hV8LaRlrPnvVKWsjhE2cN2139qhssZaRraqi\nHJXqexLbAEaFZFC8a9Qxlyqzw+/2GTjtE0Uc6sIzqyNiXX5gEbG5YfESk0k8fOouGMMwrqjU+bP7\nWSmDUMXmunlzahunSocdN/h+69vo+Lm23THvfglr8jLNZ6+i9dXzI9t3lqN4VK6jEo9+7mpZAAaJ\nQHl/bjHpFpKq4ufy/KKS77RADZEzf4XVrd3voXZ6Xbm8V600EfszXd50tulV21BYQWWB0u73Rixm\n8rLONhr7T3p+J8lc7WApm3EC+J8/s/8dzNVW8eaUdxqTfCNaG9w9kLiiupGKzHZBo7HkuNjTsKJ7\niainjx3DCyfmsDa4i+ce2lX2MnYzIipjEsLdzoOR76N1p4zWnTLqTQvzFzfx3EO7mJldcZIN3A8s\nYS2OOsVNmoTO4jAJ4kjeujG4j7XB3VhazAomOd+az1511hdi0k0WYjLoGu+Fl7BSbd9vG0HjCepe\noxKSYprssna/Hyl95xGH7FvxIkCghmXXtlN9JQUtlCFIIrtRuKCvbneHlqn+BoJietwI4dHG5rAb\nDobB5mERF2zVxdYrScgdJ3rZZXWb5Ls6VTqMzywcGZYFwZxngL3Abckb/lVbbGun1/H8mXVcbgwz\n292feWi5SOZCLicDiP26LTOv3rrtub6qTJBMnC7R7vrUgXqaIkbyS5frQ8v24rKnAJfHKn/GpEWk\nV+ekvBFG2ORJeKpc32ERD8vD60LZf2EXqi46bpe137RqfdeJEZfDb1od/wTKtEi6JM+4MZBey/lZ\nKr0ske6GDUH78PtfVXtZJXDDCuy8QkGZIbIQGvTOjsxz11x0l+E5ubgMYJgBfOFUx4lRBA7WyhRE\nvamHia0K2mYUt5ZoqXbig9gTlQfxs1rIYylVNhxxXapsoHm8j9e+V8ZzDx1zRLDKhTwJfhdRdx9k\neayXNmcx6A1/2w5GrdjubWSFOAaBg2LYi6jH26SfryiiUhCHcMw6fjLM9UGc81GFpfta4SRFuo6R\nc80rWGntN3hzHprrs3jxsWV86fJHRubJpb1KlQ3HO9Lu97DS2mtZ2wHWcRYhe3+MEDb+Lmqcnu4i\nMmgZL4HpZZmUl3O7sL0Epp+L2z3PL56yqGWEKCg1QlyszjWvDLOUpQuXyIYWDHqzWOpBWaPQXSIj\na1Q3DZXl4db9e46YFNbJMLFSfojvYL50FO3yJr50+eJ+qRsNEO5l2Yoxf6B5aTjGKVcRhoVyBZ09\n4ZF2DUlCxsHrOJVFpSrGHBitLqG6/ohEwMtbW/i//sb/jb/8v/5XGPRmJ6oQcWAfIQpo6yIm0yjj\n4zfdb1rYxBov62PUmpbjXoNZNoiMjeqJfK62CtRWsbBQwdKNs4q1hm7bzev1kaQYYL+UjFxTMqqY\ndFs1vaxF7i42cYhWURLkxuC+r5s7inXSjchkr9Z3sdOvoXm8j5YigWdSUea4wDxaSrrHJqyRIgnm\njfYT2lknATiZ+kkRm5W4YFbKScjaOhkV1fEXpSe8XN9W1NGdLm/ihRNz+KIUxjFd3hxeH+4Mlz/X\nvOI8/LbuDF3nl7e2cHvzLP7cz/3P6HbU1+s4CHLTRl03DFlZJP3mhxWNqjhJ+b3KOhnkwlbFc8rz\nVZ+zaJZJAQWlJogL1nzpKFp7gq52en3kRnvhVActl9hI6ibvdp97CcewotLLSgnAs6OKalmvbYdF\nlLdRxVWNnUkb4cLt/j7Fd7LSuoibe8s0GpuBcZPOvqVY124nerkKZzs+n0HnYuRkPJIWk1EeNifJ\n9g57/RMP23LXqmF1iWP43DcW0Ggs4bmHdp0HT3F9GFYvkEOTGhj0ZrEN7HkV0hUOQef2JBnGSSXX\nBC0XRkj6TfN7746lDIPbrT2uaIxiSc6L/KSg1Iy1wV1sbzzhBHS/+NgyXnl7ETOzK8ML4PF+qm7H\nMDeFSUQl4B94H/RZo2QWT5c3PROR3ElI4zylu62TgP/3J9/chEt5Zq9GqLgBRrHcTSwqXTF54ndN\nUkAmUiaIVkrjmERUhkW1/VOlw3vua+/wmp1+Da0+HDEZVOM2KfzOadOFZJxubbeQVLm1/cbkTuRx\nb0ee5/Xeb595RjtB6XcjyMpFk3Y84lML17B0Y1ic8NI7+5nfk8YTehHHTT2KqAQOXtyjiuRxRI6q\nb7kXUZ5AgegJEoPe7EgdUoGYNulDQ5SL2MjncLnqdYrFjQJFpTemubqTYm1wF5e3tva7PEHtQhfX\niUFvNtFqEGmTlGs7zHJpx0jKy/gJRNV8PwEaF4yhHINJL/AH2qvl5MIoiwpR5HsJQ+vWLz3+vtMy\nTHfScnMlXoJGsvQJVBc3eXnn/xDWSfl7Oj9TBQC09hIBRPci0UVn83rds0apvE+5tIlThDnA5RJW\nECchKtOICaWoNIs0rJRuFsoVLCxc22tvet+5zsoVIMSxqupGlhZhk1sm3V7YbadhlZSnR8na9rMs\nyvPDfpagJB0vAeuV6JNnUhGUSV3U5SxonZj0BnyqdBgvP3oHX3xvF2+9H/6zjSO04r6xB8VcyrjH\n63UzSUJAKus5eggz4ODFUiXAwrq65bJANwb3cap0GC89PIUTh9/HW++PLlfzqCvqjnF1t+4EcEBY\nehHGuhqXqEw7uYiicpRxr5VpWamzEJVypy7ZOunuSKVTX3iThaR7fhxxkvL/fq5ulUvb77P6xVKq\nEn5UyPu+fVW9L70UzPho5/IeB3ebuyRI2/V34vARvPTwPQBTgQW+dSSKsBSklewRVtS4hZl7uvPe\ndexF6fsuLCIitOHlR++NtJj0y7gPIu4+1pOeA1lnqhedtB+8xz1eshCVKuskoOcxm3T2dtJ4WSb9\npqnme7mi/bK1g1zbXq5z97i8Mr39RKou33+S5EJQAumIyrQ5cfgI3npf/1ItUfaRZTxemM+rsmj5\nia6wYtLvc68N7jpje/XW6oG4SlXBeq957vFHEYxhzp+ov6cON2TZeltUTLs2esVax4W7woRfzLIO\nx42X+zYsSVgmVeuk4eIW7/3+V4k82TIZ9vOpxiCmuUVlGCtl3smNoATMFZXuLGd3PUYT4ifDEiQw\nkhCcUUVNkABRHWOqcYf5LK07ZdxcXkS1votGY2nYscOj8Lq7Bqh7ntv97fcZ3Ix73qQlGOPYT1Hd\n31leEye1aidtrfQqiSXHTgIHvRRZMI6YTCrxJu0s7iBh6SXuDoQqBcwPIiiT2y9u3dflzaQcPdEl\nrtLvQup3kRTiUcT1uMWk15N02m3ukiJMQfVxt+GFl1s5zDGkKlQ+Dt31KaysD1vkvHBizre3t3s/\n7vAC+bMEicusz5O0KZK1Mi+/bdLWSnkfwMHYSV2IWid3UjE5rlXSvUxYMellqZxETPq5uIM+p3uZ\nsAk3fgmceSd3glKQhLUyyThKt5UyilWyCIWm/VytuojjqMeGeLD43LPv4m998UOoNg6hWt/F1e1u\npN/ULYgnEchFIe/Wyjz+1klZK/N4/UzLxR20TBg3t9f6fiItSEzKy6jm+2WBh3FxB8VTRhWTD2wr\n0vK6kltBCejtAg/b31qeFxc6CLBJLZFZfwY/y2SYbHVREqjbeYC/tTetdnoda1dquNR6HPWmNZIN\nLiO2r2o3KVsrJ/mOTKw9GZW8ikrdrnlxPoiHrQwRdTuCrK8rKqLWuE2KtN3c4v0kbm6VKAxj/Qz6\nDtxi1T1N3n+RLJW5FpRA/KIyysVxnAtp0gW+s7xghtn3ONnhcRBWgAXFSrqLlcu/p9xrfdCbPeC+\nWruy/1t216dQkkoGqW6c8jS5h7ufC5yMkicXeFJCUueHizxaF1WEdXV7uZ/9hF/YbQQtl4SYdG/D\nLdC83Npe891jmyRRxsviqVrO/Xnc7ML86w9QAEEJ6GupnDQ2yBQx6bdfr2L1WQjLcfalEpNyXTtg\nVFQ291pnDiobqDbqvjcKd79vgduK3e73RsSq13bC/v46C4ikMFlY6nhtMxG/2rRR4xfjYOGZVbS/\nfnY4DtFswWMc1cYhrP/BwTCpNMSkX0xikJs7bDa3l8XSy60tbz/IMhnWFR/lc4RZJ4+Yd/U0jDA3\n8ajCcKdfy6WYVE0z1br25O4MgGFylfi9FsoVnJ+pOoJQFgJetS1V7RnFdt3CVTBO6aIoy6RNmsdA\ntb5rjEAzaaymnsdZcnN5EcDQOhkkJpXTYxCTkyznhVcmtF+STtQxBZUGcv8vXsLSKItUL5e6Kq5S\n3pZXDKYS20r3lRCFsFAC8Vopo1rPwri+w1grTXLvRLmBdDvDk6/+g2qBpGs/ab8xvTm1jUvv1LF5\nfViwvFrfxVJvA0BnZN35i/vbGpwe/c6EmHyj/YTT371U2cAbnXP41EfbuLrddUSl20rp5cLX8XvU\nEZ0tlqaISDe6nsdBZGGlHGnnqurKpbBYho3/UzFuEs4k1km/fYSxSIbN6PazSKrG6ffeC1mMen2O\nIlAYQQlkW1Io7MU0btGYtmUg7P4OuLoNO+G8fkvZvf3yo3fwK9frqNZ38dTCNcyXjuKVt59Ao7EE\noIy52qpjtfzie8DzZ4Y921t3yiPbe2rhGtr93p44rTvz5ktHfasBTJqYowNZj3+cmp5JjyNNTBSA\nceGu7apqa+qFqNhQqmyMxEaHwe3qlvfnfp+kwA0TN+k1L864SdVY/JJwvDK75WlhPqNscVRZJd3v\nVUk+7nF41aHMC/o9fhtE1JudruIua6qNQ4GZjKZ8FmBfVIpakqXKhiP+uutTuL15Fs89tC8Qbgzu\nY9CbxWvfm8J86eiBZJvWnTJeODHniIqPP9nCq7duO2IyzgoAxJssXMwmubXzjl+IijxdFpOTtE11\nW0RV70NvLwZXd5Rthtl2mHI97v/9YhT9BKvbQuk3drebO0g0ut3k8jR5Hb/vwoaV6ispCmWhFGSZ\npJOW2yeP4lUXl1mYMQiRd655BU8fO4ZL79QxV1vFueYVLJQrOFWqOl1x3mg/4dx8Xnl7Ft31KXz2\nYyv4wrfPDrPB16fwavkKSpXj6KKO1p0ymsdH90PSQ3XtmNR6WRTRqMs5HBZVa1NBtW4d8HqVKuvO\neuNcE6uNQ7i5vDgiHFXxk0m73yexTgYRJt5RlcmtWj4oq9ttnRTrh83KDiNAgyiShTIVQSlOCJ2I\ny/09TjZykhnMpgjJcWsA6nxD8iq8LHp1L5zqoN3vod3voXWn7IjL29LneWrhGtqNHl69BczVbgO1\nVaAxnPdLj7+Pz9dWAQDtvnr/bvLg9jaBogjCPON3rngmup0efT9XW8XTx47hte9NSeuEd3mfXFwe\ntmKVYyMV/y88s4p252zg9oKE0LiJOH7xkV5uX79xBVkn3ev4ZXh7uby94jLl/anGFSRQvdaPZPnN\nSWHz1FzeIlNNh56oMnHFRY1z03b3i51k33FtK+o+J8HtzsuDe88rBvbFx5ad+fIy86WjePnROxj0\nZvFLj7/vJNnIyy2UK9jp1/CVjS3PwvcmJWyFhUI4n+j8u3o1E1Ato3oBwwfI5vG+83AZ1phSb1oj\noTBezF/cdGIsw25XhZfgmyS5J6qAVQnIMIk48jqq+EaVy1vlknZnZLuztL3c1e4xuPcb5rPnkUxc\n3jpaLLNkEotlFhfnuPcZVURmaaUcd79rg7t77ulhwo34Di9jeNPprp/DVza20O73RsThTr8GlPv4\npe+bxpfvKcyShJDYmeT68kZ7mHgnzuNqfRfdjv861cYhlCrruPTOMOnOr0xQ1CSf9Zat3F/YDOSk\nywSNg1voqayLYUr9yH/lMflZJt3LeWV2u7cDwNPlnWRcY5pkFkM5bnBxEsQVUzmp0NH5yV1gwhiz\nxm0tbDk6sDxi2R30ZrGN4c3r5OIylm40nOnAviWk3e/hy1j3jJcMsk7S7U10I6uOWHHgV9rtpxsn\n8av9Fee9OPf8Yh6rjUOonV5HqbKBzyyU8Knr9ZGyQDIff7KFS5ceDz3W5rNXsdK6OLItp66lQkhG\njRX0EoXjJOPEgZebO4yLO2xJInk7KstlEgLaFLRIytFBXGYZU6k7OooRnWMpVQghKY6z0ZtLDfMX\nZcuDPTwP6nuhDJUN7FQ2AJR9O+LkCR2PuaJh0vmVFnKjAblsl3jY++VvfgAXTm2NxDiXKhtAfRaA\n+t4mxOR0eRO//M19V7aqFqV46AyLEJMAlDGZMkGu7CBBNIlA9SobpMryDlO2R57nt4yX6PMSk37x\noe44zajfQR7QrmyQbjGW48IbYvKY8h27x6k6xkUBdHkZVXxvEcQkKQ6mnMPAQcvkX979yEi3qk7n\nwsjnmS5vYrq86XgaVDHi7o5YXiJerDvJ9xWmhqbboOMXO5gUYfcZJQY0zLjDxI56WTr9EpBChRbY\nU+m+EkILC6WbLC2WcXfUMfUp36QLfVpMEuMqrJNeF3PV9OG0Q3vWjcksRnR7EzI+Kjf37019C6+8\nvYhGYwkL5QpW9h4AX19vOhUcAKAN17qu87lU2XCWbR7v4yb2habj0VifwsnFZWxvnIs07qgGGr/8\nhknEpJ8lULV9v7hEMT8oAUaViOPell+2uZ910q+UkJeVswhoKShl3B0CUtlnhm0as8Y00WGyaI9K\nUT6raccgmQzdj2uvmMm1wV38yvnreOv9Y7i8tYXPfmwYO/nqrdt4+tgxJxFvoVw5KCr3EJ9bjo/+\n+JMtvNF+AsCwjJA4H5rH+3vtWw96KeYvbh5I1pEtklHqVkZJmvUq9+MlopK2cPol54Rd1ytj28vl\n7cZLlAI+STksG5QuaZcc6q5PxdpqLe2yPlHRfXymI1xek5L33yjvn4+oMen6I2rJAsBb7/ecOMoT\nh4/gxuD+SGkvVZkvd5khFTOzKyhVNtA83sdcbRVztVVnn/Wm5XTSqTctzF9UlyeqnV4fusr3plcb\nh9B81r+ytrwNvyQi975UlsC4xaNbnAXtz6+mpXs5Lze7V+yk12eTk4G8EoTyjDGCUpB2jGXc/Xt1\nu2iadCHPknEtKO71IrVLUyyrsyWHkCLR7vdweWsLrTtl3N48i893bjrv3csF4Y6N/sxCCS8/eke5\njByPKYtTd2vIudrqyP3r5OLygf141aj0wu/65duO0qMOJDB5+aA4l42yvXiz1a2UX8mgvctbRdpu\n8LhbNcoCLguBkDcBqbvLTI6VKlVmUTsNx/rw+ptN53j+2y9+B5feqTsJOvvt3DZG/uaVvB2XJpPV\nsab7uezm9uZ+e1Q5se41LGO6HK0tqhB7bWzi853egenA6LVguryJp48N3e2dzgU8tXANS5XRLHDR\n+vVU6TCubvfR7vdQb1pOTOZ0eRPrrfMAsDd9eF89ubjs24lHVVvTy9hzrnkFrc559XYU9/BxhFlY\nd/Qk3WyCCpx7rSP/ZetFjUkzeSeuskJu3DfRuC+mRblJ63wjEjFYC+UKcHzYfvGFE3P4ld9tOstU\nG4fwpcsfATC0JBBSVHSKO/erOQnsj7GLuu9yUbi9uS/kgr4D4W5vNJYAVDBXW3UEqGgFe3lry3kP\nlDEzu4JGY/i5VloX0Xz2Cm5vnkWpsoFGY5ht3rpTdlzpc7VVdDoXHEvooDeLC6c6TvJR66vn0Xz2\nKnb6tZE+5PMXN7F5vY5O5wKAYXF1UXNz7UotVH/ykU43AW72sMlEXjU+xfQgkekXk+nV4SeIvMRQ\nGi0oZdLqvhO3tdKNnwD0urgURTSaiPuG9Eb7CXzqo238yu8OszXlgPnnn/8m3mg/MVLrzt05J6/w\nGCZudH5IFDSP9zH/0DpwZh2vvL2IT320jRuD+3sJOdG3J1eFEPh9B7I7XeVa9xOnQ6E3Om+nX8PS\n5qwjHqfLm1hpXRx5yC1VNtC6U8a55hWstC5i4ZmhK33Qmx1aPZubzrZOLu4J7sbiyDaqjfow4ej0\n/m88XH+4TyFwBaL+7nRzdJ687KA3i3PNVWffLz08hS++N7xXN4/3nWvpoDf0EgFwxif2XzsN4OLe\n99/ck0fNw/sGJZfoDSrsXoS4SZncCEogP6LSC950/THhBjTMCr3vvJcD5l9/c2ix3Jmt4RONEr6y\nMbQsFKWYOSGmIlomfu4bC/jUR9uO5XBc5OuY33VNPHgu3WhgrrYaaR9PLVzD+Y9W8eqt/Wmqe8wn\nn/4WXvve1Mh8MR55nht52Y8/2ULrTtmZ9vEnW8oi7fL+3WMR8f7uZeRpHWne5zb2PYs396YddMvX\nPETffcU0EkSuBCWQXnxlUi5wkh/c1sl2v4dfbZcx6AW7x956f9TaQFFJ0kKnhzKdHxKFmFsb3MWF\nUx280Z7Fpz7axtXtrjM/TEIOEGws8Pse5ktHMX9mHZe3RuMtVYIMGP6+O/0a2tgc8YB4ibnX+gen\nCRH3Wn+/vq3f3zdcQ3+jPSslDLmvh/UA4XcfwIw0Lw/iz7j8aCW5E5QCCstiossNKCj2Chg+3b/y\n9qLzXhxL2xvncLm8lNjYdIJWdxJEljGVIg4xiPnSUczMrgCYGmnJGIaw54D72iaLwijbFNsJEpJh\ntiemOwXYcbDjl4qiuYKLQm4FpSDvbnBiFuLmdHJxY0RMyoiLvftGRislKTK6PCx68dLDyVuZVKJS\nXCeiXBtUAjHMNL8yehSP48OkHIOgqCRp4mfRWChXgHJfGT/08SdbAPzr1lFUkiKjUwZ4HIxjoXd/\nBypL47jbDhaMihaxFIy5wLKsXwDwUwB2Afx/AD5p2/YgyjZSEZShmqMnPYaUSgzJJyTFZTboasmQ\nu2bM1VbR6Y1mOP7kkTrenNoGEK4YsunQ3U3GJc1z3MvtLdoqAhhxcYv/5XM4iYfAqO5pQVCzDpWl\nkaIxabK1UFqWdQbAfw3gMdu2B5ZlvQLgPwfwL6NsJ1ULpQ7CEkjXYglQWGaBrqJSsFCu4OnHlnHp\nnToGvVk8tXANX76X9ajSg2KSTEqa1sqoojLogTBK3KQb9+f12xbFIwnJNoB7AKqWZT0AUAWwFnUj\nmaQW6XDAmtwXnJjPfOmoU2qkVNnAJ2aPjcxX9QDOCxSTJE6yPp7c4tH9fhzrpF9LXLlUjtcyfvec\nbueB8xqZXpB+01piW+m+3Lu37Q0Afx/AewA6AO7Ytv27UT9GZjGU8oGblcUyixaOAC2WaaGTlVIW\niPOlowCAlx+9g1dv3cZPN07irfd7kbNDCYkbXc6XqKRhrfTL+PaySKrEZFAG9rj4CUgZAV+FAAAg\nAElEQVTldIrHwrD5nVu4851bnvMtyzoL4FMAzgDYAvB/Wpb1X9q2/a+j7EeLpJysXeFZCUsBBab+\nJCFOTxw+gp/74GkMkLyvO2srjkCXcZB8krSwlAWiX/Kdl1Vy3LhHFVEzrikgi0vt+0+g9v0nnPfv\nvvZN9yLnAfwH27ZvA4BlWf8WwA8BiCQotfLDZn3Ap+kGH9kv3eGFZfDBAgVOEpISaTy4qETjTr8W\nWUxGQbiyg9zZB6ZTTGqNbVupvhR8C8BftCyrYlmWBeBjAP446ufQwkIpk7UrPG1rpbNfWi0TIU7L\n4iTbcgfvz5eO4q33ezhVOowbg/vO9CTQxSqoyzhIMQjbD3sSwsZHTuLmDmNwoIgkk2Db9luWZf0G\ngKsYlg36jwD+WdTtaCcoZbJ0hWclLJ39e1xEKDSjo1MspUCISi8xmbeyQRSTJEuyql85rpAcV0QC\nFJJmkr2X0rbtXwPwa5NsQ2tBKSiysHQTdKGh4FSjW906wNsi6ScmTSxqTjFJdMF9LCZ1TQjTm9uL\ncbrRABSSJHuMEJSC7rsPMk3c0UVU+kHLZrJkae0ct/xIlmS9f0L8iPt8DnO8U0wSN2y9mBG0Vo5H\nnIk/popTXayUAnetyby5uQkxAT+B53e9iPqwFLeQpIgkumGcoBRQWGaH38XPVLEZhbDC1K9uHRBN\nQJpmnaRl0jx0izPWgbiO4zjFJIVkHsmHhTL7SNAJyfLkyqrMkM4ElbXIGhOFjolxk4SQIUm4uAnR\nEWMtlDI6WCuB4losvZAvljpZLuNwfUexUgL+RZDDrB8VWicJyZZxywHRxU1MJReCUlC0jjsmoZu4\nTFNUAtGF5SRWSYpJQrKFYpJEgUk5GqOLsAQoLlXkqad5VGGatPuaYpKQ7AhzDkQRkxSSxCT0DHSL\nie67DzI/IRkH403WcZYUQPHB75IUnbjFJCkSUym/kiEVC2W3vYvqQnbiIcv6lQAtln5kba2My/UN\nZJslS8skIdkwiZAEaJkk+SE1l7cOolJAcakfWQrLuOpTZlX0nGKSkPQJe+wzk5sEwRjKMei298WC\nDuIyS2EJUFyqyEpYxikqgXSslRSShKRPlGOfBctJkchM1cniMrMxaHTidjsP+LSaMXGKpKQFFwUd\niRMWNQ9H0uedTvckkiK2le4rITLN8haiktbKfWi1HGK6pVJsC4jvZq2LiNRlHISkSdTjnq5uUjS0\nKBtEYanGfdEposDMQljGHQsp34iiblcn8abTWAhJi3GOeybhkCKihaAUZJ24A2SfEe5Ht/OgkKIy\nC5JKsHHfnFT70FG46TgmQpImbjFJiAo7J728tRKUgB6JO7pkhKsoqtUyD5ZKr33ojgljJCRukhCT\ntE6SPKOdoJShKzyYognMtIWlDjUms4JCshgU8dgOIi0xSQiARBNl0kRrQSmgsAxPUQQmhWVyUEiS\nIpPE8c8SQaQIGBXswVJD0WE5IhIFiklCosO4STIJNqZSfSWFERZKGZ2slYD+FktBXi2XtFTGA4Uk\nITwPCJkE4wSlQAdhCZjjCneTN4FJYRkd3jwJ2Wfc82Hc2EnTvF2EBGGsoBRQWMaDuOiZLizTJqv+\n3ZNCMUkIIZrApBy90KGGJZAfYSkwTWBmVV5IoLu4pJAk5CBJWSc916N1kuSQ3AhKQI8algLThaXA\n1FaQWbZuFOggLikgCckOJkSSMLCwuebo5goH8iUuATMEZlbCEshGXFJAEhINnjOExENuBaVAF1c4\nkB+rpYCtIMOTdBIPb4pkHHSwohNSeBhDaQ66WCsFeRKWprjEs7RUyoQVfvKNnmKREP1gZxxCRimE\noBToKiyBfIlLCsvJoYgkJJ8wIYe4sW09NMmkFEpQCnRK3hHkSVyaYLXsrk9pLyoJIYQQU9BDTWWI\nDu0c3eTpCVZntw/bpRFCCCHxUEgLpRvdXOEALZZpYYoLnBBCSF5hUk7u0FFYAvkUl7oKS4DikhBC\nCImKXspJE3R0gwvy4g6nK5wQQgjBsGxQmq+EoIXSAx0TdwR5sVjSFU5IdrAG5ZBSZWOsqgrV+u5Y\nD5/VRw7lxjBAiAwFZQh0dYUD+ROXFJaEkDxQbRzS2hND9IGtFwuIzsISyIe41F1YAhSXhBBCiBs9\nlZHmdNu7WsdZAubHWur8ZM8YS0LyBd3/JFNyEkPJO+ME6C4su+8+cF4m0u080FZYdtennBchxHzG\nEZVB3grdPC2EJAld3jGguyscMNsdrnPyDkB3ODEPWuTiY5zkHCbmkDyirwIyEJ2tlTImX8h0tVgK\naLUkxFwotEkW2PZUqq+k4J0vZoQbXHdxabI7XHdRCdAlTggZ4uVVMc1TREgQdHkniAmucMBMd7ju\nbnAZlaika5wQPRFWyii1KcetSUnIEJYNIiExRVgC++LSFGEJ6FtqyA+KTEL0ZtyC5ypYk5IUAf0V\nTo4wwRUuMNEdbvoFm25yQsxlnAdCkx7cSXLYtpXqKyloocwAWiyTwyRXuB9hRSWtmoQkB62UhISH\ngjJDdO4X7sbkOEuThWUQJlkzKX6JiUSJqRTHuNd5qRKVLCFEGENJYqXb3tVeVApMtFrmWVSagp/4\npdgkuhOntZKQPEJBqREmucIBs4RlEayVJhNkaaXgJDoQVlT6ZX3TSknyCgWlhpjkCgfMFJYAxaVJ\nMCs+Pli8ezLCusCjlhKiqCwuSRYbTxMKSs0xSVyaFmdJq6XZsOUlyZIw1kovUSmuObRUkjyht0Ih\nI5hScggwq70jMy/Nh+WWSBaEsfbyYYcEY6X8SgZegQ3DxFqWJojLbueB8yLmwlqeJG1KlY1AYekl\nKlXeERO8O4So4FXXUEwSloBZhdIpLPMBhSVJk3FjUykqCQubEy0wKcYSYAIPSR8hKul6JEnjl7Dj\nV6OSmd8kD+ivQEhoTLNYmgStluZDayXRGVoqC4xtpftKiIkslJZl/TqAHwNwF8AqgE/atr0Vx8DI\neJhksTQtKxxgZrjp0FpJ3IR1VUcpah5kqYxaoxIw7yGcFI9JFcfrAB63bfsHACwD+IXJh0TiwqQ4\nS5NiLAEm8ZgO4yuLh0iecb8mWT/MOiqq9d1IiTqAOQ/dpLhMdEW1bftrtm2Ls+IKgA9NPiQSN6aI\nSsDMp3AKS0L0JapwjHvbLCRPgrBhpfpKijiTcl4C8L/HuD0SI3SFJw/d4eZBF3g+SVvEBXXP8SqC\n7pWo41f4HDDzwZvojWVZxwH8cwCPA7ABvGTb9v8bZRuBgtKyrK8BOKmY9Vnbtn9nb5lfBHDXtu1/\no9rGve7vO/9PHTmNQ0cejjJGEjMm9Qw3KStccOAmQIGpPd31KYpKw9HBEugnLP0664zTUQegsNSd\ne9vv4d6fvRe8oB6tF/8RgK/Ytv2fWpZ1GMAHom4gUFDatv2jfvMty/qbAD4B4K94LXOk+sNRx0VS\ngMIyHVh+yAxorTQPHUSkCi/xGCQqAe+yQgCFpWkcmXkYR2b2DWi9G7/vs3R2WJZ1DMDTtm3/DQCw\nbfs+gMgJ1hMpCcuyngXwaQA/Ydt2f5JtkexgjGV6MN5Sf5isYwZZicnp8iamy5uBy3mNb5JxM2En\nn2gQQ/lhAH9qWdaXLMv6j5Zl/W+WZVWjfo5JYyj/CYCjAL5mWRYA/IFt2//thNskGcAYy3ShW1xv\n6ALXlyyEpEpAuqft9GsHlpnEUgn4WysBl/eD1kriwZ/9yTvY+ZN3/RY5DOAvAPhp27aXLMv6HIDP\nAPifouxnIkFp2/b3T7I+0RO6wtOHbnH9oKjUC12EZNCybmE5jqgUhBWXKmEJUFySIR/88Bl88MNn\nnPc3v37Zvch3AXzXtu2lvfe/haGgjIT+ioFkBl3h2UC3OEkKXWMOgzBp3FFEaJTP5fdwU20cYqcd\nk8m4U45t2zcBXLcsa3Fv0scAfDPqx2Avb+ILrZXZQbd49jBRJ1uyFJJewnChXFFOb/d7ntvys0aG\nsVQK3MdhmHJDtFiSkPx3AP61ZVlHsdf5MOoGKChJKBhjmT10i2cH3d/po7uYnC8ddf5fG9zFQrni\niMrp8mZo17eYB0Rr7wh4u8SD4iwBikudSLLYeOgx2PZbAC5Msg0KShIZE62WQH7FJUCBSfJD1u7t\nMC7r+dJRnCqN3j7XBncD1wuyRkb97PK2wohLQGrA4LoeUmCSSdFfERBtMSnGEsj3BZNxl8nDckLJ\nk7WY9EPl6j7d866sEiWWcly8Wj/GEW+Zpwdw7bGn0n0lBC2UZCJMcoUD+YuzdEPLZbLQ9Z0cOotJ\nFTcG9/HFP93BSw9Hu+5FiZmMsk3A22Ip48QF89pAYoaCksSGia7wvApLAQVm/FBUxo8uYtLPqtju\n9xwr5eWtLdzePAsAON27i6v31lMZn9/3NOjNhorFVB27QuR2O8P3efbm6Ii9a2c9hFigoCSxY6Kw\nBPIvLgEKzLigqIwPXcRkWJZuNHDhVAedPdH2c98GnlqIJiijWClV34+qqLq8zaDv1L3vQW92z3L5\ngGKSjI3+d3xiLIyx1B/GXpIsMU1Mtvs9PH9mKB6F63hmdsWZF4VxP7ssJoXFNGy8pirmsrs+xfhg\nEgu0UJJEMTXGEiiGxVLAkkTRYY3KyTBNTO70axj0ZvGlVt2ZNhRji8DiMqbL0QRlGNzfkRCOC+XK\nSKa5ELNhLJ/yfHEMi/O/iA/VWpAPjzctlCQ9uu1do6yW3XeL6f4RVktaL/NH3Mkg42KamAxi3O9V\n/h4GvdkRl3WY7+gL3z7u/C/EZlCcpdiHW0wSMikUlCR1TBKVAJ/aecPxh+7CYpBUGaBxxfWld4aW\n0jfaT2CnX8NCuYLm8b7nOGUxqaLo17kssXfTfSUFXd4kE+gKNwu6xElc6Gyd3OnXDggyuf7kwqkO\nWnvjbx7vo93vYadfw3MP7WJtMFwuaiwlEN4iKca4tDlqaeyuL2K7vuu5HbeQpKubJAEFJckck7LC\nAYpLZoofhFnf4dBZTKpQtVo8//AUvvjeLpZuNPDyo3fwxfd28crbiwCAFx9bBgBHaAahEq9uQXp7\n86xTEui5h3ZxGZu4ubzozK/Wd/HUwjVnPXfGtxuKSf2wcxJDSUFJtME0YQkUp56lH04rt4ILS4pK\nf0wTkwIhJF95exEvPraMq9tdTJd7aB7v49VbPUyXAcyuoHm8jx/4wDEA+1ZKt6gT34FKSIokm/nS\nUaeN49KNBkqVDaxdqQGo4RKA588AO4vLuLm86BxvYowq66hfEg4hcWLOnZsUBtNiLIHiJvDIMImH\n8ZR5wG1ZPFU6jMtbWyhVNnB5a8uxPr7RfgIrrYvodC7guYeG16y33u85YjBsoo5sBf2ho885/8+X\njmLzen3EGlmqbOC1703huYd2UTu9jhcfW8bM7Ape+17wcac6Not+zdKG3ZRfCUELJdES02IsBbRY\n0mJJDmKadVKOpbwxuI+njx3DWrkDoILWnTK2N86NCLTLW1sAMOJ2dmdTC2viXG31gGtbWBg/9d3f\ndDrwvCG3UWwcQrfzAIPeLOZqq7i8BczVtrA2qKB5HHtWzYOfQyVq6eomSUFBSbSn2941SlQCjLME\nipvIQ9e3/vhZD4X43enX0Mam03JRCMDpcg/TjSXszNawvXFuL34Rzjpi+wdiFTtAvTkL1FYBAJ3O\nheFKjSW80X5ib791J/7x7z96E19eWEfrThnN433H/S2LXTEmWciqPh8t5yQNeJQRIzCthqUMLQGM\n2SoyplknZTEmBJoqNrF5vI9SZcOxLspizrFOuo777voUdvo1vP5m0+lQ0+lccL4jrzaL7X5vZF6Y\nhB9iDrad7ispaKEkRmFi4g5AVzhQLFc4rZT6Eia2US4wrhJv0+VNnJ+ponVnAz/wgYrj8pZRPUR1\nOw9wE4sHposs7ubxPlp3ynj50Tt4c3Dfd4yqccmfje5ukjYUlMRITBeWQHHFZVFc4RSVZlgnwybP\nuC2EV7e7GPQa+OzVWTQaNzFd3gws2QMMj3/5uH9q4dpIZvcwwWd4XQtbfsj9Odzxm2K/RE/s3XzU\nDaKgJEZjavIOQHEJ5N9qWWRRaZKY9IwxrO9bKoUVUdDu97B5fa+vd2OvhiSGsY2DygaqjbqniJNF\n5etvNnGuecXJ9l4b3MXa4G6gmPQSrUECmdZJkhQUlCQ3mJi8I+i++6CwohI4aLUhJCvkhxzxQOAW\nkyIp5uTiMqbLm8Ms8MFdRxTuVDZQrc8COKQUlfKxXju9DiC4hmQYPIuZ0zqpNzl55qSgJLnCVFc4\nQItlXkVlka2UaeHXZzvIZdxdn/KMdxyyd0zWh6JS7GuhXAHKfbTu1PBK5xxmZlfw0sNTuDG4j3Z5\nE5hdwcwscBOLyu3XTq/jwqkOADhZ4u4xy7GcQTCzm2QNBSXJJSYLS6C4STxFia/MEre1zUT8BKTX\nskKkCfe1Vya2G/lBZ9CbxXR50+ls8wMfqADYwhsbw3lf+Pbw78zsirO8t1itoyUJVHmMgrC/k5+Y\nlPdPd7eesPUiIQZgcowlUGyrZZ4slkWzUiYhWKOISL/1ZWE5jIEMFllDUXYIpdP7006VDuOXv/kB\nDHqnR5Z1em5vbWHQm3WKkquI6tKOug2KSZIm5t1hCSkoRbwh5Cn2iy7I8ZlUTCbBjb2yPqXKxgGr\n55O7M8481YNEtXEolgcMZWkgHmckI2ihJIXBdGslUExXeN4zwYk3cQlJ4aIGgMsiE7s3i2p9F91O\ntG2JDjpie+4QggunOvjyvf3lS5UN1JuzI0IvKWs1xaSZ2DlxXvDoI4XE5M47wFBYildR6HYeGG+x\n5A0/PHGKSQC49E595H1Ut3y1vjvSlvHy1haeP7OOmdkVvPzoHQDAi48tY7509EDGdqmygdrpdecl\nrJqThAYElgcy/Fwh5sGrGyk0JotKQZFEJcAbJRmPQW8Wl7e2RsReqbIRyfLdPN4/MO33/pt/hRuD\n+2g0lvDK24tOgXJ5H16vtCjaNcI47JRfCUGXNyk8pmeEA8VL3jE5YSfvCTqTCqW44yXb/R5eODEH\nLFzDfOkYXvveFJrH+3vdaTpoVTbQ7pz13cawJiXwBp4AsP8ZL2MTa6/8ZyPLvv5mEx9/soV2v4ft\njWE5IdVnirsfN63fJGsoKAnZIw8xlkBx4ixNjq3MWlTmoXRQEHItxy9sAhdOdXHpnTo2r9exvffd\ni9+h2pjCueYVtL56Xrktpwd2Z+94q++7m1v9fRHcaCxhYaGC1p0yXnq4iqvla45FdKFcwfmZKm4M\n7uPy1taBrHMVQW0cST5g60VCckyerJZFEJYmikpykLitk4PerNMe8XXRJhGSkKzvOpa91lfP+5b4\ncdbtPEC3A9SbHkKv3MdLD0/hC98+jkFvATOzK2ge76N1p4w32gt4auHayOJhhKXqcxGiG+beLQlJ\ngbzEWOY9hsrEuEq6KLOluz7l2SVnXAa9WbT7vZGSQjeXFwEAn1koHRCTMnGKaRPPhyJj76b7Sgpa\nKAkJIA/WSiD/FktaKqOhm9s7qqAS2dpetCG2V/dcZhLh1V2fctoxiu/Sia3c2sLm9Y/sj6Xfw637\n1cBtTpc3Y4+tJCQtzL5DEpIippcaEuTZYmlaaSFaKaOzUK4Eisn50lE8fewYmsf7ONe8gtrp9ZH5\nIn7V/QDS7TzA/MXxLYWyKK6dXke1votPPv0t7PRruLrd3XN7P4Gdfs1TOLqFdRjRz+OI6ACPQkIi\nkgdRCbCUCNGHsNbJICEpOFU6jFOlfQecKGIuiFuAqUTfhVMdNBpLznuR9d1dn2IMJBmFZYMIKS7M\nCNcbkzLAs874NoWwYrLd72HpRmNEtMnfsZyIIzPpsTJd3hwZY+tOGdsbTwDHljFd3kSnc8HZb6my\ngZcensLV7f3l3cXQCTENc++EhGhCHlzheXWDm+L+pstyMuZLR50X4J0FLcSkLODlvtqii029afnu\nT6zjtkzOl47i/EwVSzcaAIbCcW1wd0RMAsD2xjl87hsLI+vKYnQctzcxl7wk5fAqRkhM5ElY5klc\nmiIqs8AU16vKOukWkYIXTszhqYVrniJMZaHsrk/hk09/y1nHr4OOnyVzbXAXNwb38fyZdTSP93Hh\nVMfZnrx/MW3pRgPnZ6rOZwhrhSVERygoCYkZ00VlHjFBVNJKqSaKyFob3MUXvn0crTvlkczrqlTI\nXI5rlHnte1MjlsFhwfNR8RjGLb42uDvSfrHd7zm9vE8uLuPFx5YBAP/44WH2+YnDRwDggDAmxcG2\n030lBWMoCUmAPJQayls7R5YVyg+y+JKTb06VDmPpxr7lVbbAliobaDRXcXtztM3iueYV7PRraB7v\no913uZvrswBcolKyMLqRu+LIMZHT5U1nu2uDCp5auIbfmzqK588At+4f3pt+98D2CDEJc+92hBhA\nHtzgQH4ywnW3VNJKGQ0hJoWV79Vbt515pcqGYw1sNJZw4VTH09rZPN7H0o0Gnj52DAvlCqbLmyPW\nTfklti3vBxjtdBMmwUYIyBuD+87/XuuZEppAig2vXoSkQF5EZR6Epe6iMm1MFysnDh9B6c+O4Op2\nFwAwV1sFADx/Zh2Xt7ac5US8pewCr9Z3sVCuYL50FIPe7MjyArlguXgfF273uIDFzQvGbsqvhKDL\nm5CUyFOpIdNd4Dq7v00vI5RVB55PzB4DcAy37t/DjWPDQuZPHzuGy1g6mEE9u4JSZThOYRUctkUc\ntWCKLjjifzfuaTv9WmBNzXa/d8BSypJBJA9QUBKSAd32rvGiEjA7tpKich/d2jBG4db9e0DlHjBs\nn+300QaGFkAh3mRLoBB94q8QeSphJ4vKMIQVlX7ry4xjQa42Do1Y4quPHMqFdyGv2ElmyqSIuXc0\nQgxHxFea7A433Q1O97f+qMSXHH/ofrmX8cIt+vxEnsrt7SfA6bImbkx++A4LBSUhGmCyqATMTtrR\nVVQyQUc/goSkzDiiMsw6pse8FpGHf/wwFp5Z9Zyfl8LmdHkTogmmlxoy2Q2us/s7LeJ0e0fdVpCb\nWBV3uDa4q6zd6GWZzCJOUQjEIBe4l5AMKx692kmSbKg+cgjzF4e/eamygfbXz+K9d89kOqY0oKAk\nRDPyICwpKuPB9ASdKISJPXSTVO3GuF3WOrjAGUeZLPUfPIra6XVcONXBpUuPY+3K8DfvvjsDYHhN\nvH3VY+WcnOIUlIRoisnC0lRrpXB/6yQs0xSVOifnqKyUUdYNiw7iD5jcte1OzAEoKuOk+sgh1JsW\nZmZXnAeh1lfP4zvvzqD6yHAZUx+ux4WCkhDNMTkjvGgXVJIscieaqOsQEgdCSJYqG9i8Xsd6a7/z\nklusywK+CNdBCkpCDMB0UQmYdUHVzf1tout7HGtnWLd3GGvlOEIyL9ZJP2iljE79B4+iWt/FzOwK\nAODm8iLWOzVUG8P54sFZXOOitq1NMlEmTSgoCTEEk13ggHnWSt1EZVpkmZwDRBOVeSROMalye5Ng\nqo8cQrVxCNX6LkqVDVw41cHrbzYda2T33btDYd55MHJdUz08F0m8U1ASYhimWyspKsfDRCulSehi\nnUwDWim9EdbIUmXdmXbp0uMADn5f8nfo9b98vfNKyqGFkhCSGSZbK01zgeskKtPCFCtlXOgiJiex\nTnqVDqKV0h/ZGgkATy200O730Prq+ZHvTjwMu//K89z/i/dFgYKSEIMxXVhSVEYcR4GslGmJyjyI\nySC8Mr6BYgkegdulLR54psubuHTpvLPc+h/c9RSRbgEpPyhH/k5389F6kYKSkBxgqrA0yVpZNFGZ\ntZUSCF8YfFyKICaDKIL7WwhIAI6IBOD8bX/97MjyqoddVayk2LbKQqm6tnnWocwJZt19CCG+mNrC\n0ZQbGl2H4zOJaEqi0HgexaTfg4YOD0NpIzKvhTVSiMm52irmaquYLm8eEJMClVVSni62L/8V1zG3\ntVK8vLDtdF9JQQslITnDZGslLZUhx2CglXJSZAE4rsVSFxEpSNsy6RVPmRf3tyPwJGskAJQq65ir\nrTphFK2vnpeWe3BAPKqstiqXt9tS6XaPu62VeYeCkpCcYqKwNMUFTlGZ7baiiEvdRKQgKTEZ1Nfb\nL0nHNGEpXydGReR+zUj5mDvg2u6MXm9UotLLSulePsy0vENBSQghhmJikk7cVk9dBaMfWcZMAsGZ\n3yYIIG9r5H6CzU6/hlJlQ2GVxEgNyTDeEVWWtzxPjMk9X57HskGEEKOhpTIZdLBSpoVOrm/TSUNM\nBlkpgVFhpZyvkbVSZYkE9q2Rcqa2OFZvb57F2pWDDxty5ra8fZU10S/hRjVP5RI3QZwLLMs6BOAq\ngO/atv3jUdenoCSkIJgqLCkqA8ZgqJUSQKFEaurxkiFEJRDOWilIQxi5z3f3+eW2RAqmy5tYKFfQ\nLq9ipXURJxeXAQwF5fzFTaxdqfmW/VEJySB3tpcQjXzd0uf0/RkAfwzgg+OsTEFJSMEwTVjqbq3U\nQVSmQRJWyiJYPjMtCbQnvsJaKwH/SgZe52AUoRl0HnsJSAAHakbu9GsY9GYxVxuKyBUMx19v7uLm\n8iJEd5vvvDqD6iMHrY/y+N1xkypR6eUi97JG6nrNUmFZ1ocAfALA3wXwP4yzDQpKQgqKyS0cySgm\nWimJnozTWScO4eQnJN3IiVgXTnUwXzqGljTm9ZYNISa9srX9Mrr9rJbuZeT//eInfcsG6XHq/kMA\nnwYwM+4GKCgJKTAmWSt1tlQWyUoJxOuqzqP7O0mrZKmyEXn7YS2VzvKKYzmuGqx+54nbGunFTr+G\nm8uL6HYe4LkXv4N/+sr3+5ZE8opvDOPuDhNTKf9vSrykjGVZPwbglm3bf2RZ1jPjboeCkhBilLVS\n17hKpwRJRsJSiAVTLZV5cH+n5d4eR1QCo8dGWHHprBvzce11nMpdbIRbWyAsk53OBed8E2JSjNHd\nLhGIJipVHXH8BKS8nPPZFO5wP5K2UPb/9F0M1t/1W+SHAPxVy7I+AaAMYMayrF/8OnMAABu8SURB\nVN+wbfuvR9mPZSdZNh2AZVl2ZfbTie6DEBIfpghLHUWlIEtrZVqCMknxZ5qwzCJOMu59RhWYUQk6\nLuXfXIjJF07M4dVbtx1ROYyLBM41rzhlgFSEqR/pNc0r+9v9XiU6vcSo+P/21b8H27YteayWZdmn\nf/IXfb+buLn+5b97YBzSeH4EwN8ZJ8vbjDsHISQ1TGnfaKJrKQ2SFgZpkHWdxiiYNFY/RGvCuB9I\nVNsUyTXyS8XnvrGAp48dw/bGOWxvnHOmr7QuHtxPQ20VVF0nVIkzXiE1Xu+9rJcqK6ewgBrEWJZG\nurwJIQcwxQWus/s77zGVSbuodY+tzFpIjuv2DkMSVm737+g1frHcTr/mdLsZJtkA1YY0RlfMpHB1\nO/MVZXzcNSPFcrIQFNPEcn7v3dPGjvPeTdZTHAXbtv89gH8/zroUlIQQJaYk7FBUKvadUjxlGqJP\nFh1Zi8usRaQbuZi3zqjc2SoGvVlnnihM/k+l+eKckoWkV+Y14F0bMorYdO8jitgsGnrfKQghmWOC\nC1y+CehEXJmxZEiWwkl30aYjbne2+H+hXAFwsA/7XG0VTx87hoVyxfPhwX1OuQWcX4KMV43IoOlB\nMZeqxB6xjHuaCns33VdS0EJJCAmELnAzSas+ZZoZ2mlZLE0SkElYKoO+26B9qVzcwFBEzpeOAQDa\n/d6BTO4vXf6ItFa4BzK/GpHu+W6xJ6aFmS5vX962anoRoaAkhISCLvDxKEI8JZBN2R8vURNlHCYJ\nxyC8PncSoQmTbOvy1hZub57FXG0VC+UK3ugME24Ge9sMOmdUIk8lKuVl3K7pIAHp3r5fB5xJBaYm\nhc0nhoKSEBIJE6yVOopKIJtyQml20dGllmSeRGIcZP2byPvf6dcwXd50OtxceqfuJN0ANTz/fAuX\nLj3uLK8qWK7KphbTgYNxlfK8MMJSTA8rIlXziwgFJSEkMiZYK3UTlVlSRFFJ4kP1e04q2tv9Hlp3\nynvb3nd5X7r0uGdv8bAWP792il7LRBGefvPl7buvP7evegyYFkpCSNHR3Vqpm6jMOvObopKEIcxv\n514mrMAc9GaxvXFOskoeRLboy4LSSyiKeTJRrJXyPLdVMsgyqdpnUaGgJIRMBEVlNCgqic54/WYi\nI1tOoPFaT4hLcQy4s7nXrqi3EQavdofjCEt5vuoa4SUedbqe6AQFJSFkYnQXlbrBRB2iI6rfyi0G\ng4SlvB2/mpNh8Cu75dUjO4qw9Fs+zDpxwaQcQgiR0DmukkWH90nTSglQVJpAkFVSsFCuoN3vjczz\nE5bT5U1nnU7nArrrUzjXvIJ60/J1eQuCvAteolKsK5Zxr6Nazs8aGcd1owglhSgoCSGxorO1Uif3\nd1Fc34D+bRSLTBgxKQqRz5eOOtOEsAxCFpPAfh/uaiNc4f+gh8GgMj1B2dh+14NxGib4jdMrKcfW\nqPXiJFBQEkJih9bKcBRJVAK0VupGWDEphOSTuzN4s7SNtcHdA8uqLJUL5QrOz1TxK62pkWM9bAcp\nr4LkfsvLhBGYfuuHvUYExWcWBQpKQgjJkKLEUwooKvUgym+wNriL+dJRvDm1HWkf86WjePXWbQDD\nwuXu7O0wAjOovI8fXjGQXsThlo6SFe7AGEpCCPGH7u9wZCUqs7BSAhSVJtHu97BQrmBtcBetO2Vs\nb5zDUwvXnALlMu7s7te+N6wxOTzGRkWk1/E+jvgLOo+jnOfjejD86lUWIX4SoKAkhCSM7u5visrh\n75KF+xtgXGUWjPudv/TwFK4ev4bX32wCAD77sRVc3e4CAFr94TKD3iy661NY79QA1FBtHELt9Dq6\nqCstku5jfpzWharlxj2vvdYLcpV7ddkR84ogKikoCSGpoKu1kjGVe/umtbIQjPNdiwScpRsNbF4f\nuq//1Q8Dvze4P7KcEJMj3W06D9Dt1FBtAOeaV6SknPAuayC6OzoJAedVq9KrGLrcHtI/KSf2oWYC\nBSUhJDV0FZUkW2it1AuVOxsY/j6108O/n/5TAJjCdFlke5cPiEmZbucBWp3zAB4476MQNR4yDoIy\nwL2WdT+kFsE6CVBQEkJSRlcXuC7u7yJaKQW0VuqDSlROlzfxmYUSfrU9fL+9cQ7NhWto3SlH6u3t\n1as71LoRRN442wi7Xa/kG9nFLU/zGxstlIQQMgE6WispKvUQlQCtlUkw6Xe6UK7g1v17GPRmceFU\nB+3yEtp9YHvjCTQaS9i8Xg+1HbeIdPfsHpckC5D7ZW+HEZF+Lu+8oNfVnBBCMkYX91QcN9ix972e\n/a0hisWLJINXF5yXH72Ddr/nzH9q4RoWypWxH0SyPNaB/VhHL0ukV8KNmC+my8tHcnvvpvxKCFoo\nCSGZQfd3wDgKbKkERkUlLZaTMe73J7u+2/2ek6AjxOROv+ZkeJcqG6g26mMJxKTPuUnc4ePES8oW\nSx2uJWlAQUkIyRy6v33GUXBRKaArPDtUlspBb9bJ6gZkK+P41sYsvANhywR5ZZyrhKR47/6fWd6E\nEJICFJU+46CodKCwzA7ZYqwqEWQCUZN6/KyRqmmqGMqi1KHU6+pNCCk0wgWuE0W4EQShQ0ylG8ZY\nhicO8Z20mHTHKsZN0PbDiknVOm5LpBCQ7v/zDi2UhBCt0DGuUocbQtY9v3WzVAKMsUwLt5gEkkuk\nCRPDOO52wmw3KJtbnuYWnF5WyeCyQXbgWE2AgpIQoiW6ucApKvUUlQKKy+RxxOT6FCaJlfRiEm9A\n2HNz3LJA7uleLu5xYijzAgUlIYSEhKJSb1EpYJyleSRlhQyzjzjEpN8+A2Mo9T6dQkNBSQjRFrq/\nPcZAURkKWi3jpVTZSCR2NYyYHPecCysiVcuqakiqCpaLZVRubvkvLZSEEJIxdH8rxkBRGQm3ECqK\nwIzjc7qF5PB3j6e7jZu4zis/kepnkXTPD7JKBrm8w1go7Zzk/VFQEkKMQDdrJUWleaJSpqgCc1wO\nfD/1WXQ78Wy7+sihWNovRhGRqnX8+nN7vff7XyxfFAulHldmQggxEB1KCmVdB1DHkkLjIAp1k/BM\n+jCz8Mwqqo3kxaTX8n6xkl7xkUEPkX7WTi/s3XRfSUELJSHEKHRzfxOzLZVuVKKyyNZL0XZRfi86\n55ROA9W6nPmtfsBZeGYVN5cXHYu6+Nv++llUG5M9FAUJyXFbKcrT3ct7WSr94id1ePhMGgpKQohx\n6CQq6freG8OeoMiLsJQpmsh0i0jBQrmC+dJRrJX7aGH4+edqq1goV9CaLeO5h3ZxeWsLK62LAPaF\nohCOqmN0XDEZh5B0LxckJt2iUbVNLxFJlzchhGiKTl11dLA+ZO36LhrCRZ43N3mQmPRabrq8iS9d\n/siImBTubPFe/guM5zJ3u6rjwi+W0mtamMLloayTuym/EoKCkhBiLBSVrjFoICrzElMZBVlcmiwy\nvcSkYG1wF6+8vegIS7H86282D1glZSEpC0dZZEYlbHmhsG7usPGQfnUn3f+7E3GijN106PImhBgN\n3d+uMdD9rQVeolJXV3mQmGz3e3jhxBzOf7SNV28N3dq10+sY9GZRre86vb3lBBv3cRjVOlltHML6\nH9xVz1NY/sJkcquW87NOer2X13Gf9yphydaLIbAs6+cA/DqAum3bep4phJBco1NJIYpKaRw5StaJ\ni7TLFQ16s4H7CBKTIgnnC98+js3rdQDnAGDv/33c2dpuN7dstVRZLuVlAXiKSSDYRe1exmvZMEk4\n7mlerm4/qyRjKAOwLOs0gB8F8G48wyGEkPHRxQWug3tLB/c3UEwXeBRMcpG7BaTAbXl0v8R0saxc\nKkgWlSNiNOQ5FOXhzW/ZoPaJUfYV1d2dl7JBk57p/wDAz8cxEEIIiQOKSmkMGolKCkt/soq/DLJO\nhlnGbXmULZN+Fkt5/SAxGTZRxm8b7vnjZIqr5o0rVPPG2C5vy7J+AsB3bdtuWZYV45AIIWQydImr\npPt7FLrAw5F1F5+FcmXkvSgVNDi97mmlBHDA2ihbIeXjUOUCH9nOhOdMWEGqmudVc1L1XuXu9hKp\nvuJVj+e+ifEVlJZlfQ3AScWsXwTwCwA+Li/utZ173d93/p86chqHjjwcbZSEEBIRikppDBSVRiML\nzDjFpcry6BaTgk/MHkO7v4pBb3Yk6Uq2PHsl3ahE5cnFZbS/fjb0WMdNwFEtGzZb3Ot9GOtk990H\nmKp+F1t//C7u3clH0k0QvoLStu0fVU23LOsJAB8G8NaedfJDAP7Qsqwnbdu+5V7+SPWHYxgqIYRE\nQxdRqQMUlflAiMsowjJMYo4fl7e2cOmdOga9C6Ey+FXxke7pUcTkge3H+ICmsk5Oun1hqSx/3yMo\nf98jzrZ7N34/YE2zGetKa9v2Ndu2H7Jt+8O2bX8YwHcB/AWVmCSEkCzRIaYyqWLMkcehSUwlwGSd\nSUki3lK2Ts6XjjqvTufCyL6cMkFjPBgsPLM60XE4Sdwk4F2uyG9dv+zvOGBSzijFsOcSQoxEB1EJ\nMFHHDZN1JicJYSl3xLn0zjBmslTZGLFyVuu7ONe8MvL7VRuHUG9azv/V+q6T0X1ycRnV+u7Ylskw\nhcjlZVX/A/6liNzEcb7qcM6nRSxnsm3bC6xBSQjRGV1EJSFJEJeoFGLyVOkwTpUO4+VH76BU2XCE\nq2yRFN1x3IhlhNg8ubiMm8uLWG8lZ3vyqhOZBpOKRlooCSHEMHQQlTq4v3WyUgK0VMaFn7UyiuA8\nVTqME4eP4NVbt3FjcB8A8NTCNWf+i48tY2Z2RbmuHGN5rnkFJxeXsb1xLpZjzstdPVIgPYJLfBLB\nmfU5rCNsvUgIKRS6JOpknf2tU5KOgMk68eCVhOOevtOvKTO9hYh84cQcbgzu4/kz61gbAI3G0t4S\nxzwF6sefbGG+dBSXt7aw069he+NcopZJQCpDFMP5FLSNqPsItXxOtCkFJSGkcOgiKrNGV1EJFLsP\neByMm9m9NriL+dJRR1SKacB+0s7a4C5KlQ1U68N9zNVWsVCuoN3vod0HWnfKAIZicvh7xmeddBdB\nd+Z79NL23F7KD3NsvUgIITlFF/d31ujm/hbQBZ4da4O7jogUf2Xa/R4AYGZ2BXO1VTx97Bg+MXts\nJEtcWDDjOr7krjtyS0cvvASjavqk3gK3mK02Djl/iwQtlISQwqKDpTJr1zegp6USoAt8UlQ1K/3c\n3u1+b0QU+olJsc5CueIsd36mCgBo9YfLxm2dlFs3yv3AQ29n7zwTYk/VoUee7lXc3H2u1JsW1jFa\nyL3etFCqrONcc2i9XbrRwPXfVo/L3s1HoRwKSkJIoaGo3BuDxqISoAvcC5Uld5LvSghGYN/FLU9T\nLS9E5drg7t6y5bH374WqD3iQmPRq7eie795PFKFabRzCi499C69g0XH/A8DtzbMY9Gaxcr2OVif7\nRLw0oKAkhBQeisq9MWgqKokar7AAt2XXbZUMm5zjJyTdy4kYyqRwH5dhBJ/f8ey3fhhR+fzz38R8\n6She+94ULr1Tx4uPLePSO3Xc3hzW2dy8Xt9bP1hMJlnKJ00oKAkhBBSVzhg0FZV0f48SFGMa9fvy\nEpVhEWJyp18bmR7VLa2iWt91MsWDknPc+C3jZcEURdm7naE7u9FYwgsn5vDF93YxXd7E7c2zWLrR\nQKuyge2NcwCAL10eCsh98Ri+gHpeoKAkhJA9dBCVOqCzqATo/laJSZUo8kOVBT6JqJSFpIjd3P+d\nPDKzG4dQO72OQW/Ws7RQvWkNxXFjPzFHFnxeuPuJq76fc80rWGldxPzF4Wd+/sw6Lr1Tx1xtFTv9\nGmaeAZrH+1i6cRZf2Nz/bIPe7N5v4BaRY5KTw5mCkhBCJLIWlWl3+fAch6aiEqC10gu/Y0clIL1E\npSCMuHRbJFUMf6tDrvdAqbKOlx+9g1dvraK7flEpOkuVDTz1ZAevv9kcOR6H4/a2htZOrwOoo1rf\nRaOxhE79AoBhZvp0eRM7/Rp2+jWcXFx2PuvaoIJSZcNxWwPAUk/KWF+f2tu+je67xbNABsFHcUII\nIUp0LSkEsKyQiij9rsPgJxaFIPPD3ftbvMS8UmUDV7e7+/NdDzDVxiGUKhuYLx11Ou8ILpzqOH3D\ngaElU4hDsd7JxWVnDDOzK2g0lrC9cU5Z3minXxuJARWWSGGNFGIyiU5X9oN0X0lh2Xay6eqWZdmV\n2U8nug9CCIkbHVzfWVspBbpaKgVFs1Z6JuNIdRqBg9+LqtD5OMXPw+DVSce9P2EtFOvI68lZ08Id\nLyf/yJbEudoqOp0LaDSWfIWu2EbrTlk5xn139pBYXNp73L7692DbtiVPsyzLnj7187FsPyw7N37t\nwDjigC5vQghRkLXrG6D7Oyx0gQ8ZJ25y3I46QZQqG8o6mG5RuNK6iHPNK8Np5VXsSOsB+0JSrCdP\nd4+7uz4FNPZFqlswliob2OnX0OrvC17Zne1sR5QoKkCpnzihoCSEEA9ENx0dhCVFpT9FStip1ncD\nXf5RvockRaWMEIXzpaOYLx3F2uAunn76W1gbVPCJ2WP4ygbQxsG4TWFxFELQa6yf/dgKXr3lPR63\niAT2j5ssRSTLBhFCCCEkE/xE5TiiOilRqUL0Cxd1HJvHga9sbAWuJ7vEVWOV+4+713OTlFu7yFBQ\nEkJIALq4v3WwUgJ6x1QWyf1t0ueU2zuKv53OBXTXp3ATGEm4UeEWku73O/0aXusDIvPbHY8JaOzW\nzrj1omVZpwH8BoATAGwA/8y27X8cdTsUlIQQEgId3N86iEqA7m9TCbJApmmlFDGQXdQPiEk5UUeM\nywuveX5ubUATIakP9wD8rG3b37AsaxrAH1qW9TXbtt+OspHs0xgJIYSEhjfC8LC0UHT8xFucTJc3\nceFUB9X6Lnb6tZGEm0kZqRtpgJjMumyQbds3bdv+xt7/OwDeBtCI+jl4thFCSASEpTLTMWhwQ9S5\nRqUMRWV00hCVQkB+6qNtZ3/jiEq5XqTs5vZyb+tw7uiMZVlnAPx5AP4xCAro8iaEkIjoEFOpAybE\nVALFiquMi7jd315tHW8M7uOXHn8fX9kYXVYeh2psKtwPD1rFSfqQdJb3g3vvYff+9cDl9tzdvwXg\nZ/YslZGgoCSEkDHIWlTqUqMS0D+mEqCoHEccJikq2/0eFsoVrA3uYm1w15kWZkxuTBWSaXHoyMM4\ndORh5/39wX84sIxlWUcA/DaA37Rt+9I4++EjNiGEjIkO7m8SHrq/o5Ok+1sWkHGLSRIey7IsAP8C\nwB/btv25cbfDs4sQQiYga1GpixXGlBs5RWV0VCV4xsXdFrHd742ISS93t6oE0IFakqbGSu6m/DrI\nDwP4KQB/ybKsP9p7PRv1Y9DlTQghE6KD+5uu7/AU3f09Lu4+2+Pi12vbvR/Vez+rpFFCUhNs234D\nMRgY+ahGCCE5QJcbKS2VxSCt8kJu8igmsy4bFBc8owghJAa67V26v/egqNSLpIqVx+kK99qeqkC5\n8z4HYjJP0OVNCCE5gu7vaND9PTluERhVwEbpduO8z5GYTLpsUFpQUBJCSIxkHU8JUFSSbJnUasmY\nSTMphr2fEEJSJGvXN8AbbRSK4vo2gSAxako4RSR27XRfCcGziBBCEkAHUakDpgiAvIrKpOInkyBM\nncmReXxo0op8nkGEEKIBWYtKXW64co1AncmbqMybmKSrW2/ydfYQQohmUFQS4k9WJYh0wd5N95UU\nFJSEEJJzdBGVtFISN16WSVonzYNZ3oQQkjDCSsluOmZkfrOUUPJEsUrmXUwmWWw8TfgoRgghKUH3\n9xBaKpNH5/hJPzHpZ5kkekMLJSGEkNQxwVJJ4qfo8ZJKdu9mPYJYMPsRjBBCDINWSkLUBFmFeezq\nDS2UhBCSMll302E8ZThMjaXU0d0dWLDc8BCDSbDRzXoIsVDcX5AQQjKElsohjJEjXvDYMAsKSkII\nyQiKSv0psuWMkCjQ5U0IIRmStftbB4QlSlf3t0mubxPd3UXHxvtZDyEWin0VI4QQDcjSUtl99wEt\nlTlBRzEZBlqB8wEtlIQQQrRA9yQdQpKBFkpCCCExwXhKvaEVjRB/aKEkhBBNyDqeUodyQrrHU+qK\nqe5uwhhKQgghCUBLJSkapiQ8EX8oKAkhRDMoKlmDkBDToMubEEI0JGv3NzmIruWD6O42G3bKIYQQ\nkltopSRxMa7gdcfRZh3fS/yhhTImHtx7D4eOPJz1MEhM8PfMF6b+nrRSqksJdb/7DqofOpPNgEjs\n/NmfvIsPfviRAmfSb2c9gFgo6q8XO7v3rmc9BBIj/D3zhcm/J4ueH7RUdtfezWgk+mGKu9tvnDvv\nDH9PHcMJSHhooSSEEM3J2lKpQzkhXdApjtIUMSkoVTYit2GsNg6NPFBUHzmkxUNOnNj4ZtZDiAVa\nKAkhxACyzvzOGp3iKYvrmp2cIBEcRqzz4UZPLNu2k92BZSW7A0IIIYQQA7Bt25LfZ6WR3OOIg8QF\nJSGEEEIIyTe02xNCCCGEkImgoCSEEEIIIRNBQUkIIYQQQiaCgjIBLMv6Ocuydi3LilYfgWiFZVm/\nblnW25ZlvWVZ1r+1LOtY1mMi0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"text/plain": [ "<matplotlib.figure.Figure at 0x1348bc50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotGammaiterated((-5, 5),(-5,5), 'gist_earth' )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let us zoom a subregion in the above plot:" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.rcParams['figure.figsize']=9,9" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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YU99m95nJoCqhVhijZmbLHrnQBQB4tf8+HEgct91g3S0oQ0q3rkrcEMpqOCkW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\n2J9MmM4kk7vWgVf776toeF4Pct3qvjXjPbWKUgK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LXVh+cr7qeuXrJJe30saD2TwSm5ci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"text/plain": [ "<matplotlib.figure.Figure at 0x3e6e048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotGammaiterated((1.75, 3.75),(2,3.2), 'cubehelix', N=300, filename='gammafr.png' )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to test the conjecture that the orbits of some points approach the fixed point 1, while other escape to infinity or none of these conditions is met, we define a new function that returns a tuple of four elements, $(r,g,b,a)$, representing respectively the colors `yellow`, `black` and `white`.\n", "\n", "- `yellow` encodes the points whose orbit approaches 1\n", "- `black` encodes the points whose iterates escape to infinity\n", "- `white` encodes the third type of points (fixed points and possible points in other $f$-invariant sets)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def iterGamma3cols(z, Miter):\n", " \n", " for n in range(Miter):\n", " if np.abs(z-1)<0.005:\n", " return (1.0, 0.8, 0.0, 1.0)# yellow\n", " \n", " if np.isnan(np.abs(z)):\n", " return (0,0,0,1) #black\n", " z=gamma(z+1) \n", " \n", " return (1,1,1,1) #white" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plotGammaiter3cols(re, im, N=100, Miter=50, filename=False):\n", " \n", " \n", " Nx=int((re[1]-re[0])*N)\n", " Ny=int((im[1]-im[0])*N)\n", " x=np.linspace(re[0], re[1], Nx)\n", " y=np.linspace(im[0], im[1], Ny)\n", " w=np.zeros((Ny, Nx, 4), dtype=float)\n", "\n", " for n in xrange(Ny):\n", " for m in xrange(Nx):\n", " z =x[m]+1j*y[n]\n", " w[n][m] = iterGamma3cols(z, Miter)\n", " \n", " fig=plt.figure() \n", " ax = fig.add_subplot(111) \n", "\n", " im=plt.imshow(w, extent=(re[0], re[1], im[0], im[1]), interpolation='nearest', origin='lower') \n", "\n", " if(filename):\n", " plt.savefig(filename) " ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.rcParams['figure.figsize']=10,10" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x13185908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotGammaiter3cols(re=(1,7), im=(-6,6))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us zoom the central protuberance:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.rcParams['figure.figsize']=10, 6" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11a565c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotGammaiter3cols(re=(1.75,6.5), im=(-1,1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The colored representation of this protuberance is the following:" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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G+iDccx148ueV+bj/nBx9qQtf+NUv4Jtffkf4N4H9x9d0LV7JqU68pkjKHJR5INtStSGp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\nY6v4/9u72xi5qjoM4M/TbmvZhYBJu8UtpeUDxk8ESlVCoYIBLAZfSDR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"text/plain": [ "<matplotlib.figure.Figure at 0x13a8e2b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotGammaiterated((1.75,6.5), (-1,1), 'cubehelix', N=300, Miter=100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A new zoom:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11ea22e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotGammaiter3cols(re=(1.95,3), im=(-0.25,0.25), N=500)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and its colored version illustrates the self similarity:" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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VOrUh0+DJTdVjcOte6TkvgcLgQ3S808VEwP13my9cWw6qeW5imVK/R0xkeM1L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\nbq5Gr/PxG8+rP+e+Qsd4He8lrry2ueEU3V7WSa/+3CyE9nmn504Ru+5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Yy7eqXcIiK+IG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UKD3cHtdX0soqnIu14J1/+5zuc+DGVEAvRF1re3YC8vw2O8NPnQ679xtFztVX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\n0zj60pT6+QTRrmKoty8r500tmasRUAeUW+4MJ8L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"text/plain": [ "<matplotlib.figure.Figure at 0x12daa160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotGammaiterated((-4.5,0), (-1.25,1.25), 'cubehelix', N=200, Miter=100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To be continued :)\n", "\n", "This IPython Notebook is included in the folder Math:\n", "[https://github.com/empet/Math/blob/master/README.md](https://github.com/empet/Math/blob/master/README.md)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style>\n", " /*body {\n", " background-color: #F5F5F5;\n", " }*/\n", " div.cell{\n", " width: 900px;\n", " margin-left: 13% !important;\n", " margin-right: auto;\n", " }\n", " #notebook li { /* More space between bullet points */\n", " margin-top:0.8em;\n", " }\n", "\n", " h1 {\n", " font-family: 'Alegreya Sans', sans-serif;\n", " }\n", " .text_cell_render h1 {\n", " font-weight: 200;\n", " font-size: 40pt;\n", " line-height: 100%;\n", " color: rgb(8, 66, 133);\n", " margin-bottom: 0em;\n", " margin-top: 0em;\n", " display: block;\n", " }\n", " h2 {\n", " font-family: 'Fenix', serif;\n", " text-indent:1em;\n", " }\n", " .text_cell_render h2 {\n", " font-weight: 200;\n", " font-size: 28pt;\n", " line-height: 100%;\n", " color: rgb(8, 66, 133);\n", " margin-bottom: 0.1em;\n", " margin-top: 0.1em;\n", " %margin-bottom: 1.5em;\n", " %margin-top: 0.5em;\n", " display: block;\n", " }\n", " h3 {\n", " font-family: 'Fenix', serif;\n", " %margin-top:12px;\n", " %margin-bottom: 3px;\n", " }\n", " .text_cell_render h3 {\n", " font-weight: 300;\n", " font-size: 18pt;\n", " line-height: 100%;\n", " color: rgb(8, 66, 133);\n", " margin-bottom: 0.5em;\n", " margin-top: 2em;\n", " display: block;\n", " }\n", " h4 {\n", " font-family: 'Fenix', serif;\n", " }\n", " .text_cell_render h4 {\n", " font-weight: 300;\n", " font-size: 16pt;\n", " color: rgb(8, 66, 133);\n", " margin-bottom: 0.5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\n", " h5 {\n", " font-family: 'Alegreya Sans', sans-serif;\n", " }\n", " .text_cell_render h5 {\n", " font-weight: 300;\n", " font-style: normal;\n", " font-size: 16pt;\n", " margin-bottom: 0em;\n", " margin-top: 1.5em;\n", " display: block;\n", " }\n", " div.text_cell_render{\n", " font-family: 'Alegreya Sans',Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n", " line-height: 145%;\n", " font-size: 130%;\n", " width:900px;\n", " margin-left:auto;\n", " margin-right:auto;\n", " %text-align:justify;\n", " %text-justify:inter-word;\n", " }\n", " \n", " \n", " code{\n", " font-size: 78%;\n", " }\n", " .rendered_html code{\n", " background-color: transparent;\n", " white-space: inherit; \n", " }\n", " .prompt{\n", " display: None;\n", " }\n", " .rendered_html code{\n", " background-color: transparent;\n", " }\n", "\n", " blockquote{\n", " display:block;\n", " background: #f3f3f3;\n", " font-family: \"Open sans\",verdana,arial,sans-serif;\n", " width:610px;\n", " padding: 15px 15px 15px 15px;\n", " text-align:justify;\n", " text-justify:inter-word;\n", " }\n", " blockquote p {\n", " margin-bottom: 0;\n", " line-height: 125%;\n", " font-size: 100%;\n", " }\n", " /* element.style {\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" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.core.display import HTML\n", "def css_styling():\n", " styles = open(\"./custom.css\", \"r\").read()\n", " return HTML(styles)\n", "css_styling()" ] } ], "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 }
bsd-3-clause
Cyb3rWard0g/HELK
docker/helk-jupyter/notebooks/sigma/win_susp_dns_config.ipynb
1
3010
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# DNS Server Error Failed Loading the ServerLevelPluginDLL\n", "This rule detects a DNS server error in which a specified plugin DLL (in registry) could not be loaded" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rule Content\n", "```\n", "- title: DNS Server Error Failed Loading the ServerLevelPluginDLL\n", " id: cbe51394-cd93-4473-b555-edf0144952d9\n", " description: This rule detects a DNS server error in which a specified plugin DLL\n", " (in registry) could not be loaded\n", " status: experimental\n", " date: 2017/05/08\n", " references:\n", " - https://medium.com/@esnesenon/feature-not-bug-dnsadmin-to-dc-compromise-in-one-line-a0f779b8dc83\n", " - https://technet.microsoft.com/en-us/library/cc735829(v=ws.10).aspx\n", " - https://twitter.com/gentilkiwi/status/861641945944391680\n", " tags:\n", " - attack.defense_evasion\n", " - attack.t1073\n", " author: Florian Roth\n", " logsource:\n", " product: windows\n", " service: dns-server\n", " category: null\n", " detection:\n", " selection:\n", " EventID:\n", " - 150\n", " - 770\n", " condition: selection\n", " falsepositives:\n", " - Unknown\n", " level: critical\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Querying Elasticsearch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import Libraries" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from elasticsearch import Elasticsearch\n", "from elasticsearch_dsl import Search\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialize Elasticsearch client" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "es = Elasticsearch(['http://helk-elasticsearch:9200'])\n", "searchContext = Search(using=es, index='logs-*', doc_type='doc')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run Elasticsearch Query" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s = searchContext.query('query_string', query='event_id:(\"150\" OR \"770\")')\n", "response = s.execute()\n", "if response.success():\n", " df = pd.DataFrame((d.to_dict() for d in s.scan()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Show Results" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df.head()" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 4 }
gpl-3.0
google/learned_optimization
docs/notebooks/Part3_Truncation_TruncatedStep.ipynb
1
216266
{ "cells": [ { "cell_type": "markdown", "id": "1bb8dc7b", "metadata": { "id": "1bb8dc7b" }, "source": [ "# Part 3: Truncation and TruncatedStep" ] }, { "cell_type": "code", "execution_count": 1, "id": "6ZVfhXllCTTe", "metadata": { "executionInfo": { "elapsed": 54, "status": "ok", "timestamp": 1647909291310, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 240 }, "id": "6ZVfhXllCTTe" }, "outputs": [], "source": [ "!pip install git+https://github.com/google/learned_optimization.git" ] }, { "cell_type": "code", "execution_count": 2, "id": "8573ae8d", "metadata": { "executionInfo": { "elapsed": 28361, "status": "ok", "timestamp": 1647909319784, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 240 }, "id": "8573ae8d" }, "outputs": [], "source": [ "import numpy as np\n", "import jax.numpy as jnp\n", "import jax\n", "import functools\n", "from matplotlib import pylab as plt\n", "\n", "from learned_optimization.outer_trainers import full_es\n", "from learned_optimization.outer_trainers import truncated_pes\n", "from learned_optimization.outer_trainers import gradient_learner\n", "from learned_optimization.outer_trainers import truncation_schedule\n", "from learned_optimization.outer_trainers import common\n", "from learned_optimization.outer_trainers import lopt_truncated_step\n", "from learned_optimization.outer_trainers import truncated_step as truncated_step_mod\n", "\n", "from learned_optimization.tasks import quadratics\n", "from learned_optimization.tasks.fixed import image_mlp\n", "from learned_optimization.tasks import base as tasks_base\n", "\n", "from learned_optimization.learned_optimizers import base as lopt_base\n", "from learned_optimization.learned_optimizers import mlp_lopt\n", "from learned_optimization.optimizers import base as opt_base\n", "\n", "from learned_optimization import optimizers\n", "from learned_optimization import training\n", "from learned_optimization import eval_training\n", "\n", "import haiku as hk\n", "import tqdm" ] }, { "cell_type": "markdown", "id": "b10e3fad", "metadata": { "id": "b10e3fad" }, "source": [ "## Training learned optimizers: Truncated training, and different kinds of gradient estimators.\n", "\n", "In the previous colabs we showed some of the core abstractions this library is based on: `Task`, `Optimizer`, `LearnedOptimizer`, and `TaskFamily`. We also showed a rather minimal meta-training procedure\n", "which trains learned optimizers to perform well with a handful of inner-training steps.\n", "\n", "In this colab, we discuss inner-training and meta-training in more detail and discuss some of the more heavy weight abstractions designed to facilitate this.\n", "\n", "First, we will discuss truncations, and `TruncationSchedule` which define how long to unroll a particular sequence. Next, we will discuss the `TruncatedStep` abstraction which provide an interface for performing inner-training using some meta-learned components.\n", "While this might seem abstract at the moment, this `TruncatedStep`\n", "abstraction allows us to compute gradients of the meta-learned components using ES, PES, or backprop of any\n", "iterative system and is general enough to work with learned optimizers, as well as other meta-learned systems." ] }, { "cell_type": "markdown", "id": "17f92031", "metadata": { "id": "17f92031" }, "source": [ "## Truncated training and TruncationSchedules\n", "\n", "When applying a learned optimizer to train some target task, one usually wants the optimizer to be performant for a very large number of steps as training a model can take hundreds to hundreds of thousands of iterations.\n", "Ideally we would like our meta-training procedure to mirror the testing setup but given how long these unrolls (iterative application of the learned optimizer) can be this can become challenging. Truncated training is one solution to this. The core idea is to never run an entire inner-problem to completion, but instead unroll a shorter segment, and leverage information from that shorter segment to update the weights of the learned optimizer.\n", "\n", "This is most commonly seen in the form of truncated backpropogation through time and is used to train training recurrent neural networks. More recently, truncated training has been used to train RL algorithms (e.g. A3C).\n", "\n", "Truncated training has a number of benifits. First, it greatly reduces the amount of computation needed before updating the learned optimizer. If one has length 100 truncations for a total length of 10k iterations, one 100x more updates to the weights of the learned optimizer. For some methods, like PES, we can even do these gradient estimates in an unbiased way (technically less biased, see PES paper for a discussion on hysteresis). For others, such as gradient based meta-training, and other ES variants, this comes at the cost of bias.\n", "\n", "In code, truncations are handed by a TruncationSchedule subclass which is a small, stateless classes which manage how long we should be computing training for. For example, here we see a constant length truncation which runs for 10 steps then reports done." ] }, { "cell_type": "code", "execution_count": 3, "id": "c4f944d6", "metadata": { "executionInfo": { "elapsed": 235, "status": "ok", "timestamp": 1647909320152, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 240 }, "id": "c4f944d6", "outputId": "811e029c-0bf3-4e6c-b88d-3f3c94a153b3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 False\n", "5 False\n", "6 False\n", "7 False\n", "8 False\n", "9 False\n", "10 True\n", "11 True\n" ] } ], "source": [ "trunc_sched = truncation_schedule.ConstantTruncationSchedule(10)\n", "outer_state = None\n", "key = jax.random.PRNGKey(0)\n", "trunc_state = trunc_sched.init(key, outer_state)\n", "for i in range(12):\n", " trunc_state, is_done = trunc_sched.next_state(trunc_state, i, key,\n", " outer_state)\n", " print(i, is_done)" ] }, { "cell_type": "markdown", "id": "6ee32360", "metadata": { "id": "6ee32360" }, "source": [ "In practice, we often run these sequentially.\n", "\n", "For example, here is a loop which let's us sequentially train a model over and over again.\n", "To do this, we must keep track of some state which progresses from inner-iteration to\n", "inner-iteration.\n", "In this case, this contains three values: the problem we are training's `opt_state`, the\n", "state of the truncation, and a rng key." ] }, { "cell_type": "code", "execution_count": 4, "id": "9540b593", "metadata": { "executionInfo": { "elapsed": 9207, "status": "ok", "timestamp": 1647909329490, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 240 }, "id": "9540b593" }, "outputs": [], "source": [ "key = jax.random.PRNGKey(0)\n", "task = image_mlp.ImageMLP_FashionMnist8_Relu32()\n", "opt = opt_base.Adam(3e-3)\n", "outer_state = None\n", "\n", "\n", "def init_state(key):\n", " key, key1, key2 = jax.random.split(key, 3)\n", " p = task.init(key1)\n", " opt_state = opt.init(p)\n", "\n", " trunc_sched = truncation_schedule.ConstantTruncationSchedule(50)\n", " trunc_state = trunc_sched.init(key2, outer_state)\n", "\n", " return opt_state, trunc_state, key\n", "\n", "\n", "def next_trunc_and_train(train_state_and_batch):\n", " (opt_state, trunc_state, key), batch = train_state_and_batch\n", " # progress one step on the truncation state\n", " trunc_state, is_done = trunc_sched.next_state(trunc_state,\n", " opt_state.iteration, key,\n", " outer_state)\n", "\n", " # progress one step by computing a gradient and applying an update.\n", " p = opt.get_params(opt_state)\n", " key, key1 = jax.random.split(key)\n", " l, g = jax.value_and_grad(task.loss)(p, key1, batch)\n", " opt_state = opt.update(opt_state, g, loss=l)\n", "\n", " return (opt_state, trunc_state, key), is_done, l\n", "\n", "\n", "def reset_trunc_and_init(train_state_and_batch):\n", " (opt_state, trunc_state, key), batch = train_state_and_batch\n", "\n", " # new inner problem\n", " p = task.init(key)\n", " opt_state = opt.init(p)\n", "\n", " # new truncation\n", " key, key1 = jax.random.split(key)\n", " trunc_state = trunc_sched.init(key1, outer_state)\n", "\n", " return (opt_state, trunc_state, key), False, jnp.nan\n", "\n", "\n", "is_done = False\n", "state = init_state(key)\n", "\n", "losses = []\n", "for i in range(200):\n", " batch = next(task.datasets.train)\n", " state, is_done, loss = jax.lax.cond(is_done, reset_trunc_and_init,\n", " next_trunc_and_train, (state, batch))\n", " losses.append(loss)" ] }, { "cell_type": "markdown", "id": "38a703dd", "metadata": { "id": "38a703dd" }, "source": [ "Now we can plot the losses. We see a sequence of decreasing losses, which get reset back to initialization every 25 steps." ] }, { "cell_type": "code", "execution_count": 5, "id": "c0654f72", "metadata": { "colab": { "height": 296 }, "executionInfo": { "elapsed": 295, "status": "ok", "timestamp": 1647909329901, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 240 }, "id": "c0654f72", "outputId": "345880f5-663c-4c05-858b-4c8c30accd50" }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'loss')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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VOeQ7Brvn5RhaPI/AjDwCnvW0Zx0MT0w3zPClOQVSyXGVdjBUiqOpjIjQlnHYcwTA8lcO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\nowyBQqFQNDnKECgUCkWT8/8DQ+7ICtI2XBoAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 600x400 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(losses)\n", "plt.xlabel(\"all inner-iteration\")\n", "plt.ylabel(\"loss\")" ] }, { "cell_type": "markdown", "id": "2919fe20", "metadata": { "id": "2919fe20" }, "source": [ "One key reason why this abstraction is important is when thinking about training multiple models in parallel. Naively, we could train all models starting from initialization with the exact same iteration into training.\n", "This is sometimes refered to as \"lock step\" training.\n", "\n", "One alternative is to break this lock-steping, and let our models train different parts of the inner-problem at different times.\n", "With this TruncationState abstraction we can do this by either training with variable length unrolls, or faking the initial state so models reset early.\n", "\n", "For starters, we can see training multiple models with lock step unrolls:" ] }, { "cell_type": "code", "execution_count": 6, "id": "9c2da78a", "metadata": { "executionInfo": { "elapsed": 7613, "status": "ok", "timestamp": 1647909337682, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 240 }, "id": "9c2da78a" }, "outputs": [], "source": [ "n_tasks = 8\n", "\n", "\n", "@jax.jit\n", "@jax.vmap\n", "def update(state, is_done, batch):\n", " state, is_done, loss = jax.lax.cond(is_done, reset_trunc_and_init,\n", " next_trunc_and_train, (state, batch))\n", " return state, is_done, loss\n", "\n", "\n", "keys = jax.random.split(key, n_tasks)\n", "states = jax.vmap(init_state)(keys)\n", "\n", "losses = []\n", "is_done = jnp.zeros([n_tasks])\n", "for i in range(200):\n", " vec_batch = training.vec_get_batch(task, n_tasks)\n", " states, is_done, l = update(states, is_done, vec_batch)\n", " losses.append(l)" ] }, { "cell_type": "code", "execution_count": 7, "id": "1b8fe368", "metadata": { "colab": { "height": 296 }, "executionInfo": { "elapsed": 301, "status": "ok", "timestamp": 1647909338096, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 240 }, "id": "1b8fe368", "outputId": "eaffc123-e90b-4e5d-e478-1e4c0142fdd9" }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'loss')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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7OEVJhiFczDRhoIz8+gSTZT2PgxtpESTA31dK3QG8A/jfhBB3/rnXfAh4i1Lq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KsW3/GKauhzutLAx4A0BDWLw7irEXQwaLuSOOlQSBMwpbV5uRr1O9I+yqZF0piFWJBs1YOkT3JB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\nQU5OTk7O93K1aQQ5OTk5OeeRC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"text/plain": [ "<Figure size 600x400 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "losses = np.asarray(losses)\n", "for i in range(n_tasks):\n", " plt.plot(losses[:, i])\n", "\n", "plt.xlabel(\"all inner-iteration\")\n", "plt.ylabel(\"loss\")" ] }, { "cell_type": "markdown", "id": "e24586dc", "metadata": { "id": "e24586dc" }, "source": [ "And then, we can compare this to what the curves look like if we break the lockstep by simply faking the initial iteration." ] }, { "cell_type": "code", "execution_count": 8, "id": "26d7ba6d", "metadata": { "executionInfo": { "elapsed": 3090, "status": "ok", "timestamp": 1647909341341, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 240 }, "id": "26d7ba6d" }, "outputs": [], "source": [ "key = jax.random.PRNGKey(0)\n", "keys = jax.random.split(key, n_tasks)\n", "states = jax.vmap(init_state)(keys)\n", "opt_state, trunc_state, keys = states\n", "import dataclasses\n", "\n", "opt_state = opt_state.replace(\n", " iteration=jax.random.randint(key, [n_tasks], 0, 50))\n", "states = (opt_state, trunc_state, keys)\n", "\n", "losses = []\n", "is_done = jnp.zeros([n_tasks])\n", "for i in range(200):\n", " vec_batch = training.vec_get_batch(task, n_tasks)\n", " states, is_done, l = update(states, is_done, vec_batch)\n", " losses.append(l)" ] }, { "cell_type": "code", "execution_count": 9, "id": "12bf6111", "metadata": { "colab": { "height": 265 }, "executionInfo": { "elapsed": 295, "status": "ok", "timestamp": 1647909341741, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 240 }, "id": "12bf6111", "outputId": "5eb3efdf-2ecf-4968-c65e-9e35f75578f0" }, "outputs": [ { "data": { "image/png": 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JV5O6d7ybHnbiM5/gc3/4OwD4vk8+ZyNck4a/s7IWulDtUwfkT08VHO7el+dT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"text/plain": [ "<Figure size 600x400 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "losses = np.asarray(losses)\n", "for i in range(n_tasks):\n", " plt.plot(losses[:, i])" ] }, { "cell_type": "markdown", "id": "f9014491", "metadata": { "id": "f9014491" }, "source": [ "With this version, all the tasks start lockstep-ed initially, but then reset early so as to ensure the training trajectories are not running in lock step." ] }, { "cell_type": "markdown", "id": "9ef76f1f", "metadata": { "id": "9ef76f1f" }, "source": [ "# TruncatedStep: An interface for unrolled computation graphs\n", "\n", "The TruncatedStep abstraction defines an interface which can be used to inner-train a task with some meta-learned pieces.\n", "\n", "This abstraction is entirely independent of learned optimizers and can (hopefully) be used for just about any meta-learning system.\n", "\n", "The interface exposes the following:\n", " * `outer_init`: Initialize outer parameters (weights of learned optimizer).\n", " * `init_step_state`: Which initializes the state of the inner problem.\n", " * `unroll_step`: Which trains the inner-problem state a single step using the learned algorithm.\n", " * `meta_loss_batch`: Computes a meta-loss from a given inner-problem state.\n", "\n", "Inner-training, and computing meta-losses often require data. To this end, `TruncatedStep` also provide a `get_batch` and `get_outer_batch` for getting data for inner-training, and computing meta-losses respectively.\n", "\n", "When we use this abstraction, we iterativly apply unroll_step. As such, we must ensure that this function occasionally resets as we saw in the previous section with truncation schedules." ] }, { "cell_type": "markdown", "id": "7b0d8422", "metadata": { "id": "7b0d8422" }, "source": [ "As a demonstration of this, let's define a simple `TruncatedStep` for a learned optimizer. For many cases, this is unnecessary as `VectorizedLOptTruncatedStep` should be used as it supports task family, truncation schedule and a number of different ways to calculate meta-loss." ] }, { "cell_type": "code", "execution_count": 10, "id": "f1106d1b", "metadata": { "executionInfo": { "elapsed": 61, "status": "ok", "timestamp": 1647909341952, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 240 }, "id": "f1106d1b" }, "outputs": [], "source": [ "class SimpleLOptTruncStep(truncated_step_mod.TruncatedStep):\n", "\n", " def __init__(self, lopt, task, unroll_length=5):\n", " self.lopt = lopt\n", " self.task = task\n", " self.unroll_length = unroll_length\n", "\n", " def outer_init(self, key):\n", " return self.lopt.init(key)\n", "\n", " def init_step_state(self, theta, outer_state, key):\n", " params = self.task.init(key)\n", " return self.lopt.opt_fn(theta).init(params)\n", "\n", " @functools.partial(jax.jit, static_argnums=(0,))\n", " def unroll_step(self, theta, unroll_state, key, data, outer_state):\n", " opt = self.lopt.opt_fn(theta)\n", "\n", " def train(unroll_state):\n", " params = opt.get_params(unroll_state)\n", " loss, grad = jax.value_and_grad(task.loss)(params, key, data)\n", " unroll_state = opt.update(unroll_state, grad, loss=loss)\n", " out = truncated_step_mod.TruncatedUnrollOut(\n", " loss=loss,\n", " is_done=False,\n", " task_param=None,\n", " iteration=unroll_state.iteration,\n", " mask=True)\n", " return unroll_state, out\n", "\n", " def reset(unroll_state):\n", " params = self.task.init(key) # Modify this\n", " unroll_state = self.lopt.opt_fn(theta).init(params)\n", " out = truncated_step_mod.TruncatedUnrollOut(\n", " loss=0.0,\n", " is_done=True,\n", " task_param=None,\n", " iteration=unroll_state.iteration,\n", " mask=False)\n", " return unroll_state, out\n", "\n", " return jax.lax.cond(unroll_state.iteration < self.unroll_length, train,\n", " reset, unroll_state)\n", "\n", " def meta_loss_batch(self, theta, unroll_state, key, data, outer_state):\n", " params = self.lopt.opt_fn(theta).get_params(unroll_state)\n", " return task.loss(params, key, data)\n", "\n", " def get_batch(self, steps=None):\n", " if steps is None:\n", " return next(task.train)\n", " else:\n", " return training.vec_get_batch(task, steps, split=\"train\")\n", "\n", " def get_outer_batch(self, steps=None):\n", " if steps is None:\n", " return next(task.train)\n", " else:\n", " return training.vec_get_batch(task, steps, split=\"train\")" ] }, { "cell_type": "markdown", "id": "f794ae8a", "metadata": { "id": "f794ae8a" }, "source": [ "We can now use our truncated step function in a simple loop to run 10 steps of inner-training. Because we are resetting every 5 steps, we see the loss also resets. When we reset, we report of loss as 0.0, but also have a mask=False for this iteration." ] }, { "cell_type": "code", "execution_count": 11, "id": "b6a6bcbc", "metadata": { "executionInfo": { "elapsed": 908, "status": "ok", "timestamp": 1647909342968, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 240 }, "id": "b6a6bcbc", "outputId": "13aece58-b51a-4182-95f8-c2ddf40fa2bd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration= 0\n", "Iteration= 1 Loss= 2.658556 Mask= True\n", "Iteration= 2 Loss= 2.5532773 Mask= True\n", "Iteration= 3 Loss= 2.361111 Mask= True\n", "Iteration= 4 Loss= 2.1044798 Mask= True\n", "Iteration= 5 Loss= 1.8072809 Mask= True\n", "Iteration= 0 Loss= 0.0 Mask= False\n", "Iteration= 1 Loss= 2.658556 Mask= True\n", "Iteration= 2 Loss= 2.5532773 Mask= True\n", "Iteration= 3 Loss= 2.361111 Mask= True\n", "Iteration= 4 Loss= 2.1044798 Mask= True\n" ] } ], "source": [ "lopt = lopt_base.LearnableSGDM()\n", "task = quadratics.QuadraticTask()\n", "truncated_step = SimpleLOptTruncStep(lopt, task, unroll_length=5)\n", "\n", "key = jax.random.PRNGKey(0)\n", "theta = truncated_step.outer_init(key)\n", "\n", "unroll_state = truncated_step.init_step_state(theta, None, key)\n", "print(\"Iteration=\", unroll_state.iteration)\n", "\n", "for i in range(10):\n", " batch = jax.tree_map(lambda x: x[0], truncated_step.get_batch(1))\n", " unroll_state, out = truncated_step.unroll_step(theta, unroll_state, key,\n", " batch, None)\n", " print(\"Iteration=\", unroll_state.iteration, \"Loss=\", out.loss, \"Mask=\",\n", " out.mask)" ] }, { "cell_type": "markdown", "id": "acaece93", "metadata": { "id": "acaece93" }, "source": [ "## VecTruncatedStep\n", "\n", "Now for performance reasons, we often want to inner-train batches. `VecTruncatedStep` both provides an interface for vectorized steps, as well as a wrapper function to convert non-vectorized to vectorized." ] }, { "cell_type": "code", "execution_count": 12, "id": "bd3db7cc", "metadata": { "executionInfo": { "elapsed": 1921, "status": "ok", "timestamp": 1647909345001, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 240 }, "id": "bd3db7cc", "outputId": "e26b40da-23ee-417e-ce48-9025e60bfca8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration= [0 0 0]\n", "Iteration= [1 1 1] Loss= [8.163749 7.267569 6.8465357] Mask= [ True True True]\n", "Iteration= [2 2 2] Loss= [7.8404646 6.9797735 6.5754128] Mask= [ True True True]\n", "Iteration= [3 3 3] Loss= [7.25037 6.4544573 6.080529 ] Mask= [ True True True]\n", "Iteration= [4 4 4] Loss= [6.4623203 5.752917 5.419631 ] Mask= [ True True True]\n", "Iteration= [5 5 5] Loss= [5.549699 4.9404783 4.65426 ] Mask= [ True True True]\n", "Iteration= [0 0 0] Loss= [0. 0. 0.] Mask= [False False False]\n", "Iteration= [1 1 1] Loss= [8.163749 7.267569 6.8465357] Mask= [ True True True]\n", "Iteration= [2 2 2] Loss= [7.8404646 6.9797735 6.5754128] Mask= [ True True True]\n", "Iteration= [3 3 3] Loss= [7.25037 6.4544573 6.080529 ] Mask= [ True True True]\n", "Iteration= [4 4 4] Loss= [6.4623203 5.752917 5.419631 ] Mask= [ True True True]\n" ] } ], "source": [ "truncated_step = truncated_step_mod.VectorizedTruncatedStep(truncated_step, 3)\n", "\n", "key = jax.random.PRNGKey(0)\n", "theta = truncated_step.outer_init(key)\n", "\n", "unroll_state = truncated_step.init_step_state(theta, None, key)\n", "print(\"Iteration=\", unroll_state.iteration)\n", "\n", "for i in range(10):\n", " batch = jax.tree_map(lambda x: x[0], truncated_step.get_batch(1))\n", " unroll_state, out = truncated_step.unroll_step(theta, unroll_state, key,\n", " batch, None)\n", " print(\"Iteration=\", unroll_state.iteration, \"Loss=\", out.loss, \"Mask=\",\n", " out.mask)" ] }, { "cell_type": "markdown", "id": "38fe30c7", "metadata": { "id": "38fe30c7" }, "source": [ "## VectorizedLOptTruncatedStep\n", "In the above examples, we created a simple `TruncatedStep` for a learned optimizer and task. This is great, but in reality we want to use more of the machinery we have been developing through these notebooks -- namely, `TaskFamily` and `TruncationSchedule`. To this end, we also provide a `VectorizedLOptTruncatedStep` which takes in a `TaskFamily`, `LearnedOptimizer`, and `TruncationSchedule` and constructs the appropriate `VectorizedTruncatedStep`.\n", "\n", "In the below example, we will construct one fo these `TruncatedStep` on a quadratic task family, with randomized starting iteration." ] }, { "cell_type": "code", "execution_count": 13, "id": "8ac6db44", "metadata": { "executionInfo": { "elapsed": 4333, "status": "ok", "timestamp": 1647909349458, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 240 }, "id": "8ac6db44", "outputId": "5404e568-1aa5-404c-9113-b70a1254ee8c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration= [2 4 4]\n", "Iteration= [3 5 5] Loss= [30.317793 32.50056 10.791804] Mask= [ True True True]\n", "Iteration= [4 0 0] Loss= [28.276335 0. 0. ] Mask= [ True False False]\n", "Iteration= [5 1 1] Loss= [25.218487 24.83813 36.847755] Mask= [ True True True]\n", "Iteration= [0 2 2] Loss= [ 0. 25.100714 33.250145] Mask= [False True True]\n", "Iteration= [1 3 3] Loss= [10.66427 22.762098 31.916872] Mask= [ True True True]\n", "Iteration= [2 4 4] Loss= [10.144799 20.350817 28.981028] Mask= [ True True True]\n", "Iteration= [3 5 5] Loss= [ 8.875515 17.26621 23.050066] Mask= [ True True True]\n", "Iteration= [4 0 0] Loss= [8.438987 0. 0. ] Mask= [ True False False]\n", "Iteration= [5 1 1] Loss= [ 5.835725 25.567972 35.97472 ] Mask= [ True True True]\n", "Iteration= [0 2 2] Loss= [ 0. 23.460606 33.696133] Mask= [False True True]\n" ] } ], "source": [ "task_family = quadratics.FixedDimQuadraticFamilyData(10)\n", "lopt = lopt_base.LearnableSGDM()\n", "trunc_sched = truncation_schedule.ConstantTruncationSchedule(5)\n", "truncated_step = lopt_truncated_step.VectorizedLOptTruncatedStep(\n", " task_family,\n", " lopt,\n", " trunc_sched,\n", " num_tasks=3,\n", " random_initial_iteration_offset=5)\n", "\n", "key = jax.random.PRNGKey(1)\n", "theta = truncated_step.outer_init(key)\n", "\n", "unroll_state = truncated_step.init_step_state(theta, None, key)\n", "print(\"Iteration=\", unroll_state.inner_step)\n", "\n", "for i in range(10):\n", " batch = jax.tree_map(lambda x: x[0], truncated_step.get_batch(1))\n", " unroll_state, out = truncated_step.unroll_step(theta, unroll_state, key,\n", " batch, None)\n", " print(\"Iteration=\", unroll_state.inner_step, \"Loss=\", out.loss, \"Mask=\",\n", " out.mask)" ] } ], "metadata": { "colab": { "collapsed_sections": [], "last_runtime": { "build_target": "//learning/deepmind/public/tools/ml_python:ml_notebook", "kind": "private" }, "name": "Part3_Truncation_TruncatedStep.ipynb", "provenance": [] }, "jupytext": { "formats": "ipynb,md:myst,py", "main_language": "python" }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 5 }
apache-2.0
machinelearningnanodegree/stanford-cs231
assignments/assignment2/Dropout.ipynb
4
8912
{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# Dropout\n", "Dropout [1] is a technique for regularizing neural networks by randomly setting some features to zero during the forward pass. In this exercise you will implement a dropout layer and modify your fully-connected network to optionally use dropout.\n", "\n", "[1] Geoffrey E. Hinton et al, \"Improving neural networks by preventing co-adaptation of feature detectors\", arXiv 2012" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# As usual, a bit of setup\n", "\n", "import time\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from cs231n.classifiers.fc_net import *\n", "from cs231n.data_utils import get_CIFAR10_data\n", "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n", "from cs231n.solver import Solver\n", "\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", "plt.rcParams['image.interpolation'] = 'nearest'\n", "plt.rcParams['image.cmap'] = 'gray'\n", "\n", "# for auto-reloading external modules\n", "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "def rel_error(x, y):\n", " \"\"\" returns relative error \"\"\"\n", " return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))" ], "outputs": [], "metadata": { "collapsed": false } }, { "execution_count": null, "cell_type": "code", "source": [ "# Load the (preprocessed) CIFAR10 data.\n", "\n", "data = get_CIFAR10_data()\n", "for k, v in data.iteritems():\n", " print '%s: ' % k, v.shape" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "# Dropout forward pass\n", "In the file `cs231n/layers.py`, implement the forward pass for dropout. Since dropout behaves differently during training and testing, make sure to implement the operation for both modes.\n", "\n", "Once you have done so, run the cell below to test your implementation." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "x = np.random.randn(500, 500) + 10\n", "\n", "for p in [0.3, 0.6, 0.75]:\n", " out, _ = dropout_forward(x, {'mode': 'train', 'p': p})\n", " out_test, _ = dropout_forward(x, {'mode': 'test', 'p': p})\n", "\n", " print 'Running tests with p = ', p\n", " print 'Mean of input: ', x.mean()\n", " print 'Mean of train-time output: ', out.mean()\n", " print 'Mean of test-time output: ', out_test.mean()\n", " print 'Fraction of train-time output set to zero: ', (out == 0).mean()\n", " print 'Fraction of test-time output set to zero: ', (out_test == 0).mean()\n", " print" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "# Dropout backward pass\n", "In the file `cs231n/layers.py`, implement the backward pass for dropout. After doing so, run the following cell to numerically gradient-check your implementation." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "x = np.random.randn(10, 10) + 10\n", "dout = np.random.randn(*x.shape)\n", "\n", "dropout_param = {'mode': 'train', 'p': 0.8, 'seed': 123}\n", "out, cache = dropout_forward(x, dropout_param)\n", "dx = dropout_backward(dout, cache)\n", "dx_num = eval_numerical_gradient_array(lambda xx: dropout_forward(xx, dropout_param)[0], x, dout)\n", "\n", "print 'dx relative error: ', rel_error(dx, dx_num)" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "# Fully-connected nets with Dropout\n", "In the file `cs231n/classifiers/fc_net.py`, modify your implementation to use dropout. Specificially, if the constructor the the net receives a nonzero value for the `dropout` parameter, then the net should add dropout immediately after every ReLU nonlinearity. After doing so, run the following to numerically gradient-check your implementation." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "N, D, H1, H2, C = 2, 15, 20, 30, 10\n", "X = np.random.randn(N, D)\n", "y = np.random.randint(C, size=(N,))\n", "\n", "for dropout in [0, 0.25, 0.5]:\n", " print 'Running check with dropout = ', dropout\n", " model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n", " weight_scale=5e-2, dtype=np.float64,\n", " dropout=dropout, seed=123)\n", "\n", " loss, grads = model.loss(X, y)\n", " print 'Initial loss: ', loss\n", "\n", " for name in sorted(grads):\n", " f = lambda _: model.loss(X, y)[0]\n", " grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n", " print '%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))\n", " print" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "# Regularization experiment\n", "As an experiment, we will train a pair of two-layer networks on 500 training examples: one will use no dropout, and one will use a dropout probability of 0.75. We will then visualize the training and validation accuracies of the two networks over time." ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Train two identical nets, one with dropout and one without\n", "\n", "num_train = 500\n", "small_data = {\n", " 'X_train': data['X_train'][:num_train],\n", " 'y_train': data['y_train'][:num_train],\n", " 'X_val': data['X_val'],\n", " 'y_val': data['y_val'],\n", "}\n", "\n", "solvers = {}\n", "dropout_choices = [0, 0.75]\n", "for dropout in dropout_choices:\n", " model = FullyConnectedNet([500], dropout=dropout)\n", " print dropout\n", "\n", " solver = Solver(model, small_data,\n", " num_epochs=25, batch_size=100,\n", " update_rule='adam',\n", " optim_config={\n", " 'learning_rate': 5e-4,\n", " },\n", " verbose=True, print_every=100)\n", " solver.train()\n", " solvers[dropout] = solver" ], "outputs": [], "metadata": { "scrolled": false, "collapsed": false } }, { "execution_count": null, "cell_type": "code", "source": [ "# Plot train and validation accuracies of the two models\n", "\n", "train_accs = []\n", "val_accs = []\n", "for dropout in dropout_choices:\n", " solver = solvers[dropout]\n", " train_accs.append(solver.train_acc_history[-1])\n", " val_accs.append(solver.val_acc_history[-1])\n", "\n", "plt.subplot(3, 1, 1)\n", "for dropout in dropout_choices:\n", " plt.plot(solvers[dropout].train_acc_history, 'o', label='%.2f dropout' % dropout)\n", "plt.title('Train accuracy')\n", "plt.xlabel('Epoch')\n", "plt.ylabel('Accuracy')\n", "plt.legend(ncol=2, loc='lower right')\n", " \n", "plt.subplot(3, 1, 2)\n", "for dropout in dropout_choices:\n", " plt.plot(solvers[dropout].val_acc_history, 'o', label='%.2f dropout' % dropout)\n", "plt.title('Val accuracy')\n", "plt.xlabel('Epoch')\n", "plt.ylabel('Accuracy')\n", "plt.legend(ncol=2, loc='lower right')\n", "\n", "plt.gcf().set_size_inches(15, 15)\n", "plt.show()" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "# Question\n", "Explain what you see in this experiment. What does it suggest about dropout?" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# Answer\n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.6", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }
mit
ThierryMondeel/FBA_python_tutorial
FBA_tutorials/5_biomarker_prediction_PKU.ipynb
1
6461
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Applying Shlomi et al's biomarker prediction method to PKU on RECON2\n", "\n", "<p>**Authors**: Thierry D.G.A Mondeel, Stefania Astrologo, Ewelina Weglarz-Tomczak & Hans V. Westerhoff <br/>\n", "University of Amsterdam <br/>2017\n", "</p>\n", "\n", "The original publication looked at RECON2's predecessor RECON1. We will reproduce their analysis on RECON2 instead." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import cobra\n", "from utils import findBiomarkers\n", "import pandas as pd\n", "\n", "from IPython.core.interactiveshell import InteractiveShell\n", "InteractiveShell.ast_node_interactivity = \"all\"\n", "\n", "M = cobra.io.load_json_model('models/recon_2_2_simple_medium.json')\n", "model = M.copy() # this way we can edit model but leave M unaltered" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<span style=\"color:red\">**Assignment (10 min):**</span> Take the previous tutorial's last command cell as a template (we already copy-pasted it for you) to do the same analysis here on the full RECON2 model and the PKU disease state. \n", "\n", "**Tips**\n", "- Don't reinvent the wheel, the idea is the same as the last tutorial.\n", "- Instead of giving 'R1' as the disease reaction you should now give the PKU gene HGNC:8582 or the two reactions it catalyzes as input. \n", "- Also think about the fact that there are two equivalent reactions that you have to account for not just one.\n", "- The number of exchange reactions here is much bigger than in the example. The FVA computation will take quite a bit longer as a result. It may take 2 minutes or so. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "exchanges = [ rxn for rxn in model.reactions if rxn.products == [] and 'EX_' in rxn.id ]\n", "\n", "# Shlomi et al suggested using the following medium: everything in (-1) with a few exceptions \n", "# everything out (unlimited)\n", "# This was actually first proposed in (Sahoo et al, 2012)\n", "for rxn in exchanges:\n", " rxn.lower_bound = -1\n", " rxn.upper_bound = 999999\n", " \n", "# specifics\n", "M.reactions.EX_o2_e.lower_bound = -40\n", "\n", "for rxn in ['EX_h2o_e','EX_h_e','EX_co2_e','EX_nh4_e','EX_pi_e','EX_hco3_e','EX_so4_e']:\n", " M.reactions.get_by_id(rxn).lower_bound = - 100\n", "\n", "# to reduce computation time we check all amino acids + a couple neurotransmitters\n", "biomarkers_to_check = ['EX_his_L_e','EX_ile_L_e','EX_leu_L_e','EX_lys_L_e','EX_met_L_e',\n", " 'EX_phe_L_e','EX_thr_L_e','EX_trp_L_e','EX_val_L_e','EX_cys_L_e',\n", " 'EX_glu_L_e','EX_tyr_L_e','EX_ala_L_e','EX_asp_L_e','EX_gly_e',\n", " 'EX_arg_L_e','EX_gln_L_e','EX_pro_L_e','EX_ser_L_e','EX_asn_L_e',\n", " 'EX_dopa_e','EX_adrnl_e','EX_srtn_e']\n", "\n", "# UNCOMMENT & FIX THE LINE BELOW\n", "findBiomarkers(model,fvaRxns=biomarkers_to_check,mods=['HGNC:8582'],synchronous=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "len(exchanges)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "findBiomarkers(model,fvaRxns=exchanges,mods=['HGNC:8582'],synchronous=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<span style=\"color:red\">**Assignment (3 min):**</span> If you see the prediction of the biofluids/tissue as the brain tissue: does the model correctly predict issues with neurotransmitters in the brain?\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<span style=\"color:red\">**Assignment (10 min):**</span>\n", "What biomarkers are predicted when you focus on blocking the cofactor biopterin recycling reactions that also produce PKU? To get you started we included some code below." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.reactions.DHPR.gene_reaction_rule, model.reactions.DHPR.reaction\n", "\n", "model.reactions.DHPR2.gene_reaction_rule,model.reactions.DHPR2.reaction\n", "\n", "model.reactions.r0398.gene_reaction_rule,model.reactions.r0398.reaction" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# UNCOMMENT & FIX THE LINE BELOW\n", "# findBiomarkers(model,fvaRxns=biomarkers_to_check,mods=[ADD THE RELEVANT GENES HERE],synchronous=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<span style=\"color:red\">**Assignment (3 min):**</span> Is this what you expected? If not, is the gene annotation perhaps an issue? Check what would happen if you gave 'findBiomarkers' the full list of reactions as input. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Copy what you did above but give reactions as input" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<span style=\"color:red\">**Assignment (3 min):**</span> Are there any differences between the predictions for the two different ways to get PKU?" ] }, { "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": "3.6.1" }, "toc": { "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "toc_cell": false, "toc_position": {}, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 }
mit
jrbourbeau/cr-composition
notebooks/legacy/radviz.ipynb
1
531951
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['',\n", " '/cvmfs/icecube.opensciencegrid.org/py2-v1/RHEL_6_x86_64/lib/python2.7/site-packages/setuptools-15.2-py2.7.egg',\n", " '/cvmfs/icecube.opensciencegrid.org/py2-v1/RHEL_6_x86_64/lib/python2.7/site-packages/setuptools-15.2-py2.7.egg',\n", " '/home/jbourbeau/.local/lib/python2.7/site-packages',\n", " '/cvmfs/icecube.opensciencegrid.org/py2-v1/RHEL_6_x86_64/i3ports/root-v5.34.18/lib',\n", " '/cvmfs/icecube.opensciencegrid.org/py2-v1/RHEL_6_x86_64/lib/python2.7/site-packages',\n", " '/cvmfs/icecube.opensciencegrid.org/py2-v1/RHEL_6_x86_64/i3ports/lib/python2.7/site-packages',\n", " '/data/user/jbourbeau/metaprojects/icerec/V05-00-00/build/lib',\n", " '/home/jbourbeau/cr-composition/analysis',\n", " '/home/jbourbeau',\n", " '/home/jbourbeau/useful',\n", " '/home/jbourbeau/anisotropy',\n", " '/home/jbourbeau/ShowerLLH_scripts',\n", " '/cvmfs/icecube.opensciencegrid.org/py2-v1/RHEL_6_x86_64/lib/python27.zip',\n", " '/cvmfs/icecube.opensciencegrid.org/py2-v1/RHEL_6_x86_64/lib/python2.7',\n", " '/cvmfs/icecube.opensciencegrid.org/py2-v1/RHEL_6_x86_64/lib/python2.7/plat-linux2',\n", " '/cvmfs/icecube.opensciencegrid.org/py2-v1/RHEL_6_x86_64/lib/python2.7/lib-tk',\n", " '/cvmfs/icecube.opensciencegrid.org/py2-v1/RHEL_6_x86_64/lib/python2.7/lib-old',\n", " '/cvmfs/icecube.opensciencegrid.org/py2-v1/RHEL_6_x86_64/lib/python2.7/lib-dynload',\n", " '/home/jbourbeau/.local/lib/python2.7/site-packages/IPython/extensions',\n", " '/home/jbourbeau/.ipython',\n", " '/home/jbourbeau/cr-composition']" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import sys\n", "sys.path.append('/home/jbourbeau/cr-composition')\n", "sys.path" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jbourbeau/.local/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "from pandas.tools.plotting import radviz\n", "import matplotlib.pyplot as plt\n", "from matplotlib.colors import ListedColormap\n", "import seaborn.apionly as sns\n", "\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.model_selection import validation_curve, GridSearchCV, cross_val_score, ParameterGrid\n", "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "import composition as comp\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sns.set_palette('muted')\n", "sns.set_color_codes()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jbourbeau/cr-composition/composition/load_sim.py:105: RuntimeWarning: divide by zero encountered in log10\n", " df['log_NChannels_1_30'] = np.nan_to_num(np.log10(df['NChannels_1_30']))\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "['lap_log_energy', 'InIce_log_charge_1_30', 'lap_cos_zenith', 'NChannels_1_30', 'log_s125']\n", "number training events = 73257\n" ] } ], "source": [ "df, cut_dict = comp.load_sim(return_cut_dict=True)\n", "selection_mask = np.array([True] * len(df))\n", "standard_cut_keys = ['lap_reco_success', 'lap_zenith', 'num_hits_1_30', 'IT_signal',\n", " 'StationDensity', 'max_qfrac_1_30', 'lap_containment', 'energy_range_lap']\n", "for key in standard_cut_keys:\n", " selection_mask *= cut_dict[key]\n", "\n", "df = df[selection_mask]\n", "\n", "feature_list, feature_labels = comp.get_training_features()\n", "print(feature_list)\n", "X_train, X_test, y_train, y_test, le = comp.get_train_test_sets(\n", " df, feature_list)\n", "\n", "print('number training events = ' + str(y_train.shape[0]))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "radviz?" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0xf313f10>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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izkuSJJSUlHglA86ePYv8/HzYbC1rQtia/Pz8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zZtKugcl8uFa665Bvfddx+sVt4vQUREVGrmzJmDW2+91RBjOyIiIqLiYbGAiIiISko8Hsc111xj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Bz+cwAAgJt35MgRzZ4926bm7e2t2NhY1axZ06CuAABwPYQFAADAUKtWrdKgQYPUqlUrbdmy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\n+lZcgwAMV1BQoO3bt2vJk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"text/plain": [ "<matplotlib.figure.Figure at 0x69bc6d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "feature_list = ['lap_log_energy', 'InIce_log_charge_1_30', 'lap_cos_zenith', 'lap_chi2', 'log_NChannels_1_30']\n", "tmp = df[feature_list+['MC_comp']]\n", "tmp.columns = ['energy', 'charge', 'zenith', '$\\chi^2$', '$\\mathrm{N}_{\\mathrm{channels}}$', 'comp']\n", "opts = {'alpha': 0.5}\n", "radviz(tmp.sample(2000), 'comp', color=['b', 'g', 'r'], **opts)\n", "plt.legend(title='True composition')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Y8kBZ4ASmaW41DCPv09K8WdC0wMdbyj/RX6pV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\n7XVoqVhpLRHriCv966wFGcELx+i+dJJeXzJftTFphikLpA6QcUR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xeDwej+e2fOMb39gn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Q2AyMwZKGWzSK37EAHThBwModDejlI2i2JnF4qdXbudIwcC0OJxOFYRuY9bYTSOgZVKuUOS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\nERHR4GCxgIiIiIYVtiMiI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s4444zceeedTdr+x//4H/nqV7/aZHk8AKA2+UQAAFAle+yx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\n1bqUBAAAq1dXV5eePXumZ8+eLVomaPny5Vm8eHGj8OD9m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"text/plain": [ "<matplotlib.figure.Figure at 0xf1f8150>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "feature_list = ['lap_log_energy', 'InIce_log_charge_1_30', 'lap_chi2', 'log_NChannels_1_30']\n", "tmp = df[feature_list+['MC_comp']]\n", "tmp.columns = ['energy', 'charge', '$\\chi^2$', '$\\mathrm{N}_{\\mathrm{channels}}$', 'comp']\n", "opts = {'alpha': 0.5}\n", "radviz(tmp.sample(5000), 'comp', color=['b', 'g', 'r'], ax=ax, **opts)\n", "leg = plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.05),\n", " ncol=3, fancybox=False, shadow=False)\n", "ax.set_xlim([-1.5, 1.5])\n", "# ax.set_ylim([-1.5, 1.5])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0xd134d90>" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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DmTNnKjg4WHPnzi1xf/yf7Oxsff/9944nCI4d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yv8NfXfUT/HSaQktzz6IxShpMEBYIXfjL7AT/u2Robal6a3DSfPPC/xi9wEQq4Dvf+Q5dXV2l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\nfFF33HGHZsyYkedsBABlS3p6ut5991117dp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"text/plain": [ "<matplotlib.figure.Figure at 0xd134e50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "feature_list = ['InIce_log_charge_1_30', 'lap_chi2', 'log_NChannels_1_30']\n", "tmp = df[feature_list+['MC_comp']]\n", "tmp.columns = ['charge', 'chisquared', 'nchannels','comp']\n", "opts = {'alpha': 0.75}\n", "radviz(tmp.sample(10000), 'comp', color=['b', 'g', 'r'], **opts)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>energy</th>\n", " <th>charge</th>\n", " <th>zenith</th>\n", " <th>chisquared</th>\n", " <th>nchannels</th>\n", " <th>comp</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>410625</th>\n", " <td>6.397034</td>\n", " <td>2.582136</td>\n", " <td>0.903370</td>\n", " <td>0.018033</td>\n", " 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<td>6.451769</td>\n", " <td>2.437444</td>\n", " <td>0.974462</td>\n", " <td>0.536076</td>\n", " <td>1.954243</td>\n", " <td>P</td>\n", " </tr>\n", " <tr>\n", " <th>9738</th>\n", " <td>6.248129</td>\n", " <td>2.613261</td>\n", " <td>0.977961</td>\n", " <td>0.962385</td>\n", " <td>1.819544</td>\n", " <td>P</td>\n", " </tr>\n", " <tr>\n", " <th>460497</th>\n", " <td>7.750908</td>\n", " <td>3.392241</td>\n", " <td>0.967225</td>\n", " <td>0.004413</td>\n", " <td>1.991226</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>225685</th>\n", " <td>6.417087</td>\n", " <td>3.057016</td>\n", " <td>0.909740</td>\n", " <td>0.529079</td>\n", " <td>2.000000</td>\n", " <td>Fe</td>\n", " </tr>\n", " <tr>\n", " <th>288323</th>\n", " <td>6.259881</td>\n", " <td>2.782538</td>\n", " <td>0.997396</td>\n", " <td>1.296459</td>\n", " <td>1.892095</td>\n", " <td>Fe</td>\n", " </tr>\n", " <tr>\n", " <th>312338</th>\n", " <td>7.021274</td>\n", " <td>3.385866</td>\n", " <td>0.936394</td>\n", " <td>0.008081</td>\n", " <td>2.008600</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>682676</th>\n", " <td>7.943206</td>\n", " <td>3.759114</td>\n", " <td>0.941402</td>\n", " <td>0.005726</td>\n", " <td>2.367356</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>395070</th>\n", " <td>7.206418</td>\n", " <td>2.981299</td>\n", " <td>0.971848</td>\n", " <td>0.005997</td>\n", " <td>2.190332</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>230632</th>\n", " <td>6.354967</td>\n", " <td>2.553778</td>\n", " <td>0.889854</td>\n", " <td>0.621153</td>\n", " <td>1.851258</td>\n", " <td>Fe</td>\n", " </tr>\n", " <tr>\n", " <th>335190</th>\n", " <td>7.808167</td>\n", " <td>3.407286</td>\n", " <td>0.925869</td>\n", " <td>0.004090</td>\n", " <td>2.064458</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>165148</th>\n", " <td>6.935387</td>\n", " <td>3.874148</td>\n", " <td>0.998741</td>\n", " <td>0.544677</td>\n", " <td>2.264818</td>\n", " <td>Fe</td>\n", " </tr>\n", " <tr>\n", " <th>666833</th>\n", " <td>7.469054</td>\n", " <td>3.509535</td>\n", " <td>0.997292</td>\n", " <td>0.007213</td>\n", " <td>2.206826</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>439029</th>\n", " <td>7.485090</td>\n", " <td>3.095054</td>\n", " <td>0.948641</td>\n", " <td>0.006108</td>\n", " <td>1.892095</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>359011</th>\n", " <td>7.409304</td>\n", " <td>3.031690</td>\n", " <td>0.901930</td>\n", " <td>0.011204</td>\n", " <td>1.919078</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>203077</th>\n", " <td>7.591679</td>\n", " <td>4.049980</td>\n", " <td>0.989314</td>\n", " <td>0.424587</td>\n", " <td>2.397940</td>\n", " <td>Fe</td>\n", " </tr>\n", " <tr>\n", " <th>226766</th>\n", " <td>6.279322</td>\n", " <td>2.531278</td>\n", " <td>0.999118</td>\n", " <td>0.848731</td>\n", " <td>1.954243</td>\n", " <td>Fe</td>\n", " </tr>\n", " <tr>\n", " <th>478497</th>\n", " <td>6.409206</td>\n", " <td>2.348780</td>\n", " <td>0.933751</td>\n", " <td>0.020602</td>\n", " <td>1.913814</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>751112</th>\n", " <td>7.378921</td>\n", " <td>3.698391</td>\n", " <td>0.979064</td>\n", " <td>0.006050</td>\n", " <td>2.255273</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>179042</th>\n", " <td>6.578899</td>\n", " <td>2.714567</td>\n", " <td>0.990158</td>\n", " <td>0.875237</td>\n", " <td>2.060698</td>\n", " <td>Fe</td>\n", " </tr>\n", " <tr>\n", " <th>407339</th>\n", " <td>7.246759</td>\n", " <td>3.466579</td>\n", " <td>0.995805</td>\n", " <td>0.008744</td>\n", " <td>2.107210</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>35980</th>\n", " <td>6.439591</td>\n", " <td>2.274554</td>\n", " <td>0.985640</td>\n", " <td>0.572963</td>\n", " <td>1.924279</td>\n", " <td>P</td>\n", " </tr>\n", " <tr>\n", " <th>418578</th>\n", " <td>7.737861</td>\n", " <td>3.888848</td>\n", " <td>0.982745</td>\n", " <td>0.006232</td>\n", " <td>2.551450</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>472418</th>\n", " <td>7.029702</td>\n", " <td>2.966702</td>\n", " <td>0.984996</td>\n", " <td>0.012064</td>\n", " <td>2.309630</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>72605</th>\n", " <td>6.616210</td>\n", " <td>1.863344</td>\n", " <td>0.939302</td>\n", " <td>0.908468</td>\n", " <td>1.662758</td>\n", " <td>P</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", " </tr>\n", " <tr>\n", " <th>540438</th>\n", " <td>7.635235</td>\n", " <td>3.344949</td>\n", " <td>0.916680</td>\n", " <td>0.371357</td>\n", " <td>2.068186</td>\n", " <td>P</td>\n", " </tr>\n", " <tr>\n", " <th>745381</th>\n", " <td>7.224881</td>\n", " <td>3.543076</td>\n", " <td>0.946783</td>\n", " <td>0.008685</td>\n", " <td>2.209515</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>636034</th>\n", " <td>7.694195</td>\n", " <td>3.208489</td>\n", " <td>0.875337</td>\n", " <td>0.563716</td>\n", " <td>1.845098</td>\n", " <td>Fe</td>\n", " </tr>\n", " <tr>\n", " <th>689572</th>\n", " <td>7.887085</td>\n", " <td>3.582302</td>\n", " <td>0.986074</td>\n", " <td>0.004891</td>\n", " <td>2.357935</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>197066</th>\n", " <td>6.389286</td>\n", " <td>2.821031</td>\n", " <td>0.991168</td>\n", " <td>0.871667</td>\n", " <td>2.220108</td>\n", " <td>Fe</td>\n", " </tr>\n", " <tr>\n", " <th>538707</th>\n", " <td>7.120678</td>\n", " <td>3.652542</td>\n", " <td>0.953604</td>\n", " <td>0.720714</td>\n", " <td>2.193125</td>\n", " <td>P</td>\n", " </tr>\n", " <tr>\n", " <th>266672</th>\n", " <td>6.606214</td>\n", " <td>2.834531</td>\n", " <td>0.886157</td>\n", " <td>0.701748</td>\n", " <td>1.832509</td>\n", " <td>Fe</td>\n", " </tr>\n", " <tr>\n", " <th>319847</th>\n", " <td>6.294831</td>\n", " <td>1.875603</td>\n", " <td>0.910515</td>\n", " <td>0.016761</td>\n", " <td>1.146128</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>141390</th>\n", " <td>7.361120</td>\n", " <td>2.967779</td>\n", " <td>0.929718</td>\n", " <td>0.549327</td>\n", " <td>2.012837</td>\n", " <td>P</td>\n", " </tr>\n", " <tr>\n", " <th>340039</th>\n", " <td>6.457139</td>\n", " <td>2.406038</td>\n", " <td>0.983427</td>\n", " <td>0.015175</td>\n", " <td>1.913814</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>426059</th>\n", " <td>7.891147</td>\n", " <td>4.143046</td>\n", " <td>0.943648</td>\n", " <td>0.005663</td>\n", " <td>2.264818</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>591808</th>\n", " <td>7.727805</td>\n", " <td>3.578130</td>\n", " <td>0.990121</td>\n", " <td>0.726145</td>\n", " <td>2.245513</td>\n", " <td>Fe</td>\n", " </tr>\n", " <tr>\n", " <th>704250</th>\n", " <td>7.197488</td>\n", " <td>3.609758</td>\n", " <td>0.967279</td>\n", " <td>0.012840</td>\n", " <td>2.285557</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>315139</th>\n", " <td>6.868255</td>\n", " <td>2.913922</td>\n", " <td>0.983329</td>\n", " <td>0.010590</td>\n", " <td>2.049218</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>280997</th>\n", " <td>6.213838</td>\n", " <td>2.408053</td>\n", " <td>0.926214</td>\n", " <td>0.666286</td>\n", " <td>1.799341</td>\n", " <td>Fe</td>\n", " </tr>\n", " <tr>\n", " <th>354127</th>\n", " <td>6.984879</td>\n", " <td>3.164351</td>\n", " <td>0.937214</td>\n", " <td>0.010250</td>\n", " <td>1.944483</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>201615</th>\n", " <td>6.630084</td>\n", " <td>2.876483</td>\n", " <td>0.957543</td>\n", " <td>0.649181</td>\n", " <td>2.152288</td>\n", " <td>Fe</td>\n", " </tr>\n", " <tr>\n", " <th>702213</th>\n", " <td>7.741076</td>\n", " <td>3.590199</td>\n", " <td>0.970695</td>\n", " <td>0.006664</td>\n", " <td>2.326336</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>236064</th>\n", " <td>7.929628</td>\n", " <td>3.901189</td>\n", " <td>0.930726</td>\n", " <td>0.389490</td>\n", " <td>2.232996</td>\n", " <td>Fe</td>\n", " </tr>\n", " <tr>\n", " <th>177427</th>\n", " <td>7.312445</td>\n", " <td>3.582322</td>\n", " <td>0.987446</td>\n", " <td>0.460195</td>\n", " <td>2.324282</td>\n", " <td>Fe</td>\n", " </tr>\n", " <tr>\n", " <th>767748</th>\n", " <td>7.319944</td>\n", " <td>3.342337</td>\n", " <td>0.945847</td>\n", " <td>0.009085</td>\n", " <td>1.857332</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>128234</th>\n", " <td>7.164379</td>\n", " <td>3.139925</td>\n", " <td>0.998584</td>\n", " <td>0.418169</td>\n", " <td>2.075547</td>\n", " <td>P</td>\n", " </tr>\n", " <tr>\n", " <th>750497</th>\n", " <td>7.413582</td>\n", " <td>3.334821</td>\n", " <td>0.999974</td>\n", " <td>0.006774</td>\n", " <td>2.336460</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>35284</th>\n", " <td>6.260259</td>\n", " <td>2.113491</td>\n", " <td>0.954701</td>\n", " <td>0.476327</td>\n", " <td>1.755875</td>\n", " <td>P</td>\n", " </tr>\n", " <tr>\n", " <th>154305</th>\n", " <td>6.629496</td>\n", " <td>2.921224</td>\n", " <td>0.985331</td>\n", " <td>0.775961</td>\n", " <td>2.096910</td>\n", " <td>Fe</td>\n", " </tr>\n", " <tr>\n", " <th>136074</th>\n", " <td>7.209143</td>\n", " <td>2.876873</td>\n", " <td>0.893183</td>\n", " <td>0.667331</td>\n", " <td>1.863323</td>\n", " <td>P</td>\n", " </tr>\n", " <tr>\n", " <th>656771</th>\n", " <td>7.184678</td>\n", " <td>3.439246</td>\n", " <td>0.984420</td>\n", " <td>0.014684</td>\n", " <td>2.396199</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>54070</th>\n", " <td>7.944360</td>\n", " <td>3.829021</td>\n", " <td>0.991293</td>\n", " <td>0.451271</td>\n", " <td>2.534026</td>\n", " <td>P</td>\n", " </tr>\n", " <tr>\n", " <th>484171</th>\n", " <td>6.335288</td>\n", " <td>2.416516</td>\n", " <td>0.997545</td>\n", " <td>0.017997</td>\n", " <td>1.954243</td>\n", " <td>He</td>\n", " </tr>\n", " <tr>\n", " <th>5286</th>\n", " <td>6.993968</td>\n", " <td>2.093272</td>\n", " <td>0.860772</td>\n", " <td>0.583192</td>\n", " <td>1.278754</td>\n", " <td>P</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>89659 rows × 6 columns</p>\n", "</div>" ], "text/plain": [ " energy charge zenith chisquared nchannels comp\n", "410625 6.397034 2.582136 0.903370 0.018033 1.556303 He\n", "150495 6.989937 3.040066 0.998077 0.459173 2.212188 Fe\n", "168491 6.375964 2.810805 0.876818 1.077457 1.612784 Fe\n", "310862 6.777805 2.582845 0.977905 0.018323 1.662758 He\n", "477876 7.157385 3.282042 0.930907 0.015347 2.120574 He\n", "202219 7.828750 3.303651 0.905371 1.051695 2.041393 Fe\n", "65381 6.451769 2.437444 0.974462 0.536076 1.954243 P\n", "9738 6.248129 2.613261 0.977961 0.962385 1.819544 P\n", "460497 7.750908 3.392241 0.967225 0.004413 1.991226 He\n", "225685 6.417087 3.057016 0.909740 0.529079 2.000000 Fe\n", "288323 6.259881 2.782538 0.997396 1.296459 1.892095 Fe\n", "312338 7.021274 3.385866 0.936394 0.008081 2.008600 He\n", "682676 7.943206 3.759114 0.941402 0.005726 2.367356 He\n", "395070 7.206418 2.981299 0.971848 0.005997 2.190332 He\n", "230632 6.354967 2.553778 0.889854 0.621153 1.851258 Fe\n", "335190 7.808167 3.407286 0.925869 0.004090 2.064458 He\n", "165148 6.935387 3.874148 0.998741 0.544677 2.264818 Fe\n", "666833 7.469054 3.509535 0.997292 0.007213 2.206826 He\n", "439029 7.485090 3.095054 0.948641 0.006108 1.892095 He\n", "359011 7.409304 3.031690 0.901930 0.011204 1.919078 He\n", "203077 7.591679 4.049980 0.989314 0.424587 2.397940 Fe\n", "226766 6.279322 2.531278 0.999118 0.848731 1.954243 Fe\n", "478497 6.409206 2.348780 0.933751 0.020602 1.913814 He\n", "751112 7.378921 3.698391 0.979064 0.006050 2.255273 He\n", "179042 6.578899 2.714567 0.990158 0.875237 2.060698 Fe\n", "407339 7.246759 3.466579 0.995805 0.008744 2.107210 He\n", "35980 6.439591 2.274554 0.985640 0.572963 1.924279 P\n", "418578 7.737861 3.888848 0.982745 0.006232 2.551450 He\n", "472418 7.029702 2.966702 0.984996 0.012064 2.309630 He\n", "72605 6.616210 1.863344 0.939302 0.908468 1.662758 P\n", "... ... ... ... ... ... ...\n", "540438 7.635235 3.344949 0.916680 0.371357 2.068186 P\n", "745381 7.224881 3.543076 0.946783 0.008685 2.209515 He\n", "636034 7.694195 3.208489 0.875337 0.563716 1.845098 Fe\n", "689572 7.887085 3.582302 0.986074 0.004891 2.357935 He\n", "197066 6.389286 2.821031 0.991168 0.871667 2.220108 Fe\n", "538707 7.120678 3.652542 0.953604 0.720714 2.193125 P\n", "266672 6.606214 2.834531 0.886157 0.701748 1.832509 Fe\n", "319847 6.294831 1.875603 0.910515 0.016761 1.146128 He\n", "141390 7.361120 2.967779 0.929718 0.549327 2.012837 P\n", "340039 6.457139 2.406038 0.983427 0.015175 1.913814 He\n", "426059 7.891147 4.143046 0.943648 0.005663 2.264818 He\n", "591808 7.727805 3.578130 0.990121 0.726145 2.245513 Fe\n", "704250 7.197488 3.609758 0.967279 0.012840 2.285557 He\n", "315139 6.868255 2.913922 0.983329 0.010590 2.049218 He\n", "280997 6.213838 2.408053 0.926214 0.666286 1.799341 Fe\n", "354127 6.984879 3.164351 0.937214 0.010250 1.944483 He\n", "201615 6.630084 2.876483 0.957543 0.649181 2.152288 Fe\n", "702213 7.741076 3.590199 0.970695 0.006664 2.326336 He\n", "236064 7.929628 3.901189 0.930726 0.389490 2.232996 Fe\n", "177427 7.312445 3.582322 0.987446 0.460195 2.324282 Fe\n", "767748 7.319944 3.342337 0.945847 0.009085 1.857332 He\n", "128234 7.164379 3.139925 0.998584 0.418169 2.075547 P\n", "750497 7.413582 3.334821 0.999974 0.006774 2.336460 He\n", "35284 6.260259 2.113491 0.954701 0.476327 1.755875 P\n", "154305 6.629496 2.921224 0.985331 0.775961 2.096910 Fe\n", "136074 7.209143 2.876873 0.893183 0.667331 1.863323 P\n", "656771 7.184678 3.439246 0.984420 0.014684 2.396199 He\n", "54070 7.944360 3.829021 0.991293 0.451271 2.534026 P\n", "484171 6.335288 2.416516 0.997545 0.017997 1.954243 He\n", "5286 6.993968 2.093272 0.860772 0.583192 1.278754 P\n", "\n", "[89659 rows x 6 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "feature_list = ['lap_log_energy', 'InIce_log_charge_1_30', 'lap_cos_zenith', 'lap_chi2', 'log_NChannels_1_30']\n", "tmp = df[feature_list+['MC_comp']]\n", "tmp.columns = ['energy', 'charge', 'zenith', 'chisquared', 'nchannels', 'comp']\n", "tmp.sample(frac=1)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "small.columns = ['energy', 'charge', 'zenith', 'chisquared', 'nchannels', 'comp']" ] }, { "cell_type": "code", "execution_count": 16, "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>energy</th>\n", " <th>charge</th>\n", " <th>zenith</th>\n", " <th>chisquared</th>\n", " <th>nchannels</th>\n", " <th>comp</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>7.725</td>\n", " <td>3.051990</td>\n", " <td>0.964200</td>\n", " <td>0.561476</td>\n", " <td>109</td>\n", " 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2.059584 0.944470 0.557761 68 P\n", "78 6.325 2.519856 0.946402 0.300512 69 P\n", "81 6.275 2.757371 0.945170 1.078451 83 P" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "small" ] }, { "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": "2.7.3" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
hpssjellis/forth-tensorflow
r-examples/broken/a21-tkcanvas.ipynb
2
1487
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%autosave 0\n", "%cp RplotsOriginal.pdf Rplots.pdf \n", "%load r21.R" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "!Rscript r21.R" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from wand.image import Image \n", "\n", "imageFromPdf = Image(filename='Rplots.pdf') \n", "pages = len(imageFromPdf.sequence) \n", "\n", "image = Image( \n", " width=imageFromPdf.width, \n", " height=imageFromPdf.height * pages \n", ") \n", "for i in range(pages): \n", " image.composite( \n", " imageFromPdf.sequence[i], \n", " top=imageFromPdf.height * i, \n", " left=0 \n", ") \n", "image.format=\"png\" \n", "image " ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
iABC2XYZ/abc
small/Untitled1.ipynb
1
642
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "\n", "a = tf.constant([1.0, 2.0])\n", "b = tf.constant([3.0, 4.0])\n", "c = a * b\n", "\n", "with tf.Session() as sess:\n", " print sess.run(c)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
ijstokes/bokeh-blaze-tutorial
solutions/1.4 Plotting - Climate (solution).ipynb
2
6021
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# <img src=images/continuum_analytics_b&w.png align=\"left\" width=\"15%\" style=\"margin-right:15%\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 align='center'>Bokeh Tutorial</h1>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1.4 Plotting - Climate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Exercise: Plot temperature anomaly monthly average for a given year and month by lat/lon with bokeh.plotting.image_rgba()**\n", "\n", "- Data: 'data/Land_and_Ocean_LatLong1.nc'\n", "\n", "*Note: For your convienience, I'm providing an RGBAColorMapper*\n", "\n", "[Bokeh color palettes](http://bokeh.pydata.org/en/0.11.0/docs/reference/palettes.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from utils.colormap import RGBAColorMapper" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get the netCDF4 variable for temperature anomaly\n", "import netCDF4\n", "\n", "data = netCDF4.Dataset('data/Land_and_Ocean_LatLong1.nc')\n", "t = data.variables['temperature']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Initialize the colormap with a bokeh palette and the low and high values for the scale.\n", "from bokeh.palettes import RdBu11\n", "colormap = RGBAColorMapper(-6, 6, RdBu11)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Write a function ``get_slice(temperature_variable, year, month)`` that given a temperature variable, year and month\n", "# returns an array of rgba colors. (Hint: Use RGBAColorMapper.color()). \n", "def get_slice(t, year, month):\n", " i = (year - 1850)*12 + month - 1\n", " return colormap.color(t[i, :, :])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from bokeh.plotting import figure, output_notebook, show" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Set output option\n", "output_notebook()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Get image data for a given year and month using the ``get_slice`` function\n", "image = get_slice(t, 1950, 1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create plot\n", "# Tip: Make sure to set x_range and y_range appropriately\n", "p = figure(width=900, height=500, x_axis_type=None, y_axis_type=None, x_range=[-180,180], y_range=[-90,90])\n", "\n", "p.image_rgba(\n", " image=[image],\n", " x=[-180], y=[-90],\n", " dw=[360], dh=[180], name='image'\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "# Show plot\n", "show(p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Change the year and month and rerun the plot again.\n", "image = get_slice(t, 1980, 8)\n", "p = figure(width=900, height=500, x_axis_type=None, y_axis_type=None, x_range=[-180,180], y_range=[-90,90])\n", "\n", "p.image_rgba(\n", " image=[image],\n", " x=[-180], y=[-90],\n", " dw=[360], dh=[180], name='image'\n", ")\n", "show(p)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Exercise: Overlay the worldmap boundaries in the temperature image plot**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get worldmap data\n", "import pandas as pd\n", "import utils.world_countries as wc\n", "world_countries = wc.data.copy()\n", "\n", "worldmap = pd.DataFrame.from_dict(world_countries, orient='index')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create your plot\n", "p = figure(width=900, height=500, x_axis_type=None, y_axis_type=None, x_range=[-180,180], y_range=[-90,90])\n", "\n", "p.image_rgba(\n", " image=[image],\n", " x=[-180], y=[-90],\n", " dw=[360], dh=[180], name='image'\n", ")\n", "\n", "p.patches(xs=worldmap['lons'], ys=worldmap['lats'], fill_color=\"white\", fill_alpha=0,\n", " line_color=\"black\", line_width=0.5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Show plot\n", "show(p)" ] }, { "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": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
cfjhallgren/shogun
doc/ipython-notebooks/computer_vision/Scene_classification.ipynb
5
29859
{ "metadata": { "name": "", "signature": "sha256:0c181f9f96b3b7fd14b5766e253aae3c37185ea81cfae909427b03d9993961fd" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Visual Categorization with Bags of Keypoints in Shogun" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "By Abhijeet Kislay (GitHub ID: <a href='https://github.com/kislayabhi'>kislayabhi</a>) as a GSoC'14 project under Kevin Hughes(GitHub ID: <a href='https://github.com/pickle27'>pickle27</a>)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook is about performing Object Categorization using <a href=\"http://en.wikipedia.org/wiki/Scale-invariant_feature_transform\">SIFT</a> descriptors of keypoints as features, and <a href=\"http://en.wikipedia.org/wiki/Support_vector_machine\">SVM</a>s to predict the category of the object present in the image. Shogun's <a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKMeans.html\">K-Means clustering </a> is employed for generating the <a href=\"http://en.wikipedia.org/wiki/Bag-of-words_model_in_computer_vision\">bag of keypoints</a> and its <a href=\"http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm\">k-nearest neighbours</a> <a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKNN.html\">module</a> is extensively used to construct the feature vectors. " ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Background" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook presents a bag of keypoints approach to visual categorization. A bag of keypoints corresponds to a histogram of the number of occurences of particular image patterns in a given image.The main advantages of the method are its simplicity, its computational efficiency and its invariance to affine transformations, as well as occlusion, lighting and intra-class variations.\n" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Strategy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***1. Compute (SIFT) descriptors at keypoints in all the template images and pool all of them together***" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "SIFT" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "SIFT extracts keypoints and computes its descriptors. It requires the following steps to be done:\n", "* **Scale-space Extrema Detection**: Difference of Gaussian (DOG) are used to search for local extrema over scale and space.\n", "* **Keypoint Localization**: Once potential keypoints are found, we refine them by eliminating low-contrast keypoints and edge keypoints.\n", "* **Orientation Assignment**: Now an orientation is assigned to each keypoint to achieve invariance to image rotation.\n", "* **Keypoint Descriptor**: Now a keypoint descriptor is created. A total of 128 elements are available for each keypoint.\n", "\n", "To get more details about SIFT in OpenCV, do read OpenCV python documentation <a href=\"http://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.html\">here</a>.\n", "\n", "OpenCV has a nice API for using SIFT. Let's see what we are looking at:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#import Opencv library\n", "import os\nSHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')\n", "try:\n", " import cv2\n", "except ImportError:\n", " print \"You must have OpenCV installed\"\n", " exit(1)\n", "\n", "#check the OpenCV version\n", "try:\n", " v=cv2.__version__\n", " assert (tuple(map(int,v.split(\".\")))>(2,4,2))\n", "except (AssertionError, ValueError):\n", " print \"Install newer version of OpenCV than 2.4.2, i.e from 2.4.3\"\n", " exit(1)\n", " \n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from shogun import *\n", "\n", "# get the list of all jpg images from the path provided\n", "import os\n", "def get_imlist(path):\n", " return [[os.path.join(path,f) for f in os.listdir(path) if (f.endswith('.jpg') or f.endswith('.png'))]]\n", "\n", "#Use the following function when reading an image through OpenCV and displaying through plt.\n", "def showfig(image, ucmap):\n", " #There is a difference in pixel ordering in OpenCV and Matplotlib.\n", " #OpenCV follows BGR order, while matplotlib follows RGB order.\n", " if len(image.shape)==3 :\n", " b,g,r = cv2.split(image) # get b,g,r\n", " image = cv2.merge([r,g,b]) # switch it to rgb\n", " imgplot=plt.imshow(image, ucmap)\n", " imgplot.axes.get_xaxis().set_visible(False)\n", " imgplot.axes.get_yaxis().set_visible(False)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We try to construct the vocabulary from a set of template images. It is a set of three general images belonging to the category of car, plane and train. \n", "\n", "OpenCV also provides **cv2.drawKeyPoints()** function which draws the small circles on the locations of keypoints. If you pass a flag, **cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS** to it, it will draw a circle with size of keypoint and it will even show its orientation. See below example." ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.rcParams['figure.figsize'] = 17, 4\n", "filenames=get_imlist(os.path.join(SHOGUN_DATA_DIR, 'SIFT/template/'))\n", "filenames=np.array(filenames)\n", "\n", "# for keeping all the descriptors from the template images\n", "descriptor_mat=[]\n", "\n", "# initialise OpenCV's SIFT\n", "sift=cv2.SIFT()\n", "fig = plt.figure()\n", "plt.title('SIFT detected Keypoints')\n", "plt.xticks(())\n", "plt.yticks(())\n", "for image_no in xrange(3):\n", " img=cv2.imread(filenames[0][image_no])\n", " img=cv2.resize(img, (500, 300), interpolation=cv2.INTER_AREA)\n", " gray=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n", " gray=cv2.equalizeHist(gray)\n", " \n", " #detect the SIFT keypoints and the descriptors.\n", " kp, des=sift.detectAndCompute(gray,None)\n", " # store the descriptors.\n", " descriptor_mat.append(des)\n", " # here we draw the keypoints\n", " img=cv2.drawKeypoints(img, kp, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n", " fig.add_subplot(1, 3, image_no+1)\n", " showfig(img, None)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***2. Group similar descriptors into an arbitrary number of clusters***.\n", "\n", "We take all the descriptors that we got from the three images above and find similarity in between them.\n", "Here, similarity is decided by <a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CEuclideanDistance.html\">Euclidean distance</a> between the 128-element SIFT descriptors. Similar descriptors are clustered into **k** number of groups. This can be done using Shogun's <a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKNN.html\">**KMeans class**</a>. These clusters are called **bags of keypoints** or **visual words** and they collectively represent the **vocabulary** of the program. Each cluster has a **cluster center**, which can be thought of as the representative descriptor of all the descriptors belonging to that cluster. These cluster centers can be found using the <a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKMeans.html#a5d8a09aeadada018747786a5470d3653\">**get_cluster_centers()**</a> method.\n", "\n", "To perform clustering into **k** groups, we define the **get_similar_descriptors()** function below." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def get_similar_descriptors(k, descriptor_mat):\n", "\n", " descriptor_mat=np.double(np.vstack(descriptor_mat))\n", " descriptor_mat=descriptor_mat.T\n", "\n", " #initialize KMeans in Shogun \n", " sg_descriptor_mat_features=RealFeatures(descriptor_mat)\n", "\n", " #EuclideanDistance is used for the distance measurement.\n", " distance=EuclideanDistance(sg_descriptor_mat_features, sg_descriptor_mat_features)\n", "\n", " #group the descriptors into k clusters.\n", " kmeans=KMeans(k, distance)\n", " kmeans.train()\n", "\n", " #get the cluster centers.\n", " cluster_centers=(kmeans.get_cluster_centers())\n", " \n", " return cluster_centers" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "cluster_centers=get_similar_descriptors(100, descriptor_mat)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***3. Now, compute training data for the SVM classifiers. ***.\n", "\n", "Since we have already constructed the vocabulary, our next step is to generate viable feature vectors which can be used to represent each training image so that we can use them for multiclass classification later in the code. \n", "\n", " \n", " * We begin by computing **SIFT** descriptors for each training image.\n", " \n", " \n", " * For each training image, associate each of its descriptors with one of the clusters in the vocabulary. The simplest way to do this is by using **k-Nearest Neighbour** approach. This can be done using Shogun's <a href=\"http://shogun-toolbox.org/doc/en/3.0.0/classshogun_1_1CKNN.html\">KNN class</a>. <a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CEuclideanDistance.html\">Euclidean distance</a> measure is used here for finding out the neighbours.\n", " \n", " \n", " * Making a histogram from this association. This histogram has as many bins as there are clusters in the vocabulary. Each bin counts how many descriptors in the training image are associated with the cluster corresponding to that bin. Intuitively, this histogram describes the image in the **visual words** of the **vocabulary,** and is called the **bag of visual words descriptor** of the image. \n", " \n", "In short, we approximated each training image into a **k** element vector. This can be utilized to train any multiclass classifier.\n", "\n", "\n", "First, let us see a few training images" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# name of all the folders together\n", "folders=['cars','planes','trains']\n", "training_sample=[]\n", "for folder in folders:\n", " #get all the training images from a particular class \n", " filenames=get_imlist(os.path.join(SHOGUN_DATA_DIR, 'SIFT/%s'%folder))\n", " for i in xrange(10):\n", " temp=cv2.imread(filenames[0][i])\n", " training_sample.append(temp)\n", "\n", "plt.rcParams['figure.figsize']=21,16\n", "fig=plt.figure()\n", "plt.xticks(())\n", "plt.yticks(())\n", "plt.title('10 training images for each class')\n", "for image_no in xrange(30):\n", " fig.add_subplot(6,5, image_no+1)\n", " showfig(training_sample[image_no], None)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We here define **get_sift_training()** function to get all the **SIFT** descriptors present in all the training images." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def get_sift_training():\n", " \n", " # name of all the folders together\n", " folders=['cars','planes','trains']\n", " \n", " folder_number=-1\n", " des_training=[]\n", " \n", " for folder in folders:\n", " folder_number+=1\n", "\n", " #get all the training images from a particular class \n", " filenames=get_imlist(os.path.join(SHOGUN_DATA_DIR, 'SIFT/%s'%folder))\n", " filenames=np.array(filenames)\n", " \n", " des_per_folder=[]\n", " for image_name in filenames[0]:\n", " img=cv2.imread(image_name)\n", "\n", " # carry out normal preprocessing routines\n", " gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n", " gray=cv2.resize(gray, (500, 300), interpolation=cv2.INTER_AREA)\n", " gray=cv2.equalizeHist(gray)\n", "\n", " #get all the SIFT descriptors for an image\n", " _, des=sift.detectAndCompute(gray, None)\n", " des_per_folder.append(des)\n", " \n", " des_training.append(des_per_folder)\n", " return des_training" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "descriptor_training=get_sift_training()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define the **compute_training_data()** function which returns the training data required for multiclass classification in the later stages.\n", "\n", "Inputs are:\n", "\n", "* **k**=number of clusters\n", "\n", "* **cluster_centers**=descriptors that are approximate form of all descriptors belonging to a particular cluster\n", "\n", "* **descriptors**=SIFT descriptors of the training images that are obtained from the above function." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def compute_training_data(k, cluster_centers, descriptors):\n", " \n", " # a list to hold histograms of all the training images\n", " all_histograms=[]\n", " # labels for all of the test images\n", " final_labels=[]\n", " # to hold the cluster number a descriptor belong to\n", " cluster_labels=[]\n", "\n", " #initialize a KNN in Shogun\n", " dist=EuclideanDistance()\n", " labels=MulticlassLabels(np.double(range(k)))\n", " knn=KNN(1, dist, labels)\n", "\n", " #Target descriptors are the cluster_centers that we got earlier. \n", " #All the descriptors of an image are matched against these for \n", " #calculating the histogram.\n", " sg_cluster_centers=RealFeatures(cluster_centers)\n", " knn.train(sg_cluster_centers)\n", "\n", " # name of all the folders together\n", " folders=['cars','planes','trains']\n", " folder_number=-1\n", "\n", " for folder in folders:\n", " folder_number+=1\n", "\n", " #get all the training images from a particular class \n", " filenames=get_imlist(os.path.join(SHOGUN_DATA_DIR, 'SIFT/%s'%folder))\n", "\n", " for image_name in xrange(len(filenames[0])):\n", " \n", " des=descriptors[folder_number][image_name]\n", " \n", " #Shogun works in a way in which columns are samples and rows are features.\n", " #Hence we need to transpose the observation matrix\n", " des=(np.double(des)).T\n", " sg_des=RealFeatures(np.array(des))\n", "\n", " #find all the labels of cluster_centers that are nearest to the descriptors present in the current image. \n", " cluster_labels=(knn.apply_multiclass(sg_des)).get_labels()\n", "\n", " histogram_per_image=[]\n", " for i in xrange(k):\n", " #find the histogram for the current image\n", " histogram_per_image.append(sum(cluster_labels==i))\n", "\n", " all_histograms.append(np.array(histogram_per_image))\n", " final_labels.append(folder_number)\n", "\n", " # we now have the training features(all_histograms) and labels(final_labels) \n", " all_histograms=np.array(all_histograms)\n", " final_labels=np.array(final_labels)\n", " return all_histograms, final_labels, knn" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "all_histograms, final_labels, knn=compute_training_data(100, cluster_centers, descriptor_training)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have to solve a multiclass classification problem here. In Shogun these are implemented in: <a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CMulticlassMachine.html\">MulticlassMachine</a> \n", "\n", "***4. We train a one-vs-all SVM for each category of object using the training data***:\n", "\n", "The following function returns a trained <a href=\"http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CGMNPSVM.html\">GMNPSVM</a>, which is a true Multiclass SVM in Shogun employing **one vs rest** approach.\n", "Inputs are:\n", "* **all_histograms**=Can be thought as the feature vector for all images for which the SVM has to be trained.\n", "* **final_labels**=The labels respective of the above mentioned feature vectors" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def train_svm(all_histograms, final_labels):\n", " \n", " # we will use GMNPSVM class of Shogun for one vs rest multiclass classification\n", " obs_matrix=np.double(all_histograms.T)\n", " sg_features=RealFeatures(obs_matrix)\n", " sg_labels=MulticlassLabels(np.double(final_labels))\n", " kernel=LinearKernel(sg_features, sg_features)\n", " C=1\n", " gsvm=GMNPSVM(C, kernel, sg_labels)\n", " _=gsvm.train(sg_features)\n", " return gsvm" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "gsvm=train_svm(all_histograms, final_labels)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***5. Now, classify by using the trained SVM***:\n", "\n", "First let us see all the test images" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Lets see the testing images\n", "testing_sample=[]\n", "#get all the testing images \n", "filenames=get_imlist(os.path.join(SHOGUN_DATA_DIR, 'SIFT/test_image/'))\n", "for i in xrange(len(filenames[0])):\n", " temp=cv2.imread(filenames[0][i])\n", " testing_sample.append(temp)\n", "\n", "plt.rcParams['figure.figsize']=20,8\n", "fig=plt.figure()\n", "plt.xticks(())\n", "plt.yticks(())\n", "plt.title('Test Images')\n", "for image_no in xrange(len(filenames[0])):\n", " fig.add_subplot(3,8, image_no+1)\n", " showfig(testing_sample[image_no], None)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define the function **get_sift_testing()** which returns all the descriptors present in the testing images." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def get_sift_testing():\n", " filenames=get_imlist(os.path.join(SHOGUN_DATA_DIR, 'SIFT/test_image/'))\n", " filenames=np.array(filenames)\n", " des_testing=[]\n", " for image_name in filenames[0]:\n", " result=[]\n", " #read the test image\n", " img=cv2.imread(image_name)\n", "\n", " #follow the normal preprocessing routines \n", " gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n", " gray=cv2.resize(gray, (500, 300), interpolation=cv2.INTER_AREA)\n", " gray=cv2.equalizeHist(gray)\n", "\n", " #compute all the descriptors of the test images\n", " _, des=sift.detectAndCompute(gray, None)\n", " des_testing.append(des)\n", " return des_testing" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "descriptor_testing=get_sift_testing()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the following **classify_svm()** function, we use the trained **GMNPSVM** for classifying the test images. It returns the predictions from our trained SVM." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def classify_svm(k, knn, des_testing):\n", " \n", " # a list to hold histograms of all the test images\n", " all_histograms=[]\n", " filenames=get_imlist(os.path.join(SHOGUN_DATA_DIR, 'SIFT/test_image/'))\n", " \n", " for image_name in xrange(len(filenames[0])):\n", " \n", " result=[]\n", " des=des_testing[image_name]\n", " \n", " #Shogun works in a way in which columns are samples and rows are features.\n", " #Hence we need to transpose the observation matrix\n", " des=(np.double(des)).T\n", " sg_des=RealFeatures(np.array(des))\n", "\n", " #cluster all the above found descriptors into the vocabulary\n", " cluster_labels=(knn.apply_multiclass(sg_des)).get_labels()\n", "\n", " #get the histogram for the current test image\n", " histogram=[]\n", " for i in xrange(k):\n", " histogram.append(sum(cluster_labels==i))\n", " \n", " all_histograms.append(np.array(histogram))\n", "\n", " all_histograms=np.double(np.array(all_histograms))\n", " all_histograms=all_histograms.T\n", " sg_testfeatures=RealFeatures(all_histograms)\n", " return gsvm.apply(sg_testfeatures).get_labels()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "predicted=classify_svm(100, knn, descriptor_testing)\n", "print \"the predicted labels for k=100 are as follows: \"\n", "print predicted" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "6.\n", "***Selecting the classifier that gives the best overall classification accuracy with respect to number of clusters (k)*** :\n", "\n", "We define the function **create_conf_matrix()** which creates the confusion matrix. \n", "\n", "Inputs are:\n", "* **expected**=The actual labels which the test images belong to\n", "* **predicted**=The output of our SVM\n", "* **n_classes**=number of classes (here 3 i.e cars, trains and planes)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def create_conf_matrix(expected, predicted, n_classes):\n", " m = [[0] * n_classes for i in range(n_classes)]\n", " for pred, exp in zip(predicted, expected):\n", " m[exp][int(pred)] += 1\n", " return np.array(m)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Form the **expected** list. \n", "\n", "* **0** represents **cars**\n", "* **1** represents **planes**\n", "* **2** represents **trains**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import re\n", "filenames=get_imlist(os.path.join(SHOGUN_DATA_DIR, 'SIFT/test_image/'))\n", "# get the formation of the files, later to be used for calculating the confusion matrix\n", "formation=([int(''.join(x for x in filename if x.isdigit())) for filename in filenames[0]])\n", " \n", "# associate them with the correct labels by making a dictionary\n", "keys=range(len(filenames[0]))\n", "\n", "values=[0,1,0,2,1,0,1,0,0,0,1,2,2,2,2,1,1,1,1,1]\n", "label_dict=dict(zip(keys, values))\n", "\n", "# the following list holds the actual labels\n", "expected=[]\n", "for i in formation:\n", " expected.append(label_dict[i-1])" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We extend all the steps that we did for **k**=100 to few other values of **k** and check their accuracies with respect to the **expected** labels. Alongside, we also draw their respective **confusion matrix**." ] }, { "cell_type": "code", "collapsed": false, "input": [ "best_k=1\n", "max_accuracy=0\n", "\n", "for k in xrange(1,5):\n", " k=100*k\n", " \n", " # step 2\n", " cluster_centers=get_similar_descriptors(k, descriptor_mat)\n", " \n", " # step 3\n", " all_histograms, final_labels, knn=compute_training_data(k, cluster_centers, descriptor_training)\n", " \n", " # step 4\n", " gsvm=train_svm(all_histograms, final_labels)\n", " \n", " # step 5\n", " predicted=classify_svm(k, knn, descriptor_testing)\n", " accuracy=sum(predicted==expected)*100/float(len(expected))\n", " print \"for a k=%d, accuracy is %d%%\"%(k, accuracy)\n", " \n", " #step 6\n", " m=create_conf_matrix(expected, predicted, 3)\n", "\n", " if accuracy>max_accuracy:\n", " best_k=k\n", " max_accuracy=accuracy\n", " best_prediction=predicted\n", " \n", " print \"confusion matrix for k=%d\"%k\n", " print m" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From all the above k's we choose the one which has the best accuracy. Number of k's can be extended further to enhance the overall accuracy.\n", "\n", "Test images along with their predicted labels are shown below for the most optimum value of k: " ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.rcParams['figure.figsize']=20,8\n", "fig=plt.figure()\n", "for image_no in xrange(len(filenames[0])):\n", " fig.add_subplot(3,8, image_no+1)\n", " plt.title('pred. class: '+folders[int(best_prediction[image_no])])\n", " showfig(testing_sample[image_no], None)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Conclusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we have presented a simple but novel approach to generic visual categorization using feature vectors constructed from clustered descriptors of image patches." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "References:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Visual Categorization with Bags of Keypoints by Gabriella Csurka, Christopher R. Dance, Lixin Fan, Jutta Willamowski, C\u00e9dric Bray\n", "\n", "* Distinctive Image Features from Scale-Invariant Keypoints by David G. Lowe\n", "\n", "* Practical OpenCV by Samarth Brahmbhatt, University of Pennsylvania " ] } ], "metadata": {} } ] }
gpl-3.0
mwcraig/vpython-jupyter
Demos/Lorenz.ipynb
1
4020
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "require.undef(\"nbextensions/jquery-ui.custom.min\");" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "require.undef(\"nbextensions/glow.2.1.min\");" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "require.undef(\"nbextensions/glowcomm\");" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "require([\"nbextensions/glowcomm\"], function(){console.log(\"glowcomm loaded\");})" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div id=\"glowscript\" class=\"glowscript\"></div>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "window.__context = { glowscript_container: $(\"#glowscript\").removeAttr(\"id\")}" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from vpython import *\n", "scene = canvas() # This is needed in Jupyter notebook and lab to make programs easily rerunnable\n", "from numpy import arange, clip\n", "\n", "# David Scherer\n", "\n", "scene.caption = \"\"\"Right button drag or Ctrl-drag to rotate \"camera\" to view scene.\n", "Middle button to drag or Alt-drag to zoom in or out.\n", " On a two-button mouse, middle is left + right.\"\"\"\n", "scene.title = \"Lorenz differential equation\"\n", "scene.center = vector(25,0,0)\n", "scene.background = color.white\n", "scene.forward = vector(0,-.2,-1)\n", "scene.range = 35\n", "\n", "lorenz = curve( color = color.black, radius=0.3 )\n", "\n", "# Draw grid\n", "for x in arange(0,51,10):\n", " box(pos=vector(x,0,0), axis=vector(0,0,50), height=0.4, width=0.4, color=color.gray(0.6) )\n", "for z in arange(-25,26,10):\n", " box(pos=vector(25,0,z), axis=vector(50,0,0), height=0.4, width=0., color=color.gray(0.6) )\n", "\n", "dt = 0.01\n", "y = vector(35, -10, -7)\n", "\n", "for t in arange(0,10,dt):\n", " # Integrate a funny differential equation\n", " dydt = vector( - 8.0/3*y.x + y.y*y.z,\n", " - 10*y.y + 10*y.z,\n", " - y.y*y.x + 28*y.y - y.z );\n", " y = y + dydt*dt\n", "\n", " # Draw lines colored by speed\n", " c = clip( [mag(dydt) * 0.005], 0, 1 )[0]\n", "\n", " lorenz.append( pos=y, color=vector(c,0, 1-c) )\n", "\n", " rate( 500 )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "VPython", "language": "python", "name": "vpython" }, "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 }
mit
nafitzgerald/allennlp
tutorials/notebooks/embedding_tokens.ipynb
1
11930
{ "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ "---\n", "layout: tutorial\n", "title: Embedding Tokens\n", "id: embedding-tokens\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook introduces how AllenNLP handles one of the key aspects of applying deep learning techniques to textual data: learning distributed representations of words and sentences.\n", "\n", "Recently, there has been an explosion of different techniques to represent words and sentences in NLP, including pre-trained word vectors, character level CNN encodings and sub-word token representation (e.g byte encodings). Even more complex learned representations of higher level lingustic features, such as POS tags, named entities and dependency paths have also proven successful for a wide variety of NLP tasks.\n", "\n", "In order to deal with this breadth of methods for representing words as vectors, AllenNLP introduces 3 key abstractions:\n", "\n", "- `TokenIndexers`, which generate indexed tensors representing sentences in different ways. See the [Data Pipeline notebook](data_pipeline.ipynb) for more info. \n", "\n", "- `TokenEmbedders`, which transform indexed tensors into embedded representations. At its most basic, this is just a standard `Embedding` layer you'd find in any neural network library. However, they can be more complex - for instance, AllenNLP has a `token_characters_encoder` which applies a CNN to character level representations.\n", "- `TextFieldEmbedders`, which are a wrapper around a set of `TokenEmbedders`. At it's most basic, this applies the `TokenEmbedders` which it is passed and concatenates their output.\n", "\n", "Using this hierarchy allows you to easily compose different representations of a sentence together in modular ways. For instance, in the Bidaf model, we use this to concatenate a character level CNN encoding of the words in the sentence to the pretrained word embeddings. You can also specify this completely from a JSON file, making experimenation with different representations extremely easy.\n", " " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# This cell just makes sure the library paths are correct. \n", "# You need to run this cell before you run the rest of this\n", "# tutorial, but you can ignore the contents!\n", "import os\n", "import sys\n", "module_path = os.path.abspath(os.path.join('../..'))\n", "if module_path not in sys.path:\n", " sys.path.append(module_path)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from allennlp.data.fields import TextField\n", "from allennlp.data import Instance\n", "from allennlp.data.token_indexers import SingleIdTokenIndexer, TokenCharactersIndexer\n", "from allennlp.data.tokenizers import Token\n", "\n", "words = [\"All\", \"the\", \"cool\", \"kids\", \"use\", \"character\", \"embeddings\", \".\"]\n", "sentence1 = TextField([Token(x) for x in words],\n", " token_indexers={\"tokens\": SingleIdTokenIndexer(namespace=\"tokens\"),\n", " \"characters\": TokenCharactersIndexer(namespace=\"token_characters\")})\n", "words2 = [\"I\", \"prefer\", \"word2vec\", \"though\", \"...\"]\n", "sentence2 = TextField([Token(x) for x in words2],\n", " token_indexers={\"tokens\": SingleIdTokenIndexer(namespace=\"tokens\"),\n", " \"characters\": TokenCharactersIndexer(namespace=\"token_characters\")})\n", "instance1 = Instance({\"sentence\": sentence1})\n", "instance2 = Instance({\"sentence\": sentence2})\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we need to create a small vocabulary from our sentence - note that because we have used both a\n", "`SingleIdTokenIndexer` and a `TokenCharactersIndexer`, when we call `Vocabulary.from_dataset`, the created `Vocabulary` will have two namespaces, which correspond to the namespaces of each token indexer in our `TextField`'s." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 2/2 [00:00<00:00, 5419.00it/s]\n", "100%|██████████| 2/2 [00:00<00:00, 6786.90it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "This is the token vocabulary we created: \n", "\n", "{0: '@@PADDING@@', 1: '@@UNKNOWN@@', 2: 'All', 3: 'the', 4: 'cool', 5: 'kids', 6: 'use', 7: 'character', 8: 'embeddings', 9: '.', 10: 'I', 11: 'prefer', 12: 'word2vec', 13: 'though', 14: '...'}\n", "This is the character vocabulary we created: \n", "\n", "{0: '@@PADDING@@', 1: '@@UNKNOWN@@', 2: 'e', 3: 'r', 4: 'h', 5: 'c', 6: 'o', 7: 'd', 8: '.', 9: 'l', 10: 't', 11: 's', 12: 'i', 13: 'u', 14: 'a', 15: 'g', 16: 'A', 17: 'k', 18: 'm', 19: 'b', 20: 'n', 21: 'I', 22: 'p', 23: 'f', 24: 'w', 25: '2', 26: 'v'}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "from allennlp.data import Vocabulary, Dataset\n", "\n", "# Make \n", "dataset = Dataset([instance1, instance2])\n", "vocab = Vocabulary.from_dataset(dataset)\n", "\n", "print(\"This is the token vocabulary we created: \\n\")\n", "print(vocab.get_index_to_token_vocabulary(\"tokens\"))\n", "\n", "print(\"This is the character vocabulary we created: \\n\")\n", "print(vocab.get_index_to_token_vocabulary(\"token_characters\"))\n", "\n", "dataset.index_instances(vocab)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from allennlp.modules.token_embedders import Embedding, TokenCharactersEncoder\n", "from allennlp.modules.seq2vec_encoders import CnnEncoder\n", "from allennlp.modules.text_field_embedders import BasicTextFieldEmbedder\n", "\n", "# We're going to embed both the words and the characters, so we create\n", "# embeddings with respect to the vocabulary size of each of the relevant namespaces\n", "# in the vocabulary.\n", "word_embedding = Embedding(num_embeddings=vocab.get_vocab_size(\"tokens\"), embedding_dim=10)\n", "char_embedding = Embedding(num_embeddings=vocab.get_vocab_size(\"token_characters\"), embedding_dim=5)\n", "character_cnn = CnnEncoder(embedding_dim=5, num_filters=2, output_dim=8)\n", "\n", "# This is going to embed an integer character tensor of shape: (batch_size, max_sentence_length, max_word_length) into\n", "# a 4D tensor with an additional embedding dimension, representing the vector for each character.\n", "# and then apply the character_cnn we defined above over the word dimension, resulting in a tensor\n", "# of shape: (batch_size, max_sentence_length, num_filters * ngram_filter_sizes). \n", "token_character_encoder = TokenCharactersEncoder(embedding=char_embedding, encoder=character_cnn)\n", "\n", "# Notice that these keys have the same keys as the TokenIndexers when we created our TextField.\n", "# This is how the text_field_embedder knows which function to apply to which array. \n", "# There should be a 1-1 mapping between TokenIndexers and TokenEmbedders in your model.\n", "text_field_embedder = BasicTextFieldEmbedder({\"tokens\": word_embedding, \"characters\": token_character_encoder})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we've actually created all the parts which we need to create concatenated word and character CNN embeddings, let's actually apply our `text_field_embedder` and see what happens. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Torch tensors for passing to a model: \n", "\n", " {'sentence': {'tokens': Variable containing:\n", " 2 3 4 5 6 7 8 9\n", " 10 11 12 13 14 0 0 0\n", "[torch.LongTensor of size 2x8]\n", ", 'characters': Variable containing:\n", "(0 ,.,.) = \n", " 16 9 9 0 0 0 0 0 0 0\n", " 10 4 2 0 0 0 0 0 0 0\n", " 5 6 6 9 0 0 0 0 0 0\n", " 17 12 7 11 0 0 0 0 0 0\n", " 13 11 2 0 0 0 0 0 0 0\n", " 5 4 14 3 14 5 10 2 3 0\n", " 2 18 19 2 7 7 12 20 15 11\n", " 8 0 0 0 0 0 0 0 0 0\n", "\n", "(1 ,.,.) = \n", " 21 0 0 0 0 0 0 0 0 0\n", " 22 3 2 23 2 3 0 0 0 0\n", " 24 6 3 7 25 26 2 5 0 0\n", " 10 4 6 13 15 4 0 0 0 0\n", " 8 8 8 0 0 0 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 0\n", " 0 0 0 0 0 0 0 0 0 0\n", "[torch.LongTensor of size 2x8x10]\n", "}}\n", "\n", "\n", "\n", "Post embedding with our TextFieldEmbedder: \n", "Batch Size: 2\n", "Sentence Length: 8\n", "Embedding Size: 18\n" ] } ], "source": [ "# Convert the indexed dataset into Pytorch Variables. \n", "tensors = dataset.as_tensor_dict(dataset.get_padding_lengths())\n", "print(\"Torch tensors for passing to a model: \\n\\n\", tensors)\n", "print(\"\\n\\n\")\n", "# tensors is a nested dictionary, first keyed by the\n", "# name we gave our instances (in most cases you'd have more\n", "# than one field in an instance) and then by the key of each\n", "# token indexer we passed to TextField.\n", "\n", "# This will contain two tensors: one from representing each\n", "# word as an index and one representing each _character_\n", "# in each word as an index. \n", "text_field_variables = tensors[\"sentence\"]\n", "\n", "# This will have shape: (batch_size, sentence_length, word_embedding_dim + character_cnn_output_dim)\n", "embedded_text = text_field_embedder(text_field_variables)\n", "\n", "dimensions = list(embedded_text.size())\n", "print(\"Post embedding with our TextFieldEmbedder: \")\n", "print(\"Batch Size: \", dimensions[0])\n", "print(\"Sentence Length: \", dimensions[1])\n", "print(\"Embedding Size: \", dimensions[2])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we've manually created the different TokenEmbedders which we wanted to use in our `TextFieldEmbedder`. However, all of these modules can be built using their `from_params` method, so you can have a `TextFieldEmbedder` in your model which is fixed (it encodes some sentence which is an input to your model), but vary the `TokenIndexers` and `TokenEmbedders` which it uses by changing a JSON file." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }
apache-2.0
cgivre/oreilly-sec-ds-fundamentals
Notebooks/Visualization/Data Visualization Worksheet.ipynb
2
11212
{ "cells": [ { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd \n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline\n", "plt.style.use('ggplot')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Visualization Worksheet\n", "This worksheet will walk you through the basic process of preparing a visualization using Python/Pandas/Matplotlib. \n", "\n", "For this exercise, we will be creating a line plot comparing the number of hosts infected by the Bedep and ConfickerAB Bot Families in the Government/Politic sector." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prepare the data\n", "The data we will be using is in the `dailybots.csv` file which can be found in the `data` folder. As is common, we will have to do some data wrangling to get it into a format which we can use to visualize this data. To do that, we'll need to:\n", "1. Read in the data\n", "2. Filter the data by industry and botnet\n", "The result should look something like this:\n", "\n", "| |date|ConflikerAB|Bedep|\n", "|-|----|-----------|-----|\n", "|0|2016-06-01|255|430|\n", "|1|2016-06-02|431|453|\n", "\n", "The way I chose to do this in the answer notebook, might be a little more complex, but I wanted you to see all the steps involved." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create the first chart\n", "Using the `.plot()` method, plot your dataframe and see what you get. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Customizing your plot:\n", "The default plot doesn't look horrible, but there are certainly some improvements which can be made. Try the following:\n", "1. Change the x-axis to a date by converting the date column to a date object.\n", "2. Move the Legend to the upper center of the graph\n", "3. Make the figure size larger.\n", "4. Instead of rendering both lines on one graph, split them up into two plots\n", "5. Add axis labels\n", "\n", "There are many examples in the documentation which is available: http://pandas.pydata.org/pandas-docs/version/0.18.1/visualization.html\n", "\n", "A few hints: \n", "http://stackoverflow.com/questions/4700614/how-to-put-the-legend-out-of-the-plot\n", "http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Making it Interactive\n", "Using Bokeh, create an interactive chart of the same data." ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <div class=\"bk-root\">\n", " <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n", " <span id=\"34189e35-2928-423a-8a5f-0c5e38a266ad\">Loading BokehJS ...</span>\n", " </div>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(global) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = \"1\";\n", "\n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force !== \"\") {\n", " window._bokeh_onload_callbacks = [];\n", " window._bokeh_is_loading = undefined;\n", " }\n", "\n", "\n", " \n", " if (typeof (window._bokeh_timeout) === \"undefined\" || force !== \"\") {\n", " window._bokeh_timeout = Date.now() + 5000;\n", " window._bokeh_failed_load = false;\n", " }\n", "\n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"<div style='background-color: #fdd'>\\n\"+\n", " \"<p>\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"</p>\\n\"+\n", " \"<ul>\\n\"+\n", " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n", " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n", " \"</ul>\\n\"+\n", " \"<code>\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"</code>\\n\"+\n", " \"</div>\"}};\n", "\n", " function display_loaded() {\n", " if (window.Bokeh !== undefined) {\n", " Bokeh.$(\"#34189e35-2928-423a-8a5f-0c5e38a266ad\").text(\"BokehJS successfully loaded.\");\n", " } else if (Date.now() < window._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", "\n", " function run_callbacks() {\n", " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " delete window._bokeh_onload_callbacks\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(js_urls, callback) {\n", " window._bokeh_onload_callbacks.push(callback);\n", " if (window._bokeh_is_loading > 0) {\n", " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " window._bokeh_is_loading = js_urls.length;\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = false;\n", " s.onreadystatechange = s.onload = function() {\n", " window._bokeh_is_loading--;\n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", " run_callbacks()\n", " }\n", " };\n", " s.onerror = function() {\n", " console.warn(\"failed to load library \" + url);\n", " };\n", " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", " }\n", " };var element = document.getElementById(\"34189e35-2928-423a-8a5f-0c5e38a266ad\");\n", " if (element == null) {\n", " console.log(\"Bokeh: ERROR: autoload.js configured with elementid '34189e35-2928-423a-8a5f-0c5e38a266ad' but no matching script tag was found. \")\n", " return false;\n", " }\n", "\n", " var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.12.3.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.3.min.js'];\n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " \n", " function(Bokeh) {\n", " \n", " Bokeh.$(\"#34189e35-2928-423a-8a5f-0c5e38a266ad\").text(\"BokehJS is loading...\");\n", " },\n", " function(Bokeh) {\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.3.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.3.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.3.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.3.min.css\");\n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " \n", " if ((window.Bokeh !== undefined) || (force === \"1\")) {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i](window.Bokeh);\n", " }if (force === \"1\") {\n", " display_loaded();\n", " }} else if (Date.now() < window._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!window._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " window._bokeh_failed_load = true;\n", " } else if (!force) {\n", " var cell = $(\"#34189e35-2928-423a-8a5f-0c5e38a266ad\").parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(this));" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bokeh.plotting import output_notebook\n", "output_notebook()" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from bokeh.charts import ... #Your code here..\n", "from bokeh.io import show" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }
apache-2.0
guangtunbenzhu/BGT-Cosmology
Spectroscopy/.ipynb_checkpoints/eBOSStests-checkpoint.ipynb
1
164997
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"'/Users/Benjamin/AstroData/SDSS'" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "import fitsio" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "data = fitsio.read('/Users/Benjamin/AstroData/SDSS/eBOSS/stack_goodSel_13801.fits')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "data.dtype.fields" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "data.dtype" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "dtype('uint8')" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "data = fitsio.read('/Users/Benjamin/AstroData/SDSS/eBOSS/stack_goodSel_13801.fits', ext=2)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "extension not found: 2 (case insensitive)", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-7-e24964f46f18>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfitsio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/Users/Benjamin/AstroData/SDSS/eBOSS/stack_goodSel_13801.fits'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Users/Benjamin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/fitsio/fitslib.pyc\u001b[0m in \u001b[0;36mread\u001b[0;34m(filename, ext, extver, **keys)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_make_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextver\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mextver\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfits\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 104\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 105\u001b[0m \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m 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"metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 33, "text": [ "[<matplotlib.lines.Line2D at 0x109d82550>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x109d2ed90>" ] } ], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "figure(figsize=(10,6))\n", "plot(data['lambda']+0.57, data['fluxMedian'], drawstyle='steps')\n", "xlim(2335, 2410)\n", "ylim(0.05,0.25)\n", "plot([2344.21, 2344.21], [0.05, 0.25], 'g')\n", "plot([2374.46, 2374.46], [0.05, 0.25], 'g')\n", "plot([2382.77, 2382.77], [0.05, 0.25], 'g')\n", "plot([2365.55, 2365.55], [0.05, 0.25], 'r')\n", "plot([2396.35, 2396.35], [0.05, 0.25], 'r')\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 32, "text": [ "[<matplotlib.lines.Line2D at 0x109d23750>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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b5Spjx+rSDFMp4p/8i+jEvXfvqlkiMGKEBjIPPRT88/vv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\nRugFBfbx999mUB+alSv18+F//xM599zgXmNvvWWrvYyg5aa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"text": [ "<matplotlib.figure.Figure at 0x10a0da710>" ] } ], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "import datapath\n", "import fitsio\n", "data = fitsio.read('/Users/Benjamin/AstroData/SDSS/eBOSS/stack_goodSel_13801.fits', ext=1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "figure(figsize=(10,6))\n", "plot(data['lambda']+0.585, data['fluxMedian'], drawstyle='steps')\n", "xlim(4310, 4430)\n", "ylim(0.15,0.45)\n", "plot([4341, 4341], [0.15, 0.45], 'g')\n", "plot([4363, 4363], [0.15, 0.45], 'r')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "[<matplotlib.lines.Line2D at 0x108ae9850>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10c96b9d0>" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "nobj = data.size" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "objs_dtype = [('PLATE', 'i4'),\n", " ('MJD', 'i4'),\n", " ('FIBER', 'i4'),\n", " ('Z', 'f8')]\n", "objs = np.zeros(nobj, dtype=objs_dtype)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": true, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
aflaxman/siaman16-va-minitutorial
1-tutorial-notebooks/4-va_csmf.ipynb
1
10421
{ "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" }, "name": "", "signature": "sha256:32126b2fd93f2e7edf946bfe1f3ab5c0272887029925fb03e8f6e23534ef77c9" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# We won't work through this notebook\n", "\n", "We won't have time. But I thought I'd include it, in case you want to see exactly how I implement my population-level quality metric." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np, pandas as pd" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Let's put the CSMF Accuracy calculation right at the top" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def measure_prediction_quality(csmf_pred, y_test):\n", " \"\"\"Calculate population-level prediction quality (CSMF Accuracy)\n", " \n", " Parameters\n", " ----------\n", " csmf_pred : pd.Series, predicted distribution of causes\n", " y_test : array-like, labels for test dataset\n", " \n", " Results\n", " -------\n", " csmf_acc : float\n", " \"\"\"\n", " \n", " csmf_true = pd.Series(y_test).value_counts() / float(len(y_test))\n", " csmf_acc = 1 - \n", "\n", " return csmf_acc" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# How can I test this?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "csmf_pred = pd.Series({'cause_1': .5, 'cause_2': .5})\n", "y_test = ['cause_1', 'cause_2']\n", "measure_prediction_quality(csmf_pred, y_test)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "csmf_pred = pd.Series({'cause_1': 0., 'cause_2': 1.})\n", "y_test = ['cause_1']*1000 + ['cause_2']\n", "measure_prediction_quality(csmf_pred, y_test)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Things we don't have time for\n", "\n", "An approach to really do the cross-validation *out of sample*:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "val = {}\n", "module = 'Adult'\n", "val[module] = pd.read_csv('../3-data/phmrc_cleaned.csv')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def get_data(module):\n", " X = np.array(val[module].filter(regex='(^s[0-9]+|age|sex)').fillna(0))\n", " y = np.array(val[module].gs_text34)\n", " site = np.array(val[module].site)\n", " \n", " return X, y, site" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "X, y, site = get_data(module)\n", "X.shape" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def my_resample(X, y, N2, csmf_new):\n", " \"\"\"\"Randomly resample X and y so that resampled cause distribution follows\n", " csmf_new and there are N2 samples total\n", " \n", " Parameters\n", " ----------\n", " X : array-like, feature vectors\n", " y : array-like, corresponding labels\n", " N2 : int, number of samples in resampled results\n", " csmf_new : pd.Series, distribution of resampled data\n", " \n", " Results\n", " -------\n", " X_new : array-like, resampled feature vectors\n", " y_new : array-like, corresponding resampled labels\n", " \"\"\"\n", " \n", " N, I = X.shape\n", " assert len(y) == N, 'X and y must have same length' \n", "\n", " causes = csmf_new.index\n", " J, = causes.shape # trailing comma for sneaky numpy reasons\n", " \n", " # generate count of examples for each cause according to csmf_new\n", " cnt_new = np.random.multinomial(N2, csmf_new)\n", " \n", " # replace y_new with original values\n", " y_new = []\n", " for cnt, cause in zip(cnt_new, causes):\n", " for n_j in range(cnt):\n", " y_new.append(cause)\n", " y_new = np.array(y_new)\n", " \n", " # resample rows of X appropriately\n", " X_new = np.zeros((len(y_new), I))\n", " for j in causes:\n", " new_rows, = np.where(y_new == j) # trailing comma for sneaky numpy reasons\n", " candidate_rows, = np.where(y == j) # trailing comma for sneaky numpy reasons\n", " \n", " assert len(candidate_rows) > 0, 'must have examples of each resampled cause'\n", " old_rows = np.random.choice(candidate_rows, size=len(new_rows), replace=True)\n", " X_new[new_rows,] = X[old_rows,]\n", " return X_new, y_new" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def random_allocation(X_train, y_train):\n", " \"\"\" make predictions by random allocation\"\"\"\n", " clf = sklearn.base.BaseEstimator()\n", " def my_predict(X_test):\n", " N = len(X_test)\n", " J = float(len(np.unique(y_train)))\n", " \n", " y_pred = np.ones((N, J)) / J\n", " csmf_pred = pd.Series(y_pred.sum(axis=0),\n", " index=np.unique(y_train)) / N\n", " return csmf_pred\n", " clf.my_predict = my_predict\n", " return clf" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def my_key(module, clf):\n", " return '{}-{}'.format(module, clf)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "import sklearn.model_selection" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "results = []\n", "def measure_csmf_acc(my_fit_predictor, replicates=10):\n", " \"\"\" my_fit_predictor : function that takes X,y returns clf object with my_predict method\n", " clf.my_predict takes X_test, return csmf_pred\n", " \n", " Results\n", " -------\n", " stores calculation in results dict,\n", " returns calc for adults\n", " \"\"\"\n", " X, y, site = get_data(module)\n", " acc = []\n", "\n", " np.random.seed(12345) # set seed for reproducibility\n", " cv = sklearn.model_selection.StratifiedShuffleSplit(n_iter=replicates, test_size=0.25)\n", " for train_index, test_index in cv.split(X, y):\n", " # make train test split\n", " X_train, X_test = X[train_index], X[test_index]\n", " y_train, y_test = y[train_index], y[test_index]\n", "\n", " # resample train set for equal class weights\n", " J = len(np.unique(y))\n", " csmf_flat = pd.Series(np.ones(J)/J, index=np.unique(y))\n", " X_train, y_train = my_resample(X_train, y_train, J*100, csmf_flat)\n", "\n", " clf = my_fit_predictor(X_train, y_train)\n", "\n", " # resample test set to have uninformative cause distribution\n", " csmf_rand = pd.Series(np.random.dirichlet(np.ones(J)), index=np.unique(y))\n", " X_test_resamp, y_test_resamp = my_resample(X_test, y_test, J*100, csmf_rand)\n", "\n", " # make predictions\n", " csmf_pred = clf.my_predict(X_test_resamp)\n", "\n", " # test predictions\n", " csmf_acc = measure_prediction_quality(csmf_pred, y_test_resamp)\n", "\n", " results.append({'csmf_acc':csmf_acc, 'key':my_key(module, clf)})\n", "\n", " df = pd.DataFrame(results)\n", " g = df.groupby('key')\n", " return g.csmf_acc.describe().unstack()\n", "\n", "baseline_csmf_acc = measure_csmf_acc(random_allocation)\n", "baseline_csmf_acc" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "import sklearn.naive_bayes\n", "\n", "def nb_pr_allocation(X_train, y_train):\n", " clf = sklearn.naive_bayes.BernoulliNB()\n", " clf.fit(X_train, y_train)\n", " \n", " def my_predict(X_test):\n", " y_pred = clf.predict_proba(X_test)\n", " csmf_pred = pd.Series(y_pred.sum(axis=0), index=clf.classes_) / float(len(y_pred))\n", " return csmf_pred\n", " clf.my_predict = my_predict\n", " return clf\n", " \n", "measure_csmf_acc(nb_pr_allocation)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": true, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-3.0
machinelearningnanodegree/stanford-cs231
solutions/kvn219/assignment1/knn.ipynb
1
709815
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# k-Nearest Neighbor (kNN) exercise\n", "\n", "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n", "\n", "The kNN classifier consists of two stages:\n", "\n", "- During training, the classifier takes the training data and simply remembers it\n", "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n", "- The value of k is cross-validated\n", "\n", "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:08:14.045221", "start_time": "2016-08-22T12:08:13.067201" }, "collapsed": false }, "outputs": [], "source": [ "# Run some setup code for this notebook.\n", "\n", "import random\n", "import numpy as np\n", "from cs231n.data_utils import load_CIFAR10\n", "import matplotlib.pyplot as plt\n", "import numpy.linalg as la\n", "import seaborn as sns\n", "import itertools\n", "import pandas as pd\n", "sns.set_style('whitegrid')\n", "\n", "# create a palette generator \n", "palette = itertools.cycle(sns.color_palette())\n", "\n", "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n", "# rather than in a new window.\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (12.0, 12.0) # set default size of plots\n", "plt.rcParams['image.interpolation'] = 'nearest'\n", "plt.rcParams['image.cmap'] = 'gray'\n", "\n", "# Some more magic so that the notebook will reload external python modules;\n", "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:08:16.698291", "start_time": "2016-08-22T12:08:14.046869" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training data shape: (50000, 32, 32, 3)\n", "Training labels shape: (50000,)\n", "Test data shape: (10000, 32, 32, 3)\n", "Test labels shape: (10000,)\n" ] } ], "source": [ "# Load the raw CIFAR-10 data.\n", "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n", "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n", "\n", "# As a sanity check, we print out the size of the training and test data.\n", "print 'Training data shape: ', X_train.shape\n", "print 'Training labels shape: ', y_train.shape\n", "print 'Test data shape: ', X_test.shape\n", "print 'Test labels shape: ', y_test.shape" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:08:22.710242", "start_time": "2016-08-22T12:08:16.699863" }, "collapsed": false }, "outputs": [ { "data": { "image/png": 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ZeJ7VsZJeIa8tZW4164jYV20WPEVvos9jmqTNDAyNxKoby/SyLizI8/ieeqEM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LMM4y9WHNRKapF72qJytl5BqyG/W1tj2IUFBlrwd21xDfFQBbuF3c26wpgMEgnD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\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HANDd38EcPTqdloyPDtesNQa43bm1i7ffE4TQgjxzi1ze3R2xQfNLPk6f4ljsSTs8/bSs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\n88qIbSih32+DGvKn6wtoOVT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NyNyqpVXl0IpyEocz6fO0ktAE6VVqqFC+cNyV/nzsuFhp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\nlpsy6a9vX5brjKVco16KmBsGj6byWaCbIx1YA8/BQksm7+W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s0P8yjmOj26ranQmTzARk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\nN+t87aJkyDx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"text/plain": [ "<matplotlib.figure.Figure at 0x117d5b7d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualize some examples from the dataset.\n", "# We show a few examples of training images from each class.\n", "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n", "num_classes = len(classes)\n", "samples_per_class = 7\n", "for y, cls in enumerate(classes):\n", " idxs = np.flatnonzero(y_train == y)\n", " idxs = np.random.choice(idxs, samples_per_class, replace=False)\n", " for i, idx in enumerate(idxs):\n", " plt_idx = i * num_classes + y + 1\n", " plt.subplot(samples_per_class, num_classes, plt_idx)\n", " plt.imshow(X_train[idx].astype('uint8'))\n", " plt.axis('off')\n", " if i == 0:\n", " plt.title(cls)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:08:22.966084", "start_time": "2016-08-22T12:08:22.711895" }, "collapsed": false }, "outputs": [], "source": [ "# Subsample the data for more efficient code execution in this exercise\n", "num_training = 5000\n", "mask = range(num_training)\n", "X_train = X_train[mask]\n", "y_train = y_train[mask]\n", "\n", "num_test = 500\n", "mask = range(num_test)\n", "X_test = X_test[mask]\n", "y_test = y_test[mask]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:08:23.108091", "start_time": "2016-08-22T12:08:22.967517" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(5000, 3072) (500, 3072)\n" ] } ], "source": [ "# Reshape the image data into rows\n", "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n", "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n", "print X_train.shape, X_test.shape" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:08:23.131984", "start_time": "2016-08-22T12:08:23.109606" }, "collapsed": false }, "outputs": [], "source": [ "from cs231n.classifiers import KNearestNeighbor\n", "\n", "# Create a kNN classifier instance. \n", "# Remember that training a kNN classifier is a noop: \n", "# the Classifier simply remembers the data and does no further processing \n", "classifier = KNearestNeighbor()\n", "classifier.train(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n", "\n", "1. First we must compute the distances between all test examples and all train examples. \n", "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n", "\n", "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n", "\n", "First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:08:23.153938", "start_time": "2016-08-22T12:08:23.133473" }, "collapsed": true }, "outputs": [], "source": [ "# Open cs231n/classifiers/k_nearest_neighbor.py and implement\n", "# compute_distances_two_loops.\n", "# Test your implementation:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:08:51.975630", "start_time": "2016-08-22T12:08:23.157421" }, "collapsed": false }, "outputs": [], "source": [ "dists = classifier.compute_distances_two_loops(X_test)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:09:54.394867", "start_time": "2016-08-22T12:08:51.977285" }, "collapsed": false }, "outputs": [], "source": [ "dists2 = classifier.compute_distances_one_loop(X_test)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:09:54.707422", "start_time": "2016-08-22T12:09:54.396626" }, "collapsed": false }, "outputs": [], "source": [ "dists3 = classifier.compute_distances_no_loops(X_test)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:09:57.314197", "start_time": "2016-08-22T12:09:54.708877" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "two loop\n" ] }, { "data": { "image/png": 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nQz53NptFvV5HJpORvcW/TYSPgY/XxvVMig0TQaIVXKPkFjJxYjAzmUzI5/OC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"text/plain": [ "<matplotlib.figure.Figure at 0x117e03250>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "one loop\n" ] }, { "data": { "image/png": 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nQz53NptFvV5HJpORvcW/TYSPgY/XxvVMig0TQaIVXKPkFjJxYjAzmUzI5/OC\nNjDhLBaLcLlcaLVawkc9SPsi4qZWq8VPkAdIB8yEPJvNCsWnWCwiFouh0WjA5XIJzeegHwEgianZ\nbJY9Eo/HhfpVqVQkqCmVSmSzWaFH9Ho9QVZJ3eB1Manf398/tK8BIJvNynVnMhl5r4VCQVBgBmiu\nb3Lz7Xa7dDmI2iWTSeESEh3mvuaeo/8iWp9Op+HxeNDv92UoLJVKyXujH+GaOsiN5aAs9yrfLdcm\n1x07G9wP+Xxe7o/FeafTkQ4HUWQAQo1iwUM0+SCq2W63pZOkUChQKpUkydjd3ZXnQooZO370uwCE\nOsFuJYelSHEj0sw9QL/KwF4ul2U9cE8x9rGoSafTUjDRdxyMIW63G4lEQpIsJt0mkwmdTkf2dr/f\nl45FMpkU0IGULXYTDs4o8PeNRiMymYzsOa4ftvKJKDocDtRqNWSzWfG79CVEyKvVqvg/FrAcZK1U\nKjI8nkqlxD+yO9XpdBCLxSRe5PN5GUIlf5++mYP2HK4HIHGqVqtJbAcgXdj9/X00Gg25P+YUpVIJ\n5XJZOgIsKG022yEOvU6nk4FirnPOEmSzWZkvIRWLSK/X65VuD9dmv/9sOLNarQowQG469zPpJo1G\nA3t7e7KWNjc3xefzHTK+s/Om1Wphs9lQrVZlb3L+gp2mVColfHHOnJA+eHBOgcO7JpMJpVJJRB34\nM4yd+XxeaFNc89wr3W4XpVJJcjkWIixkc7ncoXyBezKRSMBisch+Znymn+PgP+cpmPAzd3te9htP\nnu12O06cOIFut4vd3V0YDAZks1ksLS3h3r178Hq9SKfT6Pf7OHHiBHw+H7LZLE6fPi2bLRQKodPp\nwO/3o1QqwWKxwO12i7NOJpNQKBQYGRmB3+/H9va2JKpEaE+dOiWtqu3tbXi9XtRqNVELuH//vvDG\n2E7iFOnIyAhu3boFh8MBlUqF4eFhGW4wm824dOkSdnd38cknn2B4eFgW/+zsLPx+v1T8KysrGB4e\nRjabhc/nk/bb1NQUotEo3G430uk0LBYLgsGgBPZmsymUihMnTqBer6NcLuOVV17BzZs3YTAY8Pjx\nYxiNRiwtLWFlZeUQP2x9fV3aM6Ojo0gmk3A4HIcoCERZyDkfGxtDIpGQTRAKhbC8vIxyuYzR0VFp\nreXzeXS7XXg8Hrz66qv48Y9/LFPYiUQCwWAQarUaS0tL0oYBniWLnO5nksVJfLZqnE4n4vG48APj\n8TiCwSCBmhIGAAAgAElEQVTq9TqmpqZQLBbx2muvYWVlBWq1Gl//+tdx//59BINBtNtt7OzsIBgM\nCkJN+gSpNJxMJmI8MjKCSqUinYVarYZEIoGjR4/i0aNHQiN4/fXXZS3rdDpcuHABrVYLGxsbUoFz\noJOcc/Ij+/0+hoaGcPv2bUGOhoeHMT4+jn6/j3A4LIMd6+vrCAaD0lpla9rtdotzJLdscnIS/X4f\nw8PD+Oijj3Ds2DFJSEZHR/HZZ5/JdRoMBuzs7MDn88FsNmNubg6Tk5OitDI9PY0TJ06gVCohkUgA\ngCRuR44cEZ57v/9MmWN+fh6BQAA2m01oJVzzGo1Gihen04lMJoN2u42ZmRn0+30sLi4iHo9jYmIC\nADA+Po7Lly/j/fffx/z8PFZWViSZPX36NDKZDK5evYpPP/0UlUoFW1tbh9BBlUqFYDCIVCole7nV\nauHy5cu4f/8+rFYr5ubm4Ha7hZKUSqUQDodRKBRgNBqxtbWFUCgkg7TZbBYWiwU+nw/BYFCS9KGh\nIdy8eRPnzp3DF198gfPnz+ODDz7AV7/61UNT5PQzLpcLS0tL6Pf72NrakmdFVYuTJ09KstLpdHDm\nzBns7u4K0kq0u91u48SJE9Jd4sCPXq/HyZMnpV3e6/UwOTkJlUqFzz77DAsLC7Km7ty5Iz51bm4O\nn3/+ORYXF6VQGR0dRS6XkyAWDAZx/PhxpNNpHD9+XLjS5OsvLi7C4/FAoVBgZmYGm5ub6Ha7OHPm\njLynTCaDSqWCV199FZ999hlOnDiBRCIhKJnf7z9EgalUKvB4PAAgiKtarRbFEj4rj8eDhYUFjI6O\n4tGjR9BoNDhy5Ihws9mtq9VqGBsbQ6PRwOTkJO7cuYPjx4/jxIkTyGazePHFF/Hxxx9jYmIC6XQa\nTqdT9vVXvvIVxGIx3L17VwZASYfwer3Y399HOBwWXvzIyAiWl5cxNDQEAOJ7yHH2+XwCkmSzWVy9\nehWJRAIPHz7EsWPHUCqVkEql8NJLL2F4eBjJZFLu6eLFi9jZ2cHRo0cRj8fhcrlkTdrtdkxNTWF3\nd1d8BhNHg8GAWCyGcDgMAPD5fOj1erh37x5efPFF5PN53L17FxcuXMDTp09RKBRw4cIFJBIJmcHZ\n398XHq1er0e1WsXv/u7v4uOPP4bP5xOkkIX+a6+9hmQyieHhYRSLRYyMjEinMZ/Po1Ao4NSpU0gk\nEhgaGsLGxgYuX74sczMsQM+cOYNkMolutwuXyyXI+pEjR7C3t4c333wTf/VXfwWdTofh4WEsLy9j\nYmICRqMRa2tr8nterxcLCwsye7K9vS2c4H6/jxdffBGLi4vY3NwUwKZYLGJ6ehpKpRITExPI5/PQ\narVYXV2FVqvF5uYmJicnBRk2Go0SZxcXF1EqldBut6Xzk0ql4Pf7pbAhfa9cLuPKlStYXV3Fl7/8\nZaysrODs2bP4/PPPMTY2JgpJR48eFbWmI0eOoF6vIx6Pw+/34+jRo8hmsxgfHxcQhEn2lStXpNO0\nv7+PqakpJBIJTE1N4bPPPkOn08HU1JSo67TbbYyMjGB/fx/lchlzc3O4ceMGjh07hlgshhdeeAGb\nm5uwWCzyXC5dugSXy4WdnR2cPn1aaFKM61wz7XYbx48fx49//GO5h/39fUHrI5EIKpUKotEorl69\niuXlZSiVSjx+/BgbGxs4deqUzAkQwPL5fBgfH39uuauC7aTfhN2+fbt/+fLlQ60HIno2mw29Xk+Q\nEPJnSKInhykWiyEYDMqgHJEM0jC4ODj1SgTEYDCINBGRk3Q6LXxKIhrkQhN1PHitRGUsFgs6nQ5S\nqRQASHLItm0oFEK5XEan05EqlqjiQQpGKpUSegOdOluLDNQAhE9IZJlV6cjICDqdDvb392WAhAjS\nQbWLUqkk6BBbjawYmZCyrct2Va/XQzgcxu7urlSYB1vFwLMAMDw8jHQ6LVVeuVyWFi/lmnq9HorF\nIsbHx2XQyOl0SpHz3nvv4bd/+7eRyWTgcDikrV0sFhEOh5HNZgUdJpeYKJ3dbkcmk5GByYWFBTx9\n+hTNZhMvvvgirl27BqvVKkgZUV7y1ti+dLvdMiDDNiLRAb6/gzxeJkCVSkUcNtuSwWBQpKEACP84\nm83KNZPTzvsAcIibSYk+0iWYRORyOeHpc00Fg0FEo1FpB1NCj+uVw1z7+/uSbGk0Gvj9fpFeq1Qq\nUr2HQiEZZLPZbKjX6wgEAtja2jrU8mahS1kgKiw4HI5Dg75DQ0PIZDKCbPCa2MplscX3uL+/D41G\ng5/+9Ke4evWqUAI4H8ABEiqQcMCJkm68D6LHB6kf3AczMzPY2toSlQWv14vV1VVpOzMgEjUjdWJ3\ndxder1cQUg4Bsx1ZKpWEesPkzm63Y3l5GQAE3eb+CIfDWF9fP6TKAjxrnzPwb2xsiIQmB+4ajYZw\nlwEc4qtTqYGtTNJ9iBYpFApUKhXhQ7K9SdSG+9dut8taOaiwwVYwB4io0FAul4VWwyEntuMbjQby\n+Tz8fr+glUzmd3d3kcvlMDY2hr29PaFTGY3GQ2g3lQLeeecdfPWrX5VZCJvNdmiv9fvPlIj8fj/u\n378v74YdK7a2icJXq1UYDAYpXpxOJ548eYKlpSX84he/EHoaEdhWq4XFxUV0Oh3cv39fihbuUZvN\nBpfLhWg0Ku1w+suhoSHs7e0JDYUdE/KYmWRyYJ1DxaVSCUNDQ4eGCPnOxsfHsba2JpQ9j8dzaAiY\niCcHSTkEz3XTbreFMsHuIu8lk8mIFKDL5RIQgKgzkx9STWq1Gk6dOoWnT5/K94ku02esr6+LzBmv\niQh9q9XC0tISrl27Jl+HQiFsbW1BoXgm5cniuVarCYDV7/fxve99D6+99hqazSYikYh0oA5SR3gd\n5O5T1pOdEZfLJd0tjUYDp9OJ6elp3L17V/Y6/Sy7Y+zgci/2ej3Mzs5if38f2WwWBoNBBn9JI6Xo\nQSKRkFyF+5E0ByathUJB5HnZiQkEAiiVSgAgsq9UUWI3p9/vi7CC0+mU7iApm9PT08jn89jc3BS6\nGLsH3J8cNh4aGpKOJdcS6alEqUOhELa3t+WaKZUaj8ehVCrhdruxs7MjORVpHOze0Dc1m00Bgxjj\n2D3udrsYHR2VnKBWqwmFiOATu3/9fh+vvPIK/vRP//S56Dz/xjnPY2Nj8iDIs+L0OQOfw+HA8ePH\n0Wg0sLGxIdOqdC7UMAWeVe7kD5NCwAQ1GAzKQMf4+DisVivm5+dlgpcP2GazyRAb2zzhcFiqRiJr\nQ0NDUCj+WUd6eHhYKByc1CWSnU6nhTNns9ng9XplgttqtWJ7exuhUEiGl0ir0GqfaSyPj4+j0WgI\nIuxyuSQwu1wuOBwORCIRaLVauN1uTE5OSpvD6XQKmnZwyPEgD+tgmysQCMDj8cjPc1Anm83CbrfL\nYgcgCJtKpcLQ0BAWFhag1+sF8SAaysrd5XIJgh8KheDz+WRo52BgMZlMMJlMcDgcwldzOp1oNBrw\n+XxwuVzyjsi1HBkZgUr1TNsyFovBZDJhdXUVfr8fBoMBGxsbor/baj3T8+31ejKFzkIBgNAKfD4f\n3G43vF4vlEolfD6ftNDHx8cRCASkHU4EnAgtec1Pnz5FrVZDOByG2+2G2+1Gp9MR1I/65XQo/f4z\nDd/R0VGoVCqM/nKYzefzodFoSOIyPT0Nq9Uqk84ajQbFYlHafHa7HW63G8Fg8JDMF6kfDNZEx9Lp\ntARMIkdOp1MGX8PhsNCIuA/9fj/8fj9sNhtGR0dlbRLxY3JNtJ5JCRH4yclJjIyMCOeRqDwL6GKx\niFAoJNJPHN5lMct9HQ6HhV60ubkpnR8O/4TDYfh8PqFpMWFiQnz37l2EQiEprpk09Pt9TE9PS/uf\nhQw5iCwq6Q/Ynud6CoVCojYQj8dlTdJX2O120efu9XqClFutVoyPj8tznJiYkGBHSlYmk5HPoa9g\n54EdLXIAvV6vPKOpqSmo1WqMjo4Kh5ioMJPU+fl5hMNhGT7iXtFoNDh+/DjK5TKOHj0Kg8GAyclJ\naYGz47S/vw+/3y9rkNQb0q5arWca+KTmsBg4duyY8Dp3d3cRCoUwOTmJ2dlZSWbIkeQ7ZRxxuVwy\niMXBSvKBU6kUotEogsGgcIZZKPt8PoyMjGB0dFRa9yzqtra2sLq6Co1Gg7t374pPdDgcojoQDAbx\n9OlT3L9/H2NjY3LP4+Pjos09OTkpRSzpfjqdDjMzM8Kr5u85nU4BP6anp6XQZYFGZHJ+fl6G3oPB\noOhrl8tlBINBlMtl4QQbjUbxma1WS1A+m80mszTk9LLrwr0bDAZFf/ggD9VkMmFvbw87OzuifmKz\n2YS3T6748vKyFOCRSEQG+alxzb3ETpfP55Piw2azYXd3V+7L4/GI7KvNZpN3QUUGDgfSXxiNRng8\nHuzu7kKr1Qo/dnp6WjjKLpcLCwsLwuve3t5Gp9PBzMwMXnjhBXg8HvGTuVwO0WgURqNRRAtCoRDa\n7TYymQx2d3eFrsBYMTQ0JJQIdsD29vaEukFaATuB5KxzHXItuFwu7O3twe12Y2VlRYooAn583wQo\nDsrsBQIBoZAeOXJEaJZ+vx8jIyNwuVx48uSJ0IU4N6NQKJDJZGCz2eD3+zE9PS0ocSAQEKqIxWKB\nx+PB0tISAMiZCMyR2PlKJBKi7kX/SfoVu4CkxWQyGRw9elRojoFAAMPDw0KdaTafafuvrq5KsWOx\nWDA7Oyt+j1xv+gx2eJ6H/Vq0jUgk8iKAv4xGo5cjkcg4gP8JQA/Ao2g0+ie//Jk/AvBfAmgD+O+j\n0eh//HU++/Lly/jiiy9Qr9cxPDwsSABbWo1GA1NTU7h8+TIeP36MZDKJpaUlfP7556IEUavVcPHi\nRTx8+BBjY2NIp9OIx+Ow2WwSOOv1urT6XC4XLl++DLPZjHPnzmFlZUVkyer1OiYmJrC5uSkHqqjV\napw/f142UKvVwtzcHNrtNn7xi1+g3+/j+PHjkuwnk0lcuHABRqMR6+vreO+996BUKnHlyhVcu3ZN\nHDgRu2PHjiGVSuHq1avQ6/V4+eWXha/HivLcuXOIx+OYnZ0VZL3b7WJychJ+vx+ZTAZvvPEG/vZv\n/xZTU1M4e/Ysvv/978NoNMLn8+HSpUu4efMmbt++LcE1m80KPaVcLgtP+MKFC1JVctMSYRsbG0Or\n1cLy8rJMzo6OjkKpVOL48eN45ZVXZKKarWOPx4NKpYJ79+7h2LFjUm1++ctfxvr6OtbX14X3xTbm\nxYsXsbKygqNHj2Jvb08Q5VarhdHRUSQSCZETIjeSCIfb7cann36Kubk5fPjhh3j11VcRj8fx4MED\nLC0tSaU8OzuLBw8eCJWHCBWHk6rVKs6cOSOJyY0bNzA9PY379+/D4/FgbGwMJpMJP/vZz7C4uChD\nkru7u1hcXJRE6f79+xgZGcH58+cFKWZrSa/XY2ZmRriW4XAYm5ubeOmll5DL5fDkyROh4szPz4uw\nfqPRkIMh2C58+vSpOFWbzYaxsTH0+31MTU3h5z//uRRrFKe32Wxwu92o1Wp48cUX8dFHHyEUCknr\nlmuE3QG2i3d2dvD06VOcPHlSBq6y2SwuXLiAfr+PYDCI5eVlHDt2DCsrK7Db7TCbzXKAx/j4OIaH\nh1EulzE2Niadl0KhIAVTIpFAOBzG2toazp8/jw8//JC+SGhb1WoVJ06cwOzsLIrFIm7evAmNRoPt\n7W1MTEwgkUjg5MmTcLvdwq9Lp9PIZrOYmJiQxLFQKODWrVs4f/48LBYL1tbWMD4+LgOmly9fxubm\npsg1sRjjoQxXrlyRA50YrOisx8fHce/ePbjdbty7dw8vvfQS/vqv/xqnTp2STs/o6Cju3Lkj/MmJ\niQlotVq5h4sXL8JkMuH9998X9Fiv1+Phw4e4ePGiSPl5vV45zIJ60UyOIpEIXC4X7ty5g1deeQWf\nf/45AoGA8LPn5+flkCK9Xo/XX38dyWQSW1tbuHXrFi5cuIAnT55Ao9Hg/PnzKJfL+PrXvw4AWFxc\nxBdffIE7d+5gcXERy8vLUCgUeOmll0Rq64svvpCDZVhIhsNh3L59G2q1GtPT0/B4PPjqV7+Kmzdv\nwuVyYWNjAy+//DIcDocgpn6/X2Ynjh49Kt24N954Azdu3IBWq8WjR49w6tQpLC8vY2ZmBvv7+1hf\nX0e73cbZs2dFWYNKHufPn5eDc4hKZrNZnD17Fh999BEajYZQWs6dO4dPPvlEkMRKpYIzZ87gBz/4\nAfr9Pl5++WWsrq7CbrdjZGQEyWQSnU4HL7/8MsrlMhKJhEjb6fV6LC4uHtL85drn4UhnzpxBIpHA\nu+++C4PBgPHxcWxsbODSpUt48803sbKygnq9jsuXL6PZbCIajQqNoVqtwufzyTAd/TDwrOMyMjIi\niXw8HodOp0MwGJQuFpOZM2fOYGtrCx6PB4FAQPY2FaJ6vR6CwSCOHTsmCdqTJ0/EP66vr+PSpUuI\nRqNyeFW/38fRo0fxj//4j5ifn0ez2cTc3ByePn0Kp9Mp12u1WrG2tiZUmePHj+PmzZuyXuLxOEZH\nR3Ht2jUMDQ1Bq9Vi9JeyrvQXfr8f7777Li5fviwdD3YRCDbNzs5KEnr79m1YLBacOnUKV65cgU6n\nw2effQaz2YzHjx+j0+kImMeOJotmgmBKpRJLS0tYXV3F0tISotEoarWaUD1Jr7l+/boMYI6Pj2Nr\nawsTExNYX1/HxMSEHGjF+YVPPvkEZ8+exdraGi5cuIBsNovh4WFRruAMz9WrV7G/v4/V1VWYzWaM\njY3hyZMn8j63t7exu7uL2dlZmb36/ve/j7m5OXS7XczMzGB3d1f22+nTp6HT6TAyMgKDwYDV1VVc\nvXoVqVQKbrdbVF+++c1vYm1tDceOHcMHH3yAS5cuCXW2Vqthd3dXiiaCXgDkcBUKApAS+eabbwrC\nPzk5CYPBgK2tLSQSCfT7fXz5y1/G48ePBSyYmJgQv2M0GjE0NCRUV6PRKIXa87BfmTxHIpH/BsB/\nBqDyy2/9OwD/bTQavRaJRP59JBJ5E8BNAN8BsAjACODTSCTyXjQabf+qzyeCTII5qQv1eh2bm5sw\nm81YW1tDJBKRqoj6pel0WlqyiURChm04HEeuLIdjAoGAtHpTqRTS6TQymQy2t7dlgFChUGBtbU0o\nFtRqzeVy0mLNZDKyYIiEsUXNqpOybSTk9/t9SdJtNtshpQ4mhbFYDHt7e/JMOp1n2sukCFA5gNxg\nooy7u7uo1+u4e/euIC9sd1Knc3d3F5lMRpxiIpFAsViUlgf1qt1ut+g9c/iLZH2iRslkUu6BiCYA\nFAoFRKNROagjkUgI75woGweDarUalpeX0el0kM/nYbPZZLACAFZXV1EsFrG/vy/cpYODD7lcDqVS\nCdvb27J+dnd3sbGxIfdP1LtQKMiJkKxOyTkl34zJO6Wa8vm8nNDFIa98Pi8BhQOtfHd8r4lEAi6X\nC+l0Gr1eT3halUpFBosajQbS6bScrDY+Pi6JLYcednd3hZZBaTkOvQLP5Hgot8QhN57WaLfb5T0Q\n1djc3ITP54NWqxW+ZiwWk4ls6lNTl5zIEw/FsNvth4aC+DwAyBrZ3NwU+gQTVQ4dsZ3b7/dlILZc\nLmN6elpOl6MMEge4OM0ei8Vk0INUK3LcOOzHwRK2GjnkVygUZBiKgbNarWJ7e1uQHCqmcNCK9BsO\nLJHbyOQ+FotBr9cLb39jYwPJZFL4vRz0OnhqG2cTtre3D1EVuL4YiDn8RVUOFmMej0fAAnYImDxQ\nzoyDoLlcDsFgUGgk/DucWVhbWxP+Pdv2fFdEnre3t7G3tyfDw/F4HMViETabDf1+H2tra9je3pYB\nI7ajiQqSx8zT17jm+EwACE2LdINyuYwnT56Irm2328XOzg4qlYrQqMjhbrfbgrTRX1C/u1QqIR6P\no91+dhAV6TtWq1X8CK1YLGJzcxM2mw3xeFwURvR6PZaXl6FWqxEKhSTZ3tnZEV9H2lI6nRZd7Xg8\nLs+cVMB+v4+HDx9KEcouZDqdloIpEAjIABrfL4cF9/b2hIJYLBZlcPDmzZsib8bYR1968MQ/DkYf\nlI+kRCNjCWMuKVM8HTSVSgltqlqtin47Z1nUarUMvXPIngOPHDAMhULY399HPB4X+cFSqSQydzzT\ngafI0V+SdkCfBUA6jxwCTyQSgrByIPzSpUtSJHQ6HemCEjFOJpOwWq3Y2trCwsKC6BInEgmh+RAo\nY6IGPBsyZIJHmkw6ncbe3p4MfJL+0uv1hM5C3fL9/X3pZBaLRayvr4tsrl6vx9OnT9HtdrG3t4dC\noYB4PC68b9IvAcggNmVhi8WiUMdI1VlfX0cmk5ECkScjk9bJnImnZbLLyFi/t7eHdruNZDIpe5V7\nn4OXHASkTObQ0BCWl5dl/sjpdCKRSIjwAzW/ycEm+s3ZF9KuOMBIP8TB1lgsJt2FdruNQqGAjY0N\n8dekCq6vr8tcCOM0KcCk1jwP+5Wc50gk8nUADwD8z9Fo9GwkEolFo9HwL//tawC+DOCnAF6LRqP/\n1S+//wMA/0M0Gr39L3327du3+6dOncLw8LBI8nBiORKJHBKVpzai0+nE8vIy5ufnUSgUsLu7i0Ag\nIPxNTnNTuYBJOWVjOOnNwMrWxNjYGDY2NoTCQRWEgwoflCtjgGf7lINOXBwHOVkKhQKnTp1CMpmU\n5JUv1u12y/CUwWDA/fv3pd3LVhu5RJy+p+D5wWCo0WiE6zM+Pi4IEfmD5JS2Wi0MDw8L/7PT6cjX\n/JuhUAgPHz4UtI761KRIPHr0CGazWXicarVakrtkMim8XR41ygl4AHLkMkXv2XZjRRuNRtFsNvHx\nxx/jjTfekHvitTabTeFdU4uZ6iNKpVK4YDxoZnt7G2+++SZ+8pOfoN/v42tf+xr+/u//XqgRLKyo\ndEHpOpPJBLPZLIGW10gaEFuKVDIJBAJIJpOibHD69Gl8+umnwmebn5/HgwcPJNiTw0knyFPnyOni\nGiWFxOVyiWOipjmTLCa25MiyvXbv3j3hUbJ7QNWNtbU1zMzMYG1tTXiD6XQaS0tLePLkCYBnSSo5\nrHNzc3C5XPj0009htVphMpkwOTmJ27dvC8+O8kZUF2ByYDabMTIyIsH4oOMnTYZzCkTfiWTt7+9j\nenpa7vvdd9/FlStXEIlEZCCM3Gmime12Ww4kODjYywOGgGeUoEajIftIo9HgS1/6Ej766CO0Wi14\nvV7ZB1RK4P7Q6/Uol8tCe2Irnd0KIrmUo+Jx5LFYDHNzc0in01hYWMBHH30k3E5ym30+H44fP45P\nP/1UqCl6vV4SocnJSbjdbjx+/BgKhQJzc3O4c+eOcEG5jtj6pboQQQBK8q2vr4v8JoNJPB5HKBSS\nAxEYNJVKJTwejwRa8jNtNtuhU9vMZrMMYoVCIahUKmxsbEi7NhAIYGdnBxqNBkNDQygUCtje3sbC\nwgKazWdHGfOUNafTiZ2dHUxNTeHhw4fCK+VR6XwPbBf/8Ic/xOXLl2U2gsc400f0ej0cOXIEQ0ND\n+OlPfwqVSiW83YMUBCa1pBIUi0UsLi7C6/Xi5z//OS5duoR33nlH9iAVVwqFAn7nd34H+XweP/vZ\nz4TuQ5qU3++XxI7zCVSFmJubw8bGhrSu/X4/Njc34Xa7Jc6USiWcPHlSBnRHR0exvr4uEnF6vR5P\nnjwRTvqLL76Imzdvyvp2u93CN+e6ZdFNH8EEOJfLSWeDSSq7IQQ3pqamxNcfPXpUzhBgHDmoX1yv\n1/F7v/d7+N73viddGQ4Tp1IpfOlLX8Inn3yCYDAo/gKA6PuXSiV8/etfx3vvvQen04lCoYBIJIJH\njx4dkvLkIWCMPQDwN3/zN3j99ddRLpdx9epVARQUimca+C6XSwqEgxrtc3Nz2N7elmtg7FUoFBge\nHsbs7CzefvtttNvPsEHKIlJ+jTkVE8R8Po/z588LMGa1WuHz+XDv3j3JfXgYHK+FSCm7pIw9Fy9e\nxM2bN3HhwgXcunVLuphjY2PCfx8fH8edO3eEjtVqtUStZGRkBI8ePRJpzlgsJjHmlVdewdraGmKx\nmFBIuMd4CA27tPx7B9U2yBWnPzx58iRu3LiBQCAguv4nTpzA7u4u0uk0jhw5gs3NTVHbIt2NRRx9\nhtFoFHUYypEePOTo4sWLArrt7++jVqvB7XaLryLf22w249VXX8Uf/dEfPRfO8681MBiJREYA/N0v\nk+e9aDQa+uX3LwP4zwH8BMDRaDT6b375/b8B8DfRaPTDf+lzb9++/ZubVhzYwAY2sIENbGADG9j/\nr+x5JM//KVJ1vQP/bwFQAFACYP0/+f6vtD/+4z8Wnuu9e/cwPT2NRCKB+fl5QZxSqZQc6kDkZ35+\nHhaLBT/5yU9Ec5RyVuVyGUNDQ3jw4IG0Wd1utxyPvLKyIgoIwWBQKlHqKqpUKpmeVSqVmJ2dxeef\nf45eryenhLEd53A44HK5sLKyIu2ymZkZQVIo3WW1WvHhhx/C6/VK6ywYDKJUKmFubg4ajQbvv/++\n8NI4OFWr1RAMBkW7l9dIOTwelUrUuN/vS2uRklCkenBqmqg1NTez2aygm+l0WqTDDh5lSvm7arWK\n4eFhVKtVGSTgNXJymRQYHrPaaDQQDAYxPDyMJ0+eCFLr9/vx+PFjkS4ibeI//If/gLfeeuuQJJxa\n/exIdU5Lc5hmdXVVkKZer4czZ85geXkZPp8Pa2tr+NrXvoaHDx/izp07+P3f/31BhsLhsPDBOG3e\n6XTkxLO9vT2EQiE8evRIxPSnp6exubkJr9cr6zCZTOL06dO4du2aDNdMTU3hyZMnIpt46tQp3Lhx\nQ+gQw8PDMkAGPDtMgYeJcAAtnU7LIBClFz/++GPhZrI9zQGmWq2Gubk5OTo2l8uJCoXf70ckEsHj\nx49x4sQJ3Lp1C3q9HslkEmNjY6hWq1hZWcHS0hLW19cBQOQJidCTv7e7uwufz4dwOIyNjQ3ZD9Rx\n5vujtygAACAASURBVNoJh8OivDA6Oiqawjwi9fHjx3A4HLIXqLuaSCSgVCpx/vx5kY6jNvRf/uVf\n4lvf+hZOnz6NBw8eCPrPwz94LeFwGFarFdevX0e/30c8HpehH07rE2XjM5qensb6+rrIn9F/UFKM\naLJOp0O9XsfQ0JDQY6gawi6WSqUSzvS9e/fwxhtv4MMPP8Tp06fxox/9CC+99JLw9KLRKIBnyP3U\n1JQMJrLjRvoPD/lIp9PCqeSa5XT8xYsX8eGHH8rPchB2d3dXlAgWFhbw6NEjQTb5bLe2tnDhwgV8\n9NFHcsohTyIcHR1FPB7H3NycTNe73W7cunVL3nkkEkG1WpVh62w2i62tLYyNjQnytrKygkajgYmJ\nCUHez549i6dPnwr1Zn5+Hnq9Hnfv3hVVFnaFIpGIKKKwTV2r1fDd734Xf/InfyJdS/pB6q2Pj49L\nN+HGjRui0MQTDv1+P9bX16VdX6lUBC0DIOoVp0+fxueffy574tixY9jc3EQul8PXvvY11Go1fPLJ\nJ9JBValUMJlM2NzcxIULF7C1tSU8d7PZjNXVVSwuLkKtVuPatWsYHx+Xa+bQ6tTUFGKxGJaWlpDJ\nZJBKpbC4uIibN2+KupLBYBDqoU6nw9LSEq5fvy7tdCLRpM6ZzWY8efIExWIRU1NTMJvNWF5elljY\n6XRw5MgRrK6uAnhGbfF4PBgeHsb169cxPT0tVBqfz4fNzU2h5ZCWFI/H5WjlS5cu4ac//al09qgA\nY7FYcPz4cfzoRz9CMBjEzs4OAoGAHPbC7sb09DTu3Lkjyg+RSARPnz4VbftMJoPp6Wl5XzqdDtPT\n03jrrbfwZ3/2ZwCAqakpPH78GPV6HXNzc4jFYggEAvjss88k/hSLRfh8PtTrdczOzuLRo0ewWCxY\nXV2VAdRz587B5/Ph7t27IqPIQdlOpyM0SNI0KJ3KITlScyYnJ7G+vi5IPKmDqVQKNptN6KdU5fD5\nfNje3sbZs2ext7eHkydP4oc//CHm5+dFnYdDeyaTSZ6Pw+GAwWBAKpWSOYrNzU30+32h7lHu79y5\nc7h586bQXKnMEQ6HEYvF5LlsbW3h5MmTuHnzpgxFUhmLai0c/k8mk8jn85iYmEC73cbp06exurqK\nXC6HK1eu4O2335aOKwclOezfbreF004qKQcFg8GgdHOWlpZQKBTQ7XZF3pg0FXa15ubmZN6Gsxr/\nWvtPQZ7/CcC/jUajn0QikX8P4EMAnwB4D8ApAAYANwAcj0ajrX/pc2/fvt3/xje+IQcj8DSqdrst\nvDfSG0KhEB48eCAUB0pG8aFSBYOJJLU7KVwOQCSM6vU6ZmZmsL6+LpuCQ2xs2VNLlzQJtvXUarW0\nacipocQehfkp82O320XMnl+TA2owGOTap6amsLW1JQMYDocDm5ubIocFQJIdJhzkxikUChHrn5mZ\nEerDyMiIcGw1Go1sAErcMRk4eKIYhet50iD5azxtiUM7LpcL29vbMjFOhQadTodIJIK7d++i0WiI\n3jClqSggT17V+Pg4crmcHCZCubcf/OAHeOutt8Rpk8tLZ8kWJ5VEeNAG+XGkAHU6HWlPs63OA3PI\nRaWqA6eGqTvJQMxkgDw8Piu2jyhzRN4sgz0HISitw8EpAELH4YElFotFuKU8VITtYyolkHebzWZF\nT5pcZap3kIpkMBhkQBZ41pLjgTE8dIaHRXBP8MRF7g+1Wi1yWdxbLCr5/MkP5D4dHh6WRI60AwBw\nu93SFm21WvB4PKhWq3KEvFarFf71QdoPkzuHw4FsNosPPvgAr732msjfra+vw+12yz0cPAqWE+1u\nt1u4cABEH5aH/5ArSf5vNptFu92G2+3G/v4+jEYjxsbG8OjRI2nvUwGHgXNyclKkBnd3d2WWgdQv\nlUolz9Tn8yGZTEKlUgl9i3xZahZTacfpdGJtbQ1+v198GWldpEs4nU7s7e2JIgaHXjlXwEFMlUol\nKkQs/LRaLfb390V9hHKEDodDuJAsXnmKKwBRveHfpiQfqQxqtVqkzJiMdbtdSWi1Wq0UB9wL/xt7\nbx4caVqdez7aSrtSWyp3KZVaUlJpq0XVVdVNd1MU0DRUTwMGArAxxhgCiAnC4flnYmYiuNc4JmK8\nhe3rsIPwHS8YY2iwAbcN075Um26o6qrqWlWlfU2lpFyUSm2pXcr5Q/07pBwThnvdHm7MOCMIuqtV\nyszve7/3Pec5zwLX8dSpU3rw4IGSyaTKysosHVCSfR5J5jyUl5enF154QZ/97Gc1Oztra4y9DG9h\nRFlwhhG2HR4eqru729b+6uqqARJYjBKY5PV6jRePhWUmk7HYbO4dHGtoaXl5eert7dUrr7yiw8ND\ne9b29/fV1dWlRCJhaxc3C9YiAnj2M/buwsJCnT59Wq+88opdC2zteN6wBGtoaNDq6qpZQfKMFxQU\nWJIvQR9YV+Z6xRcUFBgtDZogZy4CUNYDNCwEv6lUSoFAwHiv/5xqV1paqrm5ORUVFRkFA30ETRNU\nAfjtBJKQysc9p5EAYPjLv/xLPffcc8bt5vNwpuJmsbGxIa/Xa88R55nP51NTU5OGh4cVjUYtGRNd\nEPauuTQh1jkhMbjKQJ3EXYsUSYKrDg8PFQ6HNTk5qeLiYmUyGdXX11ugWWlpqYFGWNcSugStFTof\nLlqsY9YLezl0DATTPLPQM6enp1VXV2f3C3vCvb0948PjYEZ9A5DY0tKi69evW5AVglS8qqmboNwk\nEgkVFhZamA+fC80VDdjS0pIBqOxJ0Ed3dnZMm8Nzi2aG60Ji4enTp/X5z3/+Z2ZV9z9J+o/hcPhH\nkookfWN0dDQu6fcl/VDSf9GRoPBfLJx5sSnnLrLKykq70ES6wqPjZq+vr9vCy8vLk8vlMrEXqCrx\n1bW1tUb058agQqdzoigGoa2srDQPWLrskpIS47E2NDSovr7+mIUON4iC3+FwGB8RwRqIHvZAbMy8\nRzablc/ns4KNhgILKDiB+/v7ZinmcDjM89jhcMjhcNihAf+0rq7OrJmKioqOWe5hB7e2tqaSkhJT\nBMM15EXc6u7uUVwyi9npdKq4uNiCDLCzo4jhACShiY0X5wJcDPh8kgwNwD6ovr7eGicKEzhScBvr\n6+utSOFh5PCjgCouLrbvgDUZDQC+1TQShYWFCgQCFkEL/xwBJFZHePQS+CLJvh/+1PAsUdFLMk9S\n1hGWOiBi1dXVZm1UWHgU+w6vrri42CzO4KViLs9aZc3jhYpAgzVAEYOFE5xIihpQDCKVudc0pvwO\n4mhra2vt34lwlX4ctY49EeuypKRE9fX1qqqqMh44aw4EGycOUCXs11iPXGc+N+uO71VeXn7sWcQi\nT5LZA+YWDIhXiKQFZYbDifcvFo/FxcVml4jFFxoE0DXCMkBMKYwBCUCIQZgRE1Moo2Tn2eeARoOR\nax8JalVdXS1JVqRwQBL0gnc9ugz4lbnx5DQeHHSs+4aGBqXTafn9flVWVtrezP3gGeK7w53NZrNW\naHE/8LN3OBxyOp12aINiYX1GKA1rn//GGqPJzW1QS0pKrCFZX1+3IAy32y2fz2eWagQiud1uKz4o\nGrEuhMfJOiN4hz0KMV11dbUcDodFDXN24b7CHse69vl8BkhgJ4crAKFCcOmZjq6urpoFZe73x8IL\nSznOD5pDngNsQbHsYx+VdCwtkj2WxE0afUAMCmHejz2JBo4ERJJqWau8FyE2nM8NDQ1mBYkQnec5\n17ecVFWE+IipSeWrrq62qR4R60xLPB6PrUkaDZfLZTaWnKWcQc3NzWYOANixtrZmZ44ka25zk3NB\nXgECsN+l0EVoBxhEE0LQEfZsIMqSTLdCc4SlLdedfZamOje2nQAWmrri4mKruSoqKsxm1e12H+MU\nA6Ix/UQ3trOzY1x+QD7sZ/GoRqeWGy2O9S0WfTTNNAPs+dvb23b+8v58VhpFfKcRxbJ22SOphzg/\n2QvfrNdPRdsYHR2dlXTxjX8el/T0/8PP/GdJ//m/9gNAUwBJ4wLkKs6x0EHoQ6EE+sjmXl5ebhdu\nZ2fHRjBs/KCYHHwsCEy4OWQYsbDAstmsnE6noSGEa7C4GQvjY8jYDwSG8SqdEgcU/p74GNfX15vP\na3l5uS0Akg/xOuTwBEmkkMXPF8cACnR8oPHd5XtKsg4RQVGuzycm7xQrHMDb29vmeyzJ/B5JWaMQ\npZilqMKTEpEUmwubEYWQJDsoeXhB83hgQEFRa9N0gSyg/qfZ4sCTjg6aSCSi6upqOZ1OE83gMwz1\nhAKCzZBCCXSEpoGuGLEP9ke5CAooOYULDQrjLnxjq6qqtLq6avdvfX3dLBxBA0FlvV6vJiYmVFVV\nZRs0703qJNcZlAlBHEh7rg8mzSETAK45ByzXHZcLxCisU0a4INaMIwlMYO1hYk9iIg4fUAiIbaex\nzvVVZlNHdMh9YNzOBg2Vo7q62gpJJlIUnRTEoJCsY8IcWJ+1tbX2rFLMEKhB6AnUpdnZWfvMiJRX\nV1cNFeRQxZGF4plpAp7du7u7VuxwcHNfQJgcDoeNLymQsEFj7QAGVFVVyel0WhHBc1JfX29e57gy\n4KmOiIefpaH2+XyanJxUQ0ODiawJkigvL7f3RcgKSg+thr0PJDu3qKL5LigoMGcHDsyRkRGbJLE2\neaa5Pzh+0AA4HA6l02m79rxvLjJIgYXvOeAGBWBNTY0ODw9t/6qpqVFFRYVFIKP2p7iDngAYgoUY\nAUO5YUiABqxvt9ttz/ru7q75yDN5qqiosLMSGiMNLoUZzzMNIE1uTU2N+QbnTqY4T3iuQKcJqcjd\nt3gmCKDg+5WUlNj6wCUGahBTDzzmKerZEw4ODsw9RpI1Wzzv3CPACyhLIJr19fVKp9NW9HZ0dJjg\nlbA1SSaoZi+DDoFbEXsw/+73+1VdXW3gQVlZmT1LFHpQI7jG0E2wHWVyxn0pKSkxC17WF/seFMiq\nqioDGMhxwNqNiSX7CP8DaGQqwL0mII49KzcAh2aPvRCjBUAC9geef4TnqVTKCnLWS1FRkVwul2pq\nauTxeKyxoKnivAH9Zu9k/8W6l+u8srJidQ8gR25jzp5Hc01jiQkDQVU0VEwnyCl4M14/83huOLG5\ndkg8lKBHm5ubGh4eNoXw0tKSbYAgUnNzc7aZofpPJBJaXl62BVRSUmKqz4WFBVMMJxIJU3iyaNnk\nV1ZWVFFRoUgkYhw7RmzQO7a2thSJRMx2Dds06chWhtH29PS0jTfhNJHus7OzY5GnCwsLtnhAyrk2\njCOgFbCRZDIZjY+Pa2Njw6LHsfihWJmbm7PNipFjrgUZhxIqbElmpk+OfG5YBcXQ/Py83ZehoSGt\nra2ZMhx6AJss12l/f9/uPXQYRqrSkVIZD1d+noOYe7W9va2ZmRmzrjk4OLCRzvb2tqLRqI38Gc1h\n7YVdGfY4BGzkonAFBQVmewOFZ2lpyWgnKJAzmYwWFhbMGsvtdtuYD/5bJpPR3NycoYrpdNoKhGQy\nqdXVVRsZYk+FvQ6xs1i2Yas3Pj5utljYlDGGxC4qlwLBIcWoHmV9NnsUBy3JOO8o/aHxEIKRm1aF\nrSSjtuHhYUtNXF9fVyQSsesLcicdFQxLS0va2dk5tgZY08XFxRZLXFRUZPZ9kpRIJIyrh/VcJBKx\n68IGzLWAbsN7s85Zj7joVFRUaGpqSouLi4aEoLKfnp6264xdY67TwuzsrOLxuKGPkgytpJjGlmxu\nbs6snLD9w9KLQxH7RBrOXKsyPJwpRqempizWmsOS5pZndW1tzawut7e3FYlEbCRPMitWW/BtmZiw\n5y4sLNjzh6J9bGzMxq7YnXFteR/G6TgnAFKk02lrQvm+BQUFFtTAvjk3N2cUJmgWABq5ia/T09O2\n73EfoBjw93Z2dsx3mWaGCSZrNp1OG5pGsEZxcbE9r3Nzc/b8c82xBcXnl4RVfj9oM25L0KXgeWND\ntrm5qWg0aimfrDNcp7BLZO9khL2zs2M80aWlJTuXVldXjWIoySgke3t7x/4bFB9oDBUVFbYPb25u\nWlodzx+0H/ZLziH2HGw3cUAiKIxrS8ou6OLy8rJpCbBwxMUnk8nI5/MpnU4rm80qmUza+mFyxr2F\nMsL1kqRYLGZTsMnJSTvf+DuxWMysFmdnZ416wX3ljIHul6sTwnaNxh36GjUHe8Lc3Jwh5rmptvw+\nvvPi4qKWlpasxiDwhGkCdcPMzIzZRe7u7lo9BIAFB570UQA+msqFhQWrSaLR6LHJK65hBLqwP0ci\nEUPE5+fnrYbiWQApHxsbszqJtQEYQl2BxSuONzgagcznuiRNTEyYOxrsAM567g37HmuwoKDAABTo\ncbzHmxmSUvCFL3zhTftl/7WvxcXFL/zO7/yO2tvbzVYnN9KTh3NnZ0djY2M6deqUzp49q5mZGb3z\nne80O6GTJ09qenrauvbi4mL5/X4bj3IIlpSUWEwnhzVeo4899pjxjeG6ZrNZhUIhNTU1aWpqyhBe\nDha63rNnzyoajRrHcGBgQIODg7aJPvvss3bQ5aYKhcNho2gMDAzo1Vdfld/vVywWM543PqOzs7Py\ner2GKhNlC+Kxv79voSHz8/NKJBI6c+aMNRiIPzo7O23xYT8HTxuz+AcPHtiojJFjMBiU1+vV3Nyc\nqqqqbDxHAcLnnpyclMfjMS7f1taW1tbWdHBwoMcee0wzMzNqamoyPi9WSohlNjc39dGPflRf//rX\nzdaPzSMvL0/BYFAzMzOWiodHZnFxsebn59XT06OFhQU9/fTTisfj+vSnP61bt24pPz9fn/70p/XK\nK69Yqls0GlUwGLT7DkLU0NBgyW2xWMw+V2NjownwsO9JpVLq7e3V3NycrYd3v/vdGhwcNIHpe9/7\nXq2srGh+fv7ooSsokN/v1/DwsHw+n3H+QZ8aGxs1Ojpq41wsfsbGxtTe3m40keHhYUPmMpmMvF6v\nwuGw/H6/WbhROPb09JiQZnp6WhcvXjSONUKfd77znYYog4BkMhm1t7erp6dHt27dUmHhUXT1O97x\nDj148MDWHwUYaF5ra6sloYVCIQv8yWQyhhQgCCL6Fs7uwcGBTp06pUQioYsXL+rRo0dqb2/XlStX\n9O1vf9sCg3p7e7W8vKxkMmlCoJqaGl26dMlG4whJEJSeOHHCpjegLJWVlfqFX/gFvfzyyyotLVUo\nFFJ3d7eJB0dGRiwICD/S2tpaQ0SSyaQhVrn0Da/Xq6mpKT322GNKp9N69tlnNTc3pytXruhHP/qR\nmpqazIPc4XCoqalJly9f1uzsrBU4xNmurq6qp6dHzc3NhlSdP3/eAh14X5pTpnCNjY0qLCw0u7u+\nvj7du3dPgUBA6+vrCofDNvE6ffq0IVxDQ0PG3e3t7dXMG+ENHMw9PT32LPFMnz9/XpFIRG1tbWYx\nCO/30qVLWlhYUDAY1OnTp3V4eKh0Oq1Lly7ZNA9v23A4rOXlZb31rW/V7du37UDv7+8/JtReXl5W\nYWGh3ve+9+m3f/u3DTTp7e09Nvmorq7WxYsX9eSTT+rmzZva3983kToWYHCEET4zLr506ZKeeOIJ\nzc7O6qMf/aiuXbumUChkCYsk4P3qr/6qOjs79dJLL9mkJplMGkJKge12u+3eMMLmgAfpxXLsxIkT\nVoBduXLFprRPPPGEHj16pIWFBbW2turkyZN69OiR0WaeeuopPXjwQF1dXZKk1tZWK9BZEzU1NTad\nYSrFpJC9FQRxenpaly9fVm1trRKJhAYGBoxH+swzz1hyIZ70UCdA9D/3uc/ppZdeOubLTDP+zne+\nU0NDQ2pvbzf7ve3tbaOurK+v6yMf+YgmJibU3t6ulZUVnTt3TolEQnt7e3K5XFpeXtZjjz1myOjO\nzo7a2tr01FNP6cUXX1Qmk9EHP/hB86EvKirSxMSEcbFXV1f16NEjQyqfeOIJFRcXa2hoSGNjYzYJ\nOHHihC5cuKBnnnnGwlKKiooUj8fV1tYmj8ejs2fPqra2Vk1NTVaTLCws6Ny5c4a+FxQU6MKFCxob\nG1NXV5fZlVZWVlrD0dDQYLQHJszpdFrPPfecHj58qE984hO6fv26Tp8+bR7zUCTOnz+vH/3oR2bR\nyD7rdDpNU1BXV6fm5mZNTU1ZI/2pT31KkUjE9nCmWv39/dY0Skc2pufPn9fIyIg6OjqMelpZWal7\n9+7J7/ebIPD27duG3ufn5+vKlSum3ejr69PW1pbq6urk9XqNTlVQUKC6ujp1d3fr6tWrloxKM3/i\nxAmbCkxMTOijH/2ohcGlUilNTk6qr6/PJu1MBerq6nT69Gl1dnbK6/X+h39t/fozL56/853v2PgY\nqgX+gSBRQPHLy8vmn0mnhFp0a2tLTU1NkmToFRsGi3x5efmYmIoRiN/vNxSRsRwdLmOfXBU83DhM\nyXHFSKfTysvLk9frVTwet42YcXEuMgQfEG9MRqQ8QAgncSLg/RGsIdZCgINYj4eakenm5qYVt4xP\nQOERLUqyEdH4+LjxghEpokSWjoQ6oVBIQ0NDxvuikXA4HGprazOUuqqqygpKSYa60qXjHVpYWKhY\nLGa83/e///361re+ZR7EjEOhVXBIMnKSZJxZn88n6Wj8iUiNgxkHATY7RB9Op9OQDLy7KQalI1oK\nI1C8oRntszZBylAY831xRohEIoYs19XVqbS01DhloJVc38rKSguNIeQF1TB0JsIGstmsCYLwPZ6Y\nmJAkG6e1trbK4XDYgXN4eGiTEQ4x7lU8HrfgA3i4IDRMM2iIVlZWzA88m81a2IskW3/l5eXH1hG0\nAZ5LhGUEi8BlpEBJpVI2env++ef1rW99y7yio9GoSktLDTkBVUZUScMLL5xxcX5+volxGLmibQDt\nLSgo0Pz8vI0TEZzy/DPJWF5eNj0CohVEvLmCIUmGuoC0AwzAK8alAGpaUVGRCW7Zv0BT6+rqFIlE\nVFNTY9MsaD3QhxhjE69eVFQkr9crSUZ7A7Xm8KbJqK2tlcPhsPEnOg/Q9+bmZpvOgPrkUqWYXMA3\nzaXZgBhROMZiMUOk+/v7FY1G7b5LR9zYlZUVBQIBzc3N2d4Jyvfxj39c165dMxABv38ab6h4Gxsb\nhoYTNEJqXzqdNi4lU4ampialUilFo1EtLCyoq6tLsVjMAjJI5wQVI+AC1xWi6Pf29nTq1CkLfCCs\nRZLRKKCywTXOFSMC/NA4gOT19fXZegFRBZwB2cQthbOQBNVMJmNir4aGBhOyQxtra2uz2G3OYTyL\nOccodLmuaHJApgGg9vb2FI1Gje8PYAPdZHp62ooiQJWKigoTkJWXl9tnwXGKSHcoKIjXqqurNTU1\nJb/fr6eeekpf/epXjcONSxQ0r4aGBluHHo/Hpn65aG1ra6tNRZlKSrK9OJcyAU1lZmZGy8vLVvRC\neaGJlWQAxcLCgurr67W3tye/32/UIK4jUx2Xy3Us2Ib9CfEn9wdPZ+haudRG9gOub1lZma0xqBKg\n30zsaJiYrECRhSIqyeqjeDxuRgW5tQVnNLQSGigaLp5j+OjUDkzcOFOhS3H+o63hGVtaWtLq6qrR\nHOHcLy0tyeVyqbi4WE6nU319fW9K8fzmsaf/G19YC7FhMFpCFFdaWmrokt/vVyAQUCqVUnNzs405\n6XwYOVL4gSRSdHo8HrMZAxUgqS0UCtlYAHQXdwgWmSRTHJNShDiHhzlXWUq6TkdHh43EcpuChoYG\nBQIBs1JiVML4kYUCf4n4Zx4g+GcU5nNzcwqHw8Yjbm5uNkP2SCRiQRIs5p2dHRPi8Dvb2tpsAe/u\n7ioQCJgQELcN7J8ogAoLC41rOTIyYjZ/0DAIAAkEApZ2VFZWZsUpsZqSjJ/Gwoe6QZoSBSy2bozr\ndneP0vgYCaFkbmtrsxAU4qN9Pp+2t4+SoNxut008JBniTkgAmxHNFIIw7j3jO9Ztfn6+BgYG7EBD\n3AOqJP24mIGaA++YwpGAEXirTqfTHv5gMGhuGbgKkEzHpKC3t9eKHkmanZ2VdLSJIVDiUNjf3zeu\nb3t7u/Hx6dgPDg7k9XrV399vTU8gEDDUDevAvLw8zczMGBeQqQFCntraWjmdTuOPcw9pZk+cOGEJ\naOwDe3t7am1tNUqDdFSAdnd3my3UwcGBIRJ+v1+FhYXq6+uTJLt/NJGIUauqqgz9oNELBAKWIgon\nmTXBPeTvxuNxE80VFxcrEonYJAsNgiRDVEtKSux3bG1tqaenx+wiKXrQGHR0dNjzDt8aWk8gEJDf\n77d9s62tzdYQfEgOolAoZKIh7iciL6hme3t7CgaDxs08ceKEoZBYJXLQ4njDocy+TJNfX1+v5uZm\noybw/EsyLi38ebfbLZfLJemoMQ0EAlZoDQ0Nye/3m8AtnU6b4w97MWsK/rUko03lCv4QWqFfaGtr\ns2cYLin3Zmtr61himt/vt4L9woULqq+vV1lZmRYWFkxol5ti293dbUUKDWomkzH6woMHD2wf5XrQ\nbGOpdXBwoKamJmUyGSssNzY2tLm5KZ/PZ2g53FgCZCjyS0tLjTa2tbVlBQrNH81qIBAwjQ17OvoA\nCheCJhobG7W3t6dQKGSATVNTk7lPBQIBlZeXm+aHew5H+eDgwKbI0DYAqZLJpLxerxXwgFqg1tA5\nEe8jVvb7/ceonQAptbW1ymaz8nq9cjgckmS1wdmzZzU1NWXpxIlEwnQFhYWFRk+gwcRmcG5uzsA8\nuOmdnZ0GQGWzWZt+rq+vm7i1sbHR1jTJhTT6mBpsbm4qFArZXo2QDpeMTCZjNQNnxtmzZzU7O6uG\nhgZD2NEX8P99fX1Gp6D+4Zx0u90W+sVnIMH3woULpncgaAm7XWhxuUmFCKIRjTY2Nioej5v4sKOj\nwyiG1Cn19fVqaGiwSHr+3OPx2GdmzRLjnVuDkZjodruNvsP03uVyqbKy0vRM0Gn4zLkUpjfj9VNZ\n1f1bvW7fvp392Mc+ZoVAY2OjWR+xKUO9aG9v19DQkIkxiMFG9ODz+XT37l0T2SC0mpqaUl1dn1qm\nswAAIABJREFUndbX121UX1JSYv/s9/s1OTmpyspK41MiTADtk6T+/n5NT09bShpKcyJZORDX1tZs\nHEo3hEK6paXFhDYgC4xGYrGYfD6fJiYmNDAwoOHh4WNdZktLix4+fGgpi2NjYzZW5sF1Op3yeDya\nmpqSx+PR0tKSlpeXFQgEVFJSoqmpKeOWlZWVmf8qyM/Y2JhOnz6tdDptSYtYJhUVFamhoUErKyuK\nx+NqbW01pAfk/uDgQM3NzcYTphAmyhvkjkQ53CJohu7cuaNsNqu//du/1ec+9zkbNdHZ4ssLjWN5\nedkEHxxE3F+cDYhCRZw2MTEhn8+n+/fvq7+/3/hbiD/gqtLkkDxZXV2tyclJo40gMAFVaWpqUiAQ\n0PXr1xUOh00IiA0TSBYcxbW1NXk8HsViMfX09OjRo0c2KpuamlJjY6OKi4s1OjqqCxcuGK94eXnZ\nUIFQKKTBwUGVlpbK6/Xq/v375vd77do11dbW2iYP7w7bsGAwqJGREW1vb6u5udkahrW1NUsjC77h\n79vS0mJcOw5YNjPimBEDzc7Omp0RfPNgMGifm4hbrnVnZ6fS6bTm5uYMxSwpKbG/7/F4bKR+9epV\nXblyxRoeimGfz2ccZ+mowCWKl1Q94n3h3h0cHJi1Hkr/8vJyzc7OmjhuYmJCa2trevbZZ/X666/b\n5g+6fnBwYIlnxFAPDQ1J+rFFJlSyTCZj9mIUvOPj4+ZGA6rY19enBw8eWNOSSCTMpYGx//Xr15Wf\nn6/W1lYT5yWTSRMxr66uqrW1VaOjozbRQxAE/YtEQZ5TOMnl5eUm6trZ2dHGxoYSiYRcLpd5+fb3\n9+ull17SW9/6Vo2PjysYDGpiYkIOh8PEvzyrsVhM4XBYY2Nj9qy73W41NDQY9x4EkevL5IRXeXm5\n/H6/7t+/bwgyEz6Hw6EvfvGL+uxnP2s2kaDW2WxWzc3NGh0dVWlpqXk3I1ZLJpMaGRlRQ0ODHn/8\ncU1PT1tRBZWipaXFimaaTRplpoYej0eVlZVmjci9B2EmRY5JCHvT6uqqnn/+eX35y182mlNHR4cG\nBwcVCoWUSqVsD5Fk13Z3d1ehUMjOy1gsZtOIyclJ87kGIKJowEouFouZGDEXrMFrmITcgYEBRaNR\nOZ1OLS0taWFhQXV1dSYaRgQHPQsB18jIiDkcpFIpXbx4UcvLy8cEeqDFWKoxcYX6dO/ePQMy+vr6\nNDQ0pLy8PCv+ADP4Hpzdq6ureutb36pvf/vbunr1qn7xF39R+/v7huDn5+drZmZGzc3NmpiY0KVL\nl8zujTReOOP9/f1yOBx6+PChTZUzmYxCoZCi0ajy8vIsp2FkZMS0FkxUmY5R0H7/+983yt7s7Kw1\nmey3NIjokvx+v0ZGRtTU1GS5CLhTsAbu3LmjnZ0dnTlzRjdv3jSRHVMrgDq32621tTUDHLPZrObn\n581tzOv1GrXh7t275p+NsBlHrlAopNdee007OzvmhU2N0t/fr6KiIktmJSWWNE2atKKiIk1PT5t1\nIIg4IBYWmsFgUK2trbp165aBA9QT5E64XC5z28BcAt/6XMtd7B/f/va365Of/OS/fcJgOBwulPR/\nSgpKOiHpNyQNSfozHYWlPBwdHf3cGz/7K5I+JWlP0m+Mjo7+/U9689u3b2ff+973mo0IIjweMoQk\nFRUVVljSYYHuzMzM2PgIpBhfZWgNHLIgK1jJzc3N2aYNnQGHglz/RcbCCFToIHNHOQh5QHMzmYw5\nSzB2zhWZIHSB3wOdg3EWvDe6LgpGijyKF67P/v6+mpqaFIlEbDTO5oUCms8H9xPXAYoOUF1oG9Ap\n4FhXV1dbw5JIJMzyD+cB1PLRaNQQ5lQqZephUKOdnR1tbx+FJTDaoQHIy8vT3/zN3+j555+3a8s1\noUjmWlLgog7H8oqHcGlpyQ4ljPRnZmZsxE3x5/F47CBk9EjXCmKMkTx2d1w/6C74pO7t7dmoGk54\nZWWlpqambOMD2WETJGgC2gIH3fz8vKGPeXl5SiQSNtaDTrS8vGzUDpxCWM8cboxLGTOCZuUieSCO\n09PTNgYjxAcKAWg9dCc2Y+gj3HsORVADhCGMmz0ej5LJpKGvjO9ym8W6ujobEYMU/8M//IOef/55\no2JQXDBq55nieYlGo7aOcfTJvcaIfTiUV1dXzTqNGGscaBCuofrGV5mwBdYPCG1VVZUdYDSd/H34\n+Uxu+P6BQED5+UdxwNJREzA7O2uIGg5AuYJeDiY+D3sL6wsRGc8LHGRGvPCGoddQrOcih+zNdXV1\nymQyJhxiTUFN29vbM7s0KBPYi1JI8owmk0mjwCBaPDg4UFdXl2ZnZzU/P2+uI2hbGOUi6uJafOc7\n39Ev//IvK5FI2CTs4ODARvQ4cyAaw/6LaWJXV5eF0SDUw+YRStzi4qJOnTplFI7KyspjWoxQKKTD\nw0Pz0pdkB3c2m1VHR4du3LhhThVQNM6cOaNIJGJidWhc+FQ3NjYqkUjI6XSaIB6Eu62tTTMzMwZi\nMIHyer2KxWL2/UCmsSkD4OE6cLZxThHgtbW1pd3dXZvwlZeX2x6CCwlUi0wmY/SoTCZjbj9ra2vq\n7u7Wo0ePDOXGsYnGY3R01KhCcIJ5P7jlFGRQ5YiGP3HihE0cEKeBqn7pS1/S+973PmWzWbNZo0ll\nn4GGRNBafn6+XQf2wMXFRZt+0NBPTEzY+Y82BOCNqRbTCRDflZUVJRIJVVdXq66uTrFYzLIM0IQs\nLi5anQL9iTN+a2tLHo/HrExTqZQJ5LGPy7WlTKfTOjg4MPtTXC041/b39+15hl+9tbWlhYUF219q\na2u1tLSk4uJicxFaXl5WXV2dJFk9A90QAIWsA55HKCS5YAK0GZ6juro6FRYW2tkkyShNq6ur1hBz\n7kIlAcne3t62Gg6aYXl5uWKxmLmDPfHEE/rUpz71/4rP889LWhodHX1S0jOS/pOk39GRj/NTkvLD\n4fD/EA6HXZL+R0kX3vi5/z0cDv9UniCBQEA7OzsmIKIIhP9cWlqqxsZG9fb2SpIVBalU6ljHHAqF\njK8Mai3JRHFw+uCqtrS0qK6uTuFwWF6v1+yd4A5xkzhwg8GgHTD7+0dJcnCFsHiqqqqyz+PxeOT1\nelVXV2fuGU6n0zxM2VhQgoMkwttlM2XDaW1tVSaTUV1dnVpaWuzhqq2tNRFjOBy2A7K9vV3z8/Mq\nKChQXl6e2tvbTUxB0cQhiAUZyIHL5Tpm3cSBgl8n3E14otjglJeX69SpUyoqOvKRxjoLP1KoJHwm\nn89nDwwFA5SS5uZm675JTsKiCR7xzs5RmhyCNRBSaAgIqOCdTU5O2veF54ZVF2gSBx4jOb/fb5xn\nxkuSrFBAROj1euX1eo33WFFRYWNGDuNc66Ompib5/X6jKIE8YEuEbzIITq6HrNfrVWlpqYLBoBUu\n0Dp4PkpLSxUOh+1+gjCm02nz6qS439zcVDAYVDqdVjAYNM4uKDwIZG1trbxer/Lz87W0tGQWbniI\nd3Z2WtGBXSPXFwcbaBmM5b1erwns2BBzC0SeWzZzOGwcJtBIOJwkmSvIwcGB2Ts2NjaabRIiLofD\nYcV1JBKxUStqfBpnUu+gmfj9frlcLnMqaW5ultPpVCgUkvRjP2saB6YQyWRSHo9HMzMzamxstOIS\nb+b9/X0bL1LU5+fnmzfx2traMe9qKDcVFRXG+a+pqTHK2ebmpinYmViBpuIpzefFrhFP9M7OTjU3\nN9vv8Xg8WllZMTHd9va2+vr6VFtba1MoqBI44zQ2Npr9JDzInZ0dazDgjOPIUVVVpbNnz1qi7Nra\nmlwulxwOh5qbm7W9vW0cdYowqBmNjY3HPK89Ho9cLpdqa2tVWlqqZDKphYUFW8PFxcVWsG9sbKi1\ntVVNTU2GOsPBXF5etlCbSCRiBTVrGtHagwcPLGkOz/fm5mYTQfX29hrPnD0PcRT83lxfYIpA7DQT\niYSSyaTxfl0ul86cOWPPdFNTk10DgpUoXNCa1NbWWpEJdzqbzR7zGeczUVjxfOGEgQiX9bOwsGD8\n6pKSEvl8Pmv2aMJGRkZ07tw5o9lAXejs7NTi4qKdw83NzbYfSrKzcGFhwdBqh8NhTRg6BgABQKGG\nhgZrYKAhzLyRJhwIBFRTU6Pm5ma7BidPnlRfX5/q6urMmWh//yixd2BgwPbx3d1dJRIJTUxMWFhS\nTU2NmpqaDI1HXwSN88SJE0aRQ4zLesRbmgaVc4zzMNfXn+nYxMSEpTBDYeWZZdKLJiTX/z4YDBpd\nzu122/dxuVwKBoNmSsC573a7j3l+ezweOZ1OmxqVlZWpubnZaLJY8547d85sC7lffr/fXHIWFxet\nOIdKxN7H2Y+/dzqdVnt7u50pbrdbHo/HaJeYJgAq0TxzDjONZq/Py8uzqeGb8fpJVnVfl/TCG/9c\nIGlf0unR0dFX3/iz70p6h45Q6B+Ojo7uS1oLh8Pjknol3f5JH2BhYcEM9hOJhIkXcu2m5ubmji0s\nkqOy2azGxsZUVlZmCYEc9vCEsFbizyk2RkdHlUqlrHMFWWOxgBhQUM3NzVlRk5eXp4mJCbuBpPDk\n2oglEgkTFBJYMjMzYypswitAKEkTw66IzYFRHQls8XjcRiEgTHRcU1NTZpc2Ojpq4439/X2NjIyY\nkGBvb8+S1hjbgeYhquN740eZm6iFkAjVP2K7ra0tDQ8PGzqOeAu7spKSEnP9wAaN98K3GbRkdnZW\nBwcHmpmZsY0TIQeCnqqqKkWjUUPtZ2dnreusqKhQIpGw0fTS0pL6+/s1NjYm6QidhGPOqIwRJx1/\nQUGBxd7C1cOKDX4fFIa5uTlDPJk8sI5yrdVyO2cQGqgjiEPLysq0tLRkRSK2iRziJDwR/ICjBvcJ\n5I/nhjE1/MFEImFCHvw2p6enbaJAMwe3cn9/35xN9vb2TAAGGrOzcxTPPTo6eizBEfsyVM/YI2Hb\nhVUaBS1oT15enh0M+JGCRFDEU4Dt7u5axDUjbfipkkwvQbPFgQlig1AShIVJFf7q+fn5unPnjk1M\ndnZ2LF2TIoO4Wa43hX5hYaFRHWg2xsfH5fV6FY1GzTuYYpN1BR+QyRJjeZ45SYbsplIpJRIJSUeN\nBYgLdB2QQQRqUJug0MC3XFtbU11dnRUm09PT2tjYsII9Ho+bADKZTKqqqkqDg4OWEIoNKH7EhYWF\nmpmZMaEjI1q47fF43KYKWFElk0kNDg4awllXV6fFxUVDmhEOZbNZe37YL5go0QBgd8Wezkg6mUza\nFICJy/7+voaGhuyasOchzmWt52pH0D1gX9fW1qbDw0Oz8cMvnb0WdJJ9Mi8vT+l02kRSiImxKiwq\nOgo0Yh/A2QW+dyKR0MOHD7W3t6eamhrbQySZ6JW9DNQ/N58AhximYOgnWCc0b5IM7WcyQUYAYnD2\nhe3tbY2Pj9v6I9gF6gwe6iC+i4uLcrvdGhkZUV5e3jGLPoot/NPn5+eNJsU0kj0BTQtT1VgsZpxn\nkgg9Ho8mJyft7xNRjQ1oPB63po2me2pqys4Amv6GhgYFg0FFo1Ht7x/5b09PT9t9ppBnT8NuETE6\nVoO4xVB85k6es9msNXCg/+x9TOBxSGIaBM8eISSTB/a72dlZ7e/vGwWHPyfdcn9/35IcEf8yTa+q\nqrIJBzoxPO1zbfRKSko0ODiobDZrOicoFTQXgGA7OzsKBAKKx+MGGLD3U9jTeEGHZbLI2c9zzwSa\nfTEajVpjtbKyYuuQBv/Nev2LyPPo6Ojm6OhoJhwOV+qoiP5fJOXC3euSqiRVSlrN+fMNSY6f6gO8\ngbjh88fiovtCaMdImsMbdAeCPYUsI3aKYfwxUfmyebEhc8NAHLDa4XcxmuH30yWhAmaDwauYQzbX\nw5EiRfrxpnR4eGjfIRAImNsAYwuKag5wNgpSgqBb5F4bfg7aCurWoqIiK1B58fvYcDgQeF9J9jv5\nvBR7CMIY18EhxWeZYo7vBIIHt4nfv7q6avxfVM5cT7yEKRhAS/i5/PyjpQt9B+EIGw0bCt+JsVMu\nZyz3f7n3lcKUIpT34uHlUMp9aBFlsKb4HKw//lsurYLrmOsgwnfm/RBpsD5zfU1ZGxR6cNlQ1nPf\n2fQYrUHL4JpxKOOfTaPCe1Fg40WLmCdXwApyAmLLOI8CBXEISMv+/r7dF165rjb8Hj4TqAoOIlyL\n3J+VZN+T9ci9Ycz/z9cZdokUiuwlW1tb9ozmFgMg6DR73G98mdmT4JCzxx0eHto14nPn3n8mZdC9\n4AfCUWcvoLDCqYP1y71iHSDu4r9z7UBcKfK5vxy8INV8D+4tvx9eeu49Yf1ySNGoACxwXxFo0iTx\nnqWlpbZXgG6CwmezWXtmmdL9c2Ajd0/LbXQpvrgXfA9J5mXOHkIzydrJZrNWFHDochCzv2YyGWtm\nmILwYk1wv7lOuZxW9koQQe453xHRLuuGZ4R7v729bU5HuVNCKF0831tbWyaKZDrDmmD/QETGi/M4\nV5/AusEdCmFb7l6NgxBnbi6qimg1V6zG/WK/5jnlWZJkHs/shwihc89lni/AJr4LZzLPFsU06x+a\nFHspYvnl5WVzc8h1u4JnzSSPNZvNHsWy8+/sPTwHUCxynyfOVuoFKHRcU/6Z9/nnblVcf96Hpozv\nK8lQfibVuV7f3CPWL2ud71tQUHDMz5tpAvss+xjrj32Av8vnoDmjjgDIhAXA76ZuYGoA2MG95Bng\ns/PvnE3ss+wr7OvsVWjp3qzXTwxJCYfDAUl/I+k/jY6O/nU4HP4/cv5zpaQVSWs6KqL/+Z//xNfX\nv/71n/7T/vvr/zevv/u7v/tZf4R/f/13+Pre9773s/4I//767/D1p3/6pz/rj/Dvr/8OX9/85jd/\n1h/h31//H339i8XzG1zm/0vS50ZHR19+44/vhsPhJ0dHR1+R9C5JVyXdkvQb4XD4hKRSSR2SHv40\nH+DDH/6wjYpqamqsK6PLI8ihtbVVr7/+uvLz8y0uFaQDblgqlTJUDI9mvEVBQei8EVsEg0Elk8lj\n4qTc7Pn9/SOP5mAwaNHLjMLoHldXVxUMBpVIJKyzdbvd1p2TCFZWVmbjT0bWiB7gPk5NTamlpUUz\nbyS+FRYeeaEydiKCmZEPXTo2V8vLy6Zon5ubM4s7hA+5PpqM40HaGMfn5eWpuLjYHC5A27DHAT0B\nAUREUFFRoe7ubj148EArKys2XodHur+/f0ywdubMGc3Pz1vSGGP6b3/723r/+9+vzc1NE2FwT6Sj\n0TOjfFA67gnoMx0mljVzc3MKvuEegZ0g/rGMlrnWICvwp3A0QEyFlRT8xHQ6bd7BMzMzcjgcNsZG\n5JVOp43Tz+8vLCzU4uKiqebhHyaTSQXfCG/BgYNxMsgvPGecYhiF7uzsGC0AgVp9fb15PINCwFum\nq4fWcXh4aN7hUEaw02OSAc0HagBoS3t7uwmD8CKH1gJixBgWNAmPZ3zWeRZ4QcVaXl7WSy+9pHe8\n4x2GYq+urhoXjrADrg1iytyo+Vx0E64saGpxcbFcLpc5ESDWq6mpUTAY1MOHD40Sg72Y0+nUxMSE\nent7lUgkVF5ervHxcZumIQADscexhmuaSqVsbItuAiEyaCmOM3AAcTBBxEkAwvr6urxer6FeUDHQ\neeTn55vwEsEmXvhYZ4Hm4DayublpqYCImiSppaVFExMTCoVCikQi9hnYc5kU1dXVmZc20zRG1jwb\nIJAg5Y8//rhefvll219KSkpMAJWLFiNazWaz+uY3v6lPf/rT9gzBqUdsiC9zbW2tTVgQvLIe8d3l\nWhYUFJiAjetWW1urg4MDs+jL9cIFPWdUTQQ5qObJkyd17do124+ZZpw7d0737t2z/Za9orKyUktL\nS2pqajKRG0JCYpT7+/t19+5du8+scfQKCODq6+uNygj1AmE4PFMoYNXV1TZF4blDyAZyzuTjn8fZ\nl5aWGi3s4ODARL9er9emJkwfsAEEFUcvBPUBfnlBQYE5sOTqQ3Dv4fzkOpeUlKi9vV1TU1P6xje+\noQ996EP2jMOVxU0KJxF455OTk4aMInoOBAIaHh7WwsKCIeZQ2KCg1dfXa3x83HzWc6PuCTwh1ArB\nI/eK9cy+wj7JHsSkCeQdgRw0venpaUkyKg7TGFx3+P3oktA6MbHHp5pzAVcluOU4ZtTU1NhaIs2S\n5GWQX7QJ0HCYUuEhjU4Iagp0I/bhXD0DE53Ozk5NT09b/cZkApScEDFCqrBufPTokWpqauy6QA97\n9tln9fnPf/6nKU1/4usnCQb/Z0nVkv63cDj8cjgcvirpf5X0H8Ph8I8kFUn6xujoaFzS70v6oaT/\noiNB4e5P8wFIdcIlATi/ubnZxgB4Wno8HvX09Gh+fl5dXV2mcIYbB0eXgpL0Gi5sQUGBmpubVVpa\nalHHDx48UCaTUTAYVElJicrLy21UD4kdPjJjp52dHSOn5+fnq7u72yJ82VTGx8c1Pj6uxcVF9ff3\nq66uToeHh2asv7a2pqqqKtXU1Kijo0MdHR1G2IeXC9m9srLSFjSjWkQxjGowqmcEODc3J7fbrbq6\nOmWzWT18+NB4jVAUoA8wgnI6nSZOgavsdDrNXBzlK4UVPpRwyiORiG7cuGE2bMR+s1m73W4rNA8P\nDy3el0NUkhW9eXl5NtqE+wffGp4zI1/GXpFIROXl5VpdXTXR6blz5zQ3N6eSkhKdP3/e7m1tba3i\n8bitO/jKbAo+n882kvX1dS0uLtpmCF2HCOrKykolk0njtnV0dGhpack2wu7ubpWXlyuVSmltbc3G\nd1gfIsBBcIJ4g5Ek6mgOcGgBU1NT5mKyublpnxsPXBpRmi6EI4hhED8VFRUplUqppaXF1Nm5o0gS\nFyksAoGA+vr6VFZWpuXlZS0vL2t7e9s2cg55FPxut1sFBQWWribJfv/CwoISiYQ5q0gyayVEZxSQ\n0pGDQXNzs6qrq+X3+7W1taW5uTktLi6qqqpK2WxWPT09crvdVhDt7u4qHo+bNgChH/vLzs6OTp06\npfHxcXMFQQxcVFSksbEx81zHIx4ngPLycs3MzCgej5tWAwcR7CIRPIbDYW1vb6ujo8PilXd3dy2Y\npKioyFISWffsbTRJpJFxL1knuGMkEglT1peUlJjgiLXX3Nys6elpW2sNDQ12sHg8Hms8RkdHNT8/\nb88txUNhYaEGBwfV2NiokZERSbLUURLLTpw4oba2NotdXl5eVlNTk4qLi1VXVyefz6fKykrt7e2p\nsbHRxIDLy8t67bXXLDTC5/MpFotpamrKmhPG6rmNvXSknaGIw5+W5qCyslJdXV06e/as2bfxPWmG\nZ2dnNTY2ZsUhAqP29nadPn1axcXFOn36tKLRqBV61dXVqqmpUSwW09mzZ3X27FkLrlhdXdXExIRx\nux89emROCLlj86GhIe3v7ysej5vD09ramulEpqentby8rJaWFnV2dio/P19tbW2KRqO6d++e2Z0u\nLi7aWdDa2mrPLVzbpaUlLS4uamZmRrW1tXK5XKZ/KSg4SuN0Op0GPtCcITwPh8NmM4eoMB6Pq7Oz\nU62trSZsx2kil1P/5JNPKpVKaX19XdFoVLu7u5bc6fP57D4S740Ind/BGdTc3Gz2pJKMlofQmeKW\n/V+S8W97e3uVTCY1NTWloqIiPXr0yACOtbU1PXjwwKhCzc3N2tjYUCwW061bt6wxxIq1v79f6XTa\naBOzs7PmMdze3m6OHPjXAwyEQiHTW7S1tWlra8vyEwoLC9XU1GQWiNAhcNHAGefcuXOW7BeNRtXY\n2ChJJl50OBzq7+9XIpHQ9va2PB6PgUTFxcXy+XyWIeD1em1dpFIpPfbYY7Zv4KxBLba9va1YLKZE\nImFi/Lm5OTU0NEiS8atHRkbM/aixsVGxWMxoHVjuVlVVmd8/YkCKc2hddXV1amxs1MTEhAFuiURC\n6XTacjgcDofVV4FAwCwah4eH5XK5DCACqEQQ+ma9fuY+z1/84hc1PT1t1iVwDDHgXl9fV1tbm3p7\ne/Xnf/7nOjw8VGNjo5HQSahra2s7lrKGcXleXt6xNDe8RZ9++mndu3dPHR0d2tra0quvvmr2bj6f\nT2NjY8eQg1OnTpmXI+bk6+vrFi+MqwCITjAYlNPp1Pj4uKanp1VQUKC3ve1tunXrltlfDQ4OSpKe\nfvppPXjwQOfOndPg4KCampp0+/aR1rKhoUGpVEo9PT26ffu22tvbjedM4eb1epVMJnXlyhV97Wtf\nU2lpqQYGBvSd73xHLpdLhYWFeuyxxzQ1NaV79+5ZQQOflMIO4/qOjg5D1BOJhKGuTqdTgUBARUVF\nunfvnrlYVFZWanZ2VqFQSM8884z+6q/+ShsbG2pubjbEOpVKaWJiQmfOnNGjR49UUFCgp556ygoP\nNl2Px6Pf+q3f0q//+q9bJPv4+LihGNJROhqCiCeffFL379/X5uamqbexFquvr9fU1JRFGi8sLFhR\nhiJ4bm7O/DhBmzY2Nkyc1NPTY4f29PT0sSCAYDCoqqoq3b9/35TT09PT5hGK/R5FNabv2EWRBFdd\nXa1YLGY2d+vr6zp16pRNEXw+n15//XU1NzebN2c2m1VnZ6euX79u4SCkoSEmDYVCOjg4kMvl0vDw\nsBUgLS0tun//vtnmxWIxPf7447px44YJduvr6010i6Dp5MmThujiR35wcGAplk8//bRefvllC9Vo\nbm625Di8hvk86XRa29vbcrlcKikpMU9lAigkmYcs/uh//dd/rc985jNaWFjQW97yFn3ve99TU1OT\nOjo6lE6n7fnEW3h+fl4tLS1WjHIApdNptba2WlOC6O7JJ5/UzMyM+b8jPHriiSf03e9+Vw0NDeaI\nAvd1dnZWTz75pDVCr7/+uvb39+05wXM0EAhobGxMly9f1ne/+111d3crGo2alSA2nEtLS8eem5s3\nb6qvr0+FhYW6deuWHA6H3ZuxsTE9+eSTunfvnlkuOhwOS1uLxWK2domdT6fTFudcUVFtAJF1AAAg\nAElEQVRh/rkdHR3a2NiwfeDcuXPKZDKam5vTzMyM2tvbbeKA7+yFCxd08+ZNeb1eQ+eampq0urqq\nzc1NXbhwweLRI5GI6uvrDWnCsxrxZWHhUTjQBz/4Qf3BH/yBoYmnT5+2hoRQEFBVvGl/93d/V3/y\nJ3+iW7duqbi42Dzzabr29/dt4sgeT4BVcXGxTp06Zfv6vXv3DJW7dOmSbty4oYODA2ve3G63RkdH\nzWWH8+nmzZuqr69XZ2enoaINDQ3mtfvWt75V3/zmNzU/P69QKKSFhQU5nU7V19ebPoCmlpTPZDKp\n7u5uraysaHx83NyQ8vPzFQgE9Pzzz+uLX/yidnZ2dPHiRWvMaSDu3r1r4SH44K6urhqPFc0Gvs4l\nJSUKBALmBY5nu8/n06uvvmquHFx71pkk88Qn4OfRo0cmEuVa4oNNUInX69XDhw+tKWttbdXQ0JB8\nPp8lSVZWVmpyctK4sUzmsOtkijo3N2f/Hd/nL33pS/r85z+v/Px8PXz4UD/3cz+nZDKpkpISSzbk\nPjU2Nur1119XLBYz3dXjjz+uc+fO6Qc/+IFu3rxpqCkOQISiNDU1aXBwUBUVFRZgEo1GrWDv7OzU\n1NSUaSVKS0utCRkaGrL7cObMGQ0NDRki6/F4lE6nDVQgKv3MmTP64Q9/qPPnz+v69evmAMWEZW9v\nTxcuXLA6iCC32dlZZbNZtbe3a3V11USwTqdTu7u7unPnjk6ePKm7d+/q5MmTlmswODioCxcumFCP\nqaHf79drr71mYEF+fr4+8IEP6C/+4i/kdrv18OFDnTx50oA1QD7cQ1j3TBKoSYqKiuy+/9Iv/ZJu\n3rypWCymlpYW4zIvLS2pqKhIPT095ntPoz8wMKCXXnrJLEjX1tZUW1uroqIitbS06DOf+cy/vc/z\nv/Xr9u3b2YGBAYVCIRNogIh0dnaaQwEdZjAYVENDg27duqXHH39ciURC4+PjCgQChswyusW/eXt7\n25A9p9Np44hMJmOdIzfh4cOHcjqdRq3Amo0NGaQCpJNF2dDQoEgkYh6xbAKMoTFiHxsbM8Rqd/co\nvY8EoIaGBl29etUeUBYkbgUUPqCv+CgzImPk19PTo7t37yqbzer06dPmL5xMJlVQcJTkRxqXdOQB\nitqdzvDVV1+16+d0OrW1taWKigrV19frzp07Ki8vN0QfsWV1dbUh3/zs9PS0jUALCgp0/vx5PXjw\nwEaSUAaKi4vV2tpqwS9Xr17Vu971rmMUB9Aigi1A5BnvFBYeRZ+2tLTYBjM8PKyPf/zj+upXv6q8\nvDx9/OMf1x//8R/L7XZLOpp6oExmA8MbEqssDiQOdx5+nBL29vbU0tJiQSqHh4e6dOmSvve97xnq\nc+HCBb322mvmklFQcJQaOTk5KafTad6uiGQIXCgqKjLP7dbWVt25c8eSmDCSx76Pz1VXVyeXy6Ub\nN24cU/YTy1xbW6uhoSGdPXtWDx8+tHFhNBrV008/rbt376qo6ChClYS3np4eNTQ06Pvf/76hG729\nvbp69apWV1dtnfPiQMJHHVV1bW2thZ+w+SF243tDPaFgPnPmjK5duya/368XXnhBzz77rHp7ey2J\nDjeU1dVVdXR0aHd3V729vbp//74ymYyheYlEwsQz3EdSTYuKivTcc8/pG9/4hqSj0JWamhoL1+DA\nRBSzvr5u8bYUh8Qu53q4Q8M6efKkZmdndfbsWY2Pj+vy5ct64YUXrFhGUMvG/8orr5hQGSrQwcGB\nIep3795Vfv5RkuXLL79siDs0DyzHEE/TwJWVlSkYDOrevXtyuVxmQyZJ4+Pjamlpsfs0NTUlSbbv\nTk5OqqWlxZBBt9ut8fFxKwSqq6vV1dWlmzdv2hTv/v37hpK3tbVpcnLSJgexWEyTk5M2DcIfGSvA\nsbEx9ff364c//KE9k0xkEGvzvi+88IIuXrxoFn9+v9/CKfLy8gxcCQaD+upXv2qWaQiKcil1LpfL\ngJxkMqkLFy4oGAzqxRdf1DPPPKMvf/nLhtjV19fbZO0zn/mM0um0vvKVr5ioG6ESSZfQ4rDQisfj\nam9vVyQSMaGTw+FQJBIxmgyF/OXLl5VKpTQzM6OTJ0/q9u3bhlZWVVXp+vXrRnt88skn9dJLL8nr\n9SqVSsnpdCoWixmixwg/l1LFJIbzExpTJpNRNBpVf3+/lpeXNTk5aefKiRMn9Nhjj9mkEpcM3IGY\niP3Kr/yK/vAP/9CeE1DBxcVFPfvss3rppZfU1tam6elpa575TGtra/rQhz6kF1980SYg58+f17Vr\n18ztY21tTe3t7VYYYxjwe7/3e3rve9+rpaUlffjDH9aPfvQj2ztJR8T5JdcJZmBgQHNzc4rH4+ZK\nxcSsp6dHPT09+spXvmJ7Kw4/TCKgh0UiERUXFysej+td73qX5ubmzMY0EAjo1VdfVWdnp01S6urq\nlEgkzEcZUwMopOvr63rXu96lq1ev6vnnn9ff//3fW5BKS0uLTb47Ozv1/e9/38539sGqqiq1tLTo\n5s2bamxs1IkTJ2zKXVNTo5//+Z/XrVu3DC3mnCOU7eDgwKw2e3t7rbnCUYOcCWg+nIPBN5JgM5mM\nzp8/r+HhYbPmHR8ft4kB4kBEfoFAwICptbU1a4BpCPf39zU/P68PfvCDmpiYMOcq6gDpx8JGKD7v\nec979PGPf/xNKZ4LvvCFL/xrf8d/82txcfEL169fN2Up3CxCJhgf1dTU6MKFC4pGozY+X1xcNIs0\nHp7FxUXzKCYoBI9V/Ek5rDgIenp6bBRIEUBKDVYuBwcHam9vNz4Wti4kxs3MzKipqcl4xIlEQqdP\nn7bx0sLCgiVNgQAwSiktLTUUKRgM2ogp13VEksLhsBYXF+XxeIzXmUwmVVdXJ4fDYQUowS7hcNjM\nyIkA5zpLMv4e3rSSzJqptrZWHo/H3BvgZuXyjqAuUHDj6djX16fV1VU7XIqLi83vmkaFv+/z+eR0\nOo1/nEwm5XA49IEPfEBXr141c/nchDXpCI1n5O10Os2EnYhlkGei1Bkj5tquzczMqLOz04I6UCPj\nNMDBEggEzEAeP1XQgFAopOrqasXjcQUCAbW2tmpxcdGuS01NjTY2NowyAGWI0R33qL293SgwPp/P\nEhdB7M+ePauNjQ0Li8DjmGtdVFRkKM/GxoZcLpeWlpbU0NAgh8OhhoYGxeNxKwRZI4xJ8R2HK074\nCYccDhlwGiUpHo8bEgWVoK2tTRsbG/ZZXC6XFfUo5SnWcXk4c+aMNYySrLhNJBIWzoN24WMf+5i+\n9rWvmZqaa4HHe649FugWzQSUh9yglNraWnNwAOHHAo4icWtrS0888YSi0agKCwtthEzRsbu7q/b2\ndkM1UqmUFU3ZbFaNjY1GJ2DkTIRtMpk0PQKjWSwe8/Ly1NbWZgb/fr/fnAuwgYI3yHibApwpB3Sa\n3d1dORwOo0GEQiGj32AtyNoqKiqyWOaamhqjfBDOwBTr0aNHeuqpp5ROp9Xf36/19XV7lrLZrCKR\niJqamnRwcGDj15qaGsXjcft+8IM5oOvq6nTy5ElzLoJz2djYqK6uLo2Pj6uhocE4+6FQSC6XS5cv\nX9adO3dUUFBgBQiHK0E/FIuVlZWqqqoyXv7a2pp8Pp9OnjxpxZ70Y4cW4ot3dnY0Nzdn3GasMLe3\nt9XS0qKFhQUNDg4aOlZVVaVgMKiKigq5XC75/X7T5wA27O3t6W1ve5uGh4ctErqlpUXb29sKBALa\n39/XyZMntb9/lJAWj8eNDtXY2GjTKdaR3+/X2tqaDg8PrTGjiC8pKTF+7+bmphVoOO9gxcZEgHOK\nBisSiVjgFWghxXY0GtWJEyfkcrkMhIBjj7UnAWbt7e3mdOPz+cwtAitBvJ0J9sHOdXNz0/zG0e1g\nM+n1ei00ZXt7WwMDAxocHNQHP/hBffe737UGD31Ofn6+Ojo6tLq6qpMnT6qwsFDhcNga0HQ6ra2t\nLQ0MDKi7u9sca7AQxXUKb2Umntvb24rH4xbgRPNCHPrw8LBZyTFp4qzBypWwEJ4JngFyLwgLYdIb\niUSOTVHhjXs8HhUXF5t9JI5eu7u7crvdprfxeDxyu92m94CzTjR8RUWF+aN7PB6dPHlSGxsbWllZ\nUVdXl2ZmZoziEQ6HLV0XIAn6Ta6tYHl5ueLxuAWP4b6UTqeP6QYk6fz58/azTP1y0yjRh5ESyWQV\nsBOwCh2X3+/X2bNn5fV6/8O/tn79mRfPf/RHfyS/328pW/hhdnZ2Gj91dXVVi4uLCoVChpxevnxZ\n1dXVSiQSCgaDFmmM2LCxsVFbW1vGR6UYJhAFoQPJbBz8HPKQ6H0+n43LCY9gDJjJZFRfX2+8MAQE\nPT09Gh8f19LSkhwOhy5evKja2lrdv3/fOKugaw6HQ2VlZTp16pRu3LhhXruYohO4woLGq1r6ccAM\nNIL19XV7oFKplBVlcHZJdALNLy4uthE7ftShUMiEDaAH5eXlcjqddi0rKyvNQ7qoqEjxeNwKxWQy\nKafTKb/fb16rhNl0dHRYg0PcKVwskLD19XV95CMf0Z/92Z8ZNxr0t6io6JhHrNvtNmRRkhKJhLq6\nurS0tKTz589rYWFBn/jEJ3T//n1tbGzoYx/7mH74wx+qqqrKRHQg6KQslZeXW9xuWVmZ5ufnzaKq\nqanJBFegmalUSqdOndLU1JRx1N7ylrdodHTUmoTLly8rnU4rGo2aZReIAab0HHgcBtFo1DbgjY0N\ntbe3a3h42HipJSUlGhkZsQkBBXcoFLLEQzxjd3Z21N7eboEA8/Pz6uzsNCFYeXm5pqamNDAwoOXl\nZUP0KSLLy8vV1dWlR48eSTo6CM+cOaPR0VETfh0eHtp93tnZUfCNSO76+noLpSEYCOSXESAHPrH1\nu7u76u7uVjqdVkdHh0VAX7lyRd/61rc0MDCgiYkJtbW1GZc3k8mora1NlZWV6u3ttaZzbW3NRsdY\nnBF6AOeuuLhYzz33nKHuHo/HRpvBYFBjY2PWXOQizz6fz67vysqKBSxVVVXZRGpqakp9fX3a2NjQ\nU089penpab3nPe/Ra6+9pqamJi0tLRmHr7W1VZ2dnSb0WlpasljsTCYjv99vEeu1tbUKh8MmpCop\nKTnmQ1xSUmIFKSJQiobJyUkTWXq9XrlcLk1PT6u/v1+pVMo0FhTfXV1dikajam9vt8O0tbVVN27c\nMFoKyPPq6qpCoZAaGho0NjZmNKhLly5ZEX7u3DlLc3zqqadUUlKiWCxmOgGoC319feZPn0qlFA6H\nrdhm789kMnr/+9+v3//93zdbsdbWVm1tbammpsY0NZ2dnTp37pyuX7+uTCZjRSBARCaTMWHU7u6u\noWXd3d0aGBhQJBLRpUuXNDg4KLfbrVQqpe7ubptKfuQjH1Fzc7NefvlIV39wcGBFNwJpSWYVWV1d\nbRNIuNn49vMdAXVWVlZ06dIl03NcuHBBk5OTtj6Y0qB/eNvb3qbbt29bIFUoFLJzjkKe8XdFRYU1\nPHV1daY18fl8ds7EYjH19fXJ7/drfn5eZ86cMcrCxYsXVV5ebnsN4momZhUVFXrf+96nV1991Z4T\nBKPb29s6d+6choeH1djYqMnJyWPhTNCt3v3ud2tkZER+v1/Ly8saGBiw8xaa2alTpyTJfndLS4se\nf/xx/eM//qOWl5f1zDPPKBKJmOh2amrKJgjpdFrT09MGjrW3t5v/NNfZ4XAYqv+Wt7xFIyMj5iG+\nsLBggVDd3d2GQNPUILoklKagoMDQ+2AwaOAXUxIaF0lyOp1KpVLmUX7hwgUtLi7queee0w9+8AN1\ndnYa6FRWVqbq6mp1dHTo5s2b2ts7SkgksbiiokLhcFhjY2MqLi5WY2Oj+ZJvbGzofe97n0ZGRgzc\n8fv92tzcNAocFqjJZFJnz57V0NCQmpqaDEgoKCjQw4cPDTxhQlheXm5i/ve85z2KxWIqLi5WV1eX\nBcd4PB4FAgH5/X5JR+Bec3Ozrl27pmAwaA0UDSgi6unpaX3oQx9SSUmJ0YiY6ED3IvSlsLBQPT09\n6urqelOK5585bePTn/60VlZWTH2LGIcxPQrugYEB/dM//ZM2NzdNdIfh+e7uro39SVFaXV09xp1C\nnDI6OmqirvHxcbW3t2t2dvYYD0ySoZmY9Pf19Wl8fFy1tbWKxWJqbGw0rvD09LS6u7t1cHCgxcVF\nZTIZnT59Wnt7e2bKz3ifUb0ke5AdDodmZmbU3NyswcFBDQwM6OHDhyZoOnHihHp6enTjxg35/X6L\ni00mk3K73SopKdHa2ppCoZCZzQcCAU1NTRnChDMA6Csk/N7eXqVSKa2srGhhYUGhUMg6OHycUQYT\nXU7gA6hBY2OjZt4wK+/q6tLdu3eVSqXMnxhDc5B9fJ0RqMFNnJiYUGlpqb72ta/pE5/4hN1DiisM\n9omnXV9fN3EQ3fb8/LyNr91utyoqKqzgwSDe4/FocHBQvb29xnvjO0oysV1ZWZmamposlGZ6evqY\n+hjUHLN/n8+na9eumbqcvyMdiVva29ut2FxbW5PX67XianR0VFtbW8Y1ZOw0OzurixcvGg9yfn7e\nRIZdXV3GA3W5XDZqP3nypIaHh+V2u7W3t6fS0lJNTk5auA1+y9PT06ZgJ8I1Pz9f8/PzKi8vN+SY\nUShRrtCgQErw7WT9ut1uRSIRBQIBJRIJE4MwvchFbU+dOqV0Om2fndEdfw9Xk0gkoqtXr+rKlSsW\ncAGdAnEa4hA4dNCOQFH4c9B3nmWSDdvb2zUxMWHoNyEAly9f1ve//30TvIGU5OfnKxqNamBgQMlk\nUn6/Xzdu3DC/4cPDQ0s+zGazymQy6u/vt+eHxEs476BSqVRK+/tHSaujo6NyOp2qqKiwQ3VmZkb5\n+UfJg7hV0NTW1tZqZWXF/q4kewYJaWlra9PExIQaGxuVTCYlHfEpcfmoq6sz5wCCjBAAsxe/+OKL\neuc732lo69DQkKQfR1IvLCwYch4IBDQ+Pm6aCiZHhIhUVFTYZKmrq0u3bt2yZ4SEQafTqRs3bhwL\n7vH5fCopKdFv/uZv6td+7dcszANu7e7urjwejxYWFqw4LCgoMNcY6CyNjY3q6+vT/Py8ZmZmjMqD\nlzv8VvYQePwk/rW3t2t7e9vGxQglmVahMbl9+7btmfy3559/Xt/61rcswTEYDGpiYkJut1uJRMLo\nLoAgFFmhUEgdHR26f/++0c9KSkoUiUSs8GfvYX27XC7l5eWZ6xJCXhpAXHWYNuQip1NTU9rc3DS0\nFGSZz1VfX28FzOLiojmdxGIxo/HQyNCUcY4DKOQ6pNy5c0f5+fl2HQGv/H6/aRdoltxut+LxuD3n\nV65c0YsvvqgXX3xRn/zkJ024V1VVZQK+UCikR48e6dy5c0qn06qurtbIyMgxysjAwICqqqp07949\nQ9fhnXMWZTIZhcNh3b9/35ogQJampiY7M9xut1599VU7U+CYUywXFBQco31B8cmNZm9oaLA1GI/H\n1dHRoddff91oSbdv37YEUn4WvQrCTECKjY0NRaNRSxGlYPZ6vRocHJTD4bCmgTqjoqJCzc3Nun37\ntjKZjLkrMa1vbW2Vx+PRtWvXLF0xF0lmkk6zTGNABgPOajxbNTU1GhgY0J07d0y/Bpq9srJiybiL\ni4vmHlNZWSm/368HDx6osLDQOM+cP29/+9vfNM7zT/R5/rd+kdIG15lNjTEPKEosFjMTeqzJOPBJ\nHcQyBUJ7LBazVKKCggKjYGxtbSmVShkfmqCE/Px8ez8sYlhY/DyflW6NG0Ohyd+Px+PHgkkYQ1KA\ncLjynQnm4PvjOkFYRa5zBZQFjMUZ+W5vb2ttbU3pdNquDW4Y2N/kunVQuDI6QbkMhQH1MbwuxCj8\nHNcnGo1ayhjXQ5ItWCyOsNvBiobmZHd311LvchPUQNVx+MC9IpPJ2PszdsaVAssnVMo0TrhkoKIu\nLS218RzXHqUxTitQLvb29my8T+HPYUmDwwaIewMv0IRMJmO/C/oATRURt7lrl7VJwhU2cmtra/Z7\n4aYdHh5a+iCqaKzbuBbY5PH8MH3AAg26QG6QB5zQXHswDi7Ggxw2oEoo7svKysxsHwU//8MG8ODg\nwDis2Kmx7hir42iRG4JCgcz7k56GGJPDGFSb9+W6wi/d2Ng4FrhAUAMuPVgSguayX8HV53OBLFJg\nsA8VFRUZoohuAJEMAUH8Xni2PMNMNkBJpR+HRdC4ILJBuEaS4d7eniYmJixYhxQ+xtKZTEalpaV2\nzXI/L00pSauo6OF3bm1tKRqN2p68u7urSCRiFpYUQKxv6CrYHkK9wp40N/jlxIkTymQyVswVFxdb\naAs0quXlZRsBQzvi+vF3WV+ZTMbG/7l0LBxXaAIp9plOMEFA9wJfs7q62vZE7Ai5hrjFcE5ATaFJ\nRJcA35l9OxKJ2Nl3cHBgzzRir+XlZdubKD4IaAHV5v4w+cSxiu+NloBpD896PB43Rxm+c35+vtLp\ntFkmItzHeYo9ltAx1s7GxoYFebBfEpDDpHZra8ssVtfX183RhL2Zz4jrFnogmvRsNmtFMxQwEH6e\nbSZyoOYg7kxr2D/hdo+Pj5ulGml6NNcTExM2QQNEohHZ2toyMSg2t7gcse+xJ1NvsD9zfSWZQDf3\nbOZZ5QxlOkutIskSmbmX2NSxH2NTR8DP/83emwe3fZ7nog8JLlgIkCB2gFgJEiTFXZIlarMUSZYU\nW1Zcx4osO4mT1G2aNMn0nzuTtpm5mdyezjSn05tErXvjZjKJx07bHMu23CixvEW2lGiXKIkiIXEF\nCGLnBhAkSIC8f9DPG+jOPXXujO/JzDnGTKaNImH5/b7f973v8z4L1ykLfJ4LXPPUTU1OTsqUl/sm\n9yme5aSIcY3xuS1Nvi2l9rDW4RSTU02Kuxk8o9VqUVVVJfSxXC6HSCSCQqGA6elpATZZOK+trUmQ\nEussTjZoEct/SwriRxmS8qG0jUAgUH7ixIl/OXHixP924sSJZ06cOHHxxIkTuhMnTpz64L9v/trX\nvnb6g7/77IkTJ/6vEydOfOHEiROxr33ta/f+s/eORqP/+w9/+ENs374ddXV1gpAsLy9j9+7dYuc2\nPz+Pqakp7NmzB1u3bkUkEsFjjz0mnMiuri5MTEygoaEBS0tL8Hg8CAQCKC8vFwcPrVYLp9OJtrY2\nEYcQ4SovL8fBgwcF6WRHo1ar4XA44PkgjpP8ShYO5ND09vZKQWE0GtHV1YWRkRE5aJ9++mkYDAaM\njY2hpaUFwHohsmXLFnR0dMBisWDjxo349a9/DafTiVwuB4fDIfw5KrgDgYAICIF1D0QufCIu+/bt\nE9ujw4cPS6c8MjICANixY4egjxqNBoFAAMC6uIljuKGhIWg0Guj1egQCARiNRvT09Mh10Gq16O3t\nBbCuXi91h7h165aMn+PxuCQfqdVqPPbYYzJup28p0bndu3dLsXbs2DG8/vrrMkJl0atWq0VsVF9f\nD6fTKRZbNTU1MlpOJpPYtWsXUqkUvvWtb+G9996DXq/Hd77zHbz//vviwDA7Owuv14uFhQX4/X5o\nNBpoNBr4/X6YzWZRp7Oh8Pv9WF5ehs/ng8lkQj6fRyqVwubNm5FIJIQv+Mwzz6C/v1/cSJ544gkZ\nx9Kzlg4f/FylUgmj0QiFQoHm5maEw2HxNLdardi3b5/wtAuFArxer0wx6F/rcrnQ29uL9vZ2zM/P\nS5PF0bvRaMSmTZswMTGB/fv3o1AooK6uDt3d3RgZGcFTTz2F6elpOBwOoaksLS2hr68Pe/fulTF9\nc3MzHnvsMUxOTiKXywmqMzU1JdHYbrcb09PTMJlM6OjoEJtINoqlKAPRcZ/PJ4VxR0cHFhYWsGPH\nDty9exctLS04dOgQ3n77bRw+fBgjIyPYu3cvVlZW7uPHmUwmud5E8yjsq6mpgUqlgtfrlXXHIuAr\nX/kKTp8+jZqaGnR3d6O3t1fcQGKxGJqbm1FdXS1iXaPRKCPGTCYjBw7Hj3Q7iUaj2LZtG2ZnZ/Hw\nww9jamoKf/zHf4yrV6/C4/EI99tiscDr9eLIkSPiCKRQKNDW1oZEIoH5+XkcOHAAbW1t8ps++clP\n4sqVK+LsYzKZBA1ta2uD2+0WURSpaBRT0sqrs7MTHo8HsVgMvb29MhG6ffu2FBG0aGttbRUaSVtb\nG65cuSKetz6fD7t378bExAQ6OztFDER3i/379wsNZv/+/WKP+Oijj8Lr9cohPTw8jIceeggzMzPY\nt28fBgYGpDglla+iokLuocFgwKFDh/CDH/xAJhWtra2CtpGqtnfvXhw4cADXrl0T+hLRTjZQdDrR\n6XRiO/fEE0/gU5/6FGKxGI4ePYpz586htbUV+Xwe7e3tqK+vRzKZxHe+8x34fD6cO3dO/KfZvNI9\nQaPRoKOjA8ViUXQiNptNnCTI981kMqLdIXr46U9/Gg0NDSgUCti5cyfu3buHiYkJ9Pb2oru7Gzdv\n3pRR9tGjR3Hz5k10dXVBqVTC6/UK+mq1WuH3+2EymYQ+xnVN0ZnT6RT9is1mQzwexyOPPCJWpXv2\n7BGO7uc+9znh07Mhp2hTpVKhrq4O3/rWt/Dyyy8L2OFyuVBbW4tIJIJjx46JWHV0dFTObfLt5+fn\n8ZWvfAWhUAidnZ1YWFjAQw89JM2X2+1GLpfDgQMHhCZSX18Pv9+Pvr4+nD17FktLS/jGN76B/v5+\noUiFw2E0NDQI7SMYDMqE4KGHHhLe8t27d7GysoKmpibo9Xrs3bsXx44dw5UrV6BUKqVBbmlpgc1m\nw5YtW2TPK806OHDgAHQ6nXglb9++HQMDA9i+fTt0Op24h/Aaezwe0XvRHzmbzeLZZ5/FlStX8Kd/\n+qe4ceMGduzYgVQqhc7OTuj1etjtduzbtw/Xr1+HQqFAIBCQfAvy5EdHR2E2m4UCS6/3v/qrvxIq\nHzUXhUIBO3bsEA91Zix0d3djfHxcXKVIlb1+/TpaW1sxNzeHo0ePor+/H0ajUfNZCcIAACAASURB\nVPjwR44cESCvr69PJkiBQECQbYrzd+3ahfPnz6O1tVUm1tSy8YweGRnBn//5n4sd5cLCAsbHx7Fl\nyxY5T+mLbjabsW3bNni93v8xtI1AIHAEwOFgMPjHgUDgQQB/gfWI7v8aDAbfDwQCzwH4FYALAN4E\n0AtAjXXP543BYHDlv/PWuHr16tr+/fvvExCxo6HggyIFCjUoJqKKdHR0VIooUjjIdWbnTQSWYxsi\nDxyBlxrCkyNDZX2p+pfdOfnA7K7oSkB0kqEXVF1TNMSgC3aPJLNTyEHbHwD3oTH8LCK/9Kml1zCR\nA5/PJ6IFnU4nRSf5zfTAJpJBMVbp91GpVOKLDKx3uEQX6DlLv2MiFeSoFQoFuRZEoYkg5fN52Gw2\n8S+l9+v09LRcZ/pb/vKXv8STTz4pRTJHPzwQ4/G48ImJwhARpyLb6XQiHA6ju7sbt2/fFnHab3/7\nW9mU4vG4XCMiRXRksFgsiEQiInYoReOJNhP90Ol00hmvrKyIly6vIUexRIZ4WEWjUXHk4NqjlRHp\nHaQPmUwm8dMl55kjU3baLAIoSKHTAg8irt/5+XmxjGJhpVAohDLCQAraA9I7mqNGXufx8XFBqelh\nTt6zTqcTQR/vGVFuchfLy8sxPz8vzxcRrWKxCLfbLT6mk5OTqKysxKlTp3Dw4EEp7jnaJ4pYyhMs\nFApiX0U3E6IO9LBlwbK8vCxcQKIqTqcTQ0NDInZcWFiQsTbvOcV7pFTw2eK4mZxbk8kkfrzFYhG1\ntbW4ceMG1Gq1/Lvl5WWYzWZZK2zOiSwxbIDFDJ04ksmkoNdEdaqqqgRl4WFM9Io8Tz6TpCfMzc3B\n6XQiFovJM18qNiQ3ke9rNBpl/M59ora2VqY6HP0zHtnpdEpTQBR6fn4eZrNZ1nF5eTmam5sRj8eR\nSCTg9XoRDofBuGWDwYBQKHSf4FCpVOKVV17BH/3RH8k0ijxrPs9VVVXQarUwmUziVEK7yHw+L7Qi\nThVZaNbX14uf7ODgILZv345z584J4kmKTCaTwfbt21EoFHD16lXZb4kWl7oPAbgvzIkhKET16M5A\nBJS0HPI+yZ9Np9PCLaWvLj/P7/fLZ+XzeTgcDrFFpD6ESDW98vn/c8pIih5FlXq9HsViEVNTUxJs\nVFrsccLDuO1cLif8476+Pty+fVua3NJgr4aGBgwMDMDpdGJ+fl6QUtJ5KNx7//33hS7BvYdUB65N\nTmnKy8uhUqnw/PPPC4DU3NwsCDh/I2mFAGQvofg6kUgIUBWPx2XqzULv4sWL4gnPz1OpVLJfMz9B\nqVRicXERzc3NmJ6eRiKRQEVFBex2u4j9iLzyPvFemEwmmWrRaau5uVl0XqSYUWdECp3FYpFpJZ9Z\nnvlWqxWRSARarVZEg6Q7tLS0IBaLCf2J51zpeqHgmaJj+lFzkkBKIZ05JiYmRF+RzWaFP722tgar\n1Yrh4WGZEhBYIjLN/Q+AuDOROsNchpWVFXg8HuHb8yxj4BkAQdxramqwbdu2j4y28WEhKQgGg68B\n+JMP/qsbwAyA3mAw+P4Hf/ZLAPsBPADgXDAYLASDwXkA9wB0ftj7s3grTZNjocKiSavVwuv1olgs\nyoYfiUSkYM7n80Lo58a1uLgo4jydTiciNwDC86ytrUVdXZ2odHlAkbsF/G7RGAwGKdYqKythMBjE\nniabzYrwiuOY+vp62O12KURoZQVAlP88IBcXF5FMJqVhYOfH//D3EUmnGTgTComYshCjU0Kp4b3F\nYhFuUWlKFRFmjuR4T5xOpxT3LFRUKpUUOKSzUOzH62q326XI5CZJtxMAkg5JyofZbJZNG4CMZvg7\nyGHnw87CpTThjiMgJrVxxMUDiNeG5vAajQbz8/NyD0i34APH9UBrJ3JJ19bWxG2EtCGG0NTX16Oh\noUEoIiwyKQLimqAQkfeZaCV/B0e69DhloAtdQjgCZoHPjZsOM6XoDxX2RAsACJebSX583lhcUyVN\nahFthDi9sVgs0sgolUrh2NXU1AjyxyaWHGu6eBCtYqIkxWGl3s60i2IxwMkD+eikE5BnrVar4XK5\nYDab7xvx0mmA4iyNRoO6ujr5bgDkcC4rK0Mul4PJZJJrQ7oCudwAZG2w8ALWD12bzQaDwYCGhgYp\n6mizReSIa5KFqNPplJE6bam4f7EIYQFGASL1D3TyoBMKJ1EsCrkfAJB1zD+jRy45m+T619XV3eex\nSjso0mAoCCYXk5ZURHSoSyBHuHQtUYvA30b6FYs9lUqF2tpaKcY43qXor76+XgpVrjf+Ju6p9L7m\ngcv9hKKzQqEgxYVWq4VKpRLEjGJmq9UqCa7A7wKbCIrQwpR7UzqdFheEZDIp1BneT046TCaTUBBI\ni2KRxfXBa8XrRytEItCk0PB50ul0sNvt8tyyYWH6ZinAwvAerikCGJyMce0woIJ0Oz4XDMvI5XJQ\nqVQiJKZvLwsdWiEyW4HvTRoQn0WereS98+zW6/VQq9UyUSZVoxTs0el0cnaQDsBznfRAi8UiLh8s\nppn8ZzAY5M9579bW1mA0GmW/JcXQ5XKJoJae7cvLyxgbGxPKGz+PNERSOQgoUdxaqilhc8fmhPsj\nm3PuvZwQs0Bno0kXFIvFIhRX7n+5XA7RaFQK2dJrxPtaXV0tUwDWRtQ41NfXy/0jzYrPstlslvMw\nm80KRatYLMoZSZ0Ni2+uZwbMUXRItyfqb0ilKw0wKhaLUkNR2MrfynqNDRCD3gDc5xzFM5jNGgvq\nj+L1ocUzAASDwdVAIPBjrKcIvoR15JmvDAAdAC2AuZI/zwKo/bD3ZqIa0WNaj9EDmZvDzZs3YbFY\n0NLSgkKhgD179qCnpwdlZWVizK/X6yXilhHAdNcgL8xsNsuIf3FxUSxN3G43tFotysrKZNOhHyPR\nAS4SGsUzXre9vR2pVEqQRYPBgFQqJYKsTZs2yXiJG+H8/LyIDr1eL3bu3IlIJAK9Xi8UET7QfD8i\niUQsfT6fJB4SAaQ39Pj4OGw2mxysjB6lwLC0M+Ph4Ha7YbVasbCwgLGxMYmLttvtYopPblKp/c/U\n1JQorkdGRqDT6US8SHSOSBmLWn5veiwTKWeRRNs4XmN240T5iPwAkKYglUrB6XRidXUVGzZswPLy\nMrq7u6XY2LNnjzx0pGt4PB4pNJVKpaiWPR8EoJB3SlEj0QjyIekoMTMzg/HxcVitVnR3dwtCrVKp\n0NHRIQIhfnfaLWo0GoTDYRFCUjQ0Nzcn43nyYSm4oSNC6YSAllter1csAonKZzIZdHZ2ypiWGzrt\nAtlM0LaRhvIs6FQqFdxuN+bn58XiihZPLL6Wl5eFd7e8vB4pzgkP0/po80b7KQqBKEpkzHgul0NL\nS4sgwNlsVpK0ysvL4fV6oVQq4XK5UCwWMTg4iOnpaeFgdnR0oLW1FWazWRAPHv5ra2uSHsnmqKJi\nPQUyGo0Kqs/UNIfDgRs3bqCurk6+XyaTgcPhEFV9JBJBLBbD2NiYWKEZjUa0tLTI/lZRUYHOzk5k\nMhl0dHQIQss1xKLO84GXfSm1hdedxU5tbS0MBoOIZWljWFNTg1QqJXoBo9EIh8MhB/fa2hq6u7tl\n76EfNL3D6cIAQFJBs9mshDW43W5YLBYJqSEVLBQKwW63i5NPoVBAS0uLhBPE43Fs375dGv+Ojg5p\nrL1eL2pra4VzGg6HxSHE7/djdnYW8XgcyWRSUme5rijiAyD3jlQOTpfS6bR8LsOfOMErFouCsF68\neBGRSEQKVCYvWiwWdHd3Q6fTYfPmzWLnubKygo6ODhHF9vX1oaenB6lUSniYdGBJp9NSeBPN5l5G\n319yc4m6MoU0FAphcXERGzZsQFNTEyorK0UgyyIuEAiItSvPH943g8GATZs2yZm2urqKlpYWAaq4\nx2s0GnFmqKioECocg51aW1vlebPZbCIc7OjoEFu00v2fYR0ajQY9PT1YWloS5LxUkB4IBGQ6Rj77\nwsKCNBy0BFWpVOLpTMCL4lZOTfjsxWIxdHau43bUy3R0dGB+fv6+OHmNRiNn7sjIiIBZFosFBoMB\nQ0NDGB4eFgu41dVVeL1e7Nu3T5xJ9Ho9pqam4HA4oNPpZN9hCiNFlcvLy0L9WFhYQHt7u6QEu91u\nGAwG2b8IArCwJODBsLREIiEocWtrq4g+7XY7HA4Hdu7cKVH2DGSbm5tDVVUVGhoaZBrMoKiFhQVE\nIhF0d3cL6KFUrseQl5eXIxAIYH5+HtFoFKFQSJyNpqen0dDQIGspk8lI2iIbbqLBTFndsGED6urq\nYDQasWHDBrGyM5lMMBgMMBgMUqiXesmzGOZ+6XK5ZN/q6OhAS0uLUJAYhsY1SmCMgVwf1ev/k9tG\nIBAwA7gMoCYYDBo++LNHAewDcAbAoWAw+NUP/vwkgP8jGAxe+++939WrV/9wVh8fvz5+ffz6+PXx\n6+PXx6+PXx+//pd6/Q9x2wgEAp8F0BAMBv8WwBKAIoArgUDgwWAweBbAIQDvYL2o/ptAIFAFQAWg\nBcDtD3v/vXv3SjhKJpMRGobT6UQ0GgUAGdcQDaZ4IJvNYmhoSFJu6CjAMRJH8lS3WiwWQShLYz4B\nQK1Wy98j+sluuq6uDul0WugbRFKptifXr6KiQvwRk8mk2G55PB7MzMyIQI7vz8+mhdnAwIDQG0oT\nfqhE5tix1MVjYWFBxiH0yx4dHUVtba2o7wEI1UCtVsuojagDx3gcuZNnyIx7jqc0mvV4c47C5ufn\nBfWlu0KpFzVRT16HhoYGCeugApZiPIPBgHg8jrW1Nbzxxhs4fPgwcrmcTB74qq+vF+Uyx7erq6uC\nrJFvTpvCT3ziE7h06RIWFxfx0EMP4fTp04Ks0nCeXDxOFpg4Rt4aR8Ac8xOVXVpaQi6Xg8ViERS5\nUCigra0Nd+7ckRFyY2MjgsGgjEVra2uhUCgkOpU8f475KBRi6iMpQxMTE8IX5XPBNC9ywIlo0baR\n9AqDwSAdOxPMuE7Ia9ywYQPGx8clFIdouclkgsvlEvtErVYLs9mMiYkJiakmOkIOPtcsaSlcK+l0\nWjy2yZmnDSEpTJwMRCIRuN1uTExMQK1W49SpU9i3bx+amprk+SJFhiO8QmE9hXJ2dlaCUvhckufL\n1Dty+fP5PDo6OhAMBmVM7XQ6MTg4KCNHcnt5T61WK9bW1sTGkDx/0mA4fg6HwyJypXLf7Xbj9u3b\nUKlU4tjCJDraW+VyOaEP8T2JLtPXnUEYpbQg8oxJB6BDBe3EaM9H+gepUIxBHx0dFZ0BXQPIfTeZ\nTPdxqemQQ1cbpuZxjE4Pdtp0hkIh8RCORqMSbkVXjMXFRfh8PuTzeUxOTsLhcGBqakoEqRqNRoJJ\nuCepVCq88sorePjhh4WKQqoJR78ajQZGoxFWqxWXL18Wvi73DI7rif4SfSsvL5fwkZs3b6K1tVVs\ns5aXl+X5yOVy2L17NxYXF3Hp0iUAEOEr3QiY8scxPOkITU1NIgitrq6G0WjE8PCwIJakovE6xeNx\nSeNzuVxIp9PizML119vbi/7+fqF4MBWOdEQ6DdHnmm5BpZ7zDNGiw4/RaAQAjI2Nwev1irsS00Pp\nYkPHi9nZWUGK9+zZg4sXL8ozUkoHZHKqx+MRNJh0EaLTvb29uHjxokxtPR8kXjIEg1oY8v3p2vDc\nc8/hscceQy6XEytCfi8m9NKir9SNhH77pIVFIhFxa/H7/XC73Th//rxMXokUc3pUUVEhLkWkw3Ci\nS6opeds1NTWCqJrNZkQiEaE40K2k1B2np6cH9+7dQ3t7O/r7+wX5Ju1KqVTC4XAgGAxidXVVaJJM\nP3Y6nYhEImIlx71AoVCIfzitFqkDoFc1Ofqjo6NiQ0k6xMLCAmw2m9BBWMONjIxAq9Xet8+Gw2HM\nz8/LfayoqJBJONcFp8E8F7ivkVbDkCGK4SmUTiaTQu1aWVkRFxaK8nfu3Ikvf/nLH1aW/l6v34e2\n8d8AdAcCgbNY5zd/HcBXAXw7EAicB1AJ4L8Fg8E41mkd5wC8BeAvg8Hg8oe9OQVwHJ/Ri1Ov18tC\npDUMF111dbVsauTc0VS9pqZGxinc/HhzCoWCpI3R1oUPIPliXFQ8WI1GowiQyM3RarVSPCmVSuHl\nFAoFKRZoO8MN0eFwAIA8ZHRcYCFKDiR9ZMlR5GZHkSL5qIwgZ/FaWVkpHEJuPvy3tFuih2dVVZWk\n9nGcRx9YcuX4UqvVkkVPWyMq6MkP5+eXkvnp+8nfyt9L3iaLfzotsDhnoUqeEg94ijp50LEIpScz\nH3Z+LvmAbCgqKipkPXG0S0szcnfJveQ4nsUweezk5pVyyQAIj44ND9ccRZY+n08OdHoAG41GsWQj\n75aceo6bCoWCiG5YGJLXSUoCHQMUCoVwVFm8cWNmgAa5lBQlUgzFa8vfVWraT3cMfgawzlUkN470\nBz5P5DnysGUTR9EXmzJy1FmgccPjd2GyI7+vXq+X54d8VX53ckn5vgaDQVwmWKCzQCVnmaJBps0x\nppqfydElefG8dqXrhXw+NkVchyycKQRmSiapJ+TJU9jHzyU1hA0VG3NSKdjM19bWit6CzxbH2tyD\nSBGrqakRr3OKkrivsRFiM0F9AUMzAEjzxr2U4IbJZBKaGvdBaid0Oh1sNps05MB6Yc+wJe7NFM7y\n+SKXliIqcvqZPsa9sby8XA5t8tcBCLBA14nS58lut8PpdMoBTc4s9zCKjuk5XV1dDZ1OJwmuCoVC\nqEosOMkBXVtbE7EnCx8+v2xO+J3ZCJATTF43sG5nSuCBeh/yb0mVoP96aYHJpob3ivss1zm56dw/\ndTqdfCaT/XjWkBut0+lEG8MGymg0yvdnk261WmGz2eS55Z7AM4bnNK8593sWSy6XS0J+1tbW7uPQ\nkh5ILQLXDdcp7xHPTVJ1crkcrFarrCn+PT4H1EWw4AQgFppc5xQg0uKSNDiDwQC32w2j0Sh7Nml4\nJpMJdrtdzhBS00oBB64lup2oVCpYrVbhEvM+lepIqPsqLy+X55npjNwf2TAzN4LPCbVedNei8JP7\nBYG4qqoq4eXzLGUjQhoOLRuVSqU832zICUyQ+sLagfTL2tpa4VZXVFSIWxbPKV4HvV4vYl1Ss6hr\n4HflM8h1T7oK9zQCKfzewLqWir74H9XrDx6SsmvXLkGO6NiwtLQEk8kkPrgUPtGbcWZmBmazWTiz\nXCxEGijsodcr0Ul2YfTsJEpC9I6IdKnAieR5unGQf8ODjAcynS/y+bwQ1nmwUFBAxwy++FCQPzs3\nNyfo7czMDMrLy6XgZUoYiw960vKwoYCgqqoKk5OTcpCxm6dogsgzeXuMr+X1I6JEn10WiBQshEKh\n+8QaPAQZWsEIYB56/MzV1VUxyKcvqMViEY6kyWRCJBIBAPz617/GwYMHpWgpRQdK/VL50HODA3Cf\n88Ls7KyE2zC2lQgGERZOAliAEVUu9ZXmxrq8vCwHPO85/w2FVOl0Gk1NTRgfH5fC3uFwYHx8HMDv\nQkAAyOcajUZxUKCYhpsAo3orKirk7/A/CoVCjOy59qm2vnfvnqCmpQbzRJ3o3sFCjWshGo0K6mg2\nm+9z25iYmJBiiIgHnTYomqQos1SMSGELn0f6rVKEuLKyIgEdLBZoCcc0t9XVVZw9exYPPvigoDgU\n2xCp57WjIpwWTLSqK/XK5eFAdKilpQVTU1MyjWBISlVVlcSbU8TDwreyslJCPzj54tqnoJYCH6br\nFYtFmEwmjI2NoVAoCCJOwbTT6cTY2Bjy+bwI+9jgajQa1NfXi4sIo9z57HHqw3tBJxJOUNj0E3Et\nVdPz39MnlY5DbGzIM+bBxGdjfn5e9h+6rVDEQ8EXAQmq841GIxYWFiSIgwKrfD4Pl8uFmZkZcRDg\nZEGlUsl+Umrtplar8dprr+Hhhx8W5Jh6DDbY+Xwe9fX1MJvNuH37tvx5LpcTVLUUSacYimIwo9GI\noaEhBAIB3LlzR/ZO7u/l5eVob2/H0tISRkdHZb+ii0ptbS1qamowPj4u5wbXZFdXF+7evSvizfr6\netm/iYSSWzo3Nyd84EwmI39Gr2F62/v9fuGjc4JEJwnuxfT+VqvVUhQR1SMQQq48AYSVlRWJi6bT\nBP3Qmb3Ac5C6itnZWezatQvXr18XRJHNIJFMBgGVXje6iCwtLaGnpwfXr1+XSa/NZpNkTQJA/O10\nApqdncXLL7+MQ4cOSWIxU4VLdU21tbXCY6e/MPcrFrFM4SNw5HQ6cePGDZlGcRpItwiuC7rklJWV\noaWlBePj4xJ0VFlZKdNKnrO0xSwFupizwH3R6/UiHo9LeBcdyex2O5LJpDR1pWFQPOeKxSLMZjMS\niQS0Wi2Wl5clVbmmpgZNTU2YnZ3FxMTEffscn0+eZcPDw2JqQP/nlZUV4XnTNcvhcCAUCqG+vl44\n7wyQmZubg91ulzOI9RDvOfcZelRTA8B9m9xwYD1pmRN27keljjycSFRXV2P//v34xje+8ZHQNv7g\n8dw//vGP4ff7pUiiL3N7e7sUphUVFSLuamtrQzQaxY4dO0RIR29fprLRz5LWaRTS0UuWBRPV7bW1\nteLhW+qqUF1dDZ/PJ9Y1RLtKLVTq6+tlJERksKenB2NjY9JBb9u2TaybjEajFJ0ej0dGxH6/H8Fg\nEG63W1AWdqM2m00OaVof8d8tLS3dh9xSYFQsFtHc3AwAMiIk+sXrSrSB39vj8cBqtSIajUp3RzTT\naDSKWI0CEar9c7mciJcoFPD5fLJRAevFID1qmf5FiySqoInKPvnkk3j99dcFzeSIvaqqSuztiKgA\nv7MeW1paEmFIX18fkskkHnnkEdy7dw9VVVV4+OGHcfPmTWi1WrjdbrH84e9nN0vhVrFYvC8UwOv1\nSqQxleKrq6sIBAIiKDWZTOjt7RWRiclkwq5du4S2QwSeSYV2u10KOpPJhLW1Nfh8PtkMbDYbysrK\nhI7CVEbSjjhmXF5ejxS22WzQ6XSCglP13dbWhrKyMhEj0rqJyWS5XA49PT2Ym5uDyWSSz6B/J2kw\nRB06OzsRjUZlLROBpsDF6/VKuiXRTrvdjpWVFUkJY4NDVwweKgCwceNGzM7OoqOjA9FoFI2NjXj0\n0UfxH//xH2hraxPxK8WuKysr8Pl8qKmpQWdnp0ygSos/FrQWi0Web4oZ9+7di/7+fqjVapjNZvh8\nPvGKjsfj8m+4HomSsKkiFYuok8lkgueD9KvOzk6srKygr69PfMFv3boFl8slB4VOp4PL5RIv47W1\nNRFnsVF2Op2w2WwiRGpqasLU1JTY4tG7nAi5Xq+XA41Tit7eXkQiEbhcLuTzebjdbimaN27ciGw2\nKw0on8empibMzMzA7/cLiNDX14dgMIja2loRRdJGy2q1orm5GbFYTMRDfX19yGQysFqtaGpqEpHz\nAw88IPZULH5YvHR3d2NyclIaW14bOkUQ8T527BheeOEF2SNaW1uxvLwMo9Eoh3ggEEBLSwsGBgaE\nikd0tjQQhu4/dLmgZ30qlcL+/ftx48YNOBwOFItFtLa2ipDr8ccfh8fjwYULF6R5ZBgV9ydOOujG\nQVSaaCmnDqSlsNGpqKjAli1bZAq1adMmSUbV6/VCC+L+v2HDBoTDYbjdblRVVcHv90sRptPp0NDQ\nIE2rVquVdUxBb0VFBTwfpAIajUak02n09PRI+iVF+/TD5lSNRSAnQkQbDx06hDt37shIXq/Xi6ix\nq6sLoVAIfr8f09PTEpzCBFcA2LZtG6ampuD1eoVeRutFm80mSbl0Z6DActeuXXj99dexurqK3R/k\nRjBZMZlMilixsrJS6BQajQadnZ0oL18P8KJTCxvPnp4edHd3y/leX1+PTCYDn88Hu92OxsZGmT7S\nsCCXy6G5uVnOfpoIJBIJOJ1OGAwGMScgKEDzA0542HA++OCDCIfD2L9/P/r7+xEIBLCwsCBntF6v\nR2dnJ0ZGRkSQWyq0JEjQ0NAgYlfWWIcOHcLU1JQg8PRJpy0daSFMhiUVjVMynokmkwnLy8tob28X\nASHpkNu3bxfXIb/fL6Lo2tpama7QNtVmswk9iVMughY2m01cjw4ePCgTP37P5uZmAQ2JqlssFrS2\ntqKjo+Mj8Xn+gxfPr776qvCdmSZFxI0pSjSPn5iYQCKRkMOK8apEuFiEELGmfy9tpWgWnkql0NDQ\nIFHNlZWVGB8fl46c78GinQueKBA5SERD6XNJ1SutwkhziEajYp01OzsrnTyjb7VarcTkEhWenp4W\nusX8/Dw0Go14MvL7pNNpQdqXlpbQ0tKCSCQiSnv6hzLCtKxsPbqbnGda2bHoXVtbk6KN//vMzAyK\nxaJEYBI5jUajkk4EQFw0fD4fEomEhMaQ81ddXY3p6WnpJBcWFuB0OkWNy7Q/hUKB48eP48UXXxTk\njN3x6uqqoBfks/OBIcLHkdzc3Jyk7DGtj2gXnTCWl5clFYvpTbRhW1lZkbRENicMciilX7DLZXdL\ndT09wBcXFzE7O4vJyUlBXIhq0eqJxU2hUJDPIaeTf4/JZSzYmVjHjpxIQTwel2AXIoVsTGOxmNgE\npdNpJJNJKJXraZjkKZKXVywWBTlJJpNirUT+HFEPrkf6eNNxI5/PCzpA3hlDcBh3XLo2FhYWZK2y\nuStNklpYWMCTTz6JH/3oR/JMjo+PS/NVVlaGubk5TE9PIxqNYm1tDSMjI4KsEdXmYVhapPD3MB2L\nk41IJCJoHfedUjspXlOG+XB6xXXL9QKsI7tMV2OKHJEfrmf6rhLxVSqV9/mwJpNJsYSjXadOp5ND\nnh71VVVVsj8uLCzItKeUxsP1yfQ0TlOoMyE1gFoLUghYMBDV4v9lohotBMnv5Vrg+xB14qGdyWQQ\ni8VEG8FJUSaTkWkEaWNEoLhncTL41FNP4fTp0/IevKb0uAfW0wjT6bQkktJnmmAEpwqpVErS2AwG\nA0ZGRpBIJJBIJOTP6WNbKKwnEyoU6+m13Ht5LVkwkNubTCaRSqXEj5sNRs9LTgAAIABJREFUJKeO\nnGZxzc/OzsJqtYoVVyQSkeCV1dVVNDc34+7du5idnZUchNIkTfLI+YwTheR75XI5eTaSyaRMYDnF\nmZiYkGvJhF96e7NxiUajCIfDcu8JdNAujc8kn0+O4DlpoZMMKUz8PPLXeSZNTk7Kc7+0tCRrmlNi\npiFy/S8tLeGRRx7Bz372Mzn3OCFhE8001JqaGoms5j3l7yvVK1GPMTs7K9eBU27qe5LJJBYWFhAO\nh4V+w4aTa6tYLMpaLU1cXFtbE/SYvzWXy8la53msVCrFpWh6elrsdJkUy0kd10FpOmjpGcfvQo1C\nLBZDMpkUNy8+K6xN6E7DfZPnSOmZ2NjYKGshHo8DgJxXuVwOuVwOiURCJtp0PWONxv2XPv1Wq1X2\njVKL2tI8D35nrlFOAvmepfuxTqdDX1/fR1I8/8HjuY1Go4yHiK5yTDU0NCSjfYozWJCST0iUlFxF\ntVotfF4Wu0Sg2cnSoimdTsPhcEiRyoKIvFKOXTma4mgik8lIIcQNb2lpCQaDQWgkXFylHBwS9fl9\ngfXRVWtrKyYnJ+VhKbWqITrAEQbRByKPi4vr8dJEEOiBy+vG8AGDwSDFztLSkoz6S8epVqtVxq5E\nTxKJhPCvSSsgDYCLkgcmu2Va0tlsNnkQS7mb3LDIbaL3JAD5O2tra2hoaLjvYTWZTJJiV1VVJf6b\n9K+lh2gpb5zoNO8dN5LSiYbD4RCKg16vlw3b4/HcFw3Oe1dfXy9jyVQqBbPZLNcym82K5yybHJPJ\nhHQ6LZQQjsxsNptQcRicQB6oQqEQ8YPH4xFLK6ZuMhCA1olEzRiIwckFuXNEwlpbWyVumuuMSYL8\nfaUjSwpDuX7IiWNxzI2Yo/FMJnOfpoAJl0S3ODLkuI/pcqRFcNxJQQinGxzRORwOoXBxXRCB5uFb\n6l3Ka0XEYnp6Wnh8DKYhUsSCiPxj+v6Si0oOJ1E88g+5vs1ms+wvRP0KhYLQeOrr6zE+Pi7cZ2ot\n2MQRZSL1yWQyiWCY3EA2wmzULRaLHIIul0v0EaQ8kBNM+gxH2rxOLM7peZtIJOByuQRhp+91Pp+H\nz+eTpo3FBUVnLPo4QWBIEG0pyR2maJX31mKxiPd1LpeDwWBAc3OzWFRxLyZ4YbFYhCLAwoXnCJu7\n1dVVmYzxOa2vrxdaFRs+2iNSpMhDn3s5+d+lPr9cJ9yryBk3GAxQq9VSWDLkg4UpebQ8VxiSROtB\n8nTJz2fT3tLSIjHRsVgMSuV6YuDExIRY3xFZJMrGUArS/TjZI8+Z+QdcE7z+5P9XV1fDarWK/SOb\nTqKopSFFVqtVmhyHwyGe0/RHn5iYgMPhwN27d2G1WkX0R6SdKaCks9AvPxwOy/nL9UyBvdPplCJ5\ndXX1vv2ioqICfr9firO2tjbcu3cPHo9HmozGxkZEIhGZinLKxqKMgUa0lSOnl5adVqtVmhLSF9kw\n8TvzLKK1KwtZUj+IdnMCRupEKa2M3tRchwqFAjabTaafi4uL0Gg0QpPJZDL3+SFzT+f5wAkr49oV\nCoVoZigoJnWGmQMEcIB1ik9jY6MUoWxMWTPxGWMWAMG5dDotzzzt4rjeWeByYkFgg5axvOcUCfI3\nkCNfUbEeODM2NiY0IVJRE4mE7OWs/ahP+ihef3Dk+eTJk5iYmEAmkxHPxoqKCvEOZoFms9mk+6S3\nKlGaiooKMernzeLmRWSI3Xw4HAYAGXHShYBpUyykuTkA67wxcooYeELj+Gw2i1QqJUU1kUGOH3K5\nHFKplCAL3LzLy8sxNTUl3ebU1BS0Wq0stFgsJvwdEvfHxsZENEBeIB8Sos1E8HhQkM9F3iaRcW4S\n5JoRrSVvkoU8H0weXDRi5xhoaWlJ1Murq6twOp0IhULCvSRlhqgRizNG0AKQUTH5e5/5zGfw1ltv\nSapQ6UiGmwk3YP6HGyg7XKKHRK35d4iCsNhaXFzEwsKCOKHwHvLz+Nuqq9ej3OkKks/nsbCwIFxg\ninCYSkgRGZF1Oh/wz7m5JJNJSXbjhkWuLdch+WrcyHjP6ZNMLUAikUAymRS6AIVjKpVKuLgTExNy\n8NCknmrkfD6P8vJyKaxZELFIZmE5NTUFALIh87nhmqusrJTpBtcmsI4yzMzMSKPCz2DhQtSO6n9+\n95WVFaTTaTz99NP44Q9/KAUK9QN08yCPM5fLIZ1OC1+WaYBEu7kWSkNCyLPOZrOydomqMMaWn0sh\nCptLv98vtAg2wel0WtYgJ0Czs7Nwu92IRqNQqVRIJBLy74iSlpeXIxaLIZvNYnl5WWLWFQqFpMoR\nrWHhmM/nsbi4KDxUcsHJgy1NK6STTiqVkokLVetzc3NCdaFQkoiaRqNBKpWSpvjevXtoaGhANBqV\n5D8WutPT0/JM8xnhb6XXOeku2WwWyWRS0PyWlhZcu3ZNdAal3OZUKiVrkl615eXlOHLkCF599VXZ\nP9jIFgoFac5jsZhEOpe6InDdMWSCLgmlbifcF8hx5dnA789ihDoVUr3I/aYvN7nsnBIBwIYNGzA8\nPCw882QyKeFPREmJ+BKxi8fjEsbCrAI2vslkUnRDqVTqvt/Cs4OIMpsPXgPyokv1NfQwJlDDvYwa\nm1AoJOucz3MikRBQJ5VKybSC+w2nm0ajEaOjo5IBQA91TjFIx3I6nfKd2Qyn02kBxrj3lRawg4OD\nePzxx/H8889jbW1NqIV0k+K1o3e6Wq0WJykWxQAkxZFTV6LHvPesR9LptBS8pLqQ7keqG6kea2tr\nMh2jywffj/vP3NychO+UOmyRcpVKpaBUKjExMSEFOhso7iOlZ6FGo5HfAKwXo9SDUXvF8zWZTIoo\ntqamRmox0lanpqbE6571FJ/T+vp6jIyMoKysTPZ40tMmJyeRz+elbuN0r7y8XIr28vJymX4S8AqF\nQgJCEqhLJpOCRlOrQucurVYrhTNrInLarVYrNm/e/D8HbeNHP/qRoAYUeSQSCTQ0NMjYtJR8r9Vq\nMTMzI4b5RFr5kBIJYRfOWGzymEnuX1lZEaoAN0AWiByVs9CrqqoSAj7Tfvjn5EzzoSfaSMHM9PS0\nKPmZvENEnIgjESbaTtHsn2NYokxEorlJr62tyeHK0QgAER6yqGBRPjs7K6ggVf5arVaM6/n+pE+Q\nkkAVNkNLOALmeI/dcjablbEL7bP490uFc6XoMkfUDBqpqKjAZz7zGfzzP/+ziClYxHONcJPnpsL3\no+0Xke1MJoOWlhbE43Hkcjn09vZKw8LRGQ8q3kvydpnyxkOTBwOnDlRfMz6eyOni4iK8Xq9QNxQK\nBYxGo9AcWLAxip2/gYUSOeNsaLLZrDi1ZLNZKSq5RhYXF1FbWyv3jIUWxUUU4bL4tVqtSCQSgiay\n0YvH4zCbzfcdvhzPM6yIY2Aq/Nmskm7CYosHNQ9bJiQSyaClGW22WLRQgEvrQ05uOC596qmn8PLL\nLwvqAkCQHjYnuVwOLpdLCmnuGwwiYbPN+8CD3+VyIRwOS+NEtIa/iwUfbRYpoiHqTjSaY1E6PZCv\nGIvFZKrm9/tx7949KBQKEXLShcBisYhTj8FgkOKW96G0+CcHlbQrFlosfkkz4R7H4jaVSkmhAUDQ\nM4rY2FhxXMzpFO3LKMahBR5R+VLhGptzrm06qxBByufz0gzwWebUpLa2ViYURNULhYKsy1L3CoVC\ngSNHjuDnP//5fWFR3If4TDqdTnkO2Xiw0LZarchkMnKGUHDJw9blcgk/NRwOy55IC81CoYDu7m5p\nlEiX4foi1SSRSEhDoVQqJcyB/4YAEml6pCymUinY7XZBWjk1IZ2jqqoK8Xhc1jrXBWkPBA94JvE5\noTCQzYBGoxF0ljxtcrrpgEIklgCE0+mUfY5rjfsoqVdbt27F2bNnpWGiY0IkEkFrayvGx8fF8o8U\nOYrhU6kUXC4Xpqen5ZoYjUY5D0i7K7VD46Tt4YcfxiuvvIKZmRl0dHQgFAqJ5VkymZQma2Rk5L5n\nnfeI35HNGjngLpcLw8PDACCNNqcRLNxIT2HzyT19dnZWXGvi8bg0gADEQYnnAqdr/F4UT167dg0+\nnw/RaFTqFu6DnACUiiNJAyXnfmZmRkR6fE44lSQ9he/FtUh6SXV1NSYnJ1FVVYW5uTkBCLPZrIgR\n2WBRKF3aqHE6PTMzA6PRKLUd9xjuh5wGsK4icMG9nk5bFLTTmapYLMo+QIExqYaVlZVobW1Fd3f3\n/xzF88svvyyHE4D7hGTsMjjeCYfDgsSx8CEaodVqxXmCHSE3Xh5OSqVSbqbZbEYqlRLVKD2GSWEg\nSkVXB41GI4uxtAjl5sgbziKXKmBypJgKl0gkAKyPKYjQEM3k5qBWq6VLY/eqUqkQCoVkDMPP4shn\nZmYGTU1NknDFfHse8kRgiNBx3M7FRj9jdvPk3PF/IxeLRWUsFpMCjx05KQbRaFRGYrQoKhaLSKfT\n8pBns1lJgSSSTeTzySefxJkzZzA1NQWDwSAoCNEMbuK0cON1rK+vF/cAfi8+yMvLy8hkMoIQcOPk\nITA3NydcY6JlLO74nouLi/KQcypB3ihHbeSQcqNh0lPpoUUknWuJRTjdVXjos/AA1hsDIrg8FClC\nJbqRz+eFijAzMwObzSbXjBsPfwOV89XV1SI8zGazMmVQKBRynclpJlecDRwPQxZ0pP0Q8S/lQLIB\n4qZMqg6557z/3PSIgLPgXVxcxOc//3k8//zz8huY/shGid619C3nmuSzyGKDBTTvGfcLTnTm5+eh\nVqtlGkA6A/cC2p+xKCVdhtMJUoPo1JLJZOSQIdrGpoAHC/c/rlUWyPSer6ioEJ4wn0E2ziygWQzz\nmtKPnvsW/y0LWR4uLO4UCoU4VlgsFtGLMAFyZmZGaCRsCko57OTbkrNYGhHNSQ8PYTatXBNsdlpb\nW9Hf3y8NA8f5NTU10uQz+RBYb8CfeOIJvPrqq3IWAPfrCsrLy4XTSxclXttSJT6fHyLkJpMJyWRS\nPo+gC2kjBAxIRyJyTwBHrVbLtIYaG3JW+bm8rmx0qqqqBHCglzKnCKTbkJZC9w9OBqqrq6W551SB\nyCyfVyLRvEekEnB8Tjcmni+cAFAHwXtFMIGTBE74+CqdanFvSCaTcsZRcBiLxYSuw2Kb65Callwu\nJwmSBIzm5uakWVhdXZWJAUGpVCqFo0eP4oUXXpD7T6oim4JS4Ka2tla4wuTgqlQqeL1ehMNhWSss\nYnmGUffC4psT2Xg8LogqATu+P/m6nC7QdYfuWIVCAXNzc0Kj4L7DhlWv1wtdNR6Py9rhc85zgveI\nzyz3GNZVpeLb0onA1NQUamtr5ZnktJANFtMK1Wq18KEJ5jU2Ngrfm2tKqVRienpatAJcl9TTsI7i\nfsnfPDc3B4fDIVocWglzfyTAkE6nMT8/j1QqJZSSmZkZWdu81pwGbdu27SMpnn+veO5AIGAOBAKh\nQCDQHAgEGgOBwPuBQOBsIBD4x5K/82wgELgcCAR+EwgEHv59v0AsFpPFzEWQTqfFNaJQKCCRSGB4\neBg1NTWw2+3IZrPi8Uw0iEgJD4xS7jMfqtXVdfN3LtJcLodwOCyoEFE1GnOX2rTxxpCPRmSPnrcA\n5FAjOkXze6fTCYvFIjQEjsgMBgMaGhpEVR+JRLC0tCTiBnILiaaS11XqnkFBWrG4HlNMVCccDgvN\nguN62uQQhSSKQe9LdnvcmGjlU15eDpPJJGgLERnywMhrzeVyCAaDACCx0tPT03JvnE6njJYASPRs\nZWWl+FZmMutxu1NTU6ioqJCDpXTz4mizVDRZXl6OyclJzM/PizhmcXERXV1dghx0d3eLgIPIGJEL\njuqKxaKsRRYF6XRaNj522yx+aHfIg6a8vFzU/hQ1+nw+ORgoRJyfn5fijGgauYVVVVUiSuHvpMKf\nG4xCoZDRJgsp2h1RQMfCiI0PHT1oF0cuN11rHA6HFJakH7Gjb2howOzsrPDoGhsbRQTHhnNyclLG\n2XyOeG3Js+ezQ3oMnwVOWogcEsUiGsbCkPZhLG540FJxrtFo0NbWJuM70hGIiJCzzRE/XSU2bNgg\nYlKj0ShccoVCIUEdLMymp6eFxlEoFCTwg/eOlnkcjfLZ4Bi2q6tLDjoe/LS383zg/sKmVqvVisjZ\nYrEIip/P5yWMicgtm7VkMineqiwmOBKurKzE6Oio0Lvo9c09kpOB4eFhCb9gk8YQHhZSFPYReCCS\nRI/VWCyG2dlZKe7Ih6WTAy1HrVarFBLXr18XfiKpcslkEpFIRIrDYrF4n6UV9ws287RwLC1UXC4X\n2tvbpfgjukrdA+kj9Dmm+NXr9Yr7gs/nE2oMC/Hq6mrMzMygp6cHXq9XqBIzMzOIRCJIpVJYWFjA\nwMCANGgERSgSK30OeD6wyCNlw+PxSJQ8BVH9/f3io0xB49rauuc0nweeV0Rz6cLCuGTSWziN4T6X\nz+fFjzqdTsNqtQpab7fbRcTr9/ths9mg1+ulGCf3lutpy5YtGBkZwdLSkhRLS0tLCIVCaGlpEeoU\nm12K4DOZDBKJhIj6OXnxeDz3TaoXFxfFKQT4HQe39Blsa2tDKpVCIpFAJpPB8PCw0BYmJydx584d\nQWCNRqOACTdv3hT6JJ/P9vZ2OYfZcNE2j5xaOkqtrq4iEomIWweBDjbk1AyVTsUWFxfl/AQgFMFs\nNgu/3494PI6WlhahXdKth80L93MWy4VCAfF4XEDAeDwu13h2dhZTU1Nim8himvsT+eacjHC6TPCi\nVO+gVCol/Glubk5sJ9mQs4mk00upDzN9rHktqHkaHR2VJiCdTov7DtdmJBKRuHk6U1GcT60KsD4B\nIM/+o3p9qM9zIBCoAPDvANoAPArguwD+azAYfD8QCDwH4FcALgB4E0AvADXWg1I2BoPBlf/sva9e\nvbp29uxZvPfee8I7YzE8MDAgVmzt7e3Yt28fvv71ryOZTOLxxx/HO++8I3ZL2WwW+/fvx/j4OPx+\nPwYGBqRDpxAHWEcjBgYGoFQq8eyzz+LMmTM4cOAAJicn8f3vfx/bt2+XjfDq1aviu1hTU4Ndu3bh\n4sWLUjjRnu7u3buYnJzEtm3bUFZWhuHhYSwtLeHIkSPQ6XSYmJjAv/zLv8Dr9eLQoUN477334PF4\noNfr8fbbb8vfffPNN/Fnf/ZnOHv2LHp7e/Haa6+JrVcqlcIjjzyCn/zkJ9i8ebOMZc+fP4/m5mbZ\nTL/whS/g29/+NhwOBz7xiU/ge9/7HhwOB9RqNY4fP45Lly7h1KlTsNls8Pv9wiuiW0d1dTUGBgbw\n2GOPYXV1FbOzs7hy5QpsNhsGBwdhMpmwZcsWqFQqnD59Gna7XUR5N27cwIEDB/DYY4/h7//+73Hz\n5k3s27dPuHHZbBZnzpzBoUOHcO3aNVRXV+Pxxx/H1atXEQqFMDIyArvdDrfbjc9//vM4efIk7t69\niwceeAC3b99GQ0ODuCL09vZKglJnZyfGxsaQTCaxd+9e3L17F3a7HadPn0ZXVxdOnjyJL37xixgc\nHMRbb72Fo0ePIpVKQa1Ww+Px4NKlS2JhRaEC187a2ho2btwInU6H5eVlXL9+XbhrarUabW1tqKmp\nwdmzZ9HU1ITKykrcunULly9fxvHjxwGsN4c3btyAzWbDli1bxKovlUqhvb0db731FhwOhyAOVqsV\nkUgEhw8fxvDwMNLpNJxOJ86cOYPe3l5cuXIFbW1tWFpawp49e/D2229DqVxPlYpEIrhw4QLUajUa\nGhrQ1dWFTCaDnp4evPvuu4hGoygWi9i4cSN+9atfQa/Xw+PxYHh4GIcPH8YLL7yADRs2IBQKIRAI\niChjYGAAS0tLOHz4MFKpFEZHR/Gb3/wGTz75pGysU1NTeOKJJ/DSSy/BZDLh3r172LhxI0KhkAhe\nKJil5V8mk4HX64Varcbly5cF4WBwjF6vx5UrV/CpT30Kb7/9Nr773e/ib/7mbzAyMoJPfvKTeOml\nl7Bjxw709PQgk8ngjTfegEqlwq1bt3DkyBFcu3YNe/fuleY8lUqJlqC7u1sEO7FYDG+88Qa+853v\n4PLlyxgYGEBjYyOi0Sjq6+vx+OOP4x//8R9htVphNBrl+y8vLyMYDOLw4cOSpPXOO++I841CoYDD\n4cDAwAAsFgt+85vf4IknnsA//dM/4ZlnnhHbRJfLhStXrkCv1+PChQs4ePAgAMDn8+HFF1/E448/\nDo1Gg5/+9Kdic1VXV4dXX30V3/zmN/Haa68hlUqhs7NTrPeGh4dlUrWwsICWlhbU1dUhFArh0Ucf\nxfnz52EymXDq1ClUV1djy5YtgoxqNBo8/fTTGB8fRzAYxLVr17Bnzx4MDw9DqVRi9+7deOutt/C5\nz30OJ0+eRFdXF86fP49z587hs5/9LG7duoXx8XEcP35cqFSXLl1CW1sbbt++LVz0zZs34/333wew\nntxmt9vxta99DV/4whdgNBpx+/Zt/Mmf/IkUZmfPnhUqUTQaRVNTE+bn5/H1r38d9+7dw9tvvw2F\nQoGhoSFs2bIFN27cgNvtRj6fx9DQEJaWlnDw4EFpXDktOHjwoEwUXn/9dWk4jx49iueffx7z8/PY\nvXs3IpEIjhw5gjNnzqClpUUcFHbs2IF/+Id/gMViwVe+8hWMjo7KFC6bzWJqagpf+tKX8IMf/AB3\n7txBd3c3pqamoNfr0dHRgVQqhVQqJeIoTqoikQg+9alPYXx8HKdPn0Y+n8eGDRsQj8dx+PBhPP30\n0/jSl76EyclJfPGLX8T09DTS6TRCoRC2b9+OU6dOwW63w+VyYX5+XgRaQ0NDMjZXqVTw+/24fPky\ntFoturq6MDExAavViitXrqCmpgY7d+7Ec889h46ODmi1WvT392PTpk2wWq345S9/KVzq7du3C2jx\n29/+Fj09PYhGo3j//ffx7W9/G8FgEM3NzRgYGEAqlcKBAwfwwgsvwO12I5fLYePGjbh06RKcTicm\nJydhNptRX1+PU6dOScPMtEGv1yuTAavVigsXLohAmoLTb33rW3juuedgMpnwwx/+EH/3d3+HmZkZ\nrK2toampCRcuXEAqlUJLSwt27NiBkydPYnp6Gu+++y4CgQD27t2LT3/60/jJT36Ct99+WxJDV1ZW\n8NBDD4mAs6urC2+++aac/16vF+Pj4/jEJz6B6elpbNu2Db/61a8wPz8vgS9jY2N45JFHcPLkSbjd\nbgCA1+vF8PCwJNT6fD6Ew2HhYSuVSrz88sv45je/ie9+97t4+umn8fOf/xxtbW0yedPr9RgeHsaX\nv/xlRKNR2Xt8Ph9u3rwp9q23bt1CIpFAd3c3GhoakMlk8Ld/+7c4cuQI7ty5g87OToTDYTQ2NuLM\nmTM4fvw4VlZWRDsVjUaxbds2/PSnP4XT6RTq4F//9V/jL/7iL7B9+3b89Kc/xVNPPYVoNIrZ2VmU\nlZUhGAzC84FFL4EJ0mOam5sFSFCpVLhy5Qq+//3v41//9V+Ry+WwefNmVFdXY2xsDMPDw9BqtTh4\n8CC++93vwmQyIZfLwe/34zOf+Qx+8IMfoKysDE1NTXKe0XHp+PHjH4nP8+9TPP+fAH4B4JsA/gzA\nW8Fg0PnB//YogIcAvAHgUDAY/MoHf/4ygP8SDAav/mfvffXq1bVHH30UnZ2dWFxclAXDGzMwMAAA\n0pk3NjbCarXi3Llz+PSnP418Po/Tp0/D6/Xizp074tVM1DAcDsPj8eDOnTvQ6/XQ6XSw2+0YGRmR\njrSiogIGg0H4OaSM0LaHyUD076SSkybuarUa7e3tOHv2rIyHW1tbcePGDeHT9fb2Cnqu1WpltE3/\nxM2bN6OiogKvvPIKGhoaMDExIQ4FFDWEQiFBONfW1mC1WqFSqTA2NgadTiem4wqFAsPDw6ioqEBH\nR4eMS1KpFPR6vSDg5FMD60haXV0dvF4vFhYWMD4+Ll6yFPIQnR4cHBQP52g0KhuYw+EQGgk5ozMz\nM5ienpZxj9/vx+DgICwWC9LpNCwWC+7evQu32y384mKxiBdffBHHjh27zxqNdBUKO5RKpQgRONaf\nnZ3Fzp07cevWLfT09ODChQs4duwYXn/9dczNzeHZZ5/Fj3/8YyiVSjQ1NeHmzZtwOBz3UYaIZhIZ\nunfvnohRm5qacPfuXbhcLum8s9n1yNRz586hvLwcjY2NaG1txcWLFwXF3Lx5M959912kUikoFOvp\nUhqNBiMjI4JwcXOcm5tDV1cXRkZGRHxCt4XBwUFxiamrqxORqV6vx/T0NAKBAAqFgqDWpHJUVlZi\n69atuHz5Mrq7u3Ht2jXhp3HSEIvFsHv3bty4cUOQA47ELBYLXC4XLl68iLKyMni9XjgcDly/fl1Q\nI3KOibb4/X6Mj48LR3BychJWq1UOxaGhIdTV1Qn6AkAEjrlcDnv27MHNmzfhcrkwODiIhoYGfO97\n38MXv/hFtLS0iHUcbRGZvLaysoJNmzYhn8/jzp07YmlFLiOnPETO6HG8c+dOvPXWWwDWxcTt7e24\ncOECrFYr+vv7Bf3iqNrv94tgkJZ6FKKVlZWJU8y5c+ewc+dODAwMYNu2bXj33Xexb98+nDx5El6v\nV5AUesozjpwUBNK7GA7BtcnpWjQaFbtLFuFM56KzSyqVQiQSgcViwcaNG3HhwgW4XC55foH12OW+\nvj5cunRJxp6kcNGDftOmTZifn0coFMK+ffvwi1/8AhaLBePj49i4cSPKysoQCoVgt9uh0+nk/kUi\nERw8eFAiox0Oh4gC+/r6EIvFEA6Hxf3IYDAgGAyitbUVQ0NDANbRxK6uLimCSeNQq9X4t3/7Nxw/\nflzQYHrBE63yeDzwer1wOp145ZVXBIEi31+r1WJkZESQfu7bRDtdLhfOnz+PT37yk/j3f/93ob0E\nAgEkEgncvXsXzzzzDNbW1vDSSy+JhR6fvdXVVZls8swgZUalWk/IvXXrFmpra+V8AnCfVuHBBx9E\nLpfD0NAQduzYgTNnzgig4Pf7cf36dZSXrye+9fb24syZM3C73Vh6HZVYAAAgAElEQVRYWEBHRwfO\nnj0LYH0i2NTUhHA4LCACXYzolpHNZtHa2iq0uf7+fvT09EChUODSpUvo7OwUTjUL5ImJCXk27Ha7\n7C1ra2t49NFH8cILLwiYxTCvqakpeRYoyqXjAp1J0uk02tvbZSIRCoXQ09ODwcFBrK6uJ+xNTk5i\n8+bNEjqSzWbxwAMP4Mtf/jK+9KUvYWFhAXv37sX169cRDofR1dWFO3fuwOv1SkAL90sCEcvLyxge\nHpZzgJqf7du3o6mpSRrWuro6xGIxNDY2oqysDFarVQScY2NjApSw8R4eHsbq6ip27twpNQX3b5fL\nhYGBAXGKotiUAF4ikcDRo0dx6tQpPPLII3j55ZfR19eH/v5+Qdt5tp09e1boV1VVVeLJ3traiuvX\nr0tcPYGslZUVPPHEEzh//rw8RxTNezweabgaGhowMzOD7du347333oPf70c2m0U8HheTBNoder1e\n3Lp1Sxw9KioqsHXrVty7dw+5XE4Keu5/pRonUkkGBgbgdrsxOTl5H83P5/OhrKxMmhDaf96+fRvA\nuiNLLpcTiggdN7q7u3Hs2LH//4vnQCDwDAB7MBj8L4FA4F0AXwbwTjAYdHzwv+8B8AWso88dwWDw\nmx/8+U8A/CQYDL7zn3341atX/3Dxhh+/Pn59/Pr49fHr49fHr49fH7/+l3p9FMXzh/k8fwHAaiAQ\n2A+gC8BPAZhK/nctgFkA8wB0/y9//qGvr371qzJOrqqqEg4WeV/00ty0aRNef/115HI5BAIBsYIh\nd5GpbIzzpaCLfLvy8nLU19eL7cnmzZsxMDCArq4uxGIx9Pf3SwIQ0VTathCVHRwcFI9eIgm0g/P5\nfAAgNl5dXV1ChA8Gg1AqlYK80cOQf5d2RY2NjRgeHhYaAVWoVIn+5je/kXQ2jUaD0dFRGb0tLy9j\ny5YtuHr1KlZWVtDU1IShoSEJkQgEApiYmJDkO4p99Hq9mMvT1cPn8wk95e7du6ivr5f0J3aQHFnR\nRzgWi8Hr9aKjowPvv/8+RkdHhWdGXnU6nRY0c21tDT09PQiFQnIPiV7+7Gc/w1NPPYWlpSVBqemH\nS2SDPCqNRiPCJpfLJR7FoVBIuFUulwtjY2PCceTaMJvNGBsbg8lkEv4xFfwU+DHVsaKiApOTk6IU\nrqmpEdSO1mOcEKysrAgHr1R97PF4xEaQQT3BYBAOh0Osw8i127Bhg1gCBQIB+f6MOy0rK4PP50Mw\nGBTEk3zX+vp6cemgp/nk5KTwNSlIpViGPD/yEcmvr6+vFws0vV4vIpLp6WnMzMzA5XJBo9GIyX9b\nWxuuXLki9n4UwjK8hoIzWl/xmpTSnSi2JYcwnU4LQvTOO+/g4YcfFr/ze/fuwel0CsoQDodFmGcw\nGBCJROB2uwUxphBndXVVxDgAhMe3detWDA8PI5PJSEw2R9nvvPOOxCyTn1dVVYV79+6hp6dH7JyG\nhobEV72UR0/nFI/Hg3A4LO4eZWVlEsnM6ZfZbBYU6/z580IPGhwcFIGSSqXC1NQUtm7dioGBAczO\nzsLr9Yq7CgDhipMvTR9Uinp0Oh0GBwclaZF8fJ1Oh/b2dsTjcUxPTyMWi8mUh3tbf38/Ojs7ZXoT\nCoUQiUTQ09ODZDKJyclJtLW1ybNDB5zJyUnx9dfpdDJVpHf7vn37wOAsIqD0Jx4ZGRENy8rKCgwG\nA3K5HF588UX85V/+pSC2dGiYnJyUlEFOr2hdBUCcIbhXFwoFQbYVCgWsVivGx8flmSkUCnA6nRga\nGpIUNV538v1bWlpkb+QoOZPJoK+vD2+++SaWlpbEJi+Xy+HBBx/ErVu35DxUqVQiKoxGo3jggQcw\nPj4ujhz0mfb7/XjggQdkqubz+SQgg/zau3fvQqvVig8692EGujCdVKVSYXx8XLx6iXwyqMvn8+Hy\n5cuwWq3CxWeiXSgUEo4z/eILhYI8e7FYDFVVVeKYYbfbhTLT3Nws7ifl5eXiEsKpmk6nk/tCoT+n\no6XuOnQC4jlPZP2ll17CU089Jdeyo6ND1rDVasXY2BiA9XjthoYGoakMDw/D5XLBbrejra0N7733\nHiYmJuQM4jlIkbnT6ZR9mD732WwWTU1NSKfTEknN/clsNosV3cjICOrq6uR7cN/k+5Q6d/BZbmlp\nkYnt8PCwxFVzusypCLU6arUaer1etBCcXlJkzwh7To3+n1Z+Y2NjUhPQaYRMgCtXroizUG1tLbZs\n2YJf/OIXcDgcGBsbk+kH+eHFYlEE3g6HAyMjI3Le0mN8aWlJnGH27duHGzduiIsMw3ao0fD5fDKd\nAiApkL/97W9lQh+LxdDQ0IBCoYCuri584xvf+H1K0w99/aeCwWAw+GAwGNwTDAb3ALgB4LMAfhkI\nBHZ98FcOAXgfwGUAOwKBQFUgEKgF0ALg9u/zBWhhxY2NogZupjyA8vk8nE4n3G63iIZYiPCiM2mH\nEasOh0OKAC4gwve0C2JxEAgERGxRLBZhNBplbEpREQ23SScwGo1iDM6Dlfw82m7RjkmhUAg9hONj\nnU4ncZ5cNDxsaMhP43oKRRgCsrCwICNGFnyJRAJNTU3y/mazGVqtVoIoaC1EDlVZWRmam5vh9Xol\nhS4QCKCsrExCPhobG0VAQ8EKrQIZBmM2m2G320WYRX9Vev3y+zEcgqlhLMp0Oh06OztF9ARAwgdo\n9Qb8zii9rKxMfGgpuKAamGN0Cm7sdjvy+TxMJhO6urpQVVUlMaj0peRBTLsj2vSxqAAgqn1SDBgg\nQaU0RSBlZWXo6OhAVVWV0C1IwdHr9TAajeJuwLGj1WoVIQPH/rOzs5LAxRGbyWQSnjmvIQChs9BP\ntbu7WwpWWgrRcohR1YFAQIQhDQ0NUKvVcLvdMBqNqKurk82Q43LqC2pqatDc3Izu7m7YbDYRtJba\nGnJdUrRB8aDBYBAOLgB5Jigm0Wq1IvSlUtvlckmzCEBibCsq1iOEa2trxTaQz1dbW5sECTAwiONE\nnU4n7hbcxFdXVyUCFoCIKTlCZuPL+8BDgM4hAORzuI4oKKLIigUZn3k6fTDYSafToVgsore3V8RW\nFRUVkgzKImbDhg0oFArIZrPo7e0VCzkGtHAE29jYKNfMbDZLtC2FWWxeGDNOISrpL3R/oWiS4i8K\nJyniod1mXV0dmpqakEgkUF5eDr1eD4VCIWE6pf6yNTU1chjX1tYKf7WmpgaRSET2Gsa6Ly0tSbHK\ne82gkdbWVgC/swFlAAUtHBmCxT2K4TlOp1MO42w2i7q6OhGtGgwGiQhvaWnB1q1b5fmjCwKdJJiM\n19HRIfZZdEehMNlut0txx4h6FmAssFmYMVynoaFBxOTAugDNbreL40CxWJRxPp2UuIb4+Qwm4vlF\n9wetVguj0QiPxyP7H7m4pLTE43E4HA54vV6oVCr4fD6xO6R1JT3qjUajPNscvdOthSAJ1wv3K7oZ\nWa1WKdoZIGI0GqFUKiWtj2eE1WqVc5EFG9cf99hisQiv1yvXjUL4jo4OzMzMAIA8u/Pz82hvb5d9\nUK/XY3V1VTQlFotFrPsY8qPVauF2u8Vxgvs8f1Pp+mKjRuoMQTOz2QwmDPt8PhE+1tbWora2VgTw\n9GaneUJ1dbUAUlyPZrMZGo0GjY2NwiNubm6GUqkU2hGpdfzv/CyuCTrfbN26Fe3t7VI3cU9mTDez\nDPgs8zmnM1RzczMWFhb+7/bOPTjO8zrvD+5Y3Hdx2Qt2gcUugY8ACBAUQZHUhRpLYiQqI6uOY48t\nV9PKrmN3PJ7+0yaTdPqHmyrppG2mk3ZaZxq3dl27nrZpZU8yUuLEnlpRk9hiTJEUiY8k7thdXIj7\ndYEFtn8sfscLzSTmdCixib4zw5EIksDut+973vM+5znPo1AopFwupxMnTiifz8vv91uuINehqlGc\nu1dXV+2MAfRiH+E/wSWQPcjewIgpGAzaADoqU+Fw2LTM7+fA4D2pbbwr/qGkf+o4zpuSKiT9D9d1\n5yT9lgqDgn8k6Vdc1929l292584dtba2KhwO2/T1ysqK+vv7zU1wYWFB3/3udxWNRvXUU09pbW1N\nTz75pJ588klVVlaqv79fExMThkBtbm6qo6PjiJOgVJi4HB4eVl1dncbGxrS2tqYrV65oZ2dH/f39\nRxQuSIqDg4M6d+6c3ZCYWJ2ZmdH4+LgaGxv11FNPaWVlxXQbn3rqKd24cUPXrl1TWVmZnnvuOX3o\nQx/SzZs3rZDY3NxUX1+fTZ5/5CMf0Z07dxQMBnXt2jX5/X7FYjFVVBTcFEEomaiurq5Wf3//ERWH\n69evG2KAFmR9fb3S6bTeeOMN7e7uqr293dQPKFIWFhasKOru7lYqlTJprubmZrW0tOj48ePq6+uz\ngre1tVUVFQUXuvHxcTNjeOONN+T3+3X69GnTw759+7aZSeAAlMvlNDs7q8nJSUN2cKiTZCjo3Nyc\nDTLt7e2ptrZW09PTpp+NhFFFRYXeeecdHT9+XFtbW7p48aKy2aw+/elPmyzPxz72MdOa7e/vVyqV\nsmRQVVWl6upqG7BxHEeRSEQjIyNKpVKanJxUJBJRJpOxw35sbEzj4+M6e/asUqmUOWI+9thjNnTk\n9/v18ssvKxgMynVdzczMmK7w1atXVVdXp6mpKVPKWF1dtdfmuq4ZBPT09GhmZkbRaFT5fF6tra36\nwQ9+YPzyTCajeDyus2fPWtGLbi5Dlm1tbXrkkUeUzWbV29urcDisjo4O1dfXa2RkRKdOnZJU4Ish\n2QRvs1gvu6KiQk8//bRmZ2e1urpqCgNvvfWWaaiePHlSS0tLCoVC5tbV2dlpe0wqXBSuX7+uqakp\nQ2QqKgpWt4899pj29vbU19en2dlZHTt2TFKheB4aGpIkDQwMaHd31z6jRCKhaDSqZ599VsPDw2pv\nb9fm5qZGR0dtqHRnZ0fxeFw1NTWSCoVyMBjUpz/9abPo7unp0UMPPWRudz/4wQ8UDAZtEp5ihKKB\nnz8yMqLa2lp7z9FoVOPj4zp58qR8Pp9+7ud+TqlUSh/+8Ic1MzNjjn248SUSCZ0/f94k/kZHR9XV\n1aVUKqXx8XG1tbUpGo1avuzr69Pc3JxdJltaWjQ6Oqrl5WXNzs4qFArp2LFjamlpseG2M2fO6M6d\nO2pvbzfu9rFjx7S0tKSBgQEzf7ly5Yreeecdzc/P6/Tp08pkMkokEvL7/bpy5YoGBgb02muvaX9/\n3+Y9zpw5o8rKSvX29urChQs2O7G6uqqLFy+qrKxMw8PDunTpkrlMPvHEE0okEtrb29PMzIx+9KMf\nmYvgww8/rMnJSV27dk3f//73NTAwYDJqDQ0NmpiY0MTEhCTp6tWr2traUjqd1tDQkIEHs7OzikQi\nunDhgj71qU8ZQo5yDS6ft27d0s2bN9Xc3KzKykrLnefPn9fFixdVWVmpF1980boSBwcHOn36tI4f\nP66pqSl9/OMf1+c//3lTbVhZWdHly5eVTqeVSqX01ltvmSoRMw+SdOXKFSscyHfr6+uanp423u38\n/LwuXbqkM2fOqKamRpcuXVI6ndabb76paDSqJ598Uul0WplMRsFgUE888YRmZ2d18uRJtba2GjAB\n0jgwMKDe3l4rqCjaQqGQydx1d3fL7/erpqZG77zzjs6dO6cLFy4om83qkUceMcfX5557Tk899ZS6\nurq0trZmg2FcqOLxuD75yU9qenpam5ubdiZWVlYasr66uqrGxkbTTqZD2dXVpVwup2effdZUdFgz\nqK2gof7oo4+qublZ3d3dmpmZ0WOPPSZJ1vH6mZ/5GVVVVSmVSimfz+tP//RP5ff7devWLY2Njem7\n3/2uZmdnlc/nNTg4qOHhYf3whz/Ua6+9Zm6OZWVlevbZZ/XJT37SHGErKiqs+5JMJnXp0iUlEgnT\nlWbwuLq6WkNDQ6aY9fzzz5txy+DgoNrb282HAiWu3d1d9fX1aWlpybqjjzzyiHZ3d/XSSy/p1q1b\ndjGpqKhQMplUNBrV6dOndfXqVZtXYQagqalJfX19GhsbkySdOnVKs7OzGh8ft0FiXEupoSTpzJkz\n2tjY0OjoqKmPDA8P6/r16+rp6VE4HDbzntdff93mSAYGBqx72tzcrLq6Ov38z/+8DaoPDQ2pvb1d\nkUjEOilDQ0MKBoPq6+vT8PCwXn/9deu2LCws2P7g8nf16lW9+OKLOnfunAYGBrS1taUf//jHGhwc\nNHCFGQa/32+eCfcjfurA4HsZly9fzj/33HM2oVtWVmYudOhBFt+UkXpjce3t7SmTyVgBxIALg3vo\ncILcRKNR01YNBoPWukJXlvYTFAJQOqypKRy2traMroAmLKhx8Q0WWgmTwgxnIWnGe4vFYlpaWjJN\nzKamJqVSKbvRYmKAvjSIDpJWDQ0Nunv3ro4dO6bt7W1zwkPLuby83MT2GRREugj5HxCi2tpaUyHA\nqQjkieEj5PAwhADF3tzcNPUGqCQUvDs7O2b5in4wNrIbGxsKh8NGtfjOd75jU7pYFuO219HRYTa6\nSOsx3AQCDa1ic3NTPT09dsB2d3fr7bffPiKoT6uNNUJLlfdaUVFhN2vk+1AmgQLCs0QnOH6odY3s\nUiAQMElGNLsl2b8NBAJKp9Pa3983RID3s7q6aoNiCPyD4tAGDwQCtp7y+bzC4bDGxsYsAbMfaBkj\nYzU1NXUE5YQ2QDu7WMKutrbW3KBqampsMAfXKroN75abqqiosM8dndnW1laTN0PqEOF+ujagLUjr\nHRwc6PXXX9fTTz9tzpRIKiIFx+cBmsfz4nWBspSUlNhwEF0AXLuQU2tsbNTMzIx8Pp85aXFxhu6C\nMyU0lGLdU1AWChQQYr/fb26DaBkjR9fU1GQ/C+MHNKtLSkrsdRU7tGFqQ56C/lZTU2NFCdrudFGQ\nIis2peHzm5ubs9zBs6HrwhARHRdcB8nV1dXVNgCKNrok60AgnYht9Pb2ttm+o9vM4Nji4qLRhpAA\n433QssUe/Zvf/KY+8YlP2OBtSUmJ6S3ToUJ+cGpqysy20LCmQ4cBEagl37+pqUkzMzPq6urSyMiI\nvR+6TuXl5XIcRxsbG6bzXYw8g5revHnTunoYeg0MDOj27dtmCkJbHNMo5DLpAPA5rq6u6sSJExoZ\nGTmSy5eXl+U4jtLptNbW1rS/v28DacWWxuR2On1IjUH5QeYVKTh8DABPABz8fr/JiSKLhwSk3+/X\n2tqaTp8+rVQqZWYrDKGvrKwYCIHtNHrG6FWDBAM0YLqUyWSsM4JVO7kuHA5rY2ND3/jGN/T8888r\nl8spGo1qb2/viGEH3UU0yAFoOIvC4bA55q2trVmnJB6P6+2337ZnyXAfWurkRp4dOQRXPFBznGup\nHRzH0a1bt2zthkIhu4wgNwhFiL0BEAfwRLd1cXHR6hSkP5ElZT2B5LKnY7GYNjY27Ozgc5dkXXhc\nJZHkg46GhKHf7zeHQfI8nhpcaPGbaG1tNSfcYllguu9c0FZXVy2X19TU2PnI84bqiFQi9Qb5DeOj\nxsZGPfroo/riF794XzjP/y/I830NLDdp2/ChdHd3H0nUmUxG7e3tOnnypEmUgYTQUqa9D+cwEAgo\nGo1aoQG6g9wR+rhImtTV1RnvGR5SNBpVPB43rk59fb1RBOA+9/b22iSo3+9Xb2+v1tfXtbS0pMrK\nSj300EOGmtA6LysrUzKZVHd3t3w+n8ltBQIB0+GlTcVFob293Q4Sn89nnFMKdbiJoD2xWMx0S9H6\n5d+QCNC9jkQi9l7gP/t8PkWjUTU2NioWi6mrq8v0QNvb2xUIBKyYh5fFxj527JglZtr0iURCpaWl\nRgsgwQQCAWtBESDjFFMgRbSloXCgjUmxQOuup6dHVVVVevLJJ42f+uijjx5p7+zv7xvSBIfP7/er\nra3NfnHIww+XZO1fnMwSiYQl0sbGRp06dcoO2EAgoMcff9w48vBvKQobGhrMXQ40n9YTyAqtSJAy\nrHlxYeM5tba2qq+vz9BkDh9u6tFoVI7jqKysTCdOnFA4HFZTU5NxR3t6eozLXV1dLalQBLa1tam7\nu/sIpers2bOWmNAuhadXWlqqeDxubVha8uj5sr9ABXZ3d1VXV2dFTEVFhTo6OlRaWqqenh5ls1lF\nIhFJMhoSclNccPP5vHUNQJ2j0aiZ3lBwQa+hwETt4Omnn7aiPZlMqqurS8FgUOFw2CypGxsbrYiR\nCgUGFyYuNCCLfr/fnms8HlddXZ1OnjypbDarCxcu2EGC+xvr7uzZs/a6tre31dnZaVrFIDRQgAYH\nB5XP51VbW2vtbtqh5eXlikQiRlOTZBxzCilJSiaTlt9CoZBZSaNRn8vlDBnmcAR8YAIeXuXg4KBJ\nEaJDyyWVDltXV5dOnTqlSCSi0tJSOY6jY8eOWSeAHFZXV6f+/n67BIGkQ0sopsdJBX43BW1nZ6dR\nB9iDfX191s0obpFzwdzf37dLfG1trXUnHnroIT3++OOqqqrS8PCwJBl1sL29XaFQSDs7O3r88cd1\n4cIF45ljtIWiEegj7X9mVbh04w/ApRk648HBgXZ2dnT+/Hk5jqP6+noNDg7anEBHR4cGBgbsEtTc\n3CzHcSRJPT09prBUrF/d2dmpeDyuhoYG1dfX25ptampSZWWl6urq7DWyvoeGhtTV1WUdT3j3AwMD\nGhwcNAUi+Letra1qbW1VMBjUM888Y855zF1AxTx16pSqqqqM5sE5DReX7m9TU5MSiYRxWrmgcRHr\n7e1VKBQyGkg8Hpcky69nz55VZWWlXdjRDceMifmDmpoaOY4jx3G0vb2tsbExU0upr6/Xww8/rHPn\nzpkOPIoU1CE8J2agotGoUePa29ttdmRwcNDWaiQSUTAYtFzLuVRWVqZoNGpnJvmwoqJCFy9e1N7e\nnhKJhHw+n2KxmEKhkJqbm5VIJGzuKxwOW02DzObGxobJmWJqdHBwoPPnz6uqqkqRSEQlJSVqa2tT\nXV2dHMcx4AGgkU4DyDG5cGJiwuaE4Jhz3jU1NVl+a2lpMepUKBQyRZxEIqFYLKb29nadOHFC4+Pj\npkvOHgWQQQULBZSOjg4z/WJvssb9fr/RpO5XPHCHwW9+85s2hID5AzbX8HThPmMFiwPa+vq6WXjj\nxAaHjeQFQspNH9QHJ0Ic5Gi1kVyRL4PbxvfjZgTJnQUFKgL/FSF+UDnMPHZ2dgw1xsABORyk2jDg\ngIOZzWbtlonLFc8LZGp7e1vxeFwLCwtaWlpSQ0OD1tfXDU3hsITjjS0rX8PRCamikpISk4DjmYNe\ng7Bx2+a1cGuHc4R7Ekgkt0OGgXAY5FZMO/xTn/qUXn31VTuI4HfTVeA10a7ieVLw496FTN7i4qIl\nbdd1rYNQ7ELIQVeM1jNgwYEAUghSAD+LjgE3Ytz7sGqHn8jnD6LD4BPmK6DHoJXFQ7OYteBiRdGw\ntrZmBeT+/r5ZziOLVzxTkE6ntbW1ZQ5aoLFYT0uFlimfO6Y45eXlxpvmdaEryq0+m83K7/eb+yId\nGknmVlleXm525rwXnMl4hsVW1ewHzGw+8YlP6Otf/7rtEZBi/j2GN6zx2dlZ4yyD8lPsHxwc2Dqg\n84CLIzzHyclJQ9J4DSAaSMa9mzvNHoXjt7q6asNhxW6K8/Pztq9Y48VOjKwndE/Ja+x5pM54TexP\nkFo6OritsSdAY3CSwzwAK2iMeeAi8kxZt9CqOMz29va0v79vKOzGxoYhfKxjCkJMdnK5nDn+lZWV\nKZPJ2OfW2dmpqakpc2ottktnGLQ43xwcHOjFF1/U7/7u7xqlje4M+Yd9t76+bmuCnM3FjX3LOiSP\nY/ICoj4zM2PW2z6fzy4YoKXoteOsSSegubnZzKKK3V3J7+x19gqdPPZ/SUmJMpmMmTQtLy8rmUya\nkQ3rhQstJj68P1BozqOlpSXL08i2gkaTK9Eypyu7vb1tfw+zq2w2q/n5efsMObfpJPAe+OzJuyCN\nOzs7Ju3Gv8MsiktIPp/X2NiYtre3zSEUWmHx2Q9YxczL888/r69+9avWNZRk2t7w8OmyAuZIstcL\nmMVAMXVK8WwNCCloKeZpCwsLtl+hQlFj4KSLuybft66uzkQEyB88v+JOKV9bXFw0cx2oiTxb1hPd\nI/YmdQ97G0EF+OhI0LL+QMjZcz6fz9Bz9jkDfHQr6UZyzkAPpZuAuVdpaakymYyy2ewRq++VlYLW\nBIOzrDvclPkMirtnc3NzWl1dtcFk6iccXtlDSMfeD4fBn6a28Z4HQ2csIIje0k/Qx52dHXObKr7Z\nk5zX1tZsgtvv99shQduEobVoNKqlpSVDSospFQw7UezQWimeSOVQIbFCeWDTBoNBLS0taWlpScFg\n0A4ykgqJBc1IkFQGFhimYlCJAj0QCJj+KwNlFCdMJOP0EwwGbQCFBMjAIZPG/FyURWidsIEbGxuN\nl8aG2dzcNFrL7u6uJRVJhqqC5kiFREbByy2WljNtGlB0EjwXC0nWcuIzKbbzxU7X5/PZwGTxIUjh\nK8m6Dig51NfXKxAIaHNz027zICEUoLiG0f4vdgAE7a6trTVnNhAQ2u/Fw4JLS0tG1SFJVVVV2TPc\n2toy9QxsVFl/JA5QVw4y1nVdXZ2tV9YMSFs6nTa6BuhrdXW1dQgYpMnn82ZKQquZg6u6utrUZTo7\nO4/YknMpggbE+1tdXbXhK6bO6+rqjEdXrBsOQs1FqJi2RWFbTJWQZB0gbO7RByWxUiQXF7lM5vPc\nWH9cQLi8oLLD4erz+VRZWan6+no7UED5Ga7K5XK2lhmEYY9JsosLhTmfN0NePp/PLlPkNRA82tNo\n0UuyIR9eO3mJIbqGhgYbdgJkKCkpMbRakpqamuzvUOhRaJIbQNT4edhko94wPz9vLXsQUqgm6ITz\nnriIw4WUZJcPBnuhhQUCASuMyYnV1dVmXsTfhc7GpY/hJg59hiOLNdsZ+GK4kPVWVVVldD2eGUog\nxT8P6lXxnoJ2s7e3Zwg8Fw0KQt4jHT+oXAsLC0omkxoZGe/Ge4gAAB6LSURBVDH6IFbvUPwikYjN\nSXBphIYCWCDJ0Gu0o/lMWDP8fPIpFApyGMN38ENZ62ioU5iz54tpQhRTDCeDqMNr5yxhiJEzga4q\nQ990H1pbWw0JZp3z2lhfAFOY+jAwybAs6wxKBUU1r4OhNM4AirKWlhZbK4FAwC6fgGzQ+DhrOB9x\nguSZUqjxXvf3963gp/ZgzoZ9x0BjXV2dSktLDSQjB3NucA7yvDhvoLCS24pd9sih7LPq6mobIufZ\nFgM3/HvyA2dSMpk0oJDzjvwViUSODNLCp8czg1xbX1+vpaWlI+dXTU2N1R+SDLxh37e0tBg4xaA1\nZxoXcrqJ/MyDgwM1NjZa/qdgv1/xwGkbUsFOElI47RaGYsrKymxCGaONsrIyxeNx9fX1WZswl8up\np6fHEBG/36+trS2Fw2E7CJEwotAheTJdytBNMBi0TcIQEgUzMm/FPJuhoSG7xTERu76+bpuNyemG\nhgYFg0FLvsisBQIBU7zo7u42/qDf71coFDKZN54RKgUMFBa3uuBAkjj9fr8VXRTUqHs0NDRofn7e\neLiO49gtlwKjmBvGQdTb22s3XGx4oVXAj2ZqHJoLhzgFJBzOra0t+y+UAEk2TQsHuKGhwdwMSZzQ\nclA6od3f1tam4eFhtbS02GQziYaip7e31xBSv99vSiodHR1qaGhQd3e3HfrQDeDNoV4Aghk/tFSG\nt4rN6NbWljo7O3X8+HFTkqH46+zsVC5XsL5m6CsQCCibzaq7u9uspWOxmObm5syeleI5HA5bQuX1\n4PQ0Oztrraqqqio1Njaqr6/P/h8kJhQKWeuusbHRLhEcaCR3LnccMo2NjUcm3iVZgUUblXWbSCRs\nXcUPp/uhR8GphoPKkFFtba1OnTplLTdaieQF6EfQuoqpClLhYnD+/HlbFz6fzw4DWoa0UDs7O21w\nEnSWn1NbW2tSjqxjJvuhmTBkxUHDn8diMSWTSZPg6ujoUDKZNDrQ4uKi/H7/kdeGUkdzc7O1bZES\nw/yptLRUHR0d6urqskKGgptDo62t7UhXjQN1d3fXhoxjsZjxvskhPT091raHFw6NCFOilpYWzczM\n6MSJE4ZWU0DyWdXV1SmZTKqxsdGUj2KxmEmCUcRz2QYAAP3lgga9hsICKhY8eRQTJFlhWl5ermg0\nakPIFDFDQ0Pq6emxIlCSUbEikYgBARQWpaWlOnHihNErSkpKdP78eXufTU1N6unpUTKZVC6XU3Nz\ns06fPm2FFZ0lSXYBZNYChYW6ujrdvn1b0WjUipDi4ojuSFVVlUKhkGKxmAKBgI4fP27dg2w2a59P\nWVmZGU4hldfS0qLe3l5Td0Hpo729Xfv7+1paWlI0GrWijzmURCKh3d1dxQ/lNRnyKlbyQNHq+PHj\ntl4441ibwWDQ1BAAm+rr622f8TXOrIODA929e9dofPxblEqKO4ycl8ivocI1OjqqWCwmSfY6kLCr\nqKiwfN3Z2WkFK2cNxer+/r5dWqCPoMYVCoWs+G5ubtb+/r5isZgSiYSSyaTNhEDr5HLa399vHH/W\nVnl5uZLJpFpaWjQ8PGw5ASpWIpGwz5i9WFtbq66uLnut5Af2VlNT0xGVpxMnTpgSDfuxvLxcfX19\ntvfJW3T/ARz5Ply0JycnVVNTYzVPW1ubmpubTZaVGSjoe5wLPp9PTU1NRqspzoV8xvFD2dKqqio5\njmOfR1NTk12kAedqa2tt3XZ1damzs9PWFXseBSooRslk0jrw9yMeOG3jj//4j61NB98X2TUoETU1\nNRoYGNDly5eVSqXMgSqdTpu3fHt7u2ZnZ+2QWFlZMT3b4k0xNjZmMihonjKRD5pN+6WsrMwUBWiR\n8f1bWlpsU4yNjZm7H+T2eDyu1tZWraysaGRkREtLSzb9TVHC8MrBwYEuX76sUCikyclJJRIJpdNp\nG7La3983n/fW1lb7uRMTE5YouTzQ9okf6skyuAHXcWFhwQpdBshA2BkyYDPk83krrhcXF23hrq2t\nHRkq7OjoMCRjcHBQIyMjmp6ets2IHBIDjyhpBINBaxPS8q6srNRHP/pRffvb3zZ0VZJ1FtCepLUG\nAkSC4XuPjIzYf6ETLCws2O0VxAJ6AzQLWv4rKyvGVeNmPDMzo4aGBmuVgubduXPHLjpTU1Oan583\n1HJ1dVVjY2NaX1+3gpKfx0AFOqAMm05NTdkBND8/r3g8brqnPOetrS0lk0lNTk5KktEypqamrCXP\noYEmOG1lkMHFxUU7oNEa5kAF1SumB1Ecrays2DBdsYxZMplUKpVSXV2dMpmMvV7aygsLC1agskdj\nsZgqKyuNWsPzSafT9jlhc//SSy/pK1/5ij3fsbExVVdXm7tXKpWyFl4mkzHXK5yvpALvlxzDmlxf\nX9fk5KRisZi1XXHH3NnZ0dDQkG7evGlDSaAh2WxWmUxGPT09xhmemJiwZ0YOymQy2t7etkGwa9eu\nmcOfJENwNzY2jDaBDOfY2Jja2tps3ZWXl5tr6NramhXK0I7ID8XdF15HbW2tcRLRe0+n09YdmJ+f\nN+SJqfT9/X1bj/Pz8zaQdu3aNQ0ODiqVSikej2tiYkKLi4smQTYxMWF88draWt24ccM+I9SPcrmc\naUkjN/rwww+bO+v8/LzxOFtbW3Xt2jVVVlYaD5ghwBdeeEF/9md/ZtQzcuzy8rI5qt65c8d0j4uH\nGVdWVtTQ0KDW1lZrSUPxkKSJiQktLy9rZ2dHo6Ojqq6uNne96elpZTIZhUIhua5rmvHksVAoJKlw\n0Hd3d+vmzZuWq0DqT506pdHRUUNNaafTegYYunPnjhVzqVRKgUBAvb29mpycNL3/yspKk3fz+Xym\nnQ4dDkRxcnJSc3NzpnzDGQoNobS0VBsbG9rZ2dHCwoKi0aiuX7+u5eVlG6yjQ3ft2jXNzc1pe3vb\n8sPy8rLphWcyGaXTaUN0yXlSQenm9u3bBjLhPcBaRxlpZWXF8hWIPM94bW3tyCDo9va2ksmk3nnn\nHX3sYx/Tt771LVvnXETX1tYUjUY1Pz9vMz+xWMxoektLSzZn0tPTo+XlZS0sLJhCyNjYmHUCULAa\nGxvTzs6OxsfHtb29rVQqdcRdEJ12zlzXdW2Pkhf39/dt4BppunQ6bV0gVMJQ+AmFQrpx44Y9v0wm\nY3kWZS7chcnFWHjv7u6ajj1ACW66U1NTRvusra3VnTt3zPq8s7NTi4uL5t5869Yto5yh5nH9+nXV\n1NRodnbWQBnooBMTE9re3jbKCh4fi4uLNpuSzWY1Ozuru3fvanh4WHfu3FFZWZkNsebzeS0sLGh5\neVmxWExXrlzR7Oys1UtoP+PoSTeJGvPcuXP3hbZxT2objuNclrR6+NtxSb8m6auSDiRdd133C4d/\n77OSfkHSnqRXXNf9/b/q+16+fDn/zDPPWHtHkvEEo9GoVldXjYzPh09LGivu27dv24BVU1PTEXkt\nSUeoGyQLWvJIU4Fera2tGTJXbMLBRCwoKe0kbmkkBPhaiMPTtkZrFA4jFAISGrd7eKBwiUCGJNlF\nAkRcKrTjeCbYR1PsQwWAioBEHhxlbv7w8kDAMbyglQOqBeJKAgMtZQijtLRUOzs7SiQSJigPp5PC\nl8FHDic2oiRrI9XX1+vVV1/VRz/6UdM6hoO6vb1tU8UgNM3NzcZtLi0tNavVUCikVCql3t5ejY6O\nmhzh1atXDbmnkKQ9VlJSYjqQjY2NSqfThnrs7u7apap4Kp12Oa293d1d08mFMhIKhXTr1i1bL1Ay\nlpeX7UCDjsQwEwUU7VYKSdD9qqoqSzqhUMgKIHhgKC0w7U/RxfOsqamxCxCHLYoDe3t7Nsi0vr5u\ng5yrq6uGNhcXGhQDSPlBheFnNzY2mnnLwsKC0ZsY9OOSASLCYBt2vVySvvOd7+hnf/ZnDR2cn5+3\n7gc8fRDckpISKxDZ8xTM0G/4PHK5ggX11NSU5SEE/JuamqxgAsmsqKiwZ0BhwGWMghX0HnoDa5d2\n9q1bt+xQAb0FpcPAAO4y9C1yDYcC/EVMhjCxgUZQrHQDnYZnL8k0uqHvcPmD/kArF0QUlZ1cLqf2\n9natr6/bsBFteA5q9it5oaWl5UgrnYHYSCSinZ0dU8xxHEeZTMaMDejgVVZWyufz2QWYToPP59Pv\n/d7v6cUXX9Ts7KxKS0vtPIECCOe4vr5eV65csQ4K/ONwOGzzDFA3ii8QKAicOnVKly9fPkKxgtvd\n39+vqqoqvf3228azJoeCjl27dk25XE4tLS32+kAuFxcXjbrAet/a2lJjY6NRt+CDYrCDWQata86G\nSCSiiYkJy9/BYFDpdNo6YnyeUKQwA0MXfmVlRfF43FSLyHkUocUqPm1tbdrd3dXc3Jw9V6kwO9HS\n0qKdnR2dPHlSruseOcN4f+FwWK7r2nA2FCRmFfB3cF3XLpnhcFjpdNpoRFArUYLinP/yl7+sF154\nQZubm0okEka9gT7EfoHqx5xAZ2en5ubmVFlZqZaWFs3Pz9vzYRDt6tWr2tjYMFSavM1Zvb29bfSd\nsrIy04fn+zJwK8nOV8dxNDIyYtQzOhXk3a2tLXV3d5ss6fj4uA4ODrSxsWF7XpKpG0HX4NxiqBzK\nBMU7neV4PK7FxUUrnqGywoVHY3l1ddXk76ARbW9vq6amxmhGnKHke7r0dFLJy1ChdnZ27LwCoNjZ\n2bH6B1CGrjU1Ax1N5l+K818xVxzw4PHHH9cXvvCF90dtw3GcKklyXffJw1+fkfSbKmg5PyGp1HGc\nFxzHCUr6oqTzkp6V9OuO41T8tO9fWVmp5eVlawvzX4pRbnfwQ6empuyWND09bVwiDi7aP2xSbsvA\n93APaf3Da2UQgg8AeRiQKegDxXI3UABu375t06b5fF6ZTEYtLS3WrkilUjYsAueaAQJMCyYmJmxg\nhyITRImCDA4iyYXijkl/ENLKyko1NDQcGS7j9SNDBtJLy1SSIWS8N4oNCjkSPhcLtLBxpaLtz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"text/plain": [ "<matplotlib.figure.Figure at 0x11df54f10>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "no loop\n" ] }, { "data": { "image/png": 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nQz53NptFvV5HJpORvcW/TYSPgY/XxvVMig0TQaIVXKPkFjJxYjAzmUzI5/OC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"text/plain": [ "<matplotlib.figure.Figure at 0x11e873ed0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "distances = [dists, dists2, dists3]\n", "names = ['two loop', 'one loop', 'no loop']\n", "\n", "for distance, name in zip(distances, names):\n", " print(name)\n", " plt.imshow(dists, interpolation='none')\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Inline Question #1:** Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)\n", "\n", "- What in the data is the cause behind the distinctly bright rows?\n", "- What causes the columns?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Your Answer**: *fill this in.*\n", "\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:09:57.493364", "start_time": "2016-08-22T12:09:57.316231" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Got 137 / 500 correct => accuracy: 0.274\n" ] } ], "source": [ "# Now implement the function predict_labels and run the code below:\n", "# We use k = 1 (which is Nearest Neighbor).\n", "y_test_pred = classifier.predict_labels(dists, k=1)\n", "\n", "# Compute and print the fraction of correctly predicted examples\n", "num_correct = np.sum(y_test_pred == y_test)\n", "accuracy = float(num_correct) / num_test\n", "print('Got {} / {} correct => accuracy: {:.3f}'.format(num_correct, num_test, accuracy))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:09:57.672977", "start_time": "2016-08-22T12:09:57.495075" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Got 137 / 500 correct => accuracy: 0.274\n" ] } ], "source": [ "# Now implement the function predict_labels and run the code below:\n", "# We use k = 1 (which is Nearest Neighbor).\n", "y_test_pred = classifier.predict_labels(dists3, k=1)\n", "\n", "# Compute and print the fraction of correctly predicted examples\n", "num_correct = np.sum(y_test_pred == y_test)\n", "accuracy = float(num_correct) / num_test\n", "print('Got {} / {} correct => accuracy: {:.3f}'.format(num_correct, num_test, accuracy))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:09:57.843035", "start_time": "2016-08-22T12:09:57.674591" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Got 139 / 500 correct => accuracy: 0.278000\n" ] } ], "source": [ "y_test_pred = classifier.predict_labels(dists, k=5)\n", "num_correct = np.sum(y_test_pred == y_test)\n", "accuracy = float(num_correct) / num_test\n", "print 'Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You should expect to see a slightly better performance than with `k = 1`." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:11:01.595264", "start_time": "2016-08-22T12:09:57.844559" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Difference was: 0.000005\n", "Good! The distance matrices are the same\n" ] } ], "source": [ "# Now lets speed up distance matrix computation by using partial vectorization\n", "# with one loop. Implement the function compute_distances_one_loop and run the\n", "# code below:\n", "dists_one = classifier.compute_distances_one_loop(X_test)\n", "\n", "# To ensure that our vectorized implementation is correct, we make sure that it\n", "# agrees with the naive implementation. There are many ways to decide whether\n", "# two matrices are similar; one of the simplest is the Frobenius norm. In case\n", "# you haven't seen it before, the Frobenius norm of two matrices is the square\n", "# root of the squared sum of differences of all elements; in other words, reshape\n", "# the matrices into vectors and compute the Euclidean distance between them.\n", "difference = np.linalg.norm(dists - dists_one, ord='fro')\n", "print 'Difference was: %f' % (difference, )\n", "if difference < 0.001:\n", " print 'Good! The distance matrices are the same'\n", "else:\n", " print 'Uh-oh! The distance matrices are different'" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:11:01.931864", "start_time": "2016-08-22T12:11:01.596739" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Difference was: 0.000005\n", "Good! The distance matrices are the same\n" ] } ], "source": [ "# Now implement the fully vectorized version inside compute_distances_no_loops\n", "# and run the code\n", "dists_two = classifier.compute_distances_no_loops(X_test)\n", "\n", "# check that the distance matrix agrees with the one we computed before:\n", "difference = np.linalg.norm(dists - dists_two, ord='fro')\n", "print 'Difference was: %f' % (difference, )\n", "if difference < 0.001:\n", " print 'Good! The distance matrices are the same'\n", "else:\n", " print 'Uh-oh! The distance matrices are different'" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:12:31.039800", "start_time": "2016-08-22T12:11:01.933326" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Two loop version took 28.415438 seconds\n", "One loop version took 60.432901 seconds\n", "No loop version took 0.225960 seconds\n" ] } ], "source": [ "# Let's compare how fast the implementations are:\n", "def time_function(f, *args):\n", " \"\"\"\n", " Call a function f with args and return the time (in seconds) that it took to execute.\n", " \"\"\"\n", " import time\n", " tic = time.time()\n", " f(*args)\n", " toc = time.time()\n", " return toc - tic\n", "\n", "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n", "print 'Two loop version took %f seconds' % two_loop_time\n", "\n", "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n", "print 'One loop version took %f seconds' % one_loop_time\n", "\n", "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n", "print 'No loop version took %f seconds' % no_loop_time\n", "\n", "# you should see significantly faster performance with the fully vectorized implementation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cross-validation\n", "\n", "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:12:31.061752", "start_time": "2016-08-22T12:12:31.041193" }, "collapsed": true }, "outputs": [], "source": [ "def run_knn(X_train, y_train, X_validation, y_validation, k):\n", " # initalize KNN\n", " classifer = KNearestNeighbor()\n", " # train the classifer on training set\n", " classifier.train(X_train, y_train)\n", " # get distance for X validation \n", " dist = classifier.compute_distances_no_loops(X_validation)\n", " # make prediction based on k\n", " y_pred = classifier.predict_labels(dist, k=k)\n", " # get the number of correct predictions\n", " num_correct = np.sum(y_pred == y_validation)\n", " # score the classifer\n", " accuracy = float(num_correct)/len(y_validation)\n", " \n", " return accuracy" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:13:03.291009", "start_time": "2016-08-22T12:12:31.063626" }, "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "k = 1, accuracy = 0.263000\n", "k = 1, accuracy = 0.257000\n", "k = 1, accuracy = 0.264000\n", "k = 1, accuracy = 0.278000\n", "k = 1, accuracy = 0.266000\n", "k = 3, accuracy = 0.239000\n", "k = 3, accuracy = 0.249000\n", "k = 3, accuracy = 0.240000\n", "k = 3, accuracy = 0.266000\n", "k = 3, accuracy = 0.254000\n", "k = 5, accuracy = 0.248000\n", "k = 5, accuracy = 0.266000\n", "k = 5, accuracy = 0.280000\n", "k = 5, accuracy = 0.292000\n", "k = 5, accuracy = 0.280000\n", "k = 8, accuracy = 0.262000\n", "k = 8, accuracy = 0.282000\n", "k = 8, accuracy = 0.273000\n", "k = 8, accuracy = 0.290000\n", "k = 8, accuracy = 0.273000\n", "k = 10, accuracy = 0.265000\n", "k = 10, accuracy = 0.296000\n", "k = 10, accuracy = 0.276000\n", "k = 10, accuracy = 0.284000\n", "k = 10, accuracy = 0.280000\n", "k = 12, accuracy = 0.260000\n", "k = 12, accuracy = 0.295000\n", "k = 12, accuracy = 0.279000\n", "k = 12, accuracy = 0.283000\n", "k = 12, accuracy = 0.280000\n", "k = 15, accuracy = 0.252000\n", "k = 15, accuracy = 0.289000\n", "k = 15, accuracy = 0.278000\n", "k = 15, accuracy = 0.282000\n", "k = 15, accuracy = 0.274000\n", "k = 20, accuracy = 0.270000\n", "k = 20, accuracy = 0.279000\n", "k = 20, accuracy = 0.279000\n", "k = 20, accuracy = 0.282000\n", "k = 20, accuracy = 0.285000\n", "k = 50, accuracy = 0.271000\n", "k = 50, accuracy = 0.288000\n", "k = 50, accuracy = 0.278000\n", "k = 50, accuracy = 0.269000\n", "k = 50, accuracy = 0.266000\n", "k = 100, accuracy = 0.256000\n", "k = 100, accuracy = 0.270000\n", "k = 100, accuracy = 0.263000\n", "k = 100, accuracy = 0.256000\n", "k = 100, accuracy = 0.263000\n" ] } ], "source": [ "num_folds = 5\n", "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n", "\n", "X_train_folds = []\n", "y_train_folds = []\n", "n = X_train.shape[0]\n", "\n", "\n", "################################################################################\n", "# TODO: #\n", "# Split up the training data into folds. After splitting, X_train_folds and #\n", "# y_train_folds should each be lists of length num_folds, where #\n", "# y_train_folds[i] is the label vector for the points in X_train_folds[i]. #\n", "# Hint: Look up the numpy array_split function. #\n", "################################################################################\n", "indices = range(n)\n", "cv_indices = np.array_split(np.array(indices), num_folds)\n", "################################################################################\n", "# END OF YOUR CODE #\n", "################################################################################\n", "\n", "# A dictionary holding the accuracies for different values of k that we find\n", "# when running cross-validation. After running cross-validation,\n", "# k_to_accuracies[k] should be a list of length num_folds giving the different\n", "# accuracy values that we found when using that value of k.\n", "k_to_accuracies = {}\n", "\n", "################################################################################\n", "# TODO: #\n", "# Perform k-fold cross validation to find the best value of k. For each #\n", "# possible value of k, run the k-nearest-neighbor algorithm num_folds times, #\n", "# where in each case you use all but one of the folds as training data and the #\n", "# last fold as a validation set. Store the accuracies for all fold and all #\n", "# values of k in the k_to_accuracies dictionary. #\n", "################################################################################\n", "\n", "for k in k_choices:\n", " k_to_accuracies[k] = [run_knn(X_train[np.setdiff1d(cv_indices, subset)],\n", " y_train[np.setdiff1d(cv_indices, subset)],\n", " X_train[subset],\n", " y_train[subset], k) for subset in cv_indices]\n", "\n", "################################################################################\n", "# END OF YOUR CODE #\n", "################################################################################\n", "\n", "# Print out the computed accuracies\n", "for k in sorted(k_to_accuracies):\n", " for accuracy in k_to_accuracies[k]:\n", " print 'k = %d, accuracy = %f' % (k, accuracy)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:13:03.676813", "start_time": "2016-08-22T12:13:03.292793" }, "collapsed": false }, "outputs": [ { "data": { "image/png": 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6y2rSU5MYP7aYj9dsi+q60mZwPA6keKDZ5cuv01m/pZKiPp2zgl5DYzPBlFEk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\nk4G7ACLifuCEsmt9wEkR0Vc6biKbjToWmJZS+n5K6e5SsiX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"text/plain": [ "<matplotlib.figure.Figure at 0x117e03050>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot the raw observations\n", "plt.figure(figsize=(14,6))\n", "for k in k_choices:\n", " accuracies = k_to_accuracies[k]\n", " plt.scatter([k] * len(accuracies), accuracies, label=k, c=next(palette), lw=.25)\n", "\n", "# plot the trend line with error bars that correspond to standard deviation\n", "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n", "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n", "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n", "plt.title('Cross-validation on k')\n", "plt.xlabel('k')\n", "plt.ylabel('Cross-validation accuracy')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:13:04.175199", "start_time": "2016-08-22T12:13:03.678364" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " count mean std min 25% 50% 75% max\n", "1 5.0 0.2656 0.007701 0.257 0.263 0.264 0.266 0.278\n", "3 5.0 0.2496 0.011104 0.239 0.240 0.249 0.254 0.266\n", "5 5.0 0.2732 0.016829 0.248 0.266 0.280 0.280 0.292\n", "8 5.0 0.2760 0.010559 0.262 0.273 0.273 0.282 0.290\n", "10 5.0 0.2802 0.011323 0.265 0.276 0.280 0.284 0.296\n", "12 5.0 0.2794 0.012582 0.260 0.279 0.280 0.283 0.295\n", "15 5.0 0.2750 0.014000 0.252 0.274 0.278 0.282 0.289\n", "20 5.0 0.2790 0.005612 0.270 0.279 0.279 0.282 0.285\n", "50 5.0 0.2744 0.008792 0.266 0.269 0.271 0.278 0.288\n", "100 5.0 0.2616 0.005857 0.256 0.256 0.263 0.263 0.270\n" ] }, { "data": { "image/png": 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Hsn/3QX9by05N6XLHtbhdbnZuyeCj1z7HYrXw0At9qZBYHpfTxdiRE9ixeVdA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\nQgghREj4fzqDOBmTec8dAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11e74f250>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# eye ball the variance with a heatmap\n", "k_to_accuracies_df = pd.DataFrame(k_to_accuracies)\n", "plt.figure(figsize=(10,4))\n", "sns.heatmap(k_to_accuracies_df, annot=True, linecolor='white', linewidths=.005)\n", "plt.ylabel(\"Fold\")\n", "plt.xlabel(\"K\")\n", "plt.title(\"Cross-validation accuracy\");\n", "\n", "print(k_to_accuracies_df.describe().T)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2016-08-22T12:13:04.659166", "start_time": "2016-08-22T12:13:04.176928" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Got 141 / 500 correct => accuracy: 0.282000\n" ] } ], "source": [ "# Based on the cross-validation results above, choose the best value for k, \n", "# retrain the classifier using all the training data, and test it on the test\n", "# data. You should be able to get above 28% accuracy on the test data.\n", "best_k = 10\n", "\n", "classifier = KNearestNeighbor()\n", "classifier.train(X_train, y_train)\n", "y_test_pred = classifier.predict(X_test, k=best_k)\n", "\n", "# Compute and display the accuracy\n", "num_correct = np.sum(y_test_pred == y_test)\n", "accuracy = float(num_correct) / num_test\n", "print 'Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy)" ] } ], "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" }, "latex_envs": { "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 0 } }, "nbformat": 4, "nbformat_minor": 0 }
mit
datahac/jup
test/json-MN.ipynb
2
24464
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Parsing JSON \n", "http://python-guide-pt-br.readthedocs.io/en/latest/scenarios/json/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "json_string = '{\"first_name\": \"Guido\", \"last_name\":\"Rossum\"}'" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "import json\n", "parsed_json = json.loads(json_string)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Guido\n" ] } ], "source": [ "print(parsed_json['first_name'])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "## GA data" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'error': False, 'data': {'reports': [{'data': {'maximums': [{'values': ['1030', '50']}], 'minimums': [{'values': ['1', '0']}], 'rows': [{'metrics': [{'values': ['1030', '50']}], 'dimensions': ['/']}, {'metrics': [{'values': ['20', '0']}], 'dimensions': ['/2017-data-technology-trends/']}, {'metrics': [{'values': ['1', '0']}], 'dimensions': ['/7-easy-ways-to-make-your-webpage-sell-more-of-you-product/']}, {'metrics': [{'values': ['6', '0']}], 'dimensions': ['/careers./']}, {'metrics': [{'values': ['227', '0']}], 'dimensions': ['/careers/']}, {'metrics': [{'values': ['6', '0']}], 'dimensions': ['/Careers/']}, {'metrics': [{'values': ['1', '0']}], 'dimensions': ['/careers~~pobj.']}, {'metrics': [{'values': ['101', '0']}], 'dimensions': ['/case-studies/']}, {'metrics': [{'values': ['26', '1']}], 'dimensions': ['/case-studies/c24tech-swift-blockchain/']}, {'metrics': [{'values': ['14', '0']}], 'dimensions': ['/case-studies/cools/']}, {'metrics': [{'values': ['21', '0']}], 'dimensions': ['/case-studies/toothscan-dental-app/']}, {'metrics': [{'values': ['13', '0']}], 'dimensions': ['/case-studies/tp-link-partner-portal/']}, {'metrics': [{'values': ['22', '1']}], 'dimensions': ['/case-studies/travel-weekly/']}, {'metrics': [{'values': ['8', '0']}], 'dimensions': ['/case-studies/we-are-models/']}, {'metrics': [{'values': ['11', '0']}], 'dimensions': ['/category/blog/']}, {'metrics': [{'values': ['2', '0']}], 'dimensions': ['/category/blog/dev-blog/']}, {'metrics': [{'values': ['2', '0']}], 'dimensions': ['/category/blog/lean-innovation-methodology/']}, {'metrics': [{'values': ['1', '0']}], 'dimensions': ['/category/uncategorized/']}, {'metrics': [{'values': ['1', '0']}], 'dimensions': ['/contact-kyiv/']}, {'metrics': [{'values': ['14', '3']}], 'dimensions': ['/contact-us/']}, {'metrics': [{'values': ['2', '0']}], 'dimensions': ['/development-for-technology-companies/']}, {'metrics': [{'values': ['16', '0']}], 'dimensions': ['/en_us/article/google-wins-legal-battle-against-pro-trump-spammer-over-the-letter-g']}, {'metrics': [{'values': ['1', '0']}], 'dimensions': ['/funding-for-high-growth-ventures/']}, {'metrics': [{'values': ['40', '0']}], 'dimensions': ['/google-liar-ru-spam-in-analytics/']}, {'metrics': [{'values': ['1', '0']}], 'dimensions': ['/home']}, {'metrics': [{'values': ['2', '0']}], 'dimensions': ['/how-businesses-can-use-crowd-science/']}, {'metrics': [{'values': ['23', '0']}], 'dimensions': ['/incubator-rus/']}, {'metrics': [{'values': ['1', '0']}], 'dimensions': ['/java-developer/']}, {'metrics': [{'values': ['1', '0']}], 'dimensions': ['/jmb-app-is-now-available-in-appstore/']}, {'metrics': [{'values': ['2', '0']}], 'dimensions': ['/jobs/junior-software-developer-e-mail-support/']}, {'metrics': [{'values': ['2', '0']}], 'dimensions': ['/jobs/middle-python-developer/']}, {'metrics': [{'values': ['2', '0']}], 'dimensions': ['/jobs/office-manager-ceo-assistant/']}, {'metrics': [{'values': ['6', '1']}], 'dimensions': ['/jobs/project-manager-agile-pm-scrum-master/']}, {'metrics': [{'values': ['1', '0']}], 'dimensions': ['/jobs/python-developer-new-york-company-project/']}, {'metrics': [{'values': ['4', '0']}], 'dimensions': ['/jobs/python-developer/']}, {'metrics': [{'values': ['1', '0']}], 'dimensions': ['/jobs/react-js-developer-start-now/']}, {'metrics': [{'values': ['21', '1']}], 'dimensions': ['/jobs/ux-ui-designer/']}, {'metrics': [{'values': ['1', '0']}], 'dimensions': ['/maxim-brovenko/']}, {'metrics': [{'values': ['1', '0']}], 'dimensions': ['/new-exciting-market-niche-pre-travel-shopping/']}, {'metrics': [{'values': ['33', '0']}], 'dimensions': ['/news/google-g/']}, {'metrics': [{'values': ['3', '0']}], 'dimensions': ['/partner-portal-built-skein-wins-best-partner-programme-year/']}, {'metrics': [{'values': ['1', '0']}], 'dimensions': ['/pavel-kozda/']}, {'metrics': [{'values': ['1', '0']}], 'dimensions': ['/research-phase-tech-innovation-projects/']}, {'metrics': [{'values': ['1', '0']}], 'dimensions': ['/shalaev-dmitriy/']}, {'metrics': [{'values': ['5', '0']}], 'dimensions': ['/skein-announces-release-igostories-project/']}, {'metrics': [{'values': ['1', '0']}], 'dimensions': ['/skein-partnership-with-oil-industry/']}, {'metrics': [{'values': ['1', '0']}], 'dimensions': ['/skein-presents-at-open-data-institute-summit/']}, {'metrics': [{'values': ['2', '0']}], 'dimensions': ['/startContent']}, {'metrics': [{'values': ['5', '0']}], 'dimensions': ['/svitlana-surodina/']}, {'metrics': [{'values': ['3', '0']}], 'dimensions': ['/technology-business-mentoring/']}, {'metrics': [{'values': ['2', '0']}], 'dimensions': ['/technology-consultancy-and-team-formation/']}, {'metrics': [{'values': ['1', '0']}], 'dimensions': ['/terms-and-conditions/']}, {'metrics': [{'values': ['1', '0']}], 'dimensions': ['/ux']}, {'metrics': [{'values': ['21', '0']}], 'dimensions': ['/ux/']}, {'metrics': [{'values': ['14', '0']}], 'dimensions': ['/ux/ux-analysis-result/']}, {'metrics': [{'values': ['25', '0']}], 'dimensions': ['/what_we_do_lean_innovation/']}], 'isDataGolden': True, 'totals': [{'values': ['1775', '57']}], 'rowCount': 56}, 'columnHeader': {'metricHeader': {'metricHeaderEntries': [{'type': 'INTEGER', 'name': 'ga:sessions'}, {'type': 'INTEGER', 'name': 'ga:sessionsWithEvent'}]}, 'dimensions': ['ga:pagePath']}}]}, 'msg': 'Retrieve'}\n" ] } ], "source": [ "import json\n", "\n", "#загрузить из json\n", "with open('data/SKEIN_test.json') as file: #открываем файл на чтение\n", " input0 = json.load(file) #загружаем из файла данные в словарь data\n", "print(input0)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "'dict' object is not callable", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-29-bb0059b882af>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput0\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\\"data\\reports\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: 'dict' object is not callable" ] } ], "source": [ "print(input0(\"\\\"data\\reports\"))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'reports': [{'columnHeader': {'dimensions': ['ga:pagePath'],\n", " 'metricHeader': {'metricHeaderEntries': [{'name': 'ga:sessions',\n", " 'type': 'INTEGER'},\n", " {'name': 'ga:sessionsWithEvent', 'type': 'INTEGER'}]}},\n", " 'data': {'isDataGolden': True,\n", " 'maximums': [{'values': ['1030', '50']}],\n", " 'minimums': [{'values': ['1', '0']}],\n", " 'rowCount': 56,\n", " 'rows': [{'dimensions': ['/'], 'metrics': [{'values': ['1030', '50']}]},\n", " {'dimensions': ['/2017-data-technology-trends/'],\n", " 'metrics': [{'values': ['20', '0']}]},\n", " {'dimensions': ['/7-easy-ways-to-make-your-webpage-sell-more-of-you-product/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/careers./'], 'metrics': [{'values': ['6', '0']}]},\n", " {'dimensions': ['/careers/'], 'metrics': [{'values': ['227', '0']}]},\n", " {'dimensions': ['/Careers/'], 'metrics': [{'values': ['6', '0']}]},\n", " {'dimensions': ['/careers~~pobj.'], 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/case-studies/'], 'metrics': [{'values': ['101', '0']}]},\n", " {'dimensions': ['/case-studies/c24tech-swift-blockchain/'],\n", " 'metrics': [{'values': ['26', '1']}]},\n", " {'dimensions': ['/case-studies/cools/'],\n", " 'metrics': [{'values': ['14', '0']}]},\n", " {'dimensions': ['/case-studies/toothscan-dental-app/'],\n", " 'metrics': [{'values': ['21', '0']}]},\n", " {'dimensions': ['/case-studies/tp-link-partner-portal/'],\n", " 'metrics': [{'values': ['13', '0']}]},\n", " {'dimensions': ['/case-studies/travel-weekly/'],\n", " 'metrics': [{'values': ['22', '1']}]},\n", " {'dimensions': ['/case-studies/we-are-models/'],\n", " 'metrics': [{'values': ['8', '0']}]},\n", " {'dimensions': ['/category/blog/'], 'metrics': [{'values': ['11', '0']}]},\n", " {'dimensions': ['/category/blog/dev-blog/'],\n", " 'metrics': [{'values': ['2', '0']}]},\n", " {'dimensions': ['/category/blog/lean-innovation-methodology/'],\n", " 'metrics': [{'values': ['2', '0']}]},\n", " {'dimensions': ['/category/uncategorized/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/contact-kyiv/'], 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/contact-us/'], 'metrics': [{'values': ['14', '3']}]},\n", " {'dimensions': ['/development-for-technology-companies/'],\n", " 'metrics': [{'values': ['2', '0']}]},\n", " {'dimensions': ['/en_us/article/google-wins-legal-battle-against-pro-trump-spammer-over-the-letter-g'],\n", " 'metrics': [{'values': ['16', '0']}]},\n", " {'dimensions': ['/funding-for-high-growth-ventures/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/google-liar-ru-spam-in-analytics/'],\n", " 'metrics': [{'values': ['40', '0']}]},\n", " {'dimensions': ['/home'], 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/how-businesses-can-use-crowd-science/'],\n", " 'metrics': [{'values': ['2', '0']}]},\n", " {'dimensions': ['/incubator-rus/'], 'metrics': [{'values': ['23', '0']}]},\n", " {'dimensions': ['/java-developer/'], 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/jmb-app-is-now-available-in-appstore/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/jobs/junior-software-developer-e-mail-support/'],\n", " 'metrics': [{'values': ['2', '0']}]},\n", " {'dimensions': ['/jobs/middle-python-developer/'],\n", " 'metrics': [{'values': ['2', '0']}]},\n", " {'dimensions': ['/jobs/office-manager-ceo-assistant/'],\n", " 'metrics': [{'values': ['2', '0']}]},\n", " {'dimensions': ['/jobs/project-manager-agile-pm-scrum-master/'],\n", " 'metrics': [{'values': ['6', '1']}]},\n", " {'dimensions': ['/jobs/python-developer-new-york-company-project/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/jobs/python-developer/'],\n", " 'metrics': [{'values': ['4', '0']}]},\n", " {'dimensions': ['/jobs/react-js-developer-start-now/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/jobs/ux-ui-designer/'],\n", " 'metrics': [{'values': ['21', '1']}]},\n", " {'dimensions': ['/maxim-brovenko/'], 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/new-exciting-market-niche-pre-travel-shopping/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/news/google-g/'], 'metrics': [{'values': ['33', '0']}]},\n", " {'dimensions': ['/partner-portal-built-skein-wins-best-partner-programme-year/'],\n", " 'metrics': [{'values': ['3', '0']}]},\n", " {'dimensions': ['/pavel-kozda/'], 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/research-phase-tech-innovation-projects/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/shalaev-dmitriy/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/skein-announces-release-igostories-project/'],\n", " 'metrics': [{'values': ['5', '0']}]},\n", " {'dimensions': ['/skein-partnership-with-oil-industry/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/skein-presents-at-open-data-institute-summit/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/startContent'], 'metrics': [{'values': ['2', '0']}]},\n", " {'dimensions': ['/svitlana-surodina/'],\n", " 'metrics': [{'values': ['5', '0']}]},\n", " {'dimensions': ['/technology-business-mentoring/'],\n", " 'metrics': [{'values': ['3', '0']}]},\n", " {'dimensions': ['/technology-consultancy-and-team-formation/'],\n", " 'metrics': [{'values': ['2', '0']}]},\n", " {'dimensions': ['/terms-and-conditions/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/ux'], 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/ux/'], 'metrics': [{'values': ['21', '0']}]},\n", " {'dimensions': ['/ux/ux-analysis-result/'],\n", " 'metrics': [{'values': ['14', '0']}]},\n", " {'dimensions': ['/what_we_do_lean_innovation/'],\n", " 'metrics': [{'values': ['25', '0']}]}],\n", " 'totals': [{'values': ['1775', '57']}]}}]}" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "input1 = input0['data']\n", "input1" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'isDataGolden': True,\n", " 'maximums': [{'values': ['1030', '50']}],\n", " 'minimums': [{'values': ['1', '0']}],\n", " 'rowCount': 56,\n", " 'rows': [{'dimensions': ['/'], 'metrics': [{'values': ['1030', '50']}]},\n", " {'dimensions': ['/2017-data-technology-trends/'],\n", " 'metrics': [{'values': ['20', '0']}]},\n", " {'dimensions': ['/7-easy-ways-to-make-your-webpage-sell-more-of-you-product/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/careers./'], 'metrics': [{'values': ['6', '0']}]},\n", " {'dimensions': ['/careers/'], 'metrics': [{'values': ['227', '0']}]},\n", " {'dimensions': ['/Careers/'], 'metrics': [{'values': ['6', '0']}]},\n", " {'dimensions': ['/careers~~pobj.'], 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/case-studies/'], 'metrics': [{'values': ['101', '0']}]},\n", " {'dimensions': ['/case-studies/c24tech-swift-blockchain/'],\n", " 'metrics': [{'values': ['26', '1']}]},\n", " {'dimensions': ['/case-studies/cools/'],\n", " 'metrics': [{'values': ['14', '0']}]},\n", " {'dimensions': ['/case-studies/toothscan-dental-app/'],\n", " 'metrics': [{'values': ['21', '0']}]},\n", " {'dimensions': ['/case-studies/tp-link-partner-portal/'],\n", " 'metrics': [{'values': ['13', '0']}]},\n", " {'dimensions': ['/case-studies/travel-weekly/'],\n", " 'metrics': [{'values': ['22', '1']}]},\n", " {'dimensions': ['/case-studies/we-are-models/'],\n", " 'metrics': [{'values': ['8', '0']}]},\n", " {'dimensions': ['/category/blog/'], 'metrics': [{'values': ['11', '0']}]},\n", " {'dimensions': ['/category/blog/dev-blog/'],\n", " 'metrics': [{'values': ['2', '0']}]},\n", " {'dimensions': ['/category/blog/lean-innovation-methodology/'],\n", " 'metrics': [{'values': ['2', '0']}]},\n", " {'dimensions': ['/category/uncategorized/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/contact-kyiv/'], 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/contact-us/'], 'metrics': [{'values': ['14', '3']}]},\n", " {'dimensions': ['/development-for-technology-companies/'],\n", " 'metrics': [{'values': ['2', '0']}]},\n", " {'dimensions': ['/en_us/article/google-wins-legal-battle-against-pro-trump-spammer-over-the-letter-g'],\n", " 'metrics': [{'values': ['16', '0']}]},\n", " {'dimensions': ['/funding-for-high-growth-ventures/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/google-liar-ru-spam-in-analytics/'],\n", " 'metrics': [{'values': ['40', '0']}]},\n", " {'dimensions': ['/home'], 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/how-businesses-can-use-crowd-science/'],\n", " 'metrics': [{'values': ['2', '0']}]},\n", " {'dimensions': ['/incubator-rus/'], 'metrics': [{'values': ['23', '0']}]},\n", " {'dimensions': ['/java-developer/'], 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/jmb-app-is-now-available-in-appstore/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/jobs/junior-software-developer-e-mail-support/'],\n", " 'metrics': [{'values': ['2', '0']}]},\n", " {'dimensions': ['/jobs/middle-python-developer/'],\n", " 'metrics': [{'values': ['2', '0']}]},\n", " {'dimensions': ['/jobs/office-manager-ceo-assistant/'],\n", " 'metrics': [{'values': ['2', '0']}]},\n", " {'dimensions': ['/jobs/project-manager-agile-pm-scrum-master/'],\n", " 'metrics': [{'values': ['6', '1']}]},\n", " {'dimensions': ['/jobs/python-developer-new-york-company-project/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/jobs/python-developer/'],\n", " 'metrics': [{'values': ['4', '0']}]},\n", " {'dimensions': ['/jobs/react-js-developer-start-now/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/jobs/ux-ui-designer/'],\n", " 'metrics': [{'values': ['21', '1']}]},\n", " {'dimensions': ['/maxim-brovenko/'], 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/new-exciting-market-niche-pre-travel-shopping/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/news/google-g/'], 'metrics': [{'values': ['33', '0']}]},\n", " {'dimensions': ['/partner-portal-built-skein-wins-best-partner-programme-year/'],\n", " 'metrics': [{'values': ['3', '0']}]},\n", " {'dimensions': ['/pavel-kozda/'], 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/research-phase-tech-innovation-projects/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/shalaev-dmitriy/'], 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/skein-announces-release-igostories-project/'],\n", " 'metrics': [{'values': ['5', '0']}]},\n", " {'dimensions': ['/skein-partnership-with-oil-industry/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/skein-presents-at-open-data-institute-summit/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/startContent'], 'metrics': [{'values': ['2', '0']}]},\n", " {'dimensions': ['/svitlana-surodina/'], 'metrics': [{'values': ['5', '0']}]},\n", " {'dimensions': ['/technology-business-mentoring/'],\n", " 'metrics': [{'values': ['3', '0']}]},\n", " {'dimensions': ['/technology-consultancy-and-team-formation/'],\n", " 'metrics': [{'values': ['2', '0']}]},\n", " {'dimensions': ['/terms-and-conditions/'],\n", " 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/ux'], 'metrics': [{'values': ['1', '0']}]},\n", " {'dimensions': ['/ux/'], 'metrics': [{'values': ['21', '0']}]},\n", " {'dimensions': ['/ux/ux-analysis-result/'],\n", " 'metrics': [{'values': ['14', '0']}]},\n", " {'dimensions': ['/what_we_do_lean_innovation/'],\n", " 'metrics': [{'values': ['25', '0']}]}],\n", " 'totals': [{'values': ['1775', '57']}]}" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "input2 = input1['reports']\n", "a = {'a':1}\n", "input2[0]['data']\n", "#input2" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>data</th>\n", " <th>error</th>\n", " <th>msg</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>reports</th>\n", " <td>[{'data': {'maximums': [{'values': ['1030', '5...</td>\n", " <td>False</td>\n", " <td>Retrieve</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " data error msg\n", "reports [{'data': {'maximums': [{'values': ['1030', '5... False Retrieve" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas\n", "\n", "init=pandas.read_json('data/SKEIN_test.json')\n", "init" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "reports [{'data': {'maximums': [{'values': ['1030', '5...\n", "Name: data, dtype: object" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data=init['data']\n", "data" ] }, { "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 }
apache-2.0
rouxinol/HybridSystem
Simulation - CPW-Filter-qubit/.ipynb_checkpoints/Simulation Qubit-NR-CPW Roux-Hugo-checkpoint.ipynb
1
722561
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "#import functions\n", "%pylab inline\n", "\n", "# from MyUnits import *\n", "from MyFunctions import *\n", "from qutip import *\n", "\n", "# from MyQubit import *\n", "# import mpld3\n", "import multiprocessing as mp\n", "import itertools\n", "import datetime\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "code_folding": [ 0 ], "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "IPython.load_extensions('usability/codefolding/main');\n", "IPython.load_extensions('toggle_all_line_number.js');" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "%%javascript\n", "IPython.load_extensions('usability/codefolding/main');\n", "IPython.load_extensions('toggle_all_line_number.js');" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "code_folding": [ 0 ], "collapsed": true }, "outputs": [], "source": [ "import scipy.constants as sc" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import time\n", "import datetime" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [ 0 ] }, "source": [ "## Hamiltonian derivated from Hartmann and Abdi (PRL 114 173602)\n", "\n", "$$4 E_{C} n_{{ac}}^{2} \\left({{c}^\\dagger} + {c}\\right)^{2} + 8 E_{C} n_{{ac}} n_{{dc}} \\left({{c}^\\dagger} + {c}\\right) + \\frac{4 i}{2^{0.25}} E_{C} n_{{ac}} \\left(\\frac{E_{J}}{E_{C}}\\right)^{0.25} \\left(- {{a}^\\dagger} + {a}\\right) \\left({{c}^\\dagger} + {c}\\right) + 4 E_{C} n_{{dc}}^{2} + \\frac{4 i}{2^{0.25}} E_{C} n_{{dc}} \\left(\\frac{E_{J}}{E_{C}}\\right)^{0.25} \\left(- {{a}^\\dagger} + {a}\\right) - \\frac{E_{C} \\left(\\frac{E_{J}}{E_{C}}\\right)^{0.5}}{2^{0.5}} \\left(- {{a}^\\dagger} + {a}\\right)^{2} + \\frac{E_{J} \\left(\\frac{E_{C}}{E_{J}}\\right)^{0.5}}{2^{0.5}} \\left({{a}^\\dagger} + {a}\\right)^{2} - \\frac{2^{1.0} E_{J}}{24} \\left(\\frac{E_{C}}{E_{J}}\\right)^{1.0} \\left({{a}^\\dagger} + {a}\\right)^{4} + \\left({{b}^\\dagger} + {b}\\right) \\left(4 g_{0} n_{{ac}}^{2} \\left({{c}^\\dagger} + {c}\\right)^{2} + 8 g_{0} n_{{ac}} n_{{dc}} \\left({{c}^\\dagger} + {c}\\right) + \\frac{4 i}{2^{0.25}} g_{0} n_{{ac}} \\left(\\frac{E_{J}}{E_{C}}\\right)^{0.25} \\left(- {{a}^\\dagger} + {a}\\right) \\left({{c}^\\dagger} + {c}\\right) + 4 g_{0} n_{{dc}}^{2} + \\frac{4 i}{2^{0.25}} g_{0} n_{{dc}} \\left(\\frac{E_{J}}{E_{C}}\\right)^{0.25} \\left(- {{a}^\\dagger} + {a}\\right) - \\frac{g_{0} \\left(\\frac{E_{J}}{E_{C}}\\right)^{0.5}}{2^{0.5}} \\left(- {{a}^\\dagger} + {a}\\right)^{2}\\right)$$" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "code_folding": [ 1, 147 ], "collapsed": true }, "outputs": [], "source": [ "def Ht(t, args):\n", " #\n", " # evaluate the hamiltonian at time t. \n", " #\n", " \n", " H0 = args['H0']\n", " c = args['c']\n", " cDag = args['cDag']\n", " A = args['A']\n", " \n", " w = args['w']\n", "\n", " return H0 + A * (c + cDag)*cos(w*t) #(a * exp(1j*w*t) + aDag * exp(-1j*w*t))\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "code_folding": [ 0 ], "collapsed": false }, "outputs": [], "source": [ "\n", "def calc_spectrum_5(N,M,P, Ej, Ec, w_nr, w_c, g0, n_ac, n_dc, A , w,**kwargs):\n", " \n", " w_q = sqrt(8 * Ec * Ej) - Ec\n", " zeta = Ej/Ec\n", " \n", " # qubit operators\n", " \n", " a = tensor(destroy(N),qeye(M),qeye(P))\n", " n_a = a.dag() * a\n", " x_a = a.dag() + a\n", " p_a = a - a.dag()\n", " \n", " \n", " # mechanical resonator operators\n", " \n", " b = tensor(qeye(N),destroy(M),qeye(P))\n", " n_b = b.dag() * b\n", " x_b = b.dag() + b\n", " p_b = b - b.dag()\n", " \n", " \n", " # CPW operators\n", " \n", " c = tensor(qeye(N),qeye(M),destroy(P))\n", " n_c = c.dag() * c\n", " x_c = c.dag() + c\n", " p_c = c - c.dag()\n", " \n", " # Identity\n", " \n", " I = tensor(qeye(N),qeye(M),qeye(P))\n", " \n", " \n", " # Hamiltonian\n", " \n", " \n", " H1 = sqrt(8*Ec*Ej) *(a.dag()*a) - Ec/12 * (a + a.dag())**4\n", " \n", " # H1 = w_q * a.dag() * a - Ec/12 * (a.dag())**2 * a**2\n", " \n", " H2 = w_nr * (b.dag() * b)\n", " \n", " H3 = w_c * (c.dag() * c)\n", " H3a = (w_c-w) * (c.dag() * c ) + A*(c.dag()+c)\n", " \n", " \n", " H4 = 4 * Ec * n_dc**2 * x_b**2\n", " \n", " H5 = 4 * Ec * n_ac**2 * x_c**2\n", " \n", " H6 = 4 * 1j * Ec * n_ac * (zeta/2)**(1/4) * p_a * x_c\n", " \n", " H7 = 4 * g0 * n_dc**2 * x_b**3\n", " \n", " H8 = 8 * Ec * n_dc * n_ac * x_b * x_c\n", " \n", " H9 = 4 * 1j * Ec * n_dc * (zeta/2)**(1/4) * p_a * x_b\n", " \n", " H10 = 8 * g0 * n_dc * n_ac * x_b**2 * x_c \n", " \n", " H11 = 4 * 1j * g0 * n_dc * (zeta/2)**(1/4) * p_a * x_b**2\n", " \n", " H12 = 4 * g0 * n_ac**2 * x_c**2 * x_b \n", " \n", " H13 = 4 * 1j * g0 * n_ac * (zeta/2)**(1/4) * p_a * x_b * x_c\n", " \n", " H14 = - g0 * sqrt(zeta/2) * p_a**2 * x_b\n", " \n", " # Time domain\n", " \n", " \n", " \n", " \n", " # Colapse Operators\n", " \n", " c_op_list = []\n", " \n", " kappa_n = 0.0002468 # cavity\n", " \n", " gamma_rel = 6.66e-04 # qubit\n", " gamma_dep = 0.0012 # qubit\n", " \n", " Gamma_m = 0.01 # MR\n", " \n", " Ta = 60e-3 #k\n", " Tb = 60e-3 #k\n", " \n", " n_th_a = 1/(exp(sc.h*w_q*1e9/(sc.k*Ta)-1))\n", " n_th_b = 1/(exp(sc.h*w_nr*1e9/(sc.k*Tb)-1))\n", " \n", " # cavity\n", " c_op_list = []\n", "\n", " rate = kappa_n * (1 + n_th_a)\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * c)\n", "\n", " rate = kappa_n * n_th_a\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * c.dag())\n", "\n", " rate = gamma_rel * (1 + n_th_a)\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * a)\n", "\n", " rate = gamma_rel * (n_th_a)\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * a.dag())\n", "\n", " rate = gamma_dep / 2 * (1 + n_th_a)\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * a.dag()*a)\n", " \n", " rate = Gamma_m * (1 + n_th_b)\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * b)\n", "\n", " rate = Gamma_m * n_th_b\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * b.dag()) \n", " \n", " \n", " if 'mapping' in kwargs:\n", " \n", " H0 = H1 + H2 + H3a + H4 + H5 + H6 + H8 + H9 + H7+ H10 + H11 + H12 + H13 + H14 #+ H5\n", " rho = steadystate(H0,c_op_list)\n", " rho_b = rho*b.dag()*b\n", " rho_a = rho*a.dag()*a\n", " rho_c = rho*c\n", " rho_d = rho*(a)\n", " \n", " return rho_c.tr(),rho_a.tr(),rho_b.tr(),rho_d.tr()\n", " \n", " if 'energies'in kwargs:\n", " H = H1 + H2 + H3 + H4 + H5 + H6 + H7 + H8 + H9 + H10 + H11 + H12 + H13 + H14\n", " \n", " return H.eigenenergies() #+ H4\n", " \n", " \n", " if 'states'in kwargs:\n", " H = H1 + H2 + H3 + H4 + H5 + H6 + H7 + H8 + H9 + H10 + H11 + H12 + H13 + H14\n", " evals, ekets = H.eigenstates(eigvals=5)\n", " \n", "# na_expect = [expect[n_a,ekets[i]] for i in range(5)]\n", "# nb_expect = [expect[n_b,ekets[i]] for i in range(5)]\n", "# nc_expect = [expect[n_c,ekets[i]] for i in range(5)]\n", "# c_expect = [expect[c,ekets[i]] for i in range(5)]\n", " return evals,ekets#na_expect,nb_expect,nc_expect,c_expect\n", " \n", " \n", " if 'time'in kwargs:\n", " \n", " H0 = H1 + H2 + H3 + H4 + H5 + H6 + H7 + H8 + H9 + H10 + H11 + H12 + H13 + H14 \n", " H_args = {'H0': H0, 'c': c, 'cDag': c.dag() , 'A' : A , 'w': w}\n", "# rhs_generate(Ht, c_op_list, H_args)\n", " # rho = steadystate(H,c_op_list)\n", " opts = Options(rhs_reuse=True)\n", " T = 2 * pi / w\n", "\n", " U = propagator(Ht, T, c_op_list, H_args,opts)\n", "\n", " rho = propagator_steadystate(U)\n", "\n", " rho_b = rho*b.dag()*b\n", " rho_a = rho*a.dag()*a\n", " rho_c = rho*c\n", " rho_d = rho*(a)\n", " \n", " return rho_c.tr(),rho_a.tr(),rho_b.tr(),rho_d.tr()\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "def calc_spectrum_5a(N,M,P, Ej, Ec, w_nr, w_c, g0, n_ac, n_dc, A , w,**kwargs):\n", " \n", " w_q = sqrt(8 * Ec * Ej) - Ec\n", " zeta = Ej/Ec\n", " \n", " # qubit operators\n", " \n", " sm = tensor(destroy(2),qeye(M),qeye(P))\n", " sz = tensor(sigmaz(),qeye(M),qeye(P))\n", " sx = tensor(sigmax(),qeye(M),qeye(P))\n", " na = sm.dag() * sm\n", " x_a = sm + sm.dag()\n", " p_a = sm - sm.dag()\n", " I = tensor(qeye(2), qeye(M),qeye(P))\n", " \n", " \n", " # mechanical resonator operators\n", " \n", " b = tensor(qeye(2),destroy(M),qeye(P))\n", " n_b = b.dag() * b\n", " x_b = b.dag() + b\n", " p_b = b - b.dag()\n", " \n", " \n", " # CPW operators\n", " \n", " c = tensor(qeye(2),qeye(M),destroy(P))\n", " n_c = c.dag() * c\n", " x_c = c.dag() + c\n", " p_c = c - c.dag()\n", " \n", " # Identity\n", " \n", " I = tensor(qeye(2),qeye(M),qeye(P))\n", " \n", " # Identity\n", " \n", " I = tensor(qeye(N),qeye(M),qeye(P))\n", " \n", " \n", " # Hamiltonian\n", " \n", " \n", " H1 = w_q/2 *(sz) \n", " \n", " # H1 = w_q * a.dag() * a - Ec/12 * (a.dag())**2 * a**2\n", " \n", " H2 = w_nr * (b.dag() * b - I/2)\n", " \n", " H3 = w_c * (c.dag() * c - I/2) + A*(c.dag()+c)\n", " \n", " H4 = 4 * Ec * n_dc**2 * x_b**2\n", " \n", " H5 = 4 * Ec * n_ac**2 * x_c**2\n", " \n", " H6 = 4 * 1j * Ec * n_ac * (zeta/2)**(1/4) * p_a * x_c\n", " \n", " H7 = 4 * g0 * n_dc**2 * x_b**3\n", " \n", " H8 = 8 * Ec * n_dc * n_ac * x_b * x_c\n", " \n", " H9 = 4 * 1j * Ec * n_dc * (zeta/2)**(1/4) * p_a * x_b\n", " \n", " H10 = 8 * g0 * n_dc * n_ac * x_b**2 * x_c \n", " \n", " H11 = 4 * 1j * g0 * n_dc * (zeta/2)**(1/4) * p_a * x_b**2\n", " \n", " H12 = 4 * g0 * n_ac**2 * x_c**2 * x_b \n", " \n", " H13 = 4 * 1j * g0 * n_ac * (zeta/2)**(1/4) * p_a * x_b * x_c\n", " \n", " H14 = - g0 * sqrt(zeta/2) * p_a**2 * x_b\n", " \n", " c_op_list = []\n", " \n", " kappa_n = 0.0002468 # cavity\n", " \n", " gamma_rel = 6.66e-04 # qubit\n", " gamma_dep = 0.0012 # qubit\n", " \n", " Gamma_m = 0.001 # MR\n", " \n", " Ta = 60e-3 #k\n", " Tb = 60e-3 #k\n", " \n", " n_th_a = 1/(exp(sc.h*w_q*1e9/(sc.k*Ta)-1))\n", " n_th_b = 1/(exp(sc.h*w_nr*1e9/(sc.k*Tb)-1))\n", " \n", " # cavity\n", " c_op_list = []\n", "\n", " rate = kappa_n * (1 + n_th_a)\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * c)\n", "\n", " rate = kappa_n * n_th_a\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * c.dag())\n", "\n", " rate = gamma_rel * (1 + n_th_a)\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * sm)\n", "\n", " rate = gamma_rel * (n_th_a)\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * sm.dag())\n", "\n", " rate = gamma_dep / 2 * (1 + n_th_a)\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * sz)\n", " \n", " rate = Gamma_m * (1 + n_th_b)\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * b)\n", "\n", " rate = Gamma_m * n_th_b\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * b.dag()) \n", " \n", " \n", " if 'mapping' in kwargs:\n", " \n", " H0 = H1 + H2 + H3 + H4 + H5 + H6 + H8 + H9 + H7+ H10 + H11 + H12 + H13 + H14 #+ H5\n", " rho = steadystate(H0,c_op_list)\n", " rho_c = rho*c\n", " return rho_c.tr()\n", " \n", " elif 'energies'in kwargs:\n", " H = H1 + H2 + H3 + H4 + H5 + H6 + H8 + H9 + H7+ H10 + H11 + H12 + H13 + H14\n", " \n", " return H.eigenenergies() #+ H4\n", " \n", " elif 'time'in kwargs:\n", " \n", " H0 = H1 + H2 + H3 \n", " H_args = {'H0': H0, 'c': c, 'cDag': c.dag() , 'A' : A , 'w': w}\n", "\n", " # rho = steadystate(H,c_op_list)\n", "\n", " T = 2 * pi / w\n", "\n", " U = propagator(Ht, T, c_op_list, H_args)\n", "\n", " rho_ss = propagator_steadystate(U)\n", "\n", " rho_c = rho_ss*c\n", " \n", " return rho_c.tr()\n", "\n", " \n", "\n", "# rhs_generate()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "code_folding": [], "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X_ZPM = 1.39704864016e-14\n", "g0 = 0.0310351802769\n", "N_ac = 0.100681009983\n", "n_dc = 0.0261590886036\n", "E_q = 4.85964425627\n" ] } ], "source": [ "N,M,P = 4, 4 ,4\n", "\n", "# qubit Cavity parameters\n", "Ej_max = 16 \n", "Ej = 4 \n", "Ec = 0.2 \n", "w_nr = 3.5 \n", "w_c = 5.0 \n", "Cg = 10e-15 \n", "Cc = 1e-12\n", "Cb = 50e-15\n", "Cnr = 2e-17\n", "\n", "# mechanical resonator\n", "\n", "V_dc = 30\n", "\n", "m = (700e-9)*(65e-9)*(100e-9)*2700 \n", "Xzpm = sqrt(sc.hbar/(2*m*w_nr*2*pi*1e9))\n", "print('X_ZPM =',Xzpm)\n", "d0 = 30e-9 \n", "g0 = Ec/d0 * Cnr/(Cg+Cb+Cnr)*Xzpm*1e9\n", "print('g0 =', g0)\n", "\n", "# Cavity effect\n", "n_ac = Cg /2/sc.e * sqrt(sc.h*w_c*2*pi*1e9/2/Cc)\n", "print('N_ac =',n_ac)\n", "\n", "\n", "\n", "n_dc = Cnr/d0*V_dc/2/sc.e*Xzpm**2*1e9 + Cnr*V_dc/2/sc.e*Xzpm*1e9\n", "\n", "print('n_dc = ',n_dc)\n", "\n", "d = 0.1 # asymetry \n", "\n", "A = 0.0003# field aplitude\n", "\n", "w = 5.001\n", "print('E_q =', sqrt(8 * Ec * Ej_max) - Ec)\n", "\n", "kwargs = {'energies':12}\n", "\n", "\n", "x_i, x_f = 0.3,0.33\n", "phi = pi * linspace(x_i,x_f,2)\n", "Ej_vec = Ej_max * abs(cos(phi))*sqrt(1+(d*tan(phi))**2)\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ENERGY = array([calc_spectrum_5(N,M,P, x, Ec, w_nr, w_c, g0, n_ac, n_dc, A , w,**kwargs)\n", " for x in Ej_vec])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.LineCollection at 0x7f2b94989e10>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2b9497c780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = subplots(1,1, figsize=(16,10))\n", "x_inf = -1\n", "x_sup = 10\n", "\n", "for n in range(len(ENERGY[0,:])):\n", " if n < 10:\n", " axes.plot(phi/pi, (ENERGY[:,n]-ENERGY[:,0]),'-')\n", "# axes.plot(phi/pi, (ENERGY[:,n]-ENERGY[:,0])/2,'--')\n", "\n", "# if n < 4:\n", "# axes.text(.2,energies[0,n]-energies[0,0],r'|%s>'%(n),fontsize=20)\n", " \n", "axes.set_title('Full')\n", "axes.set_ylim(x_inf, x_sup)\n", "axes.set_xlabel(r'$\\phi$', fontsize=18)\n", "axes.set_ylabel(r'$E_n-E_0$', fontsize=18)\n", "axes.hlines(w_nr,x_i,x_f,linestyles='dashed') \n", "axes.hlines(w_c,x_i,x_f,linestyles='dashed')\n", "axes.vlines(0.338,0,10,linestyles='dashed')\n", "axes.vlines(0.343,0,10,linestyles='dashed')\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "y_i,y_f = 4.99,5.01\n", "y_vec = linspace(y_i,y_f,2) \n", " \n", "# phi = linspace(0,pi,100)\n", "\n", "x_vec = Ej_max * abs(cos(phi))*sqrt(1+(d*tan(phi))**2)\n", "\n", "\n", "\n", "a , b = zip(*itertools.product(x_vec,y_vec))\n", "kwargs = {'num_cpus':15,'time':1}" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parallel Simulation with 15 CPUs \n", "[100.0%] of the simulations calculated, Estimated Remaining time: 0.0s\n", "All done! \n", "Mean time:3265.094205\n", "Total time:3:37:40\n" ] } ], "source": [ "\n", "# Create from the original vectors the new vector with the correct number copies\n", "a , b = zip(*itertools.product(x_vec,y_vec))\n", "# variable to count the total number of tasks we need to do; used to create progress bar\n", "task_count =len(x_vec)*len(y_vec)\n", "\n", "\n", "\n", "# Check number of cpus to be used\n", "if 'num_cpus' in kwargs:\n", " num_cpu = kwargs['num_cpus']\n", " if num_cpu == 1:\n", " print(\"1 CPU; Serial Simulation\")\n", " else:\n", " print(\"Parallel Simulation with %d CPUs \" % num_cpu) \n", "else:\n", " num_cpu = 1\n", " print(\"Serial Simulation\")\n", "\n", "\n", "\n", "## Program to run function in parallel: \n", "\n", "try:\n", " t_start = time.time() # start time simulation\n", " total_time=0\n", " time_1 = []\n", " pool = mp.Pool(processes=num_cpu) # create the initial pool to run the simulation \n", "# manager = mp.Manager()\n", "# queue = manager.Queue()\n", "\n", "\n", "# _update_progress_bar(1)\n", "# task_args = a,z\n", " results = [pool.apply_async(calc_spectrum_5,(N,\n", " M,\n", " P,\n", " a1,\n", " Ec,\n", " w_nr,\n", " w_c,\n", " g0,\n", " n_ac,\n", " n_dc,\n", " A ,\n", " b1)\n", " ,kwargs\n", " ,callback=None,error_callback=None) for a1,b1 in zip(a,b)]\n", "\n", "\n", "\n", " #####\n", " while True:\n", " incomplete_count = sum(1 for x in results if not x.ready())\n", "\n", " if incomplete_count == 0:\n", " \n", " print(\"[100.0%] of the simulations calculated, Estimated Remaining time: 0.0s\", end=\"\\r\")\n", " print( \"\\nAll done! \\nMean time:%f\"%(dif_time/task_count))\n", " print( \"Total time:%s\"%datetime.timedelta(seconds=int(dif_time)))\n", " break\n", "\n", " else:\n", "\n", " p = float(task_count - incomplete_count) / task_count * 100 \n", "\n", " dif_time = (time.time() - t_start) \n", "\n", "# \n", " if p > 0:\n", " rem_time = (datetime.timedelta(seconds=int(dif_time*(100-p)/p)))\n", "\n", "# rem_time_1 = (datetime.timedelta(seconds=int(dif_time/(task_count-incomplete_count))))\n", " time_1.append(float(dif_time/(task_count- incomplete_count)))\n", "# rem_time_1 = mean(time_1) *task_count\n", "# rem_time_1 = (datetime.timedelta( seconds=int(mean(time_1) *task_count)))\n", " rem_time_1 = time.strftime(\"%Z - %Y/%m/%d, %H:%M:%S\", time.localtime(t_start+mean(time_1) *task_count))\n", " else:\n", " rem_time = '?'\n", " rem_time_1 = 0\n", "\n", "\n", " print(\"[%4.1f%%] of the simulations calculated, Estimated Remaining time: %s, (%s)\"\n", " %(p,rem_time,rem_time_1) , end=\"\\r\")\n", "\n", " time.sleep(.25)\n", "\n", "\n", " while not all([ar.ready() for ar in results]):\n", "\n", " for ar in results: \n", " ar.wait(timeout=0.1)\n", "\n", " pool.terminate()\n", " pool.join()\n", "\n", "except KeyboardInterrupt as e:\n", " pool.terminate()\n", " pool.join()\n", " raise e\n", "\n", "\n", "\n", "results = [ar.get() for ar in results]\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# results_2 = asarray(results)\n", "# shape(results_2[:,0])" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "results_1 = asarray(results)\n", "# qsave(results,name='ZeroVolts')\n", "#qsave(results,name='TwentyVolts')\n", "qsave(results,name='ThirtytyVolts')\n", "\n", "tr_c = reshape(results_1[:,0],(-1,len(y_vec+1)))\n", "tr_a = reshape(results_1[:,1],(-1,len(y_vec+1)))\n", "tr_b = reshape(results_1[:,2],(-1,len(y_vec+1)))\n", "tr_d = reshape(results_1[:,3],(-1,len(y_vec+1)))" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2500, 4)" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shape(results_1)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "code_folding": [], "collapsed": false }, "outputs": [], "source": [ "# results_full_range = asarray(results)\n", "# tr_c = reshape(results_1,(-1,len(y_vec+1)))\n", "# results_small_range = asarray(results)\n", "# tr_c = reshape(results_small_range,(-1,len(y_vec+1)))\n", "\n" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fbef8832f60>" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" }, { 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D8rsBH6Hh7IsHV7UzSZIkSSqjxj+7vwbMYvKyX1cAHwDWA74J7JOzTnuDVV96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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fbee79106d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = subplots(4,1, figsize=(16,20))\n", "\n", "\n", "\n", "im = ax[0].pcolor(phi/pi,y_vec,transpose(log(abs(tr_c))))#,vmin=0, vmax=1)\n", "fig.colorbar(im, ax=ax[0])\n", "# ax[0,0].set_xlim(4.27,4.39)\n", "# ax[0,0].set_ylim(P_i,P_f)\n", "ax[0].set_ylabel(r'Qubit Tone Power(dBm)',fontsize=10)\n", "# ax[0].set_xlabel(r'Qubit Tone Frequency (GHz)',fontsize=10)\n", "ax[0].set_title(r'$Tr[\\rho c]$',fontsize=20)\n", "\n", "\n", "im = ax[1].pcolor(phi/pi,y_vec,transpose((abs(tr_a))))#,vmin=0, vmax=1)\n", "fig.colorbar(im, ax=ax[1])\n", "# ax[0,0].set_xlim(4.27,4.39)\n", "# ax[0,0].set_ylim(P_i,P_f)\n", "ax[1].set_ylabel(r'Qubit Tone Power(dBm)',fontsize=10)\n", "# ax[1].set_xlabel(r'Qubit Tone Frequency (GHz)',fontsize=10)\n", "ax[1].set_title(r'$Tr[\\rho a^{\\dagger}a]$',fontsize=20)\n", "\n", "im = ax[2].pcolor(phi/pi,y_vec,transpose((abs(tr_b))))#,vmin=0, vmax=1)\n", "fig.colorbar(im, ax=ax[2])\n", "# ax[0,0].set_xlim(4.27,4.39)\n", "# ax[0,0].set_ylim(P_i,P_f)\n", "ax[2].set_ylabel(r'Qubit Tone Power(dBm)',fontsize=10)\n", "ax[2].set_xlabel(r'Qubit Tone Frequency (GHz)',fontsize=10)\n", "ax[2].set_title(r'$Tr[\\rho b^\\dagger b]$',fontsize=20)\n", "\n", "im = ax[3].pcolor(phi/pi,y_vec,transpose((abs(tr_d))))#,vmin=0, vmax=1)\n", "fig.colorbar(im, ax=ax[3])\n", "# ax[0,0].set_xlim(4.27,4.39)\n", "# ax[0,0].set_ylim(P_i,P_f)\n", "ax[3].set_ylabel(r'Qubit Tone Power(dBm)',fontsize=10)\n", "ax[3].set_xlabel(r'Qubit Tone Frequency (GHz)',fontsize=10)\n", "ax[3].set_title(r'$Tr[\\rho a]$',fontsize=20)" ] }, { "cell_type": "code", "execution_count": 646, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.colorbar.Colorbar at 0x7fbae7783b38>" ] }, "execution_count": 646, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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nidv3gZrSSqMns/fbf91TcMtw5dHEz1xpkNGE02g7tjxP2uTGjbE0SJceGU2V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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbae961a908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = subplots(1,1, figsize=(16,10))\n", "y_inf = y_i\n", "y_sup = y_f\n", "x_inf = x_i\n", "x_sup = x_f\n", "\n", "for n in range(len(ENERGY[0,:])):\n", " axes.plot(phi/pi, (ENERGY[:,n]-ENERGY[:,0]),'-')\n", " axes.plot(phi/pi, (ENERGY[:,n]-ENERGY[:,0])/2,'--')\n", " \n", "# if n < 4:\n", "# axes.text(.2,energies[0,n]-energies[0,0],r'|%s>'%(n),fontsize=20)\n", " \n", "axes.set_title('Full')\n", "axes.set_ylim(y_inf, y_sup)\n", "# axes.set_xlim(x_inf,x_sup)\n", "\n", "\n", "axes.set_xlabel(r'$\\phi$', fontsize=18)\n", "axes.set_ylabel(r'Cavity Tone Frequency GHz', fontsize=18)\n", "axes.hlines(w_nr,x_i,x_f,linestyles='dashed')\n", "axes.hlines(w_c,x_i,x_f,linestyles='dashed')\n", "axes.vlines(0.338,0,10,linestyles='dashed',linewidth=2,color='white')\n", "axes.vlines(0.343,0,10,linestyles='dashed',linewidth=2,color='red')\n", "\n", "im = axes.pcolor(phi/pi,y_vec,transpose(\n", " (abs(tr_c))))#axes.pcolor(phi/pi,y_vec,transpose((abs(tr))))#,vmin=0, vmax=1)\n", "fig.colorbar(im, ax=axes)\n", "# ax[0,0].set_xlim(4.27,4.39)\n", "# ax[0,0].set_ylim(P_i,P_f)\n", "# axes.set_ylabel(r'Qubit Tone Power(dBm)',fontsize=10)\n", "# axes.set_xlabel(r'Qubit Tone Frequency (GHz)',fontsize=10)\n", "# axes.set_title(r'$Tr[\\rho\\sigma_z]$',fontsize=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scan coupling \n" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [], "source": [ "y_i,y_f = 4.995,5.005\n", "y_vec = linspace(y_i,y_f,10) \n", "phi= 0.319\n", "Ej = Ej_max * abs(cos(phi))*sqrt(1+(d*tan(phi))**2) \n", "# phi = linspace(0,pi,100)\n", "V_dc = linspace(0,30,5)\n", "# x_vec = Ej_max * abs(cos(phi))*sqrt(1+(d*tan(phi))**2)\n", "x_vec = Cnr/d0*V_dc/2/sc.e*Xzpm**2*1e9 + Cnr*V_dc/2/sc.e*Xzpm*1e9\n", "\n", "\n", "a , b = zip(*itertools.product(x_vec,y_vec))\n", "kwargs = {'num_cpus':15,'time':1}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parallel Simulation with 15 CPUs \n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-45-f7be59e304ab>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 95\u001b[0m \u001b[0mpool\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mterminate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 96\u001b[0m \u001b[0mpool\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 97\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 98\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 99\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m<ipython-input-45-f7be59e304ab>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 81\u001b[0m %(p,rem_time,rem_time_1) , end=\"\\r\")\n\u001b[0;32m 82\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 83\u001b[1;33m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m.25\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 84\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] }, { "name": "stderr", "output_type": "stream", "text": [ "ERROR:tornado.general:Uncaught exception, closing connection.\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.4/dist-packages/zmq/eventloop/zmqstream.py\", line 407, in _run_callback\n", " callback(*args, **kwargs)\n", " File \"/usr/local/lib/python3.4/dist-packages/tornado/stack_context.py\", line 275, in null_wrapper\n", " return fn(*args, **kwargs)\n", " File \"/usr/local/lib/python3.4/dist-packages/IPython/kernel/zmq/kernelbase.py\", line 252, in dispatcher\n", " return self.dispatch_shell(stream, msg)\n", " File \"/usr/local/lib/python3.4/dist-packages/IPython/kernel/zmq/kernelbase.py\", line 213, in dispatch_shell\n", " handler(stream, idents, msg)\n", " File \"/usr/local/lib/python3.4/dist-packages/IPython/kernel/zmq/kernelbase.py\", line 388, in execute_request\n", " self._abort_queues()\n", " File \"/usr/local/lib/python3.4/dist-packages/IPython/kernel/zmq/kernelbase.py\", line 588, in _abort_queues\n", " self._abort_queue(stream)\n", " File \"/usr/local/lib/python3.4/dist-packages/IPython/kernel/zmq/kernelbase.py\", line 611, in _abort_queue\n", " poller.poll(50)\n", " File \"/usr/local/lib/python3.4/dist-packages/zmq/sugar/poll.py\", line 101, in poll\n", " return zmq_poll(self.sockets, timeout=timeout)\n", " File \"zmq/backend/cython/_poll.pyx\", line 115, in zmq.backend.cython._poll.zmq_poll (zmq/backend/cython/_poll.c:1599)\n", " File \"zmq/backend/cython/checkrc.pxd\", line 11, in zmq.backend.cython.checkrc._check_rc (zmq/backend/cython/_poll.c:1803)\n", "KeyboardInterrupt\n", "ERROR:tornado.general:Uncaught exception, closing connection.\n", "Traceback (most recent call last):\n", " File \"/usr/local/lib/python3.4/dist-packages/zmq/eventloop/zmqstream.py\", line 433, in _handle_events\n", " self._handle_recv()\n", " File \"/usr/local/lib/python3.4/dist-packages/zmq/eventloop/zmqstream.py\", line 465, in _handle_recv\n", " self._run_callback(callback, msg)\n", " File \"/usr/local/lib/python3.4/dist-packages/zmq/eventloop/zmqstream.py\", line 407, in _run_callback\n", " callback(*args, **kwargs)\n", " File \"/usr/local/lib/python3.4/dist-packages/tornado/stack_context.py\", line 275, in null_wrapper\n", " return fn(*args, **kwargs)\n", " File \"/usr/local/lib/python3.4/dist-packages/IPython/kernel/zmq/kernelbase.py\", line 252, in dispatcher\n", " return self.dispatch_shell(stream, msg)\n", " File \"/usr/local/lib/python3.4/dist-packages/IPython/kernel/zmq/kernelbase.py\", line 213, in dispatch_shell\n", " handler(stream, idents, msg)\n", " File \"/usr/local/lib/python3.4/dist-packages/IPython/kernel/zmq/kernelbase.py\", line 388, in execute_request\n", " self._abort_queues()\n", " File \"/usr/local/lib/python3.4/dist-packages/IPython/kernel/zmq/kernelbase.py\", line 588, in _abort_queues\n", " self._abort_queue(stream)\n", " File \"/usr/local/lib/python3.4/dist-packages/IPython/kernel/zmq/kernelbase.py\", line 611, in _abort_queue\n", " poller.poll(50)\n", " File \"/usr/local/lib/python3.4/dist-packages/zmq/sugar/poll.py\", line 101, in poll\n", " return zmq_poll(self.sockets, timeout=timeout)\n", " File \"zmq/backend/cython/_poll.pyx\", line 115, in zmq.backend.cython._poll.zmq_poll (zmq/backend/cython/_poll.c:1599)\n", " File \"zmq/backend/cython/checkrc.pxd\", line 11, in zmq.backend.cython.checkrc._check_rc (zmq/backend/cython/_poll.c:1803)\n", "KeyboardInterrupt\n" ] } ], "source": [ "# Create from the original vectors the new vector with the correct number copies\n", "a , b = zip(*itertools.product(x_vec,y_vec))\n", "# variable to count the total number of tasks we need to do; used to create progress bar\n", "task_count =len(x_vec)*len(y_vec)\n", "\n", "\n", "\n", "# Check number of cpus to be used\n", "if 'num_cpus' in kwargs:\n", " num_cpu = kwargs['num_cpus']\n", " if num_cpu == 1:\n", " print(\"1 CPU; Serial Simulation\")\n", " else:\n", " print(\"Parallel Simulation with %d CPUs \" % num_cpu) \n", "else:\n", " num_cpu = 1\n", " print(\"Serial Simulation\")\n", "\n", "\n", "\n", "## Program to run function in parallel: \n", "\n", "try:\n", " t_start = time.time() # start time simulation\n", " total_time=0\n", " time_1 = []\n", " pool = mp.Pool(processes=num_cpu) # create the initial pool to run the simulation \n", "# manager = mp.Manager()\n", "# queue = manager.Queue()\n", "\n", "\n", "# _update_progress_bar(1)\n", "# task_args = a,z\n", " results = [pool.apply_async(calc_spectrum_5,(N,\n", " M,\n", " P,\n", " Ej,\n", " Ec,\n", " w_nr,\n", " w_c,\n", " g0,\n", " n_ac,\n", " a1,\n", " A ,\n", " b1)\n", " ,kwargs\n", " ,callback=None,error_callback=None) for a1,b1 in zip(a,b)]\n", "\n", "\n", "\n", " #####\n", " while True:\n", " incomplete_count = sum(1 for x in results if not x.ready())\n", "\n", " if incomplete_count == 0:\n", " print(\"[100.0%] of the simulations calculated, Estimated Remaining time: 0.0s\", end=\"\\r\")\n", " print( \"\\nAll done! \\nTotal time:%s\"%datetime.timedelta(seconds=int(dif_time)))\n", " break\n", "\n", " else:\n", "\n", " p = float(task_count - incomplete_count) / task_count * 100 \n", "\n", " dif_time = (time.time() - t_start) \n", "\n", "# \n", " if p > 0:\n", " rem_time = (datetime.timedelta(seconds=int(dif_time*(100-p)/p)))\n", "\n", "# rem_time_1 = (datetime.timedelta(seconds=int(dif_time/(task_count-incomplete_count))))\n", " time_1.append(float(dif_time/(task_count- incomplete_count)))\n", "# rem_time_1 = mean(time_1) *task_count\n", "# rem_time_1 = (datetime.timedelta( seconds=int(mean(time_1) *task_count)))\n", " rem_time_1 = time.strftime(\"%Z - %Y/%m/%d, %H:%M:%S\", time.localtime(t_start+mean(time_1) *task_count))\n", " else:\n", " rem_time = '?'\n", " rem_time_1 = 0\n", "\n", "\n", " print(\"[%4.1f%%] of the simulations calculated, Estimated Remaining time: %s, (%s)\"\n", " %(p,rem_time,rem_time_1) , end=\"\\r\")\n", "\n", " time.sleep(.25)\n", "\n", "\n", " while not all([ar.ready() for ar in results]):\n", "\n", " for ar in results: \n", " ar.wait(timeout=0.1)\n", "\n", " pool.terminate()\n", " pool.join()\n", "\n", "except KeyboardInterrupt as e:\n", " pool.terminate()\n", " pool.join()\n", " raise e\n", "\n", "\n", "\n", "results = [ar.get() for ar in results]\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "results_2 = asarray(results)\n", "tr_c = reshape(results_2[:,0],(-1,len(y_vec+1)))\n", "tr_a = reshape(results_2[:,1],(-1,len(y_vec+1)))\n", "tr_b = reshape(results_2[:,2],(-1,len(y_vec+1)))\n", "tr_d = reshape(results_2[:,3],(-1,len(y_vec+1)))" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f5e3d5a4b70>" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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R/rjxo443vnLaAbDmc5xBDIpyIHAvcA1rD4ryAuBXRG3ey4HvJGXTgLOIxO79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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5e3dbbfef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ig, ax = subplots(4,1, figsize=(16,20))\n", "\n", "\n", "\n", "im = ax[0].pcolor(V_dc,y_vec,transpose((abs(tr_c))))#,vmin=0, vmax=1)\n", "fig.colorbar(im, ax=ax[0])\n", "# ax[0,0].set_xlim(4.27,4.39)\n", "# ax[0,0].set_ylim(P_i,P_f)\n", "ax[0].set_ylabel(r'Qubit Tone Power(dBm)',fontsize=10)\n", "# ax[0].set_xlabel(r'Qubit Tone Frequency (GHz)',fontsize=10)\n", "ax[0].set_title(r'$Tr[\\rho c]$',fontsize=20)\n", "\n", "\n", "im = ax[1].pcolor(V_dc,y_vec,transpose((abs(tr_a))))#,vmin=0, vmax=1)\n", "fig.colorbar(im, ax=ax[1])\n", "# ax[0,0].set_xlim(4.27,4.39)\n", "# ax[0,0].set_ylim(P_i,P_f)\n", "ax[1].set_ylabel(r'Qubit Tone Power(dBm)',fontsize=10)\n", "# ax[1].set_xlabel(r'Qubit Tone Frequency (GHz)',fontsize=10)\n", "ax[1].set_title(r'$Tr[\\rho a^{\\dagger}a]$',fontsize=20)\n", "\n", "im = ax[2].pcolor(V_dc,y_vec,transpose(log(abs(tr_b))))#,vmin=0, vmax=1)\n", "fig.colorbar(im, ax=ax[2])\n", "# ax[0,0].set_xlim(4.27,4.39)\n", "# ax[0,0].set_ylim(P_i,P_f)\n", "ax[2].set_ylabel(r'Qubit Tone Power(dBm)',fontsize=10)\n", "ax[2].set_xlabel(r'Qubit Tone Frequency (GHz)',fontsize=10)\n", "ax[2].set_title(r'$Tr[\\rho b^\\dagger b]$',fontsize=20)\n", "\n", "im = ax[3].pcolor(V_dc,y_vec,transpose(log(abs(tr_d))))#,vmin=0, vmax=1)\n", "fig.colorbar(im, ax=ax[3])\n", "# ax[0,0].set_xlim(4.27,4.39)\n", "# ax[0,0].set_ylim(P_i,P_f)\n", "ax[3].set_ylabel(r'Qubit Tone Power(dBm)',fontsize=10)\n", "ax[3].set_xlabel(r'Qubit Tone Frequency (GHz)',fontsize=10)\n", "ax[3].set_title(r'$Tr[\\rho a]$',fontsize=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Standard Hamiltonian" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\lambda \\left({{b}^\\dagger} {\\sigma_-} + {b} {\\sigma_+} - \\frac{g^{2} {{b}^\\dagger} {\\sigma_-}}{2 \\Delta^{2}} - \\frac{g^{2} {b} {\\sigma_+}}{2 \\Delta^{2}} - \\frac{g^{2} \\left({{c}^\\dagger}\\right)^{2}}{\\Delta^{2}} {\\sigma_-} {b} - \\frac{g^{2} \\left({c}\\right)^{2}}{\\Delta^{2}} {\\sigma_+} {{b}^\\dagger}\\right) + w_{mr} {{b}^\\dagger} {b} + {{c}^\\dagger} {c} \\left(\\omega_{c} + \\frac{g^{2} {\\sigma_z}}{\\Delta} - \\frac{g^{2} \\lambda}{\\Delta^{2}} {{b}^\\dagger} {\\sigma_-} - \\frac{g^{2} \\lambda}{\\Delta^{2}} {b} {\\sigma_+}\\right) + {\\sigma_z} \\left(\\frac{w_{q}}{2} + \\frac{g^{2}}{2 \\Delta} + \\frac{g \\lambda}{\\Delta} {{b}^\\dagger} {c} + \\frac{g \\lambda}{\\Delta} {{c}^\\dagger} {b}\\right)\n", "$$" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "IPython.load_extensions('usability/codefolding/main');\n", "\n", "IPython.load_extensions('toggle_all_line_number.js');" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%javascript\n", "IPython.load_extensions('usability/codefolding/main');\n", "\n", "IPython.load_extensions('toggle_all_line_number.js');" ] }, { "cell_type": "code", "execution_count": 404, "metadata": { "code_folding": [ 1, 147, 156 ], "collapsed": false }, "outputs": [], "source": [ "# Hamiltonians Functions\n", "def Ht(t, args):\n", " #\n", " # evaluate the hamiltonian at time t. \n", " #\n", " \n", " H0 = args['H0']\n", " c = args['c']\n", " cDag = args['cDag']\n", " A = args['A']\n", " \n", " w = args['w']\n", "\n", " return H0 + A * (c + cDag)*cos(w*t) #(a * exp(1j*w*t) + aDag * exp(-1j*w*t))\n", "\n", "\n", "\n", "\n", "# Calc Spectrum\n", "def calc_spectrum_6(N,M,P, w_c,w_nr, w_q,L,g,A,w=0, **kwargs):\n", " \n", " # dispersive Qubit CPW NR \n", " Delta = w_q - w_c\n", " delta = w_q - w_nr\n", " # qubit operators\n", " \n", " sm = tensor(create(2),qeye(M),qeye(P))\n", " sz = tensor(sigmaz(),qeye(M),qeye(P))\n", " sx = tensor(sigmax(),qeye(M),qeye(P))\n", " nq = sm.dag() * sm\n", " xq = sm + sm.dag()\n", " I = tensor(qeye(2), qeye(M),qeye(P))\n", " \n", " \n", " # mechanical resonator operators\n", " \n", " b = tensor(qeye(2),destroy(M),qeye(P))\n", " n_b = b.dag() * b\n", " x_b = b.dag() + b\n", " p_b = b - b.dag()\n", " \n", " \n", " # CPW operators\n", " \n", " c = tensor(qeye(2),qeye(M),destroy(P))\n", " n_c = c.dag() * c\n", " x_c = c.dag() + c\n", " p_c = c - c.dag()\n", " \n", " # Identity\n", " \n", " I = tensor(qeye(2),qeye(M),qeye(P))\n", " \n", " \n", "# Hamiltonian\n", " if 'Full' in kwargs:\n", " H1 = w_nr * (b.dag() * b + 1/2) + L * sx *(b.dag() + b)\n", "\n", " H2 = w_c * (c.dag() * c + 1/2) + g * (c.dag()*sm + c*sm.dag())\n", " H2a = (w_c - w) * (c.dag() * c ) + g * (c.dag()*sm + c*sm.dag())\n", "\n", " H3 = -sz *(w_q/2)\n", "\n", " H4 = A * (c.dag() + c)\n", " \n", " \n", " \n", " \n", " if 'Disp' in kwargs:\n", " \n", " \n", " H1 = w_nr * (b.dag() * b + 1/2) + L * sx *(b.dag() + b)\n", "\n", " H2 = (c.dag() * c ) * ( w_c + g**2/Delta*sz)\n", " H2a = (c.dag() * c ) * ( w_c - w + g**2/Delta*sz)\n", "\n", " H3 = sz *(w_q/2 + g**2/2/Delta) \n", "\n", " H4 = A * (c.dag() + c)\n", " \n", " \n", " if 'Disp-RWA' in kwargs:\n", " \n", " \n", " \n", " H1 = w_nr * (b.dag() * b + 1/2) + L * (b.dag()*sm + b*sm.dag())\n", "\n", " H2 = (c.dag() * c ) * ( w_c + g**2/Delta*sz)\n", " H2a = (c.dag() * c ) * ( w_c - w + g**2/Delta*sz)\n", "\n", " H3 = sz *(w_q/2 + g**2/2/Delta) \n", "\n", " H4 = A * (c.dag() + c)\n", " \n", " if '2Disp' in kwargs:\n", " \n", " H1 = b.dag()*b *(w_nr - 2 * g * L**2 /Delta / delta *(sm*c.dag() + sm.dag() *c)) \n", " - g*L**2/Delta/delta*(c.dag()*sm+c*sm.dag())\n", " \n", " H2 = c.dag()*c *(w_c + g**2/Delta * sz \n", " + (Delta + delta)*(\n", " - ((g*L)/(Delta * delta))**2 \n", " * ( 1 + sz + 2* b.dag()*b * sz ) \n", " ))\n", " H2a = c.dag()*c *(w_c-w + g**2/Delta * sz \n", " + (Delta + delta)*(\n", " - ((g*L)/(Delta * delta))**2 \n", " * ( 1 + sz + 2* b.dag()*b * sz ) \n", " ))\n", " \n", " \n", " \n", " H3 = sz *(w_q/2 + g**2/2/Delta \n", " + b.dag()*b *(L**2/delta - (g*L/Delta/delta)**2*(delta+ Delta) ) \n", " + (L**2/2/delta - (g*L/Delta/delta)**2/2*(delta+Delta) ))\n", " \n", " H4 = A * (c.dag() + c) \n", " \n", " \n", " \n", " \n", " \n", "# Colapse Operators\n", " \n", " c_op_list = []\n", " \n", " kappa_n = 0.0005 # cavity\n", " \n", " gamma_rel = 0.0001 # qubit\n", " gamma_dep = 0.002 # qubit\n", " \n", " Gamma_m = 0.01 # MR\n", " \n", " Ta = 60e-3 #k\n", " Tb = 60e-3 #k\n", " Tq = 30e-3 #K\n", " \n", " n_th_a = 1/(exp(sc.h*w_q*1e9/(sc.k*Ta)-1))\n", " \n", " n_th_q = 1/(exp(sc.h*w_q*1e9/(sc.k*Tq)-1))\n", " \n", " if Tb == 0:\n", " n_th_b = 0\n", " else:\n", " \n", " n_th_b = 1/(exp(sc.h*w_nr*1e9/(sc.k*Tb)-1))\n", " \n", " # cavity\n", " c_op_list = []\n", "\n", " rate = kappa_n * (1 + n_th_a)\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * c)\n", "\n", " rate = kappa_n * n_th_a\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * c.dag())\n", "\n", " rate = gamma_rel * (1 + n_th_q)\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * sm)\n", "\n", " rate = gamma_rel * (n_th_q)\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * sm.dag())\n", "\n", " rate = gamma_dep / 2 * (1 + n_th_q)\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * sz)\n", " \n", " rate = Gamma_m * (1 + n_th_b)\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * b)\n", "\n", " rate = Gamma_m * n_th_b\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * b.dag()) \n", " \n", " \n", "# Solution Type \n", " if 'dispersive' in kwargs:\n", " \n", " H0 = H1 + H2a + H3 + H4 #+ H5\n", " rho = steadystate(H0,c_op_list)\n", " rho_b = rho*n_b\n", " rho_a = rho*sz\n", " rho_c = rho*c\n", " rho_d = rho*n_c\n", " \n", " return rho_c.tr(),rho_a.tr(),rho_b.tr(),rho_d.tr()\n", " \n", " \n", " if 'mapping' in kwargs:\n", " \n", " H0 = H1 + H2w + H3 + H4 #+ H5\n", " rho = steadystate(H0,c_op_list)\n", " rho_c = rho*c\n", " return rho_c.tr()\n", " \n", " if 'energies'in kwargs:\n", " H = H1 + H2 + H3\n", " \n", " return H.eigenenergies() #+ H4\n", " \n", " if 'time'in kwargs:\n", " \n", " H0 = H1 + H2 + H3 #+ H4 \n", " H_args = {'H0': H0, 'c': c, 'cDag': c.dag() , 'A' : A , 'w': w}\n", "\n", " # rho = steadystate(H,c_op_list)\n", "\n", " T = 2 * pi / w\n", "\n", " U = propagator(Ht, T, c_op_list, H_args)\n", "\n", " rho_ss = propagator_steadystate(U)\n", "\n", " \n", " rho_b = rho_ss*n_b\n", " rho_a = rho_ss*sz\n", " rho_c = rho_ss*c\n", " rho_d = rho_ss*n_c\n", " \n", " return rho_c.tr(),rho_a.tr(),rho_b.tr(),rho_d.tr() " ] }, { "cell_type": "code", "execution_count": 439, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4.95000686255\n" ] }, { "ename": "ZeroDivisionError", "evalue": "float division by zero", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-439-1a6b7720e1dd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'num_cpus'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m26\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'energies'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'2Disp'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m \u001b[0mE\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcalc_spectrum_6\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mM\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mP\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw_c\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mw_nr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw_q\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mL\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mA\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m<ipython-input-404-37f8b25a6617>\u001b[0m in \u001b[0;36mcalc_spectrum_6\u001b[1;34m(N, M, P, w_c, w_nr, w_q, L, g, A, w, **kwargs)\u001b[0m\n\u001b[0;32m 95\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'2Disp'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 96\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 97\u001b[1;33m \u001b[0mH1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mb\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mw_nr\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mg\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mL\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m \u001b[1;33m/\u001b[0m\u001b[0mDelta\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mdelta\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msm\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0msm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mc\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 98\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mL\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mDelta\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mdelta\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0msm\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0msm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 99\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero" ] } ], "source": [ "N = 2\n", "M = 2\n", "P = 2\n", "w_c = 5\n", "w_nr = 3.5\n", "g = 0.1\n", "L = 0.001\n", "Ej = 15\n", "Ec = 0.223\n", "w = 0\n", "w_q_max = sqrt(8 * Ec * Ej) - Ec\n", "w_q = 3.5\n", "print(w_q_max)\n", "d = 0.10\n", "A = 0.00005# field aplitude\n", "kwargs = {'num_cpus':26,'energies':1, '2Disp':1}\n", "\n", "E = calc_spectrum_6(N,M,P, w_c,w_nr, w_q,L,g,A,w=0, **kwargs)" ] }, { "cell_type": "code", "execution_count": 435, "metadata": { "code_folding": [], "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0. , 3.49942822, 3.50174838, 5.00117631,\n", " 7.00117659, 8.50060452, 8.50292469, 12.0023529 ])" ] }, "execution_count": 435, "metadata": {}, "output_type": "execute_result" } ], "source": [ "E - E[0]" ] }, { "cell_type": "code", "execution_count": 469, "metadata": { "code_folding": [ 0 ], "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4.95000686255\n" ] } ], "source": [ "N = 2\n", "M = 5\n", "P = 5\n", "w_c = 5\n", "w_nr = 3.5\n", "g = 0.1\n", "L = 0.001\n", "Ej = 15\n", "Ec = 0.223\n", "w = 0\n", "w_q_max = sqrt(8 * Ec * Ej) - Ec\n", "print(w_q_max)\n", "d = 0.10\n", "A = 0.00005# field aplitude\n", "\n", "# phi = linspace(0,pi/2,200)\n", "x_i,x_f = 0.32,0.34\n", "phi = pi*linspace(x_i,x_f,100)\n", "x_vec = sqrt( 8 * Ec * Ej* abs(cos(phi))*sqrt(1+(d*tan(phi))**2) )-Ec\n", "# energies = array([calc_spectrum_6(N,M,P, w_c,w_nr, w_q/2,L,g,A,w,**kwargs)\n", "# for w_q in x_vec])\n", "kwargs = {'num_cpus':26,'energies':1, '2Disp':1}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 470, "metadata": { "code_folding": [ 0 ], "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parallel Simulation with 26 CPUs \n", "[100.0%] of the simulations calculated, Estimated Remaining time: 0.0s\n", "All done! \n", "Total time:0:00:01\n" ] } ], "source": [ "# Energies\n", "\n", "\n", "# variable to count the total number of tasks we need to do; used to create progress bar\n", "task_count =len(x_vec)\n", "\n", "\n", "\n", "# Check number of cpus to be used\n", "if 'num_cpus' in kwargs:\n", " num_cpu = kwargs['num_cpus']\n", " if num_cpu == 1:\n", " print(\"1 CPU; Serial Simulation\")\n", " else:\n", " print(\"Parallel Simulation with %d CPUs \" % num_cpu) \n", "else:\n", " num_cpu = 1\n", " print(\"Serial Simulation\")\n", "\n", "\n", "\n", "## Program to run function in parallel: \n", "\n", "try:\n", " t_start = time.time() # start time simulation\n", " time_1 = []\n", " pool = mp.Pool(processes=num_cpu) # create the initial pool to run the simulation \n", "# manager = mp.Manager()\n", "# queue = manager.Queue()\n", "\n", "\n", "# _update_progress_bar(1)\n", "# task_args = a,z\n", " results = [pool.apply_async(calc_spectrum_6,(N,\n", " M,\n", " P,\n", " w_c,\n", " w_nr,\n", " a1,\n", " L,\n", " g,\n", " A,\n", " w),kwargs\n", " ,callback=None,error_callback=None) for a1 in x_vec]\n", "\n", "\n", "\n", " #####N,M,P, w_c,w_nr, w_q,L,g,A,w=0\n", " while True:\n", " incomplete_count = sum(1 for x in results if not x.ready())\n", "\n", " if incomplete_count == 0:\n", " print(\"[100.0%] of the simulations calculated, Estimated Remaining time: 0.0s\", end=\"\\r\")\n", " print( \"\\nAll done! \\nTotal time:%s\"%datetime.timedelta(seconds=int(dif_time)))\n", " break\n", "\n", " else:\n", "\n", " p = float(task_count - incomplete_count) / task_count * 100 \n", "\n", " dif_time = (time.time() - t_start) \n", "\n", "# \n", " if p > 0:\n", " rem_time = (datetime.timedelta(seconds=int(dif_time*(100-p)/p)))\n", "\n", "# rem_time_1 = (datetime.timedelta(seconds=int(dif_time/(task_count-incomplete_count))))\n", " time_1.append(float(dif_time/(task_count- incomplete_count)))\n", "# rem_time_1 = mean(time_1) *task_count\n", "# rem_time_1 = (datetime.timedelta( seconds=int(mean(time_1) *task_count)))\n", " rem_time_1 = time.strftime(\"%Z - %Y/%m/%d, %H:%M:%S\", time.localtime(t_start+mean(time_1) *task_count))\n", " else:\n", " rem_time = '?'\n", " rem_time_1 = 0\n", "\n", "\n", " print(\"[%4.1f%%] of the simulations calculated, Estimated Remaining time: %s, (%s)\"\n", " %(p,rem_time,rem_time_1) , end=\"\\r\")\n", "\n", " time.sleep(.25)\n", "\n", "\n", " while not all([ar.ready() for ar in results]):\n", "\n", " for ar in results: \n", " ar.wait(timeout=0.1)\n", "\n", " pool.terminate()\n", " pool.join()\n", "\n", "except KeyboardInterrupt as e:\n", " pool.terminate()\n", " pool.join()\n", " raise e\n", "\n", "\n", "\n", "\n", "energies_temp = [ar.get() for ar in results]\n", "energies = asarray(energies_temp)\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 471, "metadata": { "code_folding": [], "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.LineCollection at 0x7f10d59cf8d0>" ] }, "execution_count": 471, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f10d46d35c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot \n", "fig, axes = subplots(1,1, figsize=(16,6))\n", "x_inf = -1\n", "x_sup = 10\n", "\n", "for n in range(len(energies[0,:])):\n", " axes.plot(phi/pi, (energies[:,n]-energies[:,0]),'-',linewidth=2)\n", "# axes.plot(phi/pi, (energies[:,n]-energies[:,0])/2,'--')\n", " \n", " if n < 4:\n", " axes.text(x_i,energies[0,n]-energies[0,0],r'|%s>'%(n),fontsize=20)\n", " \n", "axes.set_title('Full')\n", "axes.set_ylim(x_inf, x_sup)\n", "axes.set_xlabel(r'$\\phi$', fontsize=18)\n", "axes.set_ylabel(r'$E_n-E_0$', fontsize=18)\n", "axes.hlines(w_nr,x_i,x_f,linestyles='dashed',linewidth=3,color='green')\n", "axes.hlines(w_c,x_i,x_f,linestyles='dashed',linewidth=3)\n", "axes.vlines(0.33,0,10,linestyles='dashed',linewidth=3)\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 472, "metadata": { "code_folding": [ 0 ], "collapsed": false }, "outputs": [], "source": [ "y_i,y_f = 4.98,5.02\n", "y_vec = linspace(y_i,y_f,100) \n", "a , b = zip(*itertools.product(x_vec,y_vec))\n", "kwargs = {'num_cpus':26,'dispersive':1, '2Disp':1}" ] }, { "cell_type": "code", "execution_count": 473, "metadata": { "code_folding": [ 0 ], "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parallel Simulation with 26 CPUs \n", "[100.0%] of the simulations calculated, Estimated Remaining time: 0.0s\n", "All done! \n", "Mean time:0.006050\n", "\n", "Total time:0:01:00\n" ] } ], "source": [ "# Run Spectrum\n", "# Create from the original vectors the new vector with the correct number copies\n", "a , b = zip(*itertools.product(x_vec,y_vec))\n", "# variable to count the total number of tasks we need to do; used to create progress bar\n", "task_count =len(x_vec)*len(y_vec)\n", "\n", "# Check number of cpus to be used\n", "if 'num_cpus' in kwargs:\n", " num_cpu = kwargs['num_cpus']\n", " if num_cpu == 1:\n", " print(\"1 CPU; Serial Simulation\")\n", " else:\n", " print(\"Parallel Simulation with %d CPUs \" % num_cpu) \n", "else:\n", " num_cpu = 1\n", " print(\"Serial Simulation\")\n", "\n", "\n", "\n", "## Program to run function in parallel: \n", "t_start = time.time() # start time simulation\n", "time_1 = []\n", "try:\n", " pool = mp.Pool(processes=num_cpu) # create the initial pool to run the simulation \n", "# manager = mp.Manager()\n", "# queue = manager.Queue()\n", "\n", "\n", "# _update_progress_bar(1)\n", "# task_args = a,z\n", " results = [pool.apply_async(calc_spectrum_6,(N,\n", " M,\n", " P,\n", " w_c,\n", " w_nr,\n", " a1,\n", " L,\n", " g,\n", " A,\n", " b1),kwargs\n", " ,callback=None,error_callback=None) for a1,b1 in zip(a,b)]\n", "\n", "\n", "\n", " #####\n", " while True:\n", " incomplete_count = sum(1 for x in results if not x.ready())\n", "\n", " if incomplete_count == 0:\n", " print(\"[100.0%] of the simulations calculated, Estimated Remaining time: 0.0s\", end=\"\\r\")\n", " print( \"\\nAll done! \\nMean time:%f\"%(dif_time/task_count))\n", " print( \"\\nTotal time:%s\"%datetime.timedelta(seconds=int(dif_time)))\n", " break\n", "\n", " else:\n", "\n", " p = float(task_count - incomplete_count) / task_count * 100 \n", "\n", " dif_time = (time.time() - t_start) \n", "\n", "# \n", " if p > 0:\n", " rem_time = (datetime.timedelta(seconds=int(dif_time*(100-p)/p)))\n", "\n", "# rem_time_1 = (datetime.timedelta(seconds=int(dif_time/(task_count-incomplete_count))))\n", " time_1.append(float(dif_time/(task_count - incomplete_count)))\n", "# rem_time_1 = mean(time_1) *task_count\n", "# rem_time_1 = (datetime.timedelta( seconds=int(mean(time_1) *task_count)))\n", " rem_time_1 = time.strftime(\"%Z - %Y/%m/%d, %H:%M:%S\", time.localtime(t_start+mean(time_1) *task_count))\n", " else:\n", " rem_time = '?'\n", " rem_time_1 = 0\n", "\n", "\n", " print(\"[%4.1f%%] of the simulations calculated, Estimated Remaining time: %s, (%s)\"\n", " %(p,rem_time,rem_time_1) , end=\"\\r\")\n", "\n", " time.sleep(.25)\n", "\n", "\n", " while not all([ar.ready() for ar in results]):\n", "\n", " for ar in results: \n", " ar.wait(timeout=0.1)\n", "\n", " pool.terminate()\n", " pool.join()\n", "\n", "except KeyboardInterrupt as e:\n", " pool.terminate()\n", " pool.join()\n", " raise e\n", "\n", "\n", "\n", "\n", "results = [ar.get() for ar in results]\n", "\n" ] }, { "cell_type": "code", "execution_count": 474, "metadata": { "code_folding": [ 0 ], "collapsed": false }, "outputs": [], "source": [ "# Reshape Results\n", "#results = qload('Two_Dispersive_Simulation')\n", "results_1 = asarray(results)\n", "# qsave(results,name='One_Dispersive_Simulation_200x300')\n", "#qsave(results,name='Two_Dispersive_Simulation')\n", "# qsave(results,name='ThirtytyVolts')\n", "\n", "tr_c = reshape(results_1[:,0],(-1,len(y_vec+1)))\n", "tr_a = reshape(results_1[:,1],(-1,len(y_vec+1)))\n", "tr_b = reshape(results_1[:,2],(-1,len(y_vec+1)))\n", "tr_d = reshape(results_1[:,3],(-1,len(y_vec+1)))" ] }, { "cell_type": "code", "execution_count": 475, "metadata": { "code_folding": [ 0 ], "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f10d3fa3cc0>" ] }, "execution_count": 475, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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TNXh/zsTu9h2z/y9t8ri/artE3dZpD28ZvILbOl+rzg3La5jyd8aWJYqdH1LTvbTYumGp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"text/plain": [ "<matplotlib.figure.Figure at 0x7f10d59dd588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot Graphics\n", "fig, ax = subplots(4,1, figsize=(16,20))\n", "\n", "\n", "\n", "im = ax[0].pcolor(phi/pi,y_vec,transpose(log10(abs(tr_c))))#,vmin=0, vmax=1)\n", "fig.colorbar(im, ax=ax[0])\n", "# ax[0,0].set_xlim(4.27,4.39)\n", "# ax[0,0].set_ylim(P_i,P_f)\n", "ax[0].set_ylabel(r'Probe Frequency (GHz)',fontsize=20)\n", "# ax[0].set_xlabel(r'Qubit Tone Frequency (GHz)',fontsize=10)\n", "ax[0].set_title(r'$Tr[\\rho c]$',fontsize=20)\n", "\n", "\n", "im = ax[1].pcolor(phi/pi,y_vec,transpose((abs(tr_a))))#,vmin=0, vmax=1)\n", "fig.colorbar(im, ax=ax[1])\n", "# ax[0,0].set_xlim(4.27,4.39)\n", "# ax[0,0].set_ylim(P_i,P_f)\n", "ax[1].set_ylabel(r'Probe Frequency (GHz)',fontsize=20)\n", "# ax[1].set_xlabel(r'Qubit Tone Frequency (GHz)',fontsize=10)\n", "ax[1].set_title(r'$Tr[\\rho \\sigma_z]$',fontsize=20)\n", "\n", "im = ax[2].pcolor(phi/pi,y_vec,transpose(log10(abs(tr_b))))#,vmin=0, vmax=1)\n", "fig.colorbar(im, ax=ax[2])\n", "# ax[0,0].set_xlim(4.27,4.39)\n", "# ax[0,0].set_ylim(P_i,P_f)\n", "ax[2].set_ylabel(r'Probe Frequency (GHz)',fontsize=20)\n", "# ax[2].set_xlabel(r'Qubit Tone Frequency (GHz)',fontsize=10)\n", "ax[2].set_title(r'$Tr[\\rho b^\\dagger b]$',fontsize=20)\n", "\n", "im = ax[3].pcolor(phi/pi,y_vec,transpose((abs(tr_d))))#,vmin=0, vmax=1)\n", "fig.colorbar(im, ax=ax[3])\n", "# ax[0,0].set_xlim(4.27,4.39)\n", "# ax[0,0].set_ylim(P_i,P_f)\n", "ax[3].set_ylabel(r'Probe Frequency (GHz)',fontsize=20)\n", "ax[3].set_xlabel(r'Flux ($\\Phi_0$)',fontsize=20)\n", "ax[3].set_title(r'$Tr[\\rho c^\\dagger c]$',fontsize=20)" ] }, { "cell_type": "code", "execution_count": 476, "metadata": { "code_folding": [ 0 ], "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.colorbar.Colorbar at 0x7f10d3be7be0>" ] }, "execution_count": 476, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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jkCSpK5jnSpKS97pC0hFIkrLOic4Be/AkSZIkqWv0dg/ewi4oPVT0q4q2KfYlHUF36oKX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\nbwNuAd4PvApcBLxAqPCdSZiE2KrRigmeJI2LCV7tFeXTgqT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"text/plain": [ "<matplotlib.figure.Figure at 0x7f10d4ab2400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot Graphic II\n", "fig, axes = subplots(1,1, figsize=(16,10))\n", "y_inf = y_i\n", "y_sup = y_f\n", "x_inf = x_i\n", "x_sup = x_f\n", "\n", "for n in range(len(energies[0,:])):\n", " axes.plot(phi/pi, (energies[:,n]-energies[:,0]),'-',linewidth=1)\n", " axes.plot(phi/pi, (energies[:,n]-energies[:,0])/2,'--')\n", " \n", "# if n < 4:\n", "# axes.text(.2,energies[0,n]-energies[0,0],r'|%s>'%(n),fontsize=20)\n", " \n", "axes.set_title('Full')\n", "axes.set_ylim(y_inf, y_sup)\n", "axes.set_xlim(x_inf,x_sup)\n", "\n", "\n", "axes.set_xlabel(r'$\\phi$', fontsize=18)\n", "axes.set_ylabel(r'Cavity Tone Frequency GHz', fontsize=18)\n", "axes.hlines(w_nr,x_i,x_f,linestyles='dashed')\n", "axes.hlines(w_c,x_i,x_f,linestyles='dashed')\n", "# axes.vlines(0.245,0,10,linestyles='dashed',linewidth=3)\n", "axes.vlines(0.33,0,10,linestyles='dashed')\n", "\n", "im = axes.pcolor(phi/pi,y_vec,transpose(log10(abs(tr_c))))#axes.pcolor(phi/pi,y_vec,transpose((abs(tr))))#,vmin=0, vmax=1)\n", "fig.colorbar(im, ax=axes)\n", "# ax[0,0].set_xlim(4.27,4.39)\n", "# ax[0,0].set_ylim(P_i,P_f)\n", "# axes.set_ylabel(r'Qubit Tone Power(dBm)',fontsize=10)\n", "# axes.set_xlabel(r'Qubit Tone Frequency (GHz)',fontsize=10)\n", "# axes.set_title(r'$Tr[\\rho\\sigma_z]$',fontsize=20)" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "collapsed": true }, "source": [ "##Scan Coupling" ] }, { "cell_type": "code", "execution_count": 397, "metadata": { "code_folding": [], "collapsed": true }, "outputs": [], "source": [ "\n", "phi = 0.33 * pi\n", "w_q = sqrt( 8 * Ec * Ej* abs(cos(phi))*sqrt(1+(d*tan(phi))**2) )-Ec\n", "\n", "\n", "x_i,x_f = 0.0,0.005\n", "x_vec= linspace(x_i,x_f,60)\n", "\n", "y_i,y_f = 4.98,5.02\n", "y_vec = linspace(y_i,y_f,30) \n", "\n", "a , b = zip(*itertools.product(x_vec,y_vec))\n", "kwargs = {'num_cpus':26,'dispersive':1, 'sc':1}" ] }, { "cell_type": "code", "execution_count": 398, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parallel Simulation with 26 CPUs \n", "[100.0%] of the simulations calculated, Estimated Remaining time: 0.0s\n", "All done! \n", "Mean time:0.114948\n", "\n", "Total time:0:03:26\n" ] } ], "source": [ "# Run Spectrum\n", "# Create from the original vectors the new vector with the correct number copies\n", "a , b = zip(*itertools.product(x_vec,y_vec))\n", "# variable to count the total number of tasks we need to do; used to create progress bar\n", "task_count =len(x_vec)*len(y_vec)\n", "\n", "# Check number of cpus to be used\n", "if 'num_cpus' in kwargs:\n", " num_cpu = kwargs['num_cpus']\n", " if num_cpu == 1:\n", " print(\"1 CPU; Serial Simulation\")\n", " else:\n", " print(\"Parallel Simulation with %d CPUs \" % num_cpu) \n", "else:\n", " num_cpu = 1\n", " print(\"Serial Simulation\")\n", "\n", "\n", "\n", "## Program to run function in parallel: \n", "t_start = time.time() # start time simulation\n", "time_1 = []\n", "try:\n", " pool = mp.Pool(processes=num_cpu) # create the initial pool to run the simulation \n", "# manager = mp.Manager()\n", "# queue = manager.Queue()\n", "\n", "\n", "# _update_progress_bar(1)\n", "# task_args = a,z\n", " results = [pool.apply_async(calc_spectrum_6,(N,\n", " M,\n", " P,\n", " w_c,\n", " w_nr,\n", " w_q,\n", " a1,\n", " g,\n", " A,\n", " b1),kwargs\n", " ,callback=None,error_callback=None) for a1,b1 in zip(a,b)]\n", "\n", "\n", "\n", " #####calc_spectrum_6(N,M,P, w_c,w_nr, w_q,L,g,A,w=0, **kwargs)\n", " while True:\n", " incomplete_count = sum(1 for x in results if not x.ready())\n", "\n", " if incomplete_count == 0:\n", " print(\"[100.0%] of the simulations calculated, Estimated Remaining time: 0.0s\", end=\"\\r\")\n", " print( \"\\nAll done! \\nMean time:%f\"%(dif_time/task_count))\n", " print( \"\\nTotal time:%s\"%datetime.timedelta(seconds=int(dif_time)))\n", " break\n", "\n", " else:\n", "\n", " p = float(task_count - incomplete_count) / task_count * 100 \n", "\n", " dif_time = (time.time() - t_start) \n", "\n", "# \n", " if p > 0:\n", " rem_time = (datetime.timedelta(seconds=int(dif_time*(100-p)/p)))\n", "\n", "# rem_time_1 = (datetime.timedelta(seconds=int(dif_time/(task_count-incomplete_count))))\n", " time_1.append(float(dif_time/(task_count - incomplete_count)))\n", "# rem_time_1 = mean(time_1) *task_count\n", "# rem_time_1 = (datetime.timedelta( seconds=int(mean(time_1) *task_count)))\n", " rem_time_1 = time.strftime(\"%Z - %Y/%m/%d, %H:%M:%S\", time.localtime(t_start+mean(time_1) *task_count))\n", " else:\n", " rem_time = '?'\n", " rem_time_1 = 0\n", "\n", "\n", " print(\"[%4.1f%%] of the simulations calculated, Estimated Remaining time: %s, (%s)\"\n", " %(p,rem_time,rem_time_1) , end=\"\\r\")\n", "\n", " time.sleep(.25)\n", "\n", "\n", " while not all([ar.ready() for ar in results]):\n", "\n", " for ar in results: \n", " ar.wait(timeout=0.1)\n", "\n", " pool.terminate()\n", " pool.join()\n", "\n", "except KeyboardInterrupt as e:\n", " pool.terminate()\n", " pool.join()\n", " raise e\n", "\n", "\n", "\n", "\n", "results = [ar.get() for ar in results]\n", "\n" ] }, { "cell_type": "code", "execution_count": 399, "metadata": { "code_folding": [ 0 ], "collapsed": true }, "outputs": [], "source": [ "#results = qload('Two_Dispersive_Simulation')\n", "results_2 = asarray(results)\n", "# qsave(results,name='One_Dispersive_Simulation_200x300')\n", "#qsave(results,name='Two_Dispersive_Simulation')\n", "# qsave(results,name='ThirtytyVolts')\n", "\n", "tr_c = reshape(results_2[:,0],(-1,len(y_vec+1)))\n", "tr_a = reshape(results_2[:,1],(-1,len(y_vec+1)))\n", "tr_b = reshape(results_2[:,2],(-1,len(y_vec+1)))\n", "tr_d = reshape(results_2[:,3],(-1,len(y_vec+1)))" ] }, { "cell_type": "code", "execution_count": 400, "metadata": { "code_folding": [ 0 ], "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f10d711ea20>" ] }, "execution_count": 400, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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I+S3AA8AM4PvAG6l/GOZDxMoxMyi/JOhZwP3EajM7FOzfl1ha9H7gywX71yWK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\nxJq4z9M/qAPYEFgBrAROJtbSLcvOvZUI2t4GHEVfA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"text/plain": [ "<matplotlib.figure.Figure at 0x7f10d8958518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot Graphics\n", "fig, ax = subplots(4,1, figsize=(16,20))\n", "\n", "\n", "\n", "im = ax[0].pcolor(x_vec,y_vec,transpose(log10(abs(tr_c))))#,vmin=0, vmax=1)\n", "fig.colorbar(im, ax=ax[0])\n", "# ax[0,0].set_xlim(4.27,4.39)\n", "# ax[0,0].set_ylim(P_i,P_f)\n", "ax[0].set_ylabel(r'Probe Frequency (GHz)',fontsize=20)\n", "# ax[0].set_xlabel(r'Qubit Tone Frequency (GHz)',fontsize=10)\n", "ax[0].set_title(r'$Tr[\\rho c]$',fontsize=20)\n", "\n", "\n", "im = ax[1].pcolor(x_vec,y_vec,transpose((abs(tr_a))))#,vmin=0, vmax=1)\n", "fig.colorbar(im, ax=ax[1])\n", "# ax[0,0].set_xlim(4.27,4.39)\n", "# ax[0,0].set_ylim(P_i,P_f)\n", "ax[1].set_ylabel(r'Probe Frequency (GHz)',fontsize=20)\n", "# ax[1].set_xlabel(r'Qubit Tone Frequency (GHz)',fontsize=10)\n", "ax[1].set_title(r'$Tr[\\rho \\sigma_z]$',fontsize=20)\n", "\n", "im = ax[2].pcolor(x_vec,y_vec,transpose(log10(abs(tr_b))))#,vmin=0, vmax=1)\n", "fig.colorbar(im, ax=ax[2])\n", "# ax[0,0].set_xlim(4.27,4.39)\n", "# ax[0,0].set_ylim(P_i,P_f)\n", "ax[2].set_ylabel(r'Probe Frequency (GHz)',fontsize=20)\n", "# ax[2].set_xlabel(r'Qubit Tone Frequency (GHz)',fontsize=10)\n", "ax[2].set_title(r'$Tr[\\rho b^\\dagger b]$',fontsize=20)\n", "\n", "im = ax[3].pcolor(x_vec,y_vec,transpose((abs(tr_d))))#,vmin=0, vmax=1)\n", "fig.colorbar(im, ax=ax[3])\n", "# ax[0,0].set_xlim(4.27,4.39)\n", "# ax[0,0].set_ylim(P_i,P_f)\n", "ax[3].set_ylabel(r'Probe Frequency (GHz)',fontsize=20)\n", "ax[3].set_xlabel(r'$\\lambda$ GHz',fontsize=20)\n", "ax[3].set_title(r'$Tr[\\rho c^\\dagger c]$',fontsize=20)" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [] }, "source": [ "## Simulation New Hamiltonian " ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "$$\n", "H = \n", "-\\dfrac{E_{el}}{2}\\sigma_x\n", "-\\dfrac{E_J}{2}\\sigma_z\n", "+\\omega_c c^\\dagger c \n", "+\\omega_{NR} b^\\dagger b\n", "$$" ] }, { "cell_type": "code", "execution_count": 515, "metadata": { "code_folding": [], "collapsed": false }, "outputs": [], "source": [ "def calc_spectrum_7(M,P, w_c,w_nr, E_j,E_c, nac, ndc, g, A, w=0, **kwargs):\n", " \n", " # dispersive Qubit CPW NR \n", " \n", " # qubit operators\n", " \n", " sm = tensor(destroy(2),qeye(M),qeye(P))\n", " sz = tensor(sigmaz(),qeye(M),qeye(P))\n", " sx = tensor(sigmax(),qeye(M),qeye(P))\n", " nq = sm.dag() * sm\n", " xq = sm + sm.dag()\n", " I = tensor(qeye(2), qeye(M),qeye(P))\n", " \n", " \n", " # mechanical resonator operators\n", " \n", " b = tensor(qeye(2),destroy(M),qeye(P))\n", " n_b = b.dag() * b\n", " x_b = b.dag() + b\n", " p_b = b - b.dag()\n", " \n", " \n", " # CPW operators\n", " \n", " c = tensor(qeye(2),qeye(M),destroy(P))\n", " n_c = c.dag() * c\n", " x_c = c.dag() + c\n", " p_c = c - c.dag()\n", " \n", " # Identity\n", " \n", " I = tensor(qeye(2),qeye(M),qeye(P))\n", " \n", " \n", " # Hamiltonian\n", " \n", " H1 = w_nr * b.dag()*b\n", " \n", " H2 = w_c * c.dag()* c\n", " \n", " H2a = (w_c-w)*c.dag()*c\n", " \n", " ng = I/2 + nac * (c.dag() + c) + ndc * (b.dag() + b)\n", " Ecx = E_c * (1 + g*x_b)\n", " \n", " E_el = 4*Ecx*(1-2*ng)\n", "\n", " \n", " H3 = - E_el/2 * sx \n", " \n", " H4 = - E_j/2 * sz\n", " \n", " H5 = A*x_c\n", " \n", " # Time domain\n", " \n", " \n", " \n", " \n", " # Colapse Operators\n", " \n", " c_op_list = []\n", " \n", " kappa_n = 0.0002468 # cavity\n", " \n", " gamma_rel = 6.66e-04 # qubit\n", " gamma_dep = 0.0012 # qubit\n", " \n", " Gamma_m = 0.001 # MR\n", " \n", " Ta = 60e-3 #k\n", " Tb = 60e-3 #k\n", " \n", " n_th_a = 1/(exp(sc.h*E_j*1e9/(sc.k*Ta)-1))\n", " n_th_b = 1/(exp(sc.h*w_nr*1e9/(sc.k*Tb)-1))\n", " \n", " # cavity\n", " c_op_list = []\n", "\n", " rate = kappa_n * (1 + n_th_a)\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * c)\n", "\n", " rate = kappa_n * n_th_a\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * c.dag())\n", "\n", " rate = gamma_rel * (1 + n_th_a)\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * sm)\n", "\n", " rate = gamma_rel * (n_th_a)\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * sm.dag())\n", "\n", " rate = gamma_dep / 2 * (1 + n_th_a)\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * sz)\n", " \n", " rate = Gamma_m * (1 + n_th_b)\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * b)\n", "\n", " rate = Gamma_m * n_th_b\n", " if rate > 0.0:\n", " c_op_list.append(sqrt(rate) * b.dag()) \n", " \n", " \n", " \n", "# if 'dispersive' in kwargs:\n", " \n", "# H0 = H1 + H2b + H4 #+ H5\n", "# rho = steadystate(H0,c_op_list)\n", "# rho_c = rho*c\n", "# return rho_c.tr()\n", " \n", " \n", " if 'mapping' in kwargs:\n", " \n", " H0 = H1 + H2a + H3 + H4 + H5\n", " rho = steadystate(H0,c_op_list)\n", " rho_c = rho*c\n", " return rho_c.tr()\n", " \n", " elif 'energies'in kwargs:\n", " H = H1 + H2 + H3 + H4 #+ H3\n", " \n", " return H.eigenenergies() #+ H4\n", " \n", "# elif 'time'in kwargs:\n", " \n", "# H0 = H1 + H2 + H3 + H4 \n", "# H_args = {'H0': H0, 'c': c, 'cDag': c.dag() , 'A' : A , 'w': w}\n", "\n", "# # rho = steadystate(H,c_op_list)\n", "\n", "# T = 1/ w\n", "\n", "# U = propagator(Ht, T, c_op_list, H_args)\n", "\n", "# rho_ss = propagator_steadystate(U)\n", "\n", "# rho_c = rho_ss*c\n", " \n", "# return rho_c.tr()\n", "\n", " \n", "\n" ] }, { "cell_type": "code", "execution_count": 516, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X_ZPM = 1.39704864016e-14\n", "g0 = 0.0310351802769\n", "N_ac = 0.100681009983\n", "n_dc = 0.0290656404686\n", "E_q = 4.85964425627\n" ] } ], "source": [ "N,M,P = 2, 3 ,3\n", "\n", "# qubit Cavity parameters\n", "Ej_max = 16 \n", "Ej = 4 \n", "Ec = 0.2 \n", "w_nr = 3.5 \n", "w_c = 5 \n", "Cg = 10e-15 \n", "Cc = 1e-12\n", "Cb = 50e-15\n", "Cnr = 2e-17\n", "\n", "# mechanical resonator\n", "\n", "m = (700e-9)*(65e-9)*(100e-9)*2700 \n", "Xzpm = sqrt(sc.hbar/(2*m*w_nr*2*pi*1e9))\n", "print('X_ZPM =',Xzpm)\n", "d0 = 30e-9 \n", "g0 = Ec/d0 *Cnr/(Cg+Cb+Cnr)*Xzpm*1e9\n", "print('g0 =', g0)\n", "\n", "# Cavity effect\n", "n_ac = Cg /2/sc.e * sqrt(sc.h*w_c*2*pi*1e9/2/Cc)\n", "print('N_ac =',n_ac)\n", "\n", "V_dc = 10\n", "\n", "n_dc = Cnr/d0*V_dc/2/sc.e*Xzpm*100\n", "\n", "print('n_dc = ',n_dc)\n", "\n", "d = 0.1 # asymetry \n", "\n", "A = 0.0005# field aplitude\n", "\n", "w = 5.001\n", "print('E_q =', sqrt(8 * Ec * Ej_max) - Ec)\n", "\n", "d = 0.1\n", "A = 0.0003# field aplitude\n", "kwargs = {'energies':12}\n", "\n", "# phi = linspace(0,pi/2,200)\n", "x_i,x_f = 0,1\n", "phi = pi*linspace(x_i,x_f,100)\n", "x_vec = (sqrt(8 * Ec * Ej_max) - Ec) * abs(cos(phi))*sqrt(1+(d*tan(phi))**2)\n", "# energies = array([calc_spectrum_6(N,M,P, w_c,w_nr, w_q/2,L,g,A,w,**kwargs)\n", "# for w_q in x_vec])" ] }, { "cell_type": "code", "execution_count": 517, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parallel Simulation with 12 CPUs \n", "[0.0%] of the simulations calculated, Estimated Remaining time: 0.0s\n", "All done! \n", "Approximated total time:0:00:02\n" ] } ], "source": [ "kwargs = {'num_cpus':12,'energies':1}\n", "\n", "# variable to count the total number of tasks we need to do; used to create progress bar\n", "task_count =len(x_vec)\n", "\n", "\n", "\n", "# Check number of cpus to be used\n", "if 'num_cpus' in kwargs:\n", " num_cpu = kwargs['num_cpus']\n", " if num_cpu == 1:\n", " print(\"1 CPU; Serial Simulation\")\n", " else:\n", " print(\"Parallel Simulation with %d CPUs \" % num_cpu) \n", "else:\n", " num_cpu = 1\n", " print(\"Serial Simulation\")\n", "\n", "\n", "\n", "## Program to run function in parallel: \n", "\n", "try:\n", " t_start = time.time() # start time simulation\n", " time_1 =[]\n", " pool = mp.Pool(processes=num_cpu) # create the initial pool to run the simulation \n", "# manager = mp.Manager()\n", "# queue = manager.Queue()\n", "\n", "\n", "# _update_progress_bar(1)\n", "# task_args = a,z\n", " results = [pool.apply_async(calc_spectrum_7,(M,\n", " P,\n", " w_c,\n", " w_nr,\n", " a1,\n", " Ec,\n", " n_ac,\n", " n_dc,\n", " g,\n", " A,\n", " w),\n", " kwargs\n", " ,callback=None,error_callback=None) for a1 in x_vec]\n", "\n", "\n", "\n", " #####\n", " while True:\n", " incomplete_count = sum(1 for x in results if not x.ready())\n", "\n", " if incomplete_count == 0:\n", " print(\"[0.0%] of the simulations calculated, Estimated Remaining time: 0.0s\", end=\"\\r\")\n", " print( \"\\nAll done! \\nApproximated total time:%s\"%datetime.timedelta(seconds=int(dif_time)))\n", " break\n", "\n", " else:\n", "\n", " p = float(task_count - incomplete_count) / task_count * 100 \n", " \n", " dif_time = (time.time() - t_start) \n", " if p > 0:\n", "\n", " rem_time = (datetime.timedelta(seconds=int(dif_time*(100-p)/p)))\n", " time_1.append(float(dif_time/(task_count- incomplete_count)))\n", "# rem_time_1 = mean(time_1) *task_count\n", " rem_time_1 = (datetime.timedelta(seconds=int(mean(time_1) * task_count)))\n", " else:\n", " rem_time = 0\n", " rem_time_1 = 0\n", "\n", " print(\"[%4.1f%%] of the simulations calculated, Estimated: Remaining: %s (Total %s)\"\n", " %(p,rem_time,rem_time_1) , end=\"\\r\")\n", "\n", " time.sleep(.25)\n", "\n", "\n", " while not all([ar.ready() for ar in results]):\n", "\n", " for ar in results: \n", " ar.wait(timeout=0.1)\n", "\n", " pool.terminate()\n", " pool.join()\n", "\n", "except KeyboardInterrupt as e:\n", " pool.terminate()\n", " pool.join()\n", " raise e\n", "\n", "\n", "\n", "energies_temp = [ar.get() for ar in results]\n", "energies = asarray(energies_temp)\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 518, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.LineCollection at 0x7f9ef8e26f60>" ] }, "execution_count": 518, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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tRQNMBD4nTsCqzx8KrO6PTwX+ATxFeYRC6/0Z2ESv0u+t9NjR/fZeV1vUMoKk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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9ef95db390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = subplots(1,1, figsize=(16,6))\n", "x_inf = 0\n", "x_sup = 10\n", "\n", "for n in range(len(energies[0,:])):\n", " axes.plot(phi/pi, (energies[:,n]-energies[:,0]),'-',linewidth=2)\n", "# axes.plot(phi/pi, (energies[:,n]-energies[:,0])/2,'--')\n", " \n", "# if n < 4:\n", "# axes.text(.2,energies[0,n]-energies[0,0],r'|%s>'%(n),fontsize=20)\n", " \n", "axes.set_title('Full')\n", "axes.set_ylim(x_inf, x_sup)\n", "axes.set_xlabel(r'$\\phi$', fontsize=18)\n", "axes.set_ylabel(r'$E_n-E_0$', fontsize=18)\n", "axes.hlines(w_nr,x_i,x_f,linestyles='dashed',linewidth=3,color='green')\n", "axes.hlines(w_c,x_i,x_f,linestyles='dashed',linewidth=3)\n", "axes.vlines(0.255,0,10,linestyles='dashed',linewidth=3)\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 519, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_i,y_f = 4.7,5.3\n", "y_vec = linspace(y_i,y_f,100) \n", " \n", "# phi = linspace(0,pi/2,100)\n", "\n", "# x_vec = w_q_max * abs(cos(phi))*sqrt(1+(d*tan(phi))**2)\n", "\n", "\n", "\n", "# a , b = zip(*itertools.product(x_vec,y_vec))\n", "kwargs = {'num_cpus':12,'mapping':1}" ] }, { "cell_type": "code", "execution_count": 520, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parallel Simulation with 12 CPUs \n", "[0.0%] of the simulations calculated, Estimated Remaining time: 0.0s\n", "All done! \n", "Approximated total time:0:08:36\n" ] } ], "source": [ "# Create from the original vectors the new vector with the correct number copies\n", "a , b = zip(*itertools.product(x_vec,y_vec))\n", "# variable to count the total number of tasks we need to do; used to create progress bar\n", "task_count =len(x_vec)*len(y_vec)\n", "\n", "\n", "\n", "# Check number of cpus to be used\n", "if 'num_cpus' in kwargs:\n", " num_cpu = kwargs['num_cpus']\n", " if num_cpu == 1:\n", " print(\"1 CPU; Serial Simulation\")\n", " else:\n", " print(\"Parallel Simulation with %d CPUs \" % num_cpu) \n", "else:\n", " num_cpu = 1\n", " print(\"Serial Simulation\")\n", "\n", "\n", "\n", "## Program to run function in parallel: \n", "\n", "try:\n", " t_start = time.time() # start time simulation\n", " pool = mp.Pool(processes=num_cpu) # create the initial pool to run the simulation \n", "# manager = mp.Manager()\n", "# queue = manager.Queue()\n", "\n", "\n", "# _update_progress_bar(1)\n", "# task_args = a,z\n", " results = [pool.apply_async(calc_spectrum_7,(M,\n", " P,\n", " w_c,\n", " w_nr,\n", " a1,\n", " n_ac,\n", " n_dc,\n", " g,\n", " A,\n", " b1),kwargs\n", " ,callback=None,error_callback=None) for a1,b1 in zip(a,b)]\n", "\n", "\n", "\n", " #####\n", " while True:\n", " incomplete_count = sum(1 for x in results if not x.ready())\n", "\n", " if incomplete_count == 0:\n", " print(\"[0.0%] of the simulations calculated, Estimated Remaining time: 0.0s\", end=\"\\r\")\n", " print( \"\\nAll done! \\nApproximated total time:%s\"%datetime.timedelta(seconds=int(dif_time)))\n", " break\n", "\n", " else:\n", "\n", " p = float(task_count - incomplete_count) / task_count * 100 \n", " \n", " dif_time = (time.time() - t_start) \n", " if p > 0:\n", "\n", " rem_time = (datetime.timedelta(seconds=int(dif_time*(100-p)/p)))\n", " time_1.append(float(dif_time/(task_count- incomplete_count)))\n", "# rem_time_1 = mean(time_1) *task_count\n", " rem_time_1 = (datetime.timedelta(seconds=int(mean(time_1) * task_count)))\n", " else:\n", " rem_time = 0\n", " rem_time_1 = 0\n", "\n", " print(\"[%4.1f%%] of the simulations calculated, Estimated Remaining: %s (Total %s)\"\n", " %(p,rem_time,rem_time_1) , end=\"\\r\")\n", "\n", " time.sleep(.25)\n", "\n", "\n", " while not all([ar.ready() for ar in results]):\n", "\n", " for ar in results: \n", " ar.wait(timeout=0.1)\n", "\n", " pool.terminate()\n", " pool.join()\n", "\n", "except KeyboardInterrupt as e:\n", " pool.terminate()\n", " pool.join()\n", " raise e\n", "\n", "\n", "\n", "results = [ar.get() for ar in results]\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 521, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(100, 100)" ] }, "execution_count": 521, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tr = reshape(results,(-1,len(y_vec+1)))\n", "shape(tr)" ] }, { "cell_type": "code", "execution_count": 522, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.colorbar.Colorbar at 0x7f9f0d7e80b8>" ] }, "execution_count": 522, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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SNTUZqauPLM/icW7jXK9DCax8i2WQpVKIP/nSJwlHNOfSLfNjxqLYIuI+JSkGzVkT8cDO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svMDiUjJZuz5rJXUqC7cQbdjFpkUmvWknXWhBBCzCBJ1oQwSVZ3gMRYGLu43uwwMlZEGeTAELqu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\n+XGr13QCc1qnQ3RUBLko5tzXf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"text/plain": [ "<matplotlib.figure.Figure at 0x7f9ef9e129b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = subplots(1,1, figsize=(16,10))\n", "\n", "\n", "for n in range(len(energies[0,:])):\n", " axes.plot(phi/pi, (energies[:,n]-energies[:,0]),'-',linewidth=3)\n", " axes.plot(phi/pi, (energies[:,n]-energies[:,0])/2,'--')\n", " \n", "# if n < 4:\n", "# axes.text(.2,energies[0,n]-energies[0,0],r'|%s>'%(n),fontsize=20)\n", " \n", "axes.set_title('Full')\n", "axes.set_ylim(y_i, y_f)\n", "axes.set_xlim(x_i,x_f)\n", "\n", "\n", "axes.set_xlabel(r'$\\phi$', fontsize=18)\n", "axes.set_ylabel(r'Cavity Tone Frequency GHz', fontsize=18)\n", "axes.hlines(w_nr,x_i,x_f,linestyles='dashed')\n", "axes.hlines(w_c,x_i,x_f,linestyles='dashed')\n", "axes.vlines(0.245,0,10,linestyles='dashed',linewidth=3)\n", "# axes.vlines(0.24,0,10,linestyles='dashed')\n", "\n", "im = axes.pcolor(phi/pi,y_vec,transpose(log(abs(tr))))#axes.pcolor(phi/pi,y_vec,transpose((abs(tr))))#,vmin=0, vmax=1)\n", "fig.colorbar(im, ax=axes)\n", "# ax[0,0].set_xlim(4.27,4.39)\n", "# ax[0,0].set_ylim(P_i,P_f)\n", "# axes.set_ylabel(r'Qubit Tone Power(dBm)',fontsize=10)\n", "# axes.set_xlabel(r'Qubit Tone Frequency (GHz)',fontsize=10)\n", "# axes.set_title(r'$Tr[\\rho\\sigma_z]$',fontsize=20)" ] }, { "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": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }
cc0-1.0
scikit-learn-contrib/hdbscan
notebooks/Looking at cluster consistency.ipynb
1
544245
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Looking at cluster consistency\n", "\n", "After clustering some data, you often want to (and should!) inspect the results. Obviously, visualization is a big part of this. It is also helpful to look at some representative examples. HDBSCAN provides methods to get an approximate representative point for each cluster. It does this by calculating the centroid/medoid for each cluster, but weighting each point by its cluster membership strength (i.e. the probability of being in that cluster). One way to get representative samples for a cluster is to look for the points closest and furthest to this representative point. This allows you to look at the cluster and determine how consistent it is. For example, when clustering text this might mean \"are the documents in my cluster talking about one topic?\". \n", "\n", "Let's get some test data and cluster it:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import hdbscan\n", "from scipy.spatial.distance import cdist\n", "#Some plotting libraries\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib notebook\n", "\n", "sns.set_context('poster')\n", "sns.set_color_codes()\n", "plot_kwds = {'alpha' : 0.25, 's' : 40, 'linewidths':0}" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data = np.load('clusterable_data.npy')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "clusterer = hdbscan.HDBSCAN(min_cluster_size=15)\n", "clusterer.fit(data)\n", "labels = clusterer.labels_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's plot our sample data. We will see that the clustering is pretty good." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\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", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.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 = $(\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 backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\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 * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\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'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\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 * mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\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 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 = $(\"#\" + 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", " var width = fig.canvas.width/mpl.ratio\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 + '\" 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/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<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 () { 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", " // 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", "\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 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\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "palette = sns.color_palette('deep', np.unique(labels).max() + 1)\n", "colors = [palette[x] if x >= 0 else (0.0, 0.0, 0.0) for x in labels]\n", "plt.scatter(data.T[0], data.T[1], c=colors, **plot_kwds)\n", "frame = plt.gca()\n", "frame.axes.get_xaxis().set_visible(False)\n", "frame.axes.get_yaxis().set_visible(False)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Finding representative points\n", "\n", "Below is a class that you can use to find the points closest to and furthest away from the cluster centroids/medoids." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "class RankedPoints:\n", " \n", " def __init__(self, points, clusterer, metric='euclidean', selection_method='centroid'):\n", " \"\"\" Rank points in a cluster based on their distance to the cluster centroid/medoid\n", " \n", " Parameters\n", " ----------\n", " \n", " points : array of shape (n_samples, n_features), and must be the same data passed into\n", " HDBSCAN\n", " \n", " clusterer : Instance of HDBSCAN that has been fit to data\n", " \n", " metric: string or callable, optional (default='euclidean')\n", " The metric to use when calculating distance between points in a cluster and \n", " the cluster centroid/medoid. If metric is a string or callable, it must be one of\n", " the options allowed by scipy.spatial.distance.cdist for its metric parameter.\n", " \n", " selection_method: string, optional (default='centroid')\n", " Method to use to find the weighted cluster center. Allowed options are 'centroid' \n", " and 'medoid'.\n", " \n", " \"\"\"\n", " self.clusterer = clusterer\n", " self.metric = metric\n", " \n", " allowed_methods = ['centroid', 'medoid']\n", " if selection_method not in allowed_methods:\n", " raise ValueError(f'Selection method must be one of {allowed_methods}')\n", " \n", " if selection_method == 'centroid' and metric != 'euclidean':\n", " raise ValueError(f'Metric must be euclidian when using selection_method centroid. '\n", " f'Current metric is {metric}')\n", " \n", " self.selection_method = selection_method\n", " \n", " self._embedding_cols = [str(i) for i in range(points.shape[1])]\n", " self.embedding_df = pd.DataFrame(points, columns=self._embedding_cols)\n", " self.embedding_df['cluster'] = clusterer.labels_\n", " \n", " def calculate_all_distances_to_center(self):\n", " \"\"\"For each cluster calculate the distance from each point to the centroid/medoid\"\"\"\n", " all_distances = pd.DataFrame()\n", " for label in np.unique(self.embedding_df['cluster']): \n", " distance_df = self.calculate_distances_for_cluster(label)\n", " all_distances = pd.concat([all_distances, distance_df])\n", " \n", " self.embedding_df = self.embedding_df.merge(all_distances, left_index=True, right_index=True)\n", " \n", " def calculate_distances_for_cluster(self, cluster_id):\n", " \"\"\"For a given cluster_id calculate the distance from each point to the centroid/medoid.\n", " \n", " Parameters\n", " ----------\n", "\n", " cluster_id : int\n", " The id of the cluster to compute the distances for. If the cluster id is -1 which\n", " corresponds to the noise point cluster, then this will return a distance of NaN.\n", "\n", " Returns\n", " -------\n", "\n", " df : A pandas DataFrame containing the distances from each point to the cluster centroid/medoid.\n", " The index of the dataframe corresponds to the index in the original data. \n", "\n", " \"\"\"\n", " cluster_of_interest = self.embedding_df[self.embedding_df['cluster'] == cluster_id].copy()\n", " \n", " if cluster_of_interest.empty:\n", " raise ValueError(f'Cluster id {cluster_id} not found')\n", " \n", " # Don't calculate distances for the noise cluster\n", " if cluster_id == -1:\n", " return pd.DataFrame(np.nan, columns=['dist_to_rep_point'], index=cluster_of_interest.index)\n", " \n", " if self.selection_method == 'centroid':\n", " rep_point = self.clusterer.weighted_cluster_centroid(cluster_id)\n", " if self.selection_method == 'medoid':\n", " rep_point = self.clusterer.weighted_cluster_medoid(cluster_id)\n", " \n", " dists = cdist(rep_point.reshape((1,len(self._embedding_cols))), cluster_of_interest[self._embedding_cols].values, metric=self.metric)\n", " return pd.DataFrame(dists[0], columns=['dist_to_rep_point'], index=cluster_of_interest.index)\n", " \n", " def rank_cluster_points_by_distance(self, cluster_id):\n", " \"\"\"For a given cluster return a pandas dataframe of points ranked \n", " by distance to the cluster centroid/medoid\n", " \"\"\"\n", " cluster_of_interest = self.embedding_df[self.embedding_df['cluster'] == cluster_id].copy()\n", " \n", " if cluster_of_interest.empty:\n", " raise ValueError(f'Cluster id {cluster_id} not found')\n", " \n", " if 'dist_to_rep_point' not in self.embedding_df.columns:\n", " distance_df = self.calculate_distances_for_cluster(cluster_id)\n", " cluster_of_interest = cluster_of_interest.merge(distance_df, left_index=True, right_index=True)\n", " \n", " cluster_of_interest.sort_values('dist_to_rep_point', inplace=True)\n", " return cluster_of_interest\n", " \n", " def get_closest_samples_for_cluster(self, cluster_id, n_samples=5):\n", " \"\"\"Get the N closest points to the cluster centroid/medoid\"\"\"\n", " return self.rank_cluster_points_by_distance(cluster_id).head(n_samples)\n", " \n", " def get_furthest_samples_for_cluster(self, cluster_id, n_samples=5):\n", " \"\"\"Get the N points furthest away from the cluster centroid/medoid\"\"\"\n", " return self.rank_cluster_points_by_distance(cluster_id).tail(n_samples)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To use it we first need to instantiate the class. We pass in our sample data and the pretrained HDBSCAN class instance." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "examples = RankedPoints(data, clusterer, metric='euclidean', selection_method='medoid')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can calculate the distances to the center for all clusters" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "examples.calculate_all_distances_to_center()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or just one" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>dist_to_rep_point</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1200</th>\n", " <td>0.185616</td>\n", " </tr>\n", " <tr>\n", " <th>1201</th>\n", " <td>0.061235</td>\n", " </tr>\n", " <tr>\n", " <th>1202</th>\n", " <td>0.225697</td>\n", " </tr>\n", " <tr>\n", " <th>1205</th>\n", " <td>0.033554</td>\n", " </tr>\n", " <tr>\n", " <th>1206</th>\n", " <td>0.025164</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>2194</th>\n", " <td>0.098091</td>\n", " </tr>\n", " <tr>\n", " <th>2220</th>\n", " <td>0.054152</td>\n", " </tr>\n", " <tr>\n", " <th>2268</th>\n", " <td>0.200824</td>\n", " </tr>\n", " <tr>\n", " <th>2272</th>\n", " <td>0.124824</td>\n", " </tr>\n", " <tr>\n", " <th>2275</th>\n", " <td>0.112542</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>275 rows × 1 columns</p>\n", "</div>" ], "text/plain": [ " dist_to_rep_point\n", "1200 0.185616\n", "1201 0.061235\n", "1202 0.225697\n", "1205 0.033554\n", "1206 0.025164\n", "... ...\n", "2194 0.098091\n", "2220 0.054152\n", "2268 0.200824\n", "2272 0.124824\n", "2275 0.112542\n", "\n", "[275 rows x 1 columns]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "distance_to_cluster_1_center = examples.calculate_distances_for_cluster(1)\n", "distance_to_cluster_1_center" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perhaps more usefully, you can get a list of points ranked based on their distance" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>cluster</th>\n", " <th>dist_to_rep_point</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1582</th>\n", " <td>-0.207633</td>\n", " <td>0.230794</td>\n", " <td>1</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>1565</th>\n", " <td>-0.209338</td>\n", " <td>0.235881</td>\n", " <td>1</td>\n", " <td>0.005365</td>\n", " </tr>\n", " <tr>\n", " <th>1448</th>\n", " <td>-0.214983</td>\n", " <td>0.231339</td>\n", " <td>1</td>\n", " <td>0.007371</td>\n", " </tr>\n", " <tr>\n", " <th>1540</th>\n", " <td>-0.202308</td>\n", " <td>0.222834</td>\n", " <td>1</td>\n", " <td>0.009577</td>\n", " </tr>\n", " <tr>\n", " <th>1219</th>\n", " <td>-0.204521</td>\n", " <td>0.240115</td>\n", " <td>1</td>\n", " <td>0.009827</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>1291</th>\n", " <td>-0.322111</td>\n", " <td>0.037692</td>\n", " <td>1</td>\n", " <td>0.224486</td>\n", " </tr>\n", " <tr>\n", " <th>1451</th>\n", " <td>-0.124656</td>\n", " <td>0.439562</td>\n", " <td>1</td>\n", " <td>0.224653</td>\n", " </tr>\n", " <tr>\n", " <th>1202</th>\n", " <td>-0.319037</td>\n", " <td>0.034508</td>\n", " <td>1</td>\n", " <td>0.225697</td>\n", " </tr>\n", " <tr>\n", " <th>1469</th>\n", " <td>-0.126015</td>\n", " <td>0.445464</td>\n", " <td>1</td>\n", " <td>0.229662</td>\n", " </tr>\n", " <tr>\n", " <th>1442</th>\n", " <td>-0.334085</td>\n", " <td>0.035491</td>\n", " <td>1</td>\n", " <td>0.232666</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>275 rows × 4 columns</p>\n", "</div>" ], "text/plain": [ " 0 1 cluster dist_to_rep_point\n", "1582 -0.207633 0.230794 1 0.000000\n", "1565 -0.209338 0.235881 1 0.005365\n", "1448 -0.214983 0.231339 1 0.007371\n", "1540 -0.202308 0.222834 1 0.009577\n", "1219 -0.204521 0.240115 1 0.009827\n", "... ... ... ... ...\n", "1291 -0.322111 0.037692 1 0.224486\n", "1451 -0.124656 0.439562 1 0.224653\n", "1202 -0.319037 0.034508 1 0.225697\n", "1469 -0.126015 0.445464 1 0.229662\n", "1442 -0.334085 0.035491 1 0.232666\n", "\n", "[275 rows x 4 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cluster_1_ranked = examples.rank_cluster_points_by_distance(1)\n", "cluster_1_ranked" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Getting the N \"most/least representative\" points for a cluster\n", "\n", "There are a couple of wrapper methods to get the N points closest/furthest to the cluster center. All of these methods will return a dataframe with the index corresponding to the original data. This makes it easy to merge back with your original data and enrich it with other features." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>cluster</th>\n", " <th>dist_to_rep_point</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1645</th>\n", " <td>-0.164494</td>\n", " <td>-0.036982</td>\n", " <td>2</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>1701</th>\n", " <td>-0.161733</td>\n", " <td>-0.039066</td>\n", " <td>2</td>\n", " <td>0.003460</td>\n", " </tr>\n", " <tr>\n", " <th>1840</th>\n", " <td>-0.164663</td>\n", " <td>-0.042350</td>\n", " <td>2</td>\n", " <td>0.005371</td>\n", " </tr>\n", " <tr>\n", " <th>1812</th>\n", " <td>-0.158787</td>\n", " <td>-0.035200</td>\n", " <td>2</td>\n", " <td>0.005979</td>\n", " </tr>\n", " <tr>\n", " <th>1679</th>\n", " <td>-0.165111</td>\n", " <td>-0.030426</td>\n", " <td>2</td>\n", " <td>0.006585</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 cluster dist_to_rep_point\n", "1645 -0.164494 -0.036982 2 0.000000\n", "1701 -0.161733 -0.039066 2 0.003460\n", "1840 -0.164663 -0.042350 2 0.005371\n", "1812 -0.158787 -0.035200 2 0.005979\n", "1679 -0.165111 -0.030426 2 0.006585" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "examples.get_closest_samples_for_cluster(2, n_samples=5)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>cluster</th>\n", " <th>dist_to_rep_point</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1620</th>\n", " <td>0.014810</td>\n", " <td>0.119051</td>\n", " <td>2</td>\n", " <td>0.237690</td>\n", " </tr>\n", " <tr>\n", " <th>1733</th>\n", " <td>0.015830</td>\n", " <td>0.119390</td>\n", " <td>2</td>\n", " <td>0.238682</td>\n", " </tr>\n", " <tr>\n", " <th>1632</th>\n", " <td>0.008484</td>\n", " <td>0.129942</td>\n", " <td>2</td>\n", " <td>0.240385</td>\n", " </tr>\n", " <tr>\n", " <th>1792</th>\n", " <td>0.003510</td>\n", " <td>0.135245</td>\n", " <td>2</td>\n", " <td>0.240598</td>\n", " </tr>\n", " <tr>\n", " <th>1671</th>\n", " <td>0.019809</td>\n", " <td>0.118011</td>\n", " <td>2</td>\n", " <td>0.240812</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 cluster dist_to_rep_point\n", "1620 0.014810 0.119051 2 0.237690\n", "1733 0.015830 0.119390 2 0.238682\n", "1632 0.008484 0.129942 2 0.240385\n", "1792 0.003510 0.135245 2 0.240598\n", "1671 0.019809 0.118011 2 0.240812" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "examples.get_furthest_samples_for_cluster(2, n_samples=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can get these points for all clusters and put them into a dataframe for plotting" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "close_samples = pd.DataFrame()\n", "far_samples = pd.DataFrame()\n", "\n", "for cluster in pd.unique(clusterer.labels_):\n", " if cluster >=0:\n", " close_samples = pd.concat([close_samples, examples.get_closest_samples_for_cluster(cluster)])\n", " far_samples = pd.concat([far_samples, examples.get_furthest_samples_for_cluster(cluster)])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>cluster</th>\n", " <th>dist_to_rep_point</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>5</th>\n", " <td>-0.031930</td>\n", " <td>-0.256859</td>\n", " <td>5</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>106</th>\n", " <td>-0.032453</td>\n", " <td>-0.258743</td>\n", " <td>5</td>\n", " <td>0.001956</td>\n", " </tr>\n", " <tr>\n", " <th>116</th>\n", " <td>-0.033843</td>\n", " <td>-0.255187</td>\n", " <td>5</td>\n", " <td>0.002540</td>\n", " </tr>\n", " <tr>\n", " <th>297</th>\n", " <td>-0.026987</td>\n", " <td>-0.260578</td>\n", " <td>5</td>\n", " <td>0.006185</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>-0.023922</td>\n", " <td>-0.256170</td>\n", " <td>5</td>\n", " <td>0.008037</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 cluster dist_to_rep_point\n", "5 -0.031930 -0.256859 5 0.000000\n", "106 -0.032453 -0.258743 5 0.001956\n", "116 -0.033843 -0.255187 5 0.002540\n", "297 -0.026987 -0.260578 5 0.006185\n", "28 -0.023922 -0.256170 5 0.008037" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "close_samples.head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>cluster</th>\n", " <th>dist_to_rep_point</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>189</th>\n", " <td>-0.238119</td>\n", " <td>-0.253511</td>\n", " <td>5</td>\n", " <td>0.206217</td>\n", " </tr>\n", " <tr>\n", " <th>160</th>\n", " <td>-0.238388</td>\n", " <td>-0.278419</td>\n", " <td>5</td>\n", " <td>0.207581</td>\n", " </tr>\n", " <tr>\n", " <th>42</th>\n", " <td>-0.244433</td>\n", " <td>-0.252980</td>\n", " <td>5</td>\n", " <td>0.212539</td>\n", " </tr>\n", " <tr>\n", " <th>146</th>\n", " <td>-0.244435</td>\n", " <td>-0.263221</td>\n", " <td>5</td>\n", " <td>0.212601</td>\n", " </tr>\n", " <tr>\n", " <th>117</th>\n", " <td>-0.262338</td>\n", " <td>-0.275841</td>\n", " <td>5</td>\n", " <td>0.231189</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 cluster dist_to_rep_point\n", "189 -0.238119 -0.253511 5 0.206217\n", "160 -0.238388 -0.278419 5 0.207581\n", "42 -0.244433 -0.252980 5 0.212539\n", "146 -0.244435 -0.263221 5 0.212601\n", "117 -0.262338 -0.275841 5 0.231189" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "far_samples.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's make the same plot from before but add black stars for the \"most representative\" points and blue X's for the \"least representative\" points" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", 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' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. 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' +\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 backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\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 * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\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'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\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 * mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\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 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 = $(\"#\" + 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", " var width = fig.canvas.width/mpl.ratio\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 + '\" 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/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<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 () { 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", " // 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", "\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 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\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "palette = sns.color_palette('deep', np.unique(labels).max() + 1)\n", "colors = [palette[x] if x >= 0 else (0.0, 0.0, 0.0) for x in labels]\n", "\n", "fig = plt.figure()\n", "ax1 = fig.add_subplot(111)\n", "\n", "ax1.scatter(data.T[0], data.T[1], c=colors, **plot_kwds)\n", "ax1.scatter(close_samples['0'], close_samples['1'], s=40, marker='*', c=[(0.0,0.0,0.0)])\n", "ax1.scatter(far_samples['0'], far_samples['1'], s=40, marker='x')\n", "\n", "frame = plt.gca()\n", "frame.axes.get_xaxis().set_visible(False)\n", "frame.axes.get_yaxis().set_visible(False)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see this does what we would expect. This is a fairly crude method but can be useful when doing exploratory data analysis." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }
bsd-3-clause
maestrotf/pymepps
examples/example_plot_stationnc.ipynb
1
29059
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Load station data based on NetCDF files\n", "\n", "In this example we show how to load station data based on NetCDF files.\n", "The data is loaded with the pymepps package. Thanks to Ingo Lange we could use original data from the Wettermast for this example. In the following the data is loaded, plotted and saved as json file." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-07-13T08:00:14.996522Z", "start_time": "2017-07-13T08:00:14.057359Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For full support please install cdo see more at: \"https://code.mpimet.mpg.de/projects/cdo/wiki/Cdo#Documentation\"\n" ] } ], "source": [ "import pymepps\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We could use the global pymepps open_station_dataset function to open the Wettermast data.\n", "We have to specify the data path and the data type." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-07-13T08:00:15.560210Z", "start_time": "2017-07-13T08:00:15.500268Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:00<00:00, 33.09it/s]\n", "100%|██████████| 1/1 [00:00<00:00, 60.78it/s]\n" ] } ], "source": [ "wm_ds = pymepps.open_station_dataset('../data/station/wettermast.nc', 'nc')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2017-07-13T08:00:16.545367Z", "start_time": "2017-07-13T08:00:16.540240Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TSDataset\n", "---------\n", "File handlers: 1\n", "Variables: ['TT002_M10', 'lat', 'lon', 'product', 'station_details', 'time_bnds', 'zsl']\n", "Lonlat: None\n" ] } ], "source": [ "print(wm_ds)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we could extract the temperature in 2 m height. For this we use the select method of the resulted dataset." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-07-13T08:00:17.721586Z", "start_time": "2017-07-13T08:00:17.679312Z" }, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1/1 [00:00<00:00, 36.08it/s]\n" ] } ], "source": [ "t2m = wm_ds.select('TT002_M10')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-07-13T08:00:31.862519Z", "start_time": "2017-07-13T08:00:31.829829Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.series.Series'>\n", "count 720.000000\n", "mean -3.317834\n", "std 4.251115\n", "min -8.500000\n", "25% -6.690000\n", "50% -5.595000\n", "75% 0.802500\n", "max 5.040000\n", "Name: TT002_M10, dtype: float64\n" ] } ], "source": [ "print(type(t2m))\n", "print(t2m.describe())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We could see that the resulting temperature is a normal pandas.Series. So it is possible to use all pandas methods, e.g. plotting of the Series." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-07-13T08:01:22.028670Z", "start_time": "2017-07-13T08:01:21.780580Z" } }, "outputs": [ { "data": { "image/png": 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mRzK8VxRbD5byyMdbnA5HKeWGVhOCfZdysohox33VLiLCq9dNAqC4osbhaJRS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\n7vI9RIcGcrbdnuWUn03rS0V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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0ac405d0f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t2m.plot()\n", "plt.xlabel('Date')\n", "plt.ylabel('Temperature in °C')\n", "plt.title('Temperature at the Wettermast Hamburg')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pymepps uses an accessor to extend the pandas functionality. The accessor could be accessed with Series.pp. At the moment there is only a lonlat attribute, update, save and load method defined, but it is planned to expand the number of additional methods." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-07-13T08:01:24.641568Z", "start_time": "2017-07-13T08:01:24.636803Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "None\n" ] } ], "source": [ "print(t2m.pp.lonlat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We could see that the logitude and latitude are None at the moment, because we haven't set the yet. We could either set them directly or set the coordintes in the open_station_dataset function with the lonlat argument." ] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
EdwardJKim/info490
week10/intro2sqldml.ipynb
2
65446
{ "metadata": { "name": "", "signature": "sha256:0fffe4793b0b481c1bf625aae04e032977c788abb008dd20461c365f5e1c4ce8" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<DIV ALIGN=CENTER>\n", "\n", "# Introduction to SQL Data Manipulation Language\n", "## Professor Robert J. Brunner\n", " \n", "</DIV> \n", "-----\n", "-----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction \n", "\n", "In this lesson, we focus on the basic task of manipulating data in relational database\n", "management systems by using SQL DML. In this Notebook we will expand on our use of the SQLite\n", "database to build and query a fictitious database. We will cover inserting data into tables, creating and executing queries, and finishing with updating and deleting data.\n", "\n", "-----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### INSERT\n", "\n", "One of the most important tasks when you're building a database\n", "application is inserting data into the database. It doesn't matter how\n", "good the database software is-if you put bad data in a database, nothing\n", "else matters. There are several different ways to insert data into a\n", "database, but the rest of this lesson focuses on inserting data into a\n", "SQLite database by using the SQL INSERT statement.\n", "\n", "Before you can insert data into a SQLite database using the SQL INSERT\n", "statement, you must know how to properly use this statement. The full\n", "syntax for the SQL INSERT statement is\n", "\n", "```\n", "INSERT INTO table-Name\n", " [ (Simple-column-Name [ , Simple-column-Name]* ) ]\n", "\t Expression\n", "```\n", "\n", "which should seem familiar. As discussed previously, the square brackets\n", "([]) enclose optional parameters. The only component whose purpose isn't\n", "immediately clear is _Expression_; but how complex can that simple\n", "phrase be? Of course, appearances can be deceiving; the Expression term\n", "can expand to one of four different structures:\n", "\n", "- a single-row VALUES list\n", "- a multiple-row VALUES list\n", "- a SELECT expression\n", "- a UNION expression\n", "\n", "Of these, the last two are beyond the scope of this lesson. The first\n", "two are similar; the only difference is that the first form inserts one\n", "row into a table, whereas the latter form inserts multiple rows into a\n", "table.\n", "\n", "You can use the optional part of the SQL INSERT statement to specify the\n", "column order of the values being inserted into the table. By default,\n", "data is inserted into a table's columns in the same order that the\n", "columns were listed when the table was created. Sometimes you may want\n", "to change this order or perhaps only specify values for columns that\n", "have NOT NULL constraints. By explicitly listing the columns in your SQL\n", "INSERT statement, you gain more control of the operation and can more\n", "easily handle these specific use cases.\n", "\n", "The syntax for the SQL VALUES expression is fairly simple,\n", "\n", "```\n", "{\n", " VALUES ( Value {, Value }* )\n", " [ , ( Value {, Value }* ) ]* |\n", " VALUES Value [ , Value ]*\n", " }\n", "```\n", "\n", "This syntax displays the multiple-row format first, followed by the\n", "single-row format (remember that the vertical line character, |, means\n", "or and that the asterisk character, \\*, means one or more). The value\n", "term stands for a value that you want to insert into a specific column.\n", "To insert data into multiple columns, you must enclose the data for a\n", "row in parentheses separated by commas.\n", "\n", "As shown below, to insert data into a table, you first need to to make\n", "sure that the table exists. If you haven't already done so, execute the\n", "table creation scripts discussed earlier.\n", "\n", "```\n", "INSERT INTO myProducts \n", " VALUES(1, 19.95, '2015-03-31', 'Hooded sweatshirt') ;\n", "\n", "INSERT INTO myProducts(itemNumber, price, stockDate, description) \n", " VALUES(2, 99.99, '2015-03-29', 'Beach umbrella') ;\n", "\n", "INSERT INTO myProducts(itemNumber, price, stockDate) \n", " VALUES(3, 0.99, '2015-02-28') ;\n", "```\n", "\n", "This example presents three single-row inserts into the myProducts\n", "table. The first SQL INSERT statement doesn't provide a list of columns;\n", "it inserts an itemNumber, a price, a stockDate, and a description.\n", "Notice that the values inserted into both the stockDate and description\n", "columns are enclosed in single quote characters. The description column\n", "is a TEXT field, so it expects a string (which you indicate by enclosing\n", "the character data within single quotes). The stockDate column is also a\n", "TEXT field; as part of our application logic, we could pass in dates in\n", "the correct day, month, and year format. (For more guidance on the\n", "format of data types during a SQL INSERT operation, read the SQLite\n", "documentation).\n", "\n", "The second SQL INSERT statement explicitly lists all four columns and\n", "inserts new values appropriately. The final SQL INSERT statement lists\n", "only three columns and inserts only three values. The description column\n", "is left empty, which means it will have a NULL value.\n", "\n", "Although single-row SQL INSERT statements can be useful, when you need\n", "to insert multiple rows, it's more efficient to do so directly, as shown\n", "below:\n", "\n", " INSERT INTO myProducts(itemNumber, price, stockDate, description)\n", " VALUES (4, 29.95, '2015-02-10', 'Male bathing suit, blue'),\n", " (5, 49.95, '2015-02-20', 'Female bathing suit, one piece, aqua'),\n", " (6, 9.95, '2015-01-15', 'Child sand toy set'),\n", " (7, 24.95, '2014-12-20', 'White beach towel'),\n", " (8, 32.95, '2014-12-22', 'Blue-striped beach towel'),\n", " (9, 12.95, '2015-03-12', 'Flip-flop'),\n", " (10, 34.95, '2015-01-24', 'Open-toed sandal') ;\n", "\n", "In this example, we insert seven rows into the database by explicitly\n", "listing all four columns and providing new values for each row. As\n", "discussed earlier, multiple-row inserts enclose the values for each new\n", "row within parentheses, and these values are separated by commas. \n", "\n", "To actually execute these statements, we can place the SQL INSERT\n", "statements in a script file and run the script to insert the data. This\n", "approach lets you more easily fix errors or reinsert the data if\n", "necessary without recreating the requisite SQL INSERT statements. In the\n", "following code cells, we first create a SQL INSERT script file, before\n", "executing this script by using the `sqlite3` client tool. After this, we\n", "use the `.dump` command in the `sqlite3` client tool to display the full\n", "schema and contents of this SQLite database.\n", "\n", "-----" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%writefile insert.sql\n", "\n", "-- Single unnamed INSERT\n", "\n", "INSERT INTO myProducts \n", "VALUES(1, 19.95, '2015-03-31', 'Hooded sweatshirt') ;\n", "\n", "-- Single named INSERT\n", "\n", "INSERT INTO myProducts (itemNumber, price, stockDate, description) \n", "VALUES(2, 99.99, '2015-03-29', 'Beach umbrella') ;\n", "\n", "-- Single named INSERT with missing data\n", "\n", "INSERT INTO myProducts (itemNumber, price, stockDate) \n", "VALUES(3, 0.99, '2015-02-28') ;\n", "\n", "-- Multiple named INSERT\n", "\n", "INSERT INTO myProducts (itemNumber, price, stockDate, description)\n", "VALUES (4, 29.95, '2015-02-10', 'Male bathing suit, blue'),\n", " (5, 49.95, '2015-02-20', 'Female bathing suit, one piece, aqua'),\n", " (6, 9.95, '2015-01-15', 'Child sand toy set'),\n", " (7, 24.95, '2014-12-20', 'White beach towel'),\n", " (8, 32.95, '2014-12-22', 'Blue-striped beach towel'),\n", " (9, 12.95, '2015-03-12', 'Flip-flop'),\n", " (10, 34.95, '2015-01-24', 'Open-toed sandal') ;\n", " \n", "-- Insert into myVendors\n", "\n", "INSERT INTO myVendors(itemNumber, vendorNumber, vendorName)\n", "VALUES (1, 1, 'Luna Vista Limited'),\n", " (2, 1, 'Luna Vista Limited'),\n", " (3, 1, 'Luna Vista Limited'),\n", " (4, 2, 'Mikal Arroyo Incorporated'),\n", " (5, 2, 'Mikal Arroyo Incorporated'),\n", " (6, 1, 'Luna Vista Limited'),\n", " (7, 1, 'Luna Vista Limited'),\n", " (8, 1, 'Luna Vista Limited'),\n", " (9, 3, 'Quiet Beach Industries'),\n", " (10, 3, 'Quiet Beach Industries') ;" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Writing insert.sql\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "!head -10 insert.sql" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\r\n", "-- Single unnamed INSERT\r\n", "\r\n", "INSERT INTO myProducts \r\n", "VALUES(1, 19.95, '2015-03-31', 'Hooded sweatshirt') ;\r\n", "\r\n", "-- Single named INSERT\r\n", "\r\n", "INSERT INTO myProducts (itemNumber, price, stockDate, description) \r\n", "VALUES(2, 99.99, '2015-03-29', 'Beach umbrella') ;\r\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "!sqlite3 test < insert.sql" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "!sqlite3 test \".dump\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "PRAGMA foreign_keys=OFF;\r\n", "BEGIN TRANSACTION;\r\n", "CREATE TABLE myVendors (\r\n", " itemNumber INT NOT NULL,\r\n", " vendornumber INT NOT NULL,\r\n", " vendorName TEXT\r\n", ");\r\n", "INSERT INTO \"myVendors\" VALUES(1,1,'Luna Vista Limited');\r\n", "INSERT INTO \"myVendors\" VALUES(2,1,'Luna Vista Limited');\r\n", "INSERT INTO \"myVendors\" VALUES(3,1,'Luna Vista Limited');\r\n", "INSERT INTO \"myVendors\" VALUES(4,2,'Mikal Arroyo Incorporated');\r\n", "INSERT INTO \"myVendors\" VALUES(5,2,'Mikal Arroyo Incorporated');\r\n", "INSERT INTO \"myVendors\" VALUES(6,1,'Luna Vista Limited');\r\n", "INSERT INTO \"myVendors\" VALUES(7,1,'Luna Vista Limited');\r\n", "INSERT INTO \"myVendors\" VALUES(8,1,'Luna Vista Limited');\r\n", "INSERT INTO \"myVendors\" VALUES(9,3,'Quiet Beach Industries');\r\n", "INSERT INTO \"myVendors\" VALUES(10,3,'Quiet Beach Industries');\r\n", "CREATE TABLE myProducts (\r\n", " itemNumber INT NOT NULL,\r\n", " price REAL,\r\n", " stockDate TEXT,\r\n", " description TEXT\r\n", ");\r\n", "INSERT INTO \"myProducts\" VALUES(1,19.95,'2015-03-31','Hooded sweatshirt');\r\n", "INSERT INTO \"myProducts\" VALUES(2,99.99,'2015-03-29','Beach umbrella');\r\n", "INSERT INTO \"myProducts\" VALUES(3,0.99,'2015-02-28',NULL);\r\n", "INSERT INTO \"myProducts\" VALUES(4,29.95,'2015-02-10','Male bathing suit, blue');\r\n", "INSERT INTO \"myProducts\" VALUES(5,49.95,'2015-02-20','Female bathing suit, one piece, aqua');\r\n", "INSERT INTO \"myProducts\" VALUES(6,9.95,'2015-01-15','Child sand toy set');\r\n", "INSERT INTO \"myProducts\" VALUES(7,24.95,'2014-12-20','White beach towel');\r\n", "INSERT INTO \"myProducts\" VALUES(8,32.95,'2014-12-22','Blue-striped beach towel');\r\n", "INSERT INTO \"myProducts\" VALUES(9,12.95,'2015-03-12','Flip-flop');\r\n", "INSERT INTO \"myProducts\" VALUES(10,34.95,'2015-01-24','Open-toed sandal');\r\n", "COMMIT;\r\n" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Transactions\n", "\n", "If you look carefully at the output of the `.dump` command, you see that\n", "near the top of the output is a `BEGIN TRANSACTION;` statement and at\n", "the end of the output is a `COMMIT;` statement. These two statements are\n", "explicit instructions, inserted by SQLite, to start a transaction, which\n", "is a logical unit of work, and save all operations in the transaction to\n", "the database. If a set of operations is not completed successfully, the\n", "transaction model requires that the commit does not occur, and instead a\n", "rollback is issued to return the database to the state that existed\n", "prior to the transaction commencing.\n", "\n", "-----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SELECT\n", "\n", "In the SQL programming language, the task of performing a query falls to\n", "the SELECT statement. To provide all the query functionality required by\n", "database applications, the SELECT statement's capabilities are\n", "extensive. Before looking at example SELECT statements, lets first look\n", "at the formal syntax of SELECT, which, as shown below is actually\n", "simple. The basic format is `SELECT ... FROM ... WHERE;`, you select the\n", "columns of interest from rows in a table or tables where certain\n", "conditions are satisfied. Of course, things can become considerably more\n", "complex. This article covers the basic features of SELECT and defers the\n", "more advanced issues to subsequent articles.\n", "\n", "```\n", "SELECT [ DISTINCT | ALL ] SelectItem [ , SelectItem ]*\n", "FROM clause\n", "[ WHERE clause ]\n", "[ GROUP BY clause ]\n", "[ HAVING clause ]\n", "```\n", "\n", "From this you can see that a basic SELECT statement requires only a\n", "SELECT and a FROM; you must specify what data to select and indicate the\n", "location of the data of interest. Everything else is optional (as\n", "indicated by the square brackets). The DISTINCT and ALL keywords are\n", "optional qualifiers to indicate that either rows with unique values or\n", "all rows should be selected, respectively. By default, ALL is implicitly\n", "assumed, and you can use only one DISTINCT qualifier per SELECT\n", "statement.\n", "\n", "A SELECT statement can have multiple columns listed following the SELECT\n", "keyword. Multiple elements (or, more generally, column names) are\n", "separated by commas. For example, `SELECT a, b, c` selects the three\n", "columns a, b, and c. To select all columns from a table, you can use the\n", "asterisk character (\\*) as a shorthand for all columns. An important\n", "point to remember is that the result of any SELECT statement is a\n", "transient SQLite table, and you can use it in many of the same ways you\n", "use a more permanent table.\n", "\n", "The FROM component of a SELECT statement indicates from which table (or\n", "multiple tables) the data will be extracted. For now, we will focus on\n", "selecting data from a single table; latter we will cover table joins and\n", "selecting data from multiple tables. In this case, the fully qualified\n", "name of the table to query must follow the FROM keyword.\n", "\n", "The rest of the SELECT statement is optional. Before you build your\n", "first query, however, lets review the order in which the SELECT\n", "statement components are evaluated:\n", "\n", "1. FROM clause\n", "2. WHERE clause\n", "3. GROUP BY clause\n", "4. HAVING clause\n", "5. SELECT clause\n", "\n", "When you break down the process SQLite follows when processing a query,\n", "this order is intuitive. First you must locate the data to be analyzed,\n", "after which you filter out the rows of interest. The next steps are to\n", "group related rows and, finally, to select the actual columns of\n", "interest.\n", "\n", "### SQLite\n", "\n", "To demonstrate a SELECT statement, we can extract all the columns from\n", "the myProducts table by using the `sqlite3` tool and passing the SQL\n", "statement in as a command line argument.\n", "\n", "-----" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!sqlite3 test \"SELECT * FROM myProducts ;\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1|19.95|2015-03-31|Hooded sweatshirt\r\n", "2|99.99|2015-03-29|Beach umbrella\r\n", "3|0.99|2015-02-28|\r\n", "4|29.95|2015-02-10|Male bathing suit, blue\r\n", "5|49.95|2015-02-20|Female bathing suit, one piece, aqua\r\n", "6|9.95|2015-01-15|Child sand toy set\r\n", "7|24.95|2014-12-20|White beach towel\r\n", "8|32.95|2014-12-22|Blue-striped beach towel\r\n", "9|12.95|2015-03-12|Flip-flop\r\n", "10|34.95|2015-01-24|Open-toed sandal\r\n" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "-----\n", "\n", "In the previous code cell, we used the asterisk character to select all\n", "columns from the myProducts table without listing them explicitly. This\n", "can be a useful shortcut, especially when you're developing database\n", "applications, but it isn't a recommended practice. By using the\n", "shortcut, you don't explicitly specify the database column names or\n", "their order. In a database application, if you always assume that the\n", "column names and their order in a table are fixed, you may end up with\n", "subtle bugs if someone else modifies the database tables on which your\n", "application depends. You should always explicitly name the database\n", "columns in your SELECT statements and list the order you require.\n", "\n", "As a result, lets look at explicitly listing the columns to extract.\n", "This is a recommended practice that also allows us to control the order\n", "in which the columns are listed in the query output.\n", "\n", "-----" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!sqlite3 test \"SELECT price, itemNumber, description FROM myProducts ;\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "19.95|1|Hooded sweatshirt\r\n", "99.99|2|Beach umbrella\r\n", "0.99|3|\r\n", "29.95|4|Male bathing suit, blue\r\n", "49.95|5|Female bathing suit, one piece, aqua\r\n", "9.95|6|Child sand toy set\r\n", "24.95|7|White beach towel\r\n", "32.95|8|Blue-striped beach towel\r\n", "12.95|9|Flip-flop\r\n", "34.95|10|Open-toed sandal\r\n" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### WHERE clause\n", "\n", "Up to this point, you have only selected columns for all rows in a\n", "single table. This can be expensive in terms of query performance,\n", "especially if you only want a subset of the rows from a large table. A\n", "more efficient technique is to filter database rows by placing\n", "conditions in the WHERE clause, which is evaluated immediately after the\n", "tables are specified within the FROM clause. The rest of this section\n", "discusses some of the basic features that are enabled by using the WHERE\n", "clause, including the ability to select rows that satisfy Boolean\n", "conditions as well as join multiple tables to perform more complex\n", "queries.\n", "\n", "The simplest and most common use of the WHERE clause is to filter the\n", "rows from a table before selecting any columns, as shown in the next two\n", "code cells.\n", "\n", "-----" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!sqlite3 test \"SELECT p.itemNumber, p.price FROM myProducts AS p WHERE p.price > 30.00 ;\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2|99.99\r\n", "5|49.95\r\n", "8|32.95\r\n", "10|34.95\r\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "!sqlite3 test \"SELECT * FROM myProducts WHERE price > 30.00 AND stockDate < '2015-01-01' ;\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "8|32.95|2014-12-22|Blue-striped beach towel\r\n" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "-----\n", "The first query shown in this example selects the `itemNumber` and\n", "`price` columns from the myProducts table for all rows where the price\n", "column has a value greater than `$30.00`. The second query extends this\n", "same query to select only those columns whose price column has a value\n", "more than `$30.00` and whose stockDate column has a value less than\n", "January 1, 2006. These two query restrictions are combined in this query\n", "by using the Boolean AND operator.\n", "\n", "You can perform a number of different Boolean operations within a WHERE\n", "clause. The following table lists and provides examples of the basic SQL Boolean\n", "operations that you can use in a query.\n", "\n", "| Operator | Example | Description |\n", "| ---- | ---- | ---- |\n", "| =\t| `p.price = 29.95` | Test if any built-in type is equal to a specified value. |\n", "|<\t| `p.price < 29.95` | Test if any built-in type is less than a specified value.|\n", "|>\t| `p.price > 29.95` | Test if any built-in type is greater than a specified value.|\n", "|<=\t| `p.price <= 29.95` | Test if any built-in type is less than or equal to a specified value.|\n", "|>=\t| `p.price >= 29.95` | Test if any built-in type is greater than or equal to a specified value.|\n", "|<>\t| `p.price <> 29.95` |Test if any built-in type is not equal to a specified value. |\n", "| IS NULL | `p.description IS NULL` | Test if an expression or value is null. |\n", "| IS NOT NULL | `p.description IS NOT NULL` | Test if an expression or value is not null.|\n", "| AND | `(p.price > 29.92) AND (p.itemNumber > 5)` | Test if two expressions are both true or evaluate as nonzero.\n", "| OR | `(p.price > 29.92) OR (p.itemNumber > 5)` | Test if one or both of two expressions are true or evaluate as nonzero.\n", "| NOT | `NOT v.vendorNumber = 1` | Test if an expression is false or evaluates as zero.\n", "| BETWEEN | `p.price BETWEEN 29.95 AND 39.95` | Test if a value lies inclusively between two other values (example is equivalent to `29.95 <= p.price <= 39.95`).\n", "| LIKE | `v.vendorName LIKE 'Lun%'` | Test if a character expression matches a pattern, with the percent character (%) matching zero or more arbitrary characters and the underscore character (\\_) matching exactly one arbitrary character.\n", "\n", "The first query above also introduces the AS clause, which you can use\n", "to create a table synonym. In these examples, you define a synonym p for\n", "the fully qualified table name myProducts. By defining a synonym,\n", "you can refer to table quantities by using a shorter notation. This may\n", "not seem important when only one table is being referenced in a query,\n", "but the next section shows how to join multiple tables together within a\n", "query; in that case, providing table synonyms is very useful. You can\n", "also use an AS clause to name the selected columns in a query. Doing so\n", "lets you control how the results are displayed, which is demonstrated in\n", "the next section.\n", "\n", "-----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Joins\n", "\n", "The second major function performed by a WHERE clause is to join\n", "multiple tables together into a single table that can be queried more\n", "easily. Joining multiple tables is a powerful technique, and it can be\n", "complex when you're dealing with several large tables. Tables can be\n", "joined either explicitly, by using the JOIN keyword, or implicitly, by\n", "using a WHERE clause.\n", "\n", "You join two tables by using an inner join or an outer join. An inner\n", "join is essentially the intersection of two tables, where the tables are\n", "matched by comparing the values of a key column, such as itemNumber. The\n", "resulting table is composed of only rows that were matched between the\n", "two tables. An outer join is more like a union of two tables, where the\n", "tables are matched by comparing the values of a key column, but\n", "non-matching rows are still included in the resulting table and filled\n", "with NULL values as appropriate. Writing SQL queries that use these more\n", "advanced table joins will be addressed in future articles.\n", "\n", "In the current simple scheme, the process is simple; In the next code\n", "cell, we perform an implicit inner join of the myProducts table and the\n", "myVendors table.\n", "\n", "-----" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%writefile select.sql\n", "\n", "SELECT p.price, p.description AS 'Item', v.vendorName AS 'Vendor' \n", "FROM myProducts AS p, myVendors AS v \n", " WHERE p.itemNumber = v.itemNumber ;" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Writing select.sql\n" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "# Execute SQL Script\n", "\n", "!sqlite3 test < select.sql" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "19.95|Hooded sweatshirt|Luna Vista Limited\r\n", "99.99|Beach umbrella|Luna Vista Limited\r\n", "0.99||Luna Vista Limited\r\n", "29.95|Male bathing suit, blue|Mikal Arroyo Incorporated\r\n", "49.95|Female bathing suit, one piece, aqua|Mikal Arroyo Incorporated\r\n", "9.95|Child sand toy set|Luna Vista Limited\r\n", "24.95|White beach towel|Luna Vista Limited\r\n", "32.95|Blue-striped beach towel|Luna Vista Limited\r\n", "12.95|Flip-flop|Quiet Beach Industries\r\n", "34.95|Open-toed sandal|Quiet Beach Industries\r\n" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "-----\n", "\n", "This query may seem complex, due primarily to its length. But by\n", "breaking it down line by line you can easily follow what's happening.\n", "First, you select two columns from the myProducts table and one column\n", "from the myVendors table and use an AS clause to name these columns for\n", "subsequent usage (in this case they are displayed by the `sqlite3`\n", "client tool. Because the query joins these two tables (by using an\n", "implicit inner join), you can select columns from both tables. In the\n", "FROM clause, you list both tables and provide aliases for them to\n", "simplify the full SQL statement. In the WHERE clause, you provide the\n", "logic for joining the two tables, by explicitly instructing the SQLite\n", "database to only select rows from the two tables that have matching\n", "values in their respective itemNumber columns. In processing this query,\n", "SQLite first pulls all rows out of the first (left) table in the query\n", "(myProducts) and finds the row with a matching value in the itemNumber\n", "column in the second (right) table in the query (myVendors).\n", "\n", "-----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DISTINCT\n", "\n", "By default, when data is selected by using an SQL query, all rows that\n", "satisfy the WHERE clause are extracted from the database. In some cases,\n", "this may result in rows that have identical column values being\n", "returned. If you need to restrict your query so that only unique row\n", "values are returned, you can use the DISTINCT qualifier, as shown in the\n", "following two code cells.\n", "\n", "-----" ] }, { "cell_type": "code", "collapsed": false, "input": [ "!sqlite3 test \"SELECT DISTINCT vendorNumber AS 'Vendor #' FROM myVendors ;\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\r\n", "2\r\n", "3\r\n" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "!sqlite3 test \"SELECT DISTINCT vendorNumber AS 'Vendor #', itemNumber as 'Item #' FROM myVendors WHERE itemNumber > 5 ;\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1|6\r\n", "1|7\r\n", "1|8\r\n", "3|9\r\n", "3|10\r\n" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "\n", "If you want to use the DISTINCT qualifier, it must be the first item\n", "listed in the SELECT clause, as shown in Listing 1, and you can have\n", "only one DISTINCT qualifier per SELECT clause. If the selected rows\n", "contain a column with NULL values, multiple NULL values are considered\n", "duplicates when identifying unique rows.\n", "\n", "The first query in this listing uses the DISTINCT qualifier to restrict\n", "the output of the query to only distinct, or unique, values of the\n", "vendorNumber column, which is the only column listed in the SELECT\n", "clause. In the example schema that these articles use, there are only\n", "three vendors (with vendorNumber being restricted to 1, 2, or 3). Thus,\n", "when the DISTINCT qualifier is used in the query, only three rows are\n", "selected.\n", "\n", "The DISTINCT qualifier, however, applies to the entire list of selected\n", "columns, so if multiple columns are listed following a DISTINCT keyword,\n", "only unique combinations of all the columns are selected. This is\n", "demonstrated in the second example, where both vendorNumber and\n", "itemNumber are listed in the SELECT clause. Because every item has a\n", "unique itemNumber, every combination of these two columns is unique, and\n", "all rows that satisfy the WHERE clause are selected; in other words, the\n", "DISTINCT qualifier has no effect on the results.\n", "\n", "One remaining point that you may have noticed from the two previous\n", "examples is that the selected rows were not in the same order. If the\n", "order of selected rows is important, you can easily control it by using\n", "an ORDER BY clause in your query.\n", "\n", "### ORDER BY\n", "\n", "In general, you can't assume that SQLite, or any database, will return\n", "rows from a query in a specific order. If the order is important, you\n", "can use the ORDER BY clause to have SQLite order the data that are\n", "returned by your query in a particular manner. Generally, you do so by\n", "specifying a column that should be used to provide the ordinal values\n", "for comparison as shown in the next two code cells.\n", "\n", "-----" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%writefile orderby.sql\n", "\n", "SELECT v.vendorNumber AS \"Vendor #\", vendorName as \"Vendor\",\n", " p.price AS \"Price\", p.itemNumber AS \"Item #\"\n", " FROM myProducts AS p, myVendors AS v\n", " WHERE p.itemNumber = v.itemNumber AND p.price > 20.0\n", " ORDER by v.vendorNumber ;\n", " \n", "SELECT v.vendorNumber AS \"Vendor #\", vendorName as \"Vendor\",\n", " p.price AS \"Price\", p.itemNumber AS \"Item #\"\n", " FROM myProducts AS p, myVendors AS v\n", " WHERE p.itemNumber = v.itemNumber AND p.price > 20.0\n", " ORDER BY v.vendorNumber ASC, p.price DESC ;" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Overwriting orderby.sql\n" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "!sqlite3 test < orderby.sql" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1|Luna Vista Limited|99.99|2\r\n", "1|Luna Vista Limited|24.95|7\r\n", "1|Luna Vista Limited|32.95|8\r\n", "2|Mikal Arroyo Incorporated|29.95|4\r\n", "2|Mikal Arroyo Incorporated|49.95|5\r\n", "3|Quiet Beach Industries|34.95|10\r\n", "1|Luna Vista Limited|99.99|2\r\n", "1|Luna Vista Limited|32.95|8\r\n", "1|Luna Vista Limited|24.95|7\r\n", "2|Mikal Arroyo Incorporated|49.95|5\r\n", "2|Mikal Arroyo Incorporated|29.95|4\r\n", "3|Quiet Beach Industries|34.95|10\r\n" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "-----\n", "\n", "In the previous example, the first query uses the ORDER BY clause to\n", "list a subset of all the rows in the table that results from joining the\n", "myVendors table to the myProducts table. The rows are ordered by\n", "vendorNumber (the subset is constructed by applying the WHERE clause).\n", "An ORDER BY clause can take either a column name, as in this example, or\n", "a column number, which is taken from the order in which the columns are\n", "listed after the SELECT keyword.\n", "\n", "You can also specify multiple columns to use during the sorting process\n", "and even specify ASC for ascending order, which is the default, or DESC\n", "for descending order. For example, if you used the ORDER BY 1 DESC, 4\n", "DESC clause in the first query, the query would return the same rows,\n", "but they would be ordered by using the vendorNumber column as the\n", "primary sort column in descending order followed by the itemNumber\n", "column as the secondary sort column in descending order.\n", "\n", "Although using column numbers may seem like a handy shortcut, it\n", "generally isn't a good idea. To see why, consider what happens if you\n", "modify the columns listed in a SELECT clause or just modify their order.\n", "If you forget to modify the numbers used in the ORDER BY clause, the\n", "query will break-or worse, return bad data. In general, it's a _best\n", "practice_ to always be explicit and specify the column names directly,\n", "even if doing so means more typing.\n", "\n", "### Query Math\n", "\n", "Selecting columns from a database provides a number of useful benefits,\n", "but being able to compute and select quantities based on data in a table\n", "opens up even more possibilities. SQLite,as does any SQL database,\n", "provides several mathematical operators, the most common of which are\n", "listed in the following table, that you can use in either a SELECT clause\n", "or a WHERE clause.\n", "\n", "|Operator | Example | Description |\n", "| --- | --- | ---- |\n", "| unary + | +1.0 | A noop, or no operation, as +4 = 4 |\n", "| unary - | -p.price | Changes the sign of the value to which it's applied |\n", "|+ | p.itemNumber + 10 | Adds the second value to the first value |\n", "| - | p.itemNumber - 10 | Subtracts the second value from the first value |\n", "| * | p.price * 1.0825 | Multiplies the first value by the second value |\n", "| / | p.price / 100.0 | Divides the first value by the second value |\n", "\n", "Using these operators is straightforward because they generally behave\n", "exactly as you expect. For example, if the sales tax is 8.25%, you can\n", "return the price for an item both before and after sales tax has been\n", "applied by using `SELECT price, price * 1.0825 FROM myProducts ;`. As\n", "another example, if you have a column called `numberItems` that tracks\n", "the number of items purchased and another column called `price` that\n", "contains the price at which they're purchased, you can return the total\n", "amount paid for those items at a given price by using \n", "`numberItems * price`. Several of the queries shown in this \n", "IPython Notebook provide additional examples of how to use these \n", "operators.\n", "\n", "The only concern when using these operators arises from complications\n", "that result from using different data types, such as integer or\n", "floating-point, in a mathematical operation. If both operands are the\n", "same data type, the result type will be the same. If you're performing\n", "division, this can result in truncation (for example, if you're using\n", "two integer values), which might cause unexpected problems. On the other\n", "hand, if the two operands are different data types, the result type is\n", "promoted to the more complex type.\n", "\n", "### SQL Functions\n", "\n", "SQL is a powerful and expressive language that can be used to perform a\n", "wide range of actions. Part of the SQL language's power comes from its\n", "ability to directly interact with a variety of data types. Some of the\n", "greatest power of a relational database arises from the inherent\n", "functions that it provides and from the extensibility enabled by\n", "allowing users to create new functions. SQLite provides functions (see\n", "the following [SQLite tutorial][1] for more information) in three\n", "different categories:\n", "\n", "1. [Core functions](https://www.sqlite.org/lang_corefunc.html)\n", "2. [Aggregate functions](https://www.sqlite.org/lang_aggfunc.html)\n", "3. [Date and Time functions](https://www.sqlite.org/lang_datefunc.html)\n", "\n", "Of these built-in functions, we will most likely use the aggregate\n", "functions, which operate on multiple rows. Aggregate functions-also\n", "known as set functions in SQL-92 or, more informally, as column\n", "functions-return a computed quantity from a column over a number of\n", "rows. SQLite supports the following five aggregate functions (a sixth\n", "function, `group_concat`, is not listed).\n", "\n", "| Function | Example | Description |\n", "| --- | --- | --- |\n", "| AVG | AVG(p.price) | Returns the average value of a column from all rows that satisfy an expression. Can only be used with built-in numeric data types. The precision of the returned value is defined by the precision of the column being evaluated. |\n", "| COUNT | COUNT(p.price) | Returns the number of rows that satisfy an expression, such as a query. Can be used with any data type. |\n", "| MAX | MAX(p.price) | Returns the maximum value of a column from all rows that satisfy an expression. Can only be used with built-in data types. |\n", "| MIN | MIN(p.price) | Returns the minimum value of a column from all rows that satisfy an expression. Can only be used with built-in data types. |\n", "| SUM | SUM(p.price) | Returns the sum of a column over all rows that satisfy an expression. Can only be used with built-in numeric data types. |\n", "\n", "These aggregate functions can often be used to quickly find useful\n", "information that might otherwise be difficult to identify, as shown in\n", "Listing 4.\n", "```\n", "SELECT COUNT(p.itemNumber) AS Number, \n", " AVG(p.price) AS Average,\n", " MIN(p.stockDate) AS \"First Date\", MAX(p.stockDate) AS \"Last Date\"\n", " FROM myProducts AS p ;\n", "```\n", "Listing 4 uses four of the five aggregate functions to get summary\n", "information about the data in the myProducts table. The COUNT function\n", "indicates that the table includes ten rows (because the query didn't use\n", "a WHERE clause to restrict the rows selected from the table). The AVG\n", "function calculates the average price of all items in the myProducts\n", "table. Finally, the MIN and MAX functions extract the minimum and\n", "maximum dates from the myProducts table.\n", "\n", "-----\n", "[1]: http://zetcode.com/db/sqlite/sqlitefunctions/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DELETE\n", "\n", "To delete data in a SQLite database, you use the SQL DELETE statement,\n", "which can delete either all rows in a table or a specific subset of\n", "rows. The formal syntax for the SQL DELETE statement is remarkably\n", "simple:\n", "\n", "```\n", "DELETE FROM tableName\n", " [WHERE clause]\n", "```\n", "\n", "The DELETE statement deletes all rows from the specified table that\n", "satisfy an optional WHERE clause. If no WHERE clause is included, all\n", "rows in the table are deleted. To demonstrate this use of the DELETE\n", "statement, we can create a temporary table, insert several rows, and\n", "delete them all.\n", "\n", "-----" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%writefile delete.sql\n", "\n", "-- First create the temporary table\n", "CREATE TABLE temp (aValue INT) ;\n", "\n", "-- Insert fake data\n", "INSERT INTO temp VALUES(0), (1), (2), (3) ;\n", "\n", "-- Count rows in the table\n", "SELECT COUNT(*) AS COUNT FROM temp ; \n", "\n", "-- Delete all rows\n", "DELETE FROM temp ;\n", "\n", "-- Count all rows in the table\n", "SELECT COUNT(*) AS COUNT FROM temp ; \n", "\n", "-- Now drop the temporary table\n", "\n", "DROP TABLE temp ;" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Writing delete.sql\n" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "!sqlite3 test < delete.sql" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "4\r\n", "0\r\n" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "-----\n", "\n", "The previous example created a single-column temporary table to hold a\n", "single integer value. Next we inserted four rows into the database and\n", "issued a SELECT statement to verify that the new table contained four\n", "rows. By using an unconstrained DELETE statement, we delete all four\n", "rows from the temporary table, which is verified by the second SELECT\n", "statement, which indicates that the temporary table contains zero rows.\n", "Finally, the DROP TABLE statement deletes the empty table from the\n", "schema.\n", "\n", "In general, however, you don't want to delete all rows from a table;\n", "instead, you'll selectively delete rows. To do this, you create an\n", "appropriate WHERE clause that identifies all rows of interest. The\n", "syntax for the WHERE clause that you can use with a DELETE statement is\n", "identical to that discussed previously when we presented the full SQL\n", "SELECT statement syntax. The basic building blocks for constructing a\n", "Boolean expression within a WHERE clause were presented in an earlier\n", "table. The following example demonstrates using a WHERE clause in a\n", "DELETE statement, where we delete all rows that satisfy at least one of\n", "two conditions.\n", "\n", "-----" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%writefile delete2.sql\n", "\n", "-- First display data\n", "SELECT itemNumber, description FROM myProducts ;\n", "\n", "-- Selectively delete rows\n", "DELETE FROM myProducts \n", " WHERE description LIKE '%towel%' OR itemNumber <= 3 ;\n", "\n", "-- Confirm the proper deletion\n", "SELECT itemNumber, description FROM myProducts ;" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Writing delete2.sql\n" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "!sqlite3 test < delete2.sql" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1|Hooded sweatshirt\r\n", "2|Beach umbrella\r\n", "3|\r\n", "4|Male bathing suit, blue\r\n", "5|Female bathing suit, one piece, aqua\r\n", "6|Child sand toy set\r\n", "7|White beach towel\r\n", "8|Blue-striped beach towel\r\n", "9|Flip-flop\r\n", "10|Open-toed sandal\r\n", "4|Male bathing suit, blue\r\n", "5|Female bathing suit, one piece, aqua\r\n", "6|Child sand toy set\r\n", "9|Flip-flop\r\n", "10|Open-toed sandal\r\n" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": {}, "source": [ "-----\n", "\n", "In this example, the DELETE statement includes a WHERE clause that\n", "identifies five rows. The WHERE clause contains two expressions that are\n", "joined by the OR operator, which means that if either expression\n", "evaluates as TRUE for a specific row, that row will be deleted.\n", "\n", "The first expression finds all rows that contain the word \"towel\" in the\n", "product description. If you recall, there are two towels in the\n", "myProducts table, with itemNumber column values of 7 and 8. The other\n", "expression selects all rows with an itemNumber column value less than or\n", "equal to 3. The contents of the myProducts table are finally displayed\n", "with a simple SELECT statement, demonstrating that only five of the\n", "original ten rows remain in the table.\n", "\n", "Although this example doesn't explicitly demonstrate their use, you can\n", "also include the SQL functions to gain more control over the selection\n", "of rows for deletion. These same functions and other operators that can\n", "be used in the WHERE clause of the DELETE statement also can be used\n", "with the UPDATE statement to selectively modify the values of rows in a\n", "table, as described in the next section.\n", "\n", "### UPDATE\n", "\n", "The last SQL task for dealing with data that you need to address is\n", "updating specific column values for selected rows in a table. At some\n", "level, the SQL UPDATE statement is the union of the SQL INSERT and\n", "DELETE statements, because you must select rows to modify as well as\n", "specify how to modify them. Formally, the UPDATE statement syntax is\n", "straightforward, because you must specify the new column values for the\n", "set of rows to be updated:\n", "\n", "```\n", "UPDATE tableName\n", " SET columnName = Value\n", " [ , columnName = Value} ]*\n", " [WHERE clause]\n", "```\n", "\n", "As shown in this SQL syntax, an SQL UPDATE statement must have, at a\n", "minimum, one SET component to update one column, along with one or more\n", "SET components and a WHERE clause, both of which are optional. If the\n", "WHERE clause isn't included, the UPDATE statement modifies the indicated\n", "columns for all rows in the table.\n", "\n", "Issuing an UPDATE statement is fairly easy, as shown in the following\n", "code example, where we modify two columns of a single row.\n", "\n", "-----" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%writefile update.sql\n", "\n", "-- Extract the test row\n", "SELECT itemNumber, price, stockDate FROM myProducts WHERE itemNumber = 6 ;\n", "\n", "-- Update the row\n", "UPDATE myProducts SET price = price * 1.25, stockDate = date('now') WHERE itemNumber = 6 ;\n", "\n", "-- Show the new result\n", "SELECT itemNumber, price, stockDate FROM myProducts WHERE itemNumber = 6 ;" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Writing update.sql\n" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "!sqlite3 test < update.sql" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "6|9.95|2015-01-15\r\n", "6|12.4375|2015-02-24\r\n" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "-----\n", "\n", "This example wraps a single UPDATE statement with SELECT statements to\n", "demonstrate the change to the target row. The SELECT statements both\n", "select three columns from the myProducts table for a single row\n", "(the row with the value 6 in the itemNumber column). The UPDATE\n", "statement modifies both the price and the stockDate columns for this\n", "specific row. The value in the price column is increased by 25% (for\n", "example, perhaps due to the item's popularity), and the stockDate column\n", "is modified to hold the current date, which can be obtained easily with\n", "SQLite by using the built-in `date` function with an argument of `now`\n", "in an SQL query.\n", "\n", "The previous example demonstrated how to modify multiple column values\n", "for a specific row in a single table. However, sometimes the logic to\n", "select rows to update is more complex. For example, suppose you need to\n", "modify the price of all objects in the myProducts table that you obtain\n", "from Quiet Beach Industries, which has a value of 3 in the vendorNumber\n", "column in the myVendors table. To do this, you need to use an embedded\n", "query:\n", "\n", "-----" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%writefile update2.sql\n", "\n", "-- Update the table\n", "UPDATE myProducts\n", " SET price = price * 1.10, description = 'NEW: ' || description\n", " WHERE itemNumber IN \n", " ( SELECT v.itemNumber \n", " FROM myProducts as p, myVendors as v \n", " WHERE p.itemNumber = v.itemNumber AND v.vendorNumber = 3 ) ;\n", "\n", "-- Show new results\n", "SELECT * FROM myProducts ;" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Writing update2.sql\n" ] } ], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "!sqlite3 test < update2.sql" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "4|29.95|2015-02-10|Male bathing suit, blue\r\n", "5|49.95|2015-02-20|Female bathing suit, one piece, aqua\r\n", "6|12.4375|2015-02-24|Child sand toy set\r\n", "9|14.245|2015-03-12|NEW: Flip-flop\r\n", "10|38.445|2015-01-24|NEW: Open-toed sandal\r\n" ] } ], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": {}, "source": [ "-----\n", "\n", "In this example, the UPDATE statement modifies the price and description\n", "columns for all products that are obtained from the vendor with a value\n", "of 3 in the vendorNumber column in the myVendors table. Because you\n", "can't do a simple join within an UPDATE statement, you must include a\n", "subquery in the WHERE clause to extract the itemNumber rows that\n", "correspond to products from Quiet Beach Industries. The WHERE clause in\n", "the UPDATE statement uses the IN operator to select those rows that have\n", "an itemNumber column in the set of values selected by the embedded query.\n", "\n", "Two types of queries can be used in the WHERE clause of an UPDATE\n", "statement: a scalar subquery and a table subquery. A scalar subquery is\n", "an embedded query that returns a single row that contains a single\n", "column-essentially, a single value, which is known as a scalar. You can\n", "use a scalar subquery to select a specific value that will be used in\n", "the expression of the WHERE clause. For example, \n", "`itemNumber = (Scalar Subquery)` updates any rows that have a \n", "value in the itemNumber column that matches the result of the \n", "scalar subquery.\n", "\n", "A table subquery, on the other hand, can return multiple rows that\n", "generally only have one column. In certain instances, a table subquery\n", "can contain multiple columns. To use a table subquery, you need to use\n", "an SQL operator to combine the embedded query with a Boolean expression.\n", "For example, this was shown in the previous code listing, where the IN\n", "operator selected all rows from the myProducts table that were produced\n", "by Quiet Beach Industries. \n", "\n", "### ALTER TABLE\n", "\n", "The previous section discussed modifying the data that already exists in\n", "a table. The other possibility is modifying the structure, or schema, of\n", "a database table. This can take the form of adding a column, changing\n", "the data type for a column, adding a constraint, or even deleting a\n", "column. This process isn't easy, which is one reason to be careful when\n", "you initially design your schema. If you do need to modify the structure\n", "of a table, you should use a temporary table.\n", "\n", "-----" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%writefile rename.sql\n", "\n", "-- Create New table with extra column\n", "\n", "CREATE TABLE newProducts (\n", " itemNumber INT NOT NULL,\n", " price REAL,\n", " stockDate TEXT,\n", " count INT NOT NULL DEFAULT 0,\n", " description TEXT\n", ") ;\n", "\n", "-- New copy old table into new table\n", "\n", "INSERT INTO newProducts(itemNumber, price, stockDate, description) \n", " SELECT itemNumber, price, stockDate, description FROM myProducts ;\n", "\n", "-- Drop old table\n", "DROP TABLE myProducts ;\n", "\n", "-- Now Rename new table to old table name\n", "ALTER TABLE newProducts RENAME TO myProducts ;\n", "\n", "-- Show the results\n", "SELECT * FROM myProducts ;" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Writing rename.sql\n" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "# Execute SQL Script\n", "\n", "!sqlite3 test < rename.sql" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "4|29.95|2015-02-10|0|Male bathing suit, blue\r\n", "5|49.95|2015-02-20|0|Female bathing suit, one piece, aqua\r\n", "6|12.4375|2015-02-24|0|Child sand toy set\r\n", "9|14.245|2015-03-12|0|NEW: Flip-flop\r\n", "10|38.445|2015-01-24|0|NEW: Open-toed sandal\r\n" ] } ], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "-----\n", "\n", "As this example shows, to modify a table-in this case, to add a new\n", "`count` column to the `myProducts` table-you first create a table that\n", "has the exact schema you require. This example requires that it always\n", "have a valid value by including the column constraint NOT NULL and\n", "assigns a default value of 0 to the count column by using the column\n", "constraint DEFAULT 0. Notice how you can combine multiple column\n", "constraints by listing them sequentially.\n", "\n", "The next step is to copy the existing data from the original table to\n", "the new table. You can do so by using an SQL INSERT statement that uses\n", "a subquery to get the values to insert. This is a powerful technique\n", "that lets you easily copy all or part of an existing table into a second\n", "table.\n", "\n", "After you've created the new table and copied the appropriate data, you\n", "drop the old table by using an SQL DROP TABLE statement and rename the\n", "new table to the original name by using an SQL RENAME TABLE statement.\n", "The rename operation is straightforward: Rename the oldTableName to the\n", "newTableName, but don't supply a schema name for the new table name\n", "because the RENAME operation can't move a table between different\n", "database schemas. This example concludes by issuing a SELECT statement\n", "to display the schema and contents of the new myProducts table. As\n", "you can see, the new table has five columns, and the count column is\n", "always zero. At this point, a real application would modify the count\n", "column appropriately by issuing the necessary SQL UPDATE statements.\n", "\n", "-----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CREATE INDEX\n", "\n", "Relational databases support the concept of an index to speed up\n", "queries. An index is often used when a particular column (or columns) is\n", "frequently involved in either a WHERE clause or a table join. The syntax\n", "for creating an index is rather simple:\n", "\n", "```\n", "CREATE [UNIQUE] INDEX idx_name ON table_name(column [column]*)\n", "[WHERE]\n", "```\n", "\n", "In this format, we create an index on one or more columns in a given\n", "table. If UNIQUE is present in the index creation, only non-duplicate\n", "entries are allowed in the index. The columns can also be followed by\n", "either ASC or DESC to indicate the column sort order for the index.\n", "Finally, if a WHERE clause is included the index is only a partial index\n", "since it does not cover the entire table.\n", "\n", "As an example, we can create an index on the myProducts table by using\n", "the itemNumber column:\n", "\n", " CREATE INDEX itn ON myProducts(itemNumber) ;\n", "\n", "-----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Advanced Features\n", "\n", "Of course we have only scratched the surface of SQL, despite the length\n", "of this IPython Notebook. We have not discussed\n", "[views](https://www.sqlite.org/lang_createview.html), which effectively\n", "turns a SQL query into a new, read-only table, or\n", "[triggers](https://www.sqlite.org/lang_createtrigger.html), which are\n", "database operations that are automatically performed when a specific\n", "event occurs. \n", "\n", "Another useful feature that we will use in the third lesson is the LIMIT\n", "clause. We can use this to restrict the number of rows returned by a\n", "particular query. For example, `SELECT * FROM myProducts LIMIT 5 ;` will\n", "only return five rows.\n", "\n", "-----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Additional References\n", "\n", "Several sites exist that allow you to try out SQL commands online.\n", "\n", "1. [W3 Schools SQL][1], a general SQL demo site.\n", "2. [SQLZoo][2], allows you to specify the Relational Database to target.\n", "-----\n", "\n", "[1]: http://www.w3schools.com/SQL/\n", "[2]: http://sqlzoo.net/wiki/SELECT_basics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Return to the [Week 10 Index](index.ipynb).\n", "\n", "-----" ] } ], "metadata": {} } ] }
mit
Aggieyixin/cjc2016
code/15.network_science.ipynb
1
301180
{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "***\n", "***\n", "# 网络科学简介\n", "***\n", "***\n", "\n", "王成军 \n", "\n", "wangchengjun@nju.edu.cn\n", "\n", "计算传播网 http://computational-communication.com" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# FROM SADDAM HUSSEIN TO NETWORK THEORY \n", "A SIMPLE STORY (1) The fate of Saddam and network science\n", "\n", "- Invasion that started in March 19, 2003. Many of the regime's high ranking officials, including Saddam Hussein, avoided capture. \n", "- Hussein was last spotted kissing a baby in Baghdad in April 2003, and then his trace went cold. \n", "\n", "- Designed a deck of cards, each card engraved with the images of the 55 most wanted. \n", " - It worked: by May 1, 2003, 15 men on the cards were captured, and by the end of the month another 12 were under custody. \n", " - Yet, the ace of spades, i.e. Hussein himself, remained at large.\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "<img src = './img/saddam.png' width = 500>" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "The capture of Saddam Hussein:\n", "\n", "- shows the strong predictive power of networks. \n", "\n", "- underlies the need to obtain accurate maps of the networks we aim to study; and the often heroic difficulties we encounter during the mapping process.\n", "\n", "- demonstrates the remarkable stability of these networks: The capture of Hussein was not based on fresh intelligence, but rather on his pre-invasion social links, unearthed from old photos stacked in his family album.\n", "\n", "- shows that the choice of network we focus on makes a huge difference: the hierarchical tree, that captured the official organization of the Iraqi government, was of no use when it came to Saddam Hussein's whereabouts. \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "A SIMPLE STORY (2): August 15, 2003 blackout.\n", "\n", "<img src='./img/blackout.png' width = 800>\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# VULNERABILITY DUE TO INTERCONNECTIVITY \n", "\n", "- The 2003 blackout is a typical example of a cascading failure. \n", "- 1997, when the International Monetary Fund pressured the central banks of several Pacific nations to limit their credit. \n", "- 2009-2011 financial melt-down" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "An important theme of this class: \n", "\n", "- we must understand how network structure affects the robustness of a complex system. \n", "\n", "- develop quantitative tools to assess the interplay between network structure and the dynamical processes on the networks, and their impact on failures. \n", "\n", "- We will learn that failures reality failures follow reproducible laws, that can be quantified and even predicted using the tools of network science.\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# NETWORKS AT THE HEART OF COMPLEX SYSTEMS \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# Complex\n", "\n", "[adj., v. kuh m-pleks, kom-pleks; n. kom-pleks] \n", "–adjective \n", "- 1. composed of many interconnected parts; compound; composite: a complex highway system. \n", "- 2. characterized by a very complicated or involved arrangement of parts, units, etc.: complex machinery. \n", "- 3. so complicated or intricate as to be hard to understand or deal with: a complex problem. \n", "\t\t\t\tSource: Dictionary.com\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "# Complexity\n", "\n", "a scientific theory which asserts that some systems display behavioral phenomena that are completely inexplicable by any conventional analysis of the systems’ constituent parts. These phenomena, commonly referred to as emergent behaviour, seem to occur in many complex systems involving living organisms, such as a stock market or the human brain.\n", " \n", "Source: John L. Casti, Encyclopædia Britannica\n", " \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# COMPLEX SYSTEMS\n", "\n", "- society\n", "- brain\n", "- market\n", "- cell\n", "\n", "## Stephen Hawking: I think the next century will be the century of complexity. " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# Behind each complex system there is a network, that defines the interactions between the component. \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "<img src = './img/facebook.png' width = 800>" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "- Social graph\n", "- Organization\n", "- Brain\n", "- finantial network\n", "- business \n", "- Internet\n", "- Genes" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "Behind each system studied in complexity there is an intricate wiring diagram, or a network, that defines the interactions between the component.   \n", "\n", "# We will never understand complex system unless we map out and understand the networks behind them.\n", "  \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# TWO FORCES HELPED THE EMERGENCE OF NETWORK SCIENCE \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# THE HISTORY OF NETWORK ANALYSIS\n", "\n", "- Graph theory: 1735, Euler\n", "\n", "- Social Network Research: 1930s, Moreno\n", "\n", "- Communication networks/internet: 1960s\n", "\n", "- Ecological Networks: May, 1979.\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "<img src = './img/citation.png' width = 500>" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "While the study of networks has a long history from graph theory to sociology, the modern chapter of network science emerged only during the first decade of the 21st century, following the publication of two seminal papers in 1998 [2] and 1999 [3]. The explosive interest in network science is well documented by the citation pattern of two classic network papers, the 1959 paper by Paul Erdos and Alfréd Rényi that marks the beginning of the study of random networks in graph theory [4] and the 1973 paper by Mark Granovetter, the most cited social network paper [5]. Both papers were hardly or only moderately cited before 2000. The explosive growth of citations to these papers in the 21st century documents the emergence of network science, drawing a new, interdisciplinary audience to these classic publications. \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# THE EMERGENCE OF NETWORK SCIENCE\n", "- Movie Actor Network, 1998;\n", "- World Wide Web, 1999.\n", "- C elegans neural wiring diagram 1990\n", "- Citation Network, 1998\n", "- Metabolic Network, 2000; \n", "- PPI network, 2001\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# The universality of network characteristics: \n", "The architecture of networks emerging in various domains of science, nature, and technology are more similar to each other than one would have expected. \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# THE CHARACTERISTICS OF NETWORK SCIENCE \n", "- Interdisciplinary \n", "- Empirical\n", "- Quantitative and Mathematical \n", "- Computational \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# THE IMPACT OF NETWORK SCIENCE \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# Google\n", "Market Cap(2010 Jan 1): \n", "$189 billion\n", "\n", "# Cisco Systems\n", "networking gear Market cap (Jan 1, 2919): \n", "$112 billion\n", "\n", "# Facebook\n", "market cap: \n", "$50 billion\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# Health: From drug design to metabolic engineering. \n", "The human genome project, completed in 2001, offered the first comprehensive list of all human genes. Yet, to fully understand how our cells function, and the origin of disease, we need accurate maps that tell us how these genes and other cellular components interact with each other. " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# Security: Fighting Terrorism. \n", "Terrorism is one of the maladies of the 21st century, absorbing significant resources to combat it worldwide. Network thinking is increasingly present in the arsenal of various law enforcement agencies in charge of limiting terrorist activities. It is used to disrupt the financial network of terrorist organizations, to map terrorist networks, and to uncover the role of their members and their capabilities. While much of the work in this area is classified, several success stories have surfaced. Examples include the use of social networks to capture Saddam Hussein or the capture of the individuals behind the March 11, 2004 Madrid train bombings through the examination of the mobile call net- work. \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# Epidemics: From forecasting to halting deadly viruses. \n", "While the H1N1 pandemic was not as devastating as it was feared at the beginning of the outbreak in 2009, it gained a special role in the history of epidemics: it was the first pandemic whose course and time evolution was accurately predicted months before the pandemic reached its peak (Image 1.9) [14]. This was possible thanks to fundamental advances in understanding the role of networks in the spread of viruses. Indeed, before 2000 epidemic modeling was dominated by compartment models, assuming that everyone can infect everyone else one word the same socio-physical compartment. The emergence of a network-based framework has fundamentally changed this, offering a new level of predictability in epidemic phenomena. \n", "Today epidemic prediction is one of the most active applications of network science. It is the source several fundamental results, covered in this book, that are used to predict the spread of both biological and electronic viruses. The impact of these advances are felt beyond biological viruses. In January 2010 network science tools have predicted the conditions necessary for the emergence of viruses spreading through mobile phones. The first major mobile epidemic outbreak that started in the fall of 2010 in China, infecting over 300,000 phones each day, closely followed the predicted scenario. \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# Brain Research: Mapping neural network. \n", "The human brain, consisting of hundreds of billions of interlinked neurons, is one of the least understood net- works from the perspective of network science. The reason is simple: we lack maps telling us which neurons link to each other. The only fully mapped neural map available for research is that of the C.Elegans worm, with only 300 neurons. Should detailed maps of mammalian brains become available, brain research could become the most prolific application area of network science. Driven by the potential impact of such maps, in 2010 the National Institutes of Health has initiated the Connectome project, aimed at developing the technologies that could provide an accurate neuron-level map of mammalian brains. \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# The Bridges of Konigsberg\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "<img src = './img/konigsberg.png' width = 500>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "# Can one walk across the seven bridges and never cross the same bridge twice and get back to the starting place? \n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Can one walk across the seven bridges and never cross the same bridge twice and get back to the starting place? \n", "\n", "<img src ='./img/euler.png' width = 300>" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "1735: Euler’s theorem:\n", "\n", "- If a graph has more than two nodes of odd degree, there is no path. \n", "- If a graph is connected and has no odd degree nodes, it has at least one path.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "COMPONENTS OF A COMPLEX SYSTEM\n", "\n", "# Networks and graphs\n", " - components: nodes, vertices\t\t N\n", " - interactions: links, edges\t\t\t L\n", " - system: \t network, graph\t\t(N,L)\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "network often refers to real systems\n", "- www, \n", "- social network\n", "- metabolic network. \n", "\n", "Language: (Network, node, link)\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "graph: mathematical representation of a network\n", "- web graph, \n", "- social graph (a Facebook term)\n", "\n", "Language: (Graph, vertex, edge)\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<img src = './img/net.png' width = 800>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# CHOOSING A PROPER REPRESENTATION\n", "\n", "The choice of the proper network representation determines our ability to use network theory successfully.\n", "\n", "In some cases there is a unique, unambiguous representation. \n", "In other cases, the representation is by no means unique.\n", " \n", "For example, the way we assign the links between a group of individuals will determine the nature of the question we can study.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "If you connect individuals that work with each other, you will explore the professional network.\n", "\n", "http://www.theyrule.net" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "If you connect those that have a romantic and sexual relationship, you will be exploring the sexual networks.\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "If you connect individuals based on their first name (all Peters connected to each other), you will be exploring what? \n", "\n", "# It is a network, nevertheless.\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "# UNDIRECTED VS. DIRECTED NETWORKS\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "# Undirected\n", "Links: undirected\n", "- coauthorship \n", "- actor network\n", "- protein interactions" ] }, { "cell_type": "code", "execution_count": 174, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10f133890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "import networkx as nx\n", "Gu = nx.Graph()\n", "for i, j in [(1, 2), (1, 4), (4, 2), (4, 3)]:\n", " Gu.add_edge(i,j)\n", "nx.draw(Gu, with_labels = True)\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "# Directed\n", "Links: directed\n", "- urls on the www\n", "- phone callss\n", "- metabolic reactions" ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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fz8qVKyksLLzmeQ8PDx5//HGef/75EnlFTFqyZAmbNm1i8+bNuma5lCv9bXIj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\nfBIfT8+ePSsvsIPTilhERG7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"text/plain": [ "<matplotlib.figure.Figure at 0x10f56ecd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import networkx as nx\n", "Gd = nx.DiGraph()\n", "for i, j in [(1, 2), (1, 4), (4, 2), (4, 3)]:\n", " Gd.add_edge(i,j)\n", "nx.draw(Gd, with_labels = True)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "<img src = './img/networks.png' width = 1000>" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "# Degree, Average Degree and Degree Distribution\n" ] }, { "cell_type": "code", "execution_count": 175, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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9EoBaZOPGjYqLi9OpU6d0fN8+bS4u/j3Cv0h6RNJaSfmSWkmaKen2s47fK6ln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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x113524550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nx.draw(Gu, with_labels = True)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "# Undirected network: \n", "Node degree: the number of links connected to the node.\n", "## $k_1 = k_2 = 2, k_3 = 3, k_4 = 1$" ] }, { "cell_type": "code", "execution_count": 177, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x111a04910>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nx.draw(Gd, with_labels = True)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "# Directed network\n", "In directed networks we can define an in-degree and out-degree. The (total) degree is the sum of in-and out-degree.\n", "\n", "## $k_3^{in} = 2, k_3^{out} = 1, k_3 = 3$\n", "\n", "Source: a node with $k^{in}= 0$; Sink: a node with $k^{out}= 0$.\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "For a sample of N values: $x_1, x_2, ..., x_N$:\n", "\n", "# Average(mean):\n", "\n", "## $<x> = \\frac{x_1 +x_2 + ...+x_N}{N} = \\frac{1}{N}\\sum_{i = 1}^{N} x_i$ " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "For a sample of N values: $x_1, x_2, ..., x_N$:\n", "\n", "# The nth moment:\n", "\n", "## $<x^n> = \\frac{x_1^n +x_2^n + ...+x_N^n}{N} = \\frac{1}{N}\\sum_{i = 1}^{N} x_i^n$ " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "For a sample of N values: $x_1, x_2, ..., x_N$:\n", "\n", "# Standard deviation:\n", "\n", "## $\\sigma_x = \\sqrt{\\frac{1}{N}\\sum_{i = 1}^{N} (x_i - <x>)^2}$ " ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "(1.6666666666666667, 10, 0.7453559924999299)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "x = [1, 1, 1, 2, 2, 3]\n", "np.mean(x), np.sum(x), np.std(x)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "For a sample of N values: $x_1, x_2, ..., x_N$:\n", "\n", "# Distribution of x:\n", "\n", "## $p_x = \\frac{The \\: frequency \\: of \\: x}{The\\: Number \\:of\\: Observations}$\n", "\n", "其中，$p_x 满足 \\sum_i p_x = 1$" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10e7cac50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(x)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "defaultdict(int, {1: 3, 2: 2, 3: 1})" ] }, "execution_count": 113, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from collections import defaultdict, Counter\n", "freq = defaultdict(int)\n", "for i in x:\n", " freq[i] +=1\n", "\n", "freq" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "freq_sum = np.sum(freq.values())\n", "freq_sum" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "[0.5, 0.3333333333333333, 0.16666666666666666]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "px = [float(i)/freq_sum for i in freq.values()]\n", "px" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x107092d10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(freq.keys(), px, 'r-o')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Average Degree" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Undirected\n", "\n", "# $<k> = \\frac{1}{N} \\sum_{i = 1}^{N} k_i = \\frac{2L}{N}$\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Directed\n", "# $<k^{in}> = \\frac{1}{N} \\sum_{i=1}^N k_i^{in}= <k^{out}> = \\frac{1}{N} \\sum_{i=1}^N k_i^{out} = \\frac{L}{N}$ " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Degree distribution \n", "P(k): probability that a randomly selected node has degree k\n", "\n", "\n", "$N_k = The \\:number\\: of \\:nodes\\:with \\:degree\\: k$\n", "\n", "## $P(k) = \\frac{N_k}{N}$\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Adjacency matrix\n", "$A_{ij} =1$ if there is a link between node i and j\n", "\n", "$A_{ij} =0$ if there is no link between node i and j" ] }, { "cell_type": "code", "execution_count": 182, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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OHZYvX864cePIzs52+foMw1BveyCFQCWcv9lm3rx5fPvtty5fn+4UFncJDQ3l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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1150c5350>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(1)\n", "plt.subplot(121)\n", "pos = nx.spring_layout(Gu) #定义一个布局，此处采用了spring布局方式\n", "nx.draw(Gu, pos, with_labels = True)\n", "plt.subplot(122)\n", "nx.draw(Gd, pos, with_labels = True)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Undirected\n", "$A_{ij} =1$ if there is a link between node i and j\n", "\n", "$A_{ij} =0$ if there is no link between node i and j\n", "\n", "## $A_{ij}=\\begin{bmatrix} 0&1 &0 &1 \\\\ 1&0 &0 &1 \\\\ 0 &0 &0 &1 \\\\ 1&1 &1 & 0 \\end{bmatrix}$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Undirected\n", "## $A_{ij} = A_{ji}, \\: A_{ii} = 0$\n", "## $k_i = \\sum_{j=1}^N A_{ij}, \\: k_j = \\sum_{i=1}^N A_{ij} $\n", "## $ L = \\frac{1}{2}\\sum_{i=1}^N k_i = \\frac{1}{2}\\sum_{ij}^N A_{ij} $" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Directed\n", "$A_{ij} =1$ if there is a link between node i and j\n", "\n", "$A_{ij} =0$ if there is no link between node i and j\n", "\n", "## $A_{ij}=\\begin{bmatrix} 0&0 &0 &0 \\\\ 1&0 &0 &1 \\\\ 0 &0 &0 &1 \\\\ 1&0 &0 & 0 \\end{bmatrix}$\n", "\n", "Note that for a directed graph the matrix is not symmetric.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Directed\n", "## $A_{ij} \\neq A_{ji}, \\: A_{ii} = 0$\n", "## $k_i^{in} = \\sum_{j=1}^N A_{ij}, \\: k_j^{out} = \\sum_{i=1}^N A_{ij} $\n", "## $ L = \\sum_{i=1}^N k_i^{in} = \\sum_{j=1}^N k_j^{out}= \\frac{1}{2}\\sum_{i,j}^N A_{ij} $" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# WEIGHTED AND UNWEIGHTED NETWORKS\n", "\n", "## $A_{ij} = W_{ij}$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# BIPARTITE NETWORKS \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "bipartite graph (or bigraph) is a graph whose nodes can be divided into two disjoint sets U and V such that every link connects a node in U to one in V; that is, U and V are independent sets. \n", "\n", "- Hits algorithm\n", "- recommendation system\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# Ingredient-Flavor Bipartite Network\n", "\n", "<img src = './img/bipartite.png' width = 800>" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# Path\n", "A path is a sequence of nodes in which each node is adjacent to the next one\n", " - In a directed network, the path can follow only the direction of an arrow. \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# Distance\n", "\n", "The distance (shortest path, geodesic path) between two nodes is defined as the number of edges along the shortest path connecting them.\n", "\n", " - *If the two nodes are disconnected, the distance is infinity.\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# Diameter: \n", "\n", "Diameter dmax is the maximum distance between any pair of nodes in the graph. \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# Shortest Path \n", "The path with the shortest length between two nodes (distance). \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# Average path length/distance, $<d>$\n", "\n", "## The average of the shortest paths for all pairs of nodes.\n", "\n", "\n", "- for a connected graph: where $d_{ij}$ is the distance from node i to node j\n", "\n", "## $<d> = \\frac{1}{2 L_{max} }\\sum_{i, j \\neq i} d_{ij}$\n", "\n", "- In an undirected graph $d_{ij} =d_{ji}$ , so we only need to count them once:\n", "\n", "## $<d> = \\frac{1}{L_{max} }\\sum_{i, j > i} d_{ij}$\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# Cycle\n", "A path with the same start and end node. \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# CONNECTEDNESS\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Connected (undirected) graph: \n", "any two vertices can be joined by a path. A disconnected graph is made up by two or more connected components. \n", "\n", "- Largest Component: Giant Component\n", "- The rest: Isolates\n", "\n", "## Bridge: \n", "if we erase it, the graph becomes disconnected. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## The adjacency matrix of a network with several components can be written in a block-diagonal form, so that nonzero elements are confined to squares, with all other elements being zero:\n", "\n", "<img src = './img/block.png' width = 600>" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "# Strongly connected directed graph: \n", "has a path from each node to every other node and vice versa (e.g. AB path and BA path).\n", "\n", "# Weakly connected directed graph: \n", "it is connected if we disregard the edge directions.\n", "\n", "Strongly connected components can be identified, but not every node is partof a nontrivial strongly connected component. \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "- In-component: nodes that can reach the scc, \n", "- Out-component: nodes that can be reached from the scc. \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "# Clustering coefficient\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "# Clustering coefficient: \n", "what fraction of your neighbors are connected? Watts & Strogatz, Nature 1998.\n", "\n", "- Node i with degree $k_i$\n", "- $C_i$ in [0,1]\n", "\n", "# $C_i = \\frac{2e_i}{k_i(k_i -1)}$\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "<img src = './img/cc.png' width = 500>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Global Clustering Coefficient:\n", " \n", "## $C = \\frac{3 \\times \\mbox{number of triangles}}{\\mbox{number of connected triplets of vertices}} = \\frac{\\mbox{number of closed triplets}}{\\mbox{number of connected triplets of vertices}}$\n", " " ] }, { "cell_type": "code", "execution_count": 215, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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KW1tb0XFIy7CI6V8GDhyI2rVrIyAgQHQUKqInT56ggZkZbmVlvfc7YeC/i/g5\ngLr6+khMStLa1dQAcOLECfTu3RsbNmyAk5OT6DikRbizFv3j4sWLiI6Ohq+vr+goVAwbwsLgChRa\nwh9SEYCrTIYNYWHyC6WGOnbsiL1792LgwIGIiIgQHYe0CIuY/jFp0iTMnDlT6+/co24S//gDbbKy\nSnUMq8xMJMbHyymR+mrXrh0iIyMxbNgw7NTiqXpSLm7oQQCA6Oho3LlzB8OGDRMdhYopLSUFpf3o\nZAwg9cULecRRe61bt8bBgwfh7OyM7Oxs9O/fX3Qk0nAsYkJ+fj78/Pwwf/58lCtXTnQcKqbyJiZI\nLeS5fAC5//s9D0A2Xv+l13nrdakAjCuUdHJb8zRv3hyHDx+Go6MjcnNz4eHhIToSaTBOTRM2b94M\nQ0NDuLq6io5CxZSZmYlXOTk4WcgtDwMBGAJYAGDz//489z2vO2dgAHNLS4XlVEdNmjRBTEwMpk+f\njtWrV4uOQxqMRazlMjMzMX36dCxcuJD3r1Uj169fx4QJE2Bqaoqnz55hf9myeN/E8iwABXg9Iv77\n18y3XvMcwLbMTMh0dJCTk6PY4GqmYcOGOHr0KAIDA7FixQrRcUhDsYi13PLly9G6dWu0b99edBT6\ngLy8POzZswcODg5o3749ypYti7NnzyImJgbdunbF+hJ+kFovk8HG2hrR0dGoX78+Vq5ciezsbDmn\nV1/16tXD8ePHsXjxYixZskR0HNJEEmmt5ORkqXLlytK1a9dER6H/8ODBAykgIECqWbOm1K5dO2nj\nxo1SZmbmv15z7tw5qZqhoXQdkKRi/LoOSNUMDaW4uDhJkiTp119/lZydnaVatWpJK1aseOc82iwp\nKUmqV6+eNG/ePNFRSMNwRKzF5s6dCzc3NzRo0EB0FHqLJEk4fvw4+vbtCwsLC9y7dw8RERH45Zdf\nMGDAAOjr6//r9VZWVvBftAiOhoa4UcRz/L3XtP+iRf9sb9m2bVtERkYiPDwckZGRqFevHpYvX46s\nUl4epQlq1aqF48ePY8OGDfD394fEvZBIXkR/EiAxbt26JVWqVEl69OiR6Cj0hpcvX0rLly+XLCws\npEaNGknLli2TXr58WeT3hwYHS9UMDaUlMpn0vJBR8DNAWiyTSdUMDaXQ4OD/PN758+el7t27S59+\n+qn0ww8/SBkZGaX9EdXeo0ePpCZNmkhTpkyRCgoKRMchDcAtLrXU119/jQYNGmDmzLeX7pAIv//+\nO4KDg7HnjjQUAAAcLklEQVR9+3bY29vDx8cH1tbWJVpAd/78eSwNCsL+yEi4ymSwysz8537Ecf+7\nH3FXFxeMnTKlyDd6uHjxIgICAnD27Fn4+vpixIgRMDQ0LHY2TZGcnAx7e3t07twZixYt4kJHKhUW\nsRb67bff0K1bNyQmJqJ8+fKi42it7OxshIeHIzg4GHfv3sWIESMwdOhQVK9eXS7Hf/r0KTaEhSEx\nPh6pL17AuEIFmFtaYuCgQSXeU/r333/HnDlzcPr0aUyYMAHe3t4wMjKSS1518/z5czg6OqJt27ZY\nunQpypThN31UMixiLSNJEmxtbeHu7o7hw4eLjqOVbt++jdDQUKxduxbNmzeHt7c3unXrhrJl1Wd/\nnfj4eMyZMwcnTpzAt99+Cx8fH638UJeSkgJnZ2c0adIEK1euZBlTifD/NVrm4MGDePjwIQYPHiw6\nilbJz8/HgQMH0KVLF1hZWSEnJwenTp1CdHQ0XF1d1aqEAcDS0hLbt29HTEwMLly4gLp16yIoKAip\nqYXt8aWZTExMcOjQISQkJGDw4MHIz88XHYnUEEfEWiQ/Px/NmzfH3Llz0b17d9FxtMLTp0+xZs0a\nhIaGonLlyvDx8UHfvn017vvVK1euYO7cuTh8+DDGjh2L0aNH46OPPhIdS2nS09PRvXt3VKtWDevX\nr1e7D1YkFkfEWmTDhg2oUKECunXrJjqKRpMkCadPn8aAAQNQv359JCYmYvv27YiLi4Onp6fGlTAA\nWFhYYPPmzTh58iSuXbuGunXrIiAgACkpKaKjKYWRkRH279+P58+fo1+/fsjNzRUdidQIR8RaIiMj\nAw0aNEB4eDg+//xz0XE0UlpaGjZv3ozg4GBkZmbC29sbHh4eqFixouhoSpeYmIh58+bhwIEDGDly\nJMaNG4ePP/5YdCyFy87OhpubG2QyGbZv3w49PT3RkUgNcESsJZYuXYp27dqxhBXg8uXLGDVqFExN\nTXHo0CEsWrQI165dw/jx47WyhAHA3NwcYWFh+PXXX5GUlIR69eph5syZeP78uehoCqWnp4fw8HCU\nK1cOrq6uyMzMFB2J1ACLWAs8ffoUixcvxrx580RH0Rg5OTnYtm0brK2tYW9vj4oVK+L333/Hrl27\nYG9vz9Wz/1OvXj2sXbsW586dw8OHD1G/fn1MmzYNz549Ex1NYXR1dbF161aYmJige/fuyMjIEB2J\nVBynprXA2LFjIUkSli1bJjqK2ktKSsKqVauwZs0aNGzYED4+PujZsyfv41xEd+7cQVBQEMLDwzF8\n+HBMmDABlStXFh1LIfLz8zF48GDcvXsX+/fv18rLu6ho+LFdw924cQObN2/GjBkzREdRWwUFBYiO\njkbPnj3RvHlzvHr1CjExMTh69Cjc3NxYwsVQu3ZthIaG4uLFi3j58iUaNGgAPz8/PHnyRHQ0udPR\n0cG6detQv359ODo6as3CNSo+FrGGmzZtGr799tsS76SkzZ49e4bFixfD3Nwcfn5+cHFxQVJSEpYt\nWwYLCwvR8dSaqakpQkJCcOnSJWRkZKBRo0aYOHEiHj9+LDqaXJUpUwahoaFo3rw57O3t8eLF++4a\nTdqORazBzp49i9OnT2PcuHGio6gNSZJw7tw5DBo0CHXr1sWlS5ewceNGXLx4EcOHD+f0opzVqlUL\nP/74I/744w/k5OSgUaNG+Pbbb/Ho0SPR0eSmTJky+PHHH/Hll1/C1tYWycnJoiORimERayhJkuDn\n5wd/f3+NvG5V3jIyMrBmzRpYWVnB3d0dFhYWuH79OjZu3Ih27dpxU38Fq1GjBpYtW4Y///wTkiSh\ncePGGDduHB48eCA6mlzIZDIsXrwYjo6O6Ny5s0ZOxVPJsYg11P79+/Hs2TN4eHiIjqLSEhISMH78\neNSqVQt79uxBQEAArl+/Dj8/P07nC/Dpp5/i+++/x+XLl6GjowNLS0uMHj0a9+7dEx2t1GQyGebN\nm4devXrBxsYGDx8+FB2JVASLWAPl5eVh0qRJWLBgAbfae4+8vDzs2rULdnZ26NixI/T19fHbb78h\nIiICLi4u0NHRER1R61WrVg2LFy/GlStXoK+vj2bNmmHkyJH466+/REcrFZlMhtmzZ2PAgAGwtrbW\niA8YVHosYg20bt06fPLJJ3BxcREdRaU8ePAA/v7+MDMzw5IlSzB48GAkJSUhKCgItWvXFh2P3uOT\nTz7BwoULce3aNRgbG6N58+bw8vLC3bt3RUcrlalTp2LEiBGwtrbGnTt3RMchwVjEGiY9PR2zZ8/G\nd999x+818fq78tjYWPTu3RuNGzfGo0ePEBUVhVOnTqF///7cglBNVKlSBfPnz0dCQgIqVqyIli1b\nYvjw4bh9+7boaCU2YcIEjBs3DjY2Nrh586boOCQQi1jDLFmyBB07doSVlZXoKEK9fPnyn8uMxowZ\ng86dO+Pu3bsICQlB06ZNRcejEqpcuTLmzZuHxMREVK1aFa1bt8aQIUNw69Yt0dFKZPTo0Zg6dSps\nbGyQkJAgOg4Jwp21NMjjx49hYWGBuLg41KlTR3QcIS5cuICQkBCEh4fDyckJ3t7e6NChA2cHNNSL\nFy/www8/YMWKFejWrRumTZuGevXqiY5VbOvWrcP06dMRHR2Nxo0bi45DSsYRsQYJCAjAwIEDta6E\ns7KysGHDBrRt2xY9e/ZE7dq1cfXqVWzZsgUdO3ZkCWuwChUqwN/fHzdu3EDt2rXRrl07eHh4IDEx\nUXS0YvH09MR3330HOzs7/P7776LjkJJxRKwhEhIS0L59eyQkJKBSpUqi4yjFzZs3sXLlSoSFhaFV\nq1bw8fGBi4sLV4prsZSUFCxfvhxLly6Fo6Mjpk+fjoYNG4qOVWQ7duzA6NGjceDAAbRq1Up0HFIS\njog1xNSpU+Hr66vxJZyfn4+IiAg4Ozujbdu2AIBff/0VBw8eRPfu3VnCWs7ExATTp0/HzZs3YWFh\ngY4dO6J///64cuWK6GhF4ubmhtDQULi4uODMmTOi45CScESsAX755Re4u7sjISEBBgYGouMoxOPH\nj7FmzRqEhoaievXq8Pb2Rp8+fTT25yX5SE1NxYoVK/D999/DxsYGM2bMQJMmTUTH+qDIyEgMGjQI\nu3btwpdffik6DikYR8RqTpIk+Pr6Ys6cORpXSpIk4eTJk+jXrx8aNmyIW7duYdeuXThz5gw8PDw0\n7ucl+TM2NsbkyZNx8+ZNtG7dGnZ2dnBzc0N8fLzoaP/JxcUFmzdvhqurK44ePSo6DikYi1jN7dmz\nB2lpaRgwYIDoKHLz6tUrBAcHo2nTphg6dCjatm2LW7du4aeffuL3ZlQi5cuXh6+vL27evIm2bdvC\nwcEBX331lUovjLK3t8eOHTvQt29fREdHi45DCsSpaTWWm5uLJk2aYNmyZXB0dBQdp9Ti4+MRHByM\nrVu3wtbWFt7e3ujcuTNXPZPcZWRkYNWqVfjuu+/Qpk0bzJw5Ey1bthQd671Onz4NV1dXrFu3Dl26\ndBEdhxSAI2I1tmbNGpiamsLBwUF0lBLLzs7Gli1b0KFDBzg5OaFatWr4888/ER4eDltbW5YwKYSh\noSHGjRuHmzdvonPnzujWrRu6d++O8+fPi472jvbt2yMiIgKDBw/G7t27RcchBeCIWE2lpqbC3Nwc\nkZGRaNGiheg4xXb37l2EhoZizZo1sLS0hLe3N7p3745y5cqJjkZaKCsrCz/99BPmz5+PZs2aYdas\nWWjTpo3oWP9y4cIFuLi4YNmyZejTp4/oOCRHHBGrqcWLF8POzk6tSrigoABRUVHo1q0bWrZsiYyM\nDBw/fhxHjhzBV199xRImYfT19TFq1CjcvHkTXbt2Re/eveHs7KxSlxC1bNkS0dHRGDt2LDZt2iQ6\nDskRR8Rq6OHDh2jSpAkuXLgAMzMz0XE+KDk5GevWrcPKlSthYmKCkSNHwt3dHUZGRqKjEb1XdnY2\nwsLCEBQUBHNzc8yaNQvt27cXHQsAcOXKFdjb22POnDkYPHiw6DgkByxiNeTl5QVjY2MsXLhQdJRC\nSZKEs2fPIjg4GPv27UPPnj3h7e2NNm3a8HtfUhs5OTnYsGED5s6di7p162LWrFno0KGD6FhITEyE\nnZ0dpk6dCi8vL9FxqJRYxGrm6tWrsLa2RkJCAipUqCA6zjvS09Px888/Izg4GKmpqfDy8oKnp6fG\n7/hFmi03NxcbN27E3LlzYWZmhlmzZsHa2lpoplu3bsHW1hbjxo3D2LFjhWah0mERq5mePXuiQ4cO\nmDBhgugo/3L16lWEhIRg8+bN+PLLL+Hj4wN7e3uUKcNlCKQ5cnNz8fPPPyMwMBCffvopZs2ahU6d\nOgmb5bl79y5sbW0xfPhw+Pn5CclApcciViMnT57EN998g2vXrkFfX190HOTm5mLv3r0IDg7GlStX\nMHToUAwfPhympqaioxEpVF5eHrZs2YLAwEBUrVoVs2bNEna53f3799G5c2cMGDAAM2bMUPr5qfRY\nxGpCkiS0a9cOo0ePxtdffy00y71797B69WqsXr0a9evXh7e3N3r16gVdXV2huYiULT8/H1u3bsWc\nOXNQqVIlzJw5Ew4ODkov5EePHsHW1ha9evVCQEAA12GoGc4bqomdO3ciJycH/fr1E3L+goICHDly\nBL169ULTpk2RnJyM6OhoHD9+HO7u7ixh0ko6Ojr4+uuvcfnyZYwaNQrjx49Hu3btEBUVBWWOcapV\nq4Zjx45h3759mDRpklLPTaXHEbEayMnJQePGjbFy5UrY2toq9dwvXrxAWFgYQkJCoK+vDx8fH3z9\n9dcwNjZWag4idVBQUIDw8HAEBATAyMgIM2fOhIuLi9JGqM+ePYODgwM6dOiA77//niNjNcEiVgM/\n/vgjDhw4gKioKKWd8/z58wgODsauXbvQpUsX+Pj44IsvvuBfbKIiKCgowK5duxAQEABdXV3MnDkT\n3bp1U8rfn5cvX8LJyQktWrTAihUruGBSDbCIVdyrV69gbm6O6OhoNG3aVKHnyszMxLZt2xAcHIwn\nT57Ay8sLgwcPRtWqVRV6XiJNVVBQgL179/7zve3MmTPRo0cPhRfyq1ev0KVLF5ibm2PVqlXQ0dFR\n6PmodFjEKm769Om4f/8+1q1bp7BzXL9+HStXrsT69evx+eefw9vbG87OzvzLSyQnkiRh3759CAgI\nQH5+PmbMmAFXV1eFjlbT0tLQvXt31KhRA+vWrUPZsmUVdi4qHRaxCrt//z6aNm2KS5cuoVatWnI9\ndl5eHvbv34/g4GBcunQJnp6eGDFiBOrUqSPX8xDR/5MkCQcOHIC/vz+ys7MxY8YMfPXVVwor5IyM\nDPTs2RMVKlTApk2buJ+7imIRq7ChQ4eiSpUqCAoKktsxHz58iJ9++gmrVq1CrVq14OPjg969e6vE\ndclE2kKSJERFRcHf3x9paWmYMWMG3NzcFDILlZWVhd69e0NXVxdbt27lFQ4qiEWsov7880/Y2toi\nMTERJiYmpTqWJEk4ceIEgoODER0djT59+sDb2xvNmzeXU1oiKglJknDo0CH4+/sjJSUF06dPR9++\nfeVeyDk5Oejbty9yc3MRHh7OD94qhkWsorp27Qo7OzuMGzeuxMdISUnBxo0bERISAkmS4O3tjYED\nB5a62IlIviRJwpEjR+Dv74/k5GRMnz4d7u7ucv1eNzc3FwMGDMDLly+xe/duGBoayu3YVDosYhV0\n9OhRDBkyBFevXoWenl6x33/p0iWEhIRg+/btsLe3h4+PD6ytrXnpEZGKkyQJsbGx8Pf3x6NHjzB9\n+nT0799fboWcl5cHT09P3L9/HxEREbwVqYpgEQvy5MkTbAgLQ+IffyAtJQXlTUxg3rQpvvHwQNeu\nXTFhwgS4u7sX+XhZWVkIDw9HcHAwkpKSMGLECAwdOhTVq1dX4E9BRIpy7Ngx+Pv746+//sK0adMw\nYMAAuSy2ys/Px7Bhw3D9+nUcOHAAH330kRzSUmmwiJUsLi4OS4OCcCAqCr0AWGVlwRhAKoBzBgbY\nmZeH8kZG2HXwID7//PMPHu/27dsIDQ3F2rVr0bx5c/j4+KBr1668VIFIQ5w4cQL+/v64ffs2pk2b\nhoEDB5a6kAsKCjBy5EhcvHgRBw8exMcffyyntFQiEilNaHCwVM3QUPpeJpOeA5L0nl/PAWmxTCZV\nMzSUQoOD33ucvLw8af/+/ZKLi4tUqVIl6dtvv5USExOV/NMQkTKdPHlSsrOzk2rXri2FhoZK2dnZ\npTpeQUGBNGbMGKlVq1bSs2fP5JSSSoJFrCShwcFSHUND6XohBfz2r+uAVOetMn78+LEUFBQkmZmZ\nSVZWVtLatWul9PR0gT8VESnb6dOnJUdHR8nU1FQKCQmRsrKySnysgoICydfXV2ratKn05MkTOaak\n4uDUtBLExcWhu40NTmZkoN4bj38D4AiADACVAQwGMO2N528A6GBoiMBlyxAbG4vIyEi4urrC29sb\nVlZWyvsBiEjlnDlzBgEBAfjzzz8xefJkDB48uESXJUmShJkzZ2LXrl2IiYlBtWrVFJCW/guLWAkG\n9OqF1nv2YNxb/6mvAKgDQB9AIoCOANYDcHzjNYsAfGdkhClz5sDDwwMVK1ZUUmoiUgdxcXEICAjA\nxYsXMWnSJAwbNqxEhTxnzhxs3rwZMTExqFGjhgKSUmFYxAr25MkTNDAzw62sLFT4j9clALADsBdA\nyzcefw6grr4+EpOSUKVKFUVGJSI19ttvvyEgIADnz5+Hn58fhg8fDgMDg2Id47vvvsOqVasQGxsL\nU1NTBSWlt/H+WAq2ISwMrkChJTwSgBGAJng9Ld3yrecrAnCVybAhLExhGYlI/bVq1Qp79+7F/v37\ncezYMdStWxdLlixBRkZGkY/h5+eHUaNGwdraGrdu3VJgWnqTzuzZs2eLDqHJwkJC0PriRbQu5Pku\nAKYCsAbgBaATgLcnhR7k5WFvair0jY1x9+5dPHjwAMnJyXj16hUyMzNRUFAAHR0d3i2JiFC9enW4\nu7vDzs4OmzdvxsSJEwEAzZo1K9I+023btoWenh6GDh2Krl27olKlSoqOrPV4samCpaWkwPgDr5Hh\ndRG7AdgC4O1lWMYAHt67h507dyItLQ3p6elIS0t7589lypSBkZERypcvj/Lly//z57d/L+5zurq6\n3JWLSM00a9YM4eHhiI+Px5w5c7Bw4UJ8++23GDlyJMqXL/+f7/X29oauri46deqEw4cPo1GjRkpK\nrZ1YxApW3sQEqUV8bR6A9+3+mgqgQ6dOCN2wodD3SpKEnJyc95b027///efHjx8X+tybv0uSJLdS\nf/M5PT09FjyRgllaWmL79u24fPkyAgMDUbduXYwbNw6jRo2CsXHhw4QhQ4ZAV1cXtra2OHToECwt\nLZWYWruwiBXMvGlTnNu5E15ZWf96/CmAWABdARgAOAxgx/9+f1ucgQEaf+AvgUwmg56eHvT09OS+\nsvrNgi9KcScnJxfpw0BeXl6RC7w4hW9gYMCCJ3pL48aNsWXLFly9evWfQh47dixGjx5d6DaX33zz\nDXR1dWFvb4+oqCi0aNFCyam1A1dNK1hhq6aTAfQG8AcACUB9ADMAdHvr/Zq8ajo3Nxfp6elFKvf/\neu7tx3JycmBoaFiiEft/vd7Q0JAFTxojISEBc+fORVRUFEaPHo0xY8YUutXlrl274O3tjYiICLRp\n00bJSTUfi1gJCruOuCi+l8lwwdUVG3fuVEAyzZSXl4eMjIwSlfp/PZednQ0DAwO5T9EbGhqiTBle\nwEBiXL9+HXPnzsX+/fsxatQojB07FhUqvHudx/79+zF48GDs2bMHX3zxhYCkmotFrASF7az1IX/v\nrBVx/Dhaty5s3TUpS35+/j8FX9pSf/O5zMxMGBgYyH2hnZGREVfSU5HduHED8+bNw759++Dt7Y3x\n48e/8zXXoUOH8M0332DHjh2wtrb+13OF3VHOw9NT42bz5I1FrCSrQkKwYOJEHCpiGd8A4GhoiEmL\nFmG4t7ei45FABQUFyMjIkPsUfXp6OvT09OQ+RW9kZMS7e2mwW7duISgoCLt27YKXlxe+/fbbf13C\nFBsbi759+2LLli2ws7P74B3ldksSujg7Y+yUKdyatxAsYiVaFRKCWRMnwi8zE4Mk6b2bfDwHECaT\nYaGBAfxZwlQKkiQhMzNT7lP06enpKFeunNyn6I2MjORyv12Sjzt37iAoKAjh4eEYPnw4JkyYgMqV\nKwMATp48ia+++gruffpgx7p1mJSZCY9C/k17gdf/pn3Hf9MKxSJWsvPnz2NpUBD2R0bCVSaDVWbm\nP58e4/736bGriwvGTpnC6WhSSZIkISsrS+5T9Onp6dDR0VHYtfBUMklJSZg/fz62bduGIUOGYOLE\niahatSom+fpi46JFOAFwlq+UWMSCPH369PX3KfHxSH3xAsYVKsDc0hIDBw3i9ymklSRJQnZ2ttyn\n6NPS0gBALpfGvf2cNm1289dff2HBggX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"text/plain": [ "<matplotlib.figure.Figure at 0x1124b9b10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "G1 = nx.complete_graph(4)\n", "pos = nx.spring_layout(G1) #定义一个布局，此处采用了spring布局方式\n", "nx.draw(G1, pos = pos, with_labels = True)" ] }, { "cell_type": "code", "execution_count": 206, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0\n" ] } ], "source": [ "print(nx.transitivity(G1))" ] }, { "cell_type": "code", "execution_count": 216, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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ZQteuXUlKStKj/nLo/PnzuLm5sX37dtzd3U3HEZE8oolY8pzVamXYsGGMGzdOJZwLJUqU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\nzPnz53nrrbd4++2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8BuwHal73up4odyMVsYiI5NjtnigHV++b3wvoA3je5H09Ue6vVMQi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\nqYhFREQMUhGLiIgY9H8jVYI4xjKLCgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1124b9cd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "G2 = nx.Graph()\n", "for i, j in [(1, 2), (1, 3), (1, 0), (3, 0)]:\n", " G2.add_edge(i,j)\n", "nx.draw(G2,pos = pos, with_labels = True)" ] }, { "cell_type": "code", "execution_count": 198, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.6\n" ] } ], "source": [ "print(nx.transitivity(G2))" ] }, { "cell_type": "code", "execution_count": 219, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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12hqA+9q+fbsSExO1Z88ePffcc5o0aRL3A3BRFHEDGPfAA+r+9tt68or/1Xsl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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11153a790>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "G3 = nx.Graph()\n", "for i, j in [(1, 2), (1, 3), (1, 0)]:\n", " G3.add_edge(i,j)\n", "nx.draw(G3, pos =pos, with_labels = True)" ] }, { "cell_type": "code", "execution_count": 200, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0\n" ] } ], "source": [ "print(nx.transitivity(G3))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "THREE CENTRAL QUANTITIES IN NETWORK SCIENCE\n", "- A. Degree distribution: \t\t\t $p_k$\n", "- B. Path length: \t\t\t\t\t\t$<d>$\t\t\t\n", "- C. Clustering coefficient: $C_i$\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "# Typical Network Science Research\n", "\n", "- Discovering, Modeling, Verification\n", " - WATTSDJ,STROGATZSH.Collective dynamics of‘small-world’ networks. Nature, 1998, 393(6684): 440–442.\n", " - BARABÁSI A-L, ALBERT R. Emergence of scaling in random networks. Science, 1999, 286(5439): 509-512." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "# Typical Math Style\n", "Fan Chung & Linyuan Lu, The average distance in random graphs with given expected degree,. PNAS, 19, 15879-15882 (2002)." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "# Typical Physical Style\n", "A.-L.Barabási,R.Albert,H.Jeong Mean-field theory for scale-free random networks. Physica A 272, 173–187 (1999)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Typical Computer Science Style\n", "\n", "- Community detection\n", "- Link prediction\n", "- Recommendation algorithms" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Typical control sytle\n", "Controllability of Complex Networks\n", "\n", "Liu Y Y, Slotine J J, Barabási A L. Nature, 2011, 473(7346): 167-173." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<img src ='./img/cocktail.png' width = 500>\n", "# The random network model\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Erdös-Rényi model (1960)\n", "\n", "Definition: A random graph is a graph of N nodes where each pair of nodes is connected by probability p.\n", "\n", "## G(N, L) Model\n", "N lableled nodes are connected with L randomly placed links. Erdös-Rényi(1959)\n", "\n", "## G(N, p) Model\n", "Each pair of N labeled nodes is connected with probability p. Gilbert (1959)\n" ] }, { "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": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 144, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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DwsLwtyeeQBFZ71zWxqgxfeDAAfTq1cvuswX33IP1JPLMQqi5VaUymUw4cvIkXvHyssud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\ndXOKbnsAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10e693ad0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import networkx as nx\n", "import matplotlib.pyplot as plt\n", "BA= nx.random_graphs.barabasi_albert_graph(500,1) #生成n=20、m=1的BA无标度网络\n", "pos = nx.spring_layout(BA) #定义一个布局，此处采用了spring布局方式\n", "nx.draw(BA,pos,with_labels=False,node_size = 30) #绘制图形\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "[0, 343, 76]" ] }, "execution_count": 145, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nx.degree_histogram(BA)[:3]" ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "[(0, 6), (1, 27), (2, 13)]" ] }, "execution_count": 146, "metadata": {}, "output_type": "execute_result" } ], "source": [ "BA.degree().items()[:3]" ] }, { "cell_type": "code", "execution_count": 147, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10fbe3290>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(BA.degree().values())\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 148, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10fcd0990>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plotDegreeDistributionLongTail(G):\n", " degs = defaultdict(int)\n", " for i in G.degree().values(): degs[i]+=1\n", " items = sorted ( degs.items () )\n", " x, y = np.array(items).T\n", " plt.plot(x, y, 'b-o')\n", " plt.legend(['Degree'])\n", " plt.xlabel('$K$', fontsize = 20)\n", " plt.ylabel('$P_K$', fontsize = 20)\n", " plt.title('$Degree\\,Distribution$', fontsize = 20)\n", " plt.show() \n", " \n", "plotDegreeDistributionLongTail(BA)" ] }, { "cell_type": "code", "execution_count": 150, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10facd6d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plotDegreeDistribution(G):\n", " degs = defaultdict(int)\n", " for i in G.degree().values(): degs[i]+=1\n", " items = sorted ( degs.items () )\n", " x, y = np.array(items).T\n", " plt.plot(x, y, 'b-o')\n", " plt.xscale('log')\n", " plt.yscale('log')\n", " plt.legend(['Degree'])\n", " plt.xlabel('$K$', fontsize = 20)\n", " plt.ylabel('$P_K$', fontsize = 20)\n", " plt.title('$Degree\\,Distribution$', fontsize = 20)\n", " plt.show() \n", " \n", "plotDegreeDistribution(BA)" ] }, { "cell_type": "code", "execution_count": 192, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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d4e6fa2f7r4Ffd/W61dXVSu6nkZyj7MEHm0gFkO2E/hzJucqORMl/kb4jV8n+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\nE5rJDuTss4czbNgI6us38uq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"text/plain": [ "<matplotlib.figure.Figure at 0x11280cd50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "BA= nx.random_graphs.barabasi_albert_graph(50000,10) #生成n=50000、m=1的BA无标度网络\n", "plotDegreeDistribution(BA)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 无标度的意义" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "度分布的n阶矩被定义为：\n", "\n", "$ <k^n> = \\sum_{k_{min}}^{k_{max}}k^np_k = \\int_{k_{min}}^{k_{max}}k^np_kdk $ （1）\n", "\n", "低阶矩具有明确的统计意义：\n", "\n", "* n = 1的时候，一阶矩是$<k^{}>$，即平均度。\n", "* n = 2的时候，二阶矩是$<k^2>$，可以帮助计算方差 $ \\delta^2 = <k^2> - <k^{}>^{2} $，测量了度的离散程度（the spread in the degrees）。\n", "* n = 3的时候，三阶矩是$<k^3>$, 决定了度分布的偏度（skewness），测量了$p_k$围绕着 <k>的对称性。\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "对于无标度网络而言，满足幂律分布：\n", "\n", "$p(k) = Ck^{-\\gamma}$ （2）\n", "\n", "由公式（1）和（2）可以得到：\n", "\n", "$ <k^n> = \\int_{k_{min}}^{k_{max}}k^np_kdk = C \\frac{k_{max}^{n- \\gamma +1} - k_{min}^{n - \\gamma +1}}{n - \\gamma + 1} $ （3）\n", "\n", "可以使用wolframalpha的积分计算器积分来进行简单验证，例如x^(n-r)dx从10到100积分 网页链接：[http://www.wolframalpha.com/input/?i=integrate+x%5E%28n-r%29+dx+from+10+to+100][1]" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "显然：\n", "\n", "* 当$n - \\gamma +1 <= 0$时，随着$k_{max}增加，$$k_{max}^{n- \\gamma +1} \\rightarrow 0$。所有满足$n <= \\gamma -1$的n阶矩都是有限的。\n", "* 当$n - \\gamma +1 >0 $时，随着$k_{max}增加，$$k_{max}^{n- \\gamma +1} \\rightarrow \\infty$。所有满足$n > \\gamma -1$的n阶矩都是无极限的。\n", "\n", "对于无标度网络而言，一般幂参数$2 < \\gamma < 3$，所以：\n", "\n", "* 对于n = 1的情况，即一阶矩平均度$<k^{}>$是有限的。\n", "\n", "* 但对于n >= 2的情况，即$k^2$或$k^3$是无极限的。二阶和高阶矩无穷大是“无标度”的来源" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<img src = \"./img/scale_free.png\" width = 500>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# 一言以蔽之，在无标度的意义是，在网络中随机抽取一个节点的度可以显著的不同于平均度$<k>$\n", "\n", "上图最为直接的描绘出了这种特点，即与正态分布等相比，`无标度网络下降的慢`。\n", "\n", "* 对于任何指数类型的分布，如泊松分布或高斯分布，随机选取一个节点的度在平均度附近，因此平均度就是这些网络的`尺度`。\n", "\n", "* 对于一个幂律分布而言，因为二阶矩发散，在网络中随机抽取一个节点的度可以显著的不同于平均度$<k^{}>$， 因此平均度不再是网络的尺度，称之为无标度。" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<img src = \"./img/scalefree.png\" width = 500>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Slideshow", "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
QInfer/qinfer-examples
scoremixin_example.ipynb
1
69893
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fisher Score Mixin Example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook demonstrates how to use the ``qinfer.ScoreMixin`` class to develop models that use numerical differentiation to calculate the Fisher information. We test the mixin class with two examples where the Fisher information is known analytically." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preamble" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import division, print_function" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/cgranade/anaconda/envs/qinfer-binder/lib/python3.5/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": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/cgranade/anaconda/envs/qinfer-binder/lib/python3.5/site-packages/qinfer/metrics.py:51: UserWarning: Could not import scikit-learn. Some features may not work.\n", " warnings.warn(\"Could not import scikit-learn. Some features may not work.\")\n", "/home/cgranade/anaconda/envs/qinfer-binder/lib/python3.5/site-packages/IPython/parallel.py:13: ShimWarning: The `IPython.parallel` package has been deprecated. You should import from ipyparallel instead.\n", " \"You should import from ipyparallel instead.\", ShimWarning)\n", "/home/cgranade/anaconda/envs/qinfer-binder/lib/python3.5/site-packages/qinfer/parallel.py:53: UserWarning: Could not import IPython parallel. Parallelization support will be disabled.\n", " \"Could not import IPython parallel. \"\n" ] } ], "source": [ "from qinfer import ScoreMixin, SimplePrecessionModel, RandomizedBenchmarkingModel\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "try:\n", " plt.style.use('ggplot')\n", "except:\n", " pass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simple Precession Model Test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create two models, one that uses ScoreMixin's numerical ``score`` method, and one that uses ``SimplePrecessionModel``'s analytic ``score`` method. To make the first model, we declare a class that does nothing but inherits from both the ``ScoreMixin`` class and ``SimplePrecessionModel``; note that ``ScoreMixin`` is first, such that its implementation of ``Model.score()`` overrides that of ``SimplePrecessionModel``." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class NumericalSimplePrecessionModel(ScoreMixin, SimplePrecessionModel):\n", " pass\n", "\n", "analytic_model = SimplePrecessionModel()\n", "numerical_model = NumericalSimplePrecessionModel()\n", "\n", "expparams = np.linspace(1, 10, 50)\n", "modelparams = np.linspace(.1,1,50)[:, np.newaxis]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We verify that both models compute the same score by plotting the score for a range of experiment and model parameters. Since this is a single-parameter model, the score is a scalar." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(50, 50)\n", "(50, 50)\n" ] }, { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7fc9bf549470>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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+xR5gN31L3IhLmgnh+2VUQFJnadNTTNGGj0fGpeL41lWEuN3cVNPtlg7iWlna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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fc9b3ccf630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "analytic_score = analytic_model.score(np.array([0],dtype=int),modelparams, expparams)[0,0,...]\n", "print(analytic_score.shape)\n", "numerical_score = numerical_model.score(np.array([0],dtype=int),modelparams, expparams)[0,0,...]\n", "print(numerical_score.shape)\n", "\n", "plt.subplot(1,2,1)\n", "plt.imshow(analytic_score)\n", "plt.subplot(1,2,2)\n", "plt.imshow(numerical_score)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we verify that both models give the same Fisher information." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7fc9bf450d30>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc9b3cc43c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "analytic_fisher_info = analytic_model.fisher_information(modelparams, expparams)[0,0,...]\n", "numerical_fisher_info = numerical_model.fisher_information(modelparams, expparams)[0,0,...]\n", "\n", "plt.subplot(1,2,1)\n", "plt.imshow(analytic_fisher_info)\n", "plt.subplot(1,2,2)\n", "plt.imshow(numerical_fisher_info)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Randomized Benchmarking Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To test that we get multiparameter Fisher information calculations correct as well, we compare to the zeroth-order non-interlaced randomized benchmarking model." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class NumericalRandomizedBenchmarkingModel(ScoreMixin, RandomizedBenchmarkingModel):\n", " pass\n", "\n", "analytic_model = RandomizedBenchmarkingModel()\n", "numerical_model = NumericalRandomizedBenchmarkingModel()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now make experiment and parameters to test with." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "expparams = np.empty((150,), dtype=analytic_model.expparams_dtype)\n", "expparams['m'] = np.arange(1, 151)\n", "\n", "modelparams = np.empty((500, 3))\n", "modelparams[:, 0] = np.linspace(0.1, 0.999, 500)\n", "modelparams[:, 1] = 0.5\n", "modelparams[:, 2] = 0.5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's make sure that the returned Fisher information has the right shape. Note that the Fisher information is a four-index tensor here, with the two indices for the information matrix itself, plus two indices that vary over the input model parameters and experiment parameters." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "afi = analytic_model.fisher_information(modelparams, expparams)\n", "assert afi.shape == (3, 3, modelparams.shape[0], expparams.shape[0])\n", "nfi = numerical_model.fisher_information(modelparams, expparams)\n", "assert nfi.shape == (3, 3, modelparams.shape[0], expparams.shape[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We check that each Fisher information matrix has errors that are small compared to the analytic FI alone." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.6626103445273564e-07" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.linalg.norm(afi - nfi) / np.linalg.norm(afi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we plot the trace-inverse of each to check that we get the same Cramer-Rao bounds." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def tr_inv(arr):\n", " try:\n", " return np.trace(np.linalg.inv(arr.reshape(3, 3)))\n", " except LinAlgError:\n", " return float('inf')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def crb(fi):\n", " return np.apply_along_axis(tr_inv, 0, np.sum(fi.reshape((9, modelparams.shape[0], expparams.shape[0])), axis=-1))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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qXKg7Z8J8tBrm2UdgjhyxOxIRkXs83IPpUXX15wNCyTtEKaiBQ6CuHgU99wHo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\nSBYQFVNunsrWSE8MkS2Pz+dDdnZ26eOcnBz4fJW7ikBERGV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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc9bf457668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(15, 6))\n", "for idx, fi in enumerate([afi, nfi]):\n", " plt.subplot(1,2, 1 + idx)\n", " plt.semilogy(modelparams[:, 0], crb(fi))\n", " plt.ylabel(r'$\\operatorname{Tr}\\left(\\left(\\sum_m F(p, m)\\right)^{-1}\\right)$')\n", " plt.xlabel('$p$')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we note that the numerical FI calculations are not much slower than the analytic calculations." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100 loops, best of 3: 18.4 ms per loop\n", "10 loops, best of 3: 36.3 ms per loop\n" ] } ], "source": [ "%timeit analytic_model.fisher_information(modelparams, expparams)\n", "%timeit numerical_model.fisher_information(modelparams, expparams)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" }, "widgets": { "state": {}, "version": "1.1.1" } }, "nbformat": 4, "nbformat_minor": 0 }
agpl-3.0
JKeun/project-02-watcha
02_preprocess/04_DESC(preprocess_df2(eng)).ipynb
1
683406
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# final_eng_df DESCRIPTION 및 변수간 분포 파악" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DESC" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pd.set_option('display.max_columns', None)" ] }, { "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>title</th>\n", " <th>rating(y)</th>\n", " <th>avg_rating</th>\n", " <th>lee_rating</th>\n", " <th>eval_count</th>\n", " <th>wish_count</th>\n", " <th>cmt_count</th>\n", " <th>director</th>\n", " <th>actors</th>\n", " <th>film_rate</th>\n", " <th>genre</th>\n", " <th>nation</th>\n", " <th>run_time</th>\n", " <th>year</th>\n", " <th>0.5</th>\n", " <th>1</th>\n", " <th>1.5</th>\n", " <th>2</th>\n", " <th>2.5</th>\n", " <th>3</th>\n", " <th>3.5</th>\n", " <th>4</th>\n", " <th>4.5</th>\n", " <th>5</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>스포트라이트</td>\n", " <td>3</td>\n", " <td>4.22683</td>\n", " <td>4</td>\n", " <td>13025</td>\n", " <td>9796</td>\n", " <td>2585</td>\n", " <td>토마스맥카시</td>\n", " <td>마이클키튼,마크러팔로,레이첼맥아담스</td>\n", " <td>15세 관람가</td>\n", " <td>드라마</td>\n", " <td>미국</td>\n", " <td>128</td>\n", " <td>2015</td>\n", " <td>7</td>\n", " <td>10</td>\n", " <td>14</td>\n", " <td>83</td>\n", " <td>50</td>\n", " <td>1472</td>\n", " <td>454</td>\n", " <td>4509</td>\n", " <td>4318</td>\n", " <td>2108</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>찌라시 : 위험한 소문</td>\n", " <td>2</td>\n", " <td>2.99629</td>\n", " <td>NaN</td>\n", " <td>58122</td>\n", " <td>3166</td>\n", " <td>965</td>\n", " <td>김광식</td>\n", " <td>김강우,정진영,박성웅</td>\n", " <td>15세 관람가</td>\n", " <td>드라마</td>\n", " <td>한국</td>\n", " <td>121</td>\n", " <td>2013</td>\n", " <td>1312</td>\n", " <td>2238</td>\n", " <td>2150</td>\n", " <td>6749</td>\n", " <td>6597</td>\n", " <td>9397</td>\n", " <td>16842</td>\n", " <td>1367</td>\n", " <td>9011</td>\n", " <td>2459</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>비포 미드나잇</td>\n", " <td>4</td>\n", " <td>3.90119</td>\n", " <td>NaN</td>\n", " <td>66296</td>\n", " <td>33565</td>\n", " <td>1539</td>\n", " <td>리처드링클레이터</td>\n", " <td>에단호크,줄리델피,시머스데이비-피츠패트릭</td>\n", " <td>청소년 관람불가</td>\n", " <td>로맨스/멜로</td>\n", " <td>미국</td>\n", " <td>108</td>\n", " <td>2013</td>\n", " <td>228</td>\n", " <td>316</td>\n", " <td>956</td>\n", " <td>1513</td>\n", " <td>2367</td>\n", " <td>7526</td>\n", " <td>9953</td>\n", " <td>7289</td>\n", " <td>21200</td>\n", " <td>14948</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>더 웹툰: 예고살인</td>\n", " <td>2</td>\n", " <td>2.62241</td>\n", " <td>NaN</td>\n", " <td>67031</td>\n", " <td>1079</td>\n", " <td>712</td>\n", " <td>김용균</td>\n", " <td>이시영,엄기준,문가영</td>\n", " <td>15세 관람가</td>\n", " <td>공포</td>\n", " <td>한국</td>\n", " <td>104</td>\n", " <td>2013</td>\n", " <td>3615</td>\n", " <td>4063</td>\n", " <td>5424</td>\n", " <td>8133</td>\n", " <td>11525</td>\n", " <td>6501</td>\n", " <td>17566</td>\n", " <td>765</td>\n", " <td>7099</td>\n", " <td>2340</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>트랜센던스</td>\n", " <td>3</td>\n", " <td>3.31175</td>\n", " <td>NaN</td>\n", " <td>68174</td>\n", " <td>9510</td>\n", " <td>2439</td>\n", " <td>월리피스터</td>\n", " <td>조니뎁,레베카홀,모건프리먼</td>\n", " <td>12세 관람가</td>\n", " <td>SF</td>\n", " <td>미국, 영국</td>\n", " <td>119</td>\n", " <td>2014</td>\n", " <td>787</td>\n", " <td>1612</td>\n", " <td>1329</td>\n", " <td>6635</td>\n", " <td>5251</td>\n", " <td>13675</td>\n", " <td>15493</td>\n", " <td>4620</td>\n", " <td>14473</td>\n", " <td>4299</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>인간중독</td>\n", " <td>2</td>\n", " <td>2.27068</td>\n", " <td>NaN</td>\n", " <td>71711</td>\n", " <td>1780</td>\n", " <td>1801</td>\n", " <td>김대우</td>\n", " <td>송승헌,임지연,온주완</td>\n", " <td>청소년 관람불가</td>\n", " <td>드라마</td>\n", " <td>한국</td>\n", " <td>132</td>\n", " <td>2014</td>\n", " <td>6464</td>\n", " <td>7653</td>\n", " <td>7810</td>\n", " <td>9750</td>\n", " <td>15110</td>\n", " <td>5136</td>\n", " <td>13452</td>\n", " <td>717</td>\n", " <td>3879</td>\n", " <td>1740</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>헝거게임: 모킹제이</td>\n", " <td>2</td>\n", " <td>3.52048</td>\n", " <td>NaN</td>\n", " <td>74705</td>\n", " <td>7661</td>\n", " <td>2829</td>\n", " <td>프란시스로렌스</td>\n", " <td>제니퍼로렌스,리암헴스워스,조쉬허처슨</td>\n", " <td>15세 관람가</td>\n", " <td>판타지</td>\n", " <td>미국</td>\n", " <td>123</td>\n", " <td>2014</td>\n", " <td>627</td>\n", " <td>994</td>\n", " <td>895</td>\n", " <td>5484</td>\n", " <td>3262</td>\n", " <td>16857</td>\n", " <td>15539</td>\n", " <td>5451</td>\n", " <td>17810</td>\n", " <td>7786</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>인턴</td>\n", " <td>3</td>\n", " <td>3.90317</td>\n", " <td>NaN</td>\n", " <td>74444</td>\n", " <td>10389</td>\n", " <td>7696</td>\n", " <td>낸시마이어스</td>\n", " <td>로버트드니로,앤해서웨이,르네루소</td>\n", " <td>12세 관람가</td>\n", " <td>코미디</td>\n", " <td>미국</td>\n", " <td>121</td>\n", " <td>2015</td>\n", " <td>68</td>\n", " <td>215</td>\n", " <td>134</td>\n", " <td>2333</td>\n", " <td>892</td>\n", " <td>16408</td>\n", " <td>8537</td>\n", " <td>13171</td>\n", " <td>23202</td>\n", " <td>9484</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>뷰티 인사이드</td>\n", " <td>2</td>\n", " <td>3.61165</td>\n", " <td>NaN</td>\n", " <td>78615</td>\n", " <td>6108</td>\n", " <td>7895</td>\n", " <td>백종열</td>\n", " <td>이현우,한효주,김대명</td>\n", " <td>12세 관람가</td>\n", " <td>로맨스/멜로</td>\n", " <td>한국</td>\n", " <td>127</td>\n", " <td>2015</td>\n", " <td>294</td>\n", " <td>764</td>\n", " <td>475</td>\n", " <td>5541</td>\n", " <td>2612</td>\n", " <td>19241</td>\n", " <td>13621</td>\n", " <td>8633</td>\n", " <td>20943</td>\n", " <td>6491</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>검은 사제들</td>\n", " <td>4</td>\n", " <td>3.60711</td>\n", " <td>NaN</td>\n", " <td>79275</td>\n", " <td>4026</td>\n", " <td>7954</td>\n", " <td>장재현</td>\n", " <td>김윤석,강동원,박소담</td>\n", " <td>15세 관람가</td>\n", " <td>스릴러</td>\n", " <td>한국</td>\n", " <td>108</td>\n", " <td>2015</td>\n", " <td>268</td>\n", " <td>495</td>\n", " <td>398</td>\n", " <td>4124</td>\n", " <td>1916</td>\n", " <td>23240</td>\n", " <td>14162</td>\n", " <td>7177</td>\n", " <td>23118</td>\n", " <td>4377</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>내부자들</td>\n", " <td>4</td>\n", " <td>3.86041</td>\n", " <td>NaN</td>\n", " <td>81082</td>\n", " <td>5085</td>\n", " <td>8404</td>\n", " <td>우민호</td>\n", " <td>이병헌,조승우,백윤식</td>\n", " <td>청소년 관람불가</td>\n", " <td>범죄</td>\n", " <td>한국</td>\n", " <td>130</td>\n", " <td>2015</td>\n", " <td>159</td>\n", " <td>280</td>\n", " <td>244</td>\n", " <td>2816</td>\n", " <td>1133</td>\n", " <td>17798</td>\n", " <td>9406</td>\n", " <td>13980</td>\n", " <td>26694</td>\n", " <td>8572</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>사일런트 힐</td>\n", " <td>3</td>\n", " <td>3.34497</td>\n", " <td>NaN</td>\n", " <td>83358</td>\n", " <td>5629</td>\n", " <td>841</td>\n", " <td>크리스토프갱스</td>\n", " <td>라다미첼,로리홀든,숀빈</td>\n", " <td>청소년 관람불가</td>\n", " <td>공포</td>\n", " <td>캐나다, 일본, 미국, 프랑스</td>\n", " <td>124</td>\n", " <td>2006</td>\n", " <td>833</td>\n", " <td>1109</td>\n", " <td>3363</td>\n", " <td>3894</td>\n", " <td>8095</td>\n", " <td>9721</td>\n", " <td>22115</td>\n", " <td>3029</td>\n", " <td>22544</td>\n", " <td>8655</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>마션</td>\n", " <td>3</td>\n", " <td>4.01025</td>\n", " <td>4</td>\n", " <td>93331</td>\n", " <td>8067</td>\n", " <td>10234</td>\n", " <td>리들리스콧</td>\n", " <td>맷데이먼,제시카차스테인,제프다니엘스</td>\n", " <td>12세 관람가</td>\n", " <td>SF</td>\n", " <td>미국</td>\n", " <td>142</td>\n", " <td>2015</td>\n", " <td>69</td>\n", " <td>186</td>\n", " <td>141</td>\n", " <td>1441</td>\n", " <td>691</td>\n", " <td>17710</td>\n", " <td>6399</td>\n", " <td>20209</td>\n", " <td>35774</td>\n", " <td>10711</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>시간 여행자의 아내</td>\n", " <td>3</td>\n", " <td>3.66282</td>\n", " <td>NaN</td>\n", " <td>96957</td>\n", " <td>21133</td>\n", " <td>1044</td>\n", " <td>로베르트슈벤트케</td>\n", " <td>에릭바나,레이첼맥아담스,알렉스페리스</td>\n", " <td>12세 관람가</td>\n", " <td>로맨스/멜로</td>\n", " <td>미국</td>\n", " <td>107</td>\n", " <td>2009</td>\n", " <td>246</td>\n", " <td>535</td>\n", " <td>1604</td>\n", " <td>3179</td>\n", " <td>4844</td>\n", " <td>12990</td>\n", " <td>21657</td>\n", " <td>6560</td>\n", " <td>31673</td>\n", " <td>13669</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>군도:민란의 시대</td>\n", " <td>3</td>\n", " <td>3.14855</td>\n", " <td>NaN</td>\n", " <td>102482</td>\n", " <td>7671</td>\n", " <td>7200</td>\n", " <td>윤종빈</td>\n", " <td>하정우,강동원,이경영</td>\n", " <td>15세 관람가</td>\n", " <td>액션</td>\n", " <td>한국</td>\n", " <td>137</td>\n", " <td>2014</td>\n", " <td>1196</td>\n", " <td>2669</td>\n", " <td>1647</td>\n", " <td>12924</td>\n", " <td>7522</td>\n", " <td>26156</td>\n", " <td>28380</td>\n", " <td>3269</td>\n", " <td>16006</td>\n", " <td>2713</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>씬 시티</td>\n", " <td>3</td>\n", " <td>3.56793</td>\n", " <td>NaN</td>\n", " <td>101752</td>\n", " <td>8883</td>\n", " <td>1061</td>\n", " <td>프랭크밀러</td>\n", " <td>브루스윌리스,미키루크,제시카알바</td>\n", " <td>청소년 관람불가</td>\n", " <td>범죄</td>\n", " <td>미국</td>\n", " <td>123</td>\n", " <td>2005</td>\n", " <td>558</td>\n", " <td>782</td>\n", " <td>3799</td>\n", " <td>2620</td>\n", " <td>8100</td>\n", " <td>9690</td>\n", " <td>22607</td>\n", " <td>5442</td>\n", " <td>31937</td>\n", " <td>16217</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>끝까지 간다</td>\n", " <td>4</td>\n", " <td>3.86662</td>\n", " <td>3</td>\n", " <td>116851</td>\n", " <td>6526</td>\n", " <td>6115</td>\n", " <td>김성훈</td>\n", " <td>이선균,조진웅,신동미</td>\n", " <td>15세 관람가</td>\n", " <td>범죄</td>\n", " <td>한국</td>\n", " <td>111</td>\n", " <td>2013</td>\n", " <td>201</td>\n", " <td>472</td>\n", " <td>353</td>\n", " <td>2972</td>\n", " <td>1644</td>\n", " <td>26814</td>\n", " <td>12364</td>\n", " <td>17296</td>\n", " <td>42627</td>\n", " <td>12108</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>빅 히어로</td>\n", " <td>4</td>\n", " <td>3.95044</td>\n", " <td>NaN</td>\n", " <td>119876</td>\n", " <td>10304</td>\n", " <td>5545</td>\n", " <td>크리스윌리엄스</td>\n", " <td>라이언포터,스콧애짓,제이미정</td>\n", " <td>전체 관람가</td>\n", " <td>애니메이션</td>\n", " <td>미국</td>\n", " <td>108</td>\n", " <td>2014</td>\n", " <td>403</td>\n", " <td>544</td>\n", " <td>543</td>\n", " <td>3478</td>\n", " <td>1960</td>\n", " <td>21940</td>\n", " <td>12859</td>\n", " <td>19271</td>\n", " <td>36988</td>\n", " <td>21890</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>루시</td>\n", " <td>3</td>\n", " <td>3.10815</td>\n", " <td>NaN</td>\n", " <td>122449</td>\n", " <td>9728</td>\n", " <td>6180</td>\n", " <td>뤽베송</td>\n", " <td>스칼렛요한슨,모건프리먼,최민식</td>\n", " <td>청소년 관람불가</td>\n", " <td>액션</td>\n", " <td>미국, 프랑스</td>\n", " <td>90</td>\n", " <td>2014</td>\n", " <td>2579</td>\n", " <td>4574</td>\n", " <td>3428</td>\n", " <td>15836</td>\n", " <td>12048</td>\n", " <td>23538</td>\n", " <td>28387</td>\n", " <td>6242</td>\n", " <td>19359</td>\n", " <td>6458</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>버킷 리스트 - 죽기 전에 꼭 하고 싶은 것들</td>\n", " <td>3</td>\n", " <td>3.94196</td>\n", " <td>NaN</td>\n", " <td>127387</td>\n", " <td>40384</td>\n", " <td>1187</td>\n", " <td>롭라이너</td>\n", " <td>잭니콜슨,모건프리먼,숀헤이즈</td>\n", " <td>12세 관람가</td>\n", " <td>드라마</td>\n", " <td>미국</td>\n", " <td>96</td>\n", " <td>2007</td>\n", " <td>254</td>\n", " <td>390</td>\n", " <td>1390</td>\n", " <td>2259</td>\n", " <td>3414</td>\n", " <td>13299</td>\n", " <td>20407</td>\n", " <td>10470</td>\n", " <td>44832</td>\n", " <td>30672</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>킬러들의 수다</td>\n", " <td>3</td>\n", " <td>3.36549</td>\n", " <td>NaN</td>\n", " <td>132497</td>\n", " <td>5907</td>\n", " <td>598</td>\n", " <td>장진</td>\n", " <td>신현준,정재영,신하균</td>\n", " <td>15세 관람가</td>\n", " <td>코미디</td>\n", " <td>한국</td>\n", " <td>120</td>\n", " <td>2001</td>\n", " <td>772</td>\n", " <td>1366</td>\n", " <td>4549</td>\n", " <td>5349</td>\n", " <td>12560</td>\n", " <td>14382</td>\n", " <td>38527</td>\n", " <td>3664</td>\n", " <td>38626</td>\n", " <td>12702</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>암살</td>\n", " <td>4</td>\n", " <td>3.94286</td>\n", " <td>NaN</td>\n", " <td>142933</td>\n", " <td>5514</td>\n", " <td>10405</td>\n", " <td>최동훈</td>\n", " <td>전지현,이정재,하정우</td>\n", " <td>15세 관람가</td>\n", " <td>액션</td>\n", " <td>한국</td>\n", " <td>140</td>\n", " <td>2015</td>\n", " <td>179</td>\n", " <td>434</td>\n", " <td>323</td>\n", " <td>3557</td>\n", " <td>1445</td>\n", " <td>30103</td>\n", " <td>14994</td>\n", " <td>26425</td>\n", " <td>45653</td>\n", " <td>19820</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>베테랑</td>\n", " <td>3</td>\n", " <td>4.00765</td>\n", " <td>3</td>\n", " <td>156282</td>\n", " <td>5621</td>\n", " <td>12327</td>\n", " <td>류승완</td>\n", " <td>황정민,유아인,유해진</td>\n", " <td>15세 관람가</td>\n", " <td>액션</td>\n", " <td>한국</td>\n", " <td>124</td>\n", " <td>2015</td>\n", " <td>236</td>\n", " <td>414</td>\n", " <td>319</td>\n", " <td>2903</td>\n", " <td>1417</td>\n", " <td>31166</td>\n", " <td>12063</td>\n", " <td>31427</td>\n", " <td>52650</td>\n", " <td>23687</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>메이즈 러너</td>\n", " <td>2</td>\n", " <td>3.69728</td>\n", " <td>NaN</td>\n", " <td>159950</td>\n", " <td>6841</td>\n", " <td>5961</td>\n", " <td>웨스볼</td>\n", " <td>딜런오브라이언,토마스생스터,윌폴터</td>\n", " <td>12세 관람가</td>\n", " <td>액션</td>\n", " <td>미국</td>\n", " <td>113</td>\n", " <td>2014</td>\n", " <td>617</td>\n", " <td>1414</td>\n", " <td>1039</td>\n", " <td>8592</td>\n", " <td>4641</td>\n", " <td>37161</td>\n", " <td>26216</td>\n", " <td>16663</td>\n", " <td>44106</td>\n", " <td>19501</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>디 워</td>\n", " <td>1</td>\n", " <td>1.98421</td>\n", " <td>NaN</td>\n", " <td>267408</td>\n", " <td>484</td>\n", " <td>2215</td>\n", " <td>심형래</td>\n", " <td>제이슨베어,아만다브룩스,크레이그로빈슨</td>\n", " <td>12세 관람가</td>\n", " <td>판타지</td>\n", " <td>한국</td>\n", " <td>90</td>\n", " <td>2007</td>\n", " <td>35355</td>\n", " <td>14910</td>\n", " <td>65926</td>\n", " <td>16272</td>\n", " <td>56686</td>\n", " <td>8712</td>\n", " <td>46132</td>\n", " <td>1406</td>\n", " <td>14679</td>\n", " <td>7330</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>건축학개론</td>\n", " <td>3</td>\n", " <td>3.58254</td>\n", " <td>NaN</td>\n", " <td>636441</td>\n", " <td>6750</td>\n", " <td>3231</td>\n", " <td>이용주</td>\n", " <td>엄태웅,한가인,이제훈</td>\n", " <td>12세 관람가</td>\n", " <td>로맨스/멜로</td>\n", " <td>한국</td>\n", " <td>118</td>\n", " <td>2012</td>\n", " <td>3472</td>\n", " <td>5626</td>\n", " <td>14338</td>\n", " <td>21799</td>\n", " <td>43984</td>\n", " <td>61959</td>\n", " <td>159911</td>\n", " <td>22984</td>\n", " <td>199358</td>\n", " <td>103010</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>어거스트 러쉬</td>\n", " <td>3</td>\n", " <td>3.94234</td>\n", " <td>NaN</td>\n", " <td>450272</td>\n", " <td>13246</td>\n", " <td>1565</td>\n", " <td>커스틴쉐리단</td>\n", " <td>프레디하이모어,조나단리스마이어스,케리러셀</td>\n", " <td>전체 관람가</td>\n", " <td>드라마</td>\n", " <td>미국</td>\n", " <td>113</td>\n", " <td>2007</td>\n", " <td>1275</td>\n", " <td>1918</td>\n", " <td>5987</td>\n", " <td>8455</td>\n", " <td>16750</td>\n", " <td>36516</td>\n", " <td>74932</td>\n", " <td>28263</td>\n", " <td>149635</td>\n", " <td>126541</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>브루스 올마이티</td>\n", " <td>3</td>\n", " <td>3.79117</td>\n", " <td>NaN</td>\n", " <td>314274</td>\n", " <td>9659</td>\n", " <td>1323</td>\n", " <td>톰새디악</td>\n", " <td>짐캐리,모건프리먼,제니퍼애니스턴</td>\n", " <td>12세 관람가</td>\n", " <td>코미디</td>\n", " <td>미국</td>\n", " <td>100</td>\n", " <td>2003</td>\n", " <td>620</td>\n", " <td>1089</td>\n", " <td>3393</td>\n", " <td>6469</td>\n", " <td>10671</td>\n", " <td>35734</td>\n", " <td>64547</td>\n", " <td>16868</td>\n", " <td>120436</td>\n", " <td>54447</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>색, 계</td>\n", " <td>4</td>\n", " <td>3.55195</td>\n", " <td>NaN</td>\n", " <td>178290</td>\n", " <td>9805</td>\n", " <td>1245</td>\n", " <td>이안</td>\n", " <td>양조위,탕웨이,왕리홍</td>\n", " <td>청소년 관람불가</td>\n", " <td>로맨스/멜로</td>\n", " <td>미국, 중국, 대만, 홍콩</td>\n", " <td>157</td>\n", " <td>2007</td>\n", " <td>978</td>\n", " <td>1517</td>\n", " <td>5044</td>\n", " <td>5093</td>\n", " <td>13926</td>\n", " <td>18714</td>\n", " <td>43671</td>\n", " <td>9587</td>\n", " <td>53611</td>\n", " <td>26149</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>펄프 픽션</td>\n", " <td>3</td>\n", " <td>3.93998</td>\n", " <td>NaN</td>\n", " <td>65199</td>\n", " <td>17580</td>\n", " <td>1466</td>\n", " <td>쿠엔틴타란티노</td>\n", " <td>존트라볼타,사무엘L.잭슨,우마서먼</td>\n", " <td>청소년 관람불가</td>\n", " <td>범죄</td>\n", " <td>미국</td>\n", " <td>154</td>\n", " <td>1994</td>\n", " <td>113</td>\n", " <td>158</td>\n", " <td>1300</td>\n", " <td>720</td>\n", " <td>2506</td>\n", " <td>5159</td>\n", " <td>9866</td>\n", " <td>6283</td>\n", " <td>22912</td>\n", " <td>16182</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", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>514</th>\n", " <td>광해, 왕이 된 남자</td>\n", " <td>4</td>\n", " <td>4.02419</td>\n", " <td>NaN</td>\n", " <td>740618</td>\n", " <td>8495</td>\n", " <td>2878</td>\n", " <td>추창민</td>\n", " <td>이병헌,류승룡,한효주</td>\n", " <td>15세 관람가</td>\n", " <td>드라마</td>\n", " <td>한국</td>\n", " <td>131</td>\n", " <td>2012</td>\n", " <td>1780</td>\n", " <td>2773</td>\n", " <td>6653</td>\n", " <td>13707</td>\n", " <td>19218</td>\n", " <td>64363</td>\n", " <td>104992</td>\n", " <td>51875</td>\n", " <td>254822</td>\n", " <td>220435</td>\n", " </tr>\n", " <tr>\n", " <th>515</th>\n", " <td>7번방의 선물</td>\n", " <td>4</td>\n", " <td>3.97763</td>\n", " <td>NaN</td>\n", " <td>718646</td>\n", " <td>6212</td>\n", " <td>3769</td>\n", " <td>이환경</td>\n", " <td>류승룡,갈소원,박신혜</td>\n", " <td>15세 관람가</td>\n", " <td>코미디</td>\n", " <td>한국</td>\n", " <td>127</td>\n", " <td>2012</td>\n", " <td>7418</td>\n", " <td>6659</td>\n", " <td>16716</td>\n", " <td>18252</td>\n", " <td>32781</td>\n", " <td>47612</td>\n", " <td>94642</td>\n", " <td>46504</td>\n", " <td>183835</td>\n", " <td>264227</td>\n", " </tr>\n", " <tr>\n", " <th>516</th>\n", " <td>도둑들</td>\n", " <td>4</td>\n", " <td>3.76309</td>\n", " <td>NaN</td>\n", " <td>710614</td>\n", " <td>4949</td>\n", " <td>2893</td>\n", " <td>최동훈</td>\n", " <td>김윤석,김혜수,이정재</td>\n", " <td>15세 관람가</td>\n", " <td>액션</td>\n", " <td>한국</td>\n", " <td>135</td>\n", " <td>2012</td>\n", " <td>2414</td>\n", " <td>4207</td>\n", " <td>8829</td>\n", " <td>20367</td>\n", " <td>31241</td>\n", " <td>76738</td>\n", " <td>148730</td>\n", " <td>34423</td>\n", " <td>243595</td>\n", " <td>140070</td>\n", " </tr>\n", " <tr>\n", " <th>517</th>\n", " <td>말죽거리 잔혹사</td>\n", " <td>3</td>\n", " <td>3.37415</td>\n", " <td>NaN</td>\n", " <td>284807</td>\n", " <td>2659</td>\n", " <td>1122</td>\n", " <td>유하</td>\n", " <td>권상우,이정진,한가인</td>\n", " <td>15세 관람가</td>\n", " <td>액션</td>\n", " <td>한국</td>\n", " <td>116</td>\n", " <td>2004</td>\n", " <td>1829</td>\n", " <td>3355</td>\n", " <td>7344</td>\n", " <td>12887</td>\n", " <td>25527</td>\n", " <td>32512</td>\n", " <td>85617</td>\n", " <td>7965</td>\n", " <td>81038</td>\n", " <td>26733</td>\n", " </tr>\n", " <tr>\n", " <th>518</th>\n", " <td>포화 속으로</td>\n", " <td>3</td>\n", " <td>3.30031</td>\n", " <td>1</td>\n", " <td>295020</td>\n", " <td>1733</td>\n", " <td>872</td>\n", " <td>이재한</td>\n", " <td>차승원,권상우,김승우</td>\n", " <td>12세 관람가</td>\n", " <td>전쟁</td>\n", " <td>한국</td>\n", " <td>120</td>\n", " <td>2010</td>\n", " <td>2911</td>\n", " <td>4867</td>\n", " <td>11753</td>\n", " <td>14956</td>\n", " <td>31202</td>\n", " <td>27829</td>\n", " <td>86515</td>\n", " <td>6797</td>\n", " <td>75137</td>\n", " <td>33053</td>\n", " </tr>\n", " <tr>\n", " <th>519</th>\n", " <td>스파이더맨 2</td>\n", " <td>3</td>\n", " <td>3.67278</td>\n", " <td>NaN</td>\n", " <td>331860</td>\n", " <td>2607</td>\n", " <td>920</td>\n", " <td>샘레이미</td>\n", " <td>토비맥과이어,커스틴던스트,J.K.시몬스</td>\n", " <td>12세 관람가</td>\n", " <td>판타지</td>\n", " <td>미국</td>\n", " <td>126</td>\n", " <td>2004</td>\n", " <td>830</td>\n", " <td>1776</td>\n", " <td>4077</td>\n", " <td>8713</td>\n", " <td>17108</td>\n", " <td>35702</td>\n", " <td>85251</td>\n", " <td>11654</td>\n", " <td>111257</td>\n", " <td>55492</td>\n", " </tr>\n", " <tr>\n", " <th>520</th>\n", " <td>센과 치히로의 행방불명</td>\n", " <td>5</td>\n", " <td>4.27281</td>\n", " <td>NaN</td>\n", " <td>647024</td>\n", " <td>7048</td>\n", " <td>3539</td>\n", " <td>미야자키하야오</td>\n", " <td>최덕희,김영선,성선녀</td>\n", " <td>전체 관람가</td>\n", " <td>애니메이션</td>\n", " <td>일본</td>\n", " <td>126</td>\n", " <td>2001</td>\n", " <td>866</td>\n", " <td>1078</td>\n", " <td>3894</td>\n", " <td>4560</td>\n", " <td>9284</td>\n", " <td>34269</td>\n", " <td>59920</td>\n", " <td>57238</td>\n", " <td>207706</td>\n", " <td>268209</td>\n", " </tr>\n", " <tr>\n", " <th>521</th>\n", " <td>나비 효과</td>\n", " <td>5</td>\n", " <td>4.04594</td>\n", " <td>NaN</td>\n", " <td>298343</td>\n", " <td>22950</td>\n", " <td>1720</td>\n", " <td>에릭브레스</td>\n", " <td>애쉬튼커쳐,에이미스마트,에릭스톨츠</td>\n", " <td>청소년 관람불가</td>\n", " <td>스릴러</td>\n", " <td>미국</td>\n", " <td>113</td>\n", " <td>2004</td>\n", " <td>664</td>\n", " <td>792</td>\n", " <td>3439</td>\n", " <td>3622</td>\n", " <td>8155</td>\n", " <td>21522</td>\n", " <td>40860</td>\n", " <td>24174</td>\n", " <td>105567</td>\n", " <td>89548</td>\n", " </tr>\n", " <tr>\n", " <th>522</th>\n", " <td>셔터 아일랜드</td>\n", " <td>4</td>\n", " <td>3.80457</td>\n", " <td>NaN</td>\n", " <td>219885</td>\n", " <td>18106</td>\n", " <td>2104</td>\n", " <td>마틴스콜세지</td>\n", " <td>레오나르도디카프리오,마크러팔로,벤킹슬리</td>\n", " <td>15세 관람가</td>\n", " <td>스릴러</td>\n", " <td>미국</td>\n", " <td>138</td>\n", " <td>2010</td>\n", " <td>599</td>\n", " <td>829</td>\n", " <td>4282</td>\n", " <td>3894</td>\n", " <td>10539</td>\n", " <td>20787</td>\n", " <td>40458</td>\n", " <td>17404</td>\n", " <td>77939</td>\n", " <td>43154</td>\n", " </tr>\n", " <tr>\n", " <th>523</th>\n", " <td>트루먼 쇼</td>\n", " <td>5</td>\n", " <td>4.22021</td>\n", " <td>NaN</td>\n", " <td>428668</td>\n", " <td>22765</td>\n", " <td>3243</td>\n", " <td>피터위어</td>\n", " <td>짐캐리,로라린니,노아엠머리히</td>\n", " <td>15세 관람가</td>\n", " <td>드라마</td>\n", " <td>미국</td>\n", " <td>102</td>\n", " <td>1998</td>\n", " <td>695</td>\n", " <td>708</td>\n", " <td>3046</td>\n", " <td>3291</td>\n", " <td>6914</td>\n", " <td>25142</td>\n", " <td>41245</td>\n", " <td>46248</td>\n", " <td>144162</td>\n", " <td>157217</td>\n", " </tr>\n", " <tr>\n", " <th>524</th>\n", " <td>이프 온리</td>\n", " <td>3</td>\n", " <td>4.03853</td>\n", " <td>NaN</td>\n", " <td>366089</td>\n", " <td>21471</td>\n", " <td>1733</td>\n", " <td>길정거</td>\n", " <td>제니퍼러브휴이트,폴니콜스,루시대번포트</td>\n", " <td>15세 관람가</td>\n", " <td>로맨스/멜로</td>\n", " <td>미국, 영국</td>\n", " <td>96</td>\n", " <td>2004</td>\n", " <td>906</td>\n", " <td>1305</td>\n", " <td>4836</td>\n", " <td>5549</td>\n", " <td>12228</td>\n", " <td>25338</td>\n", " <td>53239</td>\n", " <td>24528</td>\n", " <td>116785</td>\n", " <td>121375</td>\n", " </tr>\n", " <tr>\n", " <th>525</th>\n", " <td>공동경비구역 JSA</td>\n", " <td>5</td>\n", " <td>3.89998</td>\n", " <td>NaN</td>\n", " <td>321863</td>\n", " <td>13833</td>\n", " <td>1301</td>\n", " <td>박찬욱</td>\n", " <td>오동진,은미,원호섭</td>\n", " <td>15세 관람가</td>\n", " <td>드라마</td>\n", " <td>한국</td>\n", " <td>110</td>\n", " <td>2000</td>\n", " <td>694</td>\n", " <td>900</td>\n", " <td>3390</td>\n", " <td>4234</td>\n", " <td>9629</td>\n", " <td>27473</td>\n", " <td>55591</td>\n", " <td>21992</td>\n", " <td>131500</td>\n", " <td>66460</td>\n", " </tr>\n", " <tr>\n", " <th>526</th>\n", " <td>본 슈프리머시</td>\n", " <td>4</td>\n", " <td>4.14620</td>\n", " <td>NaN</td>\n", " <td>233050</td>\n", " <td>11009</td>\n", " <td>1049</td>\n", " <td>폴그린그래스</td>\n", " <td>맷데이먼,프랑카포텐테,브라이언콕스</td>\n", " <td>15세 관람가</td>\n", " <td>액션</td>\n", " <td>미국, 독일</td>\n", " <td>110</td>\n", " <td>2004</td>\n", " <td>415</td>\n", " <td>428</td>\n", " <td>2088</td>\n", " <td>2035</td>\n", " <td>4293</td>\n", " <td>15706</td>\n", " <td>27495</td>\n", " <td>16559</td>\n", " <td>82502</td>\n", " <td>81529</td>\n", " </tr>\n", " <tr>\n", " <th>527</th>\n", " <td>초능력자</td>\n", " <td>2</td>\n", " <td>2.85195</td>\n", " <td>NaN</td>\n", " <td>272508</td>\n", " <td>2429</td>\n", " <td>1194</td>\n", " <td>김민석</td>\n", " <td>강동원,고수,정은채</td>\n", " <td>15세 관람가</td>\n", " <td>스릴러</td>\n", " <td>한국</td>\n", " <td>114</td>\n", " <td>2010</td>\n", " <td>5817</td>\n", " <td>8388</td>\n", " <td>21666</td>\n", " <td>21128</td>\n", " <td>49212</td>\n", " <td>21120</td>\n", " <td>83282</td>\n", " <td>3237</td>\n", " <td>42724</td>\n", " <td>15934</td>\n", " </tr>\n", " <tr>\n", " <th>528</th>\n", " <td>블라인드</td>\n", " <td>2</td>\n", " <td>3.40819</td>\n", " <td>3</td>\n", " <td>284610</td>\n", " <td>3631</td>\n", " <td>785</td>\n", " <td>안상훈</td>\n", " <td>김하늘,유승호,조희봉</td>\n", " <td>청소년 관람불가</td>\n", " <td>스릴러</td>\n", " <td>한국</td>\n", " <td>111</td>\n", " <td>2011</td>\n", " <td>1550</td>\n", " <td>2868</td>\n", " <td>8508</td>\n", " <td>11249</td>\n", " <td>26329</td>\n", " <td>26360</td>\n", " <td>84131</td>\n", " <td>7054</td>\n", " <td>83611</td>\n", " <td>32950</td>\n", " </tr>\n", " <tr>\n", " <th>529</th>\n", " <td>스쿨 오브 락</td>\n", " <td>3</td>\n", " <td>3.79351</td>\n", " <td>3</td>\n", " <td>297396</td>\n", " <td>10288</td>\n", " <td>1430</td>\n", " <td>리처드링클레이터</td>\n", " <td>잭블랙,조앤쿠삭,사라실버맨</td>\n", " <td>전체 관람가</td>\n", " <td>코미디</td>\n", " <td>미국, 독일</td>\n", " <td>108</td>\n", " <td>2003</td>\n", " <td>840</td>\n", " <td>1236</td>\n", " <td>4372</td>\n", " <td>5781</td>\n", " <td>12211</td>\n", " <td>30984</td>\n", " <td>58886</td>\n", " <td>15726</td>\n", " <td>110000</td>\n", " <td>57360</td>\n", " </tr>\n", " <tr>\n", " <th>530</th>\n", " <td>500일의 썸머</td>\n", " <td>5</td>\n", " <td>3.87432</td>\n", " <td>4</td>\n", " <td>290351</td>\n", " <td>30515</td>\n", " <td>4145</td>\n", " <td>마크웹</td>\n", " <td>조셉고든-레빗,주이디샤넬,클락그레그</td>\n", " <td>15세 관람가</td>\n", " <td>로맨틱 코미디</td>\n", " <td>미국</td>\n", " <td>95</td>\n", " <td>2009</td>\n", " <td>881</td>\n", " <td>1279</td>\n", " <td>5666</td>\n", " <td>5313</td>\n", " <td>12680</td>\n", " <td>27087</td>\n", " <td>47612</td>\n", " <td>23477</td>\n", " <td>96953</td>\n", " <td>69403</td>\n", " </tr>\n", " <tr>\n", " <th>531</th>\n", " <td>쇼생크 탈출</td>\n", " <td>4</td>\n", " <td>4.42741</td>\n", " <td>NaN</td>\n", " <td>458522</td>\n", " <td>26688</td>\n", " <td>3182</td>\n", " <td>프랭크다라본트</td>\n", " <td>팀로빈스,모건프리먼,밥건톤</td>\n", " <td>15세 관람가</td>\n", " <td>드라마</td>\n", " <td>미국</td>\n", " <td>142</td>\n", " <td>1994</td>\n", " <td>749</td>\n", " <td>601</td>\n", " <td>3589</td>\n", " <td>2135</td>\n", " <td>5289</td>\n", " <td>15286</td>\n", " <td>28854</td>\n", " <td>43314</td>\n", " <td>119334</td>\n", " <td>239371</td>\n", " </tr>\n", " <tr>\n", " <th>532</th>\n", " <td>러브 레터</td>\n", " <td>5</td>\n", " <td>3.97103</td>\n", " <td>4</td>\n", " <td>226169</td>\n", " <td>20212</td>\n", " <td>2464</td>\n", " <td>이와이슌지</td>\n", " <td>나카야마미호,토요카와에츠시,카시와바라타카시</td>\n", " <td>전체 관람가</td>\n", " <td>로맨스/멜로</td>\n", " <td>일본</td>\n", " <td>117</td>\n", " <td>1995</td>\n", " <td>560</td>\n", " <td>737</td>\n", " <td>4162</td>\n", " <td>2818</td>\n", " <td>8782</td>\n", " <td>15123</td>\n", " <td>36934</td>\n", " <td>15101</td>\n", " <td>73481</td>\n", " <td>68471</td>\n", " </tr>\n", " <tr>\n", " <th>533</th>\n", " <td>범죄와의 전쟁 : 나쁜놈들 전성시대</td>\n", " <td>4</td>\n", " <td>3.99282</td>\n", " <td>NaN</td>\n", " <td>464647</td>\n", " <td>15638</td>\n", " <td>2243</td>\n", " <td>윤종빈</td>\n", " <td>최민식,하정우,조진웅</td>\n", " <td>청소년 관람불가</td>\n", " <td>범죄</td>\n", " <td>한국</td>\n", " <td>133</td>\n", " <td>2011</td>\n", " <td>1008</td>\n", " <td>1419</td>\n", " <td>5099</td>\n", " <td>6578</td>\n", " <td>13383</td>\n", " <td>37959</td>\n", " <td>69027</td>\n", " <td>31844</td>\n", " <td>170676</td>\n", " <td>127654</td>\n", " </tr>\n", " <tr>\n", " <th>534</th>\n", " <td>악마는 프라다를 입는다</td>\n", " <td>4</td>\n", " <td>3.83566</td>\n", " <td>NaN</td>\n", " <td>485522</td>\n", " <td>12069</td>\n", " <td>2109</td>\n", " <td>데이빗프랭클</td>\n", " <td>앤해서웨이,메릴스트립,스탠리투치</td>\n", " <td>12세 관람가</td>\n", " <td>로맨틱 코미디</td>\n", " <td>미국</td>\n", " <td>109</td>\n", " <td>2006</td>\n", " <td>939</td>\n", " <td>1584</td>\n", " <td>4630</td>\n", " <td>9278</td>\n", " <td>16019</td>\n", " <td>51499</td>\n", " <td>97439</td>\n", " <td>26902</td>\n", " <td>179984</td>\n", " <td>97248</td>\n", " </tr>\n", " <tr>\n", " <th>535</th>\n", " <td>이끼</td>\n", " <td>3</td>\n", " <td>3.28881</td>\n", " <td>3</td>\n", " <td>274059</td>\n", " <td>5396</td>\n", " <td>967</td>\n", " <td>강우석</td>\n", " <td>정재영,박해일,유해진</td>\n", " <td>청소년 관람불가</td>\n", " <td>스릴러</td>\n", " <td>한국</td>\n", " <td>163</td>\n", " <td>2010</td>\n", " <td>1916</td>\n", " <td>3245</td>\n", " <td>9614</td>\n", " <td>12825</td>\n", " <td>30131</td>\n", " <td>27166</td>\n", " <td>85827</td>\n", " <td>6469</td>\n", " <td>72477</td>\n", " <td>24389</td>\n", " </tr>\n", " <tr>\n", " <th>536</th>\n", " <td>리얼 스틸</td>\n", " <td>1</td>\n", " <td>3.69244</td>\n", " <td>NaN</td>\n", " <td>315856</td>\n", " <td>5589</td>\n", " <td>1423</td>\n", " <td>숀레비</td>\n", " <td>휴잭맨,에반젤린릴리,다코타고요</td>\n", " <td>12세 관람가</td>\n", " <td>SF</td>\n", " <td>미국</td>\n", " <td>127</td>\n", " <td>2011</td>\n", " <td>1088</td>\n", " <td>1966</td>\n", " <td>5026</td>\n", " <td>9465</td>\n", " <td>17349</td>\n", " <td>33556</td>\n", " <td>71885</td>\n", " <td>14244</td>\n", " <td>104337</td>\n", " <td>56940</td>\n", " </tr>\n", " <tr>\n", " <th>537</th>\n", " <td>레옹</td>\n", " <td>4</td>\n", " <td>4.29233</td>\n", " <td>NaN</td>\n", " <td>384705</td>\n", " <td>31362</td>\n", " <td>4414</td>\n", " <td>뤽베송</td>\n", " <td>장르노,나탈리포트만,게리올드만</td>\n", " <td>청소년 관람불가</td>\n", " <td>범죄</td>\n", " <td>프랑스, 미국</td>\n", " <td>133</td>\n", " <td>1994</td>\n", " <td>533</td>\n", " <td>614</td>\n", " <td>2792</td>\n", " <td>2673</td>\n", " <td>5691</td>\n", " <td>19824</td>\n", " <td>33805</td>\n", " <td>41942</td>\n", " <td>114507</td>\n", " <td>162324</td>\n", " </tr>\n", " <tr>\n", " <th>538</th>\n", " <td>내가 살인범이다</td>\n", " <td>2</td>\n", " <td>3.53845</td>\n", " <td>NaN</td>\n", " <td>309470</td>\n", " <td>8553</td>\n", " <td>1353</td>\n", " <td>정병길</td>\n", " <td>정재영,박시후,정해균</td>\n", " <td>청소년 관람불가</td>\n", " <td>스릴러</td>\n", " <td>한국</td>\n", " <td>119</td>\n", " <td>2012</td>\n", " <td>2153</td>\n", " <td>3498</td>\n", " <td>8194</td>\n", " <td>12583</td>\n", " <td>22331</td>\n", " <td>32122</td>\n", " <td>74889</td>\n", " <td>11681</td>\n", " <td>95499</td>\n", " <td>46520</td>\n", " </tr>\n", " <tr>\n", " <th>539</th>\n", " <td>말아톤</td>\n", " <td>4</td>\n", " <td>3.60102</td>\n", " <td>NaN</td>\n", " <td>375039</td>\n", " <td>2092</td>\n", " <td>799</td>\n", " <td>정윤철</td>\n", " <td>조승우,김미숙,안내상</td>\n", " <td>전체 관람가</td>\n", " <td>드라마</td>\n", " <td>한국</td>\n", " <td>115</td>\n", " <td>2005</td>\n", " <td>1174</td>\n", " <td>2050</td>\n", " <td>5835</td>\n", " <td>10014</td>\n", " <td>20412</td>\n", " <td>44313</td>\n", " <td>100190</td>\n", " <td>15177</td>\n", " <td>128117</td>\n", " <td>47757</td>\n", " </tr>\n", " <tr>\n", " <th>540</th>\n", " <td>클래식</td>\n", " <td>5</td>\n", " <td>3.85321</td>\n", " <td>NaN</td>\n", " <td>314513</td>\n", " <td>15380</td>\n", " <td>1829</td>\n", " <td>곽재용</td>\n", " <td>손예진,조승우,조인성</td>\n", " <td>12세 관람가</td>\n", " <td>로맨스/멜로</td>\n", " <td>한국</td>\n", " <td>132</td>\n", " <td>2003</td>\n", " <td>970</td>\n", " <td>1401</td>\n", " <td>5735</td>\n", " <td>5904</td>\n", " <td>13933</td>\n", " <td>25697</td>\n", " <td>60021</td>\n", " <td>18274</td>\n", " <td>104168</td>\n", " <td>78410</td>\n", " </tr>\n", " <tr>\n", " <th>541</th>\n", " <td>아이언맨 3</td>\n", " <td>4</td>\n", " <td>4.10486</td>\n", " <td>NaN</td>\n", " <td>564090</td>\n", " <td>7039</td>\n", " <td>2794</td>\n", " <td>세인블랙</td>\n", " <td>로버트다우니주니어,기네스팰트로우,가이피어스</td>\n", " <td>12세 관람가</td>\n", " <td>SF</td>\n", " <td>미국, 중국</td>\n", " <td>129</td>\n", " <td>2013</td>\n", " <td>1198</td>\n", " <td>1666</td>\n", " <td>4521</td>\n", " <td>8740</td>\n", " <td>12696</td>\n", " <td>46867</td>\n", " <td>73291</td>\n", " <td>37416</td>\n", " <td>180392</td>\n", " <td>197303</td>\n", " </tr>\n", " <tr>\n", " <th>542</th>\n", " <td>어린 신부</td>\n", " <td>3</td>\n", " <td>2.97457</td>\n", " <td>NaN</td>\n", " <td>341757</td>\n", " <td>782</td>\n", " <td>757</td>\n", " <td>김호준</td>\n", " <td>김래원,문근영,김인문</td>\n", " <td>12세 관람가</td>\n", " <td>로맨틱 코미디</td>\n", " <td>한국</td>\n", " <td>115</td>\n", " <td>2004</td>\n", " <td>4791</td>\n", " <td>7747</td>\n", " <td>21333</td>\n", " <td>21986</td>\n", " <td>54695</td>\n", " <td>26642</td>\n", " <td>116168</td>\n", " <td>3589</td>\n", " <td>65017</td>\n", " <td>19789</td>\n", " </tr>\n", " <tr>\n", " <th>543</th>\n", " <td>내 아내의 모든 것</td>\n", " <td>4</td>\n", " <td>3.65453</td>\n", " <td>NaN</td>\n", " <td>461916</td>\n", " <td>8540</td>\n", " <td>1931</td>\n", " <td>민규동</td>\n", " <td>임수정,이선균,류승룡</td>\n", " <td>15세 관람가</td>\n", " <td>로맨틱 코미디</td>\n", " <td>한국</td>\n", " <td>121</td>\n", " <td>2012</td>\n", " <td>1717</td>\n", " <td>3095</td>\n", " <td>7827</td>\n", " <td>14038</td>\n", " <td>25019</td>\n", " <td>51010</td>\n", " <td>107155</td>\n", " <td>18351</td>\n", " <td>161720</td>\n", " <td>71984</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>544 rows × 24 columns</p>\n", "</div>" ], "text/plain": [ " title rating(y) avg_rating lee_rating eval_count \\\n", "0 스포트라이트 3 4.22683 4 13025 \n", "1 찌라시 : 위험한 소문 2 2.99629 NaN 58122 \n", "2 비포 미드나잇 4 3.90119 NaN 66296 \n", "3 더 웹툰: 예고살인 2 2.62241 NaN 67031 \n", "4 트랜센던스 3 3.31175 NaN 68174 \n", "5 인간중독 2 2.27068 NaN 71711 \n", "6 헝거게임: 모킹제이 2 3.52048 NaN 74705 \n", "7 인턴 3 3.90317 NaN 74444 \n", "8 뷰티 인사이드 2 3.61165 NaN 78615 \n", "9 검은 사제들 4 3.60711 NaN 79275 \n", "10 내부자들 4 3.86041 NaN 81082 \n", "11 사일런트 힐 3 3.34497 NaN 83358 \n", "12 마션 3 4.01025 4 93331 \n", "13 시간 여행자의 아내 3 3.66282 NaN 96957 \n", "14 군도:민란의 시대 3 3.14855 NaN 102482 \n", "15 씬 시티 3 3.56793 NaN 101752 \n", "16 끝까지 간다 4 3.86662 3 116851 \n", "17 빅 히어로 4 3.95044 NaN 119876 \n", "18 루시 3 3.10815 NaN 122449 \n", "19 버킷 리스트 - 죽기 전에 꼭 하고 싶은 것들 3 3.94196 NaN 127387 \n", "20 킬러들의 수다 3 3.36549 NaN 132497 \n", "21 암살 4 3.94286 NaN 142933 \n", "22 베테랑 3 4.00765 3 156282 \n", "23 메이즈 러너 2 3.69728 NaN 159950 \n", "24 디 워 1 1.98421 NaN 267408 \n", "25 건축학개론 3 3.58254 NaN 636441 \n", "26 어거스트 러쉬 3 3.94234 NaN 450272 \n", "27 브루스 올마이티 3 3.79117 NaN 314274 \n", "28 색, 계 4 3.55195 NaN 178290 \n", "29 펄프 픽션 3 3.93998 NaN 65199 \n", ".. ... ... ... ... ... \n", "514 광해, 왕이 된 남자 4 4.02419 NaN 740618 \n", "515 7번방의 선물 4 3.97763 NaN 718646 \n", "516 도둑들 4 3.76309 NaN 710614 \n", "517 말죽거리 잔혹사 3 3.37415 NaN 284807 \n", "518 포화 속으로 3 3.30031 1 295020 \n", "519 스파이더맨 2 3 3.67278 NaN 331860 \n", "520 센과 치히로의 행방불명 5 4.27281 NaN 647024 \n", "521 나비 효과 5 4.04594 NaN 298343 \n", "522 셔터 아일랜드 4 3.80457 NaN 219885 \n", "523 트루먼 쇼 5 4.22021 NaN 428668 \n", "524 이프 온리 3 4.03853 NaN 366089 \n", "525 공동경비구역 JSA 5 3.89998 NaN 321863 \n", "526 본 슈프리머시 4 4.14620 NaN 233050 \n", "527 초능력자 2 2.85195 NaN 272508 \n", "528 블라인드 2 3.40819 3 284610 \n", "529 스쿨 오브 락 3 3.79351 3 297396 \n", "530 500일의 썸머 5 3.87432 4 290351 \n", "531 쇼생크 탈출 4 4.42741 NaN 458522 \n", "532 러브 레터 5 3.97103 4 226169 \n", "533 범죄와의 전쟁 : 나쁜놈들 전성시대 4 3.99282 NaN 464647 \n", "534 악마는 프라다를 입는다 4 3.83566 NaN 485522 \n", "535 이끼 3 3.28881 3 274059 \n", "536 리얼 스틸 1 3.69244 NaN 315856 \n", "537 레옹 4 4.29233 NaN 384705 \n", "538 내가 살인범이다 2 3.53845 NaN 309470 \n", "539 말아톤 4 3.60102 NaN 375039 \n", "540 클래식 5 3.85321 NaN 314513 \n", "541 아이언맨 3 4 4.10486 NaN 564090 \n", "542 어린 신부 3 2.97457 NaN 341757 \n", "543 내 아내의 모든 것 4 3.65453 NaN 461916 \n", "\n", " wish_count cmt_count director actors film_rate \\\n", "0 9796 2585 토마스맥카시 마이클키튼,마크러팔로,레이첼맥아담스 15세 관람가 \n", "1 3166 965 김광식 김강우,정진영,박성웅 15세 관람가 \n", "2 33565 1539 리처드링클레이터 에단호크,줄리델피,시머스데이비-피츠패트릭 청소년 관람불가 \n", "3 1079 712 김용균 이시영,엄기준,문가영 15세 관람가 \n", "4 9510 2439 월리피스터 조니뎁,레베카홀,모건프리먼 12세 관람가 \n", "5 1780 1801 김대우 송승헌,임지연,온주완 청소년 관람불가 \n", "6 7661 2829 프란시스로렌스 제니퍼로렌스,리암헴스워스,조쉬허처슨 15세 관람가 \n", "7 10389 7696 낸시마이어스 로버트드니로,앤해서웨이,르네루소 12세 관람가 \n", "8 6108 7895 백종열 이현우,한효주,김대명 12세 관람가 \n", "9 4026 7954 장재현 김윤석,강동원,박소담 15세 관람가 \n", "10 5085 8404 우민호 이병헌,조승우,백윤식 청소년 관람불가 \n", "11 5629 841 크리스토프갱스 라다미첼,로리홀든,숀빈 청소년 관람불가 \n", "12 8067 10234 리들리스콧 맷데이먼,제시카차스테인,제프다니엘스 12세 관람가 \n", "13 21133 1044 로베르트슈벤트케 에릭바나,레이첼맥아담스,알렉스페리스 12세 관람가 \n", "14 7671 7200 윤종빈 하정우,강동원,이경영 15세 관람가 \n", "15 8883 1061 프랭크밀러 브루스윌리스,미키루크,제시카알바 청소년 관람불가 \n", "16 6526 6115 김성훈 이선균,조진웅,신동미 15세 관람가 \n", "17 10304 5545 크리스윌리엄스 라이언포터,스콧애짓,제이미정 전체 관람가 \n", "18 9728 6180 뤽베송 스칼렛요한슨,모건프리먼,최민식 청소년 관람불가 \n", "19 40384 1187 롭라이너 잭니콜슨,모건프리먼,숀헤이즈 12세 관람가 \n", "20 5907 598 장진 신현준,정재영,신하균 15세 관람가 \n", "21 5514 10405 최동훈 전지현,이정재,하정우 15세 관람가 \n", "22 5621 12327 류승완 황정민,유아인,유해진 15세 관람가 \n", "23 6841 5961 웨스볼 딜런오브라이언,토마스생스터,윌폴터 12세 관람가 \n", "24 484 2215 심형래 제이슨베어,아만다브룩스,크레이그로빈슨 12세 관람가 \n", "25 6750 3231 이용주 엄태웅,한가인,이제훈 12세 관람가 \n", "26 13246 1565 커스틴쉐리단 프레디하이모어,조나단리스마이어스,케리러셀 전체 관람가 \n", "27 9659 1323 톰새디악 짐캐리,모건프리먼,제니퍼애니스턴 12세 관람가 \n", "28 9805 1245 이안 양조위,탕웨이,왕리홍 청소년 관람불가 \n", "29 17580 1466 쿠엔틴타란티노 존트라볼타,사무엘L.잭슨,우마서먼 청소년 관람불가 \n", ".. ... ... ... ... ... \n", "514 8495 2878 추창민 이병헌,류승룡,한효주 15세 관람가 \n", "515 6212 3769 이환경 류승룡,갈소원,박신혜 15세 관람가 \n", "516 4949 2893 최동훈 김윤석,김혜수,이정재 15세 관람가 \n", "517 2659 1122 유하 권상우,이정진,한가인 15세 관람가 \n", "518 1733 872 이재한 차승원,권상우,김승우 12세 관람가 \n", "519 2607 920 샘레이미 토비맥과이어,커스틴던스트,J.K.시몬스 12세 관람가 \n", "520 7048 3539 미야자키하야오 최덕희,김영선,성선녀 전체 관람가 \n", "521 22950 1720 에릭브레스 애쉬튼커쳐,에이미스마트,에릭스톨츠 청소년 관람불가 \n", "522 18106 2104 마틴스콜세지 레오나르도디카프리오,마크러팔로,벤킹슬리 15세 관람가 \n", "523 22765 3243 피터위어 짐캐리,로라린니,노아엠머리히 15세 관람가 \n", "524 21471 1733 길정거 제니퍼러브휴이트,폴니콜스,루시대번포트 15세 관람가 \n", "525 13833 1301 박찬욱 오동진,은미,원호섭 15세 관람가 \n", "526 11009 1049 폴그린그래스 맷데이먼,프랑카포텐테,브라이언콕스 15세 관람가 \n", "527 2429 1194 김민석 강동원,고수,정은채 15세 관람가 \n", "528 3631 785 안상훈 김하늘,유승호,조희봉 청소년 관람불가 \n", "529 10288 1430 리처드링클레이터 잭블랙,조앤쿠삭,사라실버맨 전체 관람가 \n", "530 30515 4145 마크웹 조셉고든-레빗,주이디샤넬,클락그레그 15세 관람가 \n", "531 26688 3182 프랭크다라본트 팀로빈스,모건프리먼,밥건톤 15세 관람가 \n", "532 20212 2464 이와이슌지 나카야마미호,토요카와에츠시,카시와바라타카시 전체 관람가 \n", "533 15638 2243 윤종빈 최민식,하정우,조진웅 청소년 관람불가 \n", "534 12069 2109 데이빗프랭클 앤해서웨이,메릴스트립,스탠리투치 12세 관람가 \n", "535 5396 967 강우석 정재영,박해일,유해진 청소년 관람불가 \n", "536 5589 1423 숀레비 휴잭맨,에반젤린릴리,다코타고요 12세 관람가 \n", "537 31362 4414 뤽베송 장르노,나탈리포트만,게리올드만 청소년 관람불가 \n", "538 8553 1353 정병길 정재영,박시후,정해균 청소년 관람불가 \n", "539 2092 799 정윤철 조승우,김미숙,안내상 전체 관람가 \n", "540 15380 1829 곽재용 손예진,조승우,조인성 12세 관람가 \n", "541 7039 2794 세인블랙 로버트다우니주니어,기네스팰트로우,가이피어스 12세 관람가 \n", "542 782 757 김호준 김래원,문근영,김인문 12세 관람가 \n", "543 8540 1931 민규동 임수정,이선균,류승룡 15세 관람가 \n", "\n", " genre nation run_time year 0.5 1 1.5 2 \\\n", "0 드라마 미국 128 2015 7 10 14 83 \n", "1 드라마 한국 121 2013 1312 2238 2150 6749 \n", "2 로맨스/멜로 미국 108 2013 228 316 956 1513 \n", "3 공포 한국 104 2013 3615 4063 5424 8133 \n", "4 SF 미국, 영국 119 2014 787 1612 1329 6635 \n", "5 드라마 한국 132 2014 6464 7653 7810 9750 \n", "6 판타지 미국 123 2014 627 994 895 5484 \n", "7 코미디 미국 121 2015 68 215 134 2333 \n", "8 로맨스/멜로 한국 127 2015 294 764 475 5541 \n", "9 스릴러 한국 108 2015 268 495 398 4124 \n", "10 범죄 한국 130 2015 159 280 244 2816 \n", "11 공포 캐나다, 일본, 미국, 프랑스 124 2006 833 1109 3363 3894 \n", "12 SF 미국 142 2015 69 186 141 1441 \n", "13 로맨스/멜로 미국 107 2009 246 535 1604 3179 \n", "14 액션 한국 137 2014 1196 2669 1647 12924 \n", "15 범죄 미국 123 2005 558 782 3799 2620 \n", "16 범죄 한국 111 2013 201 472 353 2972 \n", "17 애니메이션 미국 108 2014 403 544 543 3478 \n", "18 액션 미국, 프랑스 90 2014 2579 4574 3428 15836 \n", "19 드라마 미국 96 2007 254 390 1390 2259 \n", "20 코미디 한국 120 2001 772 1366 4549 5349 \n", "21 액션 한국 140 2015 179 434 323 3557 \n", "22 액션 한국 124 2015 236 414 319 2903 \n", "23 액션 미국 113 2014 617 1414 1039 8592 \n", "24 판타지 한국 90 2007 35355 14910 65926 16272 \n", "25 로맨스/멜로 한국 118 2012 3472 5626 14338 21799 \n", "26 드라마 미국 113 2007 1275 1918 5987 8455 \n", "27 코미디 미국 100 2003 620 1089 3393 6469 \n", "28 로맨스/멜로 미국, 중국, 대만, 홍콩 157 2007 978 1517 5044 5093 \n", "29 범죄 미국 154 1994 113 158 1300 720 \n", ".. ... ... ... ... ... ... ... ... \n", "514 드라마 한국 131 2012 1780 2773 6653 13707 \n", "515 코미디 한국 127 2012 7418 6659 16716 18252 \n", "516 액션 한국 135 2012 2414 4207 8829 20367 \n", "517 액션 한국 116 2004 1829 3355 7344 12887 \n", "518 전쟁 한국 120 2010 2911 4867 11753 14956 \n", "519 판타지 미국 126 2004 830 1776 4077 8713 \n", "520 애니메이션 일본 126 2001 866 1078 3894 4560 \n", "521 스릴러 미국 113 2004 664 792 3439 3622 \n", "522 스릴러 미국 138 2010 599 829 4282 3894 \n", "523 드라마 미국 102 1998 695 708 3046 3291 \n", "524 로맨스/멜로 미국, 영국 96 2004 906 1305 4836 5549 \n", "525 드라마 한국 110 2000 694 900 3390 4234 \n", "526 액션 미국, 독일 110 2004 415 428 2088 2035 \n", "527 스릴러 한국 114 2010 5817 8388 21666 21128 \n", "528 스릴러 한국 111 2011 1550 2868 8508 11249 \n", "529 코미디 미국, 독일 108 2003 840 1236 4372 5781 \n", "530 로맨틱 코미디 미국 95 2009 881 1279 5666 5313 \n", "531 드라마 미국 142 1994 749 601 3589 2135 \n", "532 로맨스/멜로 일본 117 1995 560 737 4162 2818 \n", "533 범죄 한국 133 2011 1008 1419 5099 6578 \n", "534 로맨틱 코미디 미국 109 2006 939 1584 4630 9278 \n", "535 스릴러 한국 163 2010 1916 3245 9614 12825 \n", "536 SF 미국 127 2011 1088 1966 5026 9465 \n", "537 범죄 프랑스, 미국 133 1994 533 614 2792 2673 \n", "538 스릴러 한국 119 2012 2153 3498 8194 12583 \n", "539 드라마 한국 115 2005 1174 2050 5835 10014 \n", "540 로맨스/멜로 한국 132 2003 970 1401 5735 5904 \n", "541 SF 미국, 중국 129 2013 1198 1666 4521 8740 \n", "542 로맨틱 코미디 한국 115 2004 4791 7747 21333 21986 \n", "543 로맨틱 코미디 한국 121 2012 1717 3095 7827 14038 \n", "\n", " 2.5 3 3.5 4 4.5 5 \n", "0 50 1472 454 4509 4318 2108 \n", "1 6597 9397 16842 1367 9011 2459 \n", "2 2367 7526 9953 7289 21200 14948 \n", "3 11525 6501 17566 765 7099 2340 \n", "4 5251 13675 15493 4620 14473 4299 \n", "5 15110 5136 13452 717 3879 1740 \n", "6 3262 16857 15539 5451 17810 7786 \n", "7 892 16408 8537 13171 23202 9484 \n", "8 2612 19241 13621 8633 20943 6491 \n", "9 1916 23240 14162 7177 23118 4377 \n", "10 1133 17798 9406 13980 26694 8572 \n", "11 8095 9721 22115 3029 22544 8655 \n", "12 691 17710 6399 20209 35774 10711 \n", "13 4844 12990 21657 6560 31673 13669 \n", "14 7522 26156 28380 3269 16006 2713 \n", "15 8100 9690 22607 5442 31937 16217 \n", "16 1644 26814 12364 17296 42627 12108 \n", "17 1960 21940 12859 19271 36988 21890 \n", "18 12048 23538 28387 6242 19359 6458 \n", "19 3414 13299 20407 10470 44832 30672 \n", "20 12560 14382 38527 3664 38626 12702 \n", "21 1445 30103 14994 26425 45653 19820 \n", "22 1417 31166 12063 31427 52650 23687 \n", "23 4641 37161 26216 16663 44106 19501 \n", "24 56686 8712 46132 1406 14679 7330 \n", "25 43984 61959 159911 22984 199358 103010 \n", "26 16750 36516 74932 28263 149635 126541 \n", "27 10671 35734 64547 16868 120436 54447 \n", "28 13926 18714 43671 9587 53611 26149 \n", "29 2506 5159 9866 6283 22912 16182 \n", ".. ... ... ... ... ... ... \n", "514 19218 64363 104992 51875 254822 220435 \n", "515 32781 47612 94642 46504 183835 264227 \n", "516 31241 76738 148730 34423 243595 140070 \n", "517 25527 32512 85617 7965 81038 26733 \n", "518 31202 27829 86515 6797 75137 33053 \n", "519 17108 35702 85251 11654 111257 55492 \n", "520 9284 34269 59920 57238 207706 268209 \n", "521 8155 21522 40860 24174 105567 89548 \n", "522 10539 20787 40458 17404 77939 43154 \n", "523 6914 25142 41245 46248 144162 157217 \n", "524 12228 25338 53239 24528 116785 121375 \n", "525 9629 27473 55591 21992 131500 66460 \n", "526 4293 15706 27495 16559 82502 81529 \n", "527 49212 21120 83282 3237 42724 15934 \n", "528 26329 26360 84131 7054 83611 32950 \n", "529 12211 30984 58886 15726 110000 57360 \n", "530 12680 27087 47612 23477 96953 69403 \n", "531 5289 15286 28854 43314 119334 239371 \n", "532 8782 15123 36934 15101 73481 68471 \n", "533 13383 37959 69027 31844 170676 127654 \n", "534 16019 51499 97439 26902 179984 97248 \n", "535 30131 27166 85827 6469 72477 24389 \n", "536 17349 33556 71885 14244 104337 56940 \n", "537 5691 19824 33805 41942 114507 162324 \n", "538 22331 32122 74889 11681 95499 46520 \n", "539 20412 44313 100190 15177 128117 47757 \n", "540 13933 25697 60021 18274 104168 78410 \n", "541 12696 46867 73291 37416 180392 197303 \n", "542 54695 26642 116168 3589 65017 19789 \n", "543 25019 51010 107155 18351 161720 71984 \n", "\n", "[544 rows x 24 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('../resource/preprocess_df2.csv')\n", "df" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": 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>title</th>\n", " <th>rating(y)</th>\n", " <th>avg_rating</th>\n", " <th>lee_rating</th>\n", " <th>eval_count</th>\n", " <th>wish_count</th>\n", " <th>cmt_count</th>\n", " <th>director</th>\n", " <th>actors</th>\n", " <th>film_rate</th>\n", " <th>genre</th>\n", " <th>nation</th>\n", " <th>run_time</th>\n", " <th>year</th>\n", " <th>0.5</th>\n", " <th>1</th>\n", " 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<td>7</td>\n", " <td>7</td>\n", " <td>203</td>\n", " <td>87</td>\n", " <td>213</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <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>mean</th>\n", " <td>NaN</td>\n", " <td>3.068015</td>\n", " <td>3.523105</td>\n", " <td>3.278481</td>\n", " <td>223929.727941</td>\n", " <td>7645.349265</td>\n", " <td>1625.865809</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>117.976103</td>\n", " <td>2006.522059</td>\n", " <td>1487.737132</td>\n", " <td>2022.419118</td>\n", " <td>5881.650735</td>\n", " <td>6941.125000</td>\n", " <td>14355.235294</td>\n", " <td>21001.687500</td>\n", " <td>46972.729779</td>\n", " <td>12159.170956</td>\n", " <td>67247.323529</td>\n", " <td>45860.656250</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>NaN</td>\n", " <td>1.114515</td>\n", " <td>0.475370</td>\n", " <td>0.973188</td>\n", " <td>158144.758766</td>\n", " <td>7751.561753</td>\n", " <td>2007.170921</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>18.524817</td>\n", " <td>5.523387</td>\n", " <td>2152.590573</td>\n", " <td>1943.604448</td>\n", " <td>5739.493759</td>\n", " <td>5248.786627</td>\n", " <td>11500.217844</td>\n", " <td>14318.018144</td>\n", " <td>32565.661357</td>\n", " <td>12809.702655</td>\n", " <td>53650.563685</td>\n", " <td>53221.358839</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>NaN</td>\n", " <td>1.000000</td>\n", " <td>1.224700</td>\n", " <td>1.000000</td>\n", " <td>5841.000000</td>\n", " <td>52.000000</td>\n", " <td>67.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>62.000000</td>\n", " <td>1978.000000</td>\n", " <td>7.000000</td>\n", " <td>10.000000</td>\n", " <td>14.000000</td>\n", " <td>83.000000</td>\n", " <td>50.000000</td>\n", " <td>38.000000</td>\n", " <td>242.000000</td>\n", " <td>11.000000</td>\n", " <td>50.000000</td>\n", " <td>57.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>NaN</td>\n", " <td>2.000000</td>\n", " <td>3.279778</td>\n", " <td>3.000000</td>\n", " <td>97290.000000</td>\n", " <td>2132.000000</td>\n", " <td>587.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>106.000000</td>\n", " <td>2004.000000</td>\n", " <td>495.500000</td>\n", " <td>741.500000</td>\n", " <td>2410.000000</td>\n", " <td>3259.500000</td>\n", " <td>6115.250000</td>\n", " <td>9667.750000</td>\n", " <td>21047.250000</td>\n", " <td>2997.500000</td>\n", " <td>25210.500000</td>\n", " <td>9819.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>NaN</td>\n", " <td>3.000000</td>\n", " <td>3.620460</td>\n", " <td>4.000000</td>\n", " <td>186910.000000</td>\n", " <td>5049.000000</td>\n", " <td>1000.500000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>116.000000</td>\n", " <td>2007.000000</td>\n", " <td>927.000000</td>\n", " <td>1403.000000</td>\n", " <td>4360.500000</td>\n", " <td>5485.000000</td>\n", " <td>10975.500000</td>\n", " <td>18502.000000</td>\n", " <td>40461.500000</td>\n", " <td>7268.500000</td>\n", " <td>52302.500000</td>\n", " <td>25122.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>NaN</td>\n", " <td>4.000000</td>\n", " <td>3.867070</td>\n", " <td>4.000000</td>\n", " <td>314562.750000</td>\n", " <td>10635.500000</td>\n", " <td>1758.250000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>127.250000</td>\n", " <td>2011.000000</td>\n", " <td>1670.500000</td>\n", " <td>2577.250000</td>\n", " <td>7449.000000</td>\n", " <td>9348.250000</td>\n", " <td>19889.000000</td>\n", " <td>29311.500000</td>\n", " <td>65592.750000</td>\n", " <td>17125.750000</td>\n", " <td>99452.500000</td>\n", " <td>64507.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>NaN</td>\n", " <td>5.000000</td>\n", " <td>4.427410</td>\n", " <td>5.000000</td>\n", " <td>740618.000000</td>\n", " <td>40384.000000</td>\n", " <td>15881.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>199.000000</td>\n", " <td>2016.000000</td>\n", " <td>35355.000000</td>\n", " <td>14910.000000</td>\n", " <td>65926.000000</td>\n", " <td>32443.000000</td>\n", " <td>65440.000000</td>\n", " <td>76738.000000</td>\n", " <td>185491.000000</td>\n", " <td>71713.000000</td>\n", " <td>261018.000000</td>\n", " <td>329120.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " title rating(y) avg_rating lee_rating eval_count wish_count \\\n", "count 544 544.000000 544.000000 79.000000 544.000000 544.000000 \n", "unique 542 NaN NaN NaN NaN NaN \n", "top 천녀유혼 NaN NaN NaN NaN NaN \n", "freq 2 NaN NaN NaN NaN NaN \n", "mean NaN 3.068015 3.523105 3.278481 223929.727941 7645.349265 \n", "std NaN 1.114515 0.475370 0.973188 158144.758766 7751.561753 \n", "min NaN 1.000000 1.224700 1.000000 5841.000000 52.000000 \n", "25% NaN 2.000000 3.279778 3.000000 97290.000000 2132.000000 \n", "50% NaN 3.000000 3.620460 4.000000 186910.000000 5049.000000 \n", "75% NaN 4.000000 3.867070 4.000000 314562.750000 10635.500000 \n", "max NaN 5.000000 4.427410 5.000000 740618.000000 40384.000000 \n", "\n", " cmt_count director actors film_rate genre nation \\\n", "count 544.000000 544 544 541 544 544 \n", "unique NaN 348 531 4 12 47 \n", "top NaN 크리스토퍼놀란 다니엘래드클리프,엠마왓슨,루퍼트그린트 15세 관람가 액션 한국 \n", "freq NaN 7 7 203 87 213 \n", "mean 1625.865809 NaN NaN NaN NaN NaN \n", "std 2007.170921 NaN NaN NaN NaN NaN \n", "min 67.000000 NaN NaN NaN NaN NaN \n", "25% 587.000000 NaN NaN NaN NaN NaN \n", "50% 1000.500000 NaN NaN NaN NaN NaN \n", "75% 1758.250000 NaN NaN NaN NaN NaN \n", "max 15881.000000 NaN NaN NaN NaN NaN \n", "\n", " run_time year 0.5 1 1.5 \\\n", "count 544.000000 544.000000 544.000000 544.000000 544.000000 \n", "unique NaN NaN NaN NaN NaN \n", "top NaN NaN NaN NaN NaN \n", "freq NaN NaN NaN NaN NaN \n", "mean 117.976103 2006.522059 1487.737132 2022.419118 5881.650735 \n", "std 18.524817 5.523387 2152.590573 1943.604448 5739.493759 \n", "min 62.000000 1978.000000 7.000000 10.000000 14.000000 \n", "25% 106.000000 2004.000000 495.500000 741.500000 2410.000000 \n", "50% 116.000000 2007.000000 927.000000 1403.000000 4360.500000 \n", "75% 127.250000 2011.000000 1670.500000 2577.250000 7449.000000 \n", "max 199.000000 2016.000000 35355.000000 14910.000000 65926.000000 \n", "\n", " 2 2.5 3 3.5 4 \\\n", "count 544.000000 544.000000 544.000000 544.000000 544.000000 \n", "unique NaN NaN NaN NaN NaN \n", "top NaN NaN NaN NaN NaN \n", "freq NaN NaN NaN NaN NaN \n", "mean 6941.125000 14355.235294 21001.687500 46972.729779 12159.170956 \n", "std 5248.786627 11500.217844 14318.018144 32565.661357 12809.702655 \n", "min 83.000000 50.000000 38.000000 242.000000 11.000000 \n", "25% 3259.500000 6115.250000 9667.750000 21047.250000 2997.500000 \n", "50% 5485.000000 10975.500000 18502.000000 40461.500000 7268.500000 \n", "75% 9348.250000 19889.000000 29311.500000 65592.750000 17125.750000 \n", "max 32443.000000 65440.000000 76738.000000 185491.000000 71713.000000 \n", "\n", " 4.5 5 \n", "count 544.000000 544.000000 \n", "unique NaN NaN \n", "top NaN NaN \n", "freq NaN NaN \n", "mean 67247.323529 45860.656250 \n", "std 53650.563685 53221.358839 \n", "min 50.000000 57.000000 \n", "25% 25210.500000 9819.000000 \n", "50% 52302.500000 25122.000000 \n", "75% 99452.500000 64507.000000 \n", "max 261018.000000 329120.000000 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe(include='all')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "다니엘래드클리프,엠마왓슨,루퍼트그린트 7\n", "로버트패틴슨,크리스틴스튜어트,테일러로트너 3\n", "맥컬리컬킨,조페시,다니엘스턴 2\n", "조니뎁,키이라나이틀리,올랜도블룸 2\n", "키아누리브스,캐리앤모스,로렌스피쉬번 2\n", "제니퍼로렌스,조쉬허처슨,리암헴스워스 2\n", "장국영,왕조현,우마 2\n", "휴잭맨,케이트베킨세일,슐러헨슬리 1\n", "누미라파스,마이클패스벤더,샤를리즈테론 1\n", "임수정,문근영,염정아 1\n", "차태현,강예원,고창석 1\n", "토니자,춤폰테피탁,렁그라위바이진다쿨 1\n", "브루스윌리스,벤애플렉,리브타일러 1\n", "정우,황정음,손호준 1\n", "전도연,황정민,나문희 1\n", "수애,정진영,정경호 1\n", "브루스윌리스,미키루크,제시카알바 1\n", "주성치,조미,오맹달 1\n", "제레미레너,안소니마키,브라이언게라그티 1\n", "이병헌,김영철,신민아 1\n", "크리스헴스워스,나탈리포트만,안소니홉킨스 1\n", "장광,마이크마이어스,에디머피 1\n", "차태현,유오성,박하선 1\n", "하정우,서장원,윤종빈 1\n", "주지훈,유아인,김재욱 1\n", "에단호크,줄리델피,마리안느플라스테그 1\n", "송강호,오달수,곽도원 1\n", "윌스미스,앨리스브라가,찰리타핸 1\n", "잭니콜슨,모건프리먼,숀헤이즈 1\n", "제이슨스타뎀,나탈리아루다코바,프랑수아벨레앙 1\n", " ..\n", "김주혁,봉태규,정경호 1\n", "리지캐플란,T.J.밀러,제시카루카스 1\n", "샬토코플리,제이슨코프,바네사헤이우드 1\n", "크리스에반스,스칼렛요한슨,사무엘L.잭슨 1\n", "브래드피트,미레일에노스,다니엘라케르테스 1\n", "김명민,염정아,유해진 1\n", "저스틴바사,제프리탬버,브래들리쿠퍼 1\n", "일라이저우드,비고모텐슨,올랜도블룸 1\n", "니콜라스케이지,존트라볼타,도미니크스웨인 1\n", "브루스윌리스,매들린스토우,브래드피트 1\n", "숀빈,브렌단글리슨,세프론버로우스 1\n", "짐캐리,주이디샤넬,브래들리쿠퍼 1\n", "김윤진,마동석,천호진 1\n", "임창정,최다니엘,오달수 1\n", "펑위옌,천이한,진연희 1\n", "하인스워드,크리스찬베일,톰하디 1\n", "타케우치유코,나카무라시도,타케이아카시 1\n", "김하늘,권상우,김혜옥 1\n", "라이언고슬링,레이첼맥아담스,제임스마스던 1\n", "박해일,류승룡,문채원 1\n", "톰크루즈,저스틴채트윈,다코타패닝 1\n", "김윤진,박희순,김미숙 1\n", "아만다사이프리드,저스틴팀버레이크,킬리언머피 1\n", "정재영,정준호,류승룡 1\n", "밀라요보비치,미셀로드리게즈,에릭마비우스 1\n", "김상경,이요원,안성기 1\n", "장르노,나탈리포트만,게리올드만 1\n", "미즈하시켄지,하나무라사토미 1\n", "짐스터게스,케빈스페이시,케이트보스워스 1\n", "고수,한효주,김성오 1\n", "Name: actors, dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 시리즈물은 배우 풀이 중복됨\n", "df['actors'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pair plot\n", "* target y\n", "* real var X : avg_rating, lee_rating, eval_count, wish_count, cmt_count, run_time, year" ] }, { "cell_type": "code", "execution_count": 5, "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>rating(y)</th>\n", " <th>avg_rating</th>\n", " <th>lee_rating</th>\n", " <th>eval_count</th>\n", " <th>wish_count</th>\n", " <th>cmt_count</th>\n", " <th>run_time</th>\n", " <th>year</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>3</td>\n", " <td>4.22683</td>\n", " <td>4</td>\n", " <td>13025</td>\n", " <td>9796</td>\n", " <td>2585</td>\n", " <td>128</td>\n", " <td>2015</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>2.99629</td>\n", " <td>0</td>\n", " <td>58122</td>\n", " <td>3166</td>\n", " <td>965</td>\n", " <td>121</td>\n", " <td>2013</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>4</td>\n", " <td>3.90119</td>\n", " <td>0</td>\n", " <td>66296</td>\n", " <td>33565</td>\n", " <td>1539</td>\n", " <td>108</td>\n", " <td>2013</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2</td>\n", " <td>2.62241</td>\n", " <td>0</td>\n", " <td>67031</td>\n", " <td>1079</td>\n", " <td>712</td>\n", " <td>104</td>\n", " <td>2013</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>3</td>\n", " <td>3.31175</td>\n", " <td>0</td>\n", " <td>68174</td>\n", " <td>9510</td>\n", " <td>2439</td>\n", " <td>119</td>\n", " <td>2014</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>2</td>\n", " <td>2.27068</td>\n", " <td>0</td>\n", " <td>71711</td>\n", " <td>1780</td>\n", " <td>1801</td>\n", " <td>132</td>\n", " <td>2014</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2</td>\n", " <td>3.52048</td>\n", " <td>0</td>\n", " <td>74705</td>\n", " <td>7661</td>\n", " <td>2829</td>\n", " <td>123</td>\n", " <td>2014</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>3</td>\n", " <td>3.90317</td>\n", " <td>0</td>\n", " <td>74444</td>\n", " <td>10389</td>\n", " <td>7696</td>\n", " <td>121</td>\n", " <td>2015</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", 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" <th>536</th>\n", " <td>1</td>\n", " <td>3.69244</td>\n", " <td>0</td>\n", " <td>315856</td>\n", " <td>5589</td>\n", " <td>1423</td>\n", " <td>127</td>\n", " <td>2011</td>\n", " </tr>\n", " <tr>\n", " <th>537</th>\n", " <td>4</td>\n", " <td>4.29233</td>\n", " <td>0</td>\n", " <td>384705</td>\n", " <td>31362</td>\n", " <td>4414</td>\n", " <td>133</td>\n", " <td>1994</td>\n", " </tr>\n", " <tr>\n", " <th>538</th>\n", " <td>2</td>\n", " <td>3.53845</td>\n", " <td>0</td>\n", " <td>309470</td>\n", " <td>8553</td>\n", " <td>1353</td>\n", " <td>119</td>\n", " <td>2012</td>\n", " </tr>\n", " <tr>\n", " <th>539</th>\n", " <td>4</td>\n", " <td>3.60102</td>\n", " <td>0</td>\n", " <td>375039</td>\n", " <td>2092</td>\n", " <td>799</td>\n", " <td>115</td>\n", " <td>2005</td>\n", " </tr>\n", " <tr>\n", " <th>540</th>\n", " <td>5</td>\n", " <td>3.85321</td>\n", " <td>0</td>\n", " <td>314513</td>\n", " <td>15380</td>\n", " <td>1829</td>\n", " <td>132</td>\n", " <td>2003</td>\n", " </tr>\n", " <tr>\n", " <th>541</th>\n", " <td>4</td>\n", " <td>4.10486</td>\n", " <td>0</td>\n", " <td>564090</td>\n", " <td>7039</td>\n", " <td>2794</td>\n", " <td>129</td>\n", " <td>2013</td>\n", " </tr>\n", " <tr>\n", " <th>542</th>\n", " <td>3</td>\n", " <td>2.97457</td>\n", " <td>0</td>\n", " <td>341757</td>\n", " <td>782</td>\n", " <td>757</td>\n", " <td>115</td>\n", " <td>2004</td>\n", " </tr>\n", " <tr>\n", " <th>543</th>\n", " <td>4</td>\n", " <td>3.65453</td>\n", " <td>0</td>\n", " <td>461916</td>\n", " <td>8540</td>\n", " <td>1931</td>\n", " <td>121</td>\n", " <td>2012</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>544 rows × 8 columns</p>\n", "</div>" ], "text/plain": [ " rating(y) avg_rating lee_rating eval_count wish_count cmt_count \\\n", "0 3 4.22683 4 13025 9796 2585 \n", "1 2 2.99629 0 58122 3166 965 \n", "2 4 3.90119 0 66296 33565 1539 \n", "3 2 2.62241 0 67031 1079 712 \n", "4 3 3.31175 0 68174 9510 2439 \n", "5 2 2.27068 0 71711 1780 1801 \n", "6 2 3.52048 0 74705 7661 2829 \n", "7 3 3.90317 0 74444 10389 7696 \n", "8 2 3.61165 0 78615 6108 7895 \n", "9 4 3.60711 0 79275 4026 7954 \n", "10 4 3.86041 0 81082 5085 8404 \n", "11 3 3.34497 0 83358 5629 841 \n", "12 3 4.01025 4 93331 8067 10234 \n", "13 3 3.66282 0 96957 21133 1044 \n", "14 3 3.14855 0 102482 7671 7200 \n", "15 3 3.56793 0 101752 8883 1061 \n", "16 4 3.86662 3 116851 6526 6115 \n", "17 4 3.95044 0 119876 10304 5545 \n", "18 3 3.10815 0 122449 9728 6180 \n", "19 3 3.94196 0 127387 40384 1187 \n", "20 3 3.36549 0 132497 5907 598 \n", "21 4 3.94286 0 142933 5514 10405 \n", "22 3 4.00765 3 156282 5621 12327 \n", "23 2 3.69728 0 159950 6841 5961 \n", "24 1 1.98421 0 267408 484 2215 \n", "25 3 3.58254 0 636441 6750 3231 \n", "26 3 3.94234 0 450272 13246 1565 \n", "27 3 3.79117 0 314274 9659 1323 \n", "28 4 3.55195 0 178290 9805 1245 \n", "29 3 3.93998 0 65199 17580 1466 \n", ".. ... ... ... ... ... ... \n", "514 4 4.02419 0 740618 8495 2878 \n", "515 4 3.97763 0 718646 6212 3769 \n", "516 4 3.76309 0 710614 4949 2893 \n", "517 3 3.37415 0 284807 2659 1122 \n", "518 3 3.30031 1 295020 1733 872 \n", "519 3 3.67278 0 331860 2607 920 \n", "520 5 4.27281 0 647024 7048 3539 \n", "521 5 4.04594 0 298343 22950 1720 \n", "522 4 3.80457 0 219885 18106 2104 \n", "523 5 4.22021 0 428668 22765 3243 \n", "524 3 4.03853 0 366089 21471 1733 \n", "525 5 3.89998 0 321863 13833 1301 \n", "526 4 4.14620 0 233050 11009 1049 \n", "527 2 2.85195 0 272508 2429 1194 \n", "528 2 3.40819 3 284610 3631 785 \n", "529 3 3.79351 3 297396 10288 1430 \n", "530 5 3.87432 4 290351 30515 4145 \n", "531 4 4.42741 0 458522 26688 3182 \n", "532 5 3.97103 4 226169 20212 2464 \n", "533 4 3.99282 0 464647 15638 2243 \n", "534 4 3.83566 0 485522 12069 2109 \n", "535 3 3.28881 3 274059 5396 967 \n", "536 1 3.69244 0 315856 5589 1423 \n", "537 4 4.29233 0 384705 31362 4414 \n", "538 2 3.53845 0 309470 8553 1353 \n", "539 4 3.60102 0 375039 2092 799 \n", "540 5 3.85321 0 314513 15380 1829 \n", "541 4 4.10486 0 564090 7039 2794 \n", "542 3 2.97457 0 341757 782 757 \n", "543 4 3.65453 0 461916 8540 1931 \n", "\n", " run_time year \n", "0 128 2015 \n", "1 121 2013 \n", "2 108 2013 \n", "3 104 2013 \n", "4 119 2014 \n", "5 132 2014 \n", "6 123 2014 \n", "7 121 2015 \n", "8 127 2015 \n", "9 108 2015 \n", "10 130 2015 \n", "11 124 2006 \n", "12 142 2015 \n", "13 107 2009 \n", "14 137 2014 \n", "15 123 2005 \n", "16 111 2013 \n", "17 108 2014 \n", "18 90 2014 \n", "19 96 2007 \n", "20 120 2001 \n", "21 140 2015 \n", "22 124 2015 \n", "23 113 2014 \n", "24 90 2007 \n", "25 118 2012 \n", "26 113 2007 \n", "27 100 2003 \n", "28 157 2007 \n", "29 154 1994 \n", ".. ... ... \n", "514 131 2012 \n", "515 127 2012 \n", "516 135 2012 \n", "517 116 2004 \n", "518 120 2010 \n", "519 126 2004 \n", "520 126 2001 \n", "521 113 2004 \n", "522 138 2010 \n", "523 102 1998 \n", "524 96 2004 \n", "525 110 2000 \n", "526 110 2004 \n", "527 114 2010 \n", "528 111 2011 \n", "529 108 2003 \n", "530 95 2009 \n", "531 142 1994 \n", "532 117 1995 \n", "533 133 2011 \n", "534 109 2006 \n", "535 163 2010 \n", "536 127 2011 \n", "537 133 1994 \n", "538 119 2012 \n", "539 115 2005 \n", "540 132 2003 \n", "541 129 2013 \n", "542 115 2004 \n", "543 121 2012 \n", "\n", "[544 rows x 8 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_real = df.ix[:,1:14].drop(['director', 'actors', 'film_rate', 'genre', 'nation'], axis=1).fillna('0').astype(float)\n", "df_real" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.PairGrid at 0x9e49e48>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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gsQkL1px3SZFazGk1k99APjOmxZQnBiglk5rzryGfGTOpOd8LQF6DmvO9mL+nWXN+Ieu1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I/VOjqt+GxovnZ60+RNMllUqw4iIT2q0u6M464tbp3A6YL+JVhlRcEhOFnsFA3BXwkbtNYunc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WY88iJ2BfMKM2O0HD9+v5fO5g9x2VsxmIvIK70alWrSu4lgmhK1H0lE3cWORWGxhXU36jHo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8+SAZOU9nLI27fXv24q+YTm9/FOOKKkoG1iVHD7lpnFWORgPFvSdJrH0C/ZQpFO/KrsNM\nBQZNe5mJ5n0u3K4A9jIHDy/8Oqd8bVSZK4gcFM/9Pg2BGVIx49MZK/22qWI6Dy3MjUm1HX5SeQY6\nW7gdKDvjOZpNuxi0tqSiWTV5+da/sHTxSiKWSuxOE5a+I3itNew5cUI4v1QHgwIaqVCYaFeX4DQd\nNPZptErqGuxoqcVujyE3vCeS/Njd+THNJ3JZ/7FoEplczpGDuXWSushEuGIJYTLs33RsYDzODHv8\nHCvOrbHGWOm7l4KRfoPzzy+osUqx+sx67dXpbrR576Qj0w3UAzCp0SkEDZjTfkJt7XRp6ijWq9Bo\nlcSiA9csijOoHRWtFgfkHrekeG7/q1j7alH/9T0Gw5jlqhWUvPIWAEneY8I37uHj+nLKPvaRWb4G\nX1qH3lmGpt0t3Kf1RB/vb83VAJLG+pEzLOP88uXL+eY3v4nP5+P3v/89L7/8MitWrLigG/b29vLV\nr36V73//+yxYsACAhoYGdu3axdy5c9m6dSsLFixg2rRp/Nd//RfxeJxYLMaJEyeYOHEis2bNYsuW\nLUybNo0tW7bQ1NSE0WhErVbT1tZGZWUl27dv54EHHkChUPD444/zla98BZfLRSaTEUXSn40HH3yQ\nBx98ULStvb2dZcuWXdD/+VxYi7QsmVVBJJZEp1FiNY++0TkYDw7ZHg009oKCsLbRjc5PpzNC8RcA\nR7mUSpPPJ91vP03Ma3Tyvbvn0eLyUV1mZv5AauO8RiffvXsupzr8PPPmYSbWF5Hfa4M2A30DaehH\nDnTx0iuDk021oJFbmnCxtA58GLHbdBT3HiE9ezaZI+9jtVjo2LBeqCsRmjg37xqwfNVUNqzfL7Qb\nZ5WTyWSYN20mzZ4uPt6am3RGwnGuvH4SmTSfuRT3T6LvRuJy/vb2UaG99IbRl8Uaq+TXXKgpMzOv\noPDxhWB3mkSZCA7nZ8dZNFykMVfi08ql6rvZb/Fcoq1tbN7cJWxvnFXOu/ujXH/5BCCbop2Nzgqg\n0Ua45poaIuEExdok1YokPeUOtm08Lpyv0ijoj2ZE9xqUkBmOsSWTStG/a7dI0k52AUFIZ4y2l5cN\nW8qmEMnIeTpjadxt7c3w2juDWR0KphkywnfRXKyjKOMhseEZ0oBCl1vbybf+hZX3PoI3rsJRZkIm\ng3VP5X7nFcscXFVio6RhOoc9YhnST0NghlTM+HTGSr+Vy+TMq5wpjEkn/voUsg2vCgY6n3oF1vlz\nz3hO3+Q4zWwAQDmgWDAY4T77kYexXtYINFKZSJIBursC2J0mDPIu8tWt07qcJM1glH1+gVeA6+74\nR6Kt26mYPJ2SeXN5+9AbyIrEY3xZRRHLVk2lv8tPPJpk17aTous0zioXXVMaPy+MsdJ3LwUj/Qbn\nn6/RKkVFVotaPwQaTztH09eJbMM64Z3UrFkt7EulUsg7jmBubSFV1chbzQogDSe6uPr6ibi9MSxl\nCn7f9TvhnHjDeCFIwWvR4A918/XuWkrtFYhi+Q1iqahYayc4HATsDWze3T5wnw7Re1UYmCWN9SNn\nWMb5e++9l23btlFeXi4UVr3qqqsu6Ia//vWv8fv9/OpXv+KXv/wlMpmMRx99lB/96EckEgkmTJjA\n8uXLkclkfOlLX+KOO+4gk8nw7W9/G7VazZo1a/iXf/kX7rjjDtRqNT/72c8A+Ld/+zceeugh0uk0\nCxcuZPr0bHTKnDlzWL16NZlMhu9///sX9MwXm/0n+tj6QU4DzmLSsGDa6BYiLTOKNeHKCjTiRgW9\njopVNxHv70ddUgJ63ahevr83NGRbQuJsyOUyLptWxmUFH4xBA+WxjqxG27Mtar5x911EXUcI2gy8\nKD/K14suY2f7h7hOxEXnhvSllN95C0cUXkwb3qAYiAOar36F1pQdT0xOiS6Dffn1JPp6MDVM5qgn\nIUSHJGIpgv6oyNOdiKU4drgbkJHJZGhaWINSKScSjtO8r4uVt89k0hQ7/bt20/b8thEZET5vfJ7H\nj8KaC9+7e95p78L5Ul1XSo87QF9PkFKbkdq6sanhLyEhMXaQy2XMnWxn86F20fZELFtQnA2CvAAA\nIABJREFUs7s7TN3iRbSYHAxqp8aiSYL9AfTrfkYEaAYm/ev3uHpFA+5OPyazln1727l8eTX/4P0a\n4f40BqsCu1k17Ofq37Wb5p/8h9Ce/N2HLyh68kKlaM7mHDibkfNs0XxSpP0nizcqXkYn4klKSg0E\nfFFUKgX+qBHDQNE/z569VN99F+lYDENNNSXzGoS525aNR0TXae+HSGsbk+UyJjU1XZTaAxezr0jF\njD89qIrEv5XMoOeFA68JmT6Dsp7pdIYewzhiX36UYm0SoypAJpnM1t/SaokGg7z/8nt4okrsRXJi\nf/wv9KEwQaDz7uVk7r4eI3UE4jqwqIRimIMyoIPfgME1UndIhrVpBRPm1COTy6kyV/CryB9Zsmxp\ntmBl7TgmTXEik8vYsvEIW949DOS+JYX/BrHhUBorJYbD2b7BQzn08/d1quqEc2PRJL7Oboo3Zg3n\nyjvXnPGe+tpaIVK9WBElZnXw7MZmasrMlHQcpufJXwDgvVZcXDyRlrHq72YQjcdI7/4S/e4IJQ49\njfZ6Wrwe3BYLekWY4udfIB0KEzLoqfjKXcijMQzV1XR6YqL7tpk9PLf/VVbI/k50H608xZxaOWZ5\nBLNWHP0vjfUjZ1jGeYDFixezePHiEd/w0Ucf5dFHHz1t+9q1a0/bduutt3LrrbeKtmm1Wn7+85+f\nduz06dNZt27dadsfeOABHnjggRE88cXHZFALkfN6jZIi/ejLA0QTsbyCsBqiydHXa08Fgij0OvAp\nUBj0pIKja/wqsYk9eiUFxWBGg0wmjbf7oKjAh0wmGT4/DVxIdHAmlUJ1ZD/Lo51MrzfzbAvs1mpI\nzyohmowxSzkVfzTInz5ez1eK/kF0rtVh4GehXZCGW+5cRlVIReXk6RwNF7P5tdxCa+miGcg2/BS1\n1UpxdYUoquPIwWzq87FD3dQ12FEo5Gi1Kg4fcNG8z8XCJUq0ah/m8ZVMnjqT+ilO+nfuHBUjwucN\nq90obtuMZznys0dhzYUWl2/ExvkPdp6C5EkqHD5iCTO7d8q5bMmEEV1TQkLis8+Jt3ZgDIq1VzU6\nFY2zyoEEvbIqtA7x8sSsipPIa7f2pNj01iGhvXzVVIhn2PXXXJTkcl0jVIrvfbYFdailRXRcqKVl\n2N/V0TDynMk5UDJ/LuNr/BhWRIklzGzfkhQWvmeL5pMi7T9ZyuqcsCMXXOWsMNPZ6kWtUfLe1hMs\nWFKL+s4H0e19C6XBQLitHWVxMe1RI/s3HcVRZmZSo+M0Y3YCBW8dVaOrymC9UIfPOdYzF7OvSMWM\nPz3oq6spXbxIMLI3a/w8t/8dAB5aeJ8QYV/YX1Ysc5A63IzjS9eSkIWIGZW880I/sWjWIL40rzir\n1uXlZE2DUEdLo1Wy6K6HCXoCFNn0XFajIRqIM3VWBQqljI92DThvd0KJrpjJ08poqpjOP86/a0CO\nx05TxSRBbjn//VHnaYeoC3RE8g2H0lgpMRzO5mgcyqGfv09TKGE2UF9HodWiP0sNnF5dBZuPuYA0\noGZckZ4/78k6n344JSS8r+qKYjiRk5S2D2Qwv7e7me0vDgZA9KGMa3jnlWPCcYPvZioURh6JUXV7\nVvf+8PYTbD7mA9JotHpm1NayPFZFlbUcl/YUsWgSjVZBfUOUjLsVWViBpv+4NNaPMsM2zktcPOQy\nmShy/q4bGkb9Hp1Bt0hzXjN+9B0ACpWK1j8+LbSr7rpzVK+fyWREMgoZMuc+6Tzxdh/E4/6IdCpG\nNJSNYLY4Tk85khh7XEh0cP+u3cT/+GsAbMA/rv4azeYQmwbeFb1SR12ikS+mb8fbl2LanEpikQSV\nJTKMnqOE5VnNxbXs46Gr78NaOZN3X/hIfI9QBiugr6lGL/PSpbKi0SoEw7veGMLhqGHzhtyHc8GS\nWhYuUaKTvQkJ8Lt2Yqu5ja2bApiSMiHiBM7PiPB5Rq6AVavNyNL9ZORWgtHPT4RMbVkRX52YxhTo\nIVBko6xs5HVNinRdRKPZ/qkFtDo9IBnnJSQkhibc2kJpNMjSOjs+jJidVjJ6NW9vzMnGTajxs/Ra\nO75jvZjlEYq72hgUD5EbDARlRuqnZI0vRw+5CXZ7CCXEY7q7Q+yUhDMsqL/3L5DJkAyGKF2yCM+e\nvaRC4WEVjh00yne7A7zzxmFh+4UYeUItLSiMeqw3LyJjSBNV9uPtPkTPqXXIyI6xa758G+Pqsgvf\ns0XzSXIinyz2aEeuoF6lgx3vHBcyIRtnldPfGyImT1JdZMZXMRVPCPRmPZvz5A1vu7tJ0BE+erCL\neCLNsUNZKZvCyHwYfhCRt/sgJz76g9CunfFlUfHCi9lXBjNI6hudHDnQxdZNR6Xo5DFKSdNsSKcI\ntbTQb1GzLrBF2DcoDQa5/jIY2d4RkDPl/tX09L2RPTiyj4VLrhko1A2e4vE47/4mmdeept9mAL8a\njTbJwiVKLJYIXq+LD/cniUX7RDIZly0VzyMH+2WhHE8mlaJv1/uEWlqwja9l5Y0TaN9/Eku0jXEL\nS+h2+bFE23BMzhCzVlE+sUxkOJTGSonhcDZH41AO/VB7u2BAVybcrLh6LgGFGUeZCVuknVBqQrYG\nTtOc0+4Hp/fNVCQXmmAqNuF++UUAtFMXi+xiSZ8XKKfXXZAp7o6I2j6MgmSOoSY310mEcgoBdQ12\ndv41a+Dv0Z5k1W0WkrFujOYi+jveJCWPghEqx02mqkEqXD+aSMb5MYA/GB+yPRrUWMbBybx28bhR\nv0esq2vI9kjx9kVE2nEaTdWoXh8gGurC0/Wh0NYZHZxJD0xi7HG26OChIuoLP67GQDdmZY3QXqJb\nyp6XeoR246xyjhx0Y6qVM3n2BB4aN+W0Qm/OCrPwsVRrlDjLNaj+6V85FEyj7T6FWRFm4RK9YHiP\neMBSslL0HJFwnJIiH/mhgt0dp9jy16xmaX5EynCMCBJQZHAT7Xsl17auBOrOfsJniNpgG4k3so5T\nLTBhejkwMuk0lbKfaEFbQkJCYihS6QyK8kqSH76PbNdbwgIxdLNYz9bo1eOfFmSiVkMwaKM9PoHS\nby1B3XOIPksj77yR05ufNqcStSxJRKFm6qwKjh7KFisrteSCUAYj5j17PxTdJ9zaSuvTzwjtcXff\nhcdSx36/Ese+riGNiYORl/VTHKLtF2LkMVTXYL15EX5T1kkf6D9Becl1omOUCq/wLGeL5pPkRD5Z\ngidOCtrA3mu/AsiF+Z+pSIter8IkU9DjtbP5b9k5av0UcV2xwf4y2Gfyo3nLJ57ej043ut/F0URK\nmIvOLpvGsYPdyJInRedFgi6Rcf6T6CtSdPLYJ78Ialf7h4R3/FXYV2WuEP492F/ys3/L7FHyR0ed\n1g9ki7vqTDqOu1PU3P4NKial0LUmqLf0opO9SdSbnYsOGvPz5Wd8fnFmf36/zF/PzU676Xnyv4V9\nkx95mMo5pYRaWkh6jhHf+KoQwld3x2qqps0uuG6u/2u0SnR6FVs2HpGcSBIiziZVZ6iuKWjn1uJK\nnZ7ebduF9vja8cy+frCOQxnWcxSmd5SJM7sVupxEXzKRJHP3I/SHMpjURo4dahEcwiaZFfO656gq\nreNg3vn2UvE3p3yCA+sdq7MOgrxnsWjPLAm1cImSgPvPAES8YHHOFGxloZTYkSAxciTj/BjAUqQZ\nsj0alOtKuX3qjbiC3ZQbHVTqHec+6TxRWSxDtkeKwSiO9tcbRz/6PxkPD9mWGHuk02l2d35MzHKK\nq65Ss/PdDKFokuoyMzB0RH3hx7U1U8T6l4LMv2wFVkcCu7uCdnLauIMfK+cEB72HD6P2monbAXPu\nGnq9WuREqqhp5JXXchGBVzeksZcE8Xvy/xN9QC7yqXycmdLiCvpylyEaNwPZSWvcUUvdGT6sEmdH\nIfMM2f4sEy5wQoVbWuCykWVbqNWl4raq9CxHSkhISGTZeaCL/9kb53vTGmBXzmhXYhRH/pYG+pjk\nL6O3ZByvvrxH2L7ohsl0nwqKjlUrYPP2XDDIoqtqUSfCTLTk3IeDEfOlSxaJzk34xQvLXk0ZrxZE\nNZ/NmDgY3TaUdMJwKZnXRGhfJ7hzGXSF80+dMfccZ4vmk+REPlliJbm1VLEiSl1DjWj+N21OJXpL\nUih6CUP3l8EI+qF+v0jQJWr39p/g8Y83CO1vjv9nNq07xrLrNOSbZPL7T/ZeF7evZFIpOo+Kn1WK\nTh67pNNp5MhZPfVGgvEgk0vrhKAjyPWXbF2sLLGEWdTHzMXlzJylRF9s5G9vZx2oRw66WRF3UGMM\nkLRHCOXindCqfYAWlUYhbCvRp7luSSm+uBq5xYRfBslUir2ufRx0naLfnV3n2Yo7RPcOtbZQtfo2\nrAvm0/feTlwvvyrsO1MQU37/1+lVbFi/X9gnOZEkzkXJvCahyGrhWjwZEM8rCtuQs13kB/gN1new\nRfIysuQRHLYwuusmUV1mxtvjY/NrRweu4hZlnVg0CdrWrkNu0HPdV75JWyBGqcOAte0wS+tSwvVU\nPQfYOdNCTZGWzPu7CLdmZf6Kve7cfa0yBgV6tWpxsGA6lXOgJVIW+t57/4za+xIXhmScHwP0eiOC\n5rxOo6TXGzn3SefJcX87z+5/WWivmbaSqYyufI7cZMoVhLWWIC8a3SgMtVbBFdfW4+0PU2zVo7wI\nvddoGU936zZRW2Jss7vzYx7f8WuhfduqNVRoJjJ/YKExlN52/se1T1PCKx8luH1cFHNzL1UxBz6D\nuECRSi3nyuU1JNc9QV9PLwCmO5fx+LHXBG3Gk6fEEcTd7WIjsCemxNHZC3n1kv1+PQuWlBMNhBhn\nV1Gl7uPkr5/H/A+rSCT7UekcbPtL7uNePrHstCgQiaGRK8XGY7nCepYjP3soiotFRX6UxSMfm109\nNozFXxRkgly9NmpG/qgSEhKfYTrdPr42NUq3z8W4O9cQbetA7XTQq1QxZ5YNvUGNpq+N0nAnPcd8\ntOjE5wd64qcZN3VqscRhqN+DP9zDybp5yAYWjTJ5thaSZ89eShcvQqHXY5k9k4xcTmfcJIyNgaQ4\n6GMoY+Jg5OXRQ9kFstGkobrWekFGTplcjslZR497h7DNaBmP0VIrki/JHX/maL4LLUgrcWGctKtp\nuPvLuNPF+JIaSoxaNNps/6xrsEMmQwADJdoUDMQYHz3k5vKrJtDrDmKvKGJ/tx/vPoSsznP9foVG\n9qhS/JJ0DziNtm9JsnDJNdjsMezl40X9By5+X+nftRtNdzv5estSJsfYpXAt1WCbKBgLIddfQMbe\nd1uBbB+7YeUXiYXcRONmfMfVWIMttMXE2fk9PWEMhz4mc6V43aIzlrHsKgMmLRhmWymxqOHPvyHR\n04sB6Ln+Tn7+hpx//Iqdp/b/UThv/mUrCHTYRMb5fAP8UIbT/P9P/cBYfexwtyjrSnIijT3GWgHf\nDDJ6DFW4LRYchiJK8nJIDDU1omPzpWMGKXzf8us7hE7mMrIANHes5vbV2cCCl54Tq1JoNEpmzbDg\nKFHjCLUSvWIJ6lIr8lAH81fdCMCHL/cg2/AkxWRlAbu/dA/+fRH8Zj9H1/5GkMkdf+/XkG34/cBx\nepbe+W3aPRmKrDECXbkgR7WulozKRDRupveUgd7f/VDYJ9XBGzmScX4MoNeqeGV7Lv3wtqsnjvo9\nXIHuIdujQToQoGP9X4R25epbhzj6/MmkYcvGvEKb108a1esDmG0NVE6+iWjQhdZYhtk2+vr/EqNL\nq69DvEEX4LLG3KSqpsws2l1VVsS7+1w5mZt587AumI9rfydfTe8k84es/EfXNrAtW8bSOjvJmgls\n3ZRLo1865xpBVsbYEwJrTptRZRAv7q1F4mG21KYj1RqlqGkZvmiAaNzMjrcT1DVEGW9TonU62Huw\nFePKr+Hu6UCrThDzBrjsiglEoykpGu4CCQRsWMfdQCreg0Jto7dv9LOHxiodKTObj8UYLC60fJx5\nhKI2oFAqWb/OBygAL8tXVZ7rFAkJic8505Jd9PxhLaWLF9G2PvsNzSxfw+Y9vcIxS+sSJP0+jpdB\niSYhOt9aombHlnZBOqRmfBHahDiSvkuu5eWjcn7oPMTR5/8g6LiX37+Kzv+7nt5t24UFZPM+F5uP\ndSGMjVPNQJtwraGMiWeKPB6JsaC7x0okcw1atY9o3Ex3TymTppaJpEgkxha1Jztp7coMFNEDjbaP\nG1YakePB6+1j+5YksWiKRUsquPJqNR5vkmQqzZ53s1IEAY2clwdqjg2nThJAsX0KtTO+LDhtTiTS\nov32MhPgJhZNsXljitvubsLiGLmh8XyNY6GWFuRbX2Hp4pX40jrKxtukuesYIz96V44cvUpHOJEN\nEMzXm89nUqODy26uoK2tl8mOKoL+NlQKkMnAYDHR79cyzqnjSJ6uhiHcS+/W7eimLiTCNVgsETwe\nHZv+4icW9bBscorxvpOkTkYxLltKtLeX/nffwxToARyc8ojXeXGFl2dbivnh1/8Zbagfj7W+QIos\nJ9UzFIWyS4NRyJITaewx1iSyhnqe4TiHXD4XX5JPw9gTImQz4vJ1CQXsDeNrsd91LRlDGllYgaEy\nFyhaWiK2MRRblGwztjAtpKXtf/Mk+u66k2c3NlNTZiYyoYjSO29B3tVLeOICtr/pBqCdmEgmNxkI\nCM/dKTfzxPZOQrEkaW0108YNBmOVsO+AmT1/CwAxrr86Riwv+CvU0cnnJ/Tt4iAZ58cAGrVcFDmv\nVSvOfdJ5Uma0i9pOo23U75EMhXMV33VakqHRlYTp7y0ocNEXOsuRF46vp5n25pyDQa0xSwujMU6+\nJuJgO1+XsLbczPfunkuLy091mRm5DH70VM4D/K93z6Uu2Ipl/36UOi3dBj2pgb6b9PuQ7XqLyG3f\nEt3Dl9YJHu2gzQBpqCkaR/M+F+lYgqarJqAI+DEGu1B6XVx5zQS83hgl+jRlsi6CpDH0p9BXXk7v\nsV7mX67AWiRHbbGw7qnsx/7m2xPEPHkFNw165hek5EsMH5PRTV/b60K7yHETY7GA6aA28mimCPZH\nZEO2L4RIOD5kW0JCQqKQlCtrZEnFY8gNBtJX3YzHOA7ISYH40jrKpk3mxeRW/kkzVzDEa3Qq1Gol\n0y93korJmDKjjMkDjnhteRfHT/TRGYjy1qHswlMb7Mf4T3fgD24GIBA9wbj/cwexD7ORZ5l0+rTC\na5Fw/IxSH2cbl0cz8rirM8CWjTGyX/wYV1wXYNJUKXpzLJPp6SdsnAx4gaw2b9ybrW2Tr6kdjClJ\nx9Mc3t9FXYOd6lorpeUmfrs9J6GUn9U5FDKZHItjqrA2mZVJ89DC+wR5hDll9ZToii9Irmao+cf5\nGscM1TWkQ2FkG56hGKj+7sOSjvcYozB6d2FVEztas79x/toqlUhwfNtmIq2t6KursdQ5+Z/29cwp\nupVk30ZIZ/u7wbKKzkwd9kA7111hwxdIovd3odj2EhgMJGNJfJESvD4Fe9/PSYaGTU48b79IKhTG\ns2s3pYsXYZk9my6jDbrArssG0+iVOpbqr8EWsLJsQZqSEgW9FfW8JOqXc7CF2oY1hy4c/zVapVCg\nWWJsMdYK+A71PENF1Q/S4JbT8/RbQDa3yPaNe4QSh7JxavzeAYk7I1jHLRDOs/TvY+miCfSHMpQY\nZBi69/CB4l1WdoszowJtnfypLdvvf7Ashf/pbBFZr7JefJx1PPKbH6TEKMNQZcQ6dw7WBfPZt+ME\noff2AfDy3g70mvH0ujXoTWomWNPMm2PB6jCg1Wh445iGwQCHlZNrGf2KkJ8vJOP8GECvUVBpM+Lu\nD+Ow6jFoRt84n0mnWT31RrqC3VlDfTp97pPOE5XZRNerrwntcX9/+6he31IqLpBhsRpG9fpwupZj\nYQEliUvLmYq7NlVM56GF9+HyupjQmSH1+j4OlvfzP3vj9PhiGLRKvnxHCVGLm86YFVnAgUGrJDRQ\nQEV9/CDNTz0p3KN08SKhkItCm02aLLKJPdWWiTaK5MswmC04UfLjsr8j4S0VLVyWLy4h7k3SpbBT\nImun3OEjljDT0ZYgvekdPLzD5O8+zJRbcpEdWzYeFv6tlHvJL4ukVnhH7e/4uSTdP3R7jDCojTzI\naKQImmzFgFtoG0uLz37wMHEUZKQUFpeT+ORJpVKcOnVqWMfW1NSgUIz+XENC4myk0hliJU4UBj3G\n2lr6LXVsblYwdZZ44Wp3GMlY09yqnYEh3YYTI9u3JKlrcLDhlZwme93EXOTu5Gll1Dc6ef9AFya7\nkeoyM/G+fkKxg6JlcajnFD0vb8L18qtM/u7DOMrE6eaOsqIzGtwvxrhcyCdZzPViOIE/j+hKrOiV\n2XmiRqvAbvMTyCWBCJraiXgSc7GOWDQp6AOPm2QT5qGAUCfpfJHL5MyrnCmKcr5Qp9FQ/fx8jWPD\niR6VuLQUZh4Xa4u4beoKQQN7kOPbNtPz898AEAQqVt/KT+VXkJT7ye8VGXpwhjqJ/i4biVtz1ZX0\nvP0OabIZUm9u6QNg6ixxUJVOo8Aye7aw9kpFoyjtdlITGvjeQjMqOcw3rKBWbWXfX3s4hRuNVoHl\nCzKIdbLsOp2QpdJ5tIu+PwxvrC4cc+sm2SU5mzHKWCt2PtTzDMeRme7oKmi72ffqZmJtbVjniqXK\nvF1HKCnLvo+2mlpUbXuxl2aj6r1WK3P009ArHeSL6JoqnPyEdtK2cmRdXgYr8JjLrHAsd2+VXsff\n3s9+k75oKhKi3q9tGkeV5xSxtjZUFZX8fG8rPb4YX52YJvmnpzEBcSDw5UfFzxqXTMsjRfoLjgGC\nkST/m2eUu3P56Mu1ZOSwLk9z/rapXxz1e8T7PEO2R0ooEOXyqyYQ8EUxmbWEgrFzn3SeKFRGLM6Z\npFMx5AoNCpXx3CdJfGKcrbjrvMqZHNnvo+fJ/xb2ff2u+9gSqUBl7RFrFRpWMKfBwdaBVGKNx01+\njodMqaT06qvQOBwE/B78X7qWk6ZDVC6zYo7aKHEYMEZa8G14i0E1e9uypbQ7xX3Fn1ARS+uorgqS\nDLyJIqOlxDmZjEND2nIdfS9sI9TSgnXBfDKpFH179qJO66if4kCtURIIeshfKpc6T9eskxg+Wn0x\nyrx3W6kZuYH6TM4i+Qgjw0IFxVsH+8hIyCQRok9VGgWkRnQ5QCo8OBY5deoUm//yfRy2ob9b7p4g\nS2/6IRMmjL3MEYnPLu8fcPHf70f50a2r6Hx+PeHl9wA+QbNdRQqHKkjyz79F9607cPYfIEIuArm9\nXTxwtX58EluoNWv0y2Tw7NpNRUsL9dU1lDTWs3WTHyXigoXyuEZIF48q+6mf0jSscexijMuFnG1M\nvRhau5+Es+HzQLinh6BCT+OscuonRtAZM8hlDcgVGvy9zci0DhpnGTh2qJuaCVauvH4SmXTWkJPJ\nwP1LJqAyqDE7TcydcuHf0NFytgzVz8/XODZcaRGJS0dh5nGDbaLIyTPYr9KHjomOC586hUKtQV9V\nKzLOKyJpDO1uYVv/zp1YVv0d7r4g4ZJxQFZS9+ghNwuW1NLfG0KlURDx9uMdNwPvtfXZukhxN1RP\noLI+Spv/OFXmChbVzKazuRuNVsHCJUrsNj8KZYxQbzNaokKWSrE2SX7lvqHGamke++lhrP1WQz3P\ncByZUYfYGauyWtEbPSSmytAYilH4taSSWZO6PK4SjlNW60RR9f1WHXuO7WNxRJar++hw0CW30aPS\nUhyIMq7MgX/g/GB3n2g96O3PWUB6unOKFP69e/D/zy8BiAGPfP1B9sprmNSxh/zQtmJtknykQK2R\nIxnnxwBd/ZEh26OBN+oXtf3R0ytHjxR1qbWgXTKq17eU6gl4Bw3yMopL9aN6fYB0MoKn60OhXVh4\nSeLScqbirvMbHezu/BhFe7Non8LTSqKiB5NFAXndX1viw2ozsrw6TqWpnNKw2NucSSbR2p3EOlwo\nUynsFQvhZBytBcxz9cysmMLhP+1BbjCQWXoLUes4jieg2KxCo1USG4iEKiKMstqKUuEmCRSVTs71\nLSNYb1mEoaKadDrDvm3N9HkUbM+rPVHmnIAt0Yi6shiTs+60Yl4S54dMJn637TUjl/Y6m7NoJBiq\nawraI3fKWG1GNr+ecwBPmTFnxNckk8YWakXvacFQVAM44AypmxKfHOnzyIg7n2MlJEaDgyf6QQYB\nTz+pUBhDuAdQC9HEVzekUZrt9F73VSIFmWK20ggqeTlHDuYKtKdUMk50hoi9+RYaSzHNP/mP7Hf5\n6lsJt+zCYNazdVu2KKZW7aPUVkHy+EH8puzCNtB/An3vOCZPm3rOaMmLMS4XcrYCnRdDa/eTcDZ8\nHtDabZi9cUKAyRSk+9Q7wj5n7XU8/VSQWDQ7b1WqFdgdJiZPK6N5n4vn/yD+Tc/XsZ/vtLFoEgSP\nduGJGSj2tDFZLsN6AZHqQ/XzsWYckxg5g5nHg5JI+dHykHPilRZIaurLK+hY/xfUzc047rqWpCqK\nKqUn05ZEbTATIFt4Mrn4Rk4ZKtFXF6FLgEbbT12DnUQshUIpR2dQEQkl0FWU8862VmLRgdofS2ai\nTHfz5M6XkGXg5sxE6qIGdFVNOK9SoU5vFDJULM6ZeLo+xGaPcdvdTdgiHeSvBocaq6UC2p8extpv\nNdTzDMeRmWyopf/OZRh7QgRtBqpsCro97wHgbduHs2IZobaTyEJyZAklreuew1BdQ8wqnhuVy3Qs\nz6wi5tDjWvtr0qFwto7P3kF7iZobKkupuGsN8Y4udFWlvPpmTkawcVauApnNbqDvvfcJtbSQiCfJ\n3P0I/aEM1iIlUUUGa5+XeGUDspV344nIKVZEcaoC0ndhlLlkxvmPPvqIxx9/nLVr19La2sojjzyC\nXC5n4sSJPPbYYwA899xzrFu3DpVKxf3338+VV15JLBbjO9/5Dn19fRiNRn76059isVj48MMP+fGP\nf4xSqeTyyy/ngQceAOCJJ55gy5YtKJVKvvvd7zJ9+vShHuuS4CwRG5kdFt1Zjrxie76/AAAgAElE\nQVRwrDpxlKhFd2Hpk0MhUypzmvNaLTKl6twnnQeJaIq/vZ1XlPMiFIRNxoNDtiUuLYXFXavLzIJm\n4l3m6Vjy9nWY43zg38PCYvECxWm28Mrhl4SiR8rJd5FZ8SXqk90oNWqSwRAxdw8972zJfuA25eRA\nVuos7GYfmVIDypu/SmvUyIHtubTQpYuc+Dq7sTuMJP/8W1KA5eGvEQXSKXGmh6rGAikFRw508dIr\nJ6ifIi5Q2tISRDtuBjOnzZDSzUeBZDw0ZPtCOJOzaKTG+YuRCu6L+0WREr6YH0ZYErb3/Z0c+ffH\nhXb9vzyE7fLLRvikEiMhk8mweVslRp1lyOOCEQ9XfDHzCT2VhESWIoOKuZdBRySBBZBv/QvLrrqF\nsMlJkTyMymTi1U3ZdGtjmV4U8d7Tq4NolMZZ5ZhMWt7bmtPqXloXo7o0OxanFt/I5gMA3Wi0ShY2\n2Yj6MpQ2TKOyvpTWRCf05K4b9Jwg0HUMRUKDNm2jpGn2Gb+3l1Ki42Jo7X4SzobPAzG/H7WzgQOb\nO5nWKJ7jxSN+rlngQOtIIpd7UcsDZHpPkE47R+U3Pb2YZSUHDnUCanRVmQsqyjdUPx9rxjGJLJlM\nGm/3QaFAcLF9CjLZ8NYMZ5JEymfQiec7eCgblRvwoy4qItKTHUSLJk+m7Wf/KxxfungRnr17KV28\nCP+Euby53QPHeoAeZsytZM5l1cI6/sjBbMbUkYNujhzMFWMF6Gn3ULxxLbfcuQyAkqffoh+QGzZh\n/c5tBPLG8MG1ldJiYXy1A1nGIckpSVxShuPInF05ld2XpQXHWKL9oGh/3N9P+ngavdNJ55+eFWrh\nVf/gPtFxvp4i2nfFaCfGDXf9M8bjO+lyVLG01otWnZXTDQRlxP6YlZqyIWdpnR1fWodFl0ZvB+Os\nYkpLdYyzpmn+YTajTnb/Y2zelHPiX37VBN7d1Zl9Tw/AoMb8iuoiZkvfhVHlkhjnf/vb3/LSSy9h\nMGQ1w3/yk5/w7W9/m6amJh577DE2bdrEzJkzWbt2LevXrycajbJmzRoWLlzIM888Q319PQ888ACv\nv/46v/rVr3j00Uf5wQ9+wBNPPEFlZSX33nsvzc3N2Srku3fz/PPP43K5ePDBB3nhhRcuxX95SOSy\nDLdcVUefL4rVrOVi2OE8UR8Lq5qIJmNolRq8Ud+5TzpPol15+lmygvYoUFgA9mIUhFXrS4dsS1xa\n5jU6+d7d82hx+aguMzO/0cmfD+0F4AX5EW65cxnlPgVxh5UX4zsgDR+49nPzlOW0eDvQKjW8cvhN\nZpU1CkWPTvnb2dCs5l8npUi+lC3kZZnbBGQL02U/QFl6ugKUntyH0mSkpSdCQi12pPk6uyne+DsM\nc5vwDBaV3XGQImcd2ionPg4Jx6q0RXS/tpXuaddRP8VBqcOEq8NLVa2VRCxFqcNISKGUDPOjhFwl\nzuSRK4c2YA6HMzmLRsrFSAXv6vBz4IOck0lhckDTyK7pO3SYzPI12eLIiij+Q4cl4/wlRqFQUGab\nSHGRY8jjvH63pDcv8YlT4yyixxPm9WQr37ntFhI9fWR8J/G99zrmhgZaHLlBafuWJCtu+iKRoJto\n3MyOrUkmT5Vx4IPO0xzZvrSOhL9b+PfgNzsWTeLr7GaSrJ3axrm8t7sZrVw87ifiATzubEZVqWoe\nsQ09lC2/7rTv7pnG5dHWbT+bfM3F0NqV9MBHB6XBQF9/thh6X7+Z/OpEiYwTvTNGyJMtRB8CbKYl\nvH+ga1R+00IDfyKWk33yRi9seS9J0Xz68HYf5MRHfxDatTO+PKxaacORyxp04hXPmEGst5dUNEom\nHscwfjz923aQikZFx6eiUVKhML3bthN0iCeZkVCCSCgh2pbfZzPpjBBEYrbKkBv0GHtCKFXqXOCf\nTksqLpbtU2rL6CqR84u9f+YflUbmVc6U+rDERWG4EnPDcWQWOsa6fD48/g+E/Yqkjr6NL5Ke2yQY\n5gFCH3VSVL+UBCFQ2dj0l5wtrL87gt7jZ9yCIN6+NyGRlQV0jv8CyYF3SGk0oNz0EsWhMBlAe/VV\n/H/2A+CDH7Xlsqp7+8TvdsCXbee/swDeiLSWGG0uiXG+urqaX/7ylzz88MMAHDhwgKam7CC+ZMkS\nduzYgVwuZ86cOSiVSoxGIzU1NTQ3N7Nnzx7uuece4dgnn3ySYDBIIpGgsrISgEWLFrFjxw7UajUL\nFy4EoKysjHQ6jcfjwWIZuVFmNFEq5fR4g0RiSdKZDBMqRl+vSavU8sbRd4T2TQ3LR/8eDgfhEzlZ\nDq1zdFNbSgoKwpZchIKwqWRUpDk/qPclMTaQy2VcNq2My6Zl3+fdnR8hH1BmDyejrGUf/+e6+/D5\no4T3Z387WTrDpI40jhMhQjYZH8gzRJO5CKdKUznQS9qW+4gqdNmYvWJFFPKWWyZZGF1/EM+bW7Hf\n8jViEfEQapZno/EHC8kC6JxOWv74NPYHrxf1rbDbRd/ExWzZlI0APHLQzZXLJ/HOhsNCe/kqqRjx\naBGPhkR//3gsfO6TzsGZnEVjEXuZifyCsPZRMO74nFPYvNnNYPTEF5ZKsksSEhJnp2mKE89BO9d3\nzSbR0wUKObKMAtuVVxB3d1OszmmXxqIpfL3FbNrkg4HS6MXOrDNcrTn9u6syFzH+3q/RJbPCCbdo\nn76qisMHuti07tiAXvE1mEp9FJuMeNr/lrtnrJf2Xz+LpqRkWIadfN12ucGA/t5/wRtXXbAu/Nnk\nay6GnIhkhB05mVQKlUIh9NutmxMsXHINFksEj0fHpnVeVqwUGyNTyggtLh/zltazfNVUurv82MuK\nqG8Y2qF6JgoN/CpNzkhSPrFMeEap8O9nm0jQdVp7OMb5s4032bXVx7T6OqipGMekRx4mdPw47c+/\nKBxrLbOhvH8NWn8aduWukb/2sdl1QE6CQ6VRICuQPszvs067js1vDqyHgKWLV6KvAWdYRsh9BIVO\ni2fPXrRTFhKRZ6XKonEzHcd1ZCKnQJ6h1ddx1iyA4XIxanxIfDa4GBJzg6giRmzaBSRkIVSYwJXE\nMrcJfdU4/AcPCgb6oHMqr73oAjRMnWUilidbbYj149m1G9s8cTCaIt1D10CxZchmuAwWX046S4QY\nRIUpZ1ezmsTfCVNx9t0unH+V1Y3Nde+nmUtinL/mmmvo6MhJQWQyufRqg8FAMBgkFAphMuUMCHq9\nXthuNBqFYwOBgGjb4Pa2tja0Wi3FxcWnXWOsGeerbSYSiQydfSHKSw3UOEe/ArVRqf//2TvzwLiq\n+95/Zt83jTTaZVne930BL4CNwS6LWWrALIkfoUlpSF5LaRKaNil9oc57DxqaUNrwKJDQJGYJSwKB\nGDAYcDA2NjbeN1mWJUujkTSLZt/u+2OsmbmyJVnyyJbt8/nHOnfOPffM+Nyz/M7vfH8yz3mLuvB6\n7alwOPuyA1SWDnyy2RfhUFQmzRAOFz4grMHkouXw29l03bRpBX+GYPDkB+C0VXp5bvcvMWoMLKiZ\njV1vZULJmKxmosOqZ19rA9Pb1YT+4zm0ZMzst969FL81M5iOLxrHirHzcK3x0NYRYNQ3v0aquRll\n3UhGjh9PqLGRFZeNxxtRYgy64d238My6mvDyWdgliRGWMEVX1hAKRCmzpolpzHSs/j5Ku4bqceNx\nJy186QlRdv/DGMxu3K0fZr+LzXEt7W55F+z3yg3G/mY3HZs9YkFVANJpKwHPB9m0uWTlWZeZLwwy\nnKfuc6ePQtMVpcMdwllqYsaM0WddZltE02daIBAI8kmmJUYdaqflv9cBGYO29o776QyB2WGBSJiF\nCyuJJaDYnMBBC1deXkRY0qAp12CrSXPNdWPwuIMs/bNx+L1hHGYVJc3baX71PVKhMOO//z1WfXUW\nzfubsGviVBjClF51Jbt/n/GOj0VTbFifomqOjSvmG2UOGMqUAWnN9/hTg4rS1FFmzxuBUp0bd2WG\nzpoaAodyQRJTi27kjd/npHYGs2jvTepEyIkMTzq3fk7jL3+FtqaGJdf9BZ3+GPG0njffaCAWPbk+\nUcpP3yoTOkaU2zi0z807r+3OXrda9af8//ZnJOy5aaNQQLHLItvAKVTgX2HkH770jI12prHSeutv\nuqVCu3lowTco3itfbytDUQzFFtreeCXr1a6YMpYufS2hsjm4XCaqSpRcf3UpLQ2dOGpKSSo0RCIJ\nli0fRaCtE3O4HVWqFcfCSqJxiYBf7gwXLqqixtjF8aidztpqnGYFLqOF5J5NKGZfg9vjxNh+FOXH\nL+AIhbn97msY1a5j+x+24IuqqRhTzrhJZQXbJBUIzlSOrLe+u69+NHTkCMd//SIgN557t35O5Vfv\n4mjrEYIlJvRRsrYwtVrJ4sVVhJpOYFNGKIm04QGUETXkHexXxfWy+qmMRkqWXoW+pobPTGaWd9yM\nwhoHqzn7Pie0Sa64Ziy+zjB2pxGDEWZMc1BeqsB1RQWdgRRFZgWu2Ak6Nh8XY0MBGRYBYZV5/4mh\nUAir1YrZbCYYDJ72eigUyl6zWCxZg35+XpvNhkajyebNz98fP/vZz3jyyScL8dXOiGZvhBfeyYUv\n+frNU5hZ4Gd0Rr04jUV4Iz4cBjudUW//Nw2QZFewz/TZYjTp2byxIZseCs15u2siddO+KtPuu1A4\n1+22J/mG89pyG3MnlQ04wFV/fLanhbXPbwXg8qsz7SuciLCp8XOuq7uW2RXTUCoyz0z7SvntumNU\nl52QadeODOs4rDFw1cjLmV05FaVCeVInvBzItanGF18i4fUSf+enVM2ZjXfr5ySWr2ZTk4nRE0x0\n+lOMqDZRcWAjnvfeJ7Lme2z4sCF7/5JldWz48ORi/UAL1y6vJSHlvD2iXdWkU3JpptIKuSeUobOJ\n/b/+zaAXVBcK56LtdvrLSOX9/nHf2W8eDkVA2KHAt2MnsYPHSKUNxANu/JbkoILF5WM063ukdWdV\n3oXI+e5zBYLBcj7a7vrNDYxuzskdphbdyFsbM1H9Js2oPKk3nHHcWbZ8FB0vPody1tWk0gZ0Jju7\n9gY4ur7bu76FSTMq+PzT46y4rDbrVRY6epQJt89hwtRcTI2Oz7bgUERkdZloMmFr01I09SsEmg8Q\nb+jAo5vChk9OBkrb2gwSzF04MntPT0Nn5S03Zf/uKYE3GA3xoZCvuRgZLv1utx537Oq72PBuRkd7\n6uwqYtHcCZAjR4yMHHkDOrUXxdFWEqoi5s0v4+P3DsnKOl176c9IeLpNm3GT5WUUKvBvoYz8lzJD\n1W4Hu249XX+TTkv4j6W5jluptupQN32Bdm8D1okTaPndm9m8zslTCJ04QWDRSg6nDdjNUdKU8MH6\n7kCTXpaMjlHkPYz9s63o7nuI9z5syt5/3ZUlRF96geTy1XzyZabPnzyjUlYfvTaKJ+Lig08as9eu\nvnoupQ2bMXccoEinJRI9QXLiRFQGPa6QHvdeHxsOn+zrNzUXdJP0Uma49LlngtwwbqYk0kzo6NGC\nGI57G6N7Gt09purT9t199aP5cWB6ykWFPW38h7Me0vDXxsV8+nFOpWLJwjLs658FIHlSktfT7iRV\nlVvv+iIlMhlSbbGS4y/8N9Ly1ew7nLNDxL46BvVkBfHGY/iw8tH6g9nPFi0bQ6ismFTEx4aNuaAP\nhsVOos8+ftrvJBgcw8I4P3HiRLZu3cqcOXP46KOPmD9/PlOmTOEnP/kJ8XicWCxGfX09Y8aMYcaM\nGWzcuJEpU6awceNGZs+ejdlsRqvVcvz4caqqqvjkk0944IEHUKlUPPbYY9x77720tLQgSZLMk743\nvvWtb/Gtb31Ldq2pqYmlS5cOyfevb5brvx9tLrwevM1g49dfvp5Nr55y9l6jPdEVy8MP6ZxFveQc\nHAG/3HM+ECi857wkpYnH/CRiflQaI5KUPuPAOuebc91ue3IuDJV76zuzf+tS8hMw7W4Nb//pKCsu\nH4lSqcgG6+yylsiM8+XjJjNp3Gw6t35O06ZXeh2wTSNqSUfjeNmalbnxpw2MnuDKBi06uBeuu3Y+\nlc4idkXl93d45e2zzRPni20xMgpwMabPCXNonzvbpouKTShJcse9M9ErD5CMe9CkDbRuKx70gupC\n4Vy03XAowqg6Dam4hEqr4Uj92fcfQxEQdig81BrbJTYc1tEtQTPYYHH52JRhWX/cLel0KXG++1yB\nYLCcj7Z7rDXA5PIKVCYjjpkzaTAWAxlDSE8d02ONQapuuI8NH7UAaajvZOHSWo7SkM3TfU9nSKL7\n7OzpApuGGxtJvvYGSxatxJ82UFlXjCW9j87OCDbLRKqn34Q3sY39DXJngrbWgCydb+hUmY0oRugp\n/85NqOJ6TMkyttWfyH4+GMP6UMjXXIwMl36325jSGc6NzwoFzF9cR2d7CI1ORf1BDyNq9KRJ45g4\nAW2Rg9aj7zFypIPNehWxaKYNn669FMJI2F/g3zOV8CiUkf9SZqjarUKhxFE6+YykbLpJp9MEHW1c\ndkslCa+CkSPKGDepjAN7Wvn01Yyx/BgRloyuIPTvL6D4/vdOiVHR9NE+NvypHp1ewYLFFoqLvCy9\n1sAnG5PEoinCJheuWAvFixdyzJeUPd/XlWTM395JWK9g6Sgdn2xMcmifm8VXjiDu9+PQpzGqOzns\nk78X7f4U6m3bSHjaqbz5Jjwfbsx+VnP3nfhDesQmaeEZTNs9X6dtem5qLhkdQ/FOxiP9bA3HYyeW\nsOYbZURCrRjM5VTVuYBTNy9jX/2+7L7udthXP5ofB0ZSKPHmyUXprHaurr6aalsFwT09Tti3dtBt\n2TSelPdu0rvYvL6DbnvD4iVqPspbA65wZe4Iqc0suUaZDRx7/LiPJzf4ABtftcjXyOFwgjtumcKG\nV7fLrndGVRjyDP+h5hNnvb681BkWxvnvfve7/OM//iOJRIJRo0axfPlyFAoF99xzD3feeSeSJPHg\ngw+i1WpZvXo13/3ud7nzzjvRarU8/nhmt+aRRx7hoYceIp1Os2DBAqZOzUhbzJo1i9tvvx1JkvjB\nD35wPr9mr5QWySVmXA5DLzkHj1Vj5sbx12Q95+3awuvaJ8KRTDT3zk60ziISkcLqtZvNGiLhTOAl\nBQpM5sLLKHiaPqNp/+uya6U1Cwr+nIuRoTBU9sRqyv2fb/lUYtVNqzncfhxtys7WTyFU10aRzcBl\nU8qzwTrXHdNyx4q7GasLUzphNEVz59C5ZWuvu9fZCUXDMZRGA5V/fgvRDi/6v/g7FCENFoMenV6d\n9Y5qPdKGRR2kqCRNPk6H3JO4yCWPkWC364hFk1lD/6QZFXS2h1i46ATuhpy0Utn9f4YxIRbpZ8vY\nMR20NfwhL33dWZc5FAFhh8JDrWdwuMEGi8unROEjaLLSCRSZ07gUgX7vEQgEly615VZO7ApTfest\nhI824DQrsxt8xaUWjtV3ZMdVjU5FZ0S+kI8E5Qaebr3ikmIjjltvxjp2DI5ZM+nY/FnWIGCfPZOI\n30c6FEbxzm+wA46/voP2xB4Auo7XgwSxY25KXHIZQ1eZfJ7cbehUmkw4v7ma9uAGyExJqao+e8O6\nkK+5sCiaO5vKu+4Apynbjg1GLdFwgoN7M3EPllyjI9n1e5JAyAeOxHS8rRmJpdVfvY3dB9VoHBJB\nh4e0VIoyzxmoEEZCWeDfmhpQKml85TXcjlpOBBM47TY2/C7nHdmbp3F/Rn7BhcUp0jWOb6BQlp+y\nIeRPG7CTOZFUc/ttsrmoL56ZRy5YrMageJewN2MK/LOVN/KHN4KYpSD+qql0hiSsZU7y9edrx8Xx\ndGyGaOaeBYuXsWF9jHQyicOQJvnbZ7DccD0lJXJ7iNGsQ7H8Tnjhp8Q7O2WfJQIB7CrIjxNm1yaQ\n0mmQpDM2FPe2SSqknQbG+Tpt01sbhrPfVPS378PT8BIAQQ9YrHocpZNPMbrbDXJng+6+2ziyTubB\nbhpZk82THwfm8Puf5IIg6/W06538/rdhoI1/uNIlK7us0oZhzmwMNdUoVCrURiOuYl12TNLq1MTj\ncvuEL67BCNRNhkBXLnBscc0tfG1MGkuXB5WlWnaP3p6xaZSPLoNNOWlyS4md9Vva6Db8rxxfRw2C\ns+G8GecrKytZty6jO1lbW8sLL7xwSp5Vq1axatUq2TW9Xs+//du/nZJ36tSpvPjii6dcf+CBB3jg\ngQcKVOuhwaBVcutVo+nwR3Ha9Oi1he/sj3edwBcNEE3GSEsSqXSy/5sGiMZkpPGFX2XTNffcVdDy\n1Rp11pAJcPX14wtaPkC0R2CdnmlB7xTSUJkfkKjGVpmVn6kts7J4RiWRWBKDTo056eRP73mBTHs2\n6NTZTYH8YJ2l5TbG58ns9BxI/Xv2Emo4htpqIRWLET5yNDMoGvQo9Xq6qqbw9ge5Y1yTZlRk26LL\nZUSVMKF+8xmW3Ph1vGEFTrsGV9M2liyeSFN7Go1OxZY/NXL1lRXEVAb8/hg+f5xFV4/GfaILjU7F\n4X1tjJ7gIhZpk9UtpY1RNOPsJEgEkIh5+kwPhqEICDsUHmoVY8plk6nuYHFngztuYcMnOYkK45JS\nKvrILxg4kiQRi/V/wkOpVKLVakmn03SF2vvN3xVqJ51O95tPICgk186rZXf7URIdxzN6qraR7Nmf\n2dQ/uNfNkmWjaGoOZsfD+YvrZPebdHDlQgsNiRSjHWWEPZ2suMxK8rX/wvq1e3HOn0fH5s84+NN/\nJ7XoRvztHlyh3RiL5SdmU9oo5MXpDHYcI9nUQbE/yJKFU+gMSRSZFFRF6ml8cWvWENNt6DzWqcAf\nOyaLM5LSxhg/QRjWLykkCZVChTKVkK1NpsyqyhpGSkrcBPO65HQq15/HUid4PvQOhICmjLZ3fjDL\nQpykyDf4dGz+jP2P/hhp+Wo2fJqRGBnbQwGlN09jmZH/pOe04MKl0d98Snpu1fRTNoS6T0SebjOm\n9OQaT6/1y/rTWKiVBbNrMejS/P79zHpGdzDCsmvr6HJ3UlysR5k6IivLWRRh0owqtn7uJhZNsmTR\nSoJHDlESinHV8vl0toewO43s3t7E6HIVZkDrlPvnpkZW4vc3cm3VJNqPdWJTRgg//WM69Rkb0Jka\ninvbJBXSTgPjfJ226a0Nw9lvKvYMvuz1NPLlTi2OskkoTUbSJ+X1yhQZWSd/2nBSC74JKKfdUMmG\nwydPA6LFPN5I6MWXTtnsGXnFZRxJpIg3HkMqreDnO3M2O4MyJivb0vAFnq2fYx49iuO/OWkHtYxg\nz/5csOWrl+Xk+QDsuiSWa5ag0co3MrTxZkre/g0AJbokS0a7ss+pDh2h8cUdaMqLqL1aTdKvAmsc\nf0DuiNvcHkdEazw7hoXn/KWOxaAlHE2hUirQaVVYTYXX7jVpjbx1cEM2vWry9QV/Rqytrc/02eLt\nCPVIh3vJOXj0PQLp9EwLeudsDZWnC/baTfeiZfbEMlISWV37tCRx3YJaNGoV8USCuLGVqKOJLU1R\nZldO5bIp5af13jf2GKATXl9WT7Hy1ptlgY2rb1+FLyl/J/UkmDfVgjnuQ6lWsidZTfnq+7G17qTL\nGMdyNI77nQ34r3FysD5nBIsmFXzyYW5SOmVWFaXlZtwtQSZMLaXE2YHeJJeDMtgqhXdGAVBrS+Rp\nTUkvOc8cpVLRaxsbLD3bprHm7D3Uxk0qK7hcgi+m7jMtOHsaGhp45Zf/QImzb4/Jji4Df/vwT5Ak\nieqGt3BqtX3nj8eRpFsKWVWBoF8USgWd5aMwnciMgUH0siPVwePNlLlctLbFGD3BRbArJpPO0noa\n6bA186a0i++cmEk67KAtbcA++xpCzSdg82d4t+8gtejGnIxX/XGuWuyi+pabCB9vQqXXI7XHZcHS\n0m0R2j/6hJKrrkTx/I+zR7J9J2PNAIz/3ndwXpYxcu5efxB1wieTyzvTIIyF4EylSARDS+fWz+k6\ndJBwXSlLrtFl27EvoKTLn6C41Ew6LTdcKFW5uaQ6ocOo1hM+GZS420DaTaFPUnQby/LjI2h18nG7\nN+/8fCO/4MKnxlZ52nT3hlB7m5/KknYIH0d/2f3YJ804pd8ZO8HFbWtmo0gclm1AReM2/CfaCFtz\nRtJYNEmgpZ1az3baX/mE4r+VO+8p0lbZBpc/baBEr6fTUsUH7xzIXp80owKnPYFz9W1YRo9m/Ngx\nhBozG0afWLz8yrON+1u82NdvBjKtvKeRGMC7fQegGJD3u5B2Ghjn67RNz03NkkgToaLbC7KpaDDJ\n107uVjUb/5hpnyu//j0c7j2YRowg1HAMxTsv5jz2i27HOXeOzKtfp1fT4Y3R1G7C7j3OeKUiGwtM\npVYxdvkVAGze1YLno5xkcKRmLBOcBkLHjhG3uUhHDDiWLiXekTtJ4otryJd3CvmCMoN+KR08VLyb\n/xO7VvZ91HmnqpMBP4qt72Mnc1qwdeUa2tvT2L1+Zpam8XQ2EFKZ0Rp7rFkNYi14tohfcBgQiaf4\n9frc4POXt0wp+DN8kR7HfHqkC4HGJveU1ljPXuIhH2OPTQvDEGxiaHUOXLVXkowFUOusaHWO/m8S\nAGdvqMzXrO8O9tpN96JFyrvW4Y/wn6/tyqbX3FnEi4d/BwH4Q/2pXkj5NFQa6Lx7KXZfgkprOdEj\n9RQvXoh323YSAfm7kYxEqJ5by6dbc57Wtq4mSkLH8ZiqeeujTHDlHXi5+va5PHf0p3yleCoOwK6K\nAlp0enXG2NBD6SkWSVBco0Ors2Ozuol519PRbMu0wWgXOk0RJVVi8lcIYgmj7N2Oxo3933QeOGyq\nwrPibixdHrosJajNVRSfZZlDIZcwFN74AjmJRAKtWkKnSfWdL57pWFQqFRMsFir0fUvjnYhGUKlU\nfeYRCArNlj2t7Djcgbk8s8DteaTaNWEJXZ0RPtqbGWt1ejULZhcTViRxFilw+GNY/Rrur7yLLqud\nj987SrcH2g11VRxf+yOKFy/Er68if2HaGVGg+eN6HDNnkopGMZumUlIzlZwj9rwAACAASURBVLCv\nCVXSgPel1wBQW+Uedyp9zvzu37sX52WZsbi03Mob65IsWJwJuOaqrD3jIIyFoL9AoYJzQ6ipCXNd\nHSMmgN+da8c1NTfw2ot+9EYNbnUJOvU1aFQ+nM5SIqkAlnQdipAS9/O/4tabL+MFMvPYngbTQtNt\nLOuelwIc2udmyY1jScUUIs7BJcTsyqk8tOAbstPJkJsruvcepKnpZADYAARSOvyRKt55bXe2jO5+\np/HVT1GWL8Uf7SIat7HpoyQLaxMoy4rIl7KxljlJuzP9cuizo1jrRiOZ0ihCSrpicocCV6kZ76vb\nCbhmya4btOCVDtI8xcXKSZnPuvvl8qaMXFSoxEx+aRmjsHzzMhUOs3/t/x6Q97uQdhoY5+u0zanr\nnfKs0ftsiXv9OMqmk07FUKp0uD25ebQvrmHa7bd110J2X3dbyffqHz3BxSefdK+heo8FNmtcMWuv\n1RBrPI6+poax40vQaCpwzp/HwXc+xPN8Jhhs8YoVSGu+R2dIwllsgvr6bBlFFiXmpuMUnZTJCbkN\n4IBoUxirKvceEs7VuzvWHkBq0Y28u6XbQVbLcqcF7R/XoQUMD8yTOVFYTOJU7tkijPPDgIYeQaca\nWgqv3eswWPtMFwKl1SLTyFLaCvsMjVbB5VeNossfxWLTo9UW3lMoEmolEfWRTsWQpDQRdSsOzt2i\n61ImX7O+Z7DX7kVLvgF/zsRSWZ6OmPykRk8vpHwaAsd5Kb2Le+xTUP53Tg6reNFCNPYeR+ImTqTo\n5E78iUMtWBVhSsJNhLvShE0uIFfvjqNRHjJ9BYPZh+W+rxI5cITrlywgIBn46IMGJs/ILb50ejU1\ndUU0u0OEI1H06oxGaSLmp63hQxymqaTawijGK/G6dxMJtmAwl2N3TbxgghQPK1LtsncbaXh6Gu6u\n9/LGISVQCq2wstzLvClDu2gfDGPHl7DyhjraWrtwlVkYO8HV/02CAaFUKvli53gs5r63Z7Rm7zmq\nkUAweI61+FEoFByuVmG5eykVKnmcGo22i/Q777Bk0iLCJhfGUBuql3/N5LvupGvPflo+/gRp+Wo+\nfs/L2IlyY05Tp0TRijvxf74e14qZUJ+bR7tGllB85214Ew78CS1BfTEf/OIAsagaSLBk1jIU7/wG\nbUkx4773d4QbG1EoVTS/los/pLHmPIrHTSpl5R0zcLd0YS62UD26DIXi3I0n/QUKlaQ0vra9Ys4w\nxKgNRo4+/QxFD6+WXVcS4Obb7WjUTXS06/B0lLBjS5R5c7VoO/ah/ePmbN7RURO3zbxeZiAdKrLG\nsqYTrBhTxYlggpJyC/NmjRabtZcYSoWSuVXTe10jRYKtsnRXoJX6erlD3IlDLRj3fozaYqVpT4wW\nfR2JWIrRE1RoXWna4lGmzanCYNASDseJRRMYqyrpKikhPH4BrY0d2FVRlB+9jnN+ihWXTaLNE8Hl\nMmJ370P1Z3+GVGaEnblxokQZxLSzGaNkJz0+ib99f7afm1UxmYcWfIMWfysl//Pr6Nv8mGpzRuHx\nD38nc7IqHMa7PRPUciDe70LaaWBcjKdtIl3NeH07smmNZi6cPEOXf+qot7Yydnxxdt2UUsvH5N5i\ngR39ZAP+f/8vAKLAUbWSsUsyHu/phsPZfJ3VM0DdQYXdTyLt4Jrrx9Hl7sRVZsEWO063oJrCaCRc\nO5OvtE8g4DKhikRpdycpMiuoq7Yw8uv3ET7WiKamhoC9Dm2nm3RRFZBzUPQntHS7t5l9R6gpH02H\nO4Kz1MS0BcJmdrYI4/wwoMiq7zNdCIKxEAtqZhNNxtCrdQRjwf5vGiBSPIGuuPhkQFgnUiLR/00D\nQKFU4vcFScRSSFIUvanwEdRVakOP9PD0rr0Yydes3/KpxP+48yu4w27MiiLwlZIoS7OnviObx9jj\nOG6NrRKO90j3QvdnZo9cKgmTEcmgp/r2VSTDEdKjq2nuaqXtV7+kePQ4/GNq0Bw7gJRIIMXjOEyS\n7HaLTYfO1oVOG0NrNdL6m+24nEW0hIvR6dWo1ArmL64jHk/gdGh598192XvHjC0lnicvrZasaMc7\naDrwJol4F4H2/aSSUeqmfRVH6eR+f0+BHL3RSiLilaWHI/lBj0+XHgxDEcjKu20bwZ/8H4xAEPDq\nhf5moVEqlThs5Tis/XgzGs6N4S2VStHQ0HBGeWtra4XBRyCjrtyGhIJgIoSzzoJCraKofDYgkUqG\n0ers2KdPRa2LEfd+ib68Au83vsee1i5s1dNRmrbTeVKSo6ccRzwp8f4hLdcuux37iT2svPEKfDEN\nrnILSZ+XxkRlnpeaWxY3JmorxXbPt9nZFKHaamHKqlV0fr4t62mv0utlcmPnO3Brf4FCfW17qd/5\ni2xazBmGhmQ4TPHSpaiNlehNs1FrjCSTITR6E+2N64kno+iBETU3sGML2JwanK5ptOUZ511jxjNh\n0tmNm2c6vmeNZSAC9gn6xGApz3d6Jxq3ndLnGvQQrnahUnTiLCvm49+6iUUzp/xMc0soNiZxK3QY\n9c0UWTOSTx51DdKt/4O33ut2ptJy/V3fgvqtqD57G8eSO2n1JQjXLeKlHc1cX+dk/gorUls7RdoE\nyVefwRsK4936OYbRtpx3P5l+bm7VdKgCJp36nTLzUwX71/7v7LWBeL9fjMZmwan0JRtnsFbi0Cez\nnvMqVTVXXFt0yqmj/LaSTkscOFmeQ5fA8OVGKgN+QpMWyp6bf/o4f4Nda4qhMhlJndSzjzQ2ZvNp\n8xQrrCUhor7MCS4toLetpMnlx2bTkzyozMn1Xn8P77/bvfkWYMqsKnZ9mYlBsrK4mODTz2TLjK24\nm6daS/n+OPnpXZs6lg0zUVRVw9jLZmTqLYImFwRhnB8GGHUqWZBLo67wC1qn0cFbh3Ka8/dMu7Xg\nz5AiEZmnUdWqwj4jFk3JNOkWOscUtHyAZDyAtzW3K6oznq2ghOBM6dasb2oLYNBp6GpJ8Nu32wAv\nsIVv3DwFX1dOF+bzfW7uv3UKYW0TQakTu0XHfVPW0OBrotpWwczy3uWhuo90avc2EMpbKMWLLbT/\n8teoTEYcM2eibtLS9fKrAHQCnhV3s65Ry8PlEcJ79yGVTWThwmrCCSUGkxaduplk13q6gC7AeetC\nkieCuCpqGW0zs39XCwsWqymy+jGaXOj0quxk9uABA6WuZbicYQwJFVq7jabjv8/WzVE2HW/rDrye\nRrHQHgQKKSh7t0trT3eA8PzTM+jxiLKz30QYikBWQn/zwmegxvaGhgY2vP4DSkvMfeZ1e4Isuemf\nGTVqVAFqKbhY8PgjrP/sKA/eXkS84Y90kBvXAPzsw3XZlUQPNNP59mckF61kw+bc0ewli1ZiV2Qk\nOQ7tyxjY9Xo10WiSw/syxp729gjJ995n/OxZTLtiHvt3tfDa60cY2+OkXSKWW2zqDXrePhncetvh\nerRFRYybPRPSqZzn22y5vML5pL9AoT2D1kWCLWLOMASkDUY66+ZSrgmSTiRpa/wo+1l+u9Zrfay8\nYRRTFo5HoVRQZLAV1PtWBKoUFJqSsZeDJNEV8dLabmTTR0nAzfKbJxMJJ9CSQKluIeT9Y/aeK6++\nlkQ8jV7rx2EJ0/GfLzL6L+8i0pmTfDKUXU/9Prnjnqc9TOS9DSj+8oe8915uXnnnspF49+zkF0c1\n3DEijsmzH28oF2uup3f/mfRzwvtd0B99ycZpHTaadubWkXXTpnLFpLEDKm/JaBeKre+j3LuX6+/5\nNl0q2ynjeM8N9sq//XMinhMowipw1rKlaQeN/mamVxZlFSvUGh/5yrlqVScv7X4bgH+Oz8+VbSgj\n3ws+/9BfhyeEdvlq/GkDdlWUokQAsOOU5AFuy3QKuPNUHX8xFhUGYZwfBjS1hfjoi5x2b0+P4ELg\njfhlnvO+iL//mwZIIhDoM322REKxPtOFIBnv6jMtGDq6Nes/3QX/8vyWU2RrWjxdjI80sbjOR7So\nlI6yOpzVAZ7/Uyay+B/qYZ7pej78QA20YV/TxmVTykmn03x+4kuaA60Y1HqC8RA19swRYkXFVDos\n5XgO7ydWakNzMsiwY+ZM2j/+BMec2bI6WLo8hGKlHNWWULzoRjbsUwKZd3f+4jo0Rl++1C260aVA\nGrMpRnvKwuKr1GjTmYlqxAeLr1rGu29njAQOsxLqA3T84g3GfvsBYlr5+5NOZdp7Mi3iIAyGZCLY\nZ3q4kB/0eES5jTkTz14DdigM6UJ/88JnoMb2dPrMtSQHkldwaXDC08XXJ8cwdHYQP3mte1zrJhZ3\nEzAfwnnrQg625gJXAkQdVYyKH2HlDePxxdWUlltRKBS8+NzWbB6bMgJAqPEYzsvmZSVgenp9jqg2\nUV49Drs2QdO+47LPumVihquXZH+e+z2D057LYLWXEq0pO5g6SUabTvksv127KkfiKMu58ha6XYmN\nckGhUarUlE68AldaIrmnlflX5DYCFUoFG17dTmlViGied72rOEBny6eQAF8nOFbMRErKDehphQeH\nqwadPsSCxWr0Wj9FDiuxtss57Jcb7TvawzjffIG7r7+HhioDk1zjYGvOyGmwlMm8+8+knxPe74L+\n6Es27tQNoVYcpX3HiexZnj9twA6kQ2HsJ3Yz82v/45R7em6wx5QddCnrwQxFIybx2KafA/CmWs/f\nz1yO2eNHEVeT/yRdPGd1j5bmPOyNJrkkoNGYS+vtVt77VEd3LJ8VV0/i+4pdKEJmXIk2igKBTCye\nxAhqsvr6OcRYVBiEcX4YYDPLddysZm0vOQdPibEEo9ZIa7CNcrMLi7rvxfhg0JWW9pk+W0wWudyP\nyTwEAWH1csOnRi8Moeeabu35nptUc1XtBN98gQSgAibd/22+DMgNrHGVDzBj1qnQH9lD4/4/kdQq\nUXhOMKKqgmfiG2mPZGZz3QFjiy+bR/HJgEIdm7fQAaSiJwMsGuRtzjDKhcmnZt0xuHdGGRzOneRI\nJJPEEjby77CUjcYxLePJ4d/VgipRT1d77vMyV5D504px2NVUattJOtKYvv0ARXPn4PPslT1bqS4n\nIlVhVtae+Y8pyHLqu20/TzXpm7MNrHw6hsKQLjyQLnwGamyXJIkNH1dhNvQ9LgYjXq64Qeozj+DS\nY5bCQ+S5XyJ99Vo4OQVVquTzuO60ZErLAlcCVE0awcgp82X5pbTEbWtm03y4FR1Jgp4OLCvuxDQy\nI9zRLQHT7Wlv0IAtcJyx9jDOuXOQ0mkUiozHfDc9ZWIuNOyuidRN+6pMc15QeHwJFXVFgVPaMIDW\nUIe9xElReR320tNobBQQsVEuGCp62wisGFNOPCRff/WUgZVMaZTuLshTi1Wli1FpVNx6hxl/y+8h\nAd42sNaNpkgln48UmTLGRVfawy8D2/lcrefWu5cyOmrCNWY8jrGz0BY5RT8nKCh9ycYNZuO7Z3m2\nChe+a+7Fropim3x6gbGe5eaPMelwa56zrZ69NombltxG/SsvY1XkgrtGvTlTfXutk8jdSzF7QmjM\nMVkAV6UlSdUcHVjjeLrkUr9t9W7s776JGyi58gq8JzfHxi+8/LT1FmNRYRDG+WGA2aCWyRiYDWev\nMdyTcDLEi7t/l03fMfnGgj8j4fPJAsImfL7+bxoAFqtOHhHaVnhtfpRaXLVXkowFUOusKJSF/7+4\nVEmlJbbsaeVYi5/achtzJ5WhVJ4aRK1be/7zfW4Wz6hErVKSTKXBvU9eXksTimL5wKZN2YEkt9ck\nCD/3HMZFC3M6a8A9X1/FT9gInD5gbLfBMXy8Ce/Wz/Fu207xooXETFpCNU5eSHzGdxfNQ9XaTtgh\n33wymoIQN6Av/TM02iAlpbW0eZx8ufMgpeVWVErQGayynW2VWkfMsBvFjIVUVMsHu/wFdjJlp7HJ\nTnGFmbEF8KS+FEmlVbJ3O50ansPfmb4nA8E+eyYl//PrRBobMdTUYJ9TAIkGKd/4OjyD617oJBIJ\nOv37iMdP9JlPUg3ulNpAje0qlYrykjHYrX1vvPsCbqE3LzgFvddNBPD+YTuOb96J3+fG7SmirHIp\nSD6kdIpA+/5MZlsprkoly6uKCcZ1VIwpP0W+BXLGIyDv6LgW54IqnORLwASwa5M4Og5imlKRlalR\nKJVMWTQRbVFRrzIxFxoKhRJH6WQhZTPE6Mt1qDVmAp6d2Eom46pZTDodJ54ow99qZdLCRSjVQ98P\nio1ywblm3KQytm+OEpGWodf6icZtJONy47zaUUm7L47XOZURChPtHjPvvRYmFu3ijrvkGtaSKY36\nlWe46s//Cp8/hUObQvPWMyQBqoogCOFklBfYxW0zr8/GaRD9nGCw9KYt35ds3GA2vvPLMxg1fPD2\nfmLRjGd69zylJ9bi8djHriQabMVusOM5tjH7mUZtYlPjewCYVHquDpbQuOslFGYj0sEYyVAIlV5P\nckYVt5Vlgo2fCLTy6/QucMKaoIPSLntGoiYeIeD38Y5iG3TBmtJ7ZfXoPokIoDDoqT6NlE0+Yiwq\nDMPTOnGJoVErKbEb6PBHcdr0aNWFD57QGvT0mS4EapOZhPekQV4BapOpoOVHook+04VAbyjCG2gk\nnYohSWnMttqCP+NSZcueVv7l+S3Z9N+vmXta7+C5k8r4hzVzUB7cjdS6mzatnf8+qmHlFTXk+2kc\niBl5+bUA8y67HmdpggllteAvpfJaP2Obt+El5wHfjaqlA05ugp8uYGz3kUfbjOnE1BBraiZeVsx/\nancxwqpgRWs14edeAEBTspVrb70Xny+F06Eh/cozJDzt+ADTN+/BrSyW6cxduWIcWqUKV9n0bDCZ\ngFZP6WWXM7Py1MllzwV2Td+ydoJ+iEX1KKXm7LudUgxPzfkzfU8GwrbW3TzmeT3jweTZzkMtpads\nTA0UoS049Gg0Gq7z7acyZugz36HqikGVL4ztgnOJprIKgPisq3n1lRCjJ9SRiKUocSYIdm7CWjwe\ns2MkWl05B464qTngxRzdh82gp7hqIQpl7/1gb0fRT/X8PNWL+XwHeBVcmOijCcI6C9bi8aSSISQp\nSSxcgfKTLdiqK1Gq+5Y7KBRCqkNwLugZ7HHG3Fls3h7ly3qYZLbg2dlJojRnrPcmi9hm3MK2w7v4\nSuIOtn+RcyJIJ22yshUhJUlPO76uTUTHV1HXpCE8cybG2hpaJ1fBp+9l855u7SYQDJTetOX7mg8M\nZuM7v7yN6w8Siyazn+VL5uSzrWU3j332KwD+ZcJNWIvHZ+0GyWjORniLNAbfk89n1Z3K776DRGsL\n8fJi9hcpWTVpBQCfH9/JPcopmD0hymxa/B+/gf1k/Iaq5Uu4X6ojVGJG5Yoy9apRRP1RRtkTJH/1\nZFZYUGGznFbKRv5dxVhUCC4J47wkSfzTP/0TBw4cQKvV8uijj1JdXX2+q5WlzRdGgVKWLjTlZpcs\nXWYuKfgzFCqFzEu55q7VBS0/FIjJAsJaLIWXtbG7JgBS3q7ohII/41KlW64mP306o6NSqWB06Dj7\nf5nRVHMA/3z/txm1dCFOm55gwzHaNHbWbUsQiiX58AO469rxzJszHqph3uRyOjYH8L5+Glmamipu\nq6il2laJ5Ctl3d79p/VOrt/0IZ7nf51Nr7h7KfVqHTZfCOlksBRXmZnUC09iDoWJAcWLFtLuybR/\nvduPG7mxIBKK8+mWGAsWl6DX+nFV1jJp1CwUioFvxomI6AOnod6AXl+SXThEowbGnJu184A40/dk\nIDT6m09Jn61xPtRw7JS0mJAJBILeCMfTFC9aSIOxmFi0KzufcxkdVGurSMYDKEJKDqhaqAvqaM2b\nTxrq6vDsajnFy62bnkfH7dpERrJGjIuCISCVlgi442zc4M/O6fTmMsLhIti/n+Jejv2DmL8Jhj89\n26hj1gxa17+H74sdqAx6Trzxe8Z8+wHmz5uDuiKG5a0t1Ccr2bYzRib0a4xp81RMcM1gn/owJqt8\ncz/cosGaHI1kAVNRDfWNB/HdvZTfKg/xN+5Kjj79TDZvzV2redR1M3tL0mgVNhoPGEh7WwpyqlRw\n6dKXtvxQ0ZdkTj75a7bg7haKqiwkFCnUkp6guyNraHcqJJmlIXrgML6T0jNj/jKnZT+iKULsv98H\noJOT9oqT8ytdKI72481oAfM3Kzhur6QjGmeSxot25syMGobVRnvJeJrXHzxl/iXGs8JzSRjn33vv\nPeLxOOvWrWPnzp2sXbuWp5566nxXK4vFqOMXb+UkO756XeENwha1mdsn30hrsI2yIdKcj7V3ytMd\nnb3kHBzOEnNW1karU1PUTxC7wXCpHQceCgmN3uiWq8lPf7qrJfvs2RNKObzPjbslgCEqoTQZSZ/c\n2dX73ajUKpzz53HQVMOmnc2EYrnBa0SPsrNHq5qaqby3joDXg76mmlFXLGWKSs2nu1r69E6ONDbK\nyqsJaegyFqEaMZn3/9gGpKE+wJJFK1G8kwlIm++lby1yQY9B2GTSctWKCUTCcczFVqpHl6FQDO63\nFl7LA8diUtPU6iQRs6PVqakoHZ7DX8/3pGfbHgw9PY0K4XmkctizG1V2VRS1/cLWaRYIBEOLzufB\nY6omqdYzeYaVQ/vcxKJJLDoJTchM/Fg7ipFVOIwKUg3yDUWPsoS3epxEc5XKj6KvvKGOpj0N2JQR\nwk//mE79A2JcFAwJW/e04nJqGT2hlKamFFpdMSq1gp1b67nmzr9hd0BB6a7WUzaRQMzfBMOfnm10\n5NfvkxnMixctzAZ7nFUxjf3VXuzNfjIRwU6ShkSTllsN95AwS1x3gwXPiSAlNjXJ156lzZMJwJVc\nfi3bp5uJJmPMUE9Ge1guidt18BDeX33OhPu/zd++2wa0AYU5VSq4dDlTQ3kh6UsyJ5/8NZpaq+P4\n4zlnwZo1X6Ho+VcA0C1eKJfK1WccEpUmE/FkMRtPGtOlI3KbhsJqpfrO20lJEu7fvZm9rjzRwc93\n7QJgm03HN2eOJnmiiWTNPN5/Oxf4vPuUAYjxbCgYntaJArNt2zYWLVoEwLRp09i9e/d5rpEcT2ek\nz3QhKDLZaO3wkJbSRJJRRtqqCv4MjV1uRNLYrL3kHByhYFTmOe90GfvILTgThkJCozfmTirj79fM\n5ViLnxHlNiQk/uX5rdnPv3fzVN5/Lfdu5hu+84OKHGvxZ/XoI7Ek40c4mNdjgMsereqlLv15JxtH\njJBJ6Bhqarhj6jL++NaXsvu6o64DGKuqUBuNSOk0yWgkOwgfq+8g2BVj0wdHiEWTskFtsIiI6AMn\nlkDWfxRfXXcea9M7Pd+Tnm17MMyunMpDC75Bo7+ZGlslsyunnnWZ7rSDDYdjQEY78foRDgYnriIQ\nCC4FfMXj2PBFI5CRVVy8dCSGrjZsTbto3fwpjpkzaW5twGUuPuVef0p+Cu7EMR8fvn1AdhTd4d5D\ncP2LQKZXEuOiYKhodAeoi8bZ80VH9tqUWZl1VcPRAAf3ugFOO98T8zfBcKdnGw0fkxv3UtFodl22\nZU8rWwM2ppZbmWQApUJBWpJIp+Vz7tvWzEYarSD55X6SJw3zAMmqOsZaTYSkTmrslai75OuzboNj\nuPEYkLMzFOJUqeDS5UwN5YXkTCX0JF8p80zXE1f5iB6XB2mNtbqzf3u3bcexaiXugJvikkq8r70N\nQGrRjbz7oYfuudayZXJd3JBrBGNvupr2zZ9xIpRT60g5aoDM8zz+GF+kRmGuHYMlKJeRPlbfkf0O\nYjwrPJeEcT4YDGKx5HbE1Go16XQa5TA5dmEyyoOOmoYgIOz0ykkkSWWNM9MrT9XdPFtUVossIKyq\nwMb5YCDWZ1owcIZCQqM3lEoFl00pz5a/bv1+2edtrfKghh3WKswLr6VoTJ0sqEhtuY1QNMlHX2Q8\n65bNHTFgb//+vJPrFi9BkqRsAM1RVyxFqVAyckQZWzmezVcxpgKddzYqvZ7W9etxzJxJ+8efMH7h\n5dlB2N3SxWcfHc3eU4ijcyIi+sAJheLydDDeS87zS8/3pCBlKpTMrZp+1lI2+fgiqj7TgouPdDpN\nV6i933xdoXbS6TSpVIqGhoYzKru2tlbo2l/kNPnTsvSxtjCLrCGiAT+OmTPxbt9O+X13U2S0c/jt\nJ7PzSfuM6QRqymFTzpteo8u0lfzxVIyLgnNFIJSgMyrvr2KRjAGju23C6ed7op0Khjs926ixtkaW\nts+Ynl2XHWvx4wvFORxScGJvG5NnVLLnixOMnSiPZeNu6WLR5Mlsl0D3zXvQuf2UjB6Pc94cpubZ\nY357YD+qFXczMuFBHwvh3b49U4eaEXAg51VfiFOlgkuX4Rxr5ugJPx9+kATMTBhrJF+I2liTk+VO\nhcL4jFY2O1XUWCqwXXUrBl8bQWsV3YZ5AG9MTXzF3Vi6PHRZSigflQlk65w7RxbA9YipGj7KOU1O\nqnNy2ZRyPn7vkKx+BpM2+7cYzwrPJWGcN5vNhEK5naf+DPM/+9nPePLJJ89F1QCwm7VZL2CDTo3N\nou3/pgEyFMaZnpQvu5qWeIJwUxPGqirKl11d0PJdPY4gucoKa/y/0BlMux0KCY3BPrvn/2+bpOaZ\n1lL+fvkkmX5ZITyL+ytDpVIzdsm1p9zXc6d97AQXXmucUMMxLBPGk4xGGL/wctlmwlAcnbvYIqKf\niz7X2EMGq2daMDAqxsiNZRVjht8Ed6g513OFQjNQY7skSVQ3vIVT2/ccpSMeR5Juob6+npee/Tuc\njr5PuXV4w9x27/9lzJgxA6q/YPCcj7brKJb3uRVVdqoXzyRQ4SJ07BjFeWOn4tsPyMa3chSyk2iH\n92WkDfLH04ttXBScnuHQ706qK6J+f1J2rbjcQlVdEZvyDBmnm++JdnppMhza7ZnSs406Zs9CV1Qk\na7Pd67LachtNbbmzxof2uZk0o4KiYlP2BAlk3gWlQsns6qlQ3fvpzUqXjUf/oMSkq+SOmjhjl7ko\nnTAa++zZ/H1lW0FPlQrOjAup7V4M5NtH1h3T8s/3fxu93517F52ZSBBohwAAIABJREFUdzFqK+UH\nHwUJxcxoZphZt98PlLKyXG7eHVnnxFfnzL47c06+Oz0DuDrS0mltI64yS1ZWWqNTUVom5l1DiUKS\nJOl8V2KoWb9+PR988AFr165lx44dPPXUUzz99NMDKqOpqYmlS5fy/vvvU1VVWEmYeDzF7zfV09QW\npMpl5oYFdWi1woOsJ+lkms83H6OtNYCrzMrs+SNQqofH6YfhSn/tNp2W+Oyk5nx3R3yuAuz0fPbc\nCaUcOqk5rzZqaYnFqXRZz2mdhgIpLXFgT6vs6FxPDVLBqRS6z41Fk3y8qZ5OT4iiEhOLFtSh018S\n+9NDgmjXp6eQ7ba+vp7P//IBKg2GPvMdqq5gzZM/48iRI2z/q29Roe87/4lohJlP/YxUKsW6NV87\nI2P7Hc//FyqVasDl//A7/4XZ4OgzfzDi5ZH/8zXGjh3bZz7B0DKU81woTB8s+h3B6RjqttuTdFpi\n694WOpoCRLtiWIsM2MoszBzXPY8V7VPQP+e63Q4FmXehlWZPF/pYGimWpK7OybgJpRwcxLtwPtel\ngjPnYmi7w5UzfQfy842ssJGWJI61BBhZYcMqQVuBxiEx7zq3XBKWiWXLlrFp0ybuuOMOANauXXue\nayRHq1Vx61XCY6w/lGolcxeOPN/VuKgYCgmNs3n2cD1idjYM56NzlxI6vZqrlwrjX6EQ7frCR6VS\nMcFiOSNj+2AkZ1QqFeUlY7BbS/vM5wu4haTNJUAh+mDR7wiGA0qlgnmTK2DyqZFWRPsUXEpk3oVy\n4NQ2P5h34XyuSwWC4cCZvgOny3fZlNyYNKFA75CYd51bLgnjvEKh4JFHHjnf1RAIBAKBQCAYEtLp\nNG2x/mOxtMViwyrujkAgEAgEAoFAIBBcylwSxnmBQCAQCASCixlJknhxhg6dvW9P+JgPll38ioYC\ngUAgEAgEAoFAcEEgjPMCgUAgEAgEwwxJkuhMxNH0o+3o7coEY1OpVNjqnBhcfQc7jrQFz4mMzEAD\nzqZSKRoaGs6o7NraWiGFIxAIBAKBQCAQCAbEyy+/zKpVq/j4448JBAJcd911A7r/X//1X7n77rtx\nuVyy65Ik8YMf/ID/9b/+16DqJYzzAoFAIBAIBMOMVCrFuokadLa+A7a6UueoQgNEkiSqG946o4Cz\nknQLDQ0NbHj9B5SW9L254PYEWXLTPzNq1KhCVlcgEAgEAoFAIBBc5Dz77LOsWrWKRYsWDfjeY8eO\nEQ6HTzHMQ0ZOfe7cufzud7/jxhtvHHDZwjgvEAgEAoFAMMxQq9WYxzkxllr6zOdos56jGg2MwQSc\nLS0xU1k2PL+PQCAQCAQCgUAgGN689tpr/Pa3vyWZTKLVatFoNHR2dvLXf/3X+Hw+Wlpa+OEPf8j0\n6dPxeDxMnz6dZ599FoCmpiYefvhhFixYwGOPPcaWLVuorKykpaWFdevW8dJLL3HNNdfQ1dXFPffc\nw+uvvw7AV77yFZ588kmWLFnC/fffL4zzAoFAIBAIBIILj3Q6jdsT7Def2xNkQjp9DmokEAgEAoFA\nIBAILjSqq6v55je/ya5du1ixYgU7d+7kueee44knnuDnP/85jzzyCK+99hoKRUY+NBAI8Otf/5ov\nvviCZ555BofDweHDh3nppZc4fvw49913HwDbtm3jvvvuw2KxUFtby/79+zGbzdjtdqzWjINRe3tG\nslOpVA6ozsI4LxAIBAKBQCDok3Q6TVss1m++tlhsUBNSSZLY8HEVZoOjz3zBiJcrbpCERr1AIBAI\nBAKBQCA4hZEjR2Kz2fjkk0/YuHEjkiSRTCaBzJqjJ2PGjAHA5XIRi8VoaGhgypQpQMbQ73Bk1ieB\nQCD7980338ybb76J2Wxm5cqV2bKsVivBYDBrrD9ThHFeIBAIBAKB4BJjoMZ2SZJ4cYYOnb1vmZqY\nD5adZtLbHyqVivKSMditpX3m8wXcqFQq6uvreenZv8PpMPaZv8Mb5rZ7/2920i0QCAQCgUAgEAgu\nbl599VXGjBnDmjVreOONN1i/fn2vebs96LsZNWoUv/vd7wA4fvw4Xq8XAJvNRjgcxmg0snDhQv7z\nP/8To9HIX/zFX2TvjUQiAzbMgzDOCwQCgUAgEAw7JElCSqVJJ/uWcEmnUyf/TRPtDPdbbrQzPChj\nu0qlwlbnxODqO2BrpC2ISqU6J572u/eNOyNP+1WS8LQXCAQCgUAgEAguBRQKBZdddhkPPvgg69ev\np7y8nM7OTgAmTZrEgw8+yOLFi3u9d9y4cYwbN47Vq1dTWVmJXq8HYPbs2ezevZu5c+eiUqmYOnUq\nqVQqu24Ih8MUFRUNqs7COC8QCAQCgUAwzEgmk3g/1BDS920klozRzL+SRPCLUcT6MVYnIl6k1QM3\ntg+UgRr/0+k0XaH2fsvtCmV0HIWnvUAgEAgEAoFAIMjn5ptvzv795ptvnvL5Y489dsq1uXPnAlBZ\nWcn/+3//j46ODkaOHMnf/M3f0NzczPe///1s2S+88EI2fyqV4qabbsqWs379etnzB8J5M86/++67\nvPPOOzz++OMA7Ny5k0cffRS1Ws3ll1/OAw88AMCTTz7Jxo0bUavVPPzww0ydOhWv18tDDz1ELBbD\n5XKxdu1adDodGzZs4KmnnkKtVnPrrbeyatUqJEnin/7pnzhw4ABarZZHH32U6urq8/W1BQKBQCAQ\nCPpFo9Fgcl2O3lrWZ75SwwkgIwtjLhmNvh9jdfSksXqgnvbdf59pfoVCgc5uQF/UtzEcMh4qkiRR\n3fAWTq22z7wd8TiSdEu/ZfZkMJ72H3/88RmVvWjRIuFpLxAIBAKBQCAQXATY7XY2btzIb37zG5RK\nJd/5zncAqKurw2Aw4Ha7Wbt2LXq9nsmTJwOZtcbWrVv50Y9+NKhnnhfj/KOPPsqmTZuYMGFC9toP\nf/hDnnzySaqqqvj617/O/v37SafTfP7557z88su0tLTwrW99i1deeYV///d/54YbbuCmm27i6aef\nZt26ddx11138+Mc/5tVXX0Wn07F69WqWLl3Ktm3biMfjrFu3jp07d7J27Vqeeuqp8/G1BQKBQCAQ\nCIYFA/W0B4Y0v0KhwKnV4tLp+q27QqEYck/7hoYGfv7E22dkzK+urqa2tlYY8wUCgUAgEAgEggsc\nlUrFT37yk9N+1m2of+KJJ2TXFQoFjz766KCfeV6M8zNnzmTZsmW8+OKLAASDQRKJBFVVVQAsXLiQ\nTZs2odVqWbBgAQDl5eWk02k6OzvZvn07999/PwCLFy/miSeeYP78+YwYMQKzOXM8e/bs2WzZsoUd\nO3awaNEiAKZNm8bu3bvP9dcVCAQCgUAgGFYM1NMeGNL8qVRqwDI4pkOvYVVr+syfTiZIpTLHTQdi\nzAcwGx1YTMV936DI6P0P1Jg/atSofusiEAgEAoFAIBAILn6G1Dj/yiuv8Itf/EJ2be3ataxYsYIt\nW7Zkr4VCoaxRHcBkMnH8+HH0ej12u112PRgMEgqFsFgs2WtdXV2yawBGo/G019Vq9aACj6VSmYBr\nra2tA7pPIOiNsrIy1Oqh3R8T7VYwFIi2K7gQudDabWtrK7FQByj6nq8Eoh00NTXhdruJhzr6LTce\n6sDtdmf/Hk75z1QGp729nVQqxeYpJjSWvmVwEl1xbvV4AM7YmN/aumDA+VUq1Rkb891uN7ozOCHQ\nzYXWdgUCODftFkTbFRQe0ecKLkREnyu4UDlXbXe4o5AkSTofD96yZQsvvvgijz/+OMFgkNtvv523\n3noLgF/+8pekUik0Gg2xWIyvfe1rQEZ8/7nnnuPee+/lmWeeoaioiP379/PEE0/w4IMP8thjj/H0\n008DmU2AWbNm8cUXXzBt2jSWL18OwJVXXsmHH37YZ91+9rOf8eSTTw7dlxcIgPfffz97WqQQiHYr\nOFeItiu4EBHtVnChItqu4EKk0O0WRNsVnBtEnyu4EBF9ruBCZSja7oXIsDDOQ8bw/tOf/pSqqiq+\n8Y1v8MADD6BSqXjsscd49tlnaWlp4a/+6q94/fXX+dGPfsTkyZOzmvNKpZI1a9Zw3XXX8fLLL6PX\n61m9ejX/8R//wY4dO/jggw9Yu3YtO3bs4Kmnnsoa8AdCNBpl2rRprF+/fsh0QpcuXcr7778/JGVf\nTM+4WL7Dnj17hnyHsNDttlC/SyF/X1Gnc1dOd1kXYtvtZijebVHmhVHmhdRuz/Y3KMRveL7rcDF8\nh0LV4UJqu/0h5ojnv/xz8Yxz1W7h3LXdnpyL/6fh8txL5Zndz71Q+tzhMkZd6HW4WL7DhdTnnu/f\nazjU4WL4DoWqw7lqu8OdYfMLPPLIIzz00EOk02kWLFjA1KlTAZg1axa33347kiTxgx/8AID777+f\n7373u7z00ks4HA4ef/xx1Go1Dz/8MPfeey+SJPHnf/7nuFwuli1bxqZNm7jjjjuAjEf9YNDr9QCM\nGDGiAN+2d87FjtHF8IyL4Tuciw5oKNptoX6XQv6+ok7nrhy4cNtuN0Pxbosyh3+ZF1q7PdvfoBC/\n4fmuw8XwHQpRxoXWdvtDzBHPf/nn4hnnaqF9LttuT86Xp9/5eO6l8ky4sPrc/8/evYdHVd3743/v\nyWRmkpnck8mFkATCHUGQEKhQSsEL1vZYlaihWDlQL+d3YvuUR6uoLdJzqmi1rZVi9fG0Vmr5gq2K\nx9oeihxR4VgCCggY7iSQ+z2ZSWaSmb1/f4RMZk8mk7nsueb9eh4fWXt21uyZWXuttT977bUioY2K\nhWOIhc8QbXVuuL+vSDiGWPgMSuQRjYH5o0eP4rnnnsO2bdtk2/fu3YutW7dCrVbj9ttvR1lZmdd5\nhu1bKC0tRWlpqSM9e/ZsxwKxzioqKlBRUSHblpGRgVdffXXYvkuXLsXSpUtl2wRBwKZNm5Q5aCIi\nIiIiIiIiIiKKaDa7CJtNhE6rTPj71Vdfxa5du6DX6+XvY7Nh8+bNeOutt6DValFeXo7ly5cjPT3d\nq3x9WxWViIiIiIiIiIiIiChCnappxyNbPsa9T+/B3/7vAkQx8FndCwsL8Zvf/GbY9nPnzqGwsBAG\ngwHx8fGYN28eKisrvc6XwXkiIiIiIiIiIiIiignb3j+J0zUd6Oi24qW/HMOZS+0B53n99de7XW/B\nZDIhKSnJkdbr9eju7vY637gnn3zyyYCPbgxZsGBBVOcfK+/BzxC+91IqLx5TdOajdF7heC/myTyD\nTYn3CjSPWDiGWPgMkXIMkfResdC/4mcIf/7hfr9wvWe43nesvGeo3zcS2gceAz9DON4v3H8fCccQ\nC58hUo7BHbso4Z0Pz6LT1OfYtmRuPnIy9B7+yjvd3d3YvXs3Vq5cKdu2b98+fOtb3wIwMP/8hAkT\nUFxc7FWegiRJgY/rJyIiIiIiIiIiIiIKs0+O1OL5Px2GzS5hWcl43HfrLOh18QHnW1tbi/Xr18vW\nTbXZbLj55pvx5ptvQqfT4a677sJvf/tbGI1Gr/KMvmVxiYiIiIiIiIiIiIjcWDxnHMZnJ6HH0o+i\nvGQkaAMPzA8SBAEA8N5776G3txdlZWXYsGED1q5dC0mSUFZW5nVgHuDIeSIiIiIiIiIiIiKikOOC\nsEREREREREREREREIcbgPBERERERERERERFRiDE4T0REREREREREREQUYgzOExERERERERERERGF\nGIPzREREREREREREREQhxuA8EREREREREREREVGIqcN9AEREREREREREREREkchms+Gxxx5DbW0t\n+vv78cADD2DZsmWO1/fu3YutW7dCrVbj9ttvR1lZmdd5MzhPRERERERERERERDHDJtphE/uhU+sC\nzuvdd99FWloann32WXR2duLb3/62Izhvs9mwefNmvPXWW9BqtSgvL8fy5cuRnp7uVd4RFZy/7bbb\nYDAYAAD5+fl44IEH8Oijj0KlUmHy5MnYuHEjAGDnzp3YsWMH4uPj8cADD2Dp0qWwWq14+OGH0dra\nCoPBgM2bNyMtLQ1HjhzBU089BbVajWuvvRYVFRXh/IhEREREREREREREFCRnWy/id5/tQHNPG+6Y\n+U0sL14EleD/7O433XQTVqxYAQAQRRFq9VBI/dy5cygsLHTEtOfNm4fKykrceOONXuUdMcH5vr4+\nAMDrr7/u2PZv//ZvWL9+PUpKSrBx40bs2bMHc+bMwbZt2/D222/DYrGgvLwcixYtwvbt2zFlyhRU\nVFTg/fffx9atW/H444/jySefxJYtW5Cfn4/77rsPVVVVmDZtWrg+JhEREREREREREREFyfZju3C2\n7SIA4NXD21GUmo/JmRP8zi8hIQEAYDKZ8IMf/AA//OEPHa+ZTCYkJSU50nq9Ht3d3V7nHTELwlZV\nVaGnpwfr1q3DmjVrcPToUZw8eRIlJSUAgCVLluDAgQM4duwY5s2bB7VaDYPBgKKiIlRVVeHw4cNY\nsmSJY99PP/0UJpMJ/f39yM/PBwAsXrwYBw4cCNtnJCIiIiIiIiIiIqLgEEURHZZOR1qCBIvdGnC+\n9fX1uOeee3DrrbfiG9/4hmO7wWCAyWRypM1mM5KTk73ON2KC8zqdDuvWrcN//dd/4cknn8RDDz0E\nSZIcr+v1ephMJpjNZtndiMTERMf2wccHBu9QOG9z3u4Pm82Gy5cvw2az+fkJiUKP5ZaiFcsuRSOW\nW4pWLLsUrVh2KRqx3FK0YtmlaKFSqbBy5s2IU8UBAL5WtBDFaYUB5dnS0oJ169bh4Ycfxq233ip7\nrbi4GNXV1ejq6kJfXx8qKysxZ84cr/OOmGltioqKUFhY6Ph3amoqTp486Xh98K7DSHcjDAYDzGaz\nY1tSUpIjoO+672hefPFFbNmyxe1rH3zwgWMkPlEkYbmlaMWyS9GI5ZaiFcsuRSuWXYpGLLcUrVh2\nKdp9pWAexiXnotfWi4KUcUiID2xR2JdffhldXV3YunUrfvOb30AQBNxxxx3o7e1FWVkZNmzYgLVr\n10KSJJSVlcFoNHqdtyA5D08Po+3bt+P06dPYuHEjGhsbsWbNGowfPx7f+973UFpaio0bN2LhwoWY\nP38+1q5diz//+c+wWq2488478c477+CNN96A2WxGRUUF/vrXv+LQoUPYuHEjbr31Vvz6179Gfn4+\n7r//flRUVGD27Nk+H9/ly5exfPlyVkIUVVhuKVqx7FI0YrmlaMWyS9GKZZeiEcstRSuWXaLgiJiR\n8ytXrsSGDRuwatUqqFQqbN68GampqXjiiSfQ39+P4uJirFixAoIg4O6778aqVasgSRLWr18PjUaD\n8vJyPPLII1i1ahU0Gg2ef/55AMCmTZvw0EMPQRRFLFq0yK/APBERERERERERERGRkiImOB8fH4/n\nnntu2PZt27YN21ZWVoaysjLZNp1OhxdeeGHYvrNnz8aOHTuUO1AiIiIiIiIiIiIiogBFzIKwRERE\nRERERERERERjBYPzREREREREREREREQhxuA8EREREREREREREVGIMThPRERERERERERERBRiDM4T\nEREREREREREREY1AFEU89thjKC8vx3e+8x2cPXtW9vrevXuxcuVK3HXXXXjzzTe9zpfBeSIiIiIi\nIiIiIiKKGaLNBpvFolh+e/fuhSAI2L59O37wgx/gF7/4heM1m82GzZs347XXXsO2bduwY8cOtLW1\neZWvWrEjJCIiIiIiIiIiIiIKo+7TZ3D+lVdhbW5Bwao7kX39dRBUgY1Rv+6667Bs2TIAQG1tLVJS\nUhyvnTt3DoWFhTAYDACAefPmobKyEjfeeOOo+TI4T0REREREREREREQxofqPb8B0ZmDamXMvvQJ9\nURGSpk4JOF+VSoVHH30Ue/bswa9//WvHdpPJhKSkJEdar9eju7vbqzwZnCciIiIiIiIiIiKiqCfZ\n7ehv73TaIMFuVW56m82bN6O1tRVlZWV4//33odPpYDAYYDKZHPuYzWYkJyd7lR/nnCciIiIiIiIi\nIiKiqCfExWH8XWUQ1ANj0rO+vhSG4kkB57tr1y688sorAACtVguVSgXVlalyiouLUV1dja6uLvT1\n9aGyshJz5szxKl+OnCciIiIiIiIiIiKimJC56FokjM+HvacHiYWFUCckBJznDTfcgA0bNmD16tWw\n2Wx47LHHsHv3bvT29qKsrAwbNmzA2rVrIUkSysrKYDQavcqXwXkiIiIiIiIiIiIiihn6ggJF80tI\nSMCvfvWrEV9funQpli5d6nO+nNaGiIiIiIiIiIiIiCjEGJwnIiIiIiIiIiIiIgoxBueJiIiIiIiI\niIiIiEKMwXkiIiIiIiIiIiIiohBjcJ6IiIiIiIiIiIiIKMQiLjjf2tqKpUuX4sKFC6ipqcGqVauw\nevVqbNq0ybHPzp07cfvtt+Ouu+7Chx9+CACwWq34/ve/j+985zu4//770d7eDgA4cuQI7rjjDqxa\ntQpbtmwJx0ciIiIiIiIiIiIiIpKJqOC8zWbDxo0bodPpAABPP/001q9fjz/+8Y8QRRF79uxBS0sL\ntm3bhh07duDVV1/F888/j/7+fmzfvh1TpkzBG2+8gVtuuQVbt24FADz55JP4xS9+gT/96U84duwY\nqqqqwvkRY5pkt6P103+iZsdOtH56EJIohvuQiKIKz6Hg4XdLYxXLPhFRaLC+pVjG8k0U2XiOho7z\noHJne/fuxcqVK3HXXXfhzTff9ClPtZIHGKhnnnkG5eXlePnllyFJEk6ePImSkhIAwJIlS7B//36o\nVCrMmzcParUaBoMBRUVFqKqqwuHDh3Hvvfc69n3ppZdgMpnQ39+P/Px8AMDixYtx4MABTJs2LWyf\nMZa1VR5C1dPPOtLTNvwIGQsXhPGIiKILz6Hg4XdLYxXLPhFRaLC+pVjG8k0U2XiOume3i7DbRGi0\nyoS/XQeVO2/fvHkz3nrrLWi1WpSXl2P58uVIT0/3Kt+IGTn/1ltvISMjA4sWLYIkSQAA0elOj16v\nh8lkgtlsRlJSkmN7YmKiY7vBYHDs293dLdvmvJ2Cw1xd7TFNRJ7xHAoefrc0VrHsExGFButbimUs\n30SRjefocLU17fj9lv148em9OPx/FyGJUsB5Dg4qNxqNsu3nzp1DYWEhDAYD4uPjMW/ePFRWVnqd\nb8SMnH/rrbcgCAL279+PU6dO4ZFHHnHMGw8AZrMZycnJMBgMMJlMbrebzWbHtqSkJEdA33Xf0bz4\n4oucn94P+sIil3RheA5kjGK5jX5j9RwKRdkdq98tBU+01Lks++QqWsoukatIL7usb8mdSC+33mL5\nHntipeyOFTxHh9v7fhXqajoAAH/9yxfIzktBfmGa3/k5Dyr/7W9/K3vNZDLJBpL7OjhckAaHqUeQ\n7373u9i0aROeffZZrF27FvPnz8fGjRuxcOFCzJ8/H2vXrsWf//xnWK1W3HnnnXjnnXfwxhtvwGw2\no6KiAn/9619x6NAhbNy4Ebfeeit+/etfIz8/H/fffz8qKiowe/Zsn4/p8uXLWL58OT744APHNDkk\nJ4ki2g5WwlxdDX1hIdJL50NQRczDGWMSy2104Tk0ROmyy++WQiES61yWffJGJJZdIm9EUtllfUve\niqRy6y2WbwKis+yOFTxH5URRwsvP70Nzw1CA/O4HFmLC5Cy/81y9ejUEQQAAVFVVYcKECXjppZeQ\nkZGBU6dO4fnnn8crr7wCYGAN1Xnz5uGGG27wKu+IGTnvziOPPIIf//jH6O/vR3FxMVasWAFBEHD3\n3Xdj1apVkCQJ69evh0ajQXl5OR555BGsWrUKGo0Gzz//PABg06ZNeOihhyCKIhYtWuRXYJ68I6hU\nyFi4gPNaEfmJ51Dw8LulsYpln4goNFjfUixj+SaKbDxH5VQqAUuun4K3//QZRLuE2SX5yBufGlCe\nf/zjHx3/vvvuu/HTn/4UGRkZAIDi4mJUV1ejq6sLOp0OlZWVWLdundd5R2Rw/vXXX3f8e9u2bcNe\nLysrQ1lZmWybTqfDCy+8MGzf2bNnY8eOHcofJBERERERERERERFFlJlz8pCVbYDVYkN2XhI02njF\n8h4cQf/ee++ht7cXZWVl2LBhA9auXQtJklBWVjZsXnpPIjI4T0RERERERERERETkD2Pu6OuO+mNw\nUPmECRMc25YuXYqlS5f6ld/YnYCIiIiIiIiIiIiIiChMOHKeyIkkiehoOoleUz0SDLlINc6AIPAe\nFoUPy6Qy+D1SLGA5JiKKXKyjiYKP5xnREJ4PsYPBeYoaNpuIzz6tRlNDF4y5yShZUAiVWtmKp6Pp\nJNobj0K0W2ExNwEQkJY9U9H3IHJHFCWcPtGAxvpu5OQlwZjVil5TPeLiE1F/9u+w2ywAgIlX34OU\nrJmOfbNzkzF1ZjYElRDmTxDZ2htPoKPp2NC5LQFpOVeF+7CIfNLRdBLnj/7BkZ549T1Iy/a/HDvX\nO4HUJXa7HbXnPkOvuQEJhlzkT5wLVVyc38dFRBSNRqujPdW5rq9NmZGFzpYvGXChsFOqr+ANb/oT\nSveFKLaFsvyGgxLnw0hxCLY9ocXgPEWNzz6txt/fPj60QQJKF08Y+Q/8YDE3oL3hiCOdYMgGwOA8\nBd/pEw3Y+dohAMCyG7Qw1f3D8VpazhxHuew11aOxKcOxLwDcsaYE02blhvaAo4y5s152bsdrsxic\np6jTa6oflg7kgtS53gH8r0tqz32G5os7AQCmZgCShIIp8/0+LiKiaDRaHe2pznV9bc39OY56FWAA\nksJHqb6CN7zpTyjdF6LYFsryGw5KnA+e4hBse0KHt0AoajQ1dHlMK8HW1+MxTRQsjfXdjn/rNJ2y\n10S71fHvBEOubF/XvyX3+qwmj2miaJBgyPWY9pVSdUmvucFjmohoLBitjvZU57q+NqxedQnAEIVK\nKK87vOlPKN0XotgW69fNSpwPnuIQbHtChyPnKWq4rrJszFF+1WVD2gQ01XwsSxOFQrZT+bb2p0Dn\n9FpSxlQkJo93PFqWndvk8rdJITrK6CWox3lME0WDVOMMTLwSacnIAAAgAElEQVT6HtmjpoHIdmlX\n/a1LEgy5AyPcBtP6nEAOi4goKo1WR3uqc11fG1avMgBJYaJUX8Eb3vQnlO4LUWwLZfkNByXOB09x\nCLY9ocPgPEWNa+YXwNZnR3NTN7KMSZhXWqD4e6QaZ7Kxp7CYOjMbd6wpQWN9NzLzkmDMzEV7yyXY\nxDR0dE3AlBlD8+M575udm4SpMxkIG8344msg2kX09TZBk2BEQfE14T4kIp8Jggpp2Vcp9nipEnWJ\nKEqw9I1DUu5tEPuakZiUi3yeX0Q0Bnmqo0VRgiBIWHrTVPSa+1A4MUNW57rWx/kTjUhK1vGahMIu\n2NcdzvNd547LR1bRHQNzzutz3PYnlO4LUWyLlevmkebOV+J8GBaHyMpj2zOK2267DQaDAQCQn5+P\np556yvHa3r17sXXrVqjVatx+++0oKyvzKk8G5ylqnD3VhD3vfelIp2fqFZ8vjI09hYugEjBtVq6j\nTFd9Aex87SKAFgAtsvnxXPel0Z073YKdr7VgYDa3FtyxpoXfH415StQl8rk8BdyxJp+LwRIRuXCd\n97hwYoZsUUJ39TGvSSgSBPu6w+2c4HO5bg0pI1aum4M5d/7w7yg3ptoeSbRDFG2IU2sVya+vrw8A\n8Prrrw97zWazYfPmzXjrrbeg1WpRXl6O5cuXIz09fdR8Oec8RY2WJhNmzs3DlBnZuGruOLQ0cc5o\nik2iKKGpsdtR1rU6dczNjxdqsT7fYKiJooSqL+qxb/dpVH3RAEmUwn1I5KdAf0ueW0REo4ukupJt\nOEWSwXNBq1Nj5tw8nD3VxHJJ5CIcbUgstBXmzhpUHfwNjn/yDJovfQpJEgPOs6qqCj09PVi3bh3W\nrFmDo0ePOl47d+4cCgsLYTAYEB8fj3nz5qGystKrfDlynhQj2e1oqzwEc3U19IVFSC8tgaBS7v6P\nRqvGic/rHOkVRbFzN4/I2ekTDfjwb6cc6Zlz87yeHy/Y52G0ys41uKRja77BUAvm6A0KrUB/y9Hm\n8mSdREQ0vK5M1fRDEsWw1IdswykcRuoPDJ4bk6YbHdf6n/1fDcslRZ1g9nnDMXd+LLQVtWf+hp6u\nSwCAmi/fQkJSLgyphQHlqdPpsG7dOpSVleHixYu499578T//8z9QqVQwmUxIShr6bfR6Pbq7vbuR\nwuA8Kaat8hCqnn7WkZ624UfIWLhAsfx7e/o8pomilescco31XbLXDUlar+fHC/Z5GK0yrfVYtjgH\nbWYJ6QYBWdY6ANHVuYgk7kZvRFtnLdqNNPekrwL9LUeby5N1EhGNNe7q56kzs3HLtybi8omLSFH1\noueVzWjTVYSlPmQbTsE0Uv9kpP7AYD/i7KkmWT4slxRtgtnnDcfc+a4xicb6rqg6JyVJRH+f82wb\nEkR74DHEoqIiFBYWOv6dmpqK5uZmZGdnw2AwwGQaek+z2Yzk5OSRspJhcJ4UY66uHpZWssOZoNfI\n04maEfYkii6ud6VX3CZ/KsR1XlJPgn0eRquaJjv2ftLoSCcuz0ZGGI8n2oVj9AbJKTWaJdDfcrS5\nPFknEdFYM1L9nNZ4AqbdOwAAIsJXH7INp2AaqfyP1B8Y7EcAAj77vxrH6yyXFG2C2ecNx9z50R5/\nEwQVcidej4tf/AmSZEd67jwkJo8PON+//OUvOH36NDZu3IjGxkaYzWZkZWUBAIqLi1FdXY2uri7o\ndDpUVlZi3bp1XuXL4DwpRl9Y5JIO7HERV7Z+O679ejG6Oy1IStHBZrMrmj9RuLiOYOqz2GR3xgFg\n3+7TXo2ODfZ5GK26bRrMnJuHfqsdGq0a3TZOqxGIcIzeIDmlRj4G47d0HjWXljMTKn0iRHMPANZJ\nRBT7Rqqf3fXRlHoKyhdswymYnMu/VqdGU2O3V/0BlkuKdt5ch4ejzvdXn8XmuH6O18ahz2oL9yH5\nLD1nNnR6I0SbBQlJuYosCrty5Ups2LABq1atgkqlwlNPPYX3338fvb29KCsrw4YNG7B27VpIkoSy\nsjIYjUav8mVwnhSTXlqCaRt+dGWOrUKklyq7yrpaHYcD/3vOkV5xK+ecp9jgOoIp02hw3Bmv+qLe\np9GxwT4Po1VCegpO7DvuSLP+CEw4Rm+QnFIjH4PxW7qOmrvlvkeR1niCdRIRjQkj1c/u+minwjCn\nL9twCibn8j9pulG2jpan/gDLJUU7b67Do2ke90xjEva+X+VIz7w6L4xH47/EJGVv9MXHx+O5556T\nbZszZ47j30uXLsXSpUt9zpfBeVKMoFIhY+GCoD2e2Wvu85gmilaDI0WaGrsd5VoSJQgqwee53oJ9\nHkYrrlkR+aJpJEkk8HaEWSi+19HWzejoi8fVd96h6HsSEUWqkepnQaVCWmkpmvUFuFDfjewTTW76\neb49BcW2kyKJKEoAJCxYMgGJei16zVbZ6679AZZfiiXeXIf7++SrN+eK0ucTn2YJLQbnKWpoE9Sy\naSm0CcoXX0kS0dF0Er2meiQYcpFqnAFB4PQXpIyRGszBRnNwZMk/P7rguIs+2lxvdrsdtec+Q6+5\nAQmGXORPnAtVXFxoPlAUSU7R4tY7UyCIbZBUGbCLunAfErmIppEkkcDbEWbefq+BdOhHWzcjOzdp\n1PaVF+hEFCvc1c+DdWB7cw1a69X4dJ8NVovdbX0JeF8nsu2kSOJNf8DT/u7Kr6dzQRQlnDlZD0Gs\nhlrVjrSsAl6/k+KUvN7298lXb84VpdsDSZZinzzYGJynqGHp6ceJz+sc6fTMRMXfo6PpBM4ffd2R\nnnj1d5GWPUvx96GxyVOD6XoXvaW5G+2NrUhNvIBb70zF6VMJgCCgtcWEqi8aHB3T2nOfofniTgCA\nqRmAJKFgCqeNcGVIrEdX/X870sm5twHg3Nf+CsaNTKXmUCc5T9+r8+9oE9Ow6/81wWoZWM9lsH7y\nJkA02roZU2fmXGlf/+DYZ+LV9yAte+iinQEmIoolrnVntrHFUQfqACxacj327rajr08+p69wpXr1\ntk50V8dPvSqbg40oqEbqB3rTH3DmTd/P07lw+kQDWuqOI0H4BwCgvW6gf5GSNZM3/EkxSl5vexqN\n7qnP7c25ovS1lPO5p9XFofyeLKjjOtiuBEnEBOdFUcQTTzyBCxcuQKVSYdOmTdBoNHj00UehUqkw\nefJkbNy4EQCwc+dO7NixA/Hx8XjggQewdOlSWK1WPPzww2htbYXBYMDmzZuRlpaGI0eO4KmnnoJa\nrca1116LioqKMH9S8pfZZPWYVoKpowZpOXMg2q1QxWkH0gzOk0I8XUCNz6vB8hu1+OTKSKqC/E6c\nP7rTsW+28Xrs3W3FzLl52PlapaNj2mtukOXpmqYB/ZYmj2nyTUfTSbQ3HoVot8JibgIgIC17ZkB5\nKjWHOsl5+l47mk7KAuaDwSJgqEPvTYAoOzcZWl0cFi1RQ6fphDG/E+MnFcv26zXVy/6m11QvC87z\n5gwRxRLXunP1v8pHWeo0nQB0MHdZZYOPsvOSYGxshWC7IOsXjlQnuqvjXet215uhRL5yDcYDgtsy\n5mkdLXe86ft56h801ndDr+kE+ode7zXVo7Epgzf8STFKXm97evLVU5/bm3PF0z7+DKxyPvcWLVE7\nblAAbFeCIWKC83v37oUgCNi+fTsOHjyIX/ziF5AkCevXr0dJSQk2btyIPXv2YM6cOdi2bRvefvtt\nWCwWlJeXY9GiRdi+fTumTJmCiooKvP/++9i6dSsef/xxPPnkk9iyZQvy8/Nx3333oaqqCtOmTQv3\nxyU/GJJ1LunAV1p2pY7Xoan6iCOdN2mF4u9BY9doF1A6ALfeeRtEoQjquOOyfdPTezFzbj7OfjkQ\nVB7smCYYcgfu4F+RoOdccO6otfJV0tWarDAdSWywmBvQ3jBUVyYYsgEEFpznvIbB4el7dQ2YDwaL\ngKEOvTdB86kzs1F+T9ZAp70faL5wEEnJOlmnfeBiHiOmeXOGiGKJa91pE9Nk6eSMfNyxpgjAwHSG\ng5wHZziPsB+pTnRXx9dfkPchXW+GEvnK9YZP3qQbZa8PljFf+3Le7O+pf5Cdm4zW+hQ4RwkSDLk4\nc443/Ek5obre9tTn9uZc8bSPPzdtnc89nZubYGxXlBUxwfnrrrsOy5YtAwDU1dUhJSUFBw4cQElJ\nCQBgyZIl2L9/P1QqFebNmwe1Wg2DwYCioiJUVVXh8OHDuPfeex37vvTSSzCZTOjv70d+fj4AYPHi\nxThw4ACD81EqJy9Z9thnTl6K4u9h6zN7TBMFwpsLKIPehLziXLQ3tsq3p4yTj6y60jHNnzgXkKSB\nOfD0Ocgvvib4HyQaxRVBk/qtK3POpwPqCeE+oqhm6+vxmPaHt3Ook288fa+uAXLjuCJ87cYUWYfe\nm6C5oBKgjuuQbXPttKcaZ2Di1ffIRuw4480ZIoolrnWnpCoaVgcKggqSKMnqPtfBGVlGK+5YUzJi\nneiujh/tZiiRr1xv5rv2+wbLmK99OW/299Q/mDozG6eFWRDEJNmc89m58idkecOfAhGq621PfW5v\nzhVP+4z2BKs7zueeMb8LzRcOOl5ju6K8iAnOA4BKpcKjjz6KPXv24IUXXsD+/fsdr+n1ephMJpjN\nZiQlDRXSxMREx3aDweDYt7u7W7ZtcPvly5dD94FIUROnGNHabEZzUzeyjEmYNMU4+h/5yJA2AU01\nH8vSRErx5gLKZk/Fvt2nkZ2XgayiO650AnIxbsIc3LFm3LCOqSoujnPMe6F4ajYOf2pBU0MKjDnJ\nmHlNdrgPKaqxroxsI81Z6bp9yozpw4JFBVPkj7h6GzQfLRgkCCqkZV814oUAb84QUSwYrGdbmkxY\ncetV6O3pg/FKwOXYUQ2yc2chd0I2hCuTy7vWfa6DM4x5E5CW7Vu9ONrNUBrb/FmA3bVNN6RNgKTK\ndVynpGROD9rxeuofCCoBU6/KBTD8iT7e8CelOF9v+3P+eMvf+ei94c9NW+dzT5JEJCXr2K4Ekd/B\n+QcffBAvvviibNs999yDP/zhDyP8hXc2b96M1tZWrFy5Elbr0JziZrMZycnJMBgMMJlMbrebzWbH\ntqSkJEdA33Xf0bz44ovYsmVLQJ9jLJLsdrRVHoK5uhr6wiKkl5ZAUCm3SMTnB2uw570vHWl1fBxK\nFysbEErJmo78ad+GxVQPnSEXKVnB62gojeU2esjnfMvDxKu/i15TA2z2VGz/QzOsloF57GbOzcOJ\nzwGgHnesGYdps3IxdYYRbZWHcOnNj4NynoVDKMru2ap69JnPICejE9aeFJyp0l3pzJM/mpoz0Std\nD52mE5a+FDQ1ZyJtjN3viOQ6d6Q5K90v7NSOuD4tuvefhpjXM6xO8TZozmBQ9IjkskvkSTSUXdf6\n95ZvTUTvpUt474NGxzZP8197qku9nTN4tJuhFFqRVm79WYDdtVw2NWdgx+8H8xi4Thm8RnEXC/Bn\nvutA8Ia/MiKt7EaC0yfqsfO1w460kusZ+DsfvTf86ae7O2/ZrgSPz8H5f//3f0dVVRWampqwfPly\nx3a73Y6cHP/vSO7atQuNjY247777oNVqoVKpcNVVV+HgwYMoLS3FRx99hIULF2LWrFn45S9/ib6+\nPlitVpw/fx6TJ0/G3LlzsW/fPsyaNQv79u1DSUkJDAYDNBoNLl26hPz8fHzyySdeLQj74IMP4sEH\nH5Rtu3z5suzz0nBthw6j5ZMDsFss6L1cC6gEZJQqN6K3tcXkmNZGo1WjtcU0+h/5qLO5Cper3nGk\nNdqUqKmAWG6jh7s533ImXIdP9p5B4UQJGq0aZ75sRL/V7tin7kw9ps7MRlvlIVQ9/axj+7QNP0LG\nwgUhPX6lhaTsWs8jQfgH0H9lRu2+RLiOsiHvNdR1Y99uKwa+TSu+dmN3wDc7gjkSJRgiuc4dac5K\nTws7JUuTcOnpN/yuUwRBhZSsmWhsysCZc93Izm0a9TcM9k19ci+Syy6RJ9FQdl3r38snLgIAtDo1\nJk03ot9qR+OlVmT11iK95JphdZ6nwDoXeo1OkVZuR+ojeOqHuZbLY0dPD8sjy1wz4jVKMMou+xDB\nF2llNxLUnWlwSdcrFpz3VKb9OW+d+XPTlm1OaPkcnH/mmWfQ0dGBn/3sZ3jiiSeGMlKrkZGR4feB\n3HDDDdiwYQNWr14Nm82GJ554AhMnTsQTTzyB/v5+FBcXY8WKFRAEAXfffTdWrVrlWDBWo9GgvLwc\njzzyCFatWgWNRoPnn38eALBp0yY89NBDEEURixYtwuzZs/0+RvKsp6YGLR9/4kgnFoxXNDifkpKA\nyo8vOtLXfVP5Ue3+zMVF5Ct35ayxKQMf/u2UY9vMuXmyfTSN59F2sB/m6mrZdnN1ddQH50MhTpA/\nJh6HljAdSWwIxuKdgY4IoSEj/T6eFnaS9CKAwOoUX3/DWLzZSERjm2v9m6LqhSAImDS9yLF20OmT\nQNwkK6aLdp/qPF6nkBJG6iP40oa7y8N88ohsm3N/Ihhll30ICodUnc1jOhCeyrQS562v2OaEls/B\neYPBAIPBgJdeeglnzpxBZ2cnJEkCANTU1GD+fP+CsQkJCfjVr341bPu2bduGbSsrK0NZWZlsm06n\nwwsvvDBs39mzZ2PHjh1+HRP5pr+r22M64Pz77R7TSuACShQK7srZmXPy88WQpEW2zgLdRBVSVL2I\n+3gXzOnfgr6wSLafvrAw2IcbE3Q2DTqd0/3xYTuWWBCMuTxHGhFCvhvp9/G0sJNgHhiZE0id4utv\nyJuNRBRrnOvZVE0/el7ZDADQfePfZft1igk+13m8TiEljNRH8KUNd5dHm7lIto9zfyIYZZd9CAqH\nwkwVlk2yolNMQIqqF4WZyj3l66lMK3He+optTmj5Pef8T3/6U+zduxfjx493bBMEAa+//roiB0bR\nJ2XmDNS/+95Qeoay881m56a4pEdfP8BXnDOXQsFdOcvObZLtUzgxA1nmSzDv/h0AQMRAJze9tATT\nNvzoyuNuhUhX8OmUWJaALCR3T4KkFyGYVUhIUX5B6bEkGHN5BmM0/lg10u8z0sJOcVYNrFWtmLbh\nRwHVKb7+hrzZSESxRlbPiiLadBUDfbacJBz+vNmxX4qq1+c6j9cppISR+gi+tOHu8vB0jRKMsss+\nBIVDesk1mC7ah8p5yTzF8vZUppU4b33FNie0BGlw2LuPbrjhBrz77rvQ6XRKH1NEGpxb64MPPkB+\nfn64DyciSaKItoOVsgZZyXnfJFHCKcd8WgN3CyN5PuJIwHIbPdyVb0AK6jkVyZQuu8GunyhwsVDH\nj/U619ffkOdl5BjrZZeiV7SU3cH6se5MPVJ1NhRmCkgvmcc6b4yKxHIbbf0w9iHCIxLLbqzwp0xH\n23lLI/N75Pz48ePhZ1yfYpSgUiFj4YKgPU4mL23BqXBCvZI80aDBu+FTr8pGR9NJ1F84jgRDLtIX\nzPf5nIq2hTVDQYKAZn0BGtPSkK1PRnqQ6hAKgCAh29iK5MSB+hdCNoJV11NweHqiwm37GkC/gfUc\nEUUTd/WjKEo49UV9SOoxXuPQaFyfrutoOhHR5WWwD5FWWorTJxpwfM9Z9gcoKELV5/SnXxyMp5kH\nsd0ILb+D8ykpKbj55psxd+5caDQax/ann35akQMjchWKxQK5IjWFmxJlkAtrDsfvJPKx/o1tSv++\nPKeJKNqFsh5jG0u+iKbywv4ABdtYLWPRVA/EAr9ve3z1q1/Fgw8+iMWLF6O0tNTxH1GwuFvsQmnu\nVqQmCiUlymAozpVow+8k8rH+jW1K/748p4ko2oWyHmMbS76IpvLC/gAF21gtY9FUD8QCv0fOL1jA\nlbAptEKxWCBXpKZwU6IMcmHN4fidRD7Wv7FN6d+X5zQRRbtQ1mNsY8kX0VRe2B+gYBurZSya6oFY\n4HdwfvXq1RAEAZIkwWazoaWlBdOnT8df/vIXJY+PFCKKIg7VHUNNZy0KUsahZNxsqKJsvqipM7Nx\nx5oSlwUzlcUVqSnckjOnIXXKLbCYGqAz5CA5a5rPeYTiXIk2k6dn4bo7J6GpvhvG3CRMnmEM9yGR\nC9a/kSMYfQalf1/Wc0QU7VzrscnTjTh4+UhQrtfYxpIv/C0v4Yg5sD9AwRbrZWyk85btRmj5HZzf\nu3evLH3s2DG88cYbAR8QBcehumN4bv/LjvRDi+5Haf6cMB6R74K52IXjPQQV0rKv4lxaFDaH64/j\nuX8O1aUPqQ0+n6uhOFeizeGGL/CrC1fqwAvAQ3nRVwfGOta/kSMYfQalf1/Wc0QU7VzrsYOXjwTt\neo1tLPnC3/ISjpgD+wMUbLFexkY6b9luhJZitzFnz56NEydOKJUdKayms9ZjmogiA8/V4OD3SuQ9\nni9ERKHHupeiHcswUfTheRsZ/B45v2XLFln67NmzyMjICPiAKDgKUsZ5TBNRZOC5Ghz8Xom8x/OF\niCj0WPdStGMZJoo+PG8jg9/BeVfz58/HzTffrFR2pLCScbPx0KL7ZfNI0XCSJKKj6aRsXi0hyubm\np+jmy7nK8uq9eXlX4ekF33HM5V+Ux8fziEYS6X0G1n1EFItCWfeyHqVg8KcMsywShddI5y3PzdDy\nOzhfUVGBtrY2HD16FHa7HXPmzEFqaqqSx0YKUgkqlObPieo5lkVRwukTDVcW4kjG1JnZEFSCou/R\n0XQS54/+wZGeePU9nGOLQmqkc9Vd+e9oZnn1VldzFTpO7wIAWAB0JaTyuwpAKOpjCiNJQHJ7NrLq\nE5EsJkPIE4AI+nnZVhNRtPLUfobyeo31KAWDP2XYn7LIfihFk0gvryOdt2wnQsvv4PzHH3+Mxx57\nDHPmzIEoivjJT36Cn/3sZ/j617+u5PEROZw60YA3XzvkSN+xpkTxRTl6e1phLFoKm7UL8doU9Pa0\nIk3RdyAa4E0j7Xy32iamYdf/a4LVYodWF4fye7Ig2aqRljMHXS1VsNss6DXVs8EcAc9tZYWiPib/\nKHEBcPpEA3b6+fsGY5SNa5695ibZ66z7iCjSjFQXOrefg/05dVxHyEcl9prqh6VZj5KvvGnzR9vH\nn7IYSD9lJJEeQKXoMtJ1POBfeQ3VKHbn82B8Xo3sNbYTweV3cP6Xv/wl/vSnP2H8+PEAgEuXLqGi\nooLBeQqa2po2zJybh36rHRqtGpdr2hQPBgmQ0HTxQ0c6bzKnaqLg8NSpHGwUVdJFdDe85dhn0ZLr\nsXe3HYuWqNF8cadje1rOHLQ3HEGCgcHRkfDcVlbN+VZZuvp8K4PzEUKJC9bG+u5haW/zCMYoG9c8\n86d9W/b6YN3HC2siihQj1YXO7adrfy6r6E6MnzQvJPWWa5+RfUjyhzdt/mj7+FMWA+mnAO77C8EI\n+NPY5VruB6/jAd/Lq7v8gjWK/fSJBpw4Wod+qx3pqUmy19hOBJffwXmbzeYIzAPA+PHjIYqiIgdF\n5I5OG48Dn593pJfdNFXx9+jrbfGYJlKKc6dSq1OjqbHb0UEEJOx87RC+8U2LbCYJnaYTgG7g//1D\n2+PUWky8+h6kGmeE6vCjjsXc4jFNvknUa2XpBL0mTEdCrny5YB0pmD1QDw3Jzk1y+/fuBGM0pmue\n9v4eTLz6HtnoISA4I+mIiPzhri5MyZqJRMNQ++nan2uqvYAe67iQ1Fupxhlu61EiX3jT5rvu095c\ng2NHNY5+hz9lMZB+CuC+vxBowJ/ImWu5H7yOB3wvr+7yC9Yo9pbGbpz4vA4AUH0+DrfddRv0iSa2\nEyHgd3A+Ly8Pr732GlauXAkA+POf/4xx47iq71gm2e1oqzwEc3U19IVFSC8tgaBS7lGb7i6rx7QS\ndC53A13TREpx7lROmm7Eh3875UgvWDIBAGDtT7nShA8wjivC125MgTG/C80XDjq2J2dM4yNmo4iL\nz5Sn1Zkj7EneMOYkOZ5kitfGITvH904mBYcvF6wjBbOnzsx2XKhm5yZh6swcr9/flxFw3vYb3OWZ\nln3VsHqPF9ZEFCkSDDnD0qdPNODA3rOO9jMpzQaT0yxdlr4UmENUbwmCym09SjQSd222N22+67a6\nWjX27R647hnsd/haFgPppwDu+wuBBvyJnLmW+8HreH/K60B+w9uUYOhyirFZLXacOWPAN277SlDe\ni+T8Ds7/7Gc/w3/8x3/gt7/9LSRJwsKFC/HTn/5UyWOjKNP6z4M49cxzjvTURx9G5lcWKpa/IVk+\nUlPvklZCVv4CAIDFVA+dIdeRJgqEa2c2bd5cZPVewjeXZ6PDokZ/nHzU8eCo5E/22bBoyfUwpnRA\nY8hHzeVUZOcmIX9iMZKSdRzt5IPaulxkZt8IiG2AKh21dTkomBbuo4peU2ZkQ5Ikx0XRlBnB6SCS\nbyS7faBuuS4Hbd0S0vVAVm8tJDF7WNBbFCVUu0xPNBjMFlQCps3K9StA5MsIuLbKQ6h6+llHetqG\nHyFj4fB219s8eWFNROHi2teTICG5exIkvQjBrIJ40YLGzm5YLTbHqMSsnImYPPkONNVehKUvBfs/\nsuGWu2Kj3uI0Y7HHXZudvmC+rH1uak7H5/s/Q6rOhsJMFdJLrpG14SazAXt2tDvy8HQT3dMN/ED6\nKYD7/kKgAX8iZ+76rgVT/B+4KlZb5G1KtQXI9n6gi7f7JbnE2AZjcKzTg8/v4HxGRgbuu+8+/OpX\nv0J3dzeOHz8Oo9Ho94HYbDY89thjqK2tRX9/Px544AFMmjQJjz76KFQqFSZPnoyNGzcCAHbu3Ikd\nO3YgPj4eDzzwAJYuXQqr1YqHH34Yra2tMBgM2Lx5M9LS0nDkyBE89dRTUKvVuPbaa1FRUeH3MZJn\nnV8cl6ePfaFocD5Br5GN1NQHYRoFQVBBo02Bvb8HGm1KyBZlotjm2pmdcN/3cOGVVwEAWgCZ64du\nbGp1cSgs6MCqVXb09SWi05yLM5eM0CZo8cVnF9Ddab0yyoSjnXyhUdlhsWogiBpIKi3iVVK4Dymq\nBXpRRMoaXCSqu+EsrJebYKjtR++evegFUAX3Qe/TJzGElOoAACAASURBVBpg7u6TbVMimD3SaEx3\nnXpzdbVsH3N1tdvgvHOeoijh1HH3Fwe8sCaicHHt6+V8YwWa3t8NAIg3ZiHu/5uK3KwGLL9Ri0/2\n2WC12JGUkoiaSxpotCWw9/fhlruSY6be4jRjsWdYm11bh+bjjWis1yA7dxa6eyXs+P3Qb75skhXT\nRTsyFi5wtOGnjtdj0ZJu6DRmWPtTkJk3cr/D9Zya+vijaEnIVyQ46K6/wL4tKSnQp5Nc+82Jly6j\n6fXdjte1q9KQUTrf64EurYc/Q9Xnl9Ap6pFhrseElE9g11iHLS5rzEl2+3Q06/Tg8zs4/9xzz+Hk\nyZP43e9+h97eXmzduhWHDh3Cgw8+6Fd+7777LtLS0vDss8+iq6sLt9xyC6ZNm4b169ejpKQEGzdu\nxJ49ezBnzhxs27YNb7/9NiwWC8rLy7Fo0SJs374dU6ZMQUVFBd5//31s3boVjz/+OJ588kls2bIF\n+fn5uO+++1BVVYVp0zhcMhjUBoM8rdcrmn+fpV+WtrqklRCqhTZobHHuzKr0etT1JaHjhrVIjbNA\n9dE7SG05jTvWLERjfTcmFHXKFgezS9fj8KcDj5dd+/ViHPjfc5yqwQ/J6Z3oqv9vRzol97YwHk30\n4+iJyOB28WgDkHlVKbBnaD93Qe/G+m6c+bLR0QHPK0wNalDIXac+q7BIto++sNCvfAbrQ15YE1G4\nmGvrIK0oR6eYgNQ4C+JThwYBpP3rLWhv/iuAgRmH/2Xlt9FtzsP//q0KVosNQOwFOjjNWOzRu7TZ\n7RlTsMupPV7qsh5cp5gwrP9hzGqFqe4fQP/AuWDMzEXVF3Dbn3S9GVDTIuG9D5QJDrK/QKHkz3WT\na3/3lm9Nkb0+2Gf2dqBLTYuEvWe1AEQsuyEBly8NXRc7x7xGejqadXrw+R2c//DDD7Fr1y4AgNFo\nxO9//3vceuutfgfnb7rpJqxYsQIAYLfbERcXh5MnT6KkpAQAsGTJEuzfvx8qlQrz5s2DWq2GwWBA\nUVERqqqqcPjwYdx7772OfV966SWYTCb09/cjPz8fALB48WIcOHBgTAbnRVHEobpjqOmsRUHKOJSM\nmw2VwqPC4zMzkfnVxbBbLIjT6RCfpeyczu1tvY5/CxBkaaX0mpuQljMHot0KVZx2IK34u9BY49yZ\ntX/1X/CPTwYf59Rg2VdvQWJeHhrSGtGsqsU4EbIyGNfVAyAOANDdaQEApGr6IYmioms6xDqbtclj\nmnxz5mQ9WuqOQ6/pRGt9Ck4LszD1KnbQQm2w4+66eLRdY5Ht5y7onZ2bLJteYU7peLcXCna7DRcv\nfQqLqQE6Qw6KChYiTjW8++juwgOS6HiEti5+kmz/xvpuTL2uBNM2/OjKI7aFSC+dP+pn5sUBEUWi\n9owp2HvgPAARWl0ilhQXwnz7D5CqtcOuHuj3xal1SM6cBgh1iEtWARgK4MdaXcZpxmJPeqm8zT7e\nJe8L9JrlT+MZsw3QFxhd9pFfa7c0X8LO1y46XncOuLveDOiwyN/P9ZzhwBEKhcGnVZ2nqxlttoXT\nJ+qx87XDjvRIN5acy7BKJUCrUztu4Hb0qXGVmz6z63ni3Od3jgEm9wydi66LkTsvLjvSjSvW6cHn\nd3DeZrPBYrFAf2V0dH9/YKOYExISAAAmkwk/+MEP8MMf/hDPPPOM43W9Xg+TyQSz2YykpKGCkJiY\n6NhuuDJyW6/Xo7u7W7ZtcPvly5cDOs5odajuGJ7b/7Ij/dCi+1GaP0fR99CmpaEvMxN9bW3QZGRA\nm56haP7JKTocPjB0Z/DrLnfnlRCn1qK94YgjnT+tSPH3oLEnvbQE0x57BD01NfiyLw/A0DzPfdkT\ncTFfjef2vwQAuGbO7bIymDH+GwBMA//O0GHZJCt6XtmMNl2F27vi5J5aa/SYJt8IYjUShKGRT4KY\nBCB2ggrRYjBQ7bp4tFaXhQn/dj9sHR3QF7kPens7BczFS5+i4/TAYAwLgIsAiosWD9vP7ch4c43j\nUVvtTasADE1Hl52bBEGlQsbCBT7VZbw4IKJI1NE3dFk9aboR//j7OUd61bRsAEBy5jSnPt5BLFpy\nPfbutgOIvbqM04zFHtc2O/uLBtnrhRMzkJMi4PKJi0hR9cL21qvAgwNTCg/Od41Uu+w6J238CgA9\njrRzwN31ZkCzPhfYX+vY1/Wc4bQbFAr+zLRQd6bBJV3vtmy6luGZc/Mcg2iyc5ORMWvqsD6z63ni\n3Of/rPYLNH56ABnNZmBiqmO763WDu4WcXbFODz6/g/N33XUXbrvtNixbtgwA8NFHH+E73/lOQAdT\nX1+PiooKrF69GjfffDN+/vOfO14zm81ITk6GwWCAyWRyu91sNju2JSUlOQL6rvuO5sUXX8SWLVsC\n+iyRpqazdlha6eB8z8WLqH37HUe6IEGHjPnzFMvf9W68a1oJ9v4ej+lIFovlNlYIKhUgSaj543bo\nXQJUGelaXOy65Ej3dHfJ/tbS041rvlKIjGQ1VDtfgK25BSJGfmQtGoWi7F4+q0Z84vXQaTph6UtB\n11k1Js8K6lvGNLWq3WN6LIiEOncwUO1YPDq5A0JdOy699gYmf78CeStuADBwUdz66T+HLQLlzSPd\nFlPDsLS7RaXcjWhPbHea0uujd/DN1Q+iW50aUKeeFweBi4SyS+SPSC67zoHCfqtd9tq5Qz2YOH0Z\nIMnrybxxNnztxqkRUZd5u1igtzhtyJBILreBGGqPu5CqsSHx9KfQdHQgdfd7ADBwvVJTjYyvLHDM\ni5317zfI8uixtGNgBa4BzueR682AdFHy2P431ncNS7P8BSZWy24gek31w9KjBedTdTaP6UGufWlD\nknbUNsL5PJHsdrQdrHTU45queqT/8QMAgEp/DNev/QH6xGRk5iXBmJUnG/0/KklElrkGie3V0CcX\nAcgGwCdTlOR3cH7NmjW45pprcOjQIajVavz85z/HjBkDP+qJEycwc+ZMn/JraWnBunXr8JOf/AQL\nFw4sIjp9+nRUVlZi/vz5+Oijj7Bw4ULMmjULv/zlL9HX1wer1Yrz589j8uTJmDt3Lvbt24dZs2Zh\n3759KCkpgcFggEajwaVLl5Cfn49PPvnEqwVhH3zwwWHT81y+fBnLly/36TNFkoKUcR7TSuhrbfOY\nDlTBxAx8+tEFWVppCYYcj+lIFovlNpYMzgen+ugdLPvqLTAnZkLf04KeVzZj+vdWQx+fgJUFs5Gc\nlIhmp1NHp83EN1dejdZPD6KqucWx3Zu5maNFKMpuktCH93dbMTDO24pvLLYqlvdYlJY1Hu118vRY\nEwl1rnOgOtHWgdYXt0E0D9xUdr6B5+1iUe7oDDlwniQnwZCDplOfoLPjJIT4ONT9+kVM/n4FsnPl\ndVJ2btKVzvsA0dyDwnQJGQvlc2b6igGfwEVC2SXyRySX3azeWiybZIU5MRPJmSrk52uh03TC2p8C\n1SUzmp7Zhkmbvw/nW9lpWQWYeFVgdaJSAmknyLNILrf+kiQRHc0nkZxYj7TxGlz4j1dhMvcgc4n8\nyTrXebEFkwA4LVNXK1qQv1wHdGkwbeJ4jzepRmv/07QuAVCN+wAoeS9my66P09I4cx1l7s2o88JM\nFZZNsqJTTECKqheFme6D2q5PhxZOzPCpv+taj2d98yYMhvtFcw/S609g+t33XNmS69Paimwjgs/v\n4DwAzJ49G7Nnzx62/YknnsDbb7/tU14vv/wyurq6sHXrVvzmN7+BIAh4/PHH8Z//+Z/o7+9HcXEx\nVqxYAUEQcPfdd2PVqlWQJAnr16+HRqNBeXk5HnnkEaxatQoajQbPP/88AGDTpk146KGHIIoiFi1a\n5PZ4x4KScbPx0KL7ZXPOK02dkuKSHv0pBV9MnZkTgtFyKuRMvB59ljZodekI8BQhchicD04090D4\n+3YUfXUxWj7+BCIAXVMnnlh4GzpO70Jb1zmk5cyBJGkh9RgQ329G3fm9iMvTYvxP70ZcvxY6MRPp\nJco9lTIWGNUd+Nd1OvT1t0MTnw7pfGe4DynKqWRzhgJc/yAcnC9UWz89iCrz0NNezjfwvF0syp2i\ngoW4iIER8wmGHOhVcei2noY6LxldLVXIuH0xzNXVmFpW6qaNzvZ5TvnRBHpRRUQUDOYLFyD8fQeK\nlixGXGoSOuNPAf2AXq2DcdHXYCu5G5r0DExM/y5M7Reg1iQCECBJYkTUYYG0ExR7RmtrXaf2yLh9\nMZpe3432w5+hYHU5JFF0Oy92658/Rsbti6GdlI2knGKgX0K37hIKUowoGTcVgiD4dBzOUltOY9kk\nuyMAmtZ6GoBvA0Yp9vkzLY2zVOMMTLz6Hp9GnaeXXIPpon2oPzzCdbzzoJvccXqkJJ1H9cn90Bly\nkZW/ACo3az45c63HE1LlqydmTfJ/7U22EcEXlMijJEmj7+Ti8ccfx+OPPz5s+7Zt24ZtKysrQ1lZ\nmWybTqfDCy+8MGzf2bNnY8eOHT4fT6xRCSqU5s9RfCobZ/EZ6fIFYdPTFc1fXqqC8wiNxdyAhvP/\ncKTzJq0AMD0o70Vji/N8cGpDEi6/vQvSinJ0iglIyi1Cpr0OHQDsNgvaG44gSZwIwdyKhqSzSIub\nI5ufceLV93AxWB/FT0lA3cW/OdJ5U1aE8WiiX6+pTlYmdXoj0rJ5ARROnuac9LRYlCeiKOHMiWY0\n1huRnTsJaQktsguatJw5sPV1QZ9aOMKINsHnOeVHE+hFFRFRMAzWs+2HP4Nx4Q3AwGyrSM6chvqa\n/wEANDfuR/60b6Op5mPH30VKHeZvO0GxabS21nVqD0kvAgDs5h4kjh/veV7scYVInzUfgkqFeQDm\n5Y88aNDdcaRkzXS78Kt+3DgIrz+DwZm19Rt+5Oenp1jmz7Q0zgRBhbTsq3z7Gy/XWHLuSzfW7Mfl\nqndkr2cXLPL49671eGJhgdtrA38WT2YbEXxBCc673vGkscH41cWwm0zora1FwrhxMC5domj+oVjk\nxdrb7jFN5CvXOTzHl60EADTrC/D3/z4PQMTh8+ex5gGXR+RS8iHl2oHGsxDt8ilY3HUiOJrUM0tf\nm0ua53Yg/Hmkk5Ql2e1oO3QYPTU16O/qRsrMGUgvne+24+8pcO+Ja7u7+l/jZK+LditSCmYgfVrg\nI+K9FehFVbCwDiYa29LmzcWE+76HnuoaJOhz0W4+BgDD+nA9XYHXYcGob/xtJyg2jdbWuvb7Ugpm\nQLcqY8Sy4yk46Wm9A3fH0diU4TYm4CjDdbXQTsuAVduF9sbjAyObRUnRNRUoevlzDaP0mhzesLiU\nfde0O671eNrcOWg//NmVV4ditGdO1qOl7jj0mk601qfgtDALU6/y/D2wjQg+ztlBimn63w9R8/of\nHek4nQ55N9+kWP7uFptTOjivS8yUpbUuaSJfjTQ/W0dfvGy/mkspmJz/TXTWnIRgVqH2tZ2Y8OPv\nAcCVaUOGuOtEcDSpZxpdusc0+cafRzpJWW2Vh9DyyQG0fPwJAKD+3fdGnP/R2xE7rlzbXZsofzw2\nKWMqjOO/EvQgtHMgKi4+UfZapNwYYh3snWNHKnHuzNERX9cb0nHDTbeF8IiIlNF++DNceOVVAEDc\nJ4nIe+DbsPQ0IV5TCOBLx35qbZbs7/xZ3yoY9Y2/7QTFptECmO76gcIM3/oCg217d8NZWOua0Lrr\nY9jNPbK+jLvjOHPOfUxgsAyrGo8POz/EC2bOl00A/LuGCcd86zqXsu+adse1Hm/99J9uj1sQq5Eg\n/APoH1iNTRCTAHjOn21E8DE4T4qxNLdg3K3fRl9bGzSZGbA4LV6pBKPT6u2AfDV3pfT3mWXzKNv6\nzIq/B40tw+Znu3wZ+BRIssmfMMrMMsD6RSOa/7THsc36ZSvyS74NU/t5GIuWwmpuhj6pECmZw+eL\ni9TRpJGi32qSndv9VlO4Dymq+fNIJynLXF0Nu8UybJuSnWbXhakkVdHwCxpRQmvlPz2OJvLn8Vln\nzoGoOLUO+dO+DXt/DxIMORCre1Hz4c6wj4RjHeydc6ePoCD19Iivf1mTAIDBeYo+g/29OH0i0q65\nBpZDlyFJIuobe2HLuR46TScsfSmQhOSA1myR7HZ0N5yVbQt1fRNonU6Rb7QApnM/UBQlnDrue3mQ\n3WQyAJl3fg32M91o/+wIAAHppSVujyM7t0mWj2tMwF17bKuWPzHrbX8pHCOmKbj8uYbxZ771QMuO\nRpsGY9FS2KxdUGuTodGmjf5HXh63WiU/H1zTg+z9NhzffwpNDQPrSV21aBpU6ji3+1LgImbOeYp+\n8UkG2cj5gnvuVjR/lQAsXTEV7a1mpGXqoQ5C6VXH69BUPTSPct6kG5V/E4pJI12oDM7PptLrYf/q\nv+C0OB4JRy5BfWg3li+4Cd3pE5CYkgBBEKCfWDy0bkOCDvr88ejsakZH47Gh97EkQtNyeFiHgNOM\neKZW69BYOzTHq7HwujAeDVHg9IVF6L1c6wgESWoNajOuwrE/H4UxNxklCwqhUru/CPA2sOK8MFV2\nbhKmzMiBoMqVXdC0VrofleMs0GnpnC+07TYL7P09yCu+fsQRQeHAOtg75y+a8LcT3SO+rkmwYPX3\nQnhARArRFxZBpdcjbuW9ONsTj0S9Brq2yxiXYEW7bgI6rEXIzkuCWjiO5gDWbGmrPARrXRNgGNo2\nWN+EKmgeiqlGKbx8CWCePtGAE0fr0G+1o7XJBEGQhk2R4Vo2J083or25RraPKlOLxt8NrA/V+D+7\nHW2663G49k0GFqAfCoZKapss3wRDLsTCFNk2b+fLDseIaYo83s637hyQVxuSHE9TAb6XnV5TLZou\nfuhIxxVr0NiUpsg88WlZBWivG9qellXg+LfzuapVS9j3QQ2sFhu0unb02ONgtal4UzZIAgpvnjt3\nDu3t7bJg/Pz58/Hiiy8GfGAUfaxN8rvY1sZGRfNvaujGh38/5Uh//aapmDRd4Wlt9Dmy0Sw6PTua\n5J2RLlQG52erbhPw3geNwNlGABosm3c9YLPh4IGhjuk3l+eg98oUFQCQee21w6aRsImpMF8efree\n04x4ZulLkZ3blr7U0f+IKIKll5YAKgGJBQWo+eOfIK15FHv/dm5oBwkoXTzB7d96G1gZXJhq6gwj\n2ioP4dKbHw8b/ePNaKJAp6UbKfDtz0imYGEd7J20tDzkZ04a8XWVvm7E14giWXppCRLvewS7/vu8\nY9vMueOgkjpRrOnG1V9bCABob2yV/Z2vN/LM1dVo3fUxMm5fDEkvQp9Z5KhvQhU0D8VUoxQ9mhq7\nceLzobo7M8cwLDjvWjZvuHky7DY1dE77xAvyp/VGatPdL0A/FEiP0yci4/bF0E7KRlLOpIHzIwt+\nzZcdSf0MCh9v51t3vpmTNr9E9pqvZce1bbDZU32u30c6bk99VtdzdebcPJz4vA6Tphux+69nfHp/\n8o3fwfkf//jH+Oijj1BQMHSXRRAEvP766xg/frwiB0fRRZOWLhv1q0lXdk7nro5ezJybh36rHRqt\nGl0dvYrmDwCpxukAJKeKarri70GxZfAO+aWL8ieGXOc/PL77NIChG1adYsKVf4mObc3NZhgwNMr+\ns2oJ6YW56FPdgPi4Dlj6UiC0JELXXYu69/8GW28v9OPyHYEyTjMysnPnE5GXOx6C2AZJlYG6+kRM\nnhXuoyLyn6BSIaN0PswXLgAA2swuddDldrR+2uz2MdrhgZUuZJlrRnz01tPIMW9GE7lOj+PrtHQj\nXUQkTpgIaUU5OsUEpMZZoJ9QMEpOQyS7HW0HK9F58kvEJycjsbAQ6SXX+P24Oqd6IhrjJAmtbfLF\nX/utdnSKanSdOYcWQyEa67uRk5cpq8/+f/beM06u6szXfSrH7kodqnNQS2ollFoiKCAaJCGSDDYI\nZBtjXwZ7ZsA+47ExPh5fj8f3DBM8d65tJvl4/LONPRjm2BiDQRZIRgGElUERhZY6V+juqupKu/L9\nUN27alcHdUslkNB+vqC1w9qri7XXXutd7/t/LY7ZDL4zVhpsIkkEU0MjqXAEz8+2AuD4+pNi3o8P\nymh+qWO6zNVNYd+MhqW5YKLh+Jh7Cvump2+YEyeSrFidlXwqKXViTOkl14zOJ7KevP30nXZh1Sdp\nKFOK3+v8tiiUKlQmo/h+1G3ehG3hyDdZwUXpZU/VY1rmo81U9dbzN3NUhvH781QpnPse2SuNCOk7\n3X/h8T1f0UShZOiP+wh35b4po3PWrDRVP+7+IEol6PRqYkL2eYlYSvLfUeRN2eJz0cb5PXv28Prr\nr6PVaovZHpmrGIVGLSamA6h/+FNFrd9qN7LtdyfF8q13jtXdlpH5oBk1Whk3bAZy42HhQqVwIVNW\nZkBrKeVon4cVq9XotQHstlI8vzeSXHUP28/oAC/s83LbXXPo9wQps2tJvPx9+kfyOZStWkn3z34h\nh1hOAYtVR8AnoNcmiCWiWCzTT8Amk0PW4PzguZB0lsMsDS216VKcfPrvqf/UQxjr6iX/jwrHo1Ii\nnP7+M6TCEWBs6O1knmMTeeXkJ3GtrKhi02fbcPVJQ9CnykSG7wFDDdvP9JPd5NTiWFGLY4p1Du3b\nz8m/+0exXLZqJaRT8lgqIyMzLUbH5r7T/eiUKYlRw2hW01QfIa01Mdh/hHd2JIkJqRGPw+x4NpE8\n10SbopN5cH5QRvOJZEVkrg0K+2bVl/9Gct5pVZJJpyXzwsK+aTcriAkptm9NodObWdGmpxc1ZV/+\nG6wDpzDVVGNfvox0MkHXyX2EBnrRmS28viPJitoIc0a+14VtKVu1UrRHFMOQPlWPaRkZkG7mBE68\nj/HP/4qBoRiVVSXY2i7NdlWqkm7+WlTCBFfmyH8/8t8NkM713z/Sx3//7KB4btRbHqChwYzDlKak\nwsKp4zlHQ3lTtvhctHG+qqqKWCwmG+dlRGIe76TlSyUUlA5I4YJyMfB7jtHx7s/EcvPCh7FVyu61\nMhMbI0eNVsqdv6F91Uaitlrq5jWMWajMmlvOpz5tQYh40GDG9+Pn0c+azQMPtDHkfhkS4PNA7Zce\n5PgZG5ALe/a6gpw86iImJGlfuhbFlucASAkCSpOJziEFR7eekvXfJsFa4iEcy2WlN5VuBGZ+2M26\nahk8cJCTh7oJpE1Yfd20KhU45AXLZaUwzHTj3c1ct3oOtqWLaXrsUQTvKdZuWMzAgECZOU254ijp\nP72NtC7D6e//gJlffBzb8uUjBv5h1t05C/+Zc5hTIYSf/L/YlywRJ+2FobeTeY5N5E0kSfQGNC/8\nDLPnF9er/FK8RAs3HFKCMK2QYzkhooyMDIwdm29rr0FARzyeZN48H57z24Ds3GPF6rVs35rC3T8M\nZMeskqQCpclIemRzdHQcmmhTdDIPzg/KaD6RrIjMB0v+JvhoVNloFMVle2YqReDYcckx68ApNt49\ni55j57Eoo0T+42mGdI9L+mh+37RqEwg//z7r125iYCCKpbqM7btd4rUPPHID9SN9y3NyF4N9r6Ag\n9w4FzhgmfE80Nit1mzdd0JA+1W+4QqnEPiJPkn2W4pIcUuS5w/S5mn4za9tSzH/xN3hcQUoqbGzd\n1jGyWeslkwGb+9iUnZoK59FO5220t8QIpA1YlFEq0mNtbYVjQrivVzyXEqTG/Pw5b+/7vZJzRq2C\n668rwW5SUBk+S/2Dd5NJZ7CWl8qbspeRaRvnv/71rwOQSqXYuHEjbW1tqFS5jL1PP/108Vonc1Wh\nK5P6q+kcxZW1MZl1krLRVPyNoZDv3JiybJyXgYllHUaNVulwBMWW51jy9SdxjLNYCQycwOd5GYAo\nYNuwhPS5CJmQNBlSUhGizF4pPZZKM2uekyMHeghgxjIi46B1mklXzslq2Y9I5sj6b+Oj1XoJF5Rl\nLp6ugcxIdEfWY9lQn5myx7LMxVFoiO450U15uAvB5SLh9+M7cJB0+NcsfexRwkIXgZIzAAQTHTg+\nvpJwZydeU73EiNTeEkOx5TnSSCfthd5mF+M5lp/EdbR8MZIvky3KpuolOl4dhRsOKr1+Wl52ckJE\nGRkZGDs2x9Nqdr/ZQfs6HbFwj+ScXhsA9BiMWsn4cestH4dXngVy4+9km6ITjYuy0fzaYrxN8Mst\nrTZ44CCDlmb86z6HVSWg3PkbTDXV0HmM0NbnR6Q5N3LwfIY6k2tM35w9t4KhvfsJ3HgDGpWfzFsv\nMrjiQckzOjsGxb6tj0lz2Om1AVAqJnxPLPPmTmmTfTrf8GImhZXnDtPnavrNTp/w5OUd8Uo80HuO\nnSe09XlUJiP1n9xMMhSc1FBfOI9OqQTKw93YBQGVXo+xZiUg/R40NQ7jPf+8eE9t693iv1UWy4RS\nkFZtQvKsksEOFFueI2kyMfjIX3BOdAJ0XrG//UeBaRvnly9fLvmvjMwomVQ6pzmv15NJZy580zQI\n+COi5rxGpyIQKL7mvFprKigbJ7hS5lpjIg+mqRqtCj+wKqeRwV/tpnaVdEKa6PYj+HpZ0T4TryuE\nRqfizAkPs+dnd6cdsxtHkrGkoWOY5Tc1ATkDv6z/Nj5qdcG7rZbf7UvBL6gnLcsUn0JDdEW5gXM/\n/L5YHg1XTQaD6OZUgvuMeC5jSmOyNnCuwIgUSBsYTY1sXbwI88wZ445jU9XazGdMMuvUxSVhnmxR\nNlUv0fHqmL28jdanniRw/Dia0pIRzfmlU26XnBBRRkYGxo7N4UDWA16vDaBUSR2LTKVVPPDIDHq6\nhiTHg/Ym5hZ4+042v7yajFUyl49ibYJPh66BDK/tGR4pabnr01/EvnwpkN00T+VJc+7Z5x3TN7OS\ncjlDd9Njj6JVOKAjZ4QPBWP8cWfWYe7hz9YC+8RzdnsllqUG8Xt9sbIz0/mGFzMprDx3mD5X029W\n2NZ8nXaLMmu7si1Zwrkf/kg8PtFmT2FC2BJnM6aVzlxfH3kH8r8HprsE8mMKUrqY+H74nPPY/tsO\nxpOCrDZERK/8sjID6ZdeyDrurLqHl3+f22SW9zfabAAAIABJREFUvzWXl2mvpu+9917x3x6Ph4qK\nCvbv38/7778vOSdz7ZGMRnIFRUG5CFhsBoRocKR6BRar4QJ3TB+9yUnVjPXEooPojQ70ptqiP0Pm\n6mQiD6apGq0M5ipszkWkUzGUKh26uIOK9nbSvQlsTXeCagiN1kiicxBbtRm79gx6jZ7dI/qkpVY9\nDzzSxoA7yKy5lWh1ak6fcGOySI3Msv7b+CRSZZJ3OxItbmTPtUb1zCrmRTJigu6aWfJE7XIze14l\nG+9upudENxang0BUwLBhM5oDW7HdvgRlhYGK5vUolFq02mz/Vqn1lJa1olIaUJYbcXqlRqTaeQ2U\n6O9CU1qCrrycqg3rpxSqPZHMV773jkZjRaFch1YToNRqRYh68bmPTjvsfrJF2VS9RCeqw3Hj9Thu\nvLgFtpwQUUZGBmBWaxn3tJejtQ6jVvlRGVM0ttrQazxoDWVUNK4hGQ+SypTTfUbHdVXdeHXSzUqj\nxUD9bQ9Ijk02v7yajFUyl49C411h+XKQ74yh06swVsY4d3wLSaON0q9+B1fvMOBFp1exYrUaRXI/\nPncTpWVzOH3cQ/f5DMYNm9Hs34qltZVIZxcNbeXiRrtCCXv+cFZ8Ro+njJm1dxMNu1CnTWi0SmJm\nP37v8ex8ouA9yaRS4yZYLmQ63/BiJoWV5w7T50r4zaYqIVXY1ubZ5VTVWbFqE0R++HekgXQmTcXD\n68iY0igiKsJeN0r30TF1FyaEtThm4zs3qgufM8GPyqQBJNI28rUlDOYqbDPm47jhes5tPSVpW9/p\nfozHd2FqaKTylpshuY1IZxd6rZPOEZm1QNpA1pife5b8rbl8XLSr27e+9S2USiWf/OQn+cu//EtW\nrFjBO++8ww9+8INitk+mSKTTafb3vUdXoJd6Sw1tNdehLLImnaa0lP7f/FYsFzshrFKhFMOCAG6r\nmVPU+gGEiJf+s78Xy9Uz7wTkxLMyeZ4Z5zsRKizsLvFR1XN4Su9SJpUiPjSEz3VYPGaJziblD9KX\nKAFTHINiT/aEAmymRfhch9EDt991N2fOmqipsxLwC2x/7X2xjnmLq6lwmuWkXFNAr/dL3m3njA0f\nYmuufjKZjGQ8nrNQ7neXG4VSwXWrs9+9XMisloc++zGGQ9uzellmUESV9H7nVzT91aPEtQKejjcA\nGOjbg9NxKxvvbsYfV1NZVUp5tIeTv31FfMZUQ7UnCvEu9Oa8YXUzVVV6ht3ZuUHQPf2w+/EWZdNN\nSHw5FnZyQkQZGRkA34ED2Et68YbfAcBmTCAMHWZUKMzmzM7p4sq1mFMRTj79faxf/Y4kGrjSOb0x\n6UowVsl8+BQa76wVcy/7M6tnVsFbWX3qFavVhAdeEmUjo5m1WGpnwl4vK1arMSheJ+SFkHcnjup7\neeEno/m0tNz28T/nTEc3VqWA9//7HrO++Dit667n5BGXmFAZoKy8BPWwj96nn6fi4XW4S94Uz42X\nG26qEjTT+YYXMymsPHeYPlfCbzZVCanx2qpQKsik0wzpHyfc2Ylmto0+z9bsDWaobmqR1F3e+ADn\nzltGZGTmic+ZKHm4waQRj3kHHNita9FrAwhxCx6vA9uIWm7hd0Pr7qB7S1YCZ/ZTT1J9Z3ZtnEmn\nMVSNeOjbqjnQkfOct2qTyFw+Lto4f+TIEX71q1/xzDPP8IlPfIInnniC++67r5htkyki+/ve47tv\n/YdY/sqKz7O8dlFRnyH0909avlR8g+FJy8VACHsmLctcu4x6Zpyt1WXfpRHJ8qm8S0P79hPwH4M8\n25Gy3IAvOYuhqJ4Ztn4Ef+5cOpVLdqxR+6hrrCOdho73pTrp5hIds+Y6Re9RmYmJRwcmLctMj/Nd\nUg3Q850e5iyo/pBac+2gUCoJprSiUUerU5PSSPNWKMuyMgpDB84zMDtBfmxN2HuO0L/9b+Z//Ukc\nC2bT9fwuyb1TDdWeKMS70JtzaCCMvdQlCbGdbtj9eAudob17p6X/ejkWdrK2s4yMDGTHv5gj57mY\nP4fLLztMftJHvQzc/hBeV4i6xnKikTiVVaXMmju9MelKMFbJfPgoFEpslfMvu5RNPrPnOcW+V119\nBl/OTwOrJYI/nGTTZ9sgsZ9Q3rIlEuwDcjJPXX0Cpzqy8hrtqzaK8whp3zZTHu3Bd/AwZatXkinY\ngwq6zo4xzk9VgmY63/CLkfabuC557jBdroTfbKoSUhO1Nb8P9Z19HfJMTPGEVObM03ueHb/XA1IZ\nmYn6dnRwWFwXpNOwfWuMbPrkGCtWuhAGhvC4glRWW7jj4wtw9QWwl6hQ/fcPGDW1e8+cpGwkklTS\n33/1Iu0tKTEJrW3wFDBvej+ezJS5aON8KpUinU6zbds2vv3tbxONRhEKMgDLXDn0BlysqG9DSMbQ\nq/X0BVxQZMUWfZV0ENI7iztRNBYkhDWYdBNcefEYTBWSsr6gLCPTFegdU15eu2jC6JRMKkWkuxt9\nRSXBWId4X0rhYPvBMC1zTKQKtJnzNUqHhgxs33qU61c3odVJh+yGZscVm63+SkOrLy8ol31ILflo\nYNenJi3LFJ9Rb3FVyiyJWli4UJpAOj0QQ3Xfo5zP6KjUDpMhF22jCGd3CMPnsxN8hVIluXeiUO3C\n5IPlTc2S80K5hf9z7HdUW6XHNToVsYQFfd6x6WrPj7fQma7+65WwsJORkfloYqyvJ5bxQyg71hbq\nzI+WlZ5hArXz2b5nGM64AfdF6/fKY5rMB8V4yYdnz63IJqQXpGsQf8DI9q0neeCRNozmKolxXqOr\nAAJ55dz8I4AZX2XdmKSThZ7Cdcs+SVDI5dNRJcbaAsZI0DQ2TknmRkZmMi5VQio/4lN3ndRGpi+o\nS4hbgOymbr6MTGHfHp17N6jLOHYoa+2fv7hGco3RpJ0wQW370rUotjwHQKK6jJNH+sckGTfV1KD4\n2d+L+alMX39yWn+3zPS4aOP8xz72MVauXMmSJUtYuHAhGzZs4MEHH7zwjTIfCiqVkre6cqHmTdcV\nPz9AMhaj7qFNCC43emclyXi8qPVrNApuumUGwYBAiUWPVlt8o6SjdjmZTBoh4kVvLKesVk58LCOl\n3lIzbnmi6JShffvp+vlzqO77LElnLsxssL+c1gWQTKY5fdrIrNn3oFMNoXL5UA+bMZVej8pgRRkI\ncut6EwqNnsN7u1m0vIaG+hBGwzBlFYNkMpXT0m++VvEO1FLdskF8t92eBmpaPuxWXb3Yuo/S3mLO\neVJ0HwGmnkxTZvoM7dvPqe//C76NX5IcP7M3xJyWG0iqI6jiegajVby2xweA7rCKtfevRZ/wou2L\ngkZJ+Z/ehsZp5/Q/PANkE8lqbFYs8+ZOGKo9bvLBkRBvodzC3/q2EPEKGNUGHtv0JwT7U4SCMc6c\n8HDmRIY7Nt5NLOxGiFvo6rFSP2tqf/NEGp/j6b+OZ0CQNy+vPIYGXPQPBCY8r/AFJzwnI3Ml4k9G\n6HHbqGi8B43SjVprxVF/J5HhAMaSUgQhhqPuDoTIMBaVmtITOob9o4YXWSte5sqm8Pt//yNtxAeH\ncJ32MWOBmvKqlSQyKTweM2/9IeuH6+4PotVaMVvvRpEeIqN00HMiwYYbS/F4o9gaq9m1O+fsZJ9R\nl2dAzHkLF27Ex08OUj53JQoLpFIxMvE0g3v3Y29bIhrcCyVoSGckCWinKt8nI5OPx+sgmhkrFzNV\nmcWhAwcZ6j1MxpImfiZMbcvdpLSxrJZ8eStanYVoqJ9owkS8N8oddwnEEhbUef57tqWLaXrsUSKd\nXahrq/iH4JsMeP38vfEu2lc6GQpncJSmaV5fTe9pFxZllIhPOt/KT1A7ZK+iZP0NhMpNKMwzeGOc\nJOPFlHSSuTAXbZz/7Gc/y8MPP4xKld31/MUvfoHdLifYu1LxhAal5fDgBFdePGqNmq6fPyeW6z/5\nUFHrz2QUvJ2XIGbNhtlFrR9gqP8QfWdeE8tKtY7K+hVFf47M1UtbzXV8ZcXnJR7y6XSaE97Tkuv6\nA/0M9sTwHczqzA+qyjiw1ctomNmqWzSgUvDOzuxk9NhhWL2mCc2Pfkbl+nXoVy9gsO9FGLmjvOYB\nNj64CGXmPEHXywwHYdizg+aFn8HtccgGqQtgt/dK3u3yxjuBGR9eg65yVHodit88J3pSqB78xIfa\nnmuBcGcnqVX3ICSk77cpHiB9Pkr/r38DgH/d58RzMSFFV2cZtUucOEw+enqy+vJBTwfVX/gYff/+\nGwZ27aZu86Yxi9X8BUefRrqT5e4P0rouG/L6f47+jog3GzkZSUbpM3bw8bvv4P1jLmxGBVplildf\nGiAmZMe+2+9Vs0P0jhs7XuVHIS02WfCfekk8N6rxOd5i4f3xNhBko9cVR5NZxY3+dyc8f6ah7gNs\njYzMpaPocaMxtJBMZBCCf2R0e0lv28iBAwZmz44y2J0bx25dfzcvPp81zsta8VNH3oD9YCiMBI72\nGyTnuzoG+ePOc7Sv0xEY3gojik6pzFpiQtbwV1lVwnBA4MXnA4AK8LNmfSOm99+kJZZk6NlfsGLV\nRgJpAxWVZgbPd0ueMbppVbgRb1twHUmLIM5lAEqDLWRSKTrqdLm12fXLxDlN1/MvSOqYqnxf9reQ\n+9zVSrHzLbr6guzIk4u5eX2Q2fOrppzjQFAOMFwyEvWhA0PYSc2cXP6zUXmq80e2ok29Dqnskxz6\ntUDW5uU7cJBzP/yReM+GT93Ks/jxJEvZvtslHr9jpY0FZWFMDQ10+qR/c37Eit1Zhmv+3Ox7flza\nrzs6XRxVHpS8T+l0hvfl9+GyMm3j/De/+U2+853v8PDDD497/mc/+9klN0qm+JQZpbIZjoJyMRAG\nhyhbtZKUIKAy6BGGhi580zQYDkhlk4L+aFHrB4iFvdici0inYihVOmJh74VvkrmmUCqULK9dJNGZ\n39t7GL8QxKTSc19mJmZvmKazCU7/8hlsN94Id30ag70UUageUGaSJAqCSyLhOBbAtmQRQyqX5Fws\n1UfrgmX0dZxAnddH/QO9vPCTc+J1skFqfBKCZ9LyR5nLsrioq6LuLzeTUITRZMzElcX/pshIMTU0\nEhjwcvqEW9SWdFboUT33j0Tm5/QfrSoB0Irl1uY6ZtU4OX/8NUl9QsSDbckSBnbtHlfOJn/Boduw\nWVKnmJR17z4WHOllnulGOo1xfpk+hllrEiUXjMd30fvSy+Ii3D6zkT+8dlJM9jbeeJUfhVTZtFSi\nmT+q8Tme/uuo3r1Or2LFajWK5H587ibR215GRkbmcpAMhgiF/ZTVCeSnytOofRw75GN2i1T2TakY\n4oa1jbQ2J1Arj+A+cYrYiUFM1bWy5MYkjBvBJc93i05hJPD/aPqi5LzBlJ0L6LUBSOSOV5T6Wdps\nwqKMUh7twR2RJp8cGBYQypdh08Th4EEUW7IOHqZlbcTLZiGdY5izUjTnO2n6/KMko1FMNTXYly+j\n/9w2Sb0ZUxrvmZN8t+sd8Vh+PrDxIu0mojBaz+N1yH3uKqXY+RYnSsI9VZnFlCY2aVk8Hh2asFz4\nLLM3DA4IxDWS4764mrZNDwAQe2MbG24sZSicQV/mwB9MMmtuZdZIn1TwiXl3AnAyL3mETq+iqm6A\nULgfv+Dj7NkBdK4hfJXzxo1wkSke0zbOb9q0CYD33nuPr33taxgMBmpqashkMkVvnEzxGIr48zTn\ndfgi/gvfNE30Dgddr/1eLNd/anNR63eUmSRle7lpgisvHo3eirf7LbFcPfOuoj9D5qNHV6CXQ/1H\n+bzhetI//G8AenmHslUrGdBXs+2kikUlceYtriaTzmC1GwkFgtgrpR96p01Jw9efzE4+O96UbBQJ\nCQd7d5/DWaHE5zos3lPeeCc6fYoVq9XotQGUmfOy1M046IzVkt9TY7x2kpdejgWtbYaNc0d+J5ab\nFnz6kuqTuTD25W3UxU5woKND1Iuc09KM4pFPoPAG4I/Z65Q7f8OG+x7Fh4HKMhPGU+9wZHAWobha\nov2uCCtRGo20jow5hYwuApQmEwqDnjXrZxDwC9grjXSr3yf8ei+us4NYVQ6Uv/sNzTfewF+tWks8\nPoDPfRRrxVxMDY2kwxFxER6t+oZomIfxJR3y83oElRqJcX4yjc/RhdOK1WoMitcJeSHk3Sl62+dT\nuGE1a245gYETY+RzZGRkZC5EvLESR1BLYFgpLqxVaj1qrYkHNocxmm14c/liUWrLaKyJ4D3/onis\nNNNC99O/kHhdTiTrda1SmHBclgS6PBTm1vKYOyXJhxWKrHNHYT4ZRZ8P69bnAQjbN1Exd5WknhKd\niaAgcOiIjxWrNopa18baWhSeTjbcuJSgqpTqFidlkR6JN3I2Gl9BJpNGpTFK6k2WzGBYbccYeJdI\nMir+DaOGWPvyNlq/8RSCcoCUJobSaSSTSY/7Lvk9x+l496diubTqPtEZQqtTM+AJTeOXlPkwmShH\n3MUyURLuMZs/TU343EfHjNslzhl43TkbU4lz/Ohtc0kV/jwznTlv3mtsaiZz+0ME0gasKoHmOj3f\n8FpJOqUm3dKSnANWMhymsiKBXREGi4bAiez7o0CB3pDbECuP9tLeEiOQNjB7iYqAeytGwAgoogLd\n//WaJDIX5DH4cjBt4/z8+dkFzk9/+lN27tzJzp07SSaTrF69mltuueWSG/Tuu+/y3e9+l2effZau\nri6eeuoplEolM2fO5Fvf+hYAL7zwAs8//zwajYYvfOELrFmzhlgsxle/+lUGBwcxm8383d/9HTab\njcOHD/O3f/u3qNVqbrrpJh5//PFLbuPViN1kY8t7O8Ty5gUbi/6MmM8n8ZyP+XxFrT+VTosfSI1O\nRTpd/A2heMFuZTxafPkfmY8Oo7IP804PoNfPJN7VIxlUU4KA36QHEpw40k/rAic2m4E3t2YlcHT6\nIW66ZQYD7hAanQqNSY992UKG9u6jVBPDO5gzwhscDWx58Sh33iPdaU/GI6IxigQEXXvxV5aMm0H+\nmiaTkmxqVDZdO9IJl2NBG/KcKyh3YK+67pLqlJkchVLJglVz0drt4uIAoKOzngGHC+dn78LZH6HE\nVkZpfXbyffJ//d+EyErdHO1TsGJ1Vi/TqLUw+JNnmfnFxycM7x5dcKRW3YNb5eTY73PSXWvWN/K7\n18+PlLS0r9qIrjXDgOcP2Tb1/ZHmhZ8ZIz/jNVXBW7kF03iSDvl5PV7oepdvLrkPQyoqLnImYva8\nSu5/pA1FfB/5yn1B1xms5XMl3qinj/cz0HcUkzbAYL+FHp0N7/lc6Pt4Bn0ZGRmZ8YjNacS2/Qhe\ny2JRk9hkt+HrzUYrCQE9jro7iASHSWGjq7OUptpTkjoypjQA4d4+vCNJ+Zoah/Gef1685kLj0lS1\nj69WJvJclSkuhbm1akuqKO/twujrxFTaiK1tKQ880saAN0R53SY0Kj/xyDDJsz5UJiOpcARTQwNR\nBZJ1eyQc4+ihXuYtriaSDtN482oMDfX0/urXpMIReOstFj71JI4FVZx4ditKk4nUqnsIpA1oMkZS\n3/8BzX/1KP2uN0Rnm4yynl+/ECcm9NN+21peGf7tmL9BoVSibDDgenc7AF73WxO+S9FQv/RAapBj\nh3L2htsb5XnB1cJEOeIulvwk3FJ5lzpav/EU4XPnMDU0oGzQSzZ4RvuatWIezQs/IzHaj0f5rBWQ\ngWjIhcHsRB0x0/X8C5gaGvEaa9l+ph9IA1puVSXglS0o7q5g3uJaErEURrOa8voIfWdfx2CuQtNs\noq9/S7by6BHs1rUc3pu1JdgrcpJV4XPnUGx5HiuQXrJJ0qZ0TRn+dZ/DWlOBrm9AdLKRx+Dic9Ga\n8wsXLmThwoV88pOfZMuWLfz7v/87//mf/8nRo0cvujE/+tGPeOmllzCZsh7RTz/9NF/+8pdpa2vj\nW9/6Fm+88QaLFi3i2Wef5cUXX0QQBB566CFWrFjBc889x6xZs3j88cd59dVX+dd//Ve+8Y1v8Nd/\n/dc888wz1NbW8thjj3Hy5ElaW1svuo2Xg1Q6w95jLjr7AzRWWVg+z4myyPpNyoySe1rX4Yv6sRus\nqFBd+KZpoitzEBnOGYF05WVFrX/AHRK9BQHU6uJPOAt341VqwwRXyshIZR9sgPOzn8Jjek+cTOpm\nVOBQm+DMWWJCkmQyTV9Pzn0qJiQZcIc4ddwNgFZVTVg4hb/Dx+zrw5Jn6dQeQIMQK5V4qtjK64Eu\n8qLRROkHmRyJmLug7Jrgyo8elVXmgvKlT6ZUCZ20HNdNcKVMMclfHJw80i+JiJi5YQHxu7Q01M5H\nqVDS9asXcx42NRXEOlxs35oC9Ky/uRTdA5/hrKkOSzLNmRPuMbJHo4b1A2dTJASpLIN/UKrJNVRS\nT2VJD+Ttbwe6j5M+F8G+PKf9ak9nxvU8ymdJ1Xw+t3gTXcNZndCG+htQKy88XVUoFSiAwUG9ZIyM\nnXEzFN0n2YRQpDvFDU09kIjdKKlLHkNlZGSmypLa+RysTdF5PsSp41lN4jvu8jG6kkslBQZcA7jd\nTQhJBbNnB1CrpY4WinB2TeNzzOKlkXHddJdA/mowf1waT65uMu3jK9lwP1XpvYk8V2WKS2FurcZu\nYUxC1daRfuVzH6Xj3ZGoeQNY/+JR4pEyShe2sO/Vk5J1+6y5lUA2IaUx7kFrtxN8/31S4YhoiN/X\nkaIkfY7SukpS9z8xoqOdho4Q7as2Eg25SCUF0dkmoykZyWcDDqGaB+bfJeqL51NodPd5u7CWzxvT\nzwqj8zJKBzCQqydSoEcqc8UyXo64YjFuRPKmbARq39nXJdeKcowKpagrn05neP/o+GOeUqWmcu7N\nAAz+cS8Du3aSEgSiPb30N6+R1O2Pa7ACvpiaYyey71r7Oh2+vlwbyqqkDjgG/TCQXbMFQznJ6PwI\ngGRaKlUaTdg50BGADhftG2Yz6A3hsOkoS7h5980hPK7smDx/RStKdfFtjNcSF22c//a3v82BAwdQ\nqVQsW7aMb33rWyxfvvySGtPQ0MC//Mu/8OSTTwJw7Ngx2traAFi9ejVvvfUWSqWSpUuXolarMZvN\nNDY2cvLkSQ4cOMCf/MmfiNf+27/9G6FQiEQiQW1tLQArV67k7bffvuKM83uPufjbn+wVy//zkeXc\nWOQQEa1aw2+PbBXLn1u8aZKrL450VGBg126xXOss7qTJXi41MNnLii9ro1LrJdIXKo1snL/WSCUS\nnN21nWhXF8aGBppXt6NSjT9UFmq/9Xm7SD78eXa9PkR2Mulize2zRM8RlUqJQiedCOYnZomnYOtr\nZ7LyNwVrJrXWBMTZvSPJitVrKS+PoVFaSHcK2BrqJMb5yaQfrlW0Boe0rL92EpjnhyqOaoHCpfUR\nQdBTrr9hRHPehBCTx8piMhVDSmFExND7bm4wl6Gsyxl5tr/dAaTR9Q1w220NhAMRjLWV/PNrxwkL\nScx7D/Ll9gZee+28WM9tm2bSZzpLvaWGpW1LKPMdJjQonWzbCmTlEqjwDJklRvFEvISTf//31H/q\nIYx19eLfMLq5MBEH+4/y40M5b1G7wTrlUGR3f5B3RsbI/AgB3UabxDivVkoj+9Rq6fxCHkNlZGSm\niiKdYXgYtLrcXLFQ8kOIW7BYDRx4vYPWWWmGB06K6w2doYbM8TCtX3+So8MT15E/LhUahzbe3Yzp\n+GHyydc+nmrSwg+DqUrv5W9Oy1w+RnNrLatawNC+/fgOjd+vMqkUQdcZyblQyMOrrwwTSamIRKWb\n+qPrnfoaA1XeOHH/MJqSrLNIatU9bD+jAwbh4CC33DWT3qFhyf2BtIEavXQ9JsQtQHajq6TEwJ0j\n+tmFFH7T+3rVDPjPE40kJMZRa8VciXezx1tGvnG+MHpD5splvBxxxWKyiOTCvjbefHKqY16ks0ti\nW7POXSk5b1FmZZzyc00V5oJQaKV9VmuoAAIAlNXm1m75ka6eeDnCSBSYELcw1FUi3tPT6RedChW3\nz2TbltGoWi+ZDCxcMw+Zi+eijfPDw8NkMhmampqYMWMGzc3NlJRcmjfe2rVr6e3NhTvn69ibTCZC\noRDhcFjyHKPRKB43m83itcFgUHJs9HhPT88ltfFy0NkfGFMutnHeXZDYtLBcDBLDwUnLl0o4LHDT\nLTMIBgRKLHrCkfETaVwKemMFyUSYZGwYta4UvbGi6M+QubI5u2s73u/9EIAQ2XFoVvt68Xy+0Uxd\nkv3gqUxGbEuWEEuq6QzqJfUNBwTRc2T+4hpJMsfaMiVKbw/atlriKThzIpukNBFL4fFqqMjbKIoI\nJdx0i5VBTwi3V4XPr0I50It16/do/cZTUwqVu5YJRcxUNK4R3+1Q9NoJxQt35EIVAcK2TTjG0Rif\nFoN+un/8X2LR+blPXVp9MhJGDSmj3mTxPjXVM52UR3tHQmcbqXDWSu6xKKMSY4w/nmfkEZL4zvex\nuD7DzoiV8EhI6qb6BB6XNEqnv3OYF9KvAPD/lH+M2LM/x/HQX3LD6mYikTiZDISTUTbcUYvLmyGe\nzHDmhIczJzLcftfdJAQ3QtzCwLE0ZiD4/mm6fv4cs596krIbL2wMuhSd0MqqUmJCSowQaG8ZRjES\nYp+PrbxesqHpdqmJjyxESh218hgqIyNzQUY9vvtO92Mwqtm3v5cbVjczNBBm0Kehsnw9FksEkiUo\n3BoGYtmxdjhoQpvKef82X7eM08tTdAV6qVbmDCm7dyS5Y+PdxMLuMeNSoXHI1elltlm6aZo/7k01\naeGHgawlf2UyOg8pWy01CI72q6F9+4n1eSBvb3vUWO51hzl9wsO8xdWkkmmqai0Eh0KsXdeE8r9/\nQJ83a/Cu3XQ/ZatWct5YBuT6gd8dlWx2ARiaS/nH86/yaNV60iEfBnMVvR0mZs1NoNGpqHROPK+3\nVsylxHkfw4M9CHELb+1M0tDsFY2Mo8bRfO9mAGv5haP9ZK49JpPYKtzgGR238yOEhKg0AmOiMS9e\nIBFt7T1Ge4s5GxGrTVBrTBJe1oY67mZ/6UvVAAAgAElEQVTj3Tfjj2uwVfjw9+acfkNhsyi3JsQt\nePqs1C4TUFtSlEW76Xr+PdEJyHHD9ThuuJ5t7/yWTFQJbgtVdgcn3stFm+c7FQ4MRiXt87iGRQme\nKyk662rioo3z//RP/wTA2bNn2bNnD1/4wheIRCLs2rWraI1T5v0PDYfDlJaWYjabCYVC4x4Ph8Pi\nsZKSEtGgX3jthfjBD37AM888U7S/40I0Vlkk5YaCcjEwFsizGNT6Ca68eDRWabs1luLuLpeY9Gx7\n9aRYbr9jdlHrB7BWtALpvAH1yoqymIwPut9+VIl2dU1azvc+UpmMND32KKlImK6fZxMbmR+RyiOU\nGFVsuKOWwQBodDpaFzgxGLWYVTHo78YXU1NWr2fnji5Rw02jU7FjW4xNDzWhN4UxmJ2cOanj7T+8\nL9Z70y0z0A9lP4rhc+eoX/7AVSvD8EH0Xd+gA28sgF6rRohrUOmuTM/5qYZ3Twd1qXTBor7EjXSA\nVI9r0vK1wOXst6OGFNGb7EwvvJWNgFBsyXqVN/3p59lwYykedwiLMopq10uYvvi4uIFYkpT2m4py\nA0lhkMZ6Cya9mlvnVKJKC5SUWdDpfeL4YzRqsjuTZMe/dDiCuvcsO07nEkdtvLuZhWvmcfKIixd+\nsk88fuasiWOH9ECM9pYYSpOJ6HVrCNevoMMHjnTmgv35UnRCc7IHw1i1SWyDpzCNk/A2f/GUTNl4\n7qceYkLWoP/AI40f+aSL8nxB5mrlSuq7+d6POr2a1e3NxJOIBj+AlStrqTm7g+iu3dj+7BsA7Nwe\nZ8XqtTjLIhjQ05HM8N23/gPIrtce2/QnBPtThIIxXn3JQ0wYOy4VGodMkQFcu35Pzb0fIxmJYFuy\nSDLujUlaWLBh+WFyLWjJX0n9dqqMzkN8Bw5StmolKqNR0q/CnZ0MvrQLx8dXkqm24Rm28tbO7DzC\nYdUQE5IcO9THvMXV/OG13PqlfelaMRlsfDjrHW/TS/PIOew63AMxrmurxWjSUmLM8B+BHxNJRnnX\nK9C9TY9OH6RljgFbmZGGZgez5k5sOFcolKQVTbz6ygCjnvajRkadXo3HHRx33i1Ha1ydffdyM5nE\nVuEGzyj534v5i6Xz2onGPG2ZNOpbpdehePm5nLPVsjZ8+/ajMhmpLrNjUyk5H5uFUpszxseGK9i+\ndZCsiGOM1RvUIEAtZgb+44ekwxFAGk1VVVLBjzt/Cgooi9r4xK0P4fMIVJZZ2Pf2ebE9ZY4C+6Kv\nl+6RNcqVFJ11NXHRxvmOjg727NnDnj17OHHiBAsXLuTmm28uZtuYO3cu+/btY9myZezcuZMbbriB\nBQsW8M///M/E43FisRgdHR3MnDmTxYsXs2PHDhYsWMCOHTtoa2vDbDaj1Wrp7u6mtraW3bt3Tykh\n7BNPPMETTzwhOdbT08Ott95a1L9vlOXznPzPR5bT2R+gocrC9ZdhV9aoNkg0501q44VvmiZKvS6X\nEFavR6kv7gaA1qBize2z8Q2GsZeZ0BkuuvtOyEQD6tXAB91vP6oYGxoI5ZUN9fWS8/neR6lwhGQo\nCGqNqO1s12RYsLSWWDTryaHzdhKLREk6WohEBE6fcBMTkqxZN5M3TyjJyt900L7SScA1iMXpIJ5K\nsfHBxUTT8P7RQYwmHc7qEm6/dz7uvgBWqwFN/xkyu14izZW1yLoYPoi+q9RAOtFIrzs7fqg0xc3r\nUSymGuo4HZLRqGRsTgrRC990AcxNjeTHX5kbGy+5zquNy9lvRw0pgbSBbOInxLJ9xJv+6IARU3SA\nmak+kr4AJfduxL58GUN794143RvZcN+jovE++esfYfri4yyf5+SJDXPZ9uJRsiqsPjExtc6gQaWH\n24P3oiiNY6oyEAKUO39D+6qNREwVWHRZozfMG7NAUSigrMKMVZvEdHYfvvv+L363IxcSXlrnumB/\nno5O6HibWdKF9Pjhtfnf+kw6w8YHXR+YZ9zl2ICbLvJ8QeZq5Urqu6Me3zq9mpY5FUSFFPve7hSj\nI8ucZmwZPwOWJvzrZmFHw8JltUTDCQZ9GpRpFUI0SVpIYlQbiCSjRJJR+kwdfPzuO3j/mIuyipJx\nx6XRsbf7WCcGXw+qXS+RCkeI9PRQeVv7GKNIYXLuwg3LYjLdMe5a0JK/kvrtVBGTwocjDOzaPcbY\nZmpoJBWO4PnZVpQmIyWPPcWSBUEMvh60r/6YDTffi09QkdLlNpV0ejVxZw3RdZ/DqhLQ1Znoijvw\nRTK0r60gHgqj9XZhUic4cqBHvGdVeyOrE7ejKI2zL/E2n9r0aRJ+Va6/ZNIM7d07qRRgfj8zGDX8\n4bWsw1/LnArezNs8KMa8+6PE1dh3LzcXs2mTHyF0+oSbNRtmk0kz6ZhnrKuXrN805YXG+qytzbZk\nCd3P/3f22IbNbDutZdQY/+DnTNx+73w8rmGsdgNvbcvmweshRvv9XyTQ58GqEgj39jFa+6rGhZT0\nP8GgN4rDbGbv7k6CgRjHcNG+NhsdZjcpqEt2cdttDQz64jhsOlS/eobkSB1XUnTW1cRFWze/9KUv\nccstt/DII4+wZMkSiZd7sfja177GN7/5TRKJBDNmzOD2229HoVDw6U9/ms2bN5PJZPjyl7+MVqvl\noYce4mtf+xqbN29Gq9WKnv3f/va3+cpXvkI6nWbFihVcd13xkkEUC6VSwY0LqoouZZOPSqXitycv\nr+Z83J8nz6OAeCAw8cUXQTSU5M3f5z6ea9YX33M+k0nj9xyXhCJ91D3oZKQ0r24nk8kQ7erCUF/P\njJulk4/xvI96BLOo7UxHH7etayLoCmAKDpBSq0c8X7sBmLe4mmOH+vD7pAbS8HCYmamzqLQu0tU2\nIMWvn/eNeHNm75u3sJrlK5tIJVMc3Z3Ec8+fU1lVgq3t6onw+LBIxNIo0+epqQwQS1hIxJo+7CaN\ny+UI7zbV1hE9N7KppABTXd0l1QfgXLcWMlk9RGNDPc71ay+5Tpkco4aUziEFdOS8MC3KaJ42qw9Q\n0d5SgWLfNipva0ehVIobiOlwhNSvf8R1924kk05j+uLj2JcvQ6FUkIwkJM9LRGNU2DWYyy1sefGY\neDy43k7d5gdJhsMkh/sh1g9KJabl2XxACqWC2XMrKA93ET5+GFNDIzNvXUJg4CRDRj0mQHdIJY5j\n+f05nU6zv+89iRFeqVBOSye0GJtZU1lkFdOgfjk24GRkZD54Rj2+W+ZUcOxQH7PmVorewgA2h44S\nZ4iUKYE1YWTnjm5a5lRy6ribBUtreeONnLNH+21reWX4t0A2WuhC49Lo+fJwNyefe07cwrUuXjSu\n4V2hVIqSBZeb6Y5xsnfylYm4odPbh88xi6PDaiqPuMYkjQ93dmJqbIR0iJLwCRIRP77BQVLPfp+l\nX3+S3piW0fi6ljkV7N6dla7T6Y2saGlm+/bc2v622xrIPPscHttXxWMtcyp449Wctv2nNn2a65e0\n4D94GCF2lu4jMXRaO+e+/79JjeMFPEp+P8ukM5Ra9NOSGJGRmQqSnFGNTZBOE+7qxObMOYvEhCQV\nlSV58+EM7x/pHzPHtLctgXRK3FS1LFmCV1eDxxWkvEyD0diD83oHGkUJqoMHs8mVd/6G2zf/GX0p\nFRVVJcxorUClykaJ7Nj6vhglC9AzlOFURxrQsrG1mVF3xHf3nGPba2fF6266ZQZv/yFbjniHaOnc\njUqvx9e2jjde7hCvy4+KudodBz8sLto4//LLLxezHSI1NTX88pe/BKCxsZFnn312zDX3338/999/\nv+SYXq/ne9/73phrr7vuOp5//vkxx6813OGBScvFQGu1kBwcyitbJ7l6+vh94UnLRXmG5xgd7/5M\nLDcvfBhb5YKiP0fmykWlUks05gvJn4wam5rwGmrp7vGIevIxIclgYJgF9NC/aytDd/wZjPgY6/Qq\nWmaEaagRsNijnDiSM1qZIgMoqtT4NcdGL2fF6rUj2smQjKdw9w/TuqCK0yc8vCR+DL1o7XZ5InkB\nrKUehKHXIZH1JdCXbgRmftjNGsNlCe9OpyUJhcpuuumSq0zEEkTqLcTLnSgMVlKJJEp18aOZrlVG\nDSn2dAZjXc6ruzzaw8GOFOAVvTV9agV1f/5XhHvOE3v1NRRaHZDLhZEYDmKZN0/iSVbYz3TqDGV2\nHcOxlFhvIpbCH87gUCpJWWJQrUYZ0xIuaePosJ7KIy5mzinn7M43xDwdAC1Pf5GenlfEcv44lt+f\n9/e9J0o5AHxlxedZXrtoSslwR7mcWsX57fBVzssbcy/NoC7rK8vIfDTIeuIuxd0b4IbVzcTiSclc\nsKYmSMD3Ogqy8477Nq3nvXez8h2Kgr29srCTT87fSJXFyVLnPAbf+ePIPLOZAUPNhBuD43nEF1vn\ndzpjMshj3EeF0XmI90g/W355mJY5FfR3BxgejjBDM0i4qwu1wQgZiA0O0vXz/xKN4/WfeghDfQOB\nyhgm3wk231+Ky1fFkD8hviMtcyro6fRLnjnoTzBr1UpCxpzMTSImTSw71BXnWPQURv+7BAw5w771\nS59j+DxYPScI9/Qi9TEu/NtyhvqTR1z8cec58dxHUVZJ5oMjX/62bNVKcf2lNBnZ+NhT+OMasY/t\n2HqKyqpSFIqMRCLtlg2tRCMJnNUlVDSZUJfbUJpNnD01IM5F29fpSIVyazvH45s5dSiNVSVQmurl\nv4RtcA6+Uv150dmlsG/n68f74yr29hymK9CLbkDqyR8MCOK/S6oc9GpuobKqhGBCuu6LVzbTsnnT\nZY/O+igjr6SvEUq1Zkm5RGua4MqLJz0S9jZKbWVlUeu32qVSPFZb8aV5Qv4ubHlJOEP+Ltk4f42S\nSaUYPHCQroEMfkFN9cwqZs6p5PQJN+5hGxVz63EHBF57PucdNOoVX6HLoEiq0W7+M2L+nE7z6nYd\nJaZu0voYSsU57rqvCY/biq7vNIrtL5F5RJp0Sa8NkF3SgWNEKgLkhc/FoNEMY8h7t5OZ4Q+7SeNy\nOcK7x00EN4XEnJPR3XGIQP+LAET9kCFDy4JLN/rLSBnrUVhFncHFnn1e0VsT4BBwa6uCsr5zDNfO\nJ/LQV3FY1Az29uIT1FgPd9MUCFC19lYUSiWz51Vyz7oqhhJ6hoMJhgXYt7WLWza0ivXq9Go0ugoq\nW8uJDr2TfbwBook6dmzNarbetqmF2Mn30Oa1ORqS5h+wOxJs/IQFvc5PRfkAmUwlCoVywsSv+Qsb\nmFy38nJqFee3w7/uc5JzlzLmXgv6yjIy1wIKpYLYkI+BAUEciwFW3tpMdVUAjaoHo3MRwwMnSSUF\nFKkurltoxBTTo7PoJHXpVRmuD9pwzFvE4Dt/FMeezO0Psf1Mv3hd4cbgZB7x0zWqT8TQvv2c/v4z\n2JYsIXT6LLGhQapuXz9hXfIYd/Uwlagwd3+wYF6gwkuKCkMZyV/8SNSsrtp4NwmfD9+Bg2TSaQKV\nMfynXspVpFzL4f3ZucOCpbUoFKDQSZ9V5tCTOitQ0nmIW1uriZY4MVdYJHkc0qkUw4Ibc2MJNkXu\n/QpFBtiyV097SwWNKhVT5VqQVZL54JDI3wo5o3Y6HMHmPsbCTQ9w8ki/JLpozYacGkTrAifd54dI\nxFLYrW463v2teK7EeZ/4b5MhjMWaW9cG/AIHOtSAljsbnPxpbzPhcjP9ARfUZu8pj/TQ3hLLJpSt\ndTIQVTJrbiVanRqdDdx73sbhDaOfJY0uL680s2hxJfYyE++83UMwEAO8rLtzluQ6h11H/ZoHLuXn\nu+aRjfPXCMF4iBX1bQjJGHq1jlC8+F7niWBo0vKlotUrRc15W5kJra74Gq1qXSkoIBkbRqOzoNLK\nE8prlaF9+zl5qHtEPgJ4q5fb753PlhePitdc11YruUdDitvmpFGm0hz1laBUazl9wi3qj9bU+PF2\nHQZApdbjqK0gOtyLbXEZQ+8Y0ZsqCYZzoZt6cxXXtZlQKECtyOBxBzl5xIWzOrvw0elVrFitprr6\nDD53XJZhmgSD2QqpjPhua1TFT7xdDC5HePflSAibELyTlmUungstlmfOqeD2e+fT3yv1OAvr7GSS\nSba/lZOUm7e4lmMn+gAtG8p16P64D8eN15MBBI2Z3VtP511bTTQSxzSSz2V0Md5QI5D/tc3fNPT0\nB1GXmyXGeZ1W6q+m0eiIDr1MKAihgV3U1t5F7IibRU47JtUi9J5hwuVmnKVZuaVxN5MmMM5PZVE9\nmVzdZL91fjusKgHy/kpntRmf+6hYZ2nZHE4f90xJ9kY2BMjIfHTo88TGePZWVvgQfL9HW9ZKOhWj\nrPZGBnr2oFTp8Pn6qKyqJqxVsvj6eoxmLZFQHFOwB6FOSd/Z18mok6hMRlLhyJi8I9PZGJzORudk\nhDs7sS1ZIjpg+fbtR2e3X9K4LHNlMJkE0ej3UalUiH083ykAoH3VRhRbnkNpMuE11TMQLce5aRk+\nqw3N4GnJs/LnDhq1gnQaFGpY2d5MZYUPjdqH2nOY9IJy0roEdSXgcccY6vXQvspJQFAgJBWUOwbR\npl/HN2KvtzkX4XMdRohbgKzhUXD1kkmnp7QZJcsqyRSTfPlblUFfcC4r9VLoZJdJxXnoYQOkh9AZ\nh/n1CwLD/jgtzdL5t0rpE/9ttpbgc70tlksr1wNZyVzX+UGsv38HLVD+pcfE9EuBo8dQbHkFK6D6\nxKMkFRW5ukMC9p9vA0BbcyM33TKDYEDAUW7CNxQlEoPkQIzmWeW8uy+bDyLo9YvGfosyKuajkrl4\nZOP8NYJVb+GVU9vE8qevu7foz9A67AVlW1HrT8YyvLklL9v7hsugOZ+K4Tn/pliumjGxvInMR5tw\nZyeBtIn8RZHHJfW2Npq0krLdpEAw1fHGiJ7i/AL7bzKZ2xQrLWvFc34rCsA/CNYnPkOn24YiYxQz\nrAcHHZw73c2CJbXs3J7Vetuz18v9n1nKA4+0ocycJ+j6Nb4+8PVB88LPXJXJjD8IlJkIrrx329l8\n7bzbyUQS7ae/yFA4g92sIJm89KgBtb5i0rLMxXMhvd7TJ9xsefEo8xfXSO6zGtKESpqZpU2i1ak5\nfcItMRoNpMz4OgTqzS4ymTTnz0n7QSKWyno8VpWy9+0u8d5E2iYxvo8ugAEqqkr4YfcpPv6pWzF7\nw9S0LmD4zBClihYyJaByzCSZlC5CAgE3pwdMVKg1WH+9m3Q4ghZorF0CdePn9ZiIqSyq/Z7jdLz7\nU7Fc3riJupalKJSKSX/r/HYod/5mJBxZjVWbxBx7j453t+XV+QAv/MQ1bj0X02YZGZmrA2OJgWgs\naxAZlQRTZHooLWvF58o6YwS8J6hoXMNgzzvE06tJqO28+VrOcNm+thmHrh/X0Pswog5atulmXH1W\nKHUwf7FWlMop9EJPp9Mc7D2KrytJwqegqcFJ6zwnCqVC3GBUjiQRP3g+Q53JNe2cGaaGRkKnz0qO\nTbZpKo9xVy6FuV6i/QbJ+dHNn0wqxZFdJ3np5Q50ejVLb2zg1HH3mI2oQNqAFUituofX94bR6dXo\nGuwMnAmxYGGZ5NpEygpkPYmNRjXBUIJ39/XQvk6H4HsdAbBVLSLgyq71g+EOBNNa3nkrO99oX+Xk\nwK5+Gu4S8pdmKNAQzazlrZ3Z6GJDdRW+tJmh/QdwyNIaMkViokikQiePmYsX0fTYo9mcXDOacNx0\nI5GuLonUS2F00YymAdznfy+W77xnPc/9DGIJC/nmfY22nHmLlSRiKRLxfkkdoWE/kHUotDhzTjJ6\nT85hR1Oae65gq+XYH86LZYe9CdXtDxFIGzCrSjmwp5OYkOSG1c0c+mOXeN2K9hmit73dEEXY8hyj\nQtamrz85nZ9UZhxk4/w1glqh4p7WdfiifmwGK2pF8f/XJ/wBSUbphL+4shG+ocik5WIQF/yTlmWu\nHUwNjVh93Ui9JS2iF7xWpyaZTIvl2hoTb+3qoqE590E8fcLNjTc38+bvTwHQ0GjGoNZTWtaKQqHE\nlhfuHI956e1W09mRomVOc9ZQVg3Lrq+lp0cahdJ/xkX7fUvoO3uUfLNXNNQvG+cnIC74Ji1/lPFZ\nmnktTy974925pD8XS8dZEybDWnEjaeCMiZmyAlhRcPcHJbrvXneI2fMyojFl1ONmNCpHrYZSSxSl\nQsPu13NSMfMWV0vqjcQzHDs0yDsHB1myohatTjoPaGwqxXjqHUx1dTzwSBv9/X5OHXfjHXBgt67F\noB9GZ3Qy6LJyywYdFU4zLXMqUFd/hq5AL5WWGoZR4tpzDtuzbwCg/XQr8VKDZHHhGbZyoCMGHcOi\n1x1ApKsTbrx+XA3lSyEaki5gPL3niMRqaF1QJf6Wo1FIiuR+fO4mrBVzx2nHHIb27uPk0/+A4U9v\ngzyHvGhYKuVT6N1azISyMjIyVw56hxKVX8HqtTOJx1K8s7MDZ4WByvJBVCPzvXQqBhkl0cwavIN2\nyEQldfiGolQ36CAvqDldX8lbe9XERqLS1myYTUXlWC/0/X3vcfRIDz3bshrd++kRNwdHNxhzScS9\n7NnnnXbODPvyNmJDg/j25TYy5WR/VyeFuV7+R9MXJefjphB7ew7T3B2j51g2P11MSHJgTydrNsxG\ngUIiMVNRaca0rI1eSzUwyKx5TjFxpNFci92amycmk7XMmhukvt6MEFeg1aVpX6ejssKPSpVdD6VT\nMUl78r3tA/2DtLfEKLE4CeWlzkudG6bEMo/rFsaIxDPs25/dyLrrVsOkuvMyMtNhokikQiePjXc3\nE/rhjyTX1W+SSr0URhclhJ3Sh6WHAAO7dyS5/4G70ZtjGMxVHDum5dih7ObV/IVlEinmRKaWWXOV\naHQqQl4vo9u4psbcWG1sbKTm3o8RHxridFS60RaKpDhwRgekKRl0ccPqZoYGwsQTScl1weGYOAY0\nrK8p6nxdRjbOXxGk0hn2HnPR2R+gscrC8nlOlEVetPnjw/z25Fax/LE5xfca1VaU4/rdq2K5/rOf\nKWr9YzTn7cXXnNcbpbv8uoKyzLWDfXkbrUoFhvoMPkGNocJKREhIwjlvu2MW+kwUg7OEgYEIM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3zWf5UhsGiwaXPERjehCD0Mo7fqkpeXRUpIqZoWqcvwZgM0/gHjRpJul55UjlU+wfKG827o7Z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XRt7S0atWzYOE3ospKAj06CybuTYsSAaNQhGpLMOovYvQNErihxSjoGdv8uDAaZ\nyJXXAWVcf+qL28jnQ1wYtVOpKBIaSBqoqKuuQV397/ZacbuLFN6NEz34DvLWz5Pc/EUq/b0c+ck4\n0oWr9K9Ue5xMnp+ama81826sNyZNH9vup8etkCMQuO3EejPuvXOYrwo/9v4HxK5+RMVRRppMwAkB\nzw1Y38nwGSbO1kxg927fxsiHGpWu/HzQFsXrtuOxNM8+Z5qRr/EwemmaeCRHb2djXipFdPIAAJmE\nkgfIzeQMhh/ZyWuvyGwe1lFKv1SNl8yuJwhOKpGM1W5kOqU8P+GIF52gyDvmCw4KCR9Da0WKkoxW\ne/1+nHRn+GdGoQxPp3pYz90Vx95pNJPznwEUizLDazvISSXMoo5icfHlWjqsrSpZmw7r4pvOFuLx\nmqyNyUghFr/xSTcBp+faspzFhlZvmbfdxPWxELNXjUbggdVtN9SZv1PswllGvllvYm3bEF1B9UQ6\neX6KaWeYvzqqsPbNOhNffeY3KCa05NI5hYE56KMoybRUTNgSQUxbh0n33MNEDFat7eD8mSAGUYun\n1EI0Z8a93AoXRjAakmh1FiUzWgedviHZJdrR6pRJtFSYVr1XyIVUbaEoYCyrjU6bmBs/L8+2xSZW\nGQ4GUYfFevuVVNFAat72rUBvthMYe6fabu3ZddvXrNdznG03FynXx8kj53jtlSk+91heVV8QD4Qw\n6DUq08hiOcmpD9OIRh06gxa9QUepWGTHYJm4pMOhyeHLTTLxk4PVc5LP78HtMuPSdzA9M/SkImfx\nL9lDfvryDEv/IJuHtzIxITO4xo9TE8WkjZPj2kS+wy3idOcQ/L9LMGfhwqUQLXozlW2/xJtnNECZ\nfmOqahxLEXIpdfJULmagUsY6/hHGaA5tq1qzvqtHKU/Xi1pSCWUB0tM9XWXvGYGlS/fz/jtUx3NJ\nhkqnS9nFmIHXK/HaKzMJ/NPwzLZfxZExUpmRxpnFzVa6aAQNGzrvvaukbOpRQSma7iv1IyGzabiP\nSHhaxdB68JGlOB0jSHXDlJQNqq4jZadIhs/g632YZPgkcukMm4d3IoxHmBZbq0zOXLaEw7cfuRCm\nLHi4Oumgt3scvVbZWDRaWlWGrzq9l/fPFnBPHsMZHiFraMG7fhfFwjgarUho8iBy51beO5DnvdN5\nti0D4dX/AUA2Nan6jDczfikSVbV+pbNY5tiorcntLHZ1VRNNNPHziZuNmyqyrPjKXJ0k7uknLesx\niHpy2QL+NgcrZghKf/Pu3/G0biV9vo1U9DoiMfOMnnyQhx7uQi6Vae1II2XUbGCNmMMgelk26COV\nTuGdIXHMrm1mK+/kUo5MQS3HVy5NkYufwe3cyUfHFG+Q1es6KWvUPnlavZvWvl0U8jH0opXY5PsU\npSTtGx8ls2QdF0PKRr0UlBhY3crHxycwWwyq2NoYu8L5QzHGdO6qOfvAHJJkjUaikUiewjf/O8t/\n73fQLLHcdmK9GfcuDLdSwTBfFX5eE6kRMqxg1nTd8DM0Mt+1lQhL4hNoTUYsXVtqn7VUJDxylNx0\nAJOtDV3KRObyJXQuF47cCspiEU1BRGexVP2bujsTBC7WfKMGV+zlaMSMVqeWsdHq1LmvslzzT2tt\nzXDv2g58viDpOg6ulhinPlT+nq0wHTkdZGw0ysatS7gwmkIvarFYc1Xiypr1at9Ii81A/0o/elFL\nS6V2z7ut+vPTQDM5/xmA3SLywsHawuS5zw3Oc/StoVgpqQxhO1ctfiCvt9sIvPSjarvrV764qNdP\nxLKqUvREPLuo1wfQ6owqNuhskrSJG2MxzV7vFLvwairA5u716AQdpYrMtDGhet8QHEV3pjYsCgLY\nXFfweATyaZFCqZUzPw2wbNBHeCqHuWcDFTnLywdqwdWm4T5Eo46f/OsIoCSAtu9dQUvbJOVSEH2D\nyXCpkMXX+zBSJohGKxIeP6SUeQKVsprpoWswLBbEXtwr193+D3MX4Ofl2c5nCipWr6/VOs/RC4Pd\nomY/2Rrat4JsLjdv+1ZQr+c4V7uJ+hLoSYxWUSljLarZvZLsodggByRXPECcZYM+Vf8a3rGcNrJo\nDXrOBdJY9vwymrd/QDmTxcoyDnx7BLvTwBNf+NxMgtNNOBCjnKslNI2GJG5vB93dSTLhA1QqyuIn\nFTmrVPfMLMijE0eRS3l8nY/y8v+cqTIanWbLli7gKqJRy9BqCaM+QWpmYdGY4CdZ4dI3/p54ywBH\nJhIMuAQ2DfdRLBRwJsbIT09XdTFFo46H96zAbL6A1lJ79qVCnN37V5HPF/nJK+cAsO9yqn7DisYD\nRGq3DRQ5/uYEbpNTlQhtXLjPJlCbmBvHTgU4+8kkV95X+mD/ymuJItnpHI5+G5JYk2/Ti2oJvNl+\nIWWC1Wo0nyNJpq2Dlq4y6aCyGaMFCoXHOT/SRYUKUECjFavVadPxS7T27SSXnqCi6WZssoVYPs10\nRUdBcOMJn0e6YiStqcXoRkMSZnpLsmyqmr6JenWJ+M2MX+4N6zF/9Y9mtJzLvD86yu4n1Qkaf53c\nTj0a+9xCZBKqCbqbKOlvYm6cyiYYNc39XryQ+XQ/TBN3HW42boodP8HZv/hLKruf5c2joyopOFA2\n+6Y0U/x+5X60J8fANE3MvZw3T9YSctpyiYOHFWKA0aOeo+MJM1qdgEarRS4VVZXCrtZ7q2254sRi\nKZGr4/HNjuv1Y6yUKxIKuTEZduJxpzCaLWi0ecLjh6uJfl/vw0Qn3kUqxEjJHZz6sJZE3TSsbODL\nZbU0rv/hLsIFPe/88DIAJ5iYc6OzUY/enAnhuu8+MpNXEZzqNNqtJNabce/CcCub0vNV4ct6SfVe\nY3su6ErqdaMcyBI/rnwm74MPVl8PjxytVdElwJ5eRuifFLb7kq/+OqVkGnFNKxNXXqqe07pkm+ra\npWKIZX0CZVpVa1c0aiPb+hjZYHLQ3jGGwehVbYSVyi6g9qDNVphK+RLplPK9BQSsdbLagoAq52Yw\naKux9ab73Dx972N3ZfXnp4Fmcv4zgEBUHbwFo4sfzMVy8Xnbi4FiOqNizhfTi/s9HC4T+ZyyKBEQ\ncDivEw3fBgq5mEqXWqtb/Hv8omKhZq8LwWKyC+vL2gxavWqT6pHeB3l4SxfpyQwOTQ7toRcRrTtg\nRva10Yxw2dLHKRZrya2R00GGH1Y7k8ciGUSjemj1uCNExpWNK63OqJgOF9JUyjLJ8EmsriUq9p6g\n0QICydBJ2pftIpNUWHvxqQ9pW7qHeDRNKmVmItRC76rmgnoh+Hl5tqeTuXnbtwKHNqcKspza279m\nGTWTSW5o3wqcvpXX+Cc0oUYidIrRj/++2t77xD7Gxm10tj+OaEgSiYiceD1PX38LS5fug3KUVMpM\nLuJENKZYtjRDT0fN/DIey1NymCkkZXQ2F1GtFe+2L6B583vESzYgzfoNAtErNdkcT9fniF6pfSbR\n4ufD18dx26cRqCXlBUFDpVKmLMuqZ69YitO/sheDqOP8mSC5kjKGbR7WUUj8EEtrbdxPRc7S1vUo\n02MXMJp9jP/VN5EzWRKP+lg2qDDiZrHncz1cLNakxaR8CZ/fht3hYOLsW9XXW/seZ6Cvl7dfP199\n7fDBEvueeAyzIY6tdSmhcAv1yXm9qBgeNyZCGxfuswnUJubG2FQSd6XEbPeZNRqrx5p7UoQu1+Tb\nfL0PE5v6AF/vbgq5JIKQqxqxarRilTlW0rWR0nsoJz5RXc9sjNDXG8ZgauXMaZFUvLYpL5fy5KYT\nBEKdHHm7QE9furoAdW7pQkhdxmjxk87VkvP5ggNQ7unq68K1eTMajYBYabnl8UvQaEgU1JumhXxp\nTh3kG/W5hcgkzCboZjHwx19rasbeIqT1HVzxT8/5nn5s8Suhm2iiHjcbN2XGxgBlYxHKKik4UOa4\nlQYd5bEgs++IXndV7kIvapkuKHN2sewkFXm7mjxE1833vl1Ayk+wdccyDPrU7FAJQAUTFf0GrM4O\n4rEcxdRPlFhBo6VSlqvjev0Y62+zotdrsNqNpCOvVQvc6hP9s5u0UrGddEYtsVjMF1jXp4GyOvl6\nJVTC7VVvLASnUrRkxlWbliuG/Dy23c/UpXB1nSivXIk44CFTmFCdfyuJ9WbcuzDcChFivip8W+tS\nwsEjqjbMz9CXRpLYe7ZRJINocBP97neq52fGx/A8oMyhuelaHApQsSgVIhqLhamSg1jFRU9eLS9d\nLKjXZXrRglD8CbEJI97OB8hnlGtOZ3QYnI8jlGO4WqwUslEcLYMz8qJ5hOIxoldQmdVmol7qk/Oz\n8SxAoVCqxjzD3uXVtWKlgtpgVl9j0nvdBh4d2nvtD97EoqCZnP8MoFFj3ncH5Frsopp9aRNvn43Z\nCIPdRuCHtV3A7i/98qJeX0BQDRTb2wfmOfrWoNWb5203cX0s1Oz100Z9Wdu6dsV4xaI18mRlOf5P\nUpick2gPfA8AwWJGtDr435PrKPidWIwC9fUZWiFBUVLvnGcktUyNYjBYS/wOrvGjKdcW9nIpj5QJ\nMh2/hLtzO0anHYPZDtSS8wajB0GjU47NxqsJCKtrCdnpBN/7jp6htW6G7ln85/gXFXfi2b4T7EOn\nTatqOxrat4IWXRrLSg3FUgy9zo0lUb7xSTfA5TEbFmNNqzAyZmPgNvfSBEHT9E9g/sVBOjCqOlan\niUPFxqsvZ1h5TzcfvjfO0Np2Pjo2wUfHQDTqeeIpCwbtCL/6FTuh8dcQSvmq+WUwXOHoWxfZNNzH\ndEpCqxModQ+g/8rXKQQV1o3JqF5gx8MJDM7HEcsBylofL/5gGikvVxn8cilPPPARrrZtZPIWTCb1\nYlmuuKuLgaG17RSKFXbu7MXhukguUUvug4G85CN0SY+1qKdozeL5nV9h9BQ4WzxMpdRjbyAJQtnA\n/bt8BONxOjrdrBhqZerSSdVxgiAhaARVklPKy2Dsp3tmsedsUUw2x0ajTKclLpxRdH0aE6HzGck1\ncS162xyUM8oiUTTq0OoEHE4jm7ctJZ8r4nCbKUkfqs4pSSmsriWkU3leesHIk8/YsbqkKqve3b6V\nXKWT119I0dNnoKvLrqqCEMghZz8il4UVg08gGlyk6tbOgaCTNw8oHbx+0ZrLFKiUy0z+zQv0fO05\ncpkA5YhEQeuhf6WSpDp0+CoPr7mXLquE+761CBrNLY9fjUl3r886pw7yjfrcQmQSZhN09e1mcr6J\nJn7+cLNxkyKhBU5tHjCoNkhFow6tWOHSlBWLtRvd2dcorttBPGPkVJ033kPbZz2LNNi9A1VWbzAk\nVI3UU8k87W3tVUk8gFK5lR+/HOWe9SY6Wq8iF5VYQaszYm95EL11PTrBw9VRC+s2QV4qc+yIMlbt\nf7JAvdNcvZyHRitSLhuJJQos6b6M027i8MESUl7GlZmgcuAbsOeXmWVeiUYdXr+VzLRUlSOV8iVE\nXYV334/g1GrRvPhf6f+938GzaSM9bsj97f9S7gs4196LLBZIBc5WNyYsjm5VYn2hMizNuHdhuBUi\nxHxV+E7f0JybIo0M/aeeX4+AshlgaFnNa9+7CIhAhm3rdiK8+i00Fgtx/xCXDozgb7PjsrVDXWG+\nYO8l8eiv4ejw8fqxCFK+hL9XnSORin58vbspSRFEs4fIxLuAEk+XirVMhJYYkbgOo6FIIZckNlX7\nrO72WiV9Mhbnxy8rZrU7HtPy4CNLSSfztPitFIsyq+/rwO40cuJoLRbIpWuyNqvWqjc2rHYja9e1\n4XUZcKTPAfPr8zdx62gm5z8DSGUK/NIjy4gm83gcRqazi290atVb+fzAo8RzCVwmJ3b94if1CsmU\nWnM+efu6xvVIp/Iqvbh0Kn/jk24SWp2lQfris6lL/VnEQs1ebweNuvbrBv28fzbARP4CmUoMv9NJ\nvpin3d7K+o41COUKhtOX+b30SnyeVrLn46yx30u3zo3+cgCD349GFhB3bENs9YHJROajTzA67Bjj\nkxgc3arkfCxuoq3LUU0sARhNeobWtqPTaijJZS6cCWEQtWzbs4K8VKLNH0OnUzM2RFMLBlMfFy74\nOPxGmhZ/iT2f3wtyBJ3eTDGfJBU9i6/3YbRaPVMXX6uea/fvAnKYzIZmIugmoNWZG57t20/O3wn2\nYbFQ4KFH+0nEsjg9ZkqLUBYvebRohXEEQUKjzZD3dN/2NUVRP5PQUoK/4Z23L5XThIJry3fX0ZK5\nQubyGKVB9TMfiYiYzAbWPdBT1VivZ8JtHtYxHXqh2q5nm7lcOY68raycY5EMI6eDbBruY/JKEqNZ\nz/kzijal1SGpFtiZnIVE0IHT6kavqyDlFUbT4YMl9j21H+QocsXFZMhHPJJFqxfoX76LSimGweTj\n1GkX/SsljGY9TpeRaDhDSWumVFbMO+uT+6l0CW9XCUoaBK2GTOQIlu6tvHEgwPY9yzn9ce1zFbJl\nLn2Y5RJZOrcLOHoUo7frlY3Pl+ScNYhbMdTKuVMBvD7bnInQ+YzkmlCjIsssnR4jK11ly7aVVIAT\nR8eqPi5tXQ7SyTx9PS0kVfYqAvHAR5hbnkDKx/j+dwpsHu7E7c6ht+/i4qibY0fGkPJKbHj4YInd\njz1OMR/E7bWSCh+tXslmmSY+dbiO6dlDZszDur6ruJZ2c+hQjQnps5SIf/ABciZL5ugokcOHcd13\nH6mWNCOnayy+SKRMG5nb3phd6EbPjfrcQmQSZhN0tXbPNcc00UQTv3hwb1jPiq9/jcjli+zc00O6\nqGP3/lXksgUEQ5k3fzgyc6SBbXv/DW8eDrBqrfoaol7ZWHfYJlRVcS7PMP0r/bS0WjlxdAyD2InN\nXCNxxCbsPPiIk1y2iGhppTCTwJRLeSYntURi3Zw7GUDKx1m3SdEA7+nzYBB1yIKaWWy2K3Hs7CZt\nS88O/JoJyrJEW6vIY08u4eqEA7R5NBYzvP0Dtj//p1y5ksLrt3L0LbX0jdUucvD180j5svLdt+4j\nc/UqmuBJpJYky/7T7yGdiWJp78C94X4S4dPVWAXA075BJR/W9AZZXNwKEWK+KvzrbYo0MvTHR6O8\n9/Yl4FoZvrR3Kbpn/xCbz8mLL9UqMZ96bi0u/+MUpAg6Uyvf+3YCKV9GnIyw7oEeIsFpYgm7yozV\nqdVWKwZdrfdSlGpqBGVZqlbXt/btwSS8AkWgopa11RvsOFoG0erMaMVWvvBMDK2hhURaqPb3TcN9\nVY+fe+/vqsZfBlFHu01m25ZWYpkKnlYL+o3dZNISelGLIMCH7ysb/3t39t7wt2/i1tFMzn8GYDeL\n/P0rNdbsc3sWX3M+XUjzw7MHqu0nB3cv+j30NguBl16utruefWZRr282Gzh26HK1/fCu/kW9PkCl\nIs/bbuL6WKjZ6+3g+KkAhz++Sk4qEYpnCMqjpEoxXrr4Y4UNf3k5nSmBgP0SL/ZN0zuRJ/u//oGW\nrVsIv/RtAOyAbusWwocO07H/Ca68UHNe79j/BPHjJ/Bu3ULk0GH0p1rw/PbTSHIajb6FibNGrHaZ\n1es6kXJF9KKWeDTL6Y+nEI06Ng33MDBgQyvEkSshXn4hxfYdGYqVs/i6h5FyYTRakejkMXTmdYSm\nlATx6ntkyoWAKsh1t62DcplcLkxb306kfJKS7OJHP1T6ZN9y7w3NcJqooVyW523fCqYvN7APL98+\n+1BjtPDWKyPV9vY9tz/O6QwZwldqfaul6/YlaPSiViWVozfcPsO/CQWNi4PJ8wGif/eXaCwWBO8f\n0Nm3h2IujGj2Eg1KVDRhIlEvxUKJBx9RSnNHTgcRjTpcrhz5OgZPPdssHjch5dWM4Wy2gF7UYrYY\nkPIlTn04icu1lJaWzyNlAuQLDo68XWLZYIk33x1n9bpOdj66hEiiSKFY5sV/DiHlYdOwiw+PXeTB\nh3p5618vcOIIgImHHm3jxJFa/x5a2w6CwFuvjiAatWwe3klb67QiXRI+is87QHyqbvHfei+lqKJH\nWyoL1cWaoIF36hbbreUaS+p6ZeMLSaw3k++Lh+ixE8SOvEP8ww/o+t98FOU4X/xVF2U5TLEwTTTm\nIFnwUirKqo1UncGOq/VeZI2yZJHyMm8ekBla2znD8opXdZPPnwmyaXgpwXCJdw8m2b5Li5EakUPK\npVUJlYrehi4wjUNTJB2Msfl+L8WygNtjJvu3f0E5o2zPm3u7kf81S+TQYax7uqlq3wEOTW5RktuL\n1dcWIpPg3rCegT/+2kzVVw/uDU0WXBNN3A2oACc8Of7X+CGIHgLgD1b/Jibg0ntK7KHMxTrcrkm2\n7zJx/L0IQ2vb0QgC5UqFVLrEhTNB7rnHikFf8wYpFB0YRB2iUYeUL2E2qz28zGYDb/xI8Xk5/bGW\n/c88SSo6UY0r1j0gVpPxTo+5eiyA27uMDv/jaDQBBKFIZOJdrK4lCILC3q/IOfVGQZubd9+OArD3\nmd/Gev49Lo1fQW90k06qyX0VKhSLMlK+5vOVLJvQLzWoJcI21yTCbjTONv1oFhe3Mj/eSnVzI0Pf\nZKmb610Gnn3OBOUYaD2cv6DhnXfD9K9UX3NsJMyxd5OAnpX3GFg26KcoyXj9Vt5/ZwwpX2JsNMrm\nrd0ETxdxaFJoHLHq+anIWfy9j1CUkuhFB5GJd6rvSbnaccnwKVxt24hFpnF6XUQmDlZ15l2tMrmE\n8jxY3fuq5+TqCMDX+DDs6efNw7PxeZAHH1lKJq2sETLp2nnhhLoatonFxV2RnK9UKvzZn/0Z586d\nw2Aw8Od//ud0dd3YlfnTQrFU5Fd3DzAZzdDutVAsLX6nTzewLxvbi4FCPDFv+3bRyJS/E8x5KRtS\nJ0jbf7HZoI1M9A1DrWjuUML3RveaixV/4kyw2r5/oAXTpVM8lJwg7/Yz2W3mH899m4d6NgHwZGU5\n7m++gQS4ANNvtSKHFfqdnFf3ldl2IRZTvV5Ipeh85gtIwTDe4S1E7Ev4x3/KABpEY5KNW90k4zml\nFOxUACmvJMP6V/oxiDq87hj52IvV620e3olUdCCQp1hIqXTlnQ4HFqsy6RsNSVXSDAABQuNvA0p1\nXEm/i4mrLvqHDPQt9zZZ8zeJkqTWnPd23r4hrORSsyjyrmvNDW8WsUhm3vatQC5l523fCi0DKJIA\nACAASURBVOyaHKUWq8Lwd5uxaxbfoPtuha/NhmjUsWzQR6kgI9rNSL/yh1hcFtq7QwRH1Xrcocsv\nYdDt5NwpCU4Fuef+Th58ZCkajUAicUUl8SFa/Lh0TvKSFznhZsPGPJmCwIUzISWZ7zETmEii1WnY\nsa2DeLxALlfkxy9Os2ywD40gsGywUpV5kXJFUoEUWqNVFeRnswVlcZ6Nsn1AJmnvwmgQyCbV2sz1\nLP/ZpOuTT2mQUsqz2jgulmWJfKEFkPC32auLtbOfBFQL676eVjQzLLZm2fhnA+GzF6jk83ie3EIq\nokggGsVaJYcBWLN6O3IpqxqrHS2DJMNnMLmtDK3tpCjJuL0WPjw2Xj1Gp9OwYshPW4eddDrPyQ8n\nGVrbTiBYYWjVfsqlMHLFhVRSxzcO0UqoIvHmBRFIASmefl7RGY6Jv1NNXrvWr0N0u5X2km7cD3Yw\ndSGA01iixyvgXv/ZMWZfSH8XNBo8mzY2pWyaaOIuw4nJn/Jx8DQAZr2JrR3rcR6/QHkyyNKlGziH\nUnFnEl4jl1BqI9dv2MmRt0NV1q+xxczwI3qSU7VYxNG2i3CoSHfHRRzeTrbv7cfXEiM1pZhzGwGT\no5YklPIy586acTjXEI9n2Ths5r23R6vzuMvVp/rcwck00ykbKwb0pALfB1Aq7GaqAd3tD6iOLxUz\nKPIjMFW00H3/o7Tmc/zwwOQ1kh09fR5APTf4/FakUlD1Wr1E2I3G2bvdj+ZnZTpeLyfkEotk/va/\nVTfZF1LdvHzQx+79qwgFUvha7ThdNZna5csjJKf+tdruX76LE0eu9c4xW/VV8lKL38bBA0rCe+R0\nUGXAXJSUhLcgCBgMNUN5uZSnkI9X+/dswh1AL3pVx01OannzgJHPPRZHqDuuPnbWa2t68/WkPimn\nzjdGIup1XCQ4XdOj37m8mutw+eb+7ZpYHNwVyfnXX3+dQqHAt7/9bT7++GP+4i/+gr/+67/+WX+s\nKvQ6/R1nzruMam0rp3Hx9cDFVv+87duFs0Gb33kHtPlFk1vdNrquc+QvBo6dCvAfv3Gs2v6T5zfc\nMeb7je41+77XIbJzfReakU8QI1N02H0cj/lxT44w/T//BgAt0Pm8UplhMyoSTdawOompuzyJ3m5D\nArQmdSJWa1TaBp9X9brBbic/GSBy6DAAmS+sZ1Y4btmgj7dfq5WtbXqoD5NZz9G3LlaDyYF+tZa3\n0ZDkjdctbB7eiblQpLVvD7n0OBqtnsTUm/h8DzG0th2jLYdGq5az0hvUz6iWOKc+zPHQrv4m++IW\ncI3m/CLI2pwytCLs+RK2dJi0rYWQoZXb5bl7neoNQY9zMTYIGyOp24+stOUsNllCKuWxySW0NKuM\nFgtpATY83MehV5WA/vLFCJ/bZ8VmTVCS1GbuJUmRjzMaFDY5QKUC778zxopVrZw7WWL4kZ143ClE\nk41kysKPXkgh5ZNAkse2tyLnslj7zVg7Wnljpmrj3Kkg2wdknC//A6av/hHvz7DoV63tUJtEiVq8\ndgOCVv3/d9l17H3Yi33iJKZohFa/llAlj8OqZhjrRS1Cw6JYb/Ex87XQaNWGbaKtD+LtPP28uqy5\nqf3+2YfBYaMUM1bN0eDazZeSNHnN2DzbB7S6FjQaDSaLBtGkU23GWKwiHx+fQGdQ+tNs1QdAm7eH\nXLyMaDGSicRZes8eyrHzaHJazBUtWVsrUDNnm2U4Niav69seYHBN+6L8Lk000UQTnxbGk1cx6pRY\nYW3bEBvGIfzNbwGgsbzLnud+F5trgkwdv87jzrHugeVVaYyR00G++CVI13lalosJDPJ7IENq6hiV\nyk6mE2p5Ww0xQKgy873eMaJRIw5nF6l4VjWmZ7PqxKHOoMVsFXntlRjbHt1PpRzBZLaRiCbJVXaS\nL6hlaKMxO7NmOVK+xA9+OM7Tz6/n6ec7iISm2d2rSPkoevBKvLDv8T4mTl3GoclR+v7/wPR//DrU\ncbhuxvD1bo9Jflam441yQtu27kN4VenfC/FWOX8myKsv1HyKlD6j/B+1wk9Vx1ZKMcDE+TP/P3t3\nHh1HdeYP/9v7LnVr6VZLsjZblmTJGCNhE2yz2CwmgMFkjAMJWxZCZswk8BIS4pwk80syzCTkhTfY\nycCPE5J4ErCHsIQhIRiTGOMk4AUMlrGNLVuylta+dLd6r3r/kNXqbrVaW6/S93MOB1cvVVddT926\ndevWczuxdnUBBtu7kC11QS4fm0xVEjHecXRAyqIaM/a9PZpGT4kNlRbkqy+BT+KEKi8PPR0jo+WH\nesKfvA/4tcjSj0xKKzcU4c3nR64JRud8GhXadvYLJqxdZMOgoEFujjx44yDPog9L05uTF972yi8Y\n6V/Js+jx7r4zwePTWlkZ8zek2ZkXnfOHDh3CmjVrAADLli3D0aNHJ/lGcrX1OGIux4UIrCppgNvv\ngVqugkSc/CvT3oRajZLP3QZXhw0aawFEzexHpobyuENyMedo4fHEPze/RKaCuewK+D1DkKuyIJHH\n929IN80dg+OWE9U5H7mt1q4h/P0jBEfGd/basWF5EXKUChi9LgR+vwM+5zCkABZc93kMD4UfF8qu\nQcAIOD3DWFXSAKVOE/a+yulF/77dyFuzGlKNBkUbb8ZwexuUFWVw5eph0Ckhzc7Ggs/eCneHDcqc\nHHTt2wd9eXlwHXmmsY7R0BGeADDs9KKv2xnWmPQL4TdzsnKLsWSZFh6/AqeafFhSfRqD3Y1j6y8Y\ngt1ZiKZTElx4UQnMJVnw+52ACEjk4aMs3N5sjI4WpemTyTVhqRKkcs3kX5pE75APr3wiBWABbMBN\n1tk/9WR1NQdz/uXoJCgcbgawdFbrPN6ig0E+ltvweIsO5bNbJeQeN1xPPQkdABcA/Vfvn90KKaij\nz4Fsnx+Ll1ig1ipQucgJd/8r8CtqoNaFX+DJVSP1wWj9AACBgACP2w+9QQWPO4DdfwoAUGH1ajO6\n+nzBCdsAoHfQB+3z/x/0AAau+ULYuh1KE6o23gxP8/u44aqL0NPrgTlfgHW5EgPKPGg0chgCdgT+\nsAPGC5bhukvr0GWzI1vqguy3P4Zl0z8hYMqGWL4YHygsKDRnY1lVPuTaE+hsG0RerhIKvQaDHnnw\nQlmRLeCIvxk1JZ+Gyu+CKb8UJssFcDlswUfHFywaP/qK6WfSn8/ugL28AYY8AegbyXkaefNFKlNh\nsLsROUVr4fP6odIY4HYNI8t6C3b/cRg9Xb2oW16Ebps9LK2Wx+1D7fLC4BMdV1y1EKJMBrM1C4r+\nY+j8r5EBOXoASuPnIfpM0JeNpHMZauzC3w+M9TLNtxGORDR/lGQX4X9P7MGqkgZIJVJIbT3B9wTn\nMAynD8CQvxChw52ypCo0RXS0+wLh1ztyRXjnuE47BLnKAm9IqviAkIPa5RosWuiE3/4qnL0jQwpM\nOTdAIoQPjtNrZbh6/UJ09nig1SrhcnmhkIno6RrGrv8ehiFbjfoVFgz06JBjkGJgQERW/o2QSfuh\nzM6D11GBiz7VBY/bHzwvdHbYcfk1Ew+hueCyGhSrHSOjvf91C0yL66HMyY2ZImwi871NkqpJxyPT\nCQ0KGhjP/zs0/dxEE/ZGS0d0+TUjg+I6W7rRNzY2BVpDAS6/tgBGpQ/DT/8HjOdH6Dvv2hr8jEIZ\n3tWaZxnp8JbJwtuxXe12OF/4HQBgwUOfC46WD/jDn7zPK1RD3QaI7TacLanEoholfJ4AevsVqKy8\nCaLbBqWhED09bkgVSri92QgMGCF5/XEYAfRKv4zGUyPXqs1Nvbjksgr09TihUMmgVohh7Sq9SoRf\nPgyvUxbW1+EdZErdRJoXnfMOhwMGw1hjWy6XQxAESJPweM1UFOWFn9AK8zJzElJv01l0/nksr73l\n2mviun6VWok9/3s8uLzu+uq4rh8AIJFCKlUAEknw/3NZmTV8dHapNf5PVEy0LY1KETaS/oEbavHh\n+x3oOL8cerfbYO+Guyx8xGWXNB83Lf40uoZtyNfn4aDQi9Vf+TyGPu5BUYEefa/9EQHnSI5Y08UN\n6PrzyKSq0mVlOGzoxd6co/je2QDk3gB63n4nuF5ZzdiTK4aWI1i7KBdDOeVQ6jRhd5gNWSoIgfC7\nXH0D+TBl3wi5rB9KRQ5e3NkPj3uk4Vu7vBDyiImYlUpdcCTKxx/JcMsNCoiD/cguWYL8skug1Rnh\ncnTAHzCipdWIW+/Wz7vRF/HiC8ig0ubD6+6DUp0Df2D2OdJrK3Lwytunw5Znq1uiQf6pd5DjdkOm\nVqN74VKUzXKdSiPw1otjk7euumXWxYR6oCvmMs2c2iPi7T1NweXKhSMjjaUyFfo6DgdvIKu0FnR3\nOOGVXoO+3jwsuxjQGVRwD/uw+spyaHvPYF11AE51LnJNCng7z0GpKg7blkktYHTsslHmRmgu7fw8\nDdxtHvQV5sBZIuCq9ZdCIgJ97x2As60ZcqkGgz19OL3yOnRLZdC3nkZtST6kHgG6f92CnBUXBx9j\nDr0cXnZF7SS/wPinB02WWd5NopRz5C3Cn97qhOqQDJ++aQO8rk4IklyYy3IR8DkgCn4Mdjci4HfD\nOWyAoz8bb/z5LACgbrkKPV0j59JPPu7E6rUV2PPHsbkLrr5uIXb/aSzNjbnYFOwUEQULTEp9WH71\n0Mfr5/sIRyKaPxqKLsA/r7gTLYNt0Ct1EKztYe9nlVVg+Ggn8stHRvEqRB08H3XB6HYitH3Q15cL\niTgy6MMXMMIvht/U1OgL8coLA1h12chn9MYinDiuRrZRDYW8Ff6Qz8oDndD7wucxypIMQ//JBwhI\ncjHYp0Gu1AW1YuypT/ugBwqpE4ahvehX6fD7/k9wg/kq/FPdWOociBLs+tWB4OJkN16jpftiSryZ\nSdWk45ED2Ipry2DK2zxubpWJJuyNlY4ov3gkLtyODqj1VuQXr0TRIjlEQUCfeiwNXrfOCuxvAzDS\nXhmZcNkHpdSPt/8yknM+MrVSbr4mODtO56//DOuXN8Dj7YXGWoCuln3Bz0mlarT89y8BADn3LMc/\n3h97tMOqNMH7q+cg3fgFtMsL4PPooVDJsNDiw+jQMaNk7Dj2uP3QKwMQ5cPIUUsw2BlA4/sh/RwX\nGrHg3N/hrLk05m9M8TUvOuf1ej2czrF7wJN1zD/55JPYtm1bMooGADBo5bj9mip09g3DkqOFQRP/\n3aKRh49OUsuVE3xy5lTWiLQ2lvgmpUrGyHm12oj+wbMQAh54hABMlvEzfKermcTtitoCfPvuFWju\nGESpNRsrE3hRGrmtyJH0g73haWlC73bbDfnwFVXCf8MdMHsH4DZZ4DaXo9d2Bn8fOhz8zoLKz6Fj\nuBz5mn4EnGO505R1VVAUZCFgzcWOwBEsQRVuLN8AuXwY/b9/GXlrViPgdkNftRiiUg6LVgu5VgtR\nAkhefA6LrlqHHk0RVq8ugcsLGLKUUMoDOPhxZ7AxWZwvg8zWhM4uA0w6Ezr8yrARqmqJH36HPGz0\ntkOuxFWbS+AbkMCoDEDVfhK6oouQUz3SeRDaKCyJ//zHaSMZda5rWA4FuoPHtk+smPxLk1hZa437\n8dNnKUebbQgGsRt2fT7UltmXM7tUhuJ1EmBICWR5kV06+xsTurKyiOXkNLzTSaLi1t4fMUfA+Sdy\nhnqOIyuvGi6HC13duVApzPCePgWnRo0ikxcQAui3e2A1SOD97bZgHVj9+dvgGfBAlWuAUupC7tpy\nOJ0e5GeJkAbGRgxL334ZN979IHr7PDBpBBiUHny4cDUKzdlYV1MAqUQCSM6n9zj/nQJBxMD5uUJy\nrA0oSeC8JRQ/yW7nAkDP8Ei72+MO4I+vOLB8RQ0+OeVE3QUK+O37kZVXDb2pHEpNOXpOS1HoOInr\nLs3HgEcOY5EM1sWV8A5IYLFmobIqH3qZH102O8wFBtRduhgmiylqB/tk+dXn+wjHTJOK2CWarXSJ\nW6lEihXFF2JF8YUQRAEfqBphkashtHVCX1aGgmuvhu2NN3Hmp/83+J3Se+6CdNf/YO2amzAoaGAu\nMEAOF159Y3TQhxtXXGmGVDH2hKatSQePuxdvvREAoMbFK1XQiG6YZAGo+50IGVAPnSILal83RHMJ\n+gYBk8QB9dFDUFjMkLz4XPBaMGvdOqxdZMagoEHOwiIolkjxeH8TIAAQgBJjeIcnb7zGx0xiN1WT\njkfb5xLp+AEhE03YGytmpFI5LCWrxq0rso2RI4hRyiBBwOuDWvCgu8sJi1UGa2UlOm1DMFsNqFta\njg7XPXC3noOqtBi+Di/8LYOA3oqsvOqxp749ShRtvBnevj5o0Ie1izwYFDTIlrpgHm5FK4A+J9DY\nNHbTzXBpIRaPfscsx1VKoN8lhVHpwwL3Wbj6m6FWWSBml4X9XQaLDnaXEaLCgctvrAG8Ao+jJJCI\nopiABCfp5Y033sBf/vIXPProo/jggw/w85//HE8//fS01tHa2op169Zhz549KC4unvwL0/D3Iy04\n1+1Ce8/IhLDF+RpcuqwkrtvYe/pv6HL1o8vZA7MuD/kaE65YeOnkX5yGno8+guv4SbjaO6AptEJT\nvRh5S+M32u3A/jP404tjKYmu21iHi1eXx/jG9ImigIGuY2GPsEkk6fGExUwkMm5n6x8fdeBHISPn\nv3XLBdgTsn+vuqoYutajEPKt8C5cgvqaAhz42IYjn3RDqZBDp5LC6xMgy+2GVzaAmoIyXFx8AaQS\n6chd7PcOwNncDE1JCT7KlqHL1wW7dwjZ6izokIOPPpDBqFFitfcM5H02yK1F6F9cj4tqCtD55h7Y\njzZCEARozGY4/C4oc03o7DyHwoJyuIftEC156NEUYbjNhVytiEKhD57WNiizs6BbuBDd2uKwu/I3\nrLOgNE8Caak6LEVDJsdXIsU7dg/s+wSe4TPBCwelthwr1qRf3jy/X8Dr/ziLZtsQSguysP6SMsjl\ns4sRQRRwsO1DtAy2oSS7CA1FFwQny5yp0GMs2mjU+Soecbt/fxP2vDiW/urCFcXIMXZDox5CtqkQ\nLR8DxiwFDLaPoczSwz00CIVGAyG/GEeQh2JzFiocLeg5dQLOXAPOWcwQh8zoG/KitiIHK2tHOiHf\nbbShvWsQS32dUA90QlfG/TifJbq98I/9TXgjJK4vvXIhejodqCjXo8jUCq+nB6ImB0OmYhR3uOBq\naYZvyI7sJUuQs5JxSRNLZVt3y8++gS5L9HSkiuYAfvvw0wgEAjh79uyE6ygrK4NMNvub5pRZ0vUa\nTfD7Yfvzbgw3t0BbWgLL1evQuXsPBt7/ADK1Gv2HD6Pywa+j1aFGp80Ok9oPhVaLP/y5LbiOy29Y\nhL3/eyq4vO5aM4QSOez+QVSeHYLKJCKg9sOlLsbHwwHY0YOaglJIBy2QnzoGVX8nFAtK4XK5Ielt\ngc9igtengOuTDgzq8mBdtRIX1xXEvW1LU5OusTtVxz+yhT1VMTpyPh2EXrMtUJtRNNADr6cPSlUO\nBvIXQHmqDa6WFhiqa6Bo7RzpdysqhKhWw326CY7FK/G/u23B9V19Sy3Ezg8h6+mAtLgcuV4PvG2t\n0BYUwvbHP8HX0wsAKPjXB/CRKwfuQTc0RjXk2Sqctdnjdi1KUzMvRs5fffXV2L9/Pz772c8CAB59\n9NEUlyhcfU0R2vuaIJFIIJNJ0VBTNPmXpmlV+Qq8eeod9LsHYFDqsLp8Rdy3kVtbiz7nMEQhAO2C\nYuTUTvbo+vTUryyFKCI4g3b9JfEfqTnZ7OsUP5Ej6VfUWJCbpY6407w87DufWlqIlbVWvHt+pOai\nkhysrF0ybqRm5F3stRHbFgQRORhZh916CVZGjPa0XnMVlNnZ6D51HANmA3rKy+DwO1F48VqUT7Hx\nN9Gdc4ApGlKhelkhPnzPh3ZbLnLzNahZlp6T+cnlUtywevaj5UOFjpSKl8lGo9LMXbKyDBIRGGzr\nhVEtQK6Xw+bMxaAmC30aH67c9CnIpXIA40ciLQr+qxD5n7oEANAwwXZG5hexAkhAijiiCBevLIMI\noKunDwaDGi6XGzULtTD1noBaW4jCFVeNdcCXAFiZnJF2RIl29uxZvPXyd2HJ1497r7PbgbU3/x8s\nXLgwBSUjGk8ql6Pw+uvCXrNedy1UuTlwNjcjb/WlyLloOXLP19eCIOLAMRsuuU4Pn9OL8vJcuOTA\nqpvKMNTvgiYHUJapUF+8dOT6KeISOyyT+wIAdePb54Ig4t1GGzrVI9eMF9eOPM0X77YtzQ/p/FRF\n5NMtIx31AkqyrWgoWgJpyfg+Kn8ggN3H38M5kxVlRgH/dHc9ujscsFgNqKyx4L2PNTijHzl2ys73\neYiCAF1RYXCQlbGhAV0fd6G5YxDm80+D80nY5JsXnfMSiQT/9m//lupiTEiplOEzVyZ2BKdcKsf6\nxVckdBuJ7qyRyqVYEeeR8pQ6UqkEn1pqDZuAdiqPlkf7Xjy2HUoilSLvUyuR96mZxzIflU8vhiwd\nVl019cmciFJFJpfi0tUVAOJ7k4YolWRyKT61KlpcczAEzX2WfD2KCpirlzJTrGt8qVSClXVWoC7y\neid+1z/xuPYjGpUp1+hTHVwll8lwXe2nwl8MGQcY7diJdkzzGEs9Pp9ARERERERERERERJRk7Jwn\nIiIiIiIiIiIiIkqyeZHWhoiIiIiIiCiZBEFAZ3f0SWM7ux2oEYQkl4iIiIjSDTvniYiIiIiIiOJM\nFEW8ta8Yeo1p3HsOVz8uv1FMQamIiIgonbBznoiIiIiIiCjOZDIZrPmVMGZZxr03MNQJmUyWglIR\nERFROmHOeSIiIiIiIiIiIiKiJGPnPBERERERERERERFRkrFznoiIiIiIiIiIiIgoydg5T0RERERE\nRERERESUZJwQloiIiIiIiGga2t4fwpBHjPqe1OdMcmmIiIgoU7FznoiIiIiIiGgazAUXwO8tjvqe\nxnM6yaUhIiKiTMW0NkREREREREREREREScbOeSIiIiIiIiIiIiKiJGPnPBERERERERERERFRkrFz\nnoiIiIiIiIiIiIgoyTghLBEREREREVGcCYIAu7Mn6nt2Zw8EQUhyiYiIiCjdsHOeiIiIiIiIKM5E\nUcSCs68hV6kc916v1wtRvCUFpSIiIqJ0ws55IiIiIiIiojiTyWSoMRhQqNaMe6/d7YJMJktBqYiI\niCidMOc8EREREREREREREVGSceQ8ERERERERUQoEAgGcPXt2wvfLyso4wp6IiGgOY+c8ERERERER\nUQo0NTVh1y+/gVyTdtx7vf3DuPULP0FlZWUKSkZERETJwM55IiIiIiIiohQQRRFHP66CXmMa957D\n1Y9NopiCUhEREVGysHOeiIiIiIiIKM4EQUCXxxP1vS6PB4IgQCaTwZpfCWOWZdxnBoY6mdKGiIho\njkv7zvnLLrsMZWVlAIDly5fjgQcewAcffIB///d/h1wux6WXXootW7YAALZt24a9e/dCLpfjkUce\nwQUXXID+/n489NBD8Hg8MJvNePTRR6FSqVL4FxEREREREdFcJ4oidi5XQWXUjHvPMwBcLYoQBAF2\nZ0/U79udPRAEAYFAAPv27Yu5rTVr1gAA89cTERFlmLTunG9paUFtbS1+8YtfhL3+/e9/H9u2bUNx\ncTHuvfdeHD9+HIIg4ODBg/if//kfdHR04P7778cLL7yA7du348Ybb8TNN9+Mp59+Gs899xzuvvvu\n1PxBRERERERENC/IZDJkV+RCY9aPe8/V5YBMJkMgEMCCs68hV6kc95lerxeieAvOnj2Lp574U9TU\nN8BI+psFCxZAEATmryciIsowad05f/ToUXR2duLOO++ERqPBI488gry8PPh8PhQXFwMAVq9ejf37\n90OpVGLVqlUAAKvVCkEQ0NfXh8OHD+OrX/0qgJFR+E888QQ754mIiIiIiCihBEGAu2846nvuvmEI\nggCJRIJcpRLmCZ7ulkgkEAQBeq0JBl1e9A1JEBxh/+5hC7SqrHEfGfYM4TN3BSYdhT86An/Xrl0x\n/7Zbb70VACZdF0fqExERxZY2nfMvvPACfv3rX4e99r3vfQ9f+cpXcO211+LQoUN46KGHsH37duj1\nYyMPdDodzp07B7VaDaPRGPa6w+GA0+mEwWAIvma322dUvkAgAACw2Wwz+j5RpIKCAsjliT0EGbeU\nCIxdykSMW8pUjF3KRMmIWyC1sWvva4cYcEV9b9jVjtbWVthsNvT9LReKKJ3lPs8QbGtHyr2jQoTC\nIIz/jF3EBef/Nt0nLyFLroi6PcHvg802MlCtou8DGKN8bsDvQ3d3N/r6+vD/PvrchB34yvMj+N/8\n8U9gkEXfnj3gQ2lpKQBMuq6SkhI8/vjjUdcDAA888AAAxPxM6OcSjXUuZaL5UOfS3JSs2E13ElFM\n3+nf3W43ZDIZFIqRRsHll1+O1157DZs3b8Zrr70GAPjNb36DQCAAhUIBj8eDL37xiwCAjRs34tln\nn8UXvvAFPPPMM8jJycHx48fxxBNP4L/+679ibvfJJ5/Etm3bEvvH0by3Z8+e4BMg8cC4pWRh7FIm\nYtxSpmLsUiaKd9wCjF1KDta5lIlY51KmSkTsZqK07px/7LHHYDQa8aUvfQnHjx/H97//fTz//PPY\nuHEjfvazn6G4uBhf+cpXsGXLFshkMjz22GP45S9/iY6ODvzzP/8zXn75Zfzwhz9EXV1dMOe8VCrF\nl770pWmXxe12Y9myZXjjjTcS9mjeunXrsGfPnoSsey5tY678DY2NjQm/QxjvuI3X7xLP35dlSt56\nRteVibE7KhHHNteZGevMpLid7W8Qj98w1WWYC39DvMqQSbE7GbYRU7/+ZGwjWXELJC92IyVjP6XL\ndufLNke3myl1brqcozK9DHPlb8ikOjfVv1c6lGEu/A3xKkOyYjfdpfUvcO+99+Ib3/gG9u7dC7lc\njkcffRTAyISwDz30EARBwKpVq3DBBRcAAOrr67F582aIoojvfve7AICvfvWr+OY3hgMEyAAAIABJ\nREFUv4ldu3bBZDLhpz/96YzKolarASD4+F6iJOOO0VzYxlz4G5JRASUibuP1u8Tz92WZkrceIHNj\nd1Qijm2uM/3XmWlxO9vfIB6/YarLMBf+hnisI9NidzJsI6Z+/cnYRrIutJMZu5FSNdIvFdudL9sE\nMqvOTYdz1Fwow1z4GzKtzk3175UOZZgLf0M81sGO+RFp/StkZWXhqaeeGvf6smXLsHPnznGvb9my\nBVu2bAl7LTc3F88880zCykhERERERERERERENF3SVBeAiIiIiIiIiIiIiGi+Yec8ERERERERERER\nEVGSyb7//e9/P9WFyCQrV67M6PXPlW3wb0jdtuK1LpYpM9cT73WlYltcJ9eZaPHY1mzXMRfKMBf+\nhnQpQzptay60r/g3pH79qd5eqraZqu3Ol20me7vpcH5gGfg3pGJ7qf5+OpRhLvwN6VKGuUAiiqKY\n6kIQEREREREREREREc0nTGtDRERERERERERERJRk7JwnIiIiIiIiIiIiIkoyds4TERERERERERER\nESUZO+eJiIiIiIiIiIiIiJKMnfNEREREREREREREREnGznkiIiIiIiIiIiIioiRj5zwRERERERER\nERERUZKxc56IiIiIiIiIiIiIKMnYOU9ERERERERERERElGTsnCciIiIiIiIiIiIiSjJ5qgswE36/\nH9/+9rfR1tYGn8+H++67D4WFhfjBD34AmUwGpVKJH//4x8jJycGuXbuwc+dOKBQK3HfffbjiiitS\nXXwiIiIiIiIiIiIimuckoiiKqS7EdL344os4ceIEHnnkEQwNDeGmm25CcXExvvOd76Cqqgo7d+7E\n2bNn8cUvfhH33HMPXnrpJbjdbtx222148cUXoVAoUv0nEBEREREREREREdE8lpEj56+77jqsX78e\nABAIBCCXy/HEE08gNzcXwMjIeqVSiQ8//BD19fWQy+XQ6/UoKyvDiRMnUFdXl8riExERERERERER\nEdE8l5E55zUaDbRaLRwOB772ta/hgQceCHbMHz58GL/73e9w9913w+FwwGAwBL+n1Wpht9tntE2/\n34/W1lb4/f64/A1EycC4pUzF2KVMxLilTMXYpUzF2KVMxLilTMXYJUqMjBw5DwAdHR3YsmULPv/5\nz+PTn/40AOCPf/wjnnrqKTz99NMwmUzQ6/VwOBzB7zidTmRlZU267ieffBLbtm2L+t6ePXtQXFwc\nnz+CKI4Yt5SpGLuUiRi3lKkYu5SpGLuUiRi3lKkYu0TJk5E553t6enDnnXfiu9/9Li655BIAwCuv\nvIJdu3bhF7/4RbADvqenB1/4whfwwgsvwOPxYPPmzXj55ZehVCqnvc3W1lasW7eOlRBlFMYtZSrG\nLmUixi1lKsYuZSrGLmUixi1lKsYuUWJk5Mj5p556CkNDQ/j5z3+O7du3QxAEnDp1CoWFhfiXf/kX\nSCQSrFixAlu2bMEdd9yB22+/HaIo4sEHH5xRxzwRERERERERERERUTxlZOf81q1bsXXr1il9dtOm\nTdi0aVOCS0RERERERERERERENHUZOSEsEREREREREREREVEmY+c8EREREREREREREVGSsXOeiIiI\niIiIiIiIiCjJ2DlPRERERERERERERJRk7JwnIiIiIiIiIiIiIkoyds4TERERERERERERESUZO+eJ\niIiIiIiIiIiIiJKMnfNEREREREREREREREnGznkiIiIiIiIiIiIioiSTp7oAM+H3+/Htb38bbW1t\n8Pl8uO+++7Bo0SJ861vfglQqRWVlJb73ve8BAHbt2oWdO3dCoVDgvvvuwxVXXJHawhMRERERERER\nERHRvJeRnfN/+MMfYDKZ8OMf/xhDQ0O46aabUF1djQcffBANDQ343ve+hzfffBMXXnghduzYgZde\neglutxu33XYbVq1aBYVCkeo/gYiIiIiIiIiIiIjmsYzsnL/uuuuwfv16AEAgEIBMJsOxY8fQ0NAA\nALjsssuwf/9+SKVS1NfXQy6XQ6/Xo6ysDCdOnEBdXV0qi09ERERERERERERE81xG5pzXaDTQarVw\nOBz42te+hgceeACiKAbf1+l0cDgccDqdMBgMwde1Wi3sdnsqikwZQhQF9HceRfvp3ejvPApRFFJd\nJKIgxifNZYxvisSYmJ+432kuYBwTEdFM8Rwy/2TkyHkA6OjowJYtW/D5z38e119/PX7yk58E33M6\nncjKyoJer4fD4Rj3+mSefPJJbNu2LSHlpvQ20HUMTUd+HVyuWHYXTJbMeNKCcTv3ZXJ8xsLYJSDz\n4ptxm3iZFhOZIt1jl/udJpLusRuKcUyjMiluiUIxdlOH55D5JyNHzvf09OCLX/wivvGNb2Djxo0A\ngJqaGhw4cAAA8Pbbb6O+vh5Lly7FoUOH4PV6Ybfb0dTUhMrKyknXf//99+PEiRNh/+3ZsyehfxOl\nB5ejI+ZyOmPczn2ZHJ+xMHYJyLz4ZtwmXqbFRKZI99jlfqeJpHvshmIc06hMiluiUIzd1OE5ZP7J\nyJHzTz31FIaGhvDzn/8c27dvh0QiwdatW/HDH/4QPp8PCxcuxPr16yGRSHDHHXfg9ttvhyiKePDB\nB6FUKlNdfJohQRBxstGGzg47LNYsVNVaIJFK4roNjd4ac5kolaLFZzKOi7mAv1P6Y/1LkdI5Jlin\nJE4y9jv3HyVarDhm/BERzT3xrNvTuQ1MiZGRnfNbt27F1q1bx72+Y8eOca9t2rQJmzZtSkaxKMFO\nNtqw61cHg8u33t2A6qXxraSM5iWoWHYXXI4OaPRWGM1L4rp+otmIFp8njib+uJgLklF/0Oyw/qVI\n6RwTrFMSJxn7nfuPEi1WHDP+iIjmnnjW7encBqbEyMjOeZqfOjvs45bj3ZCVSKQwWeqYz4vSUrT4\nTMZxMRfwd0p/rH8pUjrHBOuUxEnGfuf+o0SLFceMPyKiuSeedXs6t4EpMTIy5zzNTxZrVsSyIUUl\nIUofPC6mhr8TEcUT65TMxv1HqcT4IyKae1i302xw5DxljKpaC269u+F8Di8DqmoLUl0kopTjcTE1\n/J2IKJ5Yp2Q27j9KJcYfEdHcw7qdZoOd85QxJFIJqpda+dgnUQgeF1PD34mI4ol1Smbj/qNUYvwR\nEc09rNtpNpjWhoiIiIiIiIiIiIgoydg5T0RERERERERERESUZExrQxlDEEScbLSdz+GVhapaCyRS\nSaqLRfMc4zIzcD8RJQ6PL5opxg7NBYxjIiJKFp5z5iZ2zlPGONlow65fHQwu33p3A/N5UcoxLjMD\n9xNR4vD4opli7NBcwDgmIqJk4TlnbmJaG8oYnR32mMtEqcC4zAzcT0SJw+OLZoqxQ3MB45iIiJKF\n55y5KaM7548cOYI77rgDAPDxxx9j8+bN+NznPoetW7cGP7Nr1y585jOfwWc/+1n89a9/TVFJKR4s\n1qyIZUOKSkI0hnGZGbifiBKHxxfNFGOH5gLGMRERJQvPOXNTxqa1eeaZZ/DKK69Ap9MBALZv344t\nW7ZgzZo1eOihh/DXv/4VdXV12LFjB1566SW43W7cdtttWLVqFRQKRYpLTzNRVWvBrXc3nM+tZUBV\nbUGqi0TEuMwQ3E9EicPji2aKsUNzAeOYiIiSheecuSljO+dLS0uxfft2PPzwwwCAmpoa9Pf3QxRF\nOJ1OyOVyfPjhh6ivr4dcLoder0dZWRlOnDiBurq6FJeeZkIilaB6qZX5tCitMC4zA/cTUeLw+KKZ\nYuzQXMA4JiKiZOE5Z27K2LQ2V199NWQyWXC5rKwMP/rRj3D99dejr68PK1asgMPhgMEw9oiHVquF\n3c58TDQxURTQ33kU7ad3o7/zKERRSHWRaJ5jTMYHf0eizMBjlQDGAWU2xi8REU0Fzxc0KmNHzkf6\n0Y9+hN/97ndYuHAhfvvb3+I//uM/sGbNGjgcjuBnnE4nsrKyYqxlxJNPPolt27YlsriUpga6GtF0\n5DfB5Ypld8JkWZrCEk0d43ZuyuSYnKpkxO58+B0puVjnxpcoChjoOga304b2U38Ovl6x7C6YLHzi\nMZ4yIXZZZ1M06R67rMcomnSPW6KJMHYTb6DrGJqO/Dq4XFx9MwK+YWj0VhjNSyCRZOx4apqmObOn\njUYj9Ho9AMBisWBoaAhLly7FoUOH4PV6Ybfb0dTUhMrKyknXdf/99+PEiRNh/+3ZsyfRfwKlAUf/\nmZjL6YxxOzdlckxOVTJidz78jpRcrHPja/TixDnYEva6y9GRohLNXZkQu6yzKZp0j13WYxRNusct\n0UQYu4kXeX6w955Ax+k30HTk1xjoOpaiUlEqzJmR8z/4wQ/w9a9/HXK5HEqlEj/4wQ+Ql5eHO+64\nA7fffjtEUcSDDz4IpVKZ6qJSGpMrdRHL2hSVhGgEYzI++DsSpbfRixOpTBX2ukbPfJrzEetsykSs\nx4iIaDoizw+h5w+Xo4NPXc0jGd05X1RUhOeffx4AUF9fj+eee27cZzZt2oRNmzYlu2iUodS6ApgK\nLoQQ8EAqU0GtY2OaUosxGR/8HYnS2+jFyVDPcZgKLoRCaYDeVAGjeUmKS0apwDqbMhHrMSIimg6j\neQkqlt0Fl6MDMoUWHadeD77HG7vzS0Z3zlN6EQMB9B04CGdzM3SlZchZ0QCJNLMyJxnNNQBEuBwd\n5/N81aS6SJQmUhXfjMn4yM5dDG9vD1xuGzQqK7LzqlJdJKK4mAvnXiD84oR5NikZ5765cuxQ+hir\nx9oh86jgOdYLoXAYyAcgSXXpiIgo3UgkUpgsdTBZ6iCKApSq7LC2cLKxbZQ67JynuOk7cBDHH/1x\ncLn6kYeRe8nKFJZo+kIrR6JQqYpvxmR89B88jFOP/iy4LH9ElXH1E1E0c+HcC7Cuo3DJiIe5cuxQ\n+hiNW+GMk7FFRETTkg5tYbaNUoed8xQ3ztZW5K1ZjYDbDZlGDWdbG3JTXSiiOHE2N49bTtaJinew\nZ4/1E81VqaybANZPlLnGHTutrcA/wFimWREDAQyfOwfTxQ2QadToP3Q46fUyERGlVqa2j1N9XTGf\nsXOe4kau0aJn3zvB5fLq6riu3+8XcPgfzeiyDcFszULDylJI5elfwdHcoCsti1gundF6BEHEyUYb\nOjvssFizUFVrgUQa+1nnWHewZ7K++UiabUT3whL0OUXk6iUo1w2lukhEcRGvumk6Qusdk8oH51Pb\nIDiHAcQeYcP6ihItNMbMVgOkEsDWHj3eIo8duUbL0WI0a30HDqLlv5+DVKdDYM0GDK+vh7YgFwsE\nkfXdLPD8QUSZJFNHoGvLKyCuvw2DggZGmRu68hIArIOTgZ3zFDd+uz3m8mwd/kczXn/p6NgLIrBi\ndXlctyGKAga6jjHnLY2Ts6IB1Y88fP7udylyVlw8o/WcbLRh168OBpdvvbsBVXWWmHEX6w52tPVV\nL+XkMZHOiQV4652m4PLV11agMIXlSSbWa3NbvOqm6Yisd9auuQmS158DEHuEzVTqK8YrATOPg8gY\nq11eiMb32wGMj7fIY8d5lqPFaGZC41WU+yHTaeFfswFvnVIBGASODEKZk8P22SywvUtEmSQVI9Dj\n0Ybu0RThrVMdAAQASuSuKkYuWAcnAzvnKW50ZWURy/EdvddlG4q5HA8DXcfQdOTXweWKZXcx/y0B\nACRSKXIvWTnrk2pnh33cssXcGzPuYo2MjbY+nijH6+n3xlyey1ivzW3xqpumI7LeGRQ0MJ7/d6yR\n+1OprxivBMw8DiJjzOcJhL0XGm/jj53wEWDJeAqF5obIeM39zGqctGkw0rkxgu2z2WF7l4gySSqe\nbI1HG3qiupZ1cOKxc57iJtGj98zWrLBlS0HWBJ+cOZejY9wyOwUoniwRcSyRAv3dLWGvRcZdrGMr\ncn0WqyEBpc58lmIj8F7r2HKRMcan55ZE1Gt8tDF9JWPfRNY7xbVlMOVtnvTcP5X6iudhAmYeB5Ex\nplDJQt6LfX6caTuW9SFFxqtqkQVWTT7Q1Bl8bTT+GC8zw/YuEWWSVDzZGo82tDmibh2ta1kHJx47\n5yluEj16LztbjdrlhfB5AlCoZMgyquO+DY3eClPBhRACHkhlKmj08yXxBSVLVa0Ft97dgOamXjjs\nHvz9L6ex+nI5QqNZow+/Cx16bEU+rrZ4SQ1uvbvh/EWeAVW1Bcn9gzJEw8pSQBx54sZckIWGS+bP\niMjIeIpcnolPjnWgp/0odMpB9HZk46RkKarqOHoiHSTjsdPReiy03pFIa2f0vUizjVemxZkbZhoH\nVbUWbL6nHhKhGXJpP+QqEZbCauTl6yc9P860HctHvSkyPg0Fi7BgaS20C2zj6ruTjTa88vz7WHWZ\nHI6eQZw7XY4Fiy5iPTWJqZw/iIjSRSqebI3HNZ9MKmLj5mxIhD6I0lyMzmHLOjjx2DlPcSN4fbDt\nfhPDzS3QlpWi4JqrIJXHL8Rs7fZg3lAAyDMbEtAZJKLf9kFwyWS5IM7rp0w201nXI79XtWLkxPbu\n22cAAO/sFbFx8y3Q6xzBzqSJRHtcrXppHTsCJiGRiFgo70JBoBk6eRkk0vnTOZ+dW4Xi4hvgctig\n0VuRnTf7ybolQjM0kt2AD1ADkAgGAIzBdBCvx05j1XcSqQTVS62oXmod+dx7702pXgz93kSM5iWo\nWHZXWOf6dDAtTmYLxl17G4qrb4TH1weZTwWh2Q0xX5j0nCuRSmDO70PTkReDr9UuuwsmS2XCysxH\nvec3MRCA0OxCQe46+OGAXNBBaHYD+WLU+q6zw45Vl8mD59DuM+/BkKVmPTWJqZw/iIjmqqn0Q0y1\nDR1zXYFmeAdeDX5Wpb4FgJV1cBJkdOf8kSNH8Nhjj2HHjh3o6+vDd77zHdjtdgQCAfznf/4nFixY\ngF27dmHnzp1QKBS47777cMUVV6S62HOWbfebOPP0M2MviCIKr78ubuu3WA3BkfNKlRwFhfF/lMbl\nsI1bNlmWxn07lJlmOut6tO9ZrGOdwx53AIKkDAXlBTjRaMN7/zgGrU4Fc4EBi5eEP+7MlA8z03P4\nA5zokKLPV4ZcmwTVh99HbkN9qouVFP0HD+PUoz8LLssfUc16FIdc2h9zmVInXo+dTrW+m2m9OBGJ\nRAqTpW5a9VpomogFhbHThMUbU1TMzES/W++hwzj+/jkMChqYRRH+3/8egnMYwNRjK9nnST7qPb+N\n1oF569ahW2HGoCDAKGtGNUTkRkljYLFmwdEzCPjGXuvvboExv5Z1BxHRPBarTTmV9nasNnTouk0q\nH5xPbYvavuI1XupkbOf8M888g1deeQU6nQ4A8JOf/AQbNmzA+vXr8e6776KpqQkajQY7duzASy+9\nBLfbjdtuuw2rVq2CQqFIcennpuHmlpjLs2UfdIeNnF9Qborr+oHEpH+guWOms65H+17lZy7G+o11\nI2lWrFlYXGPByUYb/ifk0fja5YUQRTHsDjVjdGZODWjw1jung8vS9QvxqRSWJ5lmGrexmPJL0N8e\nvkzpYSqPnU6lQ3mqcZOI+Jqu0LQi665VxUwTlshtA0xpMlUT/W4tPSLeOqUCIABNQ1i75iZIXn8O\nQPTYihbLyT5P8lHv+W20DhywVOOt95wYmQRWCU2JiNyQz43F6hCKi0ow0PZe8L32Njm8oo11BxHR\nPBarTTnb9nbkuidqX0Ve48mV+dj7xkkOQEmCjO2cLy0txfbt2/Hwww8DAA4fPoyqqircc889KC4u\nxtatW/G3v/0N9fX1kMvl0Ov1KCsrw4kTJ1BXx1GmiaAtC08ToS2Nb2dNl20ofLljaIJPztxsH6en\nuW2ms65H+94nH3fi9ZeOBl/LylKPezTe5wmMezyeMTozfQO+mMtz2UzjNhbGYfqaymOnU+lQnmrc\nJCK+piu07nxnr3/KacLive3RZXawTW6i323AHX5pMihoMDp9d7TYihbLVXXJrZ/4qPf8NloHDnrC\nYzcylkNjVaWWYeNnb8FQTyvc3mzsf9uPSy5n3UFENJ/FalPOtr0due6J2leh13j+gBHP/bobHvdI\ndgkOQEmsjO2cv/rqq9HW1hZcbmtrg9FoxLPPPovt27fj6aefRllZGQyGsUdLtVot7HZ7tNVRHBRc\ncxUgiiM550tLUHDt1XFdvzFXi0uvXAj7oBuGbA00Wllc1w/M7HF6mj9mOut6tO8dffNU2GdGR/0B\ngEotx+LaAsjlUoiiiBNHbcH0NozRmckyacLSYiViQul0NdO4jUUUJejsykVnhxIWaxaM+RKAAynS\n3ujIzVMnulC3vAiffNwJj9sftUN5qnGTiPiartC6c1GNGWfOqFBaUQ5ruQUSSWIDkylNZmai362w\n0grsH2vfF9eWwZS3ecLYmuhCdirnyXilJGJqo/lttA5sHpABGAzWQz6ZEsc/sgXjITRWPe4AzjQZ\n8O7baqjUASyqscDt8oZ9noiI5tc5Nlabcrbt7ch1T9S+Cr3Gk0olAHqC73EASmJlbOd8JKPRiCuv\nvBIAsHbtWjz++ONYunQpHA5H8DNOpxNZWVkTrSLoySefxLZt2xJW1lQQBAEH2z9Ey2AbSrKL0FB0\nAaSSySeynA6pXB7XHPORJJDgb38ZS0tx1Q01CdtWJpqLcZtuQmddFwQBB6IcU9EbEONna4928h19\nNN7WNoi3d38SfC9aepu5JBmxq1LLw9Jird9Ym9DtpZNo8TdbTOWRmXVu5H6rXV6IxvfbodEqxj2y\nOtW4mexzyWh/jNadXZ12/PVPJwAA7759JilxmYkpTdIhdsd+tyEojAKOy45gqLUT9UuWjvs9JdKJ\n6+vIc6lEiil3cMarHmN9mDzpELuRRuvAHEGEpsiGzpA23Ltvn8GmuxtQs9Q6LlZLK3JRWpGbknqL\nkisd45ZoKtIhdufTOTZWm3Iq7fJYNzIqa/Jx1eZF6Oqww2w1oK5+MWSy8e2ria4VAA5ASbQ50zlf\nX1+PvXv3YsOGDThw4AAqKyuxdOlSPP744/B6vfB4PGhqakJlZeWk67r//vtx//33h73W2tqKdevW\nJar4CXew/UM8tv+p4PJDq76CFcUXprBE0zfQPxw28nWgfzjVRUorczFu09lEx9RUGxChnUkupzf4\nevVS65TS28wlyYjdvn5nWP3R1++M27rnI6byyMw6N3K/qdRyrN9Yh7/86Tg8bj+A+F/0zKT9Md1R\nUqNpRVIRl5mY0iQdYlc8/3+ndxidPW3Ye+4tDPtdI/Gx9MIp/56j59Lmpl447B78/S+n4XH7pxTH\n8YoX1ofJkw6xG81ondXS1AuJRAKVWh6sU88021Cz1Bq10yVyRD3A+JmL0jVuiSaTDrE7n+rIqbYp\nJ2onx+qHOGT7CE+cOd8ePwM8VBi9PR75e+sNKlx+bVXGDEDJZHOmc/6b3/wmvvOd7+C5556DwWDA\nT3/6UxgMBtxxxx24/fbbIYoiHnzwQSiVylQXNSVaBtvGLWda57whS42D+8cmwrjyuqoUlobmu4mO\nqak2IEY7mqKNloocXaVQyXinepY0agXe++vZ4PLl6ye/UUsTYyqPzBS53xZVmdHZYQ92IgHxv+iZ\nSftjpqOkGJeZI3IfX7ZuLV63vzbt9mnojZl33z4TfH0qcRyveGHcUayRhgrTyK2oiTpdGD9ERBNj\nHTneRO3kWP0QU22PR3vKa67eDEk3Gd05X1RUhOeffx4AUFhYiF/+8pfjPrNp0yZs2rQp2UVLOyXZ\nRTGX40EMBNB34OD5PFhlyFnRAIk0fo+uh44ujrZMlEwTHVPTaUCMP4EOId/ZAm1bO27aUAXbgACN\nTglLgQGLqi04/lHHvMi3lwhOhyfmMk1PJqbymGtmcs6Nvt/C65HpXPRMZYT7TNofMx0lxbjMHJH7\nGENKQDLz9qnFmjWW69sTgEangCiIMc+TE8XLdI8txh1FxrNcLsUll1dApxFgCXRBFIQJY4jxQ0Q0\nsXSoI+PZzxWPHPoTtZNj9UNMtT2eDr/3fJXRnfM0dQ1FF+ChVV8Jy/kab30HDuL4oz8OLlc/8nBc\ncxwb83Rhy6Zc3QSfnDlRFDDQdQwuRwc0eiuM5iWQxDk3Ls0NEx1T0dLVjHYQRJ6MIzvBjEp/8BiS\n6nQouPebGPCOTMxy6njnhCNJGbeTM+eqsfYaFdTKQXh82VCpNakuUlSZMulRJqbymGtmcs6Ntt9m\n0wifygj36bQ/RuNfKo19w2Ci4yT07xMEESemeCyxDk0+c8Q+XZRXigeNX0O9dWZPNVXVWnDlddV4\n/aWjUKllKC4ewJljH8OUXzLh/pyoHpvuscX6kCLjWadXoafTAZ1Zjq5mB1p6PkBhZWHUemii+GG9\nRESUHufYePZzRWs7A5jWtd9EnfCRbfrFS8zo7zwKl6MDi/QF+H8Wfg0dbUMwWw2oty4Ofn98u7qA\nbZoUYOf8PCGVSLGi+MKEprJxtrYib81qBNxuyDRqONvakBvH9Xs9/mDOaIVKBo/XP/mXpmmg6xia\njvw6uFyx7C6YLHVx3w5lvomOqWjpam66sQKmzkb0W2rxyqtNwc9uvqch7ASqPfkPjE5hHVizIeyz\nV0SkcQodScq4nVyWsRcS927AB6gBGIwbASye7GtJN58mPaLZcTY3Q6bTwnTRRQi43Rg+14qcFRdP\neyTPbC56pjLCfTrtj9H4V6nlqF1eCL1BhdKK3HE3DKZynEznWGIdmnxSCcLadP09w/DbBBhVXTOK\nRYlUAtewDwCw6jI5NJLd6G8H+tuB4uIb4Pmoc8qj3ZzNzeOW4znYhOae0XiWSiQwZKtx6O/N8Lj9\nOHkMqF1ejMb9bcD+tmmd01kvERGlh3i2CyLbzqGTggNTu/YL74TXI9/Vipad+6ArLUPVirHv93ce\nDTuP+MSr8eHbHgCdyNEYg5/j9Wd6SIvb76+++ioef/xxuFwuvPzyy6kuDs2QXKNFz7530H/gIHre\nfgfyOI9M7et2oPH9dpw81onG99vR1+2Y/EvT5HJ0xFwmmorIk25r41mc+90KbuglAAAgAElEQVRO\ntDaeDXvd1j7SkXX5NYtRvdQKXdHY42WDQvjxE5nGKXQkKeN2cn5Pd8zldBGts5MoGl1pGUwXXRQ8\n77b89+/Q996BpJYh3nlAR+Pd4/aj8f12qDVKVC+1jhtBNJXjZDrHEuvQ5LO128PadC6nLzj5+UyN\nxqNaORj2+mDLMZz73U4cf/Q/p3SM6ErLIpZLZ1wmmh9G41kUgZ5OR9g8Hj5PIPjv6cQ36yUiovQQ\nz3ZBZNs58hp/KueJ0YE1l1+zGPnOczj+o/+I2s6JPG+Eto9Ct8Prz/SQ8pHzjz32GGw2GxobG/Hl\nL38Zv//973H8+HF861vfSnXRaJr8w8NhI+f9Lldc159vDr/ozzPHfzIQjd4ac5loKiJPukalD3lr\nVkORpwWaHCGfC4/hnBUNqH7kYTibm2EoKMOhprGR86UVuSityI2aeoJxOzmVOh+hNZJSlZ+yssRi\nseojljnpEUWXs6IBg42NYa8le4RvvPNSxursD833aSqonfBzU1lXJNahySUGAjCpfGGvKVQyALOr\n80bjUSqehd32XvB1iXNsLNJUjpHQc7GutBQ5Ky6ecZlofhitbz75uBP1nyrFyWOdwfdGY3vkc1OP\nb9ZLRETpIZ7tgsi2M4CwCe2n2w6KNao/8rzh9mYD8IzbDifdTQ8p75x/55138NJLL2Hjxo3Q6/V4\n9tlnsWHDBnbOZyC5dmTk/Kjye6vjuv6LVpYiEBDQ2+NEbr4eF6+M/0gmo3kJKpbdFZbfkQiYXi7w\n0JOuUemD5sO96N73DqS6w1i75iZ4LRUorLSO68iSSKXIvWQlci9ZiQWCCGVOTvDEXVljwScfj17s\nhW+XcTu5LMEAoWAjfO5uKDRmmET95F9KgXxXG9Yu8mBQ0CBb6kK+qxUAL8hpPIlUiuzaWnT84X+D\nr81mJM9EdVysui/eeUBjdfaH5vuU6rS46d5vYcCrmPCmwHRuHLAOTa7eQ4dhP9mG+ous0Br1kCtk\nkChkyM/XY/GSmd/gGY1HUbRgwGKAy9EBmUeFMz/8v8HPTOUYCT0Xp0qmzD8yn4XvIwM23rwQHWd6\noLV9gnXVIgb8auRVlcMnkU6YoisW1ktEROkhnu2CyLazKIjB9qrZaoBEAux942TMc3/o+cdUUAup\nTgvBOQwgvJ0Teh6ReVVo+lsf6isU464xOQlsekh557z0fN5HiWQk6Lxeb/A1yix+uz3m8mwdOdyK\nt0LycSlVcqxYXR7XbRBNZDq52EJPuqIg4MzpkRF8gnMYktefw6LbN6Nk6UUxtxd54j7+UceE25dI\npMGLttHH1zhxWLgWpx4uewfUSi88A3YM6a1xnRMjXpxnzkDy+k4YR5dzNiOXozZpAvEcyTNRHZfM\nPJSxOvtDRwYJzmGYOhuxbPOtM1rXuM9KpDBZ6maVz5mTN05dS4+IPcdlALoAdOGGdRZcdM2KuK0/\ndH+KggD5v6qmdYykw75k/tf0F20fLfK9i45XR26YZq+/DX9+7WTw/dKK3GndYIlVL6VDjBIR0eyF\ntlePf9SBnc9Ofu6PPP/cdO+3YOpsHNfOCT2PtOzcBeEPI9eYMr0W7otvR/vp3cFzSKon3aU06Jxf\nv349vv71r2NwcBC/+tWv8Ic//AE33HBDqotFM6BdsCB8uWTBBJ+cmcEBJy69ciHsg24YsjUYHHDG\ndf0AJ1+iiU1l4sNooo1ulesNaNm5C5qKRTjjMaG3x4HsbA18Xj9M6gACDjt67YDZmoW6VVWQymWT\nbp+xG5tO0w44xiaE1Wl0AMpSXKrxmOuYpkMilSLn4gYAo53XkqgTXoamhAmdFDP4els7zqIi7Duj\ndcxU677pjvSd7ufjfWzEe2Qy6+CpG3CHX37Y/Uoc/6gDPV0OKFVyuIa90OiU8Lr9yDMbZrVvQke7\niYEA+t47MO44AMKPEdXSArS2vhpcR8WyOyGcGYazrR39uYsx4JXDYs1O6Gj2mbY5KHlC95FKLYet\nbRB2/YXI+/IFEHu70KcuwCWXK+Ee9uHjjzrQ/kkHtMf2TXli4lgSUd9MdJ4gIqKpCa1HteUV6NEU\nTaud2dPlQO3yQvg8AWh1SvR02rE3yvcj2wgDXkXMAStAeDs679bLYet7C+gbWZ7oHBJ2XigpAWQy\nOM+c4TkiQVLeOX/vvfdi3759KCwsREdHB+6//35ceeWVqS4WzYB7YABFG2+Gt68PytwcuAcGJ//S\nNGjUyrCR82uvq4rr+oHoky/x4pqA2eViM9UvR/m9X8JwcwvU1gK0vvgSAi43And+E2/tPora5YU4\nsO9s8PO1ywvR+H47gG6IoohlV9ROun3GbmzS0dbHBMvpgrmOabpG071IdToE1myAt12OwsrCsEZ8\naEoYAKh+5GHkXrIy+Lq4/jYMGwJh6x2tY6Za901lpG9oI7/fUotXXm2K+flQ8T424j0ymXXw1BVW\nWoH9bcFlTU42dv3qYMi5b0Tt8kK89cfjuPXuBiyuLcDJxg60f2KDUe1HaZ4UOQ0XTevCcKLjIPK9\n/K9eBYSs1m47jXOP7oC4/ja89bepx+xsMP9r+gvdR4tqzHh79ycAzrfhPvQDaA0uL6oxQ+cbgKu1\nDe2vvIrKf90SMz3CZDcPE1HfxDo+iIhocqH1qLj+Nrx1aqyunkqbQamSB9tBtcsLw/q+Qr8/kzZC\naDvaV6IGusfW3d/dEvUcEp5SUgf5LV9EV48Oxv5zqJZK+HR3nKW8cx4AzGYz1q5dG1w+cOAALr6Y\nOzrT+Pv60PbSy8Hlon+6Ja7r7+1xxlyOB42+AKaCCyEEPJDKVJx8iYJmk4ut/9BhnHn6meBy3prV\n6NYtQGvbSAz7PAGo1DJcdqUcuTlDUGp6UFSoxd49HnTZ7FPaPicOi80v5oYd225fOia1SUyuYz7+\nPreNpnsJrNmAt06pgFNtwP62sEb8RJNFjb4+KGjwycedwdE6haXGYB0z1bpvKiN9Qxv5A9d8YdLP\nj8RuIxz9ZyBX6qAuL8CClf8Ul/iN98hk1sFTV1VbEBZTnR1DAEbOhVlGJa7fIAOEPijUQzh3Rhnc\nV7t+dSi4jrWLPKgRAtOqK2NNmhb6nmRYBoRMSyLzqQCMHCeAEHw9kaPZmf81/Y3uo55uO3Kyu1C2\nwIMskwledwcKzCq8s9cPjzsAnycAg0qA8Mqz6HEOI2/N6kknJp7s5mEi6ptYxwcREU0utB6dSZvB\nNewdWxBFrL1GBbVyEB5fNnq6HcG3QtsIBYV6mPN70X76aMzrvNBrzBOH94W95/NE79wP/XsCazZg\n99+Hzi8poSkR0zJFbCZLeef8Aw88gGPHjsFsNgdfk0gk+M1vfjPpd48cOYLHHnsMO3bsCL726quv\n4re//S2ef/55AMCuXbuwc+dOKBQK3Hfffbjiiivi/jdkAkEQcLD9Q7QMtqEkuwgNRRdAGufOGcHj\nRd6a1Qi43ZBp1BA83sm/NA15BYZgx4FSJUeeJf4TOno9dvTbPggu64zMaU8jpjvxYegIUb8j/EZS\nwO3GoEYDpWqkClaq5Fh1mRxKYTfsPSOfMRdciFWX5UMnytHy/C7oyspQtSLKSNTRjldnF4qrb0bA\nNwyN3oqu7lx8eCT2ZDLziUolQX/b2LFtLFqYwtIkVyIefxe8Pth2v4nh5hZoy0pRcM1VkMpT3qSY\nl7Tn07uMvwgYCtYXE6WEGX3dKHPD41YGR+tcuGIBJFJJsB7TNjejrrQMObWLJqxLJhvFI4oC3PI+\n5H/1KkiGZZAN+BE6PDnaqJ+R2B1rD5oKLgQgwmSpm3VamknLO80UD5y8cerGn09H9ptSJcf1G2QY\n6vxz8LPXb7gWw/2+YAf+KKc2D47TTfAMDMI/MABd2fh9FNn2rYhIhRSaGin0GOl9YR/Kv/MlBFRe\naPRWCM1uACPHCaAMfm66o9mnE7MSqQRVdRZYzL1wOY5ioLuXN1bTzGgc93f2oOnIKwCAIdtIPeXG\nB1h12dV4640AFCoZsu0tkEgA853XQGrWQJVngeD3oe/wB2jpETHglsO6qABmdxucZ86gXbEobFuR\nnTqJqG8mOk8w3Q0RzXeBQABtpw/D5bRBo7eiuGI5pDLZuM+F1qMzaTNYrNnBfy+ucsE7MJaSNb94\nLG1NaDuqv/OjsLZyxbI7YbIsjbkdV48GLvFqqJWDcHuzIevVBN8LvcZTWwsg02kRcA6Pu84YcMvR\n+493eW6Io5RfSR8/fvz/Z++946O677zf9/TepNGMGpIQRQKBMSCaMdims8YmtoMdO65JNt48STZ3\nd7N+UvfZuzc32Vdu9m7KZveVbO5uHDuO7SSPewVcKC402xghQFioa4o0VdNnztw/RpqZM6iAGAw2\n8/nH/p4553eOmDO/8vl9vp8vL774IrJxXu7J8Jvf/IZnnnkGnU6XPXb8+HH+/Oc/Z+OhoSEeeeQR\nnnrqKaLRKHfeeSerV69GoVAU7fk/KTg0cJSf7P9VNv7m6gdZXnt1Ue+hsJizRZAA6u69u6jtKxUy\nUbpzXWPx08WjBWmihXEJJZwrPAcPcernvyS15mYipia0W43ID+0ksXQDfZZajGYtb+3tZ+HSWhQK\nKRW2GCPu3PVCKkalNYT7Xx6hd7T6evO3/h4kEtEg6HOfTbw6XeWlQnIFkKYdBfGV89u+GOnvjp27\nRNkgpNNU37j1gtosYXroqtHguXs9FQojdPqyx83KZPb/J7KEGTseOHWarattuCJKNFoFCb+XtFB5\nls1B03e/xZCmdlxycSqlr891PONvKQX0YJVJ2X7TKnxxRfb8QvLSqBW/u0Iqln1/L9SWZm6zle03\nNeJyBLFVGpg7zyb6/HwtHopRVPZKw9j3PeQaYcstC4jHEsilH4nOkTFM+NcPY3nwW9ljKrUcnVHH\nsQEjNrWK5LPPIYTCZ31HhXPfh675ylm/gyzx2NXNzAe/RDISQVdTQ1nzspwffYWQua5/gO3NjaOe\n88bzVrOf7ztbqmPwycCQp1MUC6kYAOXlEdaum4VFGSf84jOU33YtAcNpiICv90NIQ8dhdybjCWB/\nPxs21CPfuRtVq4zJSJ2L0d9MNE6U7G5KKKGEKx39Hx3B3fUkQGa9nk5TN/dspw9RPzqzjvLVteeV\nAZc/l9ZpTxPPTeuRS32c+HDwrDl44Rg05Okcl5zP32i1mS2MdAZwClpM0gAzllqy5+Wv8aQ6Hep7\nv8GQL0m51QSdp7PnWfSS0thQZFxycn7RokV0d3fT2Ng49cl5qK+v55e//CUPPfQQAF6vl5/+9Kd8\n97vf5fvf/z4AR48eZenSpcjlcvR6PQ0NDZw8eZIFCy6viW1KSHOgzUH3oJ+GKhPLWyqRFlnl6gi6\nubl5E96IjzKNGVfQPfVF54mYyz1pfKFwO8Qp6K6ClPRiQKEUT37lyuKr80v4+DFR5kixfnvjqYpC\n3d05mwmGASXrbv8G+9/sInbajUotZ8WGeqQpOW+8dBKLSYU6r02Vzk7alQQkpLfciR89pwI6/N39\nGAQZ0md+wdy//hqxCrGaMDIyiHNQKTpWKiQHMrmuINZeoif5+HEx0t/DA47MeyloMMuiRAYcU19U\nwkVBV6CXJ4UP+Zo7ybrZ1fgFDSZpBNXgB/yprSvb51mWL8etq+PMYBB7m2t0Ui/N2tvEnE4Oj5JE\nKrUcf0LJyEAKw9a7kO55GiEUpmcozfO7xycXx1Q8TQvs+FzHGTyTSa81Wps5PHgMne+06LmVDVYa\n58/Fe/gIoePv4wk14NbNEJGX9/+V+F0ds5tLp1IMdIiJe+dgcNSXfGplsiCk+XD/SfraujDLooSf\nfRqvWuwBXbJ4uDhICWkOHh+kL3oafUjLgWdz3+OqW2tQ6CpE5ytUFQS3/CWaeIwd9y2l98MutFoF\n+w+5Wb1WgUTpwvq1zzP8b78n1N2NZflyTrU5cDmD+ANptpm281roVcLJCF2BXlpX3ij6HoffeXfK\nxWU2FRyom+Rvm0oZP56VUtN824SqZK+7R3T+RJ6wJVwaCILAwf6jGFJm0XGpLNOPajV25th7SRDE\n8tXbSKSDEM6dNzziw18+C073ZY/1DkaZcdtXCfYOsHHzDOJMbyNoOpjIVi/U3Z2taeIXNHR7JJQJ\n6Ss+I7OEEkq4chAJOSaNx1DYj5YDzQurEIQ0J88lcy4tUBHqQevtRjWjEm/eR0nBnJ0jq9Qy7ryv\nArnMh0puEjUhpMv5U9sL1JlqWFrZgu/QEULd3cgNBnoefYxUKIxMp6Xx83eRHAlmNmNbl2avD3fn\n5h7pdbfROSQhEZMSIca6tVX4+5yYpBGMA8cJ5a0FQ/0DJZubC8QlJ+dXrlzJtm3bsNlsyGQy0uk0\nEomE3bt3T3rdxo0b6e/PFJMSBIHvfe97fOtb30KpzJFSIyMjGAw5slWr1RIMFp/QvVAcaHPww98e\nyMbfuX85q4pOpKV59sSr2ejuq24pcvugLLNMGl8oDCa1KNYXxMWATGkQ+VLLlcapLyrhssdEmSPF\n+u2NpyrS1TfgH3KjUktYvVaOWulHqXEz76pK3j8wuhALC/iDIyxYXMMH73m5cfuNJCKdSGUqhvve\nwVS+MY/gT8DpLloWV3PgvQTr1mwn1N2NYaY4lVmjryoVkhsHErlW9NuWyK4cct5U3kRt7TYiI5lU\nTJO1+YLbDNZfzWuv9JFJb1Ry0+biZmKVcO6oM9Wgk6mxmK3ITnZQoVHjPXyEjltW8eSxvegUGr63\n5FYkIwGGB+W8M+qDfPv9S6kI9Y5O2I34PQAJIFPc8LXn21Gp5cye14D6L76KeaQff1ScrtrdOUxT\nS6VogVGo9jXP3c5P3v09989aiT3vWkPlbLyHj4j6zth93xW139NromXRvaOe81rUuirMtnl43j2I\nytVHobL0XJXJp9ocecVoldn+NJ+UmsjioYQLw4E2B291v8e7oefZkhbPRXt7h3jK8wbfbrmRZNiF\nUl2G16cmptLyyhuDbP9cFUtnyXnnsIvVaxVoJJl070ACrHdch85ez6k2B20fDIgyLe/dcAuSM+9Q\n61KQnieI0q4n2oSZjm3SVO/feGPzZKrkpCCeRxfGJVxaHBo4yr+89StuU95JnWEj5eVBNDojiViE\nuHQj4cAI4eBr2fOtNVsgfDQbe7waYvG0qM1ym55du0azR052XhaZj7r6hry5qACdTrQzHJf8uUoo\noYQSPi5o9FWiDHeNLrNhWijQsyxdnBGdFGy4n+v81HPgICf++f8BQLGzAtv/cS+xxDAaXRUOp5l1\nm7yolX5M5Zaskl8mV2O0b8bv8RKNm/D0GnjS93sAflDxGdw/+3W2feuaaxnau480EhyScoKWWdh1\nRsrIzW+0Dbn5brR8Bm37+rPxtdfWYH71vwAIfPV7orXg9ubGSQUMJUyNS07O/+xnP+Phhx+murp6\n2m20tbXR09PDP/7jPxKLxfjoo4/40Y9+xIoVKxgZyRVOCIVCGI1Tk62/+MUv+Ld/+7dpP8/5omvQ\nL4q7B/1FJ+cdI+5J42Ig4Q/kPOfVahL+wNQXnRfSWc95hUqGhPTUl5wn4lE/CrWZZCyAXGUkHvVP\nfdFlgo/7vf0kocfff1a8vPZquif57U3ksTme2n68xf2MHZ+lfOQIq2d7sgRCIgEzG24ina7FYFSz\nZ3dOSXrNDbNwD7YhSbRnj8ViQ8TNlazbFM4Wg3E4M++9X9Cgq7eO6ztqrpB8ogrJfRzvbioeEP22\nU4nLb6MWzs+T+FzhPXSE0z/6eTaWf1t1wQrgAJpJ4ysBl6LPHa+4b2vNVXy7bIto8l17713EXGe4\n13oVino9vlMZP2Q1ZH2QBzocDD+cIwVrvvo9Do8qOBOxFJAh6XMkp5J1BVOTkWCMk21ikqbQRik6\nklEXPdnzAXfWL6FebkAqseFyW9H0dwBkVZmxFCxYXENHu5NYNIm1Qo/FPues9NxQdzfSPc+xbs12\n/IKGqpkVNLVUsmdXh+i8ibKGChXMY/3pGAQhjVs3g9h938WsTlJvlYhURZ90XMr5Qvegn7gsk6et\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P21TUe8hkMnoefSwb1919V1Hb1+tUvJandlu3tamo7UPG+mKwM6fyq569tej3KGFqHBo4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XoOnJ65mNQz9HQYzwygSYPs0vPCyxRk5vOj4POQBV/JOI/de9sIDWjQccGqEn68qx6OtPP9z1ij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\nSAsBVDo3wyNmjDofcmmQgtRGLN5ILCbgcpuYO9/O+L59U+ohXm/6o7Ptu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x7vYfikF0Mu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"text/plain": [ "<matplotlib.figure.Figure at 0x9e49b00>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns; sns.set(style=\"ticks\", color_codes=True)\n", "sns.pairplot(df_real, hue='rating(y)')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
kejbaly2/metrique
notebooks/Cube Example - osinfo_rpm.ipynb
1
103889
{ "metadata": { "name": "", "signature": "sha256:788119d28ae731eb11a37404bc2971738c81f86c10ea8a61e780b10d0ce8f525" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from metrique import pyclient # main api interface" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "osinfo_rpm = pyclient(cube='osinfo_rpm', name='localhost_rpms') # find and initialize the osinfo_rpm cube" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "osinfo_rpm.get_objects() # run the rpm -qa ... and build a list of dicts with the info we seek" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "<metrique.cubes.osinfo.rpm.Rpm at 0x3fba850>" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "print 'Total RPMs: %s' % len(osinfo_rpm)\n", "print 'Example Object:'\n", "osinfo_rpm.container[0:1]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Total RPMs: 2005\n", "Example Object:\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "[{u'__v__': u'0.3.2-1',\n", " u'_e': {},\n", " u'_end': None,\n", " u'_hash': u'12cc01ee7fe4f059d61780ba5fddef97a5b28f24',\n", " u'_id': u'cward.brq.redhat.com__AdobeReader_enu-9.5.5-1.i486',\n", " u'_oid': u'cward.brq.redhat.com__AdobeReader_enu-9.5.5-1.i486',\n", " u'_start': 1405522262.92113,\n", " u'_v': 0,\n", " u'arch': u'i486',\n", " u'host': 'cward.brq.redhat.com',\n", " u'license': u'Commercial',\n", " u'name': u'AdobeReader_enu',\n", " u'nvra': u'AdobeReader_enu-9.5.5-1.i486',\n", " u'os': u'linux',\n", " u'packager': u'Adobe Systems, Incorporated',\n", " u'platform': u'i486-redhat-linux-gnu',\n", " u'release': u'1',\n", " u'sourcepackage': None,\n", " u'sourcerpm': u'AdobeReader_enu-9.5.5-1.src.rpm',\n", " u'summary': u'Adobe Reader, an application to easily view, print and collaborate on PDF files.',\n", " u'version': u'9.5.5'}]" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Data can be persisted and queried**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "_ids = osinfo_rpm.container.flush()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "[u'cward.brq.redhat.com__AdobeReader_enu-9.5.5-1.i486',\n", " u'cward.brq.redhat.com__Field3D-1.3.2-12.fc20.x86_64',\n", " u'cward.brq.redhat.com__GConf2-3.2.6-7.fc20.x86_64',\n", " u'cward.brq.redhat.com__GREYCstoration-2.8-16.fc20.x86_64',\n", " u'cward.brq.redhat.com__GeoIP-1.5.1-3.fc20.x86_64',\n", " u'cward.brq.redhat.com__ImageMagick-6.8.6.3-4.fc20.x86_64',\n", " u'cward.brq.redhat.com__ImageMagick-c++-6.8.6.3-4.fc20.x86_64',\n", 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u'cward.brq.redhat.com__libmwaw-0.2.0-4.fc20.x86_64',\n", " u'cward.brq.redhat.com__libmx-1.4.7-9.fc20.x86_64',\n", " u'cward.brq.redhat.com__libndp-1.2-1.fc20.x86_64',\n", " u'cward.brq.redhat.com__libnetfilter_conntrack-1.0.4-1.fc20.x86_64',\n", " u'cward.brq.redhat.com__libnfnetlink-1.0.1-3.fc20.x86_64',\n", " u'cward.brq.redhat.com__libnfsidmap-0.25-7.fc20.x86_64',\n", " u'cward.brq.redhat.com__libnice-0.1.4-2.fc20.x86_64',\n", " u'cward.brq.redhat.com__libnice-gstreamer1-0.1.4-2.fc20.x86_64',\n", " ...]" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "osinfo_rpm.container.count() # queries remote container store" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "2005" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "osinfo_rpm.container.columns # list " ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "[u'__v__',\n", " u'_e',\n", " u'_end',\n", " u'_hash',\n", " u'_id',\n", " u'_oid',\n", " u'_start',\n", " u'_v',\n", " u'arch',\n", " u'host',\n", " u'id',\n", " u'license',\n", " u'name',\n", " u'nvra',\n", " u'os',\n", " u'packager',\n", " u'platform',\n", " u'release',\n", " u'sourcepackage',\n", " u'sourcerpm',\n", " u'summary',\n", " u'version']" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "bash = osinfo_rpm.container.find('name == \"bash\"', fields='~', date='~', limit=1)\n", "# date uses _start; enables easy historical querying\n", "print 'Currently Installed: %s' % bash['nvra']" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Currently Installed: bash-4.2.47-2.fc20.x86_64\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot most frequently used Licenses\n", "df = osinfo_rpm.container.find(fields='license')\n", "threshold = 10\n", "license_k = df.groupby('license').apply(len)\n", "license_k.sort()\n", "subset = license_k[license_k >= threshold]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "# shorten the names a bit\n", "subset.index = [i[0:20] + '...' if len(i) > 20 else i for i in subset.index]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "subset.plot(kind='bar', title='Most Frequent Licenses')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "<matplotlib.axes.AxesSubplot at 0x5330450>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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sli9j9WreBvwGu1ldjRnypHwEuKbBMYP4yV3rA0+490PYnySMNbCbYy33AGs2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\nPubW30H0XS0Nd2E94nUw359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"text": [ "<matplotlib.figure.Figure at 0x54a2490>" ] } ], "prompt_number": 18 } ], "metadata": {} } ] }
gpl-3.0
hoerldavid/nis-automation
transform_test.ipynb
1
9036
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import logging\n", "\n", "\n", "from skimage.transform import AffineTransform\n", "import numpy as np\n", "from numpy.linalg import inv\n", "\n", "\n", "def _pix2unit3(x, offset, fov, pixel_size, cam_rotation=None, im_flip=None):\n", " \"\"\"\n", " transform a point from pixel coordinates to NIS stage coordinates,\n", " taking into account offsets, fov, camera rotation or image flipping\n", " \n", " Parameters\n", " ----------\n", " x: 4-tuple\n", " point to transform, in pixels \n", " offset: array-like\n", " extra offset to add to transformed bounding boxes (in units)\n", " (center of image or center of first tile in large image )\n", " fov: array-like\n", " field-of-view size (in units) \n", " pixel_size: scalar\n", " pixel size in units\n", " cam_rotation: 2x2 mat, optional\n", " camera rotation matrix as provided by NIS\n", " im_flip: array-like\n", " array of 1,-1 indicating whether to flip coordinates in a dimension or not\n", " \n", " Returns\n", " -------\n", " x_tr: array-like\n", " transformed point, in units\n", " \"\"\"\n", " \n", " logger = logging.getLogger(__name__)\n", " \n", " # default: no camera rotation\n", " if cam_rotation is None:\n", " cam_rotation = np.array([[1,0], [0,1]], dtype=float)\n", " \n", " # augmented rotation matrix and inverse\n", " cam_rot_tr = AffineTransform(np.array([[cam_rotation[0,0], cam_rotation[0,1], 0],\n", " [cam_rotation[1,0], cam_rotation[1,1], 0],\n", " [0, 0, 1]])\n", " )\n", " cam_rot_tr_i = AffineTransform(inv(cam_rot_tr.params))\n", " \n", " # default image flip: along y\n", " im_flip_t = AffineTransform(scale=[1,-1] if im_flip is None else im_flip)\n", " \n", " x = np.array(x, dtype=float)\n", " x_tr = (im_flip_t+cam_rot_tr_i)(x * pixel_size - np.array(fov, dtype=float)/2)\n", "\n", " res = np.squeeze(np.array(offset, dtype=float) + x_tr)\n", " logger.debug('transformed point {} (pixels) to {} (units)'.format(x, res))\n", " return res\n", " \n", " \n", "def bbox_pix2unit3(bbox, offset, fov, pixel_size, cam_rotation=None, im_flip=None):\n", " \"\"\"\n", " transform a bounding box from pixel coordinates to NIS stage coordinates,\n", " taking into account offsets, fov, camera rotation or image flipping\n", " \n", " Parameters\n", " ----------\n", " bbox: 4-tuple\n", " ymin, xmin, ymax, xmax (as output by skimages regionprops, in pixels) \n", " offset: array-like\n", " extra offset to add to transformed bounding boxes (in units)\n", " (center of image or center of first tile in large image )\n", " fov: array-like\n", " field-of-view size (in units) \n", " pixel_size: scalar\n", " pixel size in units\n", " cam_rotation: 2x2 mat, optional\n", " camera rotation matrix as provided by NIS\n", " im_flip: array-like\n", " array of 1,-1 indicating whether to flip coordinates in a dimension or not\n", " \n", " Returns\n", " -------\n", " bbox_tr: 4-tuple\n", " transformed bounding box (ymin, xmin, ymax, xmax - in units)\n", " \"\"\"\n", " \n", " logger = logging.getLogger(__name__)\n", " \n", " # transform bbox\n", " (ymin, xmin, ymax, xmax) = bbox \n", " bbox_tr = np.apply_along_axis(lambda x: _pix2unit3(x, offset, fov, pixel_size, cam_rotation, im_flip),\n", " 1, \n", " np.array([[xmin, ymin],\n", " [xmin, ymax],\n", " [xmax, ymin],\n", " [xmax, ymax]], dtype=float)\n", " )\n", " \n", " # get new min max\n", " min_ = np.apply_along_axis(np.min, 0, bbox_tr)\n", " max_ = np.apply_along_axis(np.max, 0, bbox_tr)\n", " \n", " logger.debug('new min: {}, new max: {}'.format(min_, max_))\n", " \n", " # NB: we reverse here to preserve original ymin, xmin, ymax, xmax - order\n", " bbox_tr_arr = np.array([list(reversed(list(min_))), list(reversed(list(max_)))], dtype=float)\n", " res = bbox_tr_arr.ravel()\n", " \n", " logger.debug('bbox: {}, toUnit: {}'.format(bbox, res))\n", " return tuple(list(res))\n" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "transforming point: [0 0]\n", "[[ 256. -256.]]\n", "[[ 204.8 -204.8]]\n", "[[ 204.8 -204.8]]\n", "[[-262.49477409 122.40332338]]\n", "[[-762.49477409 -377.59667662]]\n", "transforming point: [1024 0]\n", "[[-768. -256.]]\n", "[[-614.4 -204.8]]\n", "[[-614.4 -204.8]]\n", "[[507.30142086 402.5862248 ]]\n", "[[ 7.30142086 -97.4137752 ]]\n", "transforming point: [ 0 1024]\n", "[[256. 768.]]\n", "[[204.8 614.4]]\n", "[[204.8 614.4]]\n", "[[ 17.68812732 -647.39287156]]\n", "[[ -482.31187268 -1147.39287156]]\n", "transforming point: [1024 1024]\n", "[[-768. 768.]]\n", "[[-614.4 614.4]]\n", "[[-614.4 614.4]]\n", "[[ 787.48432227 -367.20997015]]\n", "[[ 287.48432227 -867.20997015]]\n" ] } ], "source": [ "###############\n", "# some tests\n", "###############\n", "\n", "logging.basicConfig(format='%(asctime)s - %(levelname)s in %(funcName)s: %(message)s', level=logging.DEBUG)\n", "\n", "\"\"\"\n", "\n", "cam_rot = AffineTransform(np.array([[-0.99995, -0.0096, 0],\n", " [0.0096, -0.99995, 0],\n", " [0, 0, 1]]))\n", "cam_rot_i = AffineTransform(inv(cam_rot.params))\n", "\n", "im_flip = AffineTransform(scale=[1,-1])\n", "\n", "\n", "offset = np.array([5000, 5000])\n", "\"\"\"\n", "cam_pixs = np.array([512, 512], dtype=float)\n", "psz = 0.8\n", "a = 160 / 360. * 2. * np.pi\n", "flip = -1\n", "r = np.pi\n", "offset = np.array([-500,-500])\n", "\n", "img_rot = AffineTransform(np.array([[np.cos(r), -np.sin(r), 0],\n", " [np.sin(r), np.cos(r), 0],\n", " [0, 0, 1]]))\n", "\n", "cam_rot = AffineTransform(np.array([[np.cos(a), -np.sin(a), 0],\n", " [np.sin(a), np.cos(a), 0],\n", " [0, 0, 1]]))\n", "im_flip = AffineTransform(scale=[1,flip])\n", "\n", "\n", "\n", "for x,y in np.nditer(np.meshgrid([0,1024], [0,1024])):\n", " a = np.array([x,y]) \n", " print('transforming point: {}'.format(a))\n", " a1 = (im_flip + img_rot).inverse(a-cam_pixs/2)\n", " print(a1)\n", " au = a1 * psz\n", " print(au)\n", " af = au#-cam_pixs*psz/2\n", " print(af)\n", " a2 = cam_rot.inverse(af)\n", " print(a2)\n", " a3 = a2 + offset\n", " print(a3)\n", "\n", "\n", "#for a in np.nditer(np.meshgrid([-1,1], [-1,1])):\n", "# print((cam_rot+im_flip+AffineTransform(scale=[1/psz, 1/psz]))( a * cam_pixs*psz/2 + cam_pixs*psz/2))\n", " \n", "#nv(im_flip.params)\n", "\n", "#bbox = [a*cam_pixs for a in np.nditer(np.meshgrid([0,1], [0,1]))]\n", "\n", "#bbox_pix2unit3((0,0,512,512), [0,0], [512,512], 0.8, )\n", "\n", "#cr = np.array([[-0.99995, -0.0096],[0.0096, -0.99995]])\n", "#_pix2unit3([12000,300], [0,0], cam_pixs*psz, psz, cr)\n", "\n", "#cam_rot_i.params" ] } ], "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": 0 }
mit
briangoodwin/nd101_udacityDeepLearning
tv-script-generation/.ipynb_checkpoints/dlnd_tv_script_generation-checkpoint.ipynb
2
29899
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# TV Script Generation\n", "In this project, you'll generate your own [Simpsons](https://en.wikipedia.org/wiki/The_Simpsons) TV scripts using RNNs. You'll be using part of the [Simpsons dataset](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data) of scripts from 27 seasons. The Neural Network you'll build will generate a new TV script for a scene at [Moe's Tavern](https://simpsonswiki.com/wiki/Moe's_Tavern).\n", "## Get the Data\n", "The data is already provided for you. You'll be using a subset of the original dataset. It consists of only the scenes in Moe's Tavern. This doesn't include other versions of the tavern, like \"Moe's Cavern\", \"Flaming Moe's\", \"Uncle Moe's Family Feed-Bag\", etc.." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import helper\n", "\n", "data_dir = './data/simpsons/moes_tavern_lines.txt'\n", "text = helper.load_data(data_dir)\n", "# Ignore notice, since we don't use it for analysing the data\n", "text = text[81:]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Explore the Data\n", "Play around with `view_sentence_range` to view different parts of the data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "view_sentence_range = (0, 10)\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import numpy as np\n", "\n", "print('Dataset Stats')\n", "print('Roughly the number of unique words: {}'.format(len({word: None for word in text.split()})))\n", "scenes = text.split('\\n\\n')\n", "print('Number of scenes: {}'.format(len(scenes)))\n", "sentence_count_scene = [scene.count('\\n') for scene in scenes]\n", "print('Average number of sentences in each scene: {}'.format(np.average(sentence_count_scene)))\n", "\n", "sentences = [sentence for scene in scenes for sentence in scene.split('\\n')]\n", "print('Number of lines: {}'.format(len(sentences)))\n", "word_count_sentence = [len(sentence.split()) for sentence in sentences]\n", "print('Average number of words in each line: {}'.format(np.average(word_count_sentence)))\n", "\n", "print()\n", "print('The sentences {} to {}:'.format(*view_sentence_range))\n", "print('\\n'.join(text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Implement Preprocessing Functions\n", "The first thing to do to any dataset is preprocessing. Implement the following preprocessing functions below:\n", "- Lookup Table\n", "- Tokenize Punctuation\n", "\n", "### Lookup Table\n", "To create a word embedding, you first need to transform the words to ids. In this function, create two dictionaries:\n", "- Dictionary to go from the words to an id, we'll call `vocab_to_int`\n", "- Dictionary to go from the id to word, we'll call `int_to_vocab`\n", "\n", "Return these dictionaries in the following tuple `(vocab_to_int, int_to_vocab)`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import numpy as np\n", "import problem_unittests as tests\n", "\n", "def create_lookup_tables(text):\n", " \"\"\"\n", " Create lookup tables for vocabulary\n", " :param text: The text of tv scripts split into words\n", " :return: A tuple of dicts (vocab_to_int, int_to_vocab)\n", " \"\"\"\n", " # TODO: Implement Function\n", " return None, None\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_create_lookup_tables(create_lookup_tables)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Tokenize Punctuation\n", "We'll be splitting the script into a word array using spaces as delimiters. However, punctuations like periods and exclamation marks make it hard for the neural network to distinguish between the word \"bye\" and \"bye!\".\n", "\n", "Implement the function `token_lookup` to return a dict that will be used to tokenize symbols like \"!\" into \"||Exclamation_Mark||\". Create a dictionary for the following symbols where the symbol is the key and value is the token:\n", "- Period ( . )\n", "- Comma ( , )\n", "- Quotation Mark ( \" )\n", "- Semicolon ( ; )\n", "- Exclamation mark ( ! )\n", "- Question mark ( ? )\n", "- Left Parentheses ( ( )\n", "- Right Parentheses ( ) )\n", "- Dash ( -- )\n", "- Return ( \\n )\n", "\n", "This dictionary will be used to token the symbols and add the delimiter (space) around it. This separates the symbols as it's own word, making it easier for the neural network to predict on the next word. Make sure you don't use a token that could be confused as a word. Instead of using the token \"dash\", try using something like \"||dash||\"." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def token_lookup():\n", " \"\"\"\n", " Generate a dict to turn punctuation into a token.\n", " :return: Tokenize dictionary where the key is the punctuation and the value is the token\n", " \"\"\"\n", " # TODO: Implement Function\n", " return None\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_tokenize(token_lookup)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Preprocess all the data and save it\n", "Running the code cell below will preprocess all the data and save it to file." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "# Preprocess Training, Validation, and Testing Data\n", "helper.preprocess_and_save_data(data_dir, token_lookup, create_lookup_tables)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Check Point\n", "This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import helper\n", "import numpy as np\n", "import problem_unittests as tests\n", "\n", "int_text, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Build the Neural Network\n", "You'll build the components necessary to build a RNN by implementing the following functions below:\n", "- get_inputs\n", "- get_init_cell\n", "- get_embed\n", "- build_rnn\n", "- build_nn\n", "- get_batches\n", "\n", "### Check the Version of TensorFlow and Access to GPU" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "from distutils.version import LooseVersion\n", "import warnings\n", "import tensorflow as tf\n", "\n", "# Check TensorFlow Version\n", "assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer'\n", "print('TensorFlow Version: {}'.format(tf.__version__))\n", "\n", "# Check for a GPU\n", "if not tf.test.gpu_device_name():\n", " warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n", "else:\n", " print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Input\n", "Implement the `get_inputs()` function to create TF Placeholders for the Neural Network. It should create the following placeholders:\n", "- Input text placeholder named \"input\" using the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) `name` parameter.\n", "- Targets placeholder\n", "- Learning Rate placeholder\n", "\n", "Return the placeholders in the following the tuple `(Input, Targets, LearingRate)`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_inputs():\n", " \"\"\"\n", " Create TF Placeholders for input, targets, and learning rate.\n", " :return: Tuple (input, targets, learning rate)\n", " \"\"\"\n", " # TODO: Implement Function\n", " return None, None, None\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_get_inputs(get_inputs)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Build RNN Cell and Initialize\n", "Stack one or more [`BasicLSTMCells`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/BasicLSTMCell) in a [`MultiRNNCell`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/MultiRNNCell).\n", "- The Rnn size should be set using `rnn_size`\n", "- Initalize Cell State using the MultiRNNCell's [`zero_state()`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/MultiRNNCell#zero_state) function\n", " - Apply the name \"initial_state\" to the initial state using [`tf.identity()`](https://www.tensorflow.org/api_docs/python/tf/identity)\n", "\n", "Return the cell and initial state in the following tuple `(Cell, InitialState)`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_init_cell(batch_size, rnn_size):\n", " \"\"\"\n", " Create an RNN Cell and initialize it.\n", " :param batch_size: Size of batches\n", " :param rnn_size: Size of RNNs\n", " :return: Tuple (cell, initialize state)\n", " \"\"\"\n", " # TODO: Implement Function\n", " return None, None\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_get_init_cell(get_init_cell)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Word Embedding\n", "Apply embedding to `input_data` using TensorFlow. Return the embedded sequence." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_embed(input_data, vocab_size, embed_dim):\n", " \"\"\"\n", " Create embedding for <input_data>.\n", " :param input_data: TF placeholder for text input.\n", " :param vocab_size: Number of words in vocabulary.\n", " :param embed_dim: Number of embedding dimensions\n", " :return: Embedded input.\n", " \"\"\"\n", " # TODO: Implement Function\n", " return None\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_get_embed(get_embed)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Build RNN\n", "You created a RNN Cell in the `get_init_cell()` function. Time to use the cell to create a RNN.\n", "- Build the RNN using the [`tf.nn.dynamic_rnn()`](https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn)\n", " - Apply the name \"final_state\" to the final state using [`tf.identity()`](https://www.tensorflow.org/api_docs/python/tf/identity)\n", "\n", "Return the outputs and final_state state in the following tuple `(Outputs, FinalState)` " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def build_rnn(cell, inputs):\n", " \"\"\"\n", " Create a RNN using a RNN Cell\n", " :param cell: RNN Cell\n", " :param inputs: Input text data\n", " :return: Tuple (Outputs, Final State)\n", " \"\"\"\n", " # TODO: Implement Function\n", " return None, None\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_build_rnn(build_rnn)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Build the Neural Network\n", "Apply the functions you implemented above to:\n", "- Apply embedding to `input_data` using your `get_embed(input_data, vocab_size, embed_dim)` function.\n", "- Build RNN using `cell` and your `build_rnn(cell, inputs)` function.\n", "- Apply a fully connected layer with a linear activation and `vocab_size` as the number of outputs.\n", "\n", "Return the logits and final state in the following tuple (Logits, FinalState) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def build_nn(cell, rnn_size, input_data, vocab_size):\n", " \"\"\"\n", " Build part of the neural network\n", " :param cell: RNN cell\n", " :param rnn_size: Size of rnns\n", " :param input_data: Input data\n", " :param vocab_size: Vocabulary size\n", " :return: Tuple (Logits, FinalState)\n", " \"\"\"\n", " # TODO: Implement Function\n", " return None, None\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_build_nn(build_nn)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Batches\n", "Implement `get_batches` to create batches of input and targets using `int_text`. The batches should be a Numpy array with the shape `(number of batches, 2, batch size, sequence length)`. Each batch contains two elements:\n", "- The first element is a single batch of **input** with the shape `[batch size, sequence length]`\n", "- The second element is a single batch of **targets** with the shape `[batch size, sequence length]`\n", "\n", "If you can't fill the last batch with enough data, drop the last batch.\n", "\n", "For exmple, `get_batches([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 2, 3)` would return a Numpy array of the following:\n", "```\n", "[\n", " # First Batch\n", " [\n", " # Batch of Input\n", " [[ 1 2 3], [ 7 8 9]],\n", " # Batch of targets\n", " [[ 2 3 4], [ 8 9 10]]\n", " ],\n", " \n", " # Second Batch\n", " [\n", " # Batch of Input\n", " [[ 4 5 6], [10 11 12]],\n", " # Batch of targets\n", " [[ 5 6 7], [11 12 13]]\n", " ]\n", "]\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_batches(int_text, batch_size, seq_length):\n", " \"\"\"\n", " Return batches of input and target\n", " :param int_text: Text with the words replaced by their ids\n", " :param batch_size: The size of batch\n", " :param seq_length: The length of sequence\n", " :return: Batches as a Numpy array\n", " \"\"\"\n", " # TODO: Implement Function\n", " return None\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_get_batches(get_batches)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Neural Network Training\n", "### Hyperparameters\n", "Tune the following parameters:\n", "\n", "- Set `num_epochs` to the number of epochs.\n", "- Set `batch_size` to the batch size.\n", "- Set `rnn_size` to the size of the RNNs.\n", "- Set `seq_length` to the length of sequence.\n", "- Set `learning_rate` to the learning rate.\n", "- Set `show_every_n_batches` to the number of batches the neural network should print progress." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Number of Epochs\n", "num_epochs = None\n", "# Batch Size\n", "batch_size = None\n", "# RNN Size\n", "rnn_size = None\n", "# Sequence Length\n", "seq_length = None\n", "# Learning Rate\n", "learning_rate = None\n", "# Show stats for every n number of batches\n", "show_every_n_batches = None\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "save_dir = './save'" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Build the Graph\n", "Build the graph using the neural network you implemented." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "from tensorflow.contrib import seq2seq\n", "\n", "train_graph = tf.Graph()\n", "with train_graph.as_default():\n", " vocab_size = len(int_to_vocab)\n", " input_text, targets, lr = get_inputs()\n", " input_data_shape = tf.shape(input_text)\n", " cell, initial_state = get_init_cell(input_data_shape[0], rnn_size)\n", " logits, final_state = build_nn(cell, rnn_size, input_text, vocab_size)\n", "\n", " # Probabilities for generating words\n", " probs = tf.nn.softmax(logits, name='probs')\n", "\n", " # Loss function\n", " cost = seq2seq.sequence_loss(\n", " logits,\n", " targets,\n", " tf.ones([input_data_shape[0], input_data_shape[1]]))\n", "\n", " # Optimizer\n", " optimizer = tf.train.AdamOptimizer(lr)\n", "\n", " # Gradient Clipping\n", " gradients = optimizer.compute_gradients(cost)\n", " capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients]\n", " train_op = optimizer.apply_gradients(capped_gradients)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Train\n", "Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the [forms](https://discussions.udacity.com/) to see if anyone is having the same problem." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "batches = get_batches(int_text, batch_size, seq_length)\n", "\n", "with tf.Session(graph=train_graph) as sess:\n", " sess.run(tf.global_variables_initializer())\n", "\n", " for epoch_i in range(num_epochs):\n", " state = sess.run(initial_state, {input_text: batches[0][0]})\n", "\n", " for batch_i, (x, y) in enumerate(batches):\n", " feed = {\n", " input_text: x,\n", " targets: y,\n", " initial_state: state,\n", " lr: learning_rate}\n", " train_loss, state, _ = sess.run([cost, final_state, train_op], feed)\n", "\n", " # Show every <show_every_n_batches> batches\n", " if (epoch_i * len(batches) + batch_i) % show_every_n_batches == 0:\n", " print('Epoch {:>3} Batch {:>4}/{} train_loss = {:.3f}'.format(\n", " epoch_i,\n", " batch_i,\n", " len(batches),\n", " train_loss))\n", "\n", " # Save Model\n", " saver = tf.train.Saver()\n", " saver.save(sess, save_dir)\n", " print('Model Trained and Saved')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Save Parameters\n", "Save `seq_length` and `save_dir` for generating a new TV script." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "# Save parameters for checkpoint\n", "helper.save_params((seq_length, save_dir))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Checkpoint" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL\n", "\"\"\"\n", "import tensorflow as tf\n", "import numpy as np\n", "import helper\n", "import problem_unittests as tests\n", "\n", "_, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()\n", "seq_length, load_dir = helper.load_params()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Implement Generate Functions\n", "### Get Tensors\n", "Get tensors from `loaded_graph` using the function [`get_tensor_by_name()`](https://www.tensorflow.org/api_docs/python/tf/Graph#get_tensor_by_name). Get the tensors using the following names:\n", "- \"input:0\"\n", "- \"initial_state:0\"\n", "- \"final_state:0\"\n", "- \"probs:0\"\n", "\n", "Return the tensors in the following tuple `(InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)` " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_tensors(loaded_graph):\n", " \"\"\"\n", " Get input, initial state, final state, and probabilities tensor from <loaded_graph>\n", " :param loaded_graph: TensorFlow graph loaded from file\n", " :return: Tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)\n", " \"\"\"\n", " # TODO: Implement Function\n", " return None, None, None, None\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_get_tensors(get_tensors)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Choose Word\n", "Implement the `pick_word()` function to select the next word using `probabilities`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def pick_word(probabilities, int_to_vocab):\n", " \"\"\"\n", " Pick the next word in the generated text\n", " :param probabilities: Probabilites of the next word\n", " :param int_to_vocab: Dictionary of word ids as the keys and words as the values\n", " :return: String of the predicted word\n", " \"\"\"\n", " # TODO: Implement Function\n", " return None\n", "\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "tests.test_pick_word(pick_word)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Generate TV Script\n", "This will generate the TV script for you. Set `gen_length` to the length of TV script you want to generate." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "gen_length = 200\n", "# homer_simpson, moe_szyslak, or Barney_Gumble\n", "prime_word = 'moe_szyslak'\n", "\n", "\"\"\"\n", "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n", "\"\"\"\n", "loaded_graph = tf.Graph()\n", "with tf.Session(graph=loaded_graph) as sess:\n", " # Load saved model\n", " loader = tf.train.import_meta_graph(load_dir + '.meta')\n", " loader.restore(sess, load_dir)\n", "\n", " # Get Tensors from loaded model\n", " input_text, initial_state, final_state, probs = get_tensors(loaded_graph)\n", "\n", " # Sentences generation setup\n", " gen_sentences = [prime_word + ':']\n", " prev_state = sess.run(initial_state, {input_text: np.array([[1]])})\n", "\n", " # Generate sentences\n", " for n in range(gen_length):\n", " # Dynamic Input\n", " dyn_input = [[vocab_to_int[word] for word in gen_sentences[-seq_length:]]]\n", " dyn_seq_length = len(dyn_input[0])\n", "\n", " # Get Prediction\n", " probabilities, prev_state = sess.run(\n", " [probs, final_state],\n", " {input_text: dyn_input, initial_state: prev_state})\n", " \n", " pred_word = pick_word(probabilities[dyn_seq_length-1], int_to_vocab)\n", "\n", " gen_sentences.append(pred_word)\n", " \n", " # Remove tokens\n", " tv_script = ' '.join(gen_sentences)\n", " for key, token in token_dict.items():\n", " ending = ' ' if key in ['\\n', '(', '\"'] else ''\n", " tv_script = tv_script.replace(' ' + token.lower(), key)\n", " tv_script = tv_script.replace('\\n ', '\\n')\n", " tv_script = tv_script.replace('( ', '(')\n", " \n", " print(tv_script)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# The TV Script is Nonsensical\n", "It's ok if the TV script doesn't make any sense. We trained on less than a megabyte of text. In order to get good results, you'll have to use a smaller vocabulary or get more data. Luckly there's more data! As we mentioned in the begging of this project, this is a subset of [another dataset](https://www.kaggle.com/wcukierski/the-simpsons-by-the-data). We didn't have you train on all the data, because that would take too long. However, you are free to train your neural network on all the data. After you complete the project, of course.\n", "# Submitting This Project\n", "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_tv_script_generation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission." ] } ], "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" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
flohorovicic/pynoddy
docs/notebooks/Pynoddy_parallel_MC.ipynb
1
13024
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# pynoddy parallelized MC error propagation\n", "\n", "This Notebook exemplifies the use of process parallelisation via the python `multiprocess` tool for parallelised MC error propagation and stochastic modeling.\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# here the usual imports. If any of the imports fails, \n", "# make sure that pynoddy is installed\n", "# properly, ideally with 'python setup.py develop' \n", "# or 'python setup.py install'\n", "import sys, os\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "# adjust some settings for matplotlib\n", "from matplotlib import rcParams\n", "# print rcParams\n", "rcParams['font.size'] = 15\n", "# determine path of repository to set paths corretly below\n", "repo_path = os.path.realpath('../..')\n", "sys.path.append('../..')\n", "import pynoddy\n", "import importlib\n", "importlib.reload(pynoddy)\n", "import pynoddy.history\n", "import pynoddy.experiment\n", "importlib.reload(pynoddy.experiment)\n", "rcParams.update({'font.size': 15})\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "************************************************************\n", "\t\t\tModel Information\n", "************************************************************\n", "\n", "This model consists of 3 events:\n", "\t(1) - STRATIGRAPHY\n", "\t(2) - FOLD\n", "\t(3) - FOLD\n", "The model extent is:\n", "\tx - 10000.0 m\n", "\ty - 7000.0 m\n", "\tz - 5000.0 m\n", "Number of cells in each direction:\n", "\tnx = 50\n", "\tny = 35\n", "\tnz = 25\n", "The model origin is located at: \n", "\t(0.0, 0.0, 5000.0)\n", "The cubesize for model export is: \n", "\t200 m\n", "\n", "\n", "************************************************************\n", "\t\t\tMeta Data\n", "************************************************************\n", "\n", "The filename of the model is:\n", "\t typeB.his\n", "It was last saved (if origin was a history file!) at:\n", "\t 3/4/1997 17:30:4" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pynoddy.history.NoddyHistory(history=\"typeb.his\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model set-up\n", "\n", "Subsequently, we will use a model from the \"Atlas of Structural Geophysics\" as an example model." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# from pynoddy.experiment import monte_carlo\n", "model_url = 'http://tectonique.net/asg/ch3/ch3_7/his/typeb.his'\n", "ue = pynoddy.experiment.Experiment(history=\"typeb.his\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ue.set_random_seed(12345)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This model consists of 3 events:\n", "\t(1) - STRATIGRAPHY\n", "\t(2) - FOLD\n", "\t(3) - FOLD\n", "\n" ] } ], "source": [ "ue.info(events_only = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now define the parameter uncertainties:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "param_stats = [{'event' : 2, \n", " 'parameter': 'Amplitude',\n", " 'stdev': 100.0,\n", " 'type': 'normal'},\n", " {'event' : 2, \n", " 'parameter': 'Wavelength',\n", " 'stdev': 500.0,\n", " 'type': 'normal'},\n", " {'event' : 2, \n", " 'parameter': 'X',\n", " 'stdev': 500.0,\n", " 'type': 'normal'}]\n", "\n", "ue.set_parameter_statistics(param_stats)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For testing purposes, here the code for sequential sampling:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"ue.set_random_seed(112358)\n", "# perfrom random sampling\n", "resolution = 100\n", "sec = ue.get_section('y')\n", "n_draws = 100\n", "tmp = sec.block[:,50,:]\n", "#\n", "# Note: setting the dtype to 'int8' significantly reduces file size!\n", "#\n", "model_sections = np.empty((n_draws, tmp.shape[0], tmp.shape[1]), dtype='int8')\n", "\"\"\";" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "\"\"\"\n", "for i in range(n_draws):\n", " ue.random_draw()\n", " tmp_sec = ue.get_section('y', resolution = resolution, \n", " remove_tmp_files = True)\n", " model_sections[i,:,:] = tmp_sec.block[:,50,:]\n", "\n", "\"\"\";\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Testing parallel execution\n", "\n", "As a next step, use parallel execution of noddy calculation - should be relatively simple, however: note that, potentially, tmp-files may be overwritten! \n", "\n", "We therefore also implement here a very convenient python method to generate tmp-folders, that should (technically...) work on any operating system:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#\n", "# Store current directory to get back from temporary files\n", "#\n", "ori_dir = os.getcwd()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adapt model generation to use temp directory:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import tempfile\n", "import shutil" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Define function to perform one iteration\n", "# Execute iterations in temporary directories to avoid overlap\n", "#\n", "# Note: needs to take outout as argument to add results\n", "def compute_iter(ue, output):\n", " ue.random_draw()\n", " dirpath = tempfile.mkdtemp()\n", " os.chdir(dirpath)\n", " tmp_sec = ue.get_section('y', resolution = 100, \n", " remove_tmp_files = True)\n", " output.put(tmp_sec.block[:,50,:])\n", "\n", " \n", "# Note: this is not the case for the 'pool' method:\n", "# use `with` context management method to ensure that directory is deleted afterwards:\n", "def compute_iter_pool(ue, i, init_state=12345):\n", " \"\"\"Perform a single iteration of randomised noddy model\n", " \n", " Arguments:\n", " \n", " ue = pynoddy.expermiment.Experiment object\n", " i = iterator, to keep random state\n", " init_state = int: base state (i will be added to keep results separate, \n", " but overall reproducible)\n", " \"\"\"\n", " from tempfile import TemporaryDirectory\n", " with TemporaryDirectory() as temp_dir:\n", " os.chdir(temp_dir)\n", " np.random.seed(init_state+i)\n", " ue.random_draw()\n", " tmp_sec = ue.get_section('y', resolution = 100, \n", " remove_tmp_files = True)\n", " \n", " return tmp_sec.block[:,50,:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For testing only: compute_iter_pool function in normal framework (non-parallel execution):" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "ori_dir = \"/Users/Shared/git/pynoddy/docs/notebooks/\"\n", "os.chdir(\"/Users/Shared/git/pynoddy/docs/notebooks/\") \n", "ue.set_random_seed(112358)\n", "# perfrom random sampling\n", "resolution = 100\n", "sec = ue.get_section('y')\n", "\n", "tmp = sec.block[:,50,:]\n", "n_draws = 100\n", "#\n", "# Note: setting the dtype to 'int8' significantly reduces file size!\n", "#\n", "model_sections = np.empty((n_draws, tmp.shape[0], tmp.shape[1]), dtype='int8')\n", "\n", "#\n", "#\n", "for i in range(n_draws):\n", " model_sections[i,:,:] = compute_iter_pool(ue)\n", "\n", "os.chdir(\"/Users/Shared/git/pynoddy/docs/notebooks/\") \n", "\"\"\";" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Executing Noddy MC in parallel\n", "\n", "After a bit of fiddling around, it seems to work now:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import multiprocessing as mp" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# settings for MC simulation:\n", "n_draws = 10 # number of random draws\n", "resolution = 100 # cell size of noddy model\n", "init_state = 12345 # initial state of random seed\n", "\n", "# the following lines are only required to pre-define the output array model_sections:\n", "ue.change_cube_size(100)\n", "sec = ue.get_section('y')\n", "tmp = sec.block[:,50,:]\n", "# Note: setting the dtype to 'int8' significantly reduces file size!\n", "#\n", "model_sections = np.empty((n_draws, tmp.shape[0], tmp.shape[1]), dtype='int8')\n", "\n", "result_list = []\n", "def log_result(result):\n", " # This is called whenever foo_pool(i) returns a result.\n", " # result_list is modified only by the main process, not the pool workers.\n", " result_list.append(result)\n", "\n", "pool = mp.Pool(processes=20)\n", "for i in range(n_draws):\n", " # pool.apply_async(foo_pool, args = (i, ), callback = log_result)\n", " # try to use copy, but this did not fix the problem, unfortunately...\n", " # ue_copy = copy.deepcopy(ue)\n", " pool.apply_async(compute_iter_pool, args=(ue, i, init_state), callback = log_result)\n", "pool.close()\n", "pool.join()\n", "\n", "# model_sections = np.array([pool.apply(compute_iter_pool, args=(ue,)) for x in range(4)])\n", "\n", "ori_dir = \"/Users/Shared/git/pynoddy/docs/notebooks/\"\n", "os.chdir(ori_dir)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(10, 100, 40)" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_sections = np.array(result_list)\n", "model_sections.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save results to file. Note: use random number in filename to avoid overwriting:" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pickle\n", "f_out = open(\"model_sections_%d.pkl\" % np.random.randint(1000), 'wb')\n", "pickle.dump(model_sections, f_out)\n", "f_out.close()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-2.0
vargaslo/msm-diffusion
Diffusion_MSM.ipynb
1
2200482
null
gpl-3.0
turbomanage/training-data-analyst
courses/machine_learning/deepdive/10_recommend/content_based_by_hand.ipynb
1
10403
{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "2NnuRIZedJmK" }, "source": [ "## Content Based Filtering by hand\n", "\n", "This lab illustrates how to implement a content based filter using low level Tensorflow operations. \n", "The code here follows the technique explained in Module 2 of Recommendation Engines: Content Based Filtering.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To run this lab, we need to use TensorFlow version 1.13.1. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!pip install tensorflow==1.13.1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make sure to restart your kernel to ensure this change has taken place." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "id": "IzbZLmz1dJmL", "outputId": "f4f882d9-6752-4b8d-8d7d-83eb61690d89" }, "outputs": [], "source": [ "import numpy as np\n", "import tensorflow as tf\n", "\n", "tf.enable_eager_execution()\n", "print(tf.__version__)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "36uCjFhldJmR" }, "source": [ "To start, we'll create our list of users, movies and features. While the users and movies represent elements in our database, for a content-based filtering method the features of the movies are likely hand-engineered and rely on domain knowledge to provide the best embedding space. Here we use the categories of Action, Sci-Fi, Comedy, Cartoon, and Drama to describe our movies (and thus our users).\n", "\n", "In this example, we will assume our database consists of four users and six movies, listed below. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "ElQV43fxdJmS" }, "outputs": [], "source": [ "users = ['Ryan', 'Danielle', 'Vijay', 'Chris']\n", "movies = ['Star Wars', 'The Dark Knight', 'Shrek', 'The Incredibles', 'Bleu', 'Memento']\n", "features = ['Action', 'Sci-Fi', 'Comedy', 'Cartoon', 'Drama']\n", "\n", "num_users = len(users)\n", "num_movies = len(movies)\n", "num_feats = len(features)\n", "num_recommendations = 2" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "s6iJCViqdJmU" }, "source": [ "### Initialize our users, movie ratings and features\n", "\n", "We'll need to enter the user's movie ratings and the k-hot encoded movie features matrix. Each row of the users_movies matrix represents a single user's rating (from 1 to 10) for each movie. A zero indicates that the user has not seen/rated that movie. The movies_feats matrix contains the features for each of the given movies. Each row represents one of the six movies, the columns represent the five categories. A one indicates that a movie fits within a given genre/category. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "_0asiLTwdJmV" }, "outputs": [], "source": [ "# each row represents a user's rating for the different movies\n", "users_movies = tf.constant([\n", " [4, 6, 8, 0, 0, 0],\n", " [0, 0, 10, 0, 8, 3],\n", " [0, 6, 0, 0, 3, 7],\n", " [10, 9, 0, 5, 0, 2]],dtype=tf.float32)\n", "\n", "# features of the movies one-hot encoded\n", "# e.g. columns could represent ['Action', 'Sci-Fi', 'Comedy', 'Cartoon', 'Drama']\n", "movies_feats = tf.constant([\n", " [1, 1, 0, 0, 1],\n", " [1, 1, 0, 0, 0],\n", " [0, 0, 1, 1, 0],\n", " [1, 0, 1, 1, 0],\n", " [0, 0, 0, 0, 1],\n", " [1, 0, 0, 0, 1]],dtype=tf.float32)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "aCW5BtGudJmX" }, "source": [ "### Computing the user feature matrix\n", "\n", "We will compute the user feature matrix; that is, a matrix containing each user's embedding in the five-dimensional feature space. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 101 }, "colab_type": "code", "id": "isMCBMOFdJmY", "outputId": "cf7eaa50-95ab-4e8f-916b-27c26d6421dd" }, "outputs": [], "source": [ "users_feats = tf.matmul(users_movies,movies_feats)\n", "users_feats" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Ps7XXoYwdJmc" }, "source": [ "Next we normalize each user feature vector to sum to 1. Normalizing isn't strictly neccesary, but it makes it so that rating magnitudes will be comparable between users." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 118 }, "colab_type": "code", "id": "y81EeooodJmc", "outputId": "904beb39-0a6f-49e0-971f-5198003e7adb" }, "outputs": [], "source": [ "users_feats = users_feats/tf.reduce_sum(users_feats,axis=1,keepdims=True)\n", "users_feats" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "kqOPr51tdJmf" }, "source": [ "#### Ranking feature relevance for each user\n", "\n", "We can use the users_feats computed above to represent the relative importance of each movie category for each user. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 101 }, "colab_type": "code", "id": "PKLqAD3adJmg", "outputId": "d535513e-72cd-4120-ef6d-82424efb20d4" }, "outputs": [], "source": [ "top_users_features = tf.nn.top_k(users_feats, num_feats)[1]\n", "top_users_features" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 84 }, "colab_type": "code", "id": "pvUmu7MUdJmj", "outputId": "a9e89bb0-330b-4687-866e-0f209910d8c0" }, "outputs": [], "source": [ "for i in range(num_users):\n", " feature_names = [features[int(index)] for index in top_users_features[i]]\n", " print('{}: {}'.format(users[i],feature_names))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Yne0CyZMdJmn" }, "source": [ "### Determining movie recommendations. \n", "\n", "We'll now use the `users_feats` tensor we computed above to determine the movie ratings and recommendations for each user.\n", "\n", "To compute the projected ratings for each movie, we compute the similarity measure between the user's feature vector and the corresponding movie feature vector. \n", "\n", "We will use the dot product as our similarity measure. In essence, this is a weighted movie average for each user." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "users_ratings = tf.matmul(users_feats,tf.transpose(movies_feats))\n", "users_ratings" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "o07wODzddJmq" }, "source": [ "The computation above finds the similarity measure between each user and each movie in our database. To focus only on the ratings for new movies, we apply a mask to the all_users_ratings matrix. \n", "\n", "If a user has already rated a movie, we ignore that rating. This way, we only focus on ratings for previously unseen/unrated movies." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 168 }, "colab_type": "code", "id": "xUgOnV3AdJmr", "outputId": "2672899f-d626-4e33-e730-7d8b051a3954" }, "outputs": [], "source": [ "users_ratings_new = tf.where(tf.equal(users_movies, tf.zeros_like(users_movies)),\n", " users_ratings,\n", " tf.zeros_like(tf.cast(users_movies, tf.float32)))\n", "users_ratings_new" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "YyNvH46zdJmu" }, "source": [ "Finally let's grab and print out the top 2 rated movies for each user" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 101 }, "colab_type": "code", "id": "PdDGgmSpdJmv", "outputId": "a921b943-383b-4984-cffd-e0eb5c7ab41e" }, "outputs": [], "source": [ "top_movies = tf.nn.top_k(users_ratings_new, num_recommendations)[1]\n", "top_movies" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 84 }, "colab_type": "code", "id": "dCB7Dv9_dJmx", "outputId": "0d00e5c6-f7bc-4fae-a359-283f2fdb1c4c" }, "outputs": [], "source": [ "for i in range(num_users):\n", " movie_names = [movies[index] for index in top_movies[i]]\n", " print('{}: {}'.format(users[i],movie_names))" ] } ], "metadata": { "colab": { "name": "content_based_by_hand.ipynb", "provenance": [], "version": "0.3.2" }, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
EstevaoVieira/udacity_projects
titanic_survival_exploration/.ipynb_checkpoints/titanic_survival_exploration-checkpoint.ipynb
1
116798
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Machine Learning Engineer Nanodegree\n", "## Introduction and Foundations\n", "## Project: Titanic Survival Exploration\n", "\n", "In 1912, the ship RMS Titanic struck an iceberg on its maiden voyage and sank, resulting in the deaths of most of its passengers and crew. In this introductory project, we will explore a subset of the RMS Titanic passenger manifest to determine which features best predict whether someone survived or did not survive. To complete this project, you will need to implement several conditional predictions and answer the questions below. Your project submission will be evaluated based on the completion of the code and your responses to the questions.\n", "> **Tip:** Quoted sections like this will provide helpful instructions on how to navigate and use an iPython notebook. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting Started\n", "To begin working with the RMS Titanic passenger data, we'll first need to `import` the functionality we need, and load our data into a `pandas` DataFrame. \n", "Run the code cell below to load our data and display the first few entries (passengers) for examination using the `.head()` function.\n", "> **Tip:** You can run a code cell by clicking on the cell and using the keyboard shortcut **Shift + Enter** or **Shift + Return**. Alternatively, a code cell can be executed using the **Play** button in the hotbar after selecting it. Markdown cells (text cells like this one) can be edited by double-clicking, and saved using these same shortcuts. [Markdown](http://daringfireball.net/projects/markdown/syntax) allows you to write easy-to-read plain text that can be converted to HTML." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Braund, Mr. Owen Harris</td>\n", " <td>male</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>A/5 21171</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n", " <td>female</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>PC 17599</td>\n", " <td>71.2833</td>\n", " <td>C85</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Heikkinen, Miss. Laina</td>\n", " <td>female</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>STON/O2. 3101282</td>\n", " <td>7.9250</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n", " <td>female</td>\n", " <td>35.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>113803</td>\n", " <td>53.1000</td>\n", " <td>C123</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Allen, Mr. William Henry</td>\n", " <td>male</td>\n", " <td>35.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>373450</td>\n", " <td>8.0500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Import libraries necessary for this project\n", "import numpy as np\n", "import pandas as pd\n", "from IPython.display import display # Allows the use of display() for DataFrames\n", "\n", "# Import supplementary visualizations code visuals.py\n", "import visuals as vs\n", "\n", "# Pretty display for notebooks\n", "%matplotlib inline\n", "\n", "# Load the dataset\n", "in_file = 'titanic_data.csv'\n", "full_data = pd.read_csv(in_file)\n", "\n", "# Print the first few entries of the RMS Titanic data\n", "display(full_data.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From a sample of the RMS Titanic data, we can see the various features present for each passenger on the ship:\n", "- **Survived**: Outcome of survival (0 = No; 1 = Yes)\n", "- **Pclass**: Socio-economic class (1 = Upper class; 2 = Middle class; 3 = Lower class)\n", "- **Name**: Name of passenger\n", "- **Sex**: Sex of the passenger\n", "- **Age**: Age of the passenger (Some entries contain `NaN`)\n", "- **SibSp**: Number of siblings and spouses of the passenger aboard\n", "- **Parch**: Number of parents and children of the passenger aboard\n", "- **Ticket**: Ticket number of the passenger\n", "- **Fare**: Fare paid by the passenger\n", "- **Cabin** Cabin number of the passenger (Some entries contain `NaN`)\n", "- **Embarked**: Port of embarkation of the passenger (C = Cherbourg; Q = Queenstown; S = Southampton)\n", "\n", "Since we're interested in the outcome of survival for each passenger or crew member, we can remove the **Survived** feature from this dataset and store it as its own separate variable `outcomes`. We will use these outcomes as our prediction targets. \n", "Run the code cell below to remove **Survived** as a feature of the dataset and store it in `outcomes`." ] }, { "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>PassengerId</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Braund, Mr. Owen Harris</td>\n", " <td>male</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>A/5 21171</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n", " <td>female</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>PC 17599</td>\n", " <td>71.2833</td>\n", " <td>C85</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>Heikkinen, Miss. Laina</td>\n", " <td>female</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>STON/O2. 3101282</td>\n", " <td>7.9250</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n", " <td>female</td>\n", " <td>35.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>113803</td>\n", " <td>53.1000</td>\n", " <td>C123</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>3</td>\n", " <td>Allen, Mr. William Henry</td>\n", " <td>male</td>\n", " <td>35.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>373450</td>\n", " <td>8.0500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Pclass Name \\\n", "0 1 3 Braund, Mr. Owen Harris \n", "1 2 1 Cumings, Mrs. John Bradley (Florence Briggs Th... \n", "2 3 3 Heikkinen, Miss. Laina \n", "3 4 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) \n", "4 5 3 Allen, Mr. William Henry \n", "\n", " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n", "0 male 22.0 1 0 A/5 21171 7.2500 NaN S \n", "1 female 38.0 1 0 PC 17599 71.2833 C85 C \n", "2 female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S \n", "3 female 35.0 1 0 113803 53.1000 C123 S \n", "4 male 35.0 0 0 373450 8.0500 NaN S " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Store the 'Survived' feature in a new variable and remove it from the dataset\n", "outcomes = full_data['Survived']\n", "data = full_data.drop('Survived', axis = 1)\n", "\n", "# Show the new dataset with 'Survived' removed\n", "display(data.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The very same sample of the RMS Titanic data now shows the **Survived** feature removed from the DataFrame. Note that `data` (the passenger data) and `outcomes` (the outcomes of survival) are now *paired*. That means for any passenger `data.loc[i]`, they have the survival outcome `outcomes[i]`.\n", "\n", "To measure the performance of our predictions, we need a metric to score our predictions against the true outcomes of survival. Since we are interested in how *accurate* our predictions are, we will calculate the proportion of passengers where our prediction of their survival is correct. Run the code cell below to create our `accuracy_score` function and test a prediction on the first five passengers. \n", "\n", "**Think:** *Out of the first five passengers, if we predict that all of them survived, what would you expect the accuracy of our predictions to be?*" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predictions have an accuracy of 60.00%.\n" ] } ], "source": [ "def accuracy_score(truth, pred):\n", " \"\"\" Returns accuracy score for input truth and predictions. \"\"\"\n", " \n", " # Ensure that the number of predictions matches number of outcomes\n", " if len(truth) == len(pred): \n", " \n", " # Calculate and return the accuracy as a percent\n", " return \"Predictions have an accuracy of {:.2f}%.\".format((truth == pred).mean()*100)\n", " \n", " else:\n", " return \"Number of predictions does not match number of outcomes!\"\n", " \n", "# Test the 'accuracy_score' function\n", "predictions = pd.Series(np.ones(5, dtype = int))\n", "print accuracy_score(outcomes[:5], predictions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> **Tip:** If you save an iPython Notebook, the output from running code blocks will also be saved. However, the state of your workspace will be reset once a new session is started. Make sure that you run all of the code blocks from your previous session to reestablish variables and functions before picking up where you last left off.\n", "\n", "# Making Predictions\n", "\n", "If we were asked to make a prediction about any passenger aboard the RMS Titanic whom we knew nothing about, then the best prediction we could make would be that they did not survive. This is because we can assume that a majority of the passengers (more than 50%) did not survive the ship sinking. \n", "The `predictions_0` function below will always predict that a passenger did not survive." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def predictions_0(data):\n", " \"\"\" Model with no features. Always predicts a passenger did not survive. \"\"\"\n", "\n", " predictions = []\n", " for _, passenger in data.iterrows():\n", " \n", " # Predict the survival of 'passenger'\n", " predictions.append(0)\n", " \n", " # Return our predictions\n", " return pd.Series(predictions)\n", "\n", "# Make the predictions\n", "predictions = predictions_0(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 1\n", "*Using the RMS Titanic data, how accurate would a prediction be that none of the passengers survived?* \n", "**Hint:** Run the code cell below to see the accuracy of this prediction." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predictions have an accuracy of 61.62%.\n" ] } ], "source": [ "print accuracy_score(outcomes, predictions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer:** Predictions have an accuracy of 61.62%." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "Let's take a look at whether the feature **Sex** has any indication of survival rates among passengers using the `survival_stats` function. This function is defined in the `titanic_visualizations.py` Python script included with this project. The first two parameters passed to the function are the RMS Titanic data and passenger survival outcomes, respectively. The third parameter indicates which feature we want to plot survival statistics across. \n", "Run the code cell below to plot the survival outcomes of passengers based on their sex." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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h7TXaZptt2HfffTnppJPo168fe+65JxdccAHz5s2rqj3IF3V96lOfWqi8oaGB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\nnV5V11XVhqp6ZlXtV1VvqapvVdWV06sQqaq9quq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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1e4972f6d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vs.survival_stats(data, outcomes, 'Sex')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Examining the survival statistics, a large majority of males did not survive the ship sinking. However, a majority of females *did* survive the ship sinking. Let's build on our previous prediction: If a passenger was female, then we will predict that they survived. Otherwise, we will predict the passenger did not survive. \n", "Fill in the missing code below so that the function will make this prediction. \n", "**Hint:** You can access the values of each feature for a passenger like a dictionary. For example, `passenger['Sex']` is the sex of the passenger." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def predictions_1(data):\n", " \"\"\" Model with one feature: \n", " - Predict a passenger survived if they are female. \"\"\"\n", " \n", " predictions = []\n", " for _, passenger in data.iterrows():\n", " \n", " # Remove the 'pass' statement below \n", " # and write your prediction conditions here\n", " predictions.append(passenger.Sex=='female')\n", " \n", " # Return our predictions\n", " return pd.Series(predictions)\n", "\n", "# Make the predictions\n", "predictions = predictions_1(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 2\n", "*How accurate would a prediction be that all female passengers survived and the remaining passengers did not survive?* \n", "**Hint:** Run the code cell below to see the accuracy of this prediction." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predictions have an accuracy of 78.68%.\n" ] } ], "source": [ "print accuracy_score(outcomes, predictions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer**: Predictions have an accuracy of 78.68%." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "Using just the **Sex** feature for each passenger, we are able to increase the accuracy of our predictions by a significant margin. Now, let's consider using an additional feature to see if we can further improve our predictions. For example, consider all of the male passengers aboard the RMS Titanic: Can we find a subset of those passengers that had a higher rate of survival? Let's start by looking at the **Age** of each male, by again using the `survival_stats` function. This time, we'll use a fourth parameter to filter out the data so that only passengers with the **Sex** 'male' will be included. \n", "Run the code cell below to plot the survival outcomes of male passengers based on their age." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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jjjqKCy+8kEceeYRu3bo1Ws/70UcfNdheO9NFse23354NN9yQKVOmMGLECKZP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\nuAr4ZH78xcAa4L5Tve9DDz3EokWlaUOh9u3bR21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K6UREPBu4D/g/wAeAYeBZwEtSSjfO\n0z9HkgrlnF5J6lwbgS+0Bt7cJ4BLyaYuXAdcCzwA/C7wZ/kxYwAppX8HXgQ8Hfgq2cjv24DBOaxd\nkjqKI72SVDER8Wbgd1JKP1l0LZLUKZzTK0klFxGvJ1vB4TBwBfCHwK2FFiVJHcbQK0nl93TgLcBT\nyVZjeCdwc6EVSVKHcXqDJEmSKs8L2SRJklR5hl5JkiRVnqFXkiRJlWfolSRJUuUZeiVJklR5hl5J\nkiRVnqFXkiRJlWfolSRJUuUZeiVJklR5/wEj9ONdHncW/AAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f1e45a642d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vs.survival_stats(data, outcomes, 'Age', [\"Sex == 'male'\"])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Examining the survival statistics, the majority of males younger than 10 survived the ship sinking, whereas most males age 10 or older *did not survive* the ship sinking. Let's continue to build on our previous prediction: If a passenger was female, then we will predict they survive. If a passenger was male and younger than 10, then we will also predict they survive. Otherwise, we will predict they do not survive. \n", "Fill in the missing code below so that the function will make this prediction. \n", "**Hint:** You can start your implementation of this function using the prediction code you wrote earlier from `predictions_1`." ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def predictions_2(data):\n", " \"\"\" Model with two features: \n", " - Predict a passenger survived if they are female.\n", " - Predict a passenger survived if they are male and younger than 10. \"\"\"\n", " \n", " predictions = []\n", " for _, passenger in data.iterrows():\n", " if passenger.Sex=='female':\n", " predictions.append(1)\n", " elif passenger.Age < 10: #passed first if mean it is male (do not need to explicit)\n", " predictions.append(1)\n", " else:\n", " predictions.append(0)\n", " # Return our predictions\n", " return pd.Series(predictions)\n", "\n", "# Make the predictions\n", "predictions = predictions_2(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 3\n", "*How accurate would a prediction be that all female passengers and all male passengers younger than 10 survived?* \n", "**Hint:** Run the code cell below to see the accuracy of this prediction." ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predictions have an accuracy of 79.24%.\n" ] } ], "source": [ "print accuracy_score(outcomes, predictions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer**: Predictions have an accuracy of 79.24%." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "***\n", "Adding the feature **Age** as a condition in conjunction with **Sex** improves the accuracy by a small margin more than with simply using the feature **Sex** alone. Now it's your turn: Find a series of features and conditions to split the data on to obtain an outcome prediction accuracy of at least 80%. This may require multiple features and multiple levels of conditional statements to succeed. You can use the same feature multiple times with different conditions. \n", "**Pclass**, **Sex**, **Age**, **SibSp**, and **Parch** are some suggested features to try.\n", "\n", "Use the `survival_stats` function below to to examine various survival statistics. \n", "**Hint:** To use mulitple filter conditions, put each condition in the list passed as the last argument. Example: `[\"Sex == 'male'\", \"Age < 18\"]`" ] }, { "cell_type": "code", "execution_count": 155, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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qSZLa1IrduvHezJlNTr/pvvuICI4tS7iOGzGiRaehD9trr0an7rfbfHNSSrxQ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\nhrXqC9zc8CSlNBFIwJq1DqqC+4GdI2J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"text/plain": [ "<matplotlib.figure.Figure at 0x7f1e45840250>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vs.survival_stats(data, outcomes, 'Embarked', [ \"Sex == 'female'\", 'Pclass == 3','Age < 20'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After exploring the survival statistics visualization, fill in the missing code below so that the function will make your prediction. \n", "Make sure to keep track of the various features and conditions you tried before arriving at your final prediction model. \n", "**Hint:** You can start your implementation of this function using the prediction code you wrote earlier from `predictions_2`." ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def predictions_3(data):\n", " \"\"\" Model with multiple features. Makes a prediction with an accuracy of at least 80%. \"\"\"\n", " predictions = []\n", " for _, passenger in data.iterrows():\n", " if passenger.Pclass ==3:\n", " if passenger.Sex=='female' and passenger.Age<20 and passenger.Embarked!='S':\n", " predictions.append(1)\n", " else:\n", " predictions.append(0)\n", " elif passenger.Sex=='female':\n", " predictions.append(1)\n", " elif passenger.Age <= 10:\n", " if passenger.SibSp >= 3:\n", " predictions.append(0)\n", " else:\n", " predictions.append(1)\n", " else:\n", " predictions.append(0)\n", " \n", " # Return our predictions\n", " return pd.Series(predictions)\n", "\n", "# Make the predictions\n", "predictions = predictions_3(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 4\n", "*Describe the steps you took to implement the final prediction model so that it got an accuracy of at least 80%. What features did you look at? Were certain features more informative than others? Which conditions did you use to split the survival outcomes in the data? How accurate are your predictions?* \n", "**Hint:** Run the code cell below to see the accuracy of your predictions." ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predictions have an accuracy of 80.81%.\n" ] } ], "source": [ "print accuracy_score(outcomes, predictions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Answer**: Predictions have an accuracy of 80.81%. Some features were much more informative than others, like Sex and Age. Others do not had much sense such as the port of embarcation. The process of searching for features was based entirely on the graphs, such that when I found situations where there was great difference between red and green bars I made a prediction for all of the people in that category to be classified as the highest bar." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusion\n", "\n", "After several iterations of exploring and conditioning on the data, you have built a useful algorithm for predicting the survival of each passenger aboard the RMS Titanic. The technique applied in this project is a manual implementation of a simple machine learning model, the *decision tree*. A decision tree splits a set of data into smaller and smaller groups (called *nodes*), by one feature at a time. Each time a subset of the data is split, our predictions become more accurate if each of the resulting subgroups are more homogeneous (contain similar labels) than before. The advantage of having a computer do things for us is that it will be more exhaustive and more precise than our manual exploration above. [This link](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/) provides another introduction into machine learning using a decision tree.\n", "\n", "A decision tree is just one of many models that come from *supervised learning*. In supervised learning, we attempt to use features of the data to predict or model things with objective outcome labels. That is to say, each of our data points has a known outcome value, such as a categorical, discrete label like `'Survived'`, or a numerical, continuous value like predicting the price of a house.\n", "\n", "### Question 5\n", "*Think of a real-world scenario where supervised learning could be applied. What would be the outcome variable that you are trying to predict? Name two features about the data used in this scenario that might be helpful for making the predictions.* " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "**Answer**: Supervised learning could be used by banks or creditcard companies, by using localization(city,state), price and type of store as features, as well as many previous cases of fraud, or even some artificially created samples, to detect if a given creditcard use may be a fraud." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to \n", "**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "py2", "language": "python", "name": "py2" }, "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
mjbommar/scotus-predict
build_scdb_model_10_no_reset.ipynb
1
406691
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Predicting the Behavior of the Supreme Court of the United States: A General Approach\n", "==================\n", " * __Title__: Predicting the Behavior of the Supreme Court of the United States: A General Approach\n", " * __Authors__: [Daniel Martin Katz](http://www.law.msu.edu/faculty_staff/profile.php?prof=780), [Michael J Bommarito II](http://bommaritollc.com/), [Josh Blackman](http://joshblackman.com)\n", " * __Paper URL__: [http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2463244](http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2463244)\n", " * __Blog URL__: [http://lexpredict.com/portfolio/predicting-the-supreme-court/](http://lexpredict.com/portfolio/predicting-the-supreme-court/)\n", "\n", "## Paper Abstract\n", "Building upon developments in theoretical and applied machine learning, as well as the efforts of various scholars including Guimera and Sales-Pardo (2011), Ruger et al. (2004), and Martin et al. (2004), we construct a model designed to predict the voting behavior of the Supreme Court of the United States. Using the extremely randomized tree method first proposed in Geurts, et al. (2006), a method similar to the random forest approach developed in Breiman (2001), as well as novel feature engineering, we predict more than sixty years of decisions by the Supreme Court of the United States (1953-2013). Using only data available prior to the date of decision, our model correctly identifies 69.7% of the Court’s overall affirm/reverse decisions and correctly forecasts 70.9% of the votes of individual justices across 7,700 cases and more than 68,000 justice votes. Our performance is consistent with the general level of prediction offered by prior scholars. However, our model is distinctive as it is the first robust, generalized,and fully predictive model of Supreme Court voting behavior offered to date. Our model predicts six decades of behavior of thirty Justices appointed by thirteen Presidents. With a more sound methodological foundation, our results represent a major advance for the science of quantitative legal prediction and portend a range of other potential applications, such as those described in Katz (2013).\n", "\n", "## Source Description\n", "The source and data in this repository allow for the reproduction of the results in this paper. \n", "\n", "## Data Description\n", "The data used in this paper is available from the [Supreme Court Database (SCDB)](http://scdb.wustl.edu/).\n", "\n", "## Version\n", "The latest version of this model was relesed in October 2015." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ubuntu/ve/local/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": [ "%matplotlib inline\n", "\n", "# Imports\n", "import matplotlib.pyplot as plt\n", "import statsmodels.stats.proportion\n", "\n", "# seaborn\n", "import seaborn\n", "seaborn.set()\n", "seaborn.set_style(\"darkgrid\")\n", "\n", "# Project imports\n", "from model import *" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get raw data\n", "raw_data = get_raw_scdb_data(\"data/input/SCDB_2015_01_justiceCentered_Citation.csv\")\n", "\n", "# Get feature data\n", "feature_df = preprocess_raw_data(raw_data, include_direction=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(77342, 82)\n", "(77342, 1365)\n" ] } ], "source": [ "# Output some diagnostics on features\n", "print(raw_data.shape)\n", "print(feature_df.shape)\n", "assert(raw_data.shape[0] == feature_df.shape[0])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " justice_outcome_disposition\n", " 1 43951\n", " 0 26293\n", "-1 7098\n", " justice_outcome_disposition\n", " 1 0.568268\n", " 0 0.339958\n", "-1 0.091774\n" ] } ], "source": [ "# Output basic quantities for sample\n", "print(pandas.DataFrame(raw_data[\"justice_outcome_disposition\"].value_counts()))\n", "print(pandas.DataFrame(raw_data[\"justice_outcome_disposition\"].value_counts(normalize=True)))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Term: 1951\n", "Term: 1952\n", "Term: 1953\n", "Term: 1954\n", "Term: 1955\n", "Term: 1956\n", "Term: 1957\n", "Term: 1958\n", "Term: 1959\n", "Term: 1960\n", "Term: 1961\n", "Term: 1962\n", "Term: 1963\n", "Term: 1964\n", "Term: 1965\n", "Term: 1966\n", "Term: 1967\n", "Term: 1968\n", "Term: 1969\n", "Term: 1970\n", "Term: 1971\n", "Term: 1972\n", "Term: 1973\n", "Term: 1974\n", "Term: 1975\n", "Term: 1976\n", "Term: 1977\n", "Term: 1978\n", "Term: 1979\n", "Term: 1980\n", "Term: 1981\n", "Term: 1982\n", "Term: 1983\n", "Term: 1984\n", "Term: 1985\n", "Term: 1986\n", "Term: 1987\n", "Term: 1988\n", "Term: 1989\n", "Term: 1990\n", "Term: 1991\n", "Term: 1992\n", "Term: 1993\n", "Term: 1994\n", "Term: 1995\n", "Term: 1996\n", "Term: 1997\n", "Term: 1998\n", "Term: 1999\n", "Term: 2000\n", "Term: 2001\n", "Term: 2002\n", "Term: 2003\n", "Term: 2004\n", "Term: 2005\n", "Term: 2006\n", "Term: 2007\n", "Term: 2008\n", "Term: 2009\n", "Term: 2010\n", "Term: 2011\n", "Term: 2012\n", "Term: 2013\n", "Term: 2014\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/ubuntu/ve/local/lib/python2.7/site-packages/sklearn/ensemble/forest.py:487: UserWarning: class_weight presets \"balanced\" or \"balanced_subsample\" are not recommended for warm_start if the fitted data differs from the full dataset. In order to use \"balanced\" weights, use compute_class_weight(\"balanced\", classes, y). In place of y you can use a large enough sample of the full training set target to properly estimate the class frequency distributions. Pass the resulting weights as the class_weight parameter.\n", " warn('class_weight presets \"balanced\" or \"balanced_subsample\" are '\n" ] } ], "source": [ "# Setup training time period\n", "min_training_years = 5\n", "term_range = range(raw_data[\"term\"].min() + min_training_years,\n", " raw_data[\"term\"].max()+1)\n", "\n", "# Setting growing random forest parameters\n", "# Number of trees to grow per term\n", "trees_per_term = 10\n", "\n", "# Number of trees to begin with\n", "initial_trees = min_training_years * trees_per_term\n", "\n", "# Number of years between \"forest fires\"\n", "reset_interval = 9999\n", "\n", "# Setup model\n", "m = None\n", "term_count = 0\n", "\n", "for term in term_range:\n", " # Diagnostic output\n", " print(\"Term: {0}\".format(term))\n", " term_count += 1\n", " \n", " # Setup train and test periods\n", " train_index = (raw_data.loc[:, \"term\"] < term).values\n", " test_index = (raw_data.loc[:, \"term\"] == term).values\n", " \n", " # Setup train data\n", " feature_data_train = feature_df.loc[train_index, :]\n", " target_data_train = raw_data.loc[train_index, \"justice_outcome_disposition\"].astype(int).values\n", "\n", " # Setup test data\n", " feature_data_test = feature_df.loc[test_index, :]\n", " target_data_test = raw_data.loc[test_index, \"justice_outcome_disposition\"].astype(int).values\n", " \n", " # Build or grow a model depending on initial/reset condition\n", " if not m:\n", " # Grow an initial forest\n", " m = sklearn.ensemble.RandomForestClassifier(n_estimators=initial_trees + (term_count * trees_per_term), \n", " class_weight=\"balanced_subsample\",\n", " warm_start=True,\n", " n_jobs=-1)\n", " elif term_count % reset_interval == 0:\n", " # \"Forest fire;\" grow a new forest from scratch\n", " print(\"Reset interval hit; rebuilding with {0} trees\".format(initial_trees + (term_count * trees_per_term)))\n", " m = sklearn.ensemble.RandomForestClassifier(n_estimators=initial_trees + (term_count * trees_per_term),\n", " class_weight=\"balanced_subsample\",\n", " warm_start=True,\n", " n_jobs=-1)\n", " else:\n", " # Grow the forest by increasing the number of trees (requires warm_start=True)\n", " m.set_params(n_estimators=initial_trees + (term_count * trees_per_term))\n", "\n", " # Fit the forest model\n", " m.fit(feature_data_train,\n", " target_data_train)\n", "\n", " # Fit the \"dummy\" model\n", " d = sklearn.dummy.DummyClassifier(strategy=\"most_frequent\")\n", " d.fit(feature_data_train, target_data_train)\n", " \n", " # Perform forest predictions\n", " raw_data.loc[test_index, \"rf_predicted\"] = m.predict(feature_data_test)\n", " \n", " # Store scores per class\n", " scores = m.predict_proba(feature_data_test)\n", " raw_data.loc[test_index, \"rf_predicted_score_other\"] = scores[:, 0]\n", " raw_data.loc[test_index, \"rf_predicted_score_affirm\"] = scores[:, 1]\n", " raw_data.loc[test_index, \"rf_predicted_score_reverse\"] = scores[:, 2]\n", " \n", " # Store dummy predictions\n", " raw_data.loc[test_index, \"dummy_predicted\"] = d.predict(feature_data_test)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RF model\n", "================================\n", " precision recall f1-score support\n", "\n", " -1 0.96 0.28 0.44 6700\n", " 0 0.58 0.27 0.37 24325\n", " 1 0.62 0.89 0.73 41142\n", "\n", "avg / total 0.64 0.63 0.58 72167\n", "\n", "[[ 1887 390 4423]\n", " [ 48 6568 17709]\n", " [ 23 4356 36763]]\n", "0.626574473097\n", "================================\n", "\n", "Dummy model\n", "================================\n", " precision recall f1-score support\n", "\n", " -1 0.00 0.00 0.00 6700\n", " 0 0.00 0.00 0.00 24325\n", " 1 0.57 1.00 0.73 41142\n", "\n", "avg / total 0.33 0.57 0.41 72167\n", "\n", "[[ 0 0 6700]\n", " [ 0 0 24325]\n", " [ 0 0 41142]]\n", "0.57009436446\n", "================================\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/ubuntu/ve/local/lib/python2.7/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": [ "# Evaluation range\n", "evaluation_index = raw_data.loc[:, \"term\"].isin(term_range)\n", "target_actual = raw_data.loc[evaluation_index, \"justice_outcome_disposition\"]\n", "target_predicted = raw_data.loc[evaluation_index, \"rf_predicted\"]\n", "target_dummy = raw_data.loc[evaluation_index, \"dummy_predicted\"]\n", "raw_data.loc[:, \"rf_correct\"] = numpy.nan\n", "raw_data.loc[:, \"dummy_correct\"] = numpy.nan\n", "raw_data.loc[evaluation_index, \"rf_correct\"] = (target_actual == target_predicted).astype(float)\n", "raw_data.loc[evaluation_index, \"dummy_correct\"] = (target_actual == target_dummy).astype(float)\n", "\n", "# Compare model\n", "print(\"RF model\")\n", "print(\"=\"*32)\n", "print(sklearn.metrics.classification_report(target_actual, target_predicted))\n", "print(sklearn.metrics.confusion_matrix(target_actual, target_predicted))\n", "print(sklearn.metrics.accuracy_score(target_actual, target_predicted))\n", "print(\"=\"*32)\n", "print(\"\")\n", "\n", "# Dummy model\n", "print(\"Dummy model\")\n", "print(\"=\"*32)\n", "print(sklearn.metrics.classification_report(target_actual, target_dummy))\n", "print(sklearn.metrics.confusion_matrix(target_actual, target_dummy))\n", "print(sklearn.metrics.accuracy_score(target_actual, target_dummy))\n", "print(\"=\"*32)\n", "print(\"\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f70227582d0>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Y1BIRERERwZ0Vms1biEdUAEBAlep8RERUCZYfExEREREByOZNv+wYAJKaXsejIaJKMagl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YQ9MxwlqaYamG3eiDr73MSEX4AN7ceRvbjR14vpda23lnWFSWz4T/HtT5VFGUCqmz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\nCLW1Su10tSIbhHHWeizN3VNLpRY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3y3EcjY6O6uMf/3i2x5K1z58nnnhCv/Zr\nv6Y3vvGNsixLlmXpIx/5iC6//HL9/u//vl5++WVt2rRJn/nMZ7JhYvfcc4++9KUvyXVd/dEf/ZGu\nuuoqSax/Hq1k/a+55hpNTU0pDEMNDg7qvvvu09atW1n/nGl17T/72c/q3nvv1ete9zoZY2RZlu67\n7z4NDw+vaO1fk1ALAAAAAMCrgUkMAAAAAIDcItQCAAAAAHKLUAsAAAAAyC1CLQAAAAAgtwi1AAAA\nAIDcItQCAAAAAHKLUAsAAAAAyK3/DwmTyInJjkWKAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f702278b4d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Setup time series\n", "rf_correct_ts = raw_data.loc[evaluation_index, :].groupby(\"term\")[\"rf_correct\"].mean()\n", "dummy_correct_ts = raw_data.loc[evaluation_index, :].groupby(\"term\")[\"dummy_correct\"].mean()\n", "\n", "# Plot all accuracies\n", "f = plt.figure(figsize=(16, 12))\n", "plt.plot(rf_correct_ts.index, rf_correct_ts,\n", " marker='o', alpha=0.75)\n", "plt.plot(dummy_correct_ts.index, dummy_correct_ts,\n", " marker='>', alpha=0.75)\n", "plt.legend(('Random forest', 'Dummy'))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f7022654b90>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AkMIVZnYq99cBAAA9hcDMTuX+OgAAoKcQmNnp3F8H0DojcgCg6xOYASAPjMgB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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f70226e6bd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Setup time series\n", "rf_spread_ts = rf_correct_ts - dummy_correct_ts\n", "\n", "# Plot all accuracies\n", "f = plt.figure(figsize=(16, 12))\n", "plt.bar(rf_spread_ts.index, rf_spread_ts,\n", " alpha=0.75)\n", "plt.xlabel(\"Term\")\n", "plt.ylabel(\"Spread (%)\")\n", "plt.title(\"Spread over dummy model for justice accuracy\")" ] }, { "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>feature</th>\n", " <th>importance</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>130</th>\n", " <td>decision_delay</td>\n", " <td>0.028171</td>\n", " </tr>\n", " <tr>\n", " <th>1363</th>\n", " <td>justice_previous_lc_direction_diff</td>\n", " <td>0.026368</td>\n", " </tr>\n", " <tr>\n", " <th>1364</th>\n", " <td>justice_cumulative_lc_direction_diff</td>\n", " <td>0.025995</td>\n", " </tr>\n", " <tr>\n", " <th>1154</th>\n", " <td>issue_area_9</td>\n", " <td>0.016174</td>\n", " </tr>\n", " <tr>\n", " <th>1352</th>\n", " <td>justice_cumulative_action</td>\n", " <td>0.015409</td>\n", " </tr>\n", " <tr>\n", " <th>1357</th>\n", " <td>justice_previous_agreement</td>\n", " <td>0.015095</td>\n", " </tr>\n", " <tr>\n", " <th>1349</th>\n", " <td>justice_previous_court_direction_diff</td>\n", " <td>0.014940</td>\n", " </tr>\n", " <tr>\n", " <th>1351</th>\n", " <td>justice_previous_action</td>\n", " <td>0.014886</td>\n", " </tr>\n", " <tr>\n", " <th>1358</th>\n", " <td>justice_cumulative_agreement</td>\n", " <td>0.014832</td>\n", " </tr>\n", " <tr>\n", " <th>1345</th>\n", " <td>justice_previous_direction</td>\n", " <td>0.014732</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " feature importance\n", "130 decision_delay 0.028171\n", "1363 justice_previous_lc_direction_diff 0.026368\n", "1364 justice_cumulative_lc_direction_diff 0.025995\n", "1154 issue_area_9 0.016174\n", "1352 justice_cumulative_action 0.015409\n", "1357 justice_previous_agreement 0.015095\n", "1349 justice_previous_court_direction_diff 0.014940\n", "1351 justice_previous_action 0.014886\n", "1358 justice_cumulative_agreement 0.014832\n", "1345 justice_previous_direction 0.014732" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Feature importance\n", "feature_importance_df = pandas.DataFrame(zip(feature_df.columns, m.feature_importances_),\n", " columns=[\"feature\", \"importance\"])\n", "feature_importance_df.sort_values([\"importance\"], ascending=False).head(10)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "t-test:\n", "Uncalibrated:\n", "Ttest_relResult(statistic=13.317956183521696, pvalue=5.5695453951134186e-20)\n", "================\n", "ranksum-test:\n", "Uncalibrated:\n", "RanksumsResult(statistic=5.3374499616411635, pvalue=9.426290530279906e-08)\n", "================\n", "Binomial:\n", "3.16846166377e-209\n" ] } ], "source": [ "# Output stats\n", "print(\"t-test:\")\n", "print(\"Uncalibrated:\")\n", "print(scipy.stats.ttest_rel(rf_correct_ts.values,\n", " dummy_correct_ts.values))\n", "\n", "print(\"=\" * 16)\n", "print(\"ranksum-test:\")\n", "print(\"Uncalibrated:\")\n", "print(scipy.stats.ranksums(rf_correct_ts.values,\n", " dummy_correct_ts.values))\n", "\n", "print(\"=\" * 16)\n", "print(\"Binomial:\")\n", "print(statsmodels.stats.proportion.binom_test(raw_data.loc[evaluation_index, \"rf_correct\"].sum(),\n", " raw_data.loc[evaluation_index, \"rf_correct\"].shape[0],\n", " raw_data.loc[evaluation_index, \"dummy_correct\"].mean(),\n", " alternative=\"larger\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Case Outcomes" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get case-level prediction\n", "#scdb_data.loc[evaluation_index, \"rf_predicted_case\"] = \n", "rf_predicted_case = pandas.DataFrame(raw_data.loc[evaluation_index, :]\\\n", " .groupby([\"docketId\"])[\"rf_predicted\"]\\\n", " .agg(lambda x: x.value_counts().index[0]))\n", "rf_predicted_case.columns = [\"rf_predicted_case\"]\n", "\n", "dummy_predicted_case = pandas.DataFrame(raw_data.loc[evaluation_index, :]\\\n", " .groupby([\"docketId\"])[\"dummy_predicted\"]\\\n", " .agg(lambda x: x.value_counts().index[0]))\n", "dummy_predicted_case.columns = [\"dummy_predicted_case\"]\n", "\n", "# Set DFs\n", "rf_predicted_case = raw_data[[\"docketId\", \"case_outcome_disposition\", \"rf_predicted\"]].join(rf_predicted_case, on=\"docketId\")\n", "dumy_predicted_case = raw_data[[\"docketId\", \"dummy_predicted\"]].join(dummy_predicted_case, on=\"docketId\")\n", "\n", "raw_data.loc[:, \"rf_predicted_case\"] = rf_predicted_case\n", "raw_data.loc[:, \"dummy_predicted_case\"] = dumy_predicted_case" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 1 5435\n", " 0 2639\n", "-1 433\n", "Name: case_outcome_disposition, dtype: int64\n", " 1 0.629707\n", " 0 0.305758\n", "-1 0.050168\n", "Name: case_outcome_disposition, dtype: float64\n" ] } ], "source": [ "# Output case distribution\n", "case_outcomes = raw_data.groupby([\"docketId\"])[\"case_outcome_disposition\"].agg(lambda x: x.mode())\n", "case_outcomes = case_outcomes.apply(lambda x: int(x) if type(x) in [numpy.float64] else None)\n", "print(case_outcomes.value_counts())\n", "print(case_outcomes.value_counts(normalize=True))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RF model\n", "================================\n", " precision recall f1-score support\n", "\n", " -1.0 0.93 0.25 0.39 3676\n", " 0.0 0.53 0.18 0.27 21597\n", " 1.0 0.68 0.93 0.78 45782\n", "\n", "avg / total 0.64 0.66 0.60 71055\n", "\n", "[[ 903 54 2719]\n", " [ 54 3867 17676]\n", " [ 18 3359 42405]]\n", "0.663922313701\n", "================================\n", "\n", "Dummy model\n", "================================\n", " precision recall f1-score support\n", "\n", " -1.0 0.00 0.00 0.00 3676\n", " 0.0 0.00 0.00 0.00 21597\n", " 1.0 0.64 1.00 0.78 45782\n", "\n", "avg / total 0.42 0.64 0.50 71055\n", "\n", "[[ 0 0 3676]\n", " [ 0 0 21597]\n", " [ 0 0 45782]]\n", "0.644317782\n", "================================\n", "\n" ] } ], "source": [ "# Output comparison\n", "# Evaluation range\n", "evaluation_index = raw_data.loc[:, \"term\"].isin(term_range) & -raw_data.loc[:, \"case_outcome_disposition\"].isnull()\n", "target_actual = raw_data.loc[evaluation_index, \"case_outcome_disposition\"]\n", "target_predicted = raw_data.loc[evaluation_index, \"rf_predicted_case\"]\n", "target_dummy = raw_data.loc[evaluation_index, \"dummy_predicted_case\"]\n", "\n", "raw_data.loc[:, \"rf_correct_case\"] = numpy.nan\n", "raw_data.loc[:, \"dummy_correct_case\"] = numpy.nan\n", "raw_data.loc[evaluation_index, \"rf_correct_case\"] = (target_actual == target_predicted).astype(float)\n", "raw_data.loc[evaluation_index, \"dummy_correct_case\"] = (target_actual == target_dummy).astype(float)\n", "\n", "# Compare model\n", "print(\"RF model\")\n", "print(\"=\"*32)\n", "print(sklearn.metrics.classification_report(target_actual, target_predicted))\n", "print(sklearn.metrics.confusion_matrix(target_actual, target_predicted))\n", "print(sklearn.metrics.accuracy_score(target_actual, target_predicted))\n", "print(\"=\"*32)\n", "print(\"\")\n", "\n", "# Dummy model\n", "print(\"Dummy model\")\n", "print(\"=\"*32)\n", "print(sklearn.metrics.classification_report(target_actual, target_dummy))\n", "print(sklearn.metrics.confusion_matrix(target_actual, target_dummy))\n", "print(sklearn.metrics.accuracy_score(target_actual, target_dummy))\n", "print(\"=\"*32)\n", "print(\"\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f7022358110>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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iIiIiR2PgSkRERERERI7GwJWIiIiIiIgcjYErERERERERORoDVyIiIiIiInI0Bq5ERERE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\nXNPHyo7DCZt7XP04TL9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"text/plain": [ "<matplotlib.figure.Figure at 0x7f70226e6f90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Setup time series\n", "rf_correct_case_ts = raw_data.loc[evaluation_index, :].groupby(\"term\")[\"rf_correct_case\"].mean()\n", "dummy_correct_case_ts = raw_data.loc[evaluation_index, :].groupby(\"term\")[\"dummy_correct_case\"].mean()\n", "\n", "# Plot all accuracies\n", "f = plt.figure(figsize=(16, 12))\n", "plt.plot(rf_correct_case_ts.index, rf_correct_case_ts,\n", " marker='o', alpha=0.75)\n", "plt.plot(dummy_correct_case_ts.index, dummy_correct_case_ts,\n", " marker='>', alpha=0.75)\n", "plt.legend(('Random forest', 'Dummy'))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f70222788d0>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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gBgAAgBQCMwAAAKQQmAEAACCFwAwAAAApBGYAAABIITADAABACoEZAAAAUgjM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\nACgQzAAAAFAgmAEAAKBAMAMAAECBYAY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"text/plain": [ "<matplotlib.figure.Figure at 0x7f704801d950>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Setup time series\n", "rf_spread_case_ts = rf_correct_case_ts - dummy_correct_case_ts\n", "\n", "# Plot all accuracies\n", "f = plt.figure(figsize=(16, 12))\n", "plt.bar(rf_spread_case_ts.index, rf_spread_case_ts,\n", " alpha=0.75)\n", "plt.xlabel(\"Term\")\n", "plt.ylabel(\"Spread (%)\")\n", "plt.title(\"Spread over dummy model for case accuracy\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f70225b4e10>]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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PvBtdPcfisoUzY/lSqZqiWLO8PXa+3htv7x95o6VqUjVJRlM2//7E2/Hoju64746rUv39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\n0sMPPxy6LDc3V9u2bZMkbd++PTTP3Nxc7dixQ21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"text/plain": [ "<matplotlib.figure.Figure at 0x7f70225dc810>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Setup time series\n", "rf_spread_case_dir_ts = pandas.expanding_sum(numpy.sign(rf_spread_case_ts))\n", "\n", "# Plot all accuracies\n", "f = plt.figure(figsize=(16, 12))\n", "plt.plot(rf_spread_case_dir_ts.index, rf_spread_case_dir_ts,\n", " alpha=0.75)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "t-test:\n", "Uncalibrated:\n", "Ttest_relResult(statistic=3.7584207971683461, pvalue=0.0003763711009210788)\n", "================\n", "ranksum-test:\n", "Uncalibrated:\n", "RanksumsResult(statistic=1.2176057724993905, pvalue=0.2233738197596421)\n", "================\n", "Binomial:\n", "0.000125381247602\n" ] } ], "source": [ "# Output stats\n", "print(\"t-test:\")\n", "print(\"Uncalibrated:\")\n", "print(scipy.stats.ttest_rel(rf_correct_case_ts.values,\n", " dummy_correct_case_ts.values))\n", "\n", "print(\"=\" * 16)\n", "print(\"ranksum-test:\")\n", "print(\"Uncalibrated:\")\n", "print(scipy.stats.ranksums(rf_correct_case_ts.values,\n", " dummy_correct_case_ts.values))\n", "\n", "print(\"=\" * 16)\n", "print(\"Binomial:\")\n", "case_accuracy_data = raw_data.loc[evaluation_index, [\"docketId\", \"rf_correct_case\", \"dummy_correct_case\"]].drop_duplicates()\n", "print(statsmodels.stats.proportion.binom_test(case_accuracy_data[\"rf_correct_case\"].sum(),\n", " case_accuracy_data[\"rf_correct_case\"].shape[0],\n", " case_accuracy_data[\"dummy_correct_case\"].mean(),\n", " alternative=\"larger\"))" ] }, { "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>22281</th>\n", " <th>22282</th>\n", " <th>22283</th>\n", " <th>22284</th>\n", " <th>22285</th>\n", " <th>22286</th>\n", " <th>22287</th>\n", " <th>22288</th>\n", " <th>22289</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>caseName</th>\n", " <td>MIRANDA v. ARIZONA</td>\n", " <td>MIRANDA v. ARIZONA</td>\n", " <td>MIRANDA v. ARIZONA</td>\n", " <td>MIRANDA v. ARIZONA</td>\n", " <td>MIRANDA v. ARIZONA</td>\n", " <td>MIRANDA v. ARIZONA</td>\n", " <td>MIRANDA v. ARIZONA</td>\n", " <td>MIRANDA v. ARIZONA</td>\n", " <td>MIRANDA v. ARIZONA</td>\n", " </tr>\n", " <tr>\n", " <th>justiceName</th>\n", " <td>JHarlan2</td>\n", " <td>HLBlack</td>\n", " <td>WODouglas</td>\n", " <td>PStewart</td>\n", " <td>WJBrennan</td>\n", " <td>BRWhite</td>\n", " <td>EWarren</td>\n", " <td>TCClark</td>\n", " <td>AFortas</td>\n", " </tr>\n", " <tr>\n", " <th>case_outcome_disposition</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>justice_outcome_disposition</th>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>rf_predicted</th>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>rf_predicted_score_affirm</th>\n", " <td>0.59</td>\n", " <td>0.15</td>\n", " <td>0.13</td>\n", " <td>0.375</td>\n", " <td>0.265</td>\n", " <td>0.31</td>\n", " <td>0.24</td>\n", " <td>0.51</td>\n", " <td>0.525</td>\n", " </tr>\n", " <tr>\n", " <th>rf_predicted_score_reverse</th>\n", " <td>0.365</td>\n", " <td>0.78</td>\n", " <td>0.78</td>\n", " <td>0.57</td>\n", " <td>0.695</td>\n", " <td>0.61</td>\n", " <td>0.74</td>\n", " <td>0.46</td>\n", " <td>0.415</td>\n", " </tr>\n", " <tr>\n", " <th>rf_correct_case</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>dummy_correct_case</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 22281 22282 \\\n", "caseName MIRANDA v. ARIZONA MIRANDA v. ARIZONA \n", "justiceName JHarlan2 HLBlack \n", "case_outcome_disposition 1 1 \n", "justice_outcome_disposition 0 1 \n", "rf_predicted 0 1 \n", "rf_predicted_score_affirm 0.59 0.15 \n", "rf_predicted_score_reverse 0.365 0.78 \n", "rf_correct_case 1 1 \n", "dummy_correct_case 1 1 \n", "\n", " 22283 22284 \\\n", "caseName MIRANDA v. ARIZONA MIRANDA v. ARIZONA \n", "justiceName WODouglas PStewart \n", "case_outcome_disposition 1 1 \n", "justice_outcome_disposition 1 0 \n", "rf_predicted 1 1 \n", "rf_predicted_score_affirm 0.13 0.375 \n", "rf_predicted_score_reverse 0.78 0.57 \n", "rf_correct_case 1 1 \n", "dummy_correct_case 1 1 \n", "\n", " 22285 22286 \\\n", "caseName MIRANDA v. ARIZONA MIRANDA v. ARIZONA \n", "justiceName WJBrennan BRWhite \n", "case_outcome_disposition 1 1 \n", "justice_outcome_disposition 1 0 \n", "rf_predicted 1 1 \n", "rf_predicted_score_affirm 0.265 0.31 \n", "rf_predicted_score_reverse 0.695 0.61 \n", "rf_correct_case 1 1 \n", "dummy_correct_case 1 1 \n", "\n", " 22287 22288 \\\n", "caseName MIRANDA v. ARIZONA MIRANDA v. ARIZONA \n", "justiceName EWarren TCClark \n", "case_outcome_disposition 1 1 \n", "justice_outcome_disposition 1 0 \n", "rf_predicted 1 0 \n", "rf_predicted_score_affirm 0.24 0.51 \n", "rf_predicted_score_reverse 0.74 0.46 \n", "rf_correct_case 1 1 \n", "dummy_correct_case 1 1 \n", "\n", " 22289 \n", "caseName MIRANDA v. ARIZONA \n", "justiceName AFortas \n", "case_outcome_disposition 1 \n", "justice_outcome_disposition 1 \n", "rf_predicted 0 \n", "rf_predicted_score_affirm 0.525 \n", "rf_predicted_score_reverse 0.415 \n", "rf_correct_case 1 \n", "dummy_correct_case 1 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data.loc[raw_data.loc[:, \"caseName\"] == \"MIRANDA v. ARIZONA\", \n", " [\"caseName\", \"justiceName\", \"case_outcome_disposition\", \"justice_outcome_disposition\",\n", " \"rf_predicted\", \"rf_predicted_score_affirm\", \"rf_predicted_score_reverse\", \"rf_correct_case\", \"dummy_correct_case\"]].T" ] }, { "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>77288</th>\n", " <th>77289</th>\n", " <th>77290</th>\n", " <th>77291</th>\n", " <th>77292</th>\n", " <th>77293</th>\n", " <th>77294</th>\n", " <th>77295</th>\n", " <th>77296</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>caseName</th>\n", " <td>OBERGEFELL v. HODGES</td>\n", " <td>OBERGEFELL v. HODGES</td>\n", " <td>OBERGEFELL v. HODGES</td>\n", " <td>OBERGEFELL v. HODGES</td>\n", " <td>OBERGEFELL v. HODGES</td>\n", " <td>OBERGEFELL v. HODGES</td>\n", " <td>OBERGEFELL v. HODGES</td>\n", " <td>OBERGEFELL v. HODGES</td>\n", " <td>OBERGEFELL v. HODGES</td>\n", " </tr>\n", " <tr>\n", " <th>justiceName</th>\n", " <td>JGRoberts</td>\n", " <td>AScalia</td>\n", " <td>AMKennedy</td>\n", " <td>CThomas</td>\n", " <td>RBGinsburg</td>\n", " <td>SGBreyer</td>\n", " <td>SAAlito</td>\n", " <td>SSotomayor</td>\n", " <td>EKagan</td>\n", " </tr>\n", " <tr>\n", " <th>case_outcome_disposition</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>justice_outcome_disposition</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>rf_predicted</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>rf_predicted_score_affirm</th>\n", " <td>0.394203</td>\n", " <td>0.386957</td>\n", " <td>0.363768</td>\n", " <td>0.4</td>\n", " <td>0.315942</td>\n", " <td>0.326087</td>\n", " <td>0.369565</td>\n", " <td>0.286957</td>\n", " <td>0.301449</td>\n", " </tr>\n", " <tr>\n", " <th>rf_predicted_score_reverse</th>\n", " <td>0.562319</td>\n", " <td>0.584058</td>\n", " <td>0.597101</td>\n", " <td>0.571014</td>\n", " <td>0.642029</td>\n", " <td>0.636232</td>\n", " <td>0.597101</td>\n", " <td>0.676812</td>\n", " <td>0.649275</td>\n", " </tr>\n", " <tr>\n", " <th>rf_correct_case</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>dummy_correct_case</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 77288 77289 \\\n", "caseName OBERGEFELL v. HODGES OBERGEFELL v. HODGES \n", "justiceName JGRoberts AScalia \n", "case_outcome_disposition 1 1 \n", "justice_outcome_disposition 0 0 \n", "rf_predicted 1 1 \n", "rf_predicted_score_affirm 0.394203 0.386957 \n", "rf_predicted_score_reverse 0.562319 0.584058 \n", "rf_correct_case 1 1 \n", "dummy_correct_case 1 1 \n", "\n", " 77290 77291 \\\n", "caseName OBERGEFELL v. HODGES OBERGEFELL v. HODGES \n", "justiceName AMKennedy CThomas \n", "case_outcome_disposition 1 1 \n", "justice_outcome_disposition 1 0 \n", "rf_predicted 1 1 \n", "rf_predicted_score_affirm 0.363768 0.4 \n", "rf_predicted_score_reverse 0.597101 0.571014 \n", "rf_correct_case 1 1 \n", "dummy_correct_case 1 1 \n", "\n", " 77292 77293 \\\n", "caseName OBERGEFELL v. HODGES OBERGEFELL v. HODGES \n", "justiceName RBGinsburg SGBreyer \n", "case_outcome_disposition 1 1 \n", "justice_outcome_disposition 1 1 \n", "rf_predicted 1 1 \n", "rf_predicted_score_affirm 0.315942 0.326087 \n", "rf_predicted_score_reverse 0.642029 0.636232 \n", "rf_correct_case 1 1 \n", "dummy_correct_case 1 1 \n", "\n", " 77294 77295 \\\n", "caseName OBERGEFELL v. HODGES OBERGEFELL v. HODGES \n", "justiceName SAAlito SSotomayor \n", "case_outcome_disposition 1 1 \n", "justice_outcome_disposition 0 1 \n", "rf_predicted 1 1 \n", "rf_predicted_score_affirm 0.369565 0.286957 \n", "rf_predicted_score_reverse 0.597101 0.676812 \n", "rf_correct_case 1 1 \n", "dummy_correct_case 1 1 \n", "\n", " 77296 \n", "caseName OBERGEFELL v. HODGES \n", "justiceName EKagan \n", "case_outcome_disposition 1 \n", "justice_outcome_disposition 1 \n", "rf_predicted 1 \n", "rf_predicted_score_affirm 0.301449 \n", "rf_predicted_score_reverse 0.649275 \n", "rf_correct_case 1 \n", "dummy_correct_case 1 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data.loc[raw_data.loc[:, \"caseName\"] == \"OBERGEFELL v. HODGES\", \n", " [\"caseName\", \"justiceName\", \"case_outcome_disposition\", \"justice_outcome_disposition\",\n", " \"rf_predicted\", \"rf_predicted_score_affirm\", \"rf_predicted_score_reverse\", \"rf_correct_case\", \"dummy_correct_case\"]].T" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>77126</th>\n", " <th>77127</th>\n", " <th>77128</th>\n", " <th>77129</th>\n", " <th>77130</th>\n", " <th>77131</th>\n", " <th>77132</th>\n", " <th>77133</th>\n", " <th>77134</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>caseName</th>\n", " <td>KING v. BURWELL</td>\n", " <td>KING v. BURWELL</td>\n", " <td>KING v. BURWELL</td>\n", " <td>KING v. BURWELL</td>\n", " <td>KING v. BURWELL</td>\n", " <td>KING v. BURWELL</td>\n", " <td>KING v. BURWELL</td>\n", " <td>KING v. BURWELL</td>\n", " <td>KING v. BURWELL</td>\n", " </tr>\n", " <tr>\n", " <th>justiceName</th>\n", " <td>JGRoberts</td>\n", " <td>AScalia</td>\n", " <td>AMKennedy</td>\n", " <td>CThomas</td>\n", " <td>RBGinsburg</td>\n", " <td>SGBreyer</td>\n", " <td>SAAlito</td>\n", " <td>SSotomayor</td>\n", " <td>EKagan</td>\n", " </tr>\n", " <tr>\n", " <th>case_outcome_disposition</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>justice_outcome_disposition</th>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>rf_predicted</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>rf_predicted_score_affirm</th>\n", " <td>0.516785</td>\n", " <td>0.509511</td>\n", " <td>0.512553</td>\n", " <td>0.494203</td>\n", " <td>0.559045</td>\n", " <td>0.537588</td>\n", " <td>0.521433</td>\n", " <td>0.574987</td>\n", " <td>0.542496</td>\n", " </tr>\n", " <tr>\n", " <th>rf_predicted_score_reverse</th>\n", " <td>0.448433</td>\n", " <td>0.451359</td>\n", " <td>0.446868</td>\n", " <td>0.463768</td>\n", " <td>0.398926</td>\n", " <td>0.430528</td>\n", " <td>0.449581</td>\n", " <td>0.387332</td>\n", " <td>0.429968</td>\n", " </tr>\n", " <tr>\n", " <th>rf_correct_case</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>dummy_correct_case</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 77126 77127 \\\n", "caseName KING v. BURWELL KING v. BURWELL \n", "justiceName JGRoberts AScalia \n", "case_outcome_disposition 0 0 \n", "justice_outcome_disposition 0 1 \n", "rf_predicted 0 0 \n", "rf_predicted_score_affirm 0.516785 0.509511 \n", "rf_predicted_score_reverse 0.448433 0.451359 \n", "rf_correct_case 1 1 \n", "dummy_correct_case 0 0 \n", "\n", " 77128 77129 \\\n", "caseName KING v. BURWELL KING v. BURWELL \n", "justiceName AMKennedy CThomas \n", "case_outcome_disposition 0 0 \n", "justice_outcome_disposition 0 1 \n", "rf_predicted 0 0 \n", "rf_predicted_score_affirm 0.512553 0.494203 \n", "rf_predicted_score_reverse 0.446868 0.463768 \n", "rf_correct_case 1 1 \n", "dummy_correct_case 0 0 \n", "\n", " 77130 77131 \\\n", "caseName KING v. BURWELL KING v. BURWELL \n", "justiceName RBGinsburg SGBreyer \n", "case_outcome_disposition 0 0 \n", "justice_outcome_disposition 0 0 \n", "rf_predicted 0 0 \n", "rf_predicted_score_affirm 0.559045 0.537588 \n", "rf_predicted_score_reverse 0.398926 0.430528 \n", "rf_correct_case 1 1 \n", "dummy_correct_case 0 0 \n", "\n", " 77132 77133 77134 \n", "caseName KING v. BURWELL KING v. BURWELL KING v. BURWELL \n", "justiceName SAAlito SSotomayor EKagan \n", "case_outcome_disposition 0 0 0 \n", "justice_outcome_disposition 1 0 0 \n", "rf_predicted 0 0 0 \n", "rf_predicted_score_affirm 0.521433 0.574987 0.542496 \n", "rf_predicted_score_reverse 0.449581 0.387332 0.429968 \n", "rf_correct_case 1 1 1 \n", "dummy_correct_case 0 0 0 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data.loc[raw_data.loc[:, \"caseName\"] == \"KING v. BURWELL\", \n", " [\"caseName\", \"justiceName\", \"case_outcome_disposition\", \"justice_outcome_disposition\",\n", " \"rf_predicted\", \"rf_predicted_score_affirm\", \"rf_predicted_score_reverse\", \"rf_correct_case\", \"dummy_correct_case\"]].T" ] }, { "cell_type": "code", "execution_count": 21, "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>73130</th>\n", " <th>73131</th>\n", " <th>73132</th>\n", " <th>73133</th>\n", " <th>73134</th>\n", " <th>73135</th>\n", " <th>73136</th>\n", " <th>73137</th>\n", " <th>73138</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>caseName</th>\n", " <td>CITIZENS UNITED v. FEDERAL ELECTION COMMISSION</td>\n", " <td>CITIZENS UNITED v. FEDERAL ELECTION COMMISSION</td>\n", " <td>CITIZENS UNITED v. FEDERAL ELECTION COMMISSION</td>\n", " <td>CITIZENS UNITED v. FEDERAL ELECTION COMMISSION</td>\n", " <td>CITIZENS UNITED v. FEDERAL ELECTION COMMISSION</td>\n", " <td>CITIZENS UNITED v. FEDERAL ELECTION COMMISSION</td>\n", " <td>CITIZENS UNITED v. FEDERAL ELECTION COMMISSION</td>\n", " <td>CITIZENS UNITED v. FEDERAL ELECTION COMMISSION</td>\n", " <td>CITIZENS UNITED v. FEDERAL ELECTION COMMISSION</td>\n", " </tr>\n", " <tr>\n", " <th>justiceName</th>\n", " <td>JGRoberts</td>\n", " <td>JPStevens</td>\n", " <td>AScalia</td>\n", " <td>AMKennedy</td>\n", " <td>CThomas</td>\n", " <td>RBGinsburg</td>\n", " <td>SGBreyer</td>\n", " <td>SAAlito</td>\n", " <td>SSotomayor</td>\n", " </tr>\n", " <tr>\n", " <th>case_outcome_disposition</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>justice_outcome_disposition</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>rf_predicted</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>rf_predicted_score_affirm</th>\n", " <td>0.390625</td>\n", " <td>0.50625</td>\n", " <td>0.382812</td>\n", " <td>0.370312</td>\n", " <td>0.379688</td>\n", " <td>0.490371</td>\n", " <td>0.463808</td>\n", " <td>0.392188</td>\n", " <td>0.354094</td>\n", " </tr>\n", " <tr>\n", " <th>rf_predicted_score_reverse</th>\n", " <td>0.55</td>\n", " <td>0.434375</td>\n", " <td>0.553125</td>\n", " <td>0.5625</td>\n", " <td>0.55625</td>\n", " <td>0.442442</td>\n", " <td>0.481504</td>\n", " <td>0.551562</td>\n", " <td>0.477156</td>\n", " </tr>\n", " <tr>\n", " <th>rf_correct_case</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>dummy_correct_case</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 73130 \\\n", "caseName CITIZENS UNITED v. FEDERAL ELECTION COMMISSION \n", "justiceName JGRoberts \n", "case_outcome_disposition 1 \n", "justice_outcome_disposition 1 \n", "rf_predicted 1 \n", "rf_predicted_score_affirm 0.390625 \n", "rf_predicted_score_reverse 0.55 \n", "rf_correct_case 1 \n", "dummy_correct_case 1 \n", "\n", " 73131 \\\n", "caseName CITIZENS UNITED v. FEDERAL ELECTION COMMISSION \n", "justiceName JPStevens \n", "case_outcome_disposition 1 \n", "justice_outcome_disposition 0 \n", "rf_predicted 0 \n", "rf_predicted_score_affirm 0.50625 \n", "rf_predicted_score_reverse 0.434375 \n", "rf_correct_case 1 \n", "dummy_correct_case 1 \n", "\n", " 73132 \\\n", "caseName CITIZENS UNITED v. FEDERAL ELECTION COMMISSION \n", "justiceName AScalia \n", "case_outcome_disposition 1 \n", "justice_outcome_disposition 1 \n", "rf_predicted 1 \n", "rf_predicted_score_affirm 0.382812 \n", "rf_predicted_score_reverse 0.553125 \n", "rf_correct_case 1 \n", "dummy_correct_case 1 \n", "\n", " 73133 \\\n", "caseName CITIZENS UNITED v. FEDERAL ELECTION COMMISSION \n", "justiceName AMKennedy \n", "case_outcome_disposition 1 \n", "justice_outcome_disposition 1 \n", "rf_predicted 1 \n", "rf_predicted_score_affirm 0.370312 \n", "rf_predicted_score_reverse 0.5625 \n", "rf_correct_case 1 \n", "dummy_correct_case 1 \n", "\n", " 73134 \\\n", "caseName CITIZENS UNITED v. FEDERAL ELECTION COMMISSION \n", "justiceName CThomas \n", "case_outcome_disposition 1 \n", "justice_outcome_disposition 1 \n", "rf_predicted 1 \n", "rf_predicted_score_affirm 0.379688 \n", "rf_predicted_score_reverse 0.55625 \n", "rf_correct_case 1 \n", "dummy_correct_case 1 \n", "\n", " 73135 \\\n", "caseName CITIZENS UNITED v. FEDERAL ELECTION COMMISSION \n", "justiceName RBGinsburg \n", "case_outcome_disposition 1 \n", "justice_outcome_disposition 0 \n", "rf_predicted 0 \n", "rf_predicted_score_affirm 0.490371 \n", "rf_predicted_score_reverse 0.442442 \n", "rf_correct_case 1 \n", "dummy_correct_case 1 \n", "\n", " 73136 \\\n", "caseName CITIZENS UNITED v. FEDERAL ELECTION COMMISSION \n", "justiceName SGBreyer \n", "case_outcome_disposition 1 \n", "justice_outcome_disposition 0 \n", "rf_predicted 1 \n", "rf_predicted_score_affirm 0.463808 \n", "rf_predicted_score_reverse 0.481504 \n", "rf_correct_case 1 \n", "dummy_correct_case 1 \n", "\n", " 73137 \\\n", "caseName CITIZENS UNITED v. FEDERAL ELECTION COMMISSION \n", "justiceName SAAlito \n", "case_outcome_disposition 1 \n", "justice_outcome_disposition 1 \n", "rf_predicted 1 \n", "rf_predicted_score_affirm 0.392188 \n", "rf_predicted_score_reverse 0.551562 \n", "rf_correct_case 1 \n", "dummy_correct_case 1 \n", "\n", " 73138 \n", "caseName CITIZENS UNITED v. FEDERAL ELECTION COMMISSION \n", "justiceName SSotomayor \n", "case_outcome_disposition 1 \n", "justice_outcome_disposition 0 \n", "rf_predicted 1 \n", "rf_predicted_score_affirm 0.354094 \n", "rf_predicted_score_reverse 0.477156 \n", "rf_correct_case 1 \n", "dummy_correct_case 1 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data.loc[raw_data.loc[:, \"caseName\"] == 'CITIZENS UNITED v. FEDERAL ELECTION COMMISSION',\n", " [\"caseName\", \"justiceName\", \"case_outcome_disposition\", \"justice_outcome_disposition\",\n", " \"rf_predicted\", \"rf_predicted_score_affirm\", \"rf_predicted_score_reverse\", \"rf_correct_case\", \"dummy_correct_case\"]].T" ] }, { "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>72257</th>\n", " <th>72258</th>\n", " <th>72259</th>\n", " <th>72260</th>\n", " <th>72261</th>\n", " <th>72262</th>\n", " <th>72263</th>\n", " <th>72264</th>\n", " <th>72265</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>caseName</th>\n", " <td>DISTRICT OF COLUMBIA v. HELLER</td>\n", " <td>DISTRICT OF COLUMBIA v. HELLER</td>\n", " <td>DISTRICT OF COLUMBIA v. HELLER</td>\n", " <td>DISTRICT OF COLUMBIA v. HELLER</td>\n", " <td>DISTRICT OF COLUMBIA v. HELLER</td>\n", " <td>DISTRICT OF COLUMBIA v. HELLER</td>\n", " <td>DISTRICT OF COLUMBIA v. HELLER</td>\n", " <td>DISTRICT OF COLUMBIA v. HELLER</td>\n", " <td>DISTRICT OF COLUMBIA v. 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HELLER \n", "justiceName JPStevens \n", "case_outcome_disposition 0 \n", "justice_outcome_disposition 1 \n", "rf_predicted 1 \n", "rf_predicted_score_affirm 0.372581 \n", "rf_predicted_score_reverse 0.577419 \n", "rf_correct_case 0 \n", "dummy_correct_case 0 \n", "\n", " 72258 \\\n", "caseName DISTRICT OF COLUMBIA v. HELLER \n", "justiceName AScalia \n", "case_outcome_disposition 0 \n", "justice_outcome_disposition 0 \n", "rf_predicted 1 \n", "rf_predicted_score_affirm 0.403226 \n", "rf_predicted_score_reverse 0.543548 \n", "rf_correct_case 0 \n", "dummy_correct_case 0 \n", "\n", " 72259 \\\n", "caseName DISTRICT OF COLUMBIA v. HELLER \n", "justiceName AMKennedy \n", "case_outcome_disposition 0 \n", "justice_outcome_disposition 0 \n", "rf_predicted 1 \n", "rf_predicted_score_affirm 0.375806 \n", "rf_predicted_score_reverse 0.559677 \n", "rf_correct_case 0 \n", "dummy_correct_case 0 \n", "\n", " 72260 \\\n", "caseName DISTRICT OF COLUMBIA v. HELLER \n", "justiceName DHSouter \n", "case_outcome_disposition 0 \n", "justice_outcome_disposition 1 \n", "rf_predicted 1 \n", "rf_predicted_score_affirm 0.396774 \n", "rf_predicted_score_reverse 0.553226 \n", "rf_correct_case 0 \n", "dummy_correct_case 0 \n", "\n", " 72261 \\\n", "caseName DISTRICT OF COLUMBIA v. HELLER \n", "justiceName CThomas \n", "case_outcome_disposition 0 \n", "justice_outcome_disposition 0 \n", "rf_predicted 1 \n", "rf_predicted_score_affirm 0.4 \n", "rf_predicted_score_reverse 0.535484 \n", "rf_correct_case 0 \n", "dummy_correct_case 0 \n", "\n", " 72262 \\\n", "caseName DISTRICT OF COLUMBIA v. HELLER \n", "justiceName RBGinsburg \n", "case_outcome_disposition 0 \n", "justice_outcome_disposition 1 \n", "rf_predicted 1 \n", "rf_predicted_score_affirm 0.359677 \n", "rf_predicted_score_reverse 0.572581 \n", "rf_correct_case 0 \n", "dummy_correct_case 0 \n", "\n", " 72263 \\\n", "caseName DISTRICT OF COLUMBIA v. HELLER \n", "justiceName SGBreyer \n", "case_outcome_disposition 0 \n", "justice_outcome_disposition 1 \n", "rf_predicted 1 \n", "rf_predicted_score_affirm 0.403226 \n", "rf_predicted_score_reverse 0.53871 \n", "rf_correct_case 0 \n", "dummy_correct_case 0 \n", "\n", " 72264 \\\n", "caseName DISTRICT OF COLUMBIA v. HELLER \n", "justiceName JGRoberts \n", "case_outcome_disposition 0 \n", "justice_outcome_disposition 0 \n", "rf_predicted 1 \n", "rf_predicted_score_affirm 0.382258 \n", "rf_predicted_score_reverse 0.556452 \n", "rf_correct_case 0 \n", "dummy_correct_case 0 \n", "\n", " 72265 \n", "caseName DISTRICT OF COLUMBIA v. HELLER \n", "justiceName SAAlito \n", "case_outcome_disposition 0 \n", "justice_outcome_disposition 0 \n", "rf_predicted 1 \n", "rf_predicted_score_affirm 0.38871 \n", "rf_predicted_score_reverse 0.562903 \n", "rf_correct_case 0 \n", "dummy_correct_case 0 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data.loc[raw_data.loc[:, \"caseName\"] == 'DISTRICT OF COLUMBIA v. HELLER',\n", " [\"caseName\", \"justiceName\", \"case_outcome_disposition\", \"justice_outcome_disposition\",\n", " \"rf_predicted\", \"rf_predicted_score_affirm\", \"rf_predicted_score_reverse\", \"rf_correct_case\", \"dummy_correct_case\"]].T" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Write output\n", "import cPickle as pickle\n", "import time\n", "\n", "timestamp_suffix = time.strftime(\"%Y%m%d%H%M%S\")\n", "raw_data.to_csv(\"data/output/model_output_{0}_no_reset_{1}.csv\".format(trees_per_term, timestamp_suffix),\n", " index=False)\n", "# You probably don't want to do this, as the object can be tens of GB\n", "#pickle.dump(m, open(\"scotus_scdb_model_{0}.pickle\".format(timestamp_suffix), \"w\"))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
dodo325/school
Tim/plot.ipynb
1
42428
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "matplotlib.style.use('ggplot')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.read_csv(r'C:\\Users\\MadT\\Documents\\Visual Studio 2017\\Projects\\11\\11\\3.txt', header=None, sep=\"\\t\", names=[\"n\", \"timsort\",\"quick\"])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x4b28970>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.plot(x=\"n\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [ { "data": { "image/png": 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5wy936TmSyTTskaLPDiP56w/KqplZHcUdP70+HB/6hNyoNQztyk/h+Mq3i9si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\n6rVpT/Oz4jANaEfw+QmJKIrCA2+f4FCTh1npVvY29PDr0iZ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TjmisQ/zx11QbbPxl7NVk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\nUUQGfUmSpFHk/wOq37u/tjhcagAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7254510>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ax = df.plot(x=\"n\")\n", "fig = ax.get_figure()\n", "fig.savefig('tim.png',dpi=200)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.read_csv(r'C:\\Users\\MadT\\Documents\\Visual Studio 2017\\Projects\\11\\11\\3d_tim.txt', header=None, sep=\"\\t\", names=[\"random\", \"n\",\"tim\"])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from mpl_toolkits.mplot3d import Axes3D" ] }, { "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
basnijholt/holoviews
examples/user_guide/Notebook_Magics.ipynb
1
9825
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Notebook Magics" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import holoviews as hv\n", "from holoviews import opts\n", "hv.extension('bokeh', 'matplotlib')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The [Applying Customization](03-Applying_Customization.ipynb) user guide describes the currently *recommended* way to customize your visualizations in HoloViews. Those mechanisms use standard Python syntax but they are not the only way to apply options as there is a much older approach for working with HoloViews that is specific to notebooks.\n", "\n", "From the start, HoloViews aimed to enable rapid exploration of data in Jupyter Notebooks. For this reason, when you load the HoloViews extension in a notebook, you also get a set of IPython magics. IPython magics use a syntax that is not standard Python and the HoloViews magics only apply in the notebook environment (and not the IPython terminal for instance).\n", "\n", "The advantages of the notebook magics are:\n", "\n", "* They allow tab-completion in the notebook environment (but so do the more recent option builders and `hv.output` mechanisms).\n", "* They allow very concise expression of options and settings.\n", "\n", "\n", "Unfortunately, they also have some serious disadvantages:\n", "\n", "* They are not Python syntax which makes it difficult to use code written with magics in notebooks anywhere else. For instance, it makes it harder to use such code with [bokeh server](https://bokeh.pydata.org/en/latest/docs/reference/server.html) or [panel](http://panel.pyviz.org/).\n", "* They have their own special syntax which is very concise but also rather mysterious.\n", "\n", "\n", "These disadvantages means the magics can be bewildering to anyone unfamiliar with the IPython specific syntax and HoloViews itself, and are no longer recommended for these reasons. This user guide documents these magics to allow people to understand older notebooks using HoloViews and to help people update these old notebooks to use the recommended Python API." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Line and cell magics\n", "\n", "There are two types of magic supported in Jupyter notebooks called *line magics* and *cell magics* respectively. Both typically appear at the top of code cells prefixed by `%` (line magics) or `%%` (cell magics).\n", "\n", "* **line magics**: These can appear anywhere in a code cell and effect global changes to the current notebook session. HoloViews has the `%opts` and `%output` line magics.\n", "* **cell magics**: These have to appear at the top of the cell and are used to modify how that cell is executed. HoloViews has the `%%opts` and `%%ouput` cell magics.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The %opts and %%opts magics\n", "\n", "These two magics are now served by `opts.defaults` and the `.opts` method respectively, as described in the [Applying Customization](03-Applying_Customization.ipynb) user guide.\n", "\n", "* *The ``%opts`` line magic*: IPython specific syntax applied globally *[string format]*\n", "* *The ``%%opts`` cell magic*: IPython specific syntax applies to displayed object *[string format]*\n", "\n", "These magics have their own syntax that separates between *style*, *plot* and *norm* options described towards the end of the [Applying Customization](03-Applying_Customization.ipynb) user guide. The definition of the syntax is as follows:\n", "\n", "```\n", "[[spec] [normalization] [plotting options] [style options]]+\n", "\n", "spec: A dotted type.group.label specification\n", " (e.g. Curve,Sinusoid.Squared)\n", "\n", "normalization: List of normalization options delimited by braces.\n", " One of | -axiswise | -framewise | +axiswise | +framewise |\n", " E.g. { +axiswise +framewise }\n", "\n", "plotting options: List of plotting option keywords delimited by\n", " square brackets. E.g. [show_title=False]\n", "\n", "style options: List of style option keywords delimited by\n", " parentheses. E.g. (lw=10 marker='+')\n", "```\n", "\n", "In other words, you have a list of spec strings (for instance `Curve` or `Curve.Sinusoid`) followed by keywords in either parentheses, square brackets or braces to represent the style, plot and normalization options respectively.\n", "\n", "## Example `%%opts` cell magic\n", "\n", "Here is the example from the [Customization](../getting_started/2-Customization.ipynb) section of the 'Getting Started' customized using the `%%opts` cell magic:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%opts Curve [height=200 width=900 xaxis=None tools=['hover']]\n", "%%opts Curve (color='red' line_width=1.5)\n", "%%opts Spikes [height=150 width=900 yaxis=None] (color='grey' line_width=0.25)\n", "\n", "spike_train = pd.read_csv('../assets/spike_train.csv.gz')\n", "curve = hv.Curve( spike_train, 'milliseconds', vdims='Hertz')\n", "spikes = hv.Spikes(spike_train, 'milliseconds', vdims=[])\n", "layout = (curve+spikes).cols(1)\n", "layout" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This `layout` object is now customized in a way that will persist, just like it would using the recommended `.opts` method together with option builders. It is worth noting that instead of just using element names, you can specify the group and label (e.g `Curve.Sinusoid.Squared`) to condition on that metadata, just the way you can using the option builders.\n", "\n", "\n", "## Example `%opts` line magic\n", "\n", "Here is how you could use the `%opts` line magic instead of `opts.default` as detailed in the [Applying Customization](03-Applying_Customization.ipynb) user guide:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%opts HeatMap (cmap='Summer') [colorbar=True, toolbar='above']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now all `HeatMap` elements will use the 'Summer' colormap, showing a colorbar with the Bokeh toolbar at the top:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = [(chr(65+i), chr(97+j), i*j) for i in range(5) for j in range(5) if i!=j]\n", "hv.HeatMap(data).sort()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The `%output` line magic\n", "\n", "The `%output` line magic has been fairly directly replaced by the `hv.output` utility. Here is an example of the `%output` line magic:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%output backend='matplotlib', fig='svg'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This ensures the following `Path` (and all subsequent `Path` objects) are rendered as SVG with matplotlib:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lin = np.linspace(0, np.pi*2, 200)\n", "\n", "def lissajous(t, a, b, delta):\n", " return (np.sin(a * t + delta), np.sin(b * t), t)\n", "\n", "path = hv.Path([lissajous(lin, 3, 5, np.pi/2)])\n", "path.opts(opts.Path(linewidth=2, color='red', linestyle='dotted'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the purposes of this notebook, let us switch the plotting extension back to bokeh:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%output backend='bokeh'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the `%output` magic accepts the same set of output settings as the `hv.output` utility." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The `%%output` cell magic\n", "\n", "If we want to *temporarily* switch to matplotlib with some custom output settings, we can use the `%%output` cell magic in an example combines the `%%output` and `%%opts` cell magics in the same cell:\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%output backend='matplotlib' fig='svg' size=50\n", "%%opts Path (linewidth=3 color='blue')\n", "lin = np.linspace(0, np.pi*2, 200)\n", "\n", "def lissajous(t, a, b, delta):\n", " return (np.sin(a * t + delta), np.sin(b * t), t)\n", "\n", "hv.Path([lissajous(lin, 3, 5, np.pi/2)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The recommended approach would now be to pass the `path` object to the `hv.output` utility as detailed in the [Applying Customization](03-Applying_Customization.ipynb) user guide. The magic processes the same set of output settings as the `hv.output` utility." ] } ], "metadata": { "language_info": { "name": "python", "pygments_lexer": "ipython3" } }, "nbformat": 4, "nbformat_minor": 2 }
bsd-3-clause
ClaudioVZ/Teoria-FEM-Python
Teoría y ejemplos/trabajos virtuales viga.ipynb
1
18567
{ "metadata": { "name": "trabajos virtuales viga" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Principio de los trabajos virtuales para viga" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para un elemento con dos nodos:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{equation}\n", "\\iiint \\delta \\varepsilon \\ \\sigma \\ dV = \\int \\delta w \\ q \\ dx + \\sum_{i=1}^{2} \\delta w_{i} \\ Z_{i} + \\sum_{i=1}^{2} \\delta \\theta_{i} \\ M_{i}\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recordando expresiones conocidas:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{eqnarray}\n", "\\sigma &=& -z \\ E \\ \\frac{\\partial^{2} w}{\\partial x^{2}} \\\\\\\n", "\\delta \\varepsilon &=& -z \\ \\delta \\Big ( \\frac{\\partial^{2} w}{\\partial x^{2}} \\Big )\n", "\\end{eqnarray}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reemplazando:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{equation}\n", "\\iiint z^{2} \\ \\delta \\Big ( \\frac{\\partial^{2} w}{\\partial x^{2}} \\Big ) \\ E \\ \\frac{\\partial^{2} w}{\\partial x^{2}} \\ dx \\ dy \\ dz = \\int \\delta w \\ q \\ dx + \\delta w_{1} \\ Z_{1} + \\delta w_{2} \\ Z_{2} + \\delta \\theta_{1} \\ M_{1} + \\delta \\theta_{2} \\ M_{2}\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simplificando:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{equation}\n", "\\int \\delta \\Big ( \\frac{\\partial^{2} w}{\\partial x^{2}} \\Big ) \\ EI \\ \\frac{\\partial^{2} w}{\\partial x^{2}} \\ dx = \\int \\delta w \\ q \\ dx + \\delta w_{1} \\ Z_{1} + \\delta w_{2} \\ Z_{2} + \\delta \\theta_{1} \\ M_{1} + \\delta \\theta_{2} \\ M_{2}\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Realizando un cambio de variable:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{equation}\n", "\\int \\delta \\chi \\ EI \\ \\chi \\ dx = \\int \\delta w \\ q \\ dx + \\delta w_{1} \\ Z_{1} + \\delta w_{2} \\ Z_{2} + \\delta \\theta_{1} \\ M_{1} + \\delta \\theta_{2} \\ M_{2}\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interpolando el campo $w$ (desplazamientos) y $\\chi$ (curvatura):" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{eqnarray}\n", "w &=& N_{1} w_{1} + N_{2} \\theta_{1} + N_{3} w_{2} + N_{4} \\theta_{2} \\\\\\\n", "\\chi &=& \\frac{\\partial^{2} w}{\\partial x^{2}} = \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} w_{1} + \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\theta_{1} + \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} w_{2} + \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\theta_{2}\n", "\\end{eqnarray}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interpolando el campo $\\delta w$ (deformaciones virtuales) y $\\delta \\chi$ (curvatura virtual):" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{eqnarray}\n", "\\delta w &=& N_{1} \\delta w_{1} + N_{2} \\delta \\theta_{1} + N_{3} \\delta w_{2} + N_{4} \\delta \\theta_{2} \\\\\\\n", "\\delta \\chi &=& \\frac{\\partial^{2} \\delta w}{\\partial x^{2}} = \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\delta w_{1} + \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\delta \\theta_{1} + \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} \\delta w_{2} + \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\delta \\theta_{2}\n", "\\end{eqnarray}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reemplazando:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{equation}\n", "\\int \\Big ( \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\delta w_{1} + \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\delta \\theta_{1} + \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} \\delta w_{2} + \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\delta \\theta_{2} \\Big ) \\ E I \\ \\Big ( \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} w_{1} + \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\theta_{1} + \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} w_{2} + \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\theta_{2} \\Big ) \\ dx = \\int ( N_{1} \\delta w_{1} + N_{2} \\delta \\theta_{1} + N_{3} \\delta w_{2} + N_{4} \\delta \\theta_{2} ) \\ q \\ dx + \\delta w_{1} \\ Z_{1} + \\delta w_{2} \\ Z_{2} + \\delta \\theta_{1} \\ M_{1} + \\delta \\theta_{2} \\ M_{2}\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Expandiendo y agrupando t\u00e9rminos:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{equation}\n", "\\int \\Big [ \\Big ( \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} w_{1} + \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\theta_{1} + \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} w_{2} + \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\theta_{2} \\Big ) \\delta w_{1} + \\Big ( \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} w_{1} + \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\theta_{1} + \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} w_{2} + \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\theta_{2} \\Big ) \\delta \\theta_{1} + \\Big ( \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} w_{1} + \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} \\theta_{1} + \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} w_{2} + \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\theta_{2} \\Big ) \\delta w_{2} + \\Big ( \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} w_{1} + \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\theta_{1} + \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} w_{2} + \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\theta_{2} \\Big ) \\delta \\theta_{2} \\Big ] \\ E I \\ dx = \\int ( N_{1} \\ q \\ \\delta w_{1} + N_{2} \\ q \\ \\delta \\theta_{1} + N_{3} \\ q \\ \\delta w_{2} + N_{4} \\ q \\ \\delta \\theta_{2} ) \\ dx + ( \\delta w_{1} \\ Z_{1} + \\delta w_{2} \\ Z_{2}) + (\\delta \\theta_{1} \\ M_{1} + \\delta \\theta_{2} \\ M_{2} \uff09\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Las deformaciones virtuales y rotaciones virtuales son arbitrarias, para simplificar $\\delta w_{1} = \\delta \\theta_{1} = \\delta w_{2} = \\delta \\theta_{2} = 1$:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{equation}\n", "\\int \\Big [ \\Big ( \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} w_{1} + \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\theta_{1} + \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} w_{2} + \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\theta_{2} \\Big ) + \\Big ( \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} w_{1} + \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\theta_{1} + \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} w_{2} + \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\theta_{2} \\Big ) + \\Big ( \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} w_{1} + \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} \\theta_{1} + \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} w_{2} + \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\theta_{2} \\Big ) + \\Big ( \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} w_{1} + \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\theta_{1} + \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} w_{2} + \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\theta_{2} \\Big ) \\Big ] \\ E I \\ dx = \\int ( N_{1} \\ q + N_{2} \\ q + N_{3} \\ q + N_{4} \\ q ) \\ dx + (Z_{1} + Z_{2}) + (M_{1} + M_{2} \uff09\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Representando como un sistema de ecuaciones:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{eqnarray}\n", "\\int \\Big ( \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} w_{1} + \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\theta_{1} + \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} w_{2} + \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\theta_{2} \\Big ) \\ E I \\ dx &=& \\int N_{1} \\ q \\ dx + Z_{1} + 0 \\\\\\\n", "\\int \\Big ( \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} w_{1} + \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\theta_{1} + \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} w_{2} + \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\theta_{2} \\Big ) \\ E I \\ dx &=& \\int N_{2} \\ q \\ dx + 0 + M_{1} \\\\\\\n", "\\int \\Big ( \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} w_{1} + \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} \\theta_{1} + \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} w_{2} + \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\theta_{2} \\Big ) \\ E I \\ dx &=& \\int N_{3} \\ q \\ dx + Z_{2} + 0 \\\\\\\n", "\\int \\Big ( \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} w_{1} + \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\theta_{1} + \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} w_{2} + \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\theta_{2} \\Big ) \\ E I \\ dx &=& \\int N_{4} \\ q \\ dx + 0 + M_{2}\n", "\\end{eqnarray}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Representando en forma matricial:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{equation}\n", "\\int \\left [\n", "\\begin{matrix}\n", "\\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} & \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} & \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} & \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\\\\\\n", "\\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} & \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} & \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} & \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\\\\\\n", "\\frac{\\partial^{2} N_{3}}{\\partial x^{2}} \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} & \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} & \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} & \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\\\\\\n", "\\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\frac{\\partial^{2} N_{1}}{\\partial x^{2}} & \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} & \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} & \\frac{\\partial^{2} N_{4}}{\\partial x^{2}} \\frac{\\partial^{2} N_{4}}{\\partial x^{2}}\n", "\\end{matrix}\n", "\\right ] E I\n", "\\left [\n", "\\begin{matrix}\n", "w_{1} \\\\\\\n", "\\theta_{1} \\\\\\\n", "w_{2} \\\\\\\n", "\\theta_{2}\n", "\\end{matrix}\n", "\\right ]\n", "\\ dx = \\int\n", "\\left [\n", "\\begin{matrix}\n", "N_{1} \\\\\\\n", "N_{2} \\\\\\\n", "N_{3} \\\\\\\n", "N_{4}\n", "\\end{matrix}\n", "\\right ]\n", "q \\ dx +\n", "\\left [\n", "\\begin{matrix}\n", "Z_{1} \\\\\\\n", "M_{1} \\\\\\\n", "Z_{2} \\\\\\\n", "M_{2}\n", "\\end{matrix}\n", "\\right ]\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Factorizando:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{equation}\n", "\\int \\left [\n", "\\begin{matrix}\n", "\\frac{\\partial^{2} N_{1}}{\\partial x^{2}} \\\\\\\n", "\\frac{\\partial^{2} N_{2}}{\\partial x^{2}} \\\\\\\n", "\\frac{\\partial^{2} N_{3}}{\\partial x^{2}} \\\\\\\n", "\\frac{\\partial^{2} N_{4}}{\\partial x^{2}}\n", "\\end{matrix}\n", "\\right ] E I\n", "\\left [\n", "\\begin{matrix}\n", "\\frac{\\partial^{2} N_{1}}{\\partial x^{2}} & \\frac{\\partial^{2} N_{2}}{\\partial x^{2}} & \\frac{\\partial^{2} N_{3}}{\\partial x^{2}} & \\frac{\\partial^{2} N_{4}}{\\partial x^{2}}\n", "\\end{matrix}\n", "\\right ]\n", "\\left [\n", "\\begin{matrix}\n", "w_{1} \\\\\\\n", "\\theta_{1} \\\\\\\n", "w_{2} \\\\\\\n", "\\theta_{2}\n", "\\end{matrix}\n", "\\right ]\n", "dx = \\int\n", "\\left [\n", "\\begin{matrix}\n", "N_{1} \\\\\\\n", "N_{2} \\\\\\\n", "N_{3} \\\\\\\n", "N_{4}\n", "\\end{matrix}\n", "\\right ]\n", "q \\ dx +\n", "\\left [\n", "\\begin{matrix}\n", "Z_{1} \\\\\\\n", "M_{1} \\\\\\\n", "Z_{2} \\\\\\\n", "M_{2}\n", "\\end{matrix}\n", "\\right ]\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Representando en forma matricial reducida:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{equation}\n", "\\int \\boldsymbol{B_{f}^{T}} \\ E I \\ \\boldsymbol{B_{f}} \\ dx \\ \\boldsymbol{u} = \\int \\boldsymbol{N^{T}} \\ q \\ dx + \\boldsymbol{q}\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Siendo la matriz constitutiva:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{equation}\n", "\\boldsymbol{D} = E I\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reemplazando:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{equation}\n", "\\int \\boldsymbol{B_{f}^{T}} \\ \\boldsymbol{D} \\ \\boldsymbol{B_{f}} \\ dx \\ \\boldsymbol{u} = \\int \\boldsymbol{N^{T}} \\ q \\ dx + \\boldsymbol{q}\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La anterior ecuaci\u00f3n es una generalizaci\u00f3n para un elemento con cualquier n\u00famero de nodos, teniendo en cuenta que est\u00e1 en coordenadas globales." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reescribiendo la anterior expresi\u00f3n en coordenadas naturales:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{equation}\n", "\\int_{-1}^{+1} \\color{Blue} {\\boldsymbol{B_{f}^{T}}} \\ \\boldsymbol{D} \\ \\color{Blue} { \\boldsymbol{B_{f}}} \\ J \\ d\\xi \\ \\boldsymbol{u} = \\int_{-1}^{+1} \\color{Blue} {\\boldsymbol{N^{T}}} \\ q \\ J \\ d\\xi + \\boldsymbol{q}\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tambi\u00e9n puede escribirse como:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\\begin{equation}\n", "\\boldsymbol{K} \\ \\boldsymbol{u} = \\boldsymbol{f} + \\boldsymbol{q}\n", "\\end{equation}" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
artistic-2.0
ottogroup/dstoolbox
notebooks/Examples_cluster.ipynb
1
4071
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using dstoolbox cluster" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of contents" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. [HierarchicalClustering](#HierarchicalClustering)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from dstoolbox.cluster import HierarchicalClustering" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## HierarchicalClustering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A variant of `sklearn.cluster.AgglomerativeClustering` that returns a dynamic number of labels.\n", "\n", "`HierarchicalClustering` uses the same `scipy` algorithms as `sklearn` but `sklearn` requires to determine beforehand how many clusters you want. With `HierarchicalClustering` we can set the `max_dist` parameter and let the data decide how many clusters occur. This way, `HierarchicalClustering` is similar to `sklearn.cluster.DBSCAN`, which also returns a variable amount of different clusters." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = np.random.random((100, 5))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "labels = HierarchicalClustering(max_dist=0.5).fit_predict(X)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "74" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(set(labels))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "labels = HierarchicalClustering(max_dist=0.9).fit_predict(X)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "38" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(set(labels))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "labels = HierarchicalClustering(max_dist=1.1).fit_predict(X)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "23" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(set(labels))" ] }, { "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": 2 }
apache-2.0
salvor7/salvor7.github.io
content/A Million Pseudo-Random Digits/How uniformly Random are RAND's 'Million Psueod-Random Digits'.ipynb
1
17930
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "I found this [data set](https://www.kaggle.com/datacanary/a-million-pseudorandom-digits) on [Kaggle](http://www.kaggle.com). It is 1 million single digit numbers from 1 to 9, and I was curious if their distribution is uniform. Or said another way, how reasonable is it suggest the numbers in the list were generated uniformly at random? I've recently learned the basics of using the PyMC3's Markov Chain Monte Carlo methods to answer these types of questions with a Baysian framework, and I think the ideas and methods can be used for this. Lets start with the data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## One Million Pseudo-Random Digits" ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "the_digits = pd.read_csv('data.csv')\n", "the_digits.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First thing to notice is that the digits are \"arranged into ten columns for your convenience,\" as the data set [description](https://www.kaggle.com/datacanary/a-million-pseudorandom-digits) states. I think it wil be easier to work with as a flattened set of digits." ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "the_digits = the_digits.values.flatten()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, do the individual digit counts make sense supposing these 1 million digitis were drawn uniformly at random? " ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "from collections import Counter" ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "digit_counts = Counter(the_digits)\n", "digit_counts" ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "for digit in digit_counts:\n", " print(round(digit_counts[digit] - 1000000/9))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Zero was left out of the million single digits for some reason. The other digits appear with very similar counts varied around\n", "$$\\frac{1000000}{9} = \\text{~}111111$$\n", "as you'd expect from a list proporting to be uniformly generated. More, the differences are tiny compared to $1000000/9$, so there is obvious problem with saying these digits were drawn from a uniform distribution. But with a Bayesian framework, and PyMC3, I can ask a subtler question: how likely is it that this sample was drawn from a uniform distribution over the 9 digits? There are other distributions over the 9 digits which could have produced the observed data set, and one of those might be a better candidate for generatng distribution of these 1 million digits. \n", "\n", "I'm going attempt to frame thise question in a Bayesian framework using MCMC in PyMC3 to compute the other candidates. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## MCMC in PyMC3\n", "\n", "I've recently worked through Cam Davison-Pilon's great book \"[Probabilistic Programming and Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers).\" Well actualy, I worked through the PyMC3 port of that same book by Max Margenot and Thomas Wiecki. I'm going to leave in some of my failed attempts, as seeing the error messages and wrong thinking will be helpful for troubleshooting my problem on other projects. Like always... more imports! " ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "import pymc3 as pm\n", "import theano.tensor as tt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Aside\n", "Most of the reasons for my failures involve exceptions. In IPython however, the error output is too large to be a reasonable part of a narrative, at least I find it so. I still want to report back the exceptions I encounter, so heres a quick function to avoid some boiler plate. " ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "def print_err(err):\n", " \"\"\"Print the error name and message \"\"\"\n", " print(type(err).__name__,':', err)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fail 1\n", "The first thing I tried was based on the fact that PyMC3 has a Discrete Uniform distribution in it. In the PyMC3 approach, you link data to disributions with the observed keyword, and run an MCMC algorithm to find posterior distribution(s) to fit your data. The million digits ostensibly is given by a discrete, uniform distribution over the digits 1 to 9, so the PyMC3 model should link the given data with that distribution. (the CD-P book above explains this code)" ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "with pm.Model() as fail1:\n", " digits = pm.DiscreteUniform('bias', 1,9, observed=the_digits)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Seems small for being the whole model, but lets run it using the Metroplois algorithm. " ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "with fail1:\n", " \n", " try:\n", " step = pm.Metropolis()\n", " \n", " except ValueError as err:\n", " print_err(err)\n", " else:\n", " trace = pm.sample(10000, step=step)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fail! The error message gives a hint at what went wrong though. The one variable in the model, digits, is fixed to the data, so there is no posterior distribution being calculated. The \"one array to concatenate\" is the one array representing a variable we're calculating a posterior distribution for. There are none, so... fail!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fail 2 & 3\n", "To fix fail 1, I came up with the idea of taking another uniform distribution over 1 to 9, and look at the differences to the data. Not sure why I thought this might be a good idea; I was spit balling. But it failed in another way." ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "with pm.Model() as fail2:\n", " digits = pm.DiscreteUniform('digits', 1,9, observed=the_digits) # from fail 1\n", " base = pm.DiscreteUniform('base', 1,9) # the other uniform \n", " \n", " diff = pm.Deterministic('diff', base-digits) # look at the difference\n", " \n", " try:\n", " trace = pm.sample(10000, step=pm.Metropolis())\n", " except MemoryError as err:\n", " print_err(err)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It seems likely that I got this memory error because the million data points is too large, so I'll try a subset of the . " ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "with pm.Model() as fail3:\n", " base = pm.DiscreteUniform('base', 1,9)\n", " digits = pm.DiscreteUniform('digits', 1,9, observed=the_digits[:10000]) # use first 10000 points\n", " diff = pm.Deterministic('diff', base-digits)\n", " \n", " step = pm.Metropolis()\n", " trace = pm.sample(10000, step=step)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It runs this time, but the output is odd, sometimes. On occasion, it outputs progress bar over several lines, but not always. Regardless lets see what the \"diff\" posterior looks like. " ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I take a burned trace to remove the typically wild fluctuations at the beginning of the MC algorithm where the priors are the least constrained, and then I try to chart the results using PyMC traceplot." ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "burned_trace = trace[1000:] # remove the first 1000 instances, as this is the \"burn in period\" of the algorithm\n", "\n", "pm.plots.traceplot(trace=burned_trace, varnames=['diff']);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The difference graph doesn't complete, even after many hours. Something is wrong with this formulation, and I'm going to try a different approach. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SUCCESS \n", "\n", "The idea this time is to build a model where I estimate the probabilities of the digits themselves, and use the PyMC3 categorical distribution with those probabilities as the prior data generation process. Then I can compare the entropy of the estimated distribution fitting the digit data to the entropy of a 9 element uniform distribution, $U(9)$, which is\n", "$$H(U(9)) = \\sum_i p_i \\log p_i^{-1} = \\sum_i \\frac{\\log 9}{9} = \\log 9.$$\n", "I should mention that this is the maximum entropy of a distribution over 9 elements, as this comes up below.\n", "\n", "Noe, I could just calculate the entropy of the given set of digits. The entropy estimate of finite set is \n", "$$H(S) = -\\sum_i \\frac{n_i}{N}\\text{log}\\left( \\frac{n_i}{N} \\right)$$\n", "where $n_i$ is the count of the $i$th unique element and $N$ is the size of $S$. The estimate for the of digits is..." ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "from math import log, exp\n", "\n", "N = 1000000\n", "\n", "entropy = log(9)\n", "ent_est = -sum((n/N) * log(n/N) for n in digit_counts.values())\n", "\n", "entropy, ent_est, entropy - ent_est" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which is the same to 5 decimales places. Now that I am considering entropy, that proximity is enough to say that in terms of frequency the 1 million digits are generted approximately uniformly, but lets throw PyMC3 at the problem anyways to practice formulating the problem." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To start, I need 9 prior probabilities, one for the probability of drawing each of the nine digits, to define other 9 element distributions. Each prior is a probability valued real number, so a good prior is a uniform distribution from 0 to 1. I'll use a test value of $1/9$ to initialize $p_1$, so the algorithm will converges more quickly assuming I am correct that $p_1$ should be close to $1/9$." ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "with pm.Model() as model:\n", " p1 = pm.Uniform('p1', 0, 1, testval=1/9)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is one constraint, that the 9 probabilities sum to 1, so $p_2$ can't have the same prior since that would allow $p_1 + p_2$ to be greater than $1$ in some cases. Given $p_1$, $p_2$ can range from $0$ to $1-p_1$. A PyMC prior can be defined so it is dependent on other variables, like $p_1$." ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "with model:\n", " p2 = pm.Uniform('p2', 0, 1-p1, testval=1/9)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next 6 are similar, each depending on the previously defined probabilities. " ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "with model:\n", " p3 = pm.Uniform('p3', 0, 1-p1-p2, testval=1/9)\n", " p4 = pm.Uniform('p4', 0, 1-p1-p2-p3, testval=1/9)\n", " p5 = pm.Uniform('p5', 0, 1-p1-p2-p3-p4, testval=1/9)\n", " p6 = pm.Uniform('p6', 0, 1-p1-p2-p3-p4-p5, testval=1/9)\n", " p7 = pm.Uniform('p7', 0, 1-p1-p2-p3-p4-p5-p6, testval=1/9)\n", " p8 = pm.Uniform('p8', 0, 1-p1-p2-p3-p4-p5-p6-p7, testval=1/9)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, $p_9$ is deterministic given the other 8, as the sum must be 1. " ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "with model:\n", " p9 = pm.Deterministic('p9', 1-p1-p2-p3-p4-p5-p6-p7-p8)\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the failed models above, I implicitly assumed the probabilities were 1/9 by using the discrete uniform distribution. Here, I let the probabilities wander to match the categorical data. There is one small hitch with using the PyMC categorical distribution; it indexes from 0 to $n$. But the digits in the digit data are from 1 to 9. To get around that, I added $p_0$ with a 0 probability so that the digit 0 has a zero probability instead of something else like subtracting 1 from each data point. " ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "with model:\n", " p_ext = tt.stack([0,p1,p2,p3,p4,p5,p6,p7,p8,p9])\n", " \n", " digits = pm.Categorical('digits', p_ext, observed=the_digits)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, I want to calculate the entropy, so that we can see if it comes out near $\\log 9$. I'm first going to create another tensor of the 9 probabilities, and calculate the entropy from elementwise operations." ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "with model: \n", " p = tt.stack([p1,p2,p3,p4,p5,p6,p7,p8,p9])\n", " logp = tt.log(p)\n", " entropy = -tt.sum(tt.mul(logp,p))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But instead of charting the entropy itself, I'll chart $e^H$, which we expect to be close to $e^{\\log 9} = 9$, so that the values coming out are close to a whole number. I find this makes the charts easier to read, and I don't think it adds much overhead to the algorithm." ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "with model: \n", " e_entropy = pm.Deterministic('e^entropy', tt.exp(entropy))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, I run the MC algorithm over on the model using the algorithm and sample size I've observed in the learning materials. " ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "with model:\n", " step = pm.Metropolis()\n", " trace = pm.sample(20000, step=step)\n" ] }, { "cell_type": "code", "metadata": {}, "outputs": [], "source": [ "burned_trace = trace[1000:]\n", "pm.plots.traceplot(trace=burned_trace, varnames=['e^entropy']);\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A few things jump out from the plot. All the density is very close to 9, as I expected. We can also see the effect of the maximum possible entropy being $\\log 9$. The maximum value possible in the chart above is 9, and that only occurs if all 9 prior probabilities are simultaneously sampled at $1/9$, which has close to no chance of happening given the inherent numerical imprecision. Hence, the \"pressure\" of matching the data is not enough to push the mode on to the max of 9. The mode will always be less than the max, but the mode is still very close confirming again that it is reasonable to treat these digits as having been generated by a uniform distribution. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Final Thoughts\n", "\n", "Uniform distributions are one of those simple concepts that gets complicated in the real world because we can only ever observe samples from a distribution, never the distribution itself. So the frequency of the digits is not the only thing to measure to determine how well these digits approximate 1 million draws from a uniform distribution. The ordered correlations between the digits also needs to be checked. For instance, can earlier digits be used to predict future ones? I'm going to leave that question alone here, and perhaps come back to it at some point. \n", "\n", "Next, the algorithm takes way too long to complete (3 hours for 20000 samples) to repeatedly cycle through a build and test cycle using all the data. Some of that is the size of the data set, at 1 million, and a reduced data set can be used instead. But much of the slowness is due to Theano not yet using the GPUs on my video card. I am running windows, and it takes some additional setup for Theano to work easily. I'm going to take on setting up Theano to use GPUs despite running on windows very soon. " ] } ], "metadata": { "anaconda-cloud": {}, "git": { "suppress_outputs": true }, "kernelspec": { "display_name": "Python [conda env:ml]", "language": "python", "name": "conda-env-ml-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
McIntyre-Lab/papers
fear_ase_2016/scripts/qsim_bayesian/qsim_Bayesian_Distributions_Multiimage.ipynb
1
8623
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up output" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mclab = os.getenv('MCLAB')\n", "odir = os.path.join(mclab, 'cegs_ase_paper/pipeline_output/qsim_bayesian/')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import data" ] }, { "cell_type": "code", "execution_count": 3, "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>line</th>\n", " <th>mating_status</th>\n", " <th>fusion_id</th>\n", " <th>qsim_mean_theta</th>\n", " <th>qsim_q025</th>\n", " <th>qsim_q975</th>\n", " <th>Bayesianpvalue_qsim</th>\n", " <th>flag_AI_qsim</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>r324</td>\n", " <td>V</td>\n", " <td>S24441_SI</td>\n", " <td>0.430</td>\n", " <td>0.297</td>\n", " <td>0.566</td>\n", " <td>0.336</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>r324</td>\n", " <td>M</td>\n", " <td>S24447_SI</td>\n", " <td>0.923</td>\n", " <td>0.876</td>\n", " <td>0.956</td>\n", " <td>0.000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>r324</td>\n", " <td>V</td>\n", " <td>S24447_SI</td>\n", " <td>0.916</td>\n", " <td>0.868</td>\n", " <td>0.952</td>\n", " <td>0.000</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " line mating_status fusion_id qsim_mean_theta qsim_q025 qsim_q975 \\\n", "0 r324 V S24441_SI 0.430 0.297 0.566 \n", "1 r324 M S24447_SI 0.923 0.876 0.956 \n", "2 r324 V S24447_SI 0.916 0.868 0.952 \n", "\n", " Bayesianpvalue_qsim flag_AI_qsim \n", "0 0.336 0 \n", "1 0.000 1 \n", "2 0.000 1 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fname = '/home/jfear/mclab/cegs_ase_paper/pipeline_output/qsim_bayesian/output/ase_dataset_for_bayesian_w_qsim_summary.csv'\n", "dat = pd.read_csv(fname)\n", "dat.head(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate Plots for Mated and Virgin" ] }, { "cell_type": "code", "execution_count": 4, "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>line</th>\n", " <th>qsim_mean_theta</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>r324</td>\n", " <td>0.43</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " line qsim_mean_theta\n", "0 r324 0.43" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Split dataset by sex\n", "mated = dat[dat['mating_status'] == 'M'][['line','qsim_mean_theta']]\n", "virgin = dat[dat['mating_status'] == 'V'][['line','qsim_mean_theta']]\n", "virgin.head(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot Mated" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get group levels\n", "mLines = np.array([x for x in set(mated['line'])])\n", "mLines.sort()\n", "\n", "# Create a mask to split into multiple figures of 6x4 plots\n", "grp = np.concatenate([np.ones(16), np.ones(16)*2, np.ones(16)*3, np.ones(16)*4, np.ones(4)*5])\n", "\n", "# Iterate over multi figure groups and plot\n", "for g in range(1,6):\n", " curr = mLines[grp == g]\n", " \n", " # Figure out the number of subplots I need\n", " num = int(np.sqrt(curr.shape[0]))\n", " \n", " # Plot figure\n", " fig, axes = plt.subplots(num, num, figsize=(8, 8))\n", " fig.suptitle(u'Distribution of QSIM Thetas\\nMated', fontsize=12)\n", " axs = np.ravel(axes)\n", " for i, line in enumerate(curr):\n", " p = mated[mated['line'] == line]\n", " p.plot(kind='kde', ax=axs[i], fontsize=8, title=line, legend=False, color = 'r')\n", " #axs[i].set_xlabel('Line <------> Tester', fontsize=12)\n", " axs[i].axvline(0.5, lw=1, c='k')\n", " axs[i].get_yaxis().set_visible(False)\n", " \n", " fig.text(0.5, 0.04, 'Line <------------> Tester', ha='center', fontsize=20)\n", " plt.savefig(os.path.join(odir, 'mated_dist_qsim_theta_g{0}.png'.format(str(g))), bbox_inches='tight')\n", " plt.close(fig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot Virgin" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get group levels\n", "vLines = np.array([x for x in set(virgin['line'])])\n", "vLines.sort()\n", " \n", "# Create a mask to split into multiple figures of 6x4 plots\n", "grp = np.concatenate([np.ones(16), np.ones(16)*2, np.ones(16)*3, np.ones(16)*4, np.ones(4)*5])\n", "\n", "# Iterate over multi figure groups and plot\n", "for g in range(1,6):\n", " curr = vLines[grp == g]\n", " \n", " # Figure out the number of subplots I need\n", " num = int(np.sqrt(curr.shape[0]))\n", " \n", " # Plot figure\n", " fig, axes = plt.subplots(num, num, figsize=(8,8))\n", " fig.suptitle(u'Distribution of QSIM Thetas\\nVirgin', fontsize=12)\n", "\n", " axs = np.ravel(axes)\n", " for i, line in enumerate(curr):\n", " p = virgin[virgin['line'] == line]\n", " p.plot(kind='kde', ax=axs[i], fontsize=8, title=line, legend=False, color = 'r')\n", " #axs[i].set_xlabel('Line <--- AB ---> Tester', fontsize=12)\n", " axs[i].axvline(0.5, lw=1, c='k')\n", " axs[i].get_yaxis().set_visible(False)\n", " \n", " fig.text(0.5, 0.04, 'Line <------------> Tester', ha='center', fontsize=20)\n", " plt.savefig(os.path.join(odir, 'virgin_dist_qsim_theta_g{0}.png'.format(str(g))), bbox_inches='tight')\n", " plt.close(fig)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
lgpl-3.0
thehackerwithin/berkeley
code_examples/keras_introduction/Single_layer_by_hand.ipynb
1
6176
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Building and training a single layer neural network by hand" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Import packages\n", "import pandas as pd\n", "import numpy as np\n", "from ipywidgets import interact\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Defining the model\n", "\n", "As mentioned previously, our model is \n", "\n", "$ p_{setosa} = f( w_0 + w_1 \\times width + w_2 \\times length ) \\qquad with \\;\\; f(x) = 1/(1 + e^{-x})$\n", "\n", "Let us define this using Python:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def probability_setosa( petal_length, petal_width, w0, w1, w2 ):\n", " \"Return the probability that a given specimen belongs to the species setosa\"\n", " # Compute sum of features times weights\n", " x = w0 + w1*petal_width + w2*petal_length\n", " # Apply non-linear function: sigmoid\n", " p = 1./( 1. + np.exp( -x ) )\n", " return( p )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training the network: finding the right weights so that the model fits the training data\n", "\n", "In order to get a sense of what training the network implies, we will try to find the right weights *by hand*. Once, we use Keras, this process will be automated.\n", "\n", "Let us first load the data from the training set." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.read_csv('./data/setosa/train.csv')\n", "df.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then define a function that plots the prediction of the model **for a given set of weights**, along with the training data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_model( w0, w1, w2 ):\n", " \"Plot the model, along with the training data.\"\n", "\n", " # Calculate the probability on a mesh\n", " petal_width_mesh, petal_length_mesh = \\\n", " np.meshgrid( np.linspace(0,3,100), np.linspace(0,8,100) )\n", " p = probability_setosa( petal_width_mesh, petal_length_mesh, w0, w1, w2 )\n", " # Plot the probability on the mesh\n", " plt.clf()\n", " plt.imshow( p.T, extent=[0,3,0,8], origin='lower', \n", " vmin=0, vmax=1, cmap='RdBu', aspect='auto', alpha=0.5 )\n", " # Plot the data points\n", " plt.scatter( df['petal width (cm)'], df['petal length (cm)'], c=df['setosa'], cmap='RdBu')\n", " plt.xlabel('petal width (cm)')\n", " plt.ylabel('petal length (cm)')\n", " cb = plt.colorbar()\n", " cb.set_label('setosa')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can then use the function `interact` of `ipywidgets` to call this function with adjustable weights:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "interact( plot_model, w0=(-4.,5.), w1=(-2.,2.), w2=(-2., 3.))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Optimal weights: fill these values\n", "w0 = \n", "w1 = \n", "w2 = " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Performing predictions on the test sets\n", "\n", "Now that we trained the model by finding the optimal weights for the training dataset, let us perform predictions on the test dataset.\n", "\n", "Let us first load the test set." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_test = pd.read_csv('./data/setosa/test.csv')\n", "df_test.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now check the accuracy of our model on the first point for instance:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "probability_setosa( 4.2, 1.5, w0, w1, w2 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "More generally, by using pandas syntax, we can perform predictions on the whole dataset:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df_test['probability_setosa_predicted'] = \\\n", " probability_setosa( df_test['petal length (cm)'], df_test['petal width (cm)'], w0, w1, w2 )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Next steps\n", "\n", "While the above procedure yields good results, it is very cumbersome to try to find the weights by hand. Let us use keras [here](Single_layer_keras.ipynb) to automate this process." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
bsd-3-clause
rjurney/Agile_Data_Code_2
ch09/Debugging Prediction Problems.ipynb
1
162952
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys, os, re\n", "import json\n", "import datetime, iso8601\n", "\n", "base_path = \"..\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup PySpark Environment" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PySpark initiated...\n" ] } ], "source": [ "APP_NAME = \"Debugging Prediction Problems\"\n", "\n", "# If there is no SparkSession, create the environment\n", "try:\n", " sc and spark\n", "except NameError as e:\n", " import findspark\n", " findspark.init()\n", " import pyspark\n", " import pyspark.sql\n", "\n", " sc = pyspark.SparkContext()\n", " spark = pyspark.sql.SparkSession(sc).builder.appName(APP_NAME).getOrCreate()\n", "\n", "print(\"PySpark initiated...\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Training DataFrame" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------+--------------------+--------------------+-------+--------+------+----+----------+---------+--------+\n", "|ArrDelay| CRSArrTime| CRSDepTime|Carrier|DepDelay|Origin|Dest|FlightDate|FlightNum|Distance|\n", "+--------+--------------------+--------------------+-------+--------+------+----+----------+---------+--------+\n", "| 13.0|2015-01-01 10:10:...|2015-01-01 07:30:...| AA| 14.0| ABQ| DFW|2014-12-31| 1024| 569.0|\n", "| 17.0|2015-01-01 02:15:...|2014-12-31 23:25:...| AA| 14.0| ABQ| DFW|2014-12-31| 1184| 569.0|\n", "| 36.0|2015-01-01 03:45:...|2015-01-01 01:00:...| AA| -2.0| ABQ| DFW|2014-12-31| 336| 569.0|\n", "| -21.0|2015-01-01 11:30:...|2015-01-01 09:55:...| AA| -1.0| ATL| DFW|2014-12-31| 125| 731.0|\n", "| -14.0|2015-01-01 02:25:...|2015-01-01 00:55:...| AA| -4.0| ATL| DFW|2014-12-31| 1455| 731.0|\n", "| 16.0|2015-01-01 07:15:...|2015-01-01 05:45:...| AA| 15.0| ATL| DFW|2014-12-31| 1473| 731.0|\n", "| -7.0|2015-01-01 04:15:...|2015-01-01 02:45:...| AA| -2.0| ATL| DFW|2014-12-31| 1513| 731.0|\n", "| 13.0|2015-01-01 08:50:...|2015-01-01 07:25:...| AA| 9.0| ATL| DFW|2014-12-31| 194| 731.0|\n", "| 25.0|2015-01-01 12:30:...|2015-01-01 11:00:...| AA| -2.0| ATL| DFW|2014-12-31| 232| 731.0|\n", "| 58.0|2015-01-01 13:40:...|2015-01-01 12:15:...| AA| 14.0| ATL| DFW|2014-12-31| 276| 731.0|\n", "| 14.0|2015-01-01 05:25:...|2015-01-01 03:55:...| AA| 15.0| ATL| DFW|2014-12-31| 314| 731.0|\n", "| 1.0|2015-01-01 10:05:...|2015-01-01 08:40:...| AA| -5.0| ATL| DFW|2014-12-31| 356| 731.0|\n", "+--------+--------------------+--------------------+-------+--------+------+----+----------+---------+--------+\n", "only showing top 12 rows\n", "\n" ] } ], "source": [ "from pyspark.sql.types import StringType, IntegerType, FloatType, DoubleType, DateType, TimestampType\n", "from pyspark.sql.types import StructType, StructField\n", "from pyspark.sql.functions import udf\n", "\n", "schema = StructType([\n", " StructField(\"ArrDelay\", DoubleType(), True), # \"ArrDelay\":5.0\n", " StructField(\"CRSArrTime\", TimestampType(), True), # \"CRSArrTime\":\"2015-12-31T03:20:00.000-08:00\"\n", " StructField(\"CRSDepTime\", TimestampType(), True), # \"CRSDepTime\":\"2015-12-31T03:05:00.000-08:00\"\n", " StructField(\"Carrier\", StringType(), True), # \"Carrier\":\"WN\"\n", " StructField(\"DayOfMonth\", IntegerType(), True), # \"DayOfMonth\":31\n", " StructField(\"DayOfWeek\", IntegerType(), True), # \"DayOfWeek\":4\n", " StructField(\"DayOfYear\", IntegerType(), True), # \"DayOfYear\":365\n", " StructField(\"DepDelay\", DoubleType(), True), # \"DepDelay\":14.0\n", " StructField(\"Dest\", StringType(), True), # \"Dest\":\"SAN\"\n", " StructField(\"Distance\", DoubleType(), True), # \"Distance\":368.0\n", " StructField(\"FlightDate\", DateType(), True), # \"FlightDate\":\"2015-12-30T16:00:00.000-08:00\"\n", " StructField(\"FlightNum\", StringType(), True), # \"FlightNum\":\"6109\"\n", " StructField(\"Origin\", StringType(), True), # \"Origin\":\"TUS\"\n", "])\n", "\n", "input_path = \"{}/data/simple_flight_delay_features.json\".format(base_path)\n", "\n", "features = spark.read.json(\n", " input_path,\n", " schema=schema\n", ")\n", "features.select(\"ArrDelay\", \"CRSArrTime\", \"CRSDepTime\", \"Carrier\", \"DepDelay\", \n", " \"Origin\", \"Dest\", \"FlightDate\", \"FlightNum\", \"Distance\").show(12)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Reproduce the ArrDelay Bucketing" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------+--------------+\n", "|ArrDelay|ArrDelayBucket|\n", "+--------+--------------+\n", "| 13.0| 0.0|\n", "| 17.0| 1.0|\n", "| 36.0| 1.0|\n", "| -21.0| 0.0|\n", "| -14.0| 0.0|\n", "| 16.0| 1.0|\n", "| -7.0| 0.0|\n", "| 13.0| 0.0|\n", "| 25.0| 1.0|\n", "| 58.0| 1.0|\n", "+--------+--------------+\n", "only showing top 10 rows\n", "\n" ] } ], "source": [ "# Load the arrival delay bucketizer\n", "from pyspark.ml.feature import Bucketizer\n", "\n", "# Load the departure delay bucketizer\n", "arrival_bucketizer_path = \"{}/models/arrival_bucketizer.bin\".format(base_path)\n", "arrival_bucketizer = Bucketizer.load(arrival_bucketizer_path)\n", "\n", "# Bucketize the departure and arrival delays for classification\n", "ml_bucketized_features = arrival_bucketizer.transform(features)\n", "ml_bucketized_features.select(\"ArrDelay\", \"ArrDelayBucket\").show(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Check the `ArrDelayBucket` Distribution\n", "Lets start by checking the overall range of flight delays." ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-------------+-------------+\n", "|min(ArrDelay)|max(ArrDelay)|\n", "+-------------+-------------+\n", "| -87.0| 1971.0|\n", "+-------------+-------------+\n", "\n" ] } ], "source": [ "# Check the frequency of each category\n", "ml_bucketized_features.registerTempTable(\"ml_bucketized_features\")\n", "\n", "spark.sql(\"SELECT MIN(ArrDelay), MAX(ArrDelay) FROM ml_bucketized_features\").show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now lets check the distribution of arrival delays as they fall into our bucketing scheme." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------+-------+---------+\n", "|ArrDelayBucket| Total|Total_Pct|\n", "+--------------+-------+---------+\n", "| 0.0|4650569| 81.39|\n", "| 1.0| 737848| 12.91|\n", "| 2.0| 325591| 5.7|\n", "+--------------+-------+---------+\n", "\n" ] } ], "source": [ "spark.sql(\n", "\"\"\"\n", "SELECT\n", " ArrDelayBucket,\n", " COUNT(*) AS Total,\n", " ROUND(\n", " 100 * (COUNT(*)/(SELECT COUNT(*) FROM ml_bucketized_features)),\n", " 2\n", " ) AS Total_Pct\n", "FROM ml_bucketized_features\n", "GROUP BY ArrDelayBucket\n", "\"\"\"\n", ").show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results are pretty skewed, with 81.4% in `ArrDelayBucket` `0.0`. Note that our model always predicts `0.0`..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sampling `ArrDelayBucket` `0.0`\n", "Lets take a look at some example values in the largest bucket, bucket `0.0`. Lets sample it before we inspect it." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------+--------------+\n", "|ArrDelay|ArrDelayBucket|\n", "+--------+--------------+\n", "| -31.0| 0.0|\n", "| -26.0| 0.0|\n", "| -7.0| 0.0|\n", "| -9.0| 0.0|\n", "| -7.0| 0.0|\n", "| 6.0| 0.0|\n", "| 0.0| 0.0|\n", "| -2.0| 0.0|\n", "| -15.0| 0.0|\n", "| -21.0| 0.0|\n", "| -5.0| 0.0|\n", "| 10.0| 0.0|\n", "| -13.0| 0.0|\n", "| -21.0| 0.0|\n", "| -21.0| 0.0|\n", "+--------+--------------+\n", "only showing top 15 rows\n", "\n" ] } ], "source": [ "# Check out some values from the field\n", "spark.sql(\"SELECT ArrDelay, ArrDelayBucket \"\n", " \"FROM ml_bucketized_features \"\n", " \"WHERE ArrDelayBucket == 0.0\")\\\n", " .sample(False, 0.01, 11).show(15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There looks to be a pretty broad range of values in `ArrDelayBucket` `0.0`..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Computing `ArrDelayBucket` Scope\n", "Lets look at the `MIN` and `MAX` values of `ArrDelay` in `ArrDelayBucket` `0.0`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-------------+-------------+\n", "|min(ArrDelay)|max(ArrDelay)|\n", "+-------------+-------------+\n", "| -87.0| 14.0|\n", "+-------------+-------------+\n", "\n" ] } ], "source": [ "# Check out the min/max for the field\n", "spark.sql(\"SELECT MIN(ArrDelay), MAX(ArrDelay) \"\n", " \"FROM ml_bucketized_features \"\n", " \"WHERE ArrDelayBucket == 0.0\").show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a range of 101 in this bucket. This encodes a large range... from 14 minute late all the way to over an hour early." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Baseline Histogram of `ArrDelay`\n", "Now, what is the distribution of values in `ArrDelayBucket` `0.0`? I want to get a sense of what this bucket encodes. We can filter to a relation with only `ArrDelayBucket` values of `0.0`, convert our `DataFrame` to an `RDD` and then use the [`RDD.histogram()`](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.histogram) function to get the raw data for our histogram. \n", "\n", "First, however, I need the histogram of the entire range of data to compare this to. We need to supply our own buckets, or the histogram is hard to interpret." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "([-87.0, -60, -30, -15, 0, 15, 30, 60, 120],\n", " [392, 108707, 989113, 2402687, 1149670, 412835, 325013, 211171])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Look at overall histogram\n", "ml_bucketized_features\\\n", " .select(\"ArrDelay\")\\\n", " .rdd\\\n", " .flatMap(lambda x: x)\\\n", " .histogram([-87.0, -60, -30, -15, 0, 15, 30, 60, 120])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating a PyPlot Bar Chart\n", "We must use [matplotlib.pyplot.bar](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.bar) instead of [matplotlib.pyplot.hist](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hist) because we have already computed our bins and their weights." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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uk3r5waO9gb/nk88xr4WhBkjyRuBTwLnA0cA/ASuTzJrSjkmSpFEz1DSWAl8o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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10d155780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib.mlab as mlab\n", "import matplotlib.pyplot as plt\n", "\n", "# Look at overall histogram\n", "data_tuple = ml_bucketized_features\\\n", " .select(\"ArrDelay\")\\\n", " .rdd\\\n", " .flatMap(lambda x: x)\\\n", " .histogram([-87.0, -60, -30, -15, 0, 15, 30, 60, 120])\n", "\n", "heights = np.array(data_tuple[1])\n", "\n", "# The bins are 1 > length than the values\n", "full_bins = data_tuple[0]\n", "\n", "# Bars are drawn from the left\n", "mid_point_bins = full_bins[:-1]\n", "\n", "# The width of a bar should be the range it maps in the data\n", "widths = [abs(i - j) for i, j in zip(full_bins[:-1], full_bins[1:])]\n", "\n", "# And now the bar should plot nicely\n", "bar = plt.bar(mid_point_bins, heights, width=widths, color='b')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see here that there is a strong mean in our data around 0-15, with some right left, and that most of the data was falling either in `ArrDelayBucket` `0.0` or very nearly so in the next bin. This must make the bins hard to reckon for the classifier." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Repartitioning `ArrDelayBucket`\n", "This histogram seems to indicate that we might address our problem by re-bucketizing `ArrDelayBucket` into something more balanced. While the original schema of `<15,15-60,60+` seemed to make sense from a user perspective... it seems to have wrecked our model. So lets try something that respects the distribution of delay...\n", "\n", "### Using Histograms to Determine Balanced Buckets\n", "Before we dive in and change our buckets, lets play with defining different buckets for our histogram and see if we can create balanced bars! To play around with this without repeating ourselves, lets create a function `create_hist` to help." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def create_hist(rdd_histogram_data):\n", " \"\"\"Given an RDD.histogram, plot a pyplot histogram\"\"\"\n", " heights = np.array(rdd_histogram_data[1])\n", " full_bins = rdd_histogram_data[0]\n", " mid_point_bins = full_bins[:-1]\n", " widths = [abs(i - j) for i, j in zip(full_bins[:-1], full_bins[1:])]\n", " bar = plt.bar(mid_point_bins, heights, width=widths, color='b')\n", " return bar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualizing the Original Bucketing Schema\n", "First, lets check out the distribution of the data using the original bucketing scheme. Although the maximum arrival delay is 1971 minutes, let use 200 to keep our chart from distorting." ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<Container object of 3 artists>" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x110e3aac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "buckets = [-87.0, 15, 60, 200]\n", "rdd_histogram_data = ml_bucketized_features\\\n", " .select(\"ArrDelay\")\\\n", " .rdd\\\n", " .flatMap(lambda x: x)\\\n", " .histogram(buckets)\n", "\n", "create_hist(rdd_histogram_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wow, that is hugeloy... distorted! This doesn't look like a good scheme!\n", "\n", "### Trying To Find Balanced Classes\n", "Instead, lets try a bucketing scheme that might result in more balanced bars..." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<Container object of 6 artists>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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oPYVIkqTdT39/P/39/VusW79+/bjtb8xDTa9Syj1JBoFDaULNWmCfJNN6Zmv62jbar71X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\nYKiRJElVMNRIkqQq/H9wRKmYJvYgnwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10cf94b00>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "buckets = [-87.0, -30, -15, 0, 15, 30, 120]\n", "rdd_histogram_data = ml_bucketized_features\\\n", " .select(\"ArrDelay\")\\\n", " .rdd\\\n", " .flatMap(lambda x: x)\\\n", " .histogram(buckets)\n", "\n", "create_hist(rdd_histogram_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hmmmm lets try again." ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<Container object of 5 artists>" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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xCPhcKeVLpZT7gHcBjwFvm9xuSZKkkdrrQ02S/YG5wG2D+0opBbgVOHGy+iVJ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\n6FJ76lc2QtIAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1110af588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "buckets = [-87.0, -15, 0, 15, 30, 120]\n", "rdd_histogram_data = ml_bucketized_features\\\n", " .select(\"ArrDelay\")\\\n", " .rdd\\\n", " .flatMap(lambda x: x)\\\n", " .histogram(buckets)\n", "\n", "create_hist(rdd_histogram_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets knock out our smallest bucket and try again..." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<Container object of 4 artists>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x110c13d30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "buckets = [-87.0, -15, 0, 30, 120]\n", "rdd_histogram_data = ml_bucketized_features\\\n", " .select(\"ArrDelay\")\\\n", " .rdd\\\n", " .flatMap(lambda x: x)\\\n", " .histogram(buckets)\n", "\n", "create_hist(rdd_histogram_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These look about right! The buckets `[-87, -15, 0, 30, 120]` correspond to human interprettable categories: `[early, on-time, late, very late]`. Lets use these buckets to recompute `ArrDelayBucket` and see if our problem goes away!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Recomputing Partitions for `ArrDelayBucket`\n", "\n", "Now that we have partitions that seem balanced, we can apply them to our features `DataFrame`. Lets recompute the Bucketizer and save it for use below." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pyspark.ml.feature import Bucketizer\n", "\n", "# Setup the Bucketizer\n", "splits = [-float(\"inf\"), -15.0, 0, 30.0, float(\"inf\")]\n", "arrival_bucketizer = Bucketizer(\n", " splits=splits,\n", " inputCol=\"ArrDelay\",\n", " outputCol=\"ArrDelayBucket\"\n", ")\n", "\n", "# Save the model\n", "arrival_bucketizer_path = \"{}/models/arrival_bucketizer_2.0.bin\".format(base_path)\n", "arrival_bucketizer.write().overwrite().save(arrival_bucketizer_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inspecting the new `ArrDelayBucket`\n", "\n", "Now lets apply the new buckets to our data, take a sample and take a peek..." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------+--------------+\n", "|ArrDelay|ArrDelayBucket|\n", "+--------+--------------+\n", "| 8.0| 2.0|\n", "| 49.0| 3.0|\n", "| 151.0| 3.0|\n", "| -11.0| 1.0|\n", "| 28.0| 2.0|\n", "| 0.0| 2.0|\n", "| -6.0| 1.0|\n", "| -1.0| 1.0|\n", "| -18.0| 0.0|\n", "| -27.0| 0.0|\n", "| -8.0| 1.0|\n", "| 1.0| 2.0|\n", "+--------+--------------+\n", "only showing top 12 rows\n", "\n" ] } ], "source": [ "# Apply it to our data\n", "ml_bucketized_features = arrival_bucketizer.transform(features)\n", "\n", "ml_bucketized_features\\\n", " .select(\"ArrDelay\", \"ArrDelayBucket\")\\\n", " .sample(False, 0.01, 27)\\\n", " .show(12)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're seeing examples of each label in our data. This is good. This looks differentiable. Now lets load all our models and see what happens..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading Our Feature Models\n", "Lets load all the models we've saved that vecotirize our raw data, using code from [`ch08/make_predictions.py`](https://github.com/rjurney/Agile_Data_Code_2/blob/master/ch08/make_predictions.py)." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# Load the arrival delay bucketizer\n", "from pyspark.ml.feature import Bucketizer\n", "arrival_bucketizer_path = \"{}/models/arrival_bucketizer_2.0.bin\".format(base_path)\n", "arrival_bucketizer = Bucketizer.load(arrival_bucketizer_path)\n", "\n", "# Load the departure delay bucketizer\n", "departure_bucketizer_path = \"{}/models/departure_bucketizer.bin\".format(base_path)\n", "departure_bucketizer = Bucketizer.load(departure_bucketizer_path)\n", "\n", "# Load all the string field vectorizer pipelines into a dict\n", "from pyspark.ml import PipelineModel\n", "\n", "string_vectorizer_pipeline_models = {}\n", "for column in [\"Carrier\", \"DayOfMonth\", \"DayOfWeek\", \"DayOfYear\",\n", " \"Origin\", \"Dest\", \"FlightNum\", \"DepDelayBucket\"]:\n", " string_pipeline_model_path = \"{}/models/string_indexer_pipeline_model_{}.bin\".format(\n", " base_path,\n", " column\n", " )\n", " string_pipeline_model = PipelineModel.load(string_pipeline_model_path)\n", " string_vectorizer_pipeline_models[column] = string_pipeline_model\n", "\n", "# Load the numeric vector assembler\n", "from pyspark.ml.feature import VectorAssembler\n", "vector_assembler_path = \"{}/models/numeric_vector_assembler.bin\".format(base_path)\n", "vector_assembler = VectorAssembler.load(vector_assembler_path)\n", "\n", "# Load the final assembler\n", "final_assembler_path = \"{}/models/final_vector_assembler.bin\".format(base_path)\n", "final_assembler = VectorAssembler.load(final_assembler_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Vectorize our Training Data\n", "Having loaded all the models that vectorize our data, lets use them to vectorize our data! This will prepare it for re-training our classifier model in the next step." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------+--------------+\n", "|ArrDelay|ArrDelayBucket|\n", "+--------+--------------+\n", "| 13.0| 2.0|\n", "| 17.0| 2.0|\n", "| 36.0| 3.0|\n", "| -21.0| 0.0|\n", "| -14.0| 1.0|\n", "| 16.0| 2.0|\n", "| -7.0| 1.0|\n", "| 13.0| 2.0|\n", "| 25.0| 2.0|\n", "| 58.0| 3.0|\n", "| 14.0| 2.0|\n", "| 1.0| 2.0|\n", "+--------+--------------+\n", "only showing top 12 rows\n", "\n" ] } ], "source": [ "# Bucketize the departure and arrival delays for classification\n", "ml_bucketized_features = arrival_bucketizer.transform(features)\n", "\n", "# Check the buckets\n", "ml_bucketized_features.select(\"ArrDelay\", \"ArrDelayBucket\").show(12)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# Vectorize string fields with the corresponding pipeline for that column\n", "# Turn category fields into categoric feature vectors, then drop intermediate fields\n", "for column in [\"Carrier\", \"DayOfMonth\", \"DayOfWeek\", \"DayOfYear\",\n", " \"Origin\", \"Dest\", \"FlightNum\"]:\n", " string_pipeline_path = \"{}/models/string_indexer_pipeline_{}.bin\".format(\n", " base_path,\n", " column\n", " )\n", " string_pipeline_model = string_vectorizer_pipeline_models[column]\n", " ml_bucketized_features = string_pipeline_model.transform(ml_bucketized_features)\n", " ml_bucketized_features = ml_bucketized_features.drop(column + \"_index\")\n", "\n", "# Vectorize numeric columns: DepDelay and Distance\n", "ml_bucketized_features = vector_assembler.transform(ml_bucketized_features)\n", "\n", "# Combine various features into one feature vector, 'features'\n", "final_vectorized_features = final_assembler.transform(ml_bucketized_features)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cleanup by dropping the individual vectors and check the data out..." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+--------------------+\n", "|ArrDelay| CRSArrTime| CRSDepTime|Carrier|DayOfMonth|DayOfWeek|DayOfYear|DepDelay|Dest|Distance|FlightDate|FlightNum|Origin|ArrDelayBucket| Features_vec|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+--------------------+\n", "| 13.0|2015-01-01 10:10:...|2015-01-01 07:30:...| AA| 1| 4| 1| 14.0| DFW| 569.0|2014-12-31| 1024| ABQ| 2.0|(8009,[2,39,45,37...|\n", "| 17.0|2015-01-01 02:15:...|2014-12-31 23:25:...| AA| 1| 4| 1| 14.0| DFW| 569.0|2014-12-31| 1184| ABQ| 2.0|(8009,[2,39,45,37...|\n", "| 36.0|2015-01-01 03:45:...|2015-01-01 01:00:...| AA| 1| 4| 1| -2.0| DFW| 569.0|2014-12-31| 336| ABQ| 3.0|(8009,[2,39,45,37...|\n", "| -21.0|2015-01-01 11:30:...|2015-01-01 09:55:...| AA| 1| 4| 1| -1.0| DFW| 731.0|2014-12-31| 125| ATL| 0.0|(8009,[2,39,45,37...|\n", "| -14.0|2015-01-01 02:25:...|2015-01-01 00:55:...| AA| 1| 4| 1| -4.0| DFW| 731.0|2014-12-31| 1455| ATL| 1.0|(8009,[2,39,45,37...|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+--------------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "# Drop the individual vector columns\n", "feature_columns = [\"Carrier_vec\", \"DayOfMonth_vec\", \"DayOfWeek_vec\", \"DayOfYear_vec\",\n", " \"Origin_vec\", \"Dest_vec\", \"FlightNum_vec\", \"NumericFeatures_vec\"]\n", "for column in feature_columns:\n", " final_vectorized_features = final_vectorized_features.drop(column)\n", "\n", "final_vectorized_features.show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Retraining Our Classifier Model\n", "And now we can retrain our classifier using the new `ArrDelayBuckets`, save it for use elsewhere and then test the new model and see if it still has the same problem." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Test Run: 100K\n", "Lets start out with just 100K training items to see that it runs ok... this way we don't wait a long time just to find out the model fails to fit." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+--------------------+\n", "|ArrDelay| CRSArrTime| CRSDepTime|Carrier|DayOfMonth|DayOfWeek|DayOfYear|DepDelay|Dest|Distance|FlightDate|FlightNum|Origin|ArrDelayBucket| Features_vec|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+--------------------+\n", "| 13.0|2015-01-01 10:10:...|2015-01-01 07:30:...| AA| 1| 4| 1| 14.0| DFW| 569.0|2014-12-31| 1024| ABQ| 2.0|(8009,[2,39,45,37...|\n", "| 17.0|2015-01-01 02:15:...|2014-12-31 23:25:...| AA| 1| 4| 1| 14.0| DFW| 569.0|2014-12-31| 1184| ABQ| 2.0|(8009,[2,39,45,37...|\n", "| 36.0|2015-01-01 03:45:...|2015-01-01 01:00:...| AA| 1| 4| 1| -2.0| DFW| 569.0|2014-12-31| 336| ABQ| 3.0|(8009,[2,39,45,37...|\n", "| -21.0|2015-01-01 11:30:...|2015-01-01 09:55:...| AA| 1| 4| 1| -1.0| DFW| 731.0|2014-12-31| 125| ATL| 0.0|(8009,[2,39,45,37...|\n", "| -14.0|2015-01-01 02:25:...|2015-01-01 00:55:...| AA| 1| 4| 1| -4.0| DFW| 731.0|2014-12-31| 1455| ATL| 1.0|(8009,[2,39,45,37...|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+--------------------+\n", "only showing top 5 rows\n", "\n", "Model trained on 100K items successfully!\n" ] } ], "source": [ "# Inspect the finalized features - try first with just 100K items\n", "limited_final_vectorized_features = final_vectorized_features.limit(100000)\n", "limited_final_vectorized_features.show(5)\n", "\n", "# Instantiate and fit random forest classifier on all the data\n", "from pyspark.ml.classification import RandomForestClassifier\n", "rfc = RandomForestClassifier(featuresCol=\"Features_vec\", labelCol=\"ArrDelayBucket\", predictionCol=\"Prediction\")\n", "model = rfc.fit(limited_final_vectorized_features)\n", "\n", "print(\"Model trained on 100K items successfully!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Training Run: 5.7MM\n", "The model having successfully fitted on limited data, lets train it on all 1.5 million flights and save the result to our `models/` directory." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model trained on 5.7MM items successfully!\n" ] } ], "source": [ "# Instantiate and fit random forest classifier on all the data\n", "from pyspark.ml.classification import RandomForestClassifier\n", "rfc = RandomForestClassifier(featuresCol=\"Features_vec\", labelCol=\"ArrDelayBucket\", predictionCol=\"Prediction\")\n", "#model = rfc.fit(final_vectorized_features)\n", "model = rfc.fit(limited_final_vectorized_features)\n", "\n", "# Save the new model over the old one\n", "model_output_path = \"{}/models/spark_random_forest_classifier.flight_delays.2.0.bin\".format(\n", " base_path\n", ")\n", "model.write().overwrite().save(model_output_path)\n", "\n", "print(\"Model trained on 5.7MM items successfully!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Testing Our New Classifier Model\n", "Now we need to test and see whether the new model makes predictions other than 0.0 :) Lets make a prediction for our training data and count the different labels it predicts." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+----------+-------+\n", "|Prediction| count|\n", "+----------+-------+\n", "| 1.0| 32887|\n", "| 2.0|5681121|\n", "+----------+-------+\n", "\n" ] } ], "source": [ "# Make the predictions...\n", "predictions = model.transform(final_vectorized_features)\n", "predictions.groupBy(\"Prediction\").count().show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Shit! Ok, the bucketing wasn't the issue. DAMN. Well, this is a learning experience. We'll have to figure out something else." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### In Which We Regroup And Figure Things Out\n", "This seems more like a bug. What the hell is going on?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Take 2: Simplify The Model\n", "Lets pull all but one feature out and see what happens.\n", "\n", "### Load the Data Again" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Row(ArrDelay=13.0, CRSArrTime=datetime.datetime(2015, 1, 1, 10, 10), CRSDepTime=datetime.datetime(2015, 1, 1, 7, 30), Carrier='AA', DayOfMonth=1, DayOfWeek=4, DayOfYear=1, DepDelay=14.0, Dest='DFW', Distance=569.0, FlightDate=datetime.date(2014, 12, 31), FlightNum='1024', Origin='ABQ')" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "schema = StructType([\n", " StructField(\"ArrDelay\", DoubleType(), True), # \"ArrDelay\":5.0\n", " StructField(\"CRSArrTime\", TimestampType(), True), # \"CRSArrTime\":\"2015-12-31T03:20:00.000-08:00\"\n", " StructField(\"CRSDepTime\", TimestampType(), True), # \"CRSDepTime\":\"2015-12-31T03:05:00.000-08:00\"\n", " StructField(\"Carrier\", StringType(), True), # \"Carrier\":\"WN\"\n", " StructField(\"DayOfMonth\", IntegerType(), True), # \"DayOfMonth\":31\n", " StructField(\"DayOfWeek\", IntegerType(), True), # \"DayOfWeek\":4\n", " StructField(\"DayOfYear\", IntegerType(), True), # \"DayOfYear\":365\n", " StructField(\"DepDelay\", DoubleType(), True), # \"DepDelay\":14.0\n", " StructField(\"Dest\", StringType(), True), # \"Dest\":\"SAN\"\n", " StructField(\"Distance\", DoubleType(), True), # \"Distance\":368.0\n", " StructField(\"FlightDate\", DateType(), True), # \"FlightDate\":\"2015-12-30T16:00:00.000-08:00\"\n", " StructField(\"FlightNum\", StringType(), True), # \"FlightNum\":\"6109\"\n", " StructField(\"Origin\", StringType(), True), # \"Origin\":\"TUS\"\n", "])\n", "\n", "input_path = \"{}/data/simple_flight_delay_features.json\".format(\n", " base_path\n", ")\n", "features = spark.read.json(input_path, schema=schema)\n", "features.first()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bucketize Features" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Bucketize the departure and arrival delays for classification\n", "ml_bucketized_features = arrival_bucketizer.transform(features)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Only Vectorize the Numeric Columns\n", "`DepDelay` and `Distance` will be the entirety of our model." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# Vectorize numeric columns: DepDelay and Distance\n", "numeric_features = vector_assembler.transform(ml_bucketized_features)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train a New Random Forest Classifier Model" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model trained on 5.7MM items successfully!\n" ] } ], "source": [ "# Instantiate and fit random forest classifier on all the data\n", "from pyspark.ml.classification import RandomForestClassifier\n", "rfc = RandomForestClassifier(featuresCol=\"NumericFeatures_vec\", labelCol=\"ArrDelayBucket\", predictionCol=\"Prediction\")\n", "model = rfc.fit(numeric_features)\n", "\n", "# Save the new model over the old one\n", "model_output_path = \"{}/models/spark_random_forest_classifier.flight_delays.3.0.bin\".format(\n", " base_path\n", ")\n", "model.write().overwrite().save(model_output_path)\n", "\n", "print(\"Model trained on 5.7MM items successfully!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make the Predictions" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------------------+--------------------+--------------------+----------+\n", "|ArrDelay| CRSArrTime| CRSDepTime|Carrier|DayOfMonth|DayOfWeek|DayOfYear|DepDelay|Dest|Distance|FlightDate|FlightNum|Origin|ArrDelayBucket|NumericFeatures_vec| rawPrediction| probability|Prediction|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------------------+--------------------+--------------------+----------+\n", "| 13.0|2015-01-01 10:10:...|2015-01-01 07:30:...| AA| 1| 4| 1| 14.0| DFW| 569.0|2014-12-31| 1024| ABQ| 2.0| [14.0,569.0]|[0.04395894123984...|[0.00219794706199...| 2.0|\n", "| 17.0|2015-01-01 02:15:...|2014-12-31 23:25:...| AA| 1| 4| 1| 14.0| DFW| 569.0|2014-12-31| 1184| ABQ| 2.0| [14.0,569.0]|[0.04395894123984...|[0.00219794706199...| 2.0|\n", "| 36.0|2015-01-01 03:45:...|2015-01-01 01:00:...| AA| 1| 4| 1| -2.0| DFW| 569.0|2014-12-31| 336| ABQ| 3.0| [-2.0,569.0]|[2.25610771946355...|[0.11280538597317...| 1.0|\n", "| -21.0|2015-01-01 11:30:...|2015-01-01 09:55:...| AA| 1| 4| 1| -1.0| DFW| 731.0|2014-12-31| 125| ATL| 0.0| [-1.0,731.0]|[5.44416799386120...|[0.27220839969306...| 1.0|\n", "| -14.0|2015-01-01 02:25:...|2015-01-01 00:55:...| AA| 1| 4| 1| -4.0| DFW| 731.0|2014-12-31| 1455| ATL| 1.0| [-4.0,731.0]|[7.51562274287558...|[0.37578113714377...| 1.0|\n", "| 16.0|2015-01-01 07:15:...|2015-01-01 05:45:...| AA| 1| 4| 1| 15.0| DFW| 731.0|2014-12-31| 1473| ATL| 2.0| [15.0,731.0]|[0.04395894123984...|[0.00219794706199...| 2.0|\n", "| -7.0|2015-01-01 04:15:...|2015-01-01 02:45:...| AA| 1| 4| 1| -2.0| DFW| 731.0|2014-12-31| 1513| ATL| 1.0| [-2.0,731.0]|[5.44416799386120...|[0.27220839969306...| 1.0|\n", "| 13.0|2015-01-01 08:50:...|2015-01-01 07:25:...| AA| 1| 4| 1| 9.0| DFW| 731.0|2014-12-31| 194| ATL| 2.0| [9.0,731.0]|[1.24591307802481...|[0.06229565390124...| 2.0|\n", "| 25.0|2015-01-01 12:30:...|2015-01-01 11:00:...| AA| 1| 4| 1| -2.0| DFW| 731.0|2014-12-31| 232| ATL| 2.0| [-2.0,731.0]|[5.44416799386120...|[0.27220839969306...| 1.0|\n", "| 58.0|2015-01-01 13:40:...|2015-01-01 12:15:...| AA| 1| 4| 1| 14.0| DFW| 731.0|2014-12-31| 276| ATL| 3.0| [14.0,731.0]|[0.04395894123984...|[0.00219794706199...| 2.0|\n", "| 14.0|2015-01-01 05:25:...|2015-01-01 03:55:...| AA| 1| 4| 1| 15.0| DFW| 731.0|2014-12-31| 314| ATL| 2.0| [15.0,731.0]|[0.04395894123984...|[0.00219794706199...| 2.0|\n", "| 1.0|2015-01-01 10:05:...|2015-01-01 08:40:...| AA| 1| 4| 1| -5.0| DFW| 731.0|2014-12-31| 356| ATL| 2.0| [-5.0,731.0]|[7.51562274287558...|[0.37578113714377...| 1.0|\n", "| -29.0|2015-01-01 02:12:...|2015-01-01 00:15:...| AA| 1| 4| 1| -9.0| MIA| 594.0|2014-12-31| 1652| ATL| 0.0| [-9.0,594.0]|[8.19704519542880...|[0.40985225977144...| 1.0|\n", "| -10.0|2015-01-01 00:52:...|2014-12-31 23:00:...| AA| 1| 4| 1| -4.0| MIA| 594.0|2014-12-31| 17| ATL| 1.0| [-4.0,594.0]|[4.45234793730754...|[0.22261739686537...| 1.0|\n", "| -3.0|2015-01-01 15:02:...|2015-01-01 13:10:...| AA| 1| 4| 1| -7.0| MIA| 594.0|2014-12-31| 349| ATL| 1.0| [-7.0,594.0]|[4.45234793730754...|[0.22261739686537...| 1.0|\n", "| -8.0|2015-01-01 06:35:...|2015-01-01 05:30:...| AA| 1| 4| 1| -2.0| DFW| 190.0|2014-12-31| 1023| AUS| 1.0| [-2.0,190.0]|[2.25610771946355...|[0.11280538597317...| 1.0|\n", "| -1.0|2014-12-31 22:50:...|2014-12-31 21:50:...| AA| 1| 4| 1| -2.0| DFW| 190.0|2014-12-31| 1178| AUS| 1.0| [-2.0,190.0]|[2.25610771946355...|[0.11280538597317...| 1.0|\n", "| -14.0|2015-01-01 01:40:...|2015-01-01 00:30:...| AA| 1| 4| 1| -6.0| DFW| 190.0|2014-12-31| 1296| AUS| 1.0| [-6.0,190.0]|[4.45234793730754...|[0.22261739686537...| 1.0|\n", "| -16.0|2015-01-01 02:15:...|2015-01-01 01:05:...| AA| 1| 4| 1| -4.0| DFW| 190.0|2014-12-31| 1356| AUS| 0.0| [-4.0,190.0]|[4.45234793730754...|[0.22261739686537...| 1.0|\n", "| 18.0|2015-01-01 08:55:...|2015-01-01 07:55:...| AA| 1| 4| 1| 3.0| DFW| 190.0|2014-12-31| 1365| AUS| 2.0| [3.0,190.0]|[0.87508697792321...|[0.04375434889616...| 1.0|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------------------+--------------------+--------------------+----------+\n", "only showing top 20 rows\n", "\n" ] } ], "source": [ "# Make the predictions...\n", "predictions = model.transform(numeric_features)\n", "predictions.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check the Distribution of Predictions" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+----------+--------+\n", "|Prediction|count(1)|\n", "+----------+--------+\n", "| 0.0| 244210|\n", "| 1.0| 3916886|\n", "| 3.0| 542936|\n", "| 2.0| 1009976|\n", "+----------+--------+\n", "\n" ] } ], "source": [ "predictions.registerTempTable(\"predictions\")\n", "spark.sql(\"\"\"SELECT Prediction, COUNT(*) FROM predictions GROUP BY Prediction\"\"\").show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compare to Actual Distribution" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------+--------+\n", "|ArrDelayBucket|count(1)|\n", "+--------------+--------+\n", "| 0.0| 1098212|\n", "| 1.0| 2402687|\n", "| 3.0| 650604|\n", "| 2.0| 1562505|\n", "+--------------+--------+\n", "\n" ] } ], "source": [ "spark.sql(\"\"\"Select ArrDelayBucket, COUNT(*) FROM predictions GROUP BY ArrDelayBucket\"\"\").show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ok, this looks respectable, or at least... not completely erroneous! Now lets add in another feature and see what happens." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adding Back String Columns\n", "Now, starting with just `Origin` and `Dest`, we'll try adding back the string columns and see what happens.\n", "\n", "Start by creating a vector for each field, `Origin` and `Dest`, and check them out. Note that we repeat the numeric feature vectorization to refresh the `numeric_features` relation, which we modify by looping." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------------------+----------------+---------------+\n", "|ArrDelay| CRSArrTime| CRSDepTime|Carrier|DayOfMonth|DayOfWeek|DayOfYear|DepDelay|Dest|Distance|FlightDate|FlightNum|Origin|ArrDelayBucket|NumericFeatures_vec| Origin_vec| Dest_vec|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------------------+----------------+---------------+\n", "| 13.0|2015-01-01 10:10:...|2015-01-01 07:30:...| AA| 1| 4| 1| 14.0| DFW| 569.0|2014-12-31| 1024| ABQ| 2.0| [14.0,569.0]|(322,[53],[1.0])|(322,[2],[1.0])|\n", "| 17.0|2015-01-01 02:15:...|2014-12-31 23:25:...| AA| 1| 4| 1| 14.0| DFW| 569.0|2014-12-31| 1184| ABQ| 2.0| [14.0,569.0]|(322,[53],[1.0])|(322,[2],[1.0])|\n", "| 36.0|2015-01-01 03:45:...|2015-01-01 01:00:...| AA| 1| 4| 1| -2.0| DFW| 569.0|2014-12-31| 336| ABQ| 3.0| [-2.0,569.0]|(322,[53],[1.0])|(322,[2],[1.0])|\n", "| -21.0|2015-01-01 11:30:...|2015-01-01 09:55:...| AA| 1| 4| 1| -1.0| DFW| 731.0|2014-12-31| 125| ATL| 0.0| [-1.0,731.0]| (322,[0],[1.0])|(322,[2],[1.0])|\n", "| -14.0|2015-01-01 02:25:...|2015-01-01 00:55:...| AA| 1| 4| 1| -4.0| DFW| 731.0|2014-12-31| 1455| ATL| 1.0| [-4.0,731.0]| (322,[0],[1.0])|(322,[2],[1.0])|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------------------+----------------+---------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "# Vectorize numeric columns: DepDelay and Distance\n", "numeric_features = vector_assembler.transform(ml_bucketized_features)\n", "\n", "# Vectorize string fields with the corresponding pipeline for that column\n", "# Turn category fields into categoric feature vectors, then drop intermediate fields\n", "string_columns = [\"Origin\", \"Dest\"]\n", "for column in string_columns:\n", " string_pipeline_path = \"{}/models/string_indexer_pipeline_{}.bin\".format(\n", " base_path,\n", " column\n", " )\n", " string_pipeline_model = string_vectorizer_pipeline_models[column]\n", " numeric_features = string_pipeline_model.transform(numeric_features)\n", " numeric_features = numeric_features.drop(column + \"_index\")\n", "\n", "numeric_features.show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Assemble Our Vectors\n", "Now we need to assemble the `Origin`/`Dest`/`NumericFeatures` vectors into one `Features_vec`." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+--------------------+\n", "|ArrDelay| CRSArrTime| CRSDepTime|Carrier|DayOfMonth|DayOfWeek|DayOfYear|DepDelay|Dest|Distance|FlightDate|FlightNum|Origin|ArrDelayBucket| Features_vec|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+--------------------+\n", "| 13.0|2015-01-01 10:10:...|2015-01-01 07:30:...| AA| 1| 4| 1| 14.0| DFW| 569.0|2014-12-31| 1024| ABQ| 2.0|(646,[53,324,644,...|\n", "| 17.0|2015-01-01 02:15:...|2014-12-31 23:25:...| AA| 1| 4| 1| 14.0| DFW| 569.0|2014-12-31| 1184| ABQ| 2.0|(646,[53,324,644,...|\n", "| 36.0|2015-01-01 03:45:...|2015-01-01 01:00:...| AA| 1| 4| 1| -2.0| DFW| 569.0|2014-12-31| 336| ABQ| 3.0|(646,[53,324,644,...|\n", "| -21.0|2015-01-01 11:30:...|2015-01-01 09:55:...| AA| 1| 4| 1| -1.0| DFW| 731.0|2014-12-31| 125| ATL| 0.0|(646,[0,324,644,6...|\n", "| -14.0|2015-01-01 02:25:...|2015-01-01 00:55:...| AA| 1| 4| 1| -4.0| DFW| 731.0|2014-12-31| 1455| ATL| 1.0|(646,[0,324,644,6...|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+--------------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "vector_columns = [\"Origin_vec\", \"Dest_vec\", \"NumericFeatures_vec\"]\n", "final_assembler = VectorAssembler(\n", " inputCols=vector_columns,\n", " outputCol=\"Features_vec\"\n", ")\n", "final_vectorized_features = final_assembler.transform(numeric_features)\n", "for column in vector_columns:\n", " final_vectorized_features = final_vectorized_features.drop(column)\n", "\n", "final_vectorized_features.show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Retrain the Model\n", "Note that as we add features, the model fitting slows down..." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model trained on 5.7MM items successfully!\n" ] } ], "source": [ "# Instantiate and fit random forest classifier on all the data\n", "from pyspark.ml.classification import RandomForestClassifier\n", "rfc = RandomForestClassifier(featuresCol=\"Features_vec\", labelCol=\"ArrDelayBucket\", predictionCol=\"Prediction\")\n", "model = rfc.fit(final_vectorized_features)\n", "\n", "# Save the new model over the old one\n", "model_output_path = \"{}/models/spark_random_forest_classifier.flight_delays.4.0.bin\".format(\n", " base_path\n", ")\n", "model.write().overwrite().save(model_output_path)\n", "\n", "print(\"Model trained on 5.7MM items successfully!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make our Predictions" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+--------------------+--------------------+--------------------+----------+\n", "|ArrDelay| CRSArrTime| CRSDepTime|Carrier|DayOfMonth|DayOfWeek|DayOfYear|DepDelay|Dest|Distance|FlightDate|FlightNum|Origin|ArrDelayBucket| Features_vec| rawPrediction| probability|Prediction|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+--------------------+--------------------+--------------------+----------+\n", "| 13.0|2015-01-01 10:10:...|2015-01-01 07:30:...| AA| 1| 4| 1| 14.0| DFW| 569.0|2014-12-31| 1024| ABQ| 2.0|(646,[53,324,644,...|[3.65207272881038...|[0.18260363644051...| 1.0|\n", "| 17.0|2015-01-01 02:15:...|2014-12-31 23:25:...| AA| 1| 4| 1| 14.0| DFW| 569.0|2014-12-31| 1184| ABQ| 2.0|(646,[53,324,644,...|[3.65207272881038...|[0.18260363644051...| 1.0|\n", "| 36.0|2015-01-01 03:45:...|2015-01-01 01:00:...| AA| 1| 4| 1| -2.0| DFW| 569.0|2014-12-31| 336| ABQ| 3.0|(646,[53,324,644,...|[3.88802642603634...|[0.19440132130181...| 1.0|\n", "| -21.0|2015-01-01 11:30:...|2015-01-01 09:55:...| AA| 1| 4| 1| -1.0| DFW| 731.0|2014-12-31| 125| ATL| 0.0|(646,[0,324,644,6...|[3.99362073652591...|[0.19968103682629...| 1.0|\n", "| -14.0|2015-01-01 02:25:...|2015-01-01 00:55:...| AA| 1| 4| 1| -4.0| DFW| 731.0|2014-12-31| 1455| ATL| 1.0|(646,[0,324,644,6...|[3.99362073652591...|[0.19968103682629...| 1.0|\n", "| 16.0|2015-01-01 07:15:...|2015-01-01 05:45:...| AA| 1| 4| 1| 15.0| DFW| 731.0|2014-12-31| 1473| ATL| 2.0|(646,[0,324,644,6...|[3.75766703929995...|[0.18788335196499...| 1.0|\n", "| -7.0|2015-01-01 04:15:...|2015-01-01 02:45:...| AA| 1| 4| 1| -2.0| DFW| 731.0|2014-12-31| 1513| ATL| 1.0|(646,[0,324,644,6...|[3.99362073652591...|[0.19968103682629...| 1.0|\n", "| 13.0|2015-01-01 08:50:...|2015-01-01 07:25:...| AA| 1| 4| 1| 9.0| DFW| 731.0|2014-12-31| 194| ATL| 2.0|(646,[0,324,644,6...|[3.75766703929995...|[0.18788335196499...| 1.0|\n", "| 25.0|2015-01-01 12:30:...|2015-01-01 11:00:...| AA| 1| 4| 1| -2.0| DFW| 731.0|2014-12-31| 232| ATL| 2.0|(646,[0,324,644,6...|[3.99362073652591...|[0.19968103682629...| 1.0|\n", "| 58.0|2015-01-01 13:40:...|2015-01-01 12:15:...| AA| 1| 4| 1| 14.0| DFW| 731.0|2014-12-31| 276| ATL| 3.0|(646,[0,324,644,6...|[3.75766703929995...|[0.18788335196499...| 1.0|\n", "| 14.0|2015-01-01 05:25:...|2015-01-01 03:55:...| AA| 1| 4| 1| 15.0| DFW| 731.0|2014-12-31| 314| ATL| 2.0|(646,[0,324,644,6...|[3.75766703929995...|[0.18788335196499...| 1.0|\n", "| 1.0|2015-01-01 10:05:...|2015-01-01 08:40:...| AA| 1| 4| 1| -5.0| DFW| 731.0|2014-12-31| 356| ATL| 2.0|(646,[0,324,644,6...|[3.99362073652591...|[0.19968103682629...| 1.0|\n", "| -29.0|2015-01-01 02:12:...|2015-01-01 00:15:...| AA| 1| 4| 1| -9.0| MIA| 594.0|2014-12-31| 1652| ATL| 0.0|(646,[0,346,644,6...|[3.79791319714944...|[0.18989565985747...| 1.0|\n", "| -10.0|2015-01-01 00:52:...|2014-12-31 23:00:...| AA| 1| 4| 1| -4.0| MIA| 594.0|2014-12-31| 17| ATL| 1.0|(646,[0,346,644,6...|[3.79791319714944...|[0.18989565985747...| 1.0|\n", "| -3.0|2015-01-01 15:02:...|2015-01-01 13:10:...| AA| 1| 4| 1| -7.0| MIA| 594.0|2014-12-31| 349| ATL| 1.0|(646,[0,346,644,6...|[3.79791319714944...|[0.18989565985747...| 1.0|\n", "| -8.0|2015-01-01 06:35:...|2015-01-01 05:30:...| AA| 1| 4| 1| -2.0| DFW| 190.0|2014-12-31| 1023| AUS| 1.0|(646,[34,324,644,...|[3.8828148227923,...|[0.19414074113961...| 1.0|\n", "| -1.0|2014-12-31 22:50:...|2014-12-31 21:50:...| AA| 1| 4| 1| -2.0| DFW| 190.0|2014-12-31| 1178| AUS| 1.0|(646,[34,324,644,...|[3.8828148227923,...|[0.19414074113961...| 1.0|\n", "| -14.0|2015-01-01 01:40:...|2015-01-01 00:30:...| AA| 1| 4| 1| -6.0| DFW| 190.0|2014-12-31| 1296| AUS| 1.0|(646,[34,324,644,...|[3.8828148227923,...|[0.19414074113961...| 1.0|\n", "| -16.0|2015-01-01 02:15:...|2015-01-01 01:05:...| AA| 1| 4| 1| -4.0| DFW| 190.0|2014-12-31| 1356| AUS| 0.0|(646,[34,324,644,...|[3.8828148227923,...|[0.19414074113961...| 1.0|\n", "| 18.0|2015-01-01 08:55:...|2015-01-01 07:55:...| AA| 1| 4| 1| 3.0| DFW| 190.0|2014-12-31| 1365| AUS| 2.0|(646,[34,324,644,...|[3.8828148227923,...|[0.19414074113961...| 1.0|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+--------------------+--------------------+--------------------+----------+\n", "only showing top 20 rows\n", "\n" ] } ], "source": [ "# Make the predictions...\n", "predictions = model.transform(final_vectorized_features)\n", "predictions.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Uh oh... all 1.0s..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check that the Results are Sane\n", "And drum roll... are the results sane?" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+----------+--------+\n", "|Prediction|count(1)|\n", "+----------+--------+\n", "| 1.0| 5699785|\n", "| 3.0| 14223|\n", "+----------+--------+\n", "\n" ] } ], "source": [ "predictions.registerTempTable(\"predictions\")\n", "spark.sql(\"\"\"SELECT Prediction, COUNT(*) FROM predictions GROUP BY Prediction\"\"\").show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So... how can the performance of the model completely disappear when we add `Origin` and `Dest`? Even if these items have no contribution to the model... shouldn't it still compensate? It is a decision tree!\n", "\n", "### Classifier Model Feature Importances\n", "Lets look at the feature importances. What are they?" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SparseVector(646, {0: 0.0303, 1: 0.061, 2: 0.0, 3: 0.0001, 4: 0.0087, 6: 0.0077, 7: 0.0048, 8: 0.0, 9: 0.0, 10: 0.0051, 13: 0.0006, 14: 0.0012, 15: 0.0657, 16: 0.001, 17: 0.0416, 18: 0.0001, 19: 0.0, 20: 0.0, 21: 0.0057, 22: 0.0048, 23: 0.0022, 25: 0.0006, 27: 0.011, 28: 0.0246, 29: 0.0004, 31: 0.0, 32: 0.0119, 33: 0.0138, 34: 0.0, 35: 0.0001, 36: 0.0003, 37: 0.013, 38: 0.0152, 39: 0.001, 40: 0.006, 41: 0.0009, 42: 0.0, 43: 0.0005, 46: 0.0, 47: 0.0004, 48: 0.0, 51: 0.0003, 53: 0.0002, 54: 0.0071, 56: 0.0002, 58: 0.0002, 61: 0.0, 66: 0.0, 67: 0.0002, 75: 0.0, 76: 0.0002, 79: 0.0, 84: 0.0, 86: 0.0025, 102: 0.0, 103: 0.0, 104: 0.0, 126: 0.0001, 127: 0.0, 137: 0.0, 138: 0.0, 139: 0.0001, 142: 0.0, 151: 0.0002, 155: 0.0004, 158: 0.0, 190: 0.0002, 195: 0.0001, 232: 0.0003, 301: 0.0, 322: 0.0202, 323: 0.0584, 324: 0.0072, 325: 0.0015, 326: 0.0009, 327: 0.0298, 328: 0.0036, 329: 0.0, 330: 0.0006, 331: 0.0004, 332: 0.0001, 334: 0.0007, 335: 0.0005, 336: 0.0006, 337: 0.0175, 338: 0.0048, 339: 0.0194, 340: 0.0507, 341: 0.0004, 342: 0.0, 343: 0.0005, 344: 0.0002, 345: 0.0002, 346: 0.0002, 347: 0.0005, 350: 0.0003, 351: 0.0001, 352: 0.0005, 354: 0.0094, 355: 0.0, 356: 0.0001, 357: 0.0001, 359: 0.0003, 361: 0.0004, 363: 0.0001, 365: 0.0001, 366: 0.0, 369: 0.0003, 373: 0.0003, 376: 0.0054, 378: 0.0002, 382: 0.0001, 388: 0.0, 389: 0.0002, 393: 0.0024, 395: 0.0, 396: 0.0034, 397: 0.0, 398: 0.0, 413: 0.0004, 417: 0.0003, 419: 0.0, 420: 0.0001, 428: 0.001, 436: 0.0002, 439: 0.0, 442: 0.0002, 444: 0.0, 448: 0.0, 453: 0.0008, 455: 0.0, 471: 0.0003, 472: 0.0002, 514: 0.0001, 524: 0.0002, 585: 0.0002, 644: 0.3348, 645: 0.0657})" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "feature_importances = model.featureImportances\n", "feature_importances" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Great, WTF Do I Do with This SparseVector?\n", "\n", "I have no idea what to do with this. This tells me nothing. How do I reverse the process to get the feature names?" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "# I dunno" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Try Combining `Origin`/`Dest` Into One `Route` Column\n", "\n", "Try making it one feature, the route as defined by the `Origin`/`Dest` pair." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+\n", "|ArrDelay| CRSArrTime| CRSDepTime|Carrier|DayOfMonth|DayOfWeek|DayOfYear|DepDelay|Dest|Distance|FlightDate|FlightNum|Origin|ArrDelayBucket| Route|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+\n", "| 13.0|2015-01-01 10:10:...|2015-01-01 07:30:...| AA| 1| 4| 1| 14.0| DFW| 569.0|2014-12-31| 1024| ABQ| 2.0|ABQ-DFW|\n", "| 17.0|2015-01-01 02:15:...|2014-12-31 23:25:...| AA| 1| 4| 1| 14.0| DFW| 569.0|2014-12-31| 1184| ABQ| 2.0|ABQ-DFW|\n", "| 36.0|2015-01-01 03:45:...|2015-01-01 01:00:...| AA| 1| 4| 1| -2.0| DFW| 569.0|2014-12-31| 336| ABQ| 3.0|ABQ-DFW|\n", "| -21.0|2015-01-01 11:30:...|2015-01-01 09:55:...| AA| 1| 4| 1| -1.0| DFW| 731.0|2014-12-31| 125| ATL| 0.0|ATL-DFW|\n", "| -14.0|2015-01-01 02:25:...|2015-01-01 00:55:...| AA| 1| 4| 1| -4.0| DFW| 731.0|2014-12-31| 1455| ATL| 1.0|ATL-DFW|\n", "| 16.0|2015-01-01 07:15:...|2015-01-01 05:45:...| AA| 1| 4| 1| 15.0| DFW| 731.0|2014-12-31| 1473| ATL| 2.0|ATL-DFW|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+\n", "only showing top 6 rows\n", "\n" ] } ], "source": [ "# Bucketize the departure and arrival delays for classification\n", "ml_bucketized_features = arrival_bucketizer.transform(features)\n", "from pyspark.sql.functions import lit, concat\n", "features_with_route = ml_bucketized_features.withColumn(\n", " 'Route',\n", " concat(\n", " ml_bucketized_features.Origin,\n", " lit('-'),\n", " ml_bucketized_features.Dest\n", " )\n", ")\n", "features_with_route.show(6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Vectorize `Route`\n", "\n", "Now we must create a string indexer pipeline for the `Route` column." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saved route string pipeline model to ../models/string_indexer_pipeline_model_Route.bin...\n" ] } ], "source": [ "from pyspark.ml import Pipeline\n", "from pyspark.ml.feature import StringIndexer, OneHotEncoder, VectorIndexer\n", "\n", "route_string_indexer = StringIndexer(\n", " inputCol='Route',\n", " outputCol='Route' + \"_index\"\n", ")\n", "\n", "route_one_hot_encoder = OneHotEncoder(\n", " dropLast=False,\n", " inputCol='Route' + \"_index\",\n", " outputCol='Route' + \"_vec\"\n", ")\n", "route_string_pipeline = Pipeline(\n", " stages=[route_string_indexer, route_one_hot_encoder]\n", ")\n", "\n", "# Fit/transform to apply to the data\n", "route_string_pipeline_model = route_string_pipeline.fit(features_with_route)\n", "features_with_route_vec = route_string_pipeline_model.transform(features_with_route)\n", "\n", "# Drop the intermediate index\n", "features_with_route_vec = features_with_route_vec.drop(\"Route\" + \"_index\")\n", "\n", "# Save the pipeline model\n", "route_string_pipeline_output_path = \"{}/models/string_indexer_pipeline_model_{}.bin\".format(\n", " base_path,\n", " \"Route\"\n", ")\n", "route_string_pipeline_model.write().overwrite().save(route_string_pipeline_output_path)\n", "\n", "print(\"Saved route string pipeline model to {}...\".format(route_string_pipeline_output_path))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Verify `Route_vec`\n", "And lets check the resulting `Route_vec` column..." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+-------------------+\n", "|ArrDelay| CRSArrTime| CRSDepTime|Carrier|DayOfMonth|DayOfWeek|DayOfYear|DepDelay|Dest|Distance|FlightDate|FlightNum|Origin|ArrDelayBucket| Route| Route_vec|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+-------------------+\n", "| 38.0|2015-01-01 08:50:...|2015-01-01 03:45:...| AA| 1| 4| 1| 29.0| DFW| 1235.0|2014-12-31| 2419| LAX| 3.0|LAX-DFW| (4657,[45],[1.0])|\n", "| 53.0|2015-01-01 14:04:...|2015-01-01 12:55:...| B6| 1| 4| 1| 62.0| BOS| 187.0|2014-12-31| 418| JFK| 3.0|JFK-BOS| (4657,[129],[1.0])|\n", "| 1.0|2015-01-01 13:05:...|2015-01-01 09:54:...| UA| 1| 4| 1| 3.0| MCO| 854.0|2014-12-31| 1586| IAH| 2.0|IAH-MCO| (4657,[494],[1.0])|\n", "| -17.0|2015-01-01 09:13:...|2015-01-01 04:55:...| UA| 1| 4| 1| 0.0| PHL| 1325.0|2014-12-31| 234| IAH| 0.0|IAH-PHL| (4657,[673],[1.0])|\n", "| -19.0|2015-01-01 07:55:...|2015-01-01 03:30:...| WN| 1| 4| 1| -5.0| EWR| 1504.0|2014-12-31| 1760| AUS| 0.0|AUS-EWR|(4657,[1768],[1.0])|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+-------------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "# Show a sample of the output\n", "features_with_route_vec.sample(False, 0.001, 11).show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the thing exploded in size, up to 4657 routes.\n", "\n", "### Recreate `NumericFeatures_vec`\n", "We need to recreate the vectors for our numeric columns, `DepDelay` and `Distance`." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+------------------+-------------------+\n", "|ArrDelay| CRSArrTime| CRSDepTime|Carrier|DayOfMonth|DayOfWeek|DayOfYear|DepDelay|Dest|Distance|FlightDate|FlightNum|Origin|ArrDelayBucket| Route| Route_vec|NumericFeatures_vec|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+------------------+-------------------+\n", "| 13.0|2015-01-01 10:10:...|2015-01-01 07:30:...| AA| 1| 4| 1| 14.0| DFW| 569.0|2014-12-31| 1024| ABQ| 2.0|ABQ-DFW|(4657,[938],[1.0])| [14.0,569.0]|\n", "| 17.0|2015-01-01 02:15:...|2014-12-31 23:25:...| AA| 1| 4| 1| 14.0| DFW| 569.0|2014-12-31| 1184| ABQ| 2.0|ABQ-DFW|(4657,[938],[1.0])| [14.0,569.0]|\n", "| 36.0|2015-01-01 03:45:...|2015-01-01 01:00:...| AA| 1| 4| 1| -2.0| DFW| 569.0|2014-12-31| 336| ABQ| 3.0|ABQ-DFW|(4657,[938],[1.0])| [-2.0,569.0]|\n", "| -21.0|2015-01-01 11:30:...|2015-01-01 09:55:...| AA| 1| 4| 1| -1.0| DFW| 731.0|2014-12-31| 125| ATL| 0.0|ATL-DFW| (4657,[37],[1.0])| [-1.0,731.0]|\n", "| -14.0|2015-01-01 02:25:...|2015-01-01 00:55:...| AA| 1| 4| 1| -4.0| DFW| 731.0|2014-12-31| 1455| ATL| 1.0|ATL-DFW| (4657,[37],[1.0])| [-4.0,731.0]|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+------------------+-------------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "# Load the numeric vector assembler\n", "from pyspark.ml.feature import VectorAssembler\n", "vector_assembler_path = \"{}/models/numeric_vector_assembler.bin\".format(base_path)\n", "vector_assembler = VectorAssembler.load(vector_assembler_path)\n", "\n", "# Vectorize numeric columns: DepDelay and Distance\n", "features_with_route_and_numeric = vector_assembler.transform(features_with_route_vec)\n", "features_with_route_and_numeric.show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Combine `Route_vec` with `NumericFeatures_vec`\n", "Now we need to combine our new `Route_vec` with our numeric features. \n", "\n", "First we create and save the new `VectorAssembler`" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Created and stored VectorAssembler...\n" ] } ], "source": [ "# Create and save the VectorAssembler\n", "route_feature_columns = [\"Route_vec\", \"NumericFeatures_vec\"]\n", "route_final_assembler = VectorAssembler(\n", " inputCols=route_feature_columns,\n", " outputCol=\"Features_vec\"\n", ")\n", "route_final_assembler_path = \"{}/models/final_vector_assembler_simplified_with_route.bin\".format(base_path)\n", "route_final_assembler.write().overwrite().save(route_final_assembler_path)\n", "\n", "print(\"Created and stored VectorAssembler...\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Apply the `VectorAssembler` via `transform` and check it out." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+-------------------+-------------------+--------------------+\n", "|ArrDelay| CRSArrTime| CRSDepTime|Carrier|DayOfMonth|DayOfWeek|DayOfYear|DepDelay|Dest|Distance|FlightDate|FlightNum|Origin|ArrDelayBucket| Route| Route_vec|NumericFeatures_vec| Features_vec|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+-------------------+-------------------+--------------------+\n", "| 32.0|2015-01-01 04:35:...|2015-01-01 03:10:...| AA| 1| 4| 1| -1.0| HDN| 769.0|2014-12-31| 35| DFW| 3.0|DFW-HDN|(4657,[3987],[1.0])| [-1.0,769.0]|(4659,[3987,4657,...|\n", "| -10.0|2015-01-01 05:20:...|2015-01-01 03:55:...| AA| 1| 4| 1| -5.0| DFW| 328.0|2014-12-31| 2377| ICT| 1.0|ICT-DFW|(4657,[2142],[1.0])| [-5.0,328.0]|(4659,[2142,4657,...|\n", "| -8.0|2015-01-01 05:00:...|2015-01-01 01:45:...| AA| 1| 4| 1| -6.0| MIA| 1096.0|2014-12-31| 1084| LGA| 1.0|LGA-MIA| (4657,[64],[1.0])| [-6.0,1096.0]|(4659,[64,4657,46...|\n", "| 52.0|2015-01-01 06:45:...|2015-01-01 00:40:...| AA| 1| 4| 1| 54.0| ORD| 1671.0|2014-12-31| 1033| RNO| 3.0|RNO-ORD|(4657,[3108],[1.0])| [54.0,1671.0]|(4659,[3108,4657,...|\n", "| -6.0|2015-01-01 14:18:...|2015-01-01 10:50:...| B6| 1| 4| 1| -2.0| SJU| 1046.0|2014-12-31| 1253| FLL| 1.0|FLL-SJU| (4657,[435],[1.0])| [-2.0,1046.0]|(4659,[435,4657,4...|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+-------------------+-------------------+--------------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "final_vectorized_features_with_route = route_final_assembler.transform(features_with_route_and_numeric)\n", "final_vectorized_features_with_route.sample(False, 0.001, 99).show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And drop the intermediate feature vector columns." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+--------------------+\n", "|ArrDelay| CRSArrTime| CRSDepTime|Carrier|DayOfMonth|DayOfWeek|DayOfYear|DepDelay|Dest|Distance|FlightDate|FlightNum|Origin|ArrDelayBucket| Route| Features_vec|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+--------------------+\n", "| -2.0|2015-01-01 06:19:...|2015-01-01 04:35:...| B6| 1| 4| 1| -1.0| IAD| 413.0|2014-12-31| 457| BOS| 1.0|BOS-IAD|(4659,[774,4657,4...|\n", "| -34.0|2015-01-01 11:38:...|2015-01-01 08:10:...| B6| 1| 4| 1| -3.0| LAX| 2475.0|2014-12-31| 423| JFK| 0.0|JFK-LAX|(4659,[2,4657,465...|\n", "| -11.0|2015-01-01 09:54:...|2015-01-01 09:30:...| DL| 1| 4| 1| -1.0| MEM| 332.0|2014-12-31| 1587| ATL| 1.0|ATL-MEM|(4659,[354,4657,4...|\n", "| -4.0|2015-01-01 13:02:...|2015-01-01 10:15:...| DL| 1| 4| 1| 25.0| ATL| 760.0|2014-12-31| 2510| JFK| 1.0|JFK-ATL|(4659,[855,4657,4...|\n", "| -5.0|2015-01-01 00:50:...|2014-12-31 23:30:...| EV| 1| 4| 1| -6.0| ORD| 632.0|2014-12-31| 5948| AVP| 1.0|AVP-ORD|(4659,[2836,4657,...|\n", "| 3.0|2015-01-01 03:26:...|2015-01-01 02:11:...| EV| 1| 4| 1| 7.0| IAD| 212.0|2014-12-31| 5976| EWR| 2.0|EWR-IAD|(4659,[2398,4657,...|\n", "| -11.0|2015-01-01 06:11:...|2015-01-01 03:40:...| EV| 1| 4| 1| -4.0| ORD| 794.0|2014-12-31| 4103| PNS| 1.0|PNS-ORD|(4659,[3740,4657,...|\n", "| -12.0|2015-01-01 04:55:...|2015-01-01 03:20:...| MQ| 1| 4| 1| -3.0| ORD| 438.0|2014-12-31| 3010| SGF| 1.0|SGF-ORD|(4659,[840,4657,4...|\n", "| 18.0|2015-01-01 11:00:...|2015-01-01 08:10:...| NK| 1| 4| 1| 27.0| LGA| 1076.0|2014-12-31| 174| FLL| 2.0|FLL-LGA|(4659,[111,4657,4...|\n", "| -18.0|2015-01-01 07:26:...|2015-01-01 06:20:...| OO| 1| 4| 1| -10.0| SFO| 190.0|2014-12-31| 5385| SBP| 0.0|SBP-SFO|(4659,[1652,4657,...|\n", "| 63.0|2015-01-01 15:19:...|2015-01-01 12:00:...| UA| 1| 4| 1| 21.0| ORD| 888.0|2014-12-31| 354| DEN| 3.0|DEN-ORD|(4659,[97,4657,46...|\n", "| 14.0|2015-01-01 10:25:...|2015-01-01 07:35:...| UA| 1| 4| 1| 9.0| EWR| 937.0|2014-12-31| 1100| MCO| 2.0|MCO-EWR|(4659,[145,4657,4...|\n", "| -12.0|2015-01-01 04:52:...|2015-01-01 03:30:...| US| 1| 4| 1| -11.0| BOS| 399.0|2014-12-31| 2112| DCA| 1.0|DCA-BOS|(4659,[24,4657,46...|\n", "| 115.0|2015-01-02 12:30:...|2015-01-02 10:35:...| AA| 2| 5| 2| 114.0| DFW| 985.0|2015-01-01| 1252| MCO| 3.0|MCO-DFW|(4659,[250,4657,4...|\n", "| -8.0|2015-01-02 06:25:...|2015-01-02 01:35:...| AA| 2| 5| 2| -5.0| DFW| 1171.0|2015-01-01| 36| SAN| 1.0|SAN-DFW|(4659,[321,4657,4...|\n", "| 21.0|2015-01-02 11:44:...|2015-01-02 10:30:...| DL| 2| 5| 2| 18.0| ATL| 214.0|2015-01-01| 1475| SAV| 2.0|SAV-ATL|(4659,[340,4657,4...|\n", "| 0.0|2015-01-02 09:10:...|2015-01-02 07:00:...| EV| 2| 5| 2| 9.0| ORD| 584.0|2015-01-01| 6132| BHM| 2.0|BHM-ORD|(4659,[1672,4657,...|\n", "| -29.0|2015-01-02 12:17:...|2015-01-02 12:08:...| OO| 2| 5| 2| -20.0| ORD| 157.0|2015-01-01| 5322| FWA| 0.0|FWA-ORD|(4659,[671,4657,4...|\n", "| 3.0|2015-01-02 07:21:...|2015-01-02 06:35:...| OO| 2| 5| 2| 4.0| FLG| 119.0|2015-01-01| 6584| PHX| 2.0|PHX-FLG|(4659,[1028,4657,...|\n", "| 6.0|2015-01-02 01:24:...|2015-01-02 00:25:...| UA| 2| 5| 2| 0.0| IAH| 140.0|2015-01-01| 1014| AUS| 2.0|AUS-IAH|(4659,[729,4658],...|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+--------------------+\n", "only showing top 20 rows\n", "\n" ] } ], "source": [ "for column in route_feature_columns:\n", " final_vectorized_features_with_route = final_vectorized_features_with_route.drop(column)\n", "\n", "final_vectorized_features_with_route.sample(False, 0.001, 12).show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a New Random Forest Classifier Model\n", "We need a new model that is fitted with our new feature vectors." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "# Instantiate and fit random forest classifier on all the data\n", "from pyspark.ml.classification import RandomForestClassifier, RandomForestClassificationModel\n", "rfc = RandomForestClassifier(featuresCol=\"Features_vec\", labelCol=\"ArrDelayBucket\", predictionCol=\"Prediction\")\n", "model = rfc.fit(final_vectorized_features)\n", "# Save the new model over the old one\n", "model_output_path = \"{}/models/spark_random_forest_classifier.flight_delays_simplified_with_route.bin\".format(\n", " base_path\n", ")\n", "model.write().overwrite().save(model_output_path)\n", "print(\"Model trained on 5.7MM items successfully!\")" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+--------------------+--------------------+--------------------+----------+\n", "|ArrDelay| CRSArrTime| CRSDepTime|Carrier|DayOfMonth|DayOfWeek|DayOfYear|DepDelay|Dest|Distance|FlightDate|FlightNum|Origin|ArrDelayBucket| Features_vec| rawPrediction| probability|Prediction|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+--------------------+--------------------+--------------------+----------+\n", "| 13.0|2015-01-01 10:10:...|2015-01-01 07:30:...| AA| 1| 4| 1| 14.0| DFW| 569.0|2014-12-31| 1024| ABQ| 2.0|(646,[53,324,644,...|[3.66319242779629...|[0.18315962138981...| 1.0|\n", "| 17.0|2015-01-01 02:15:...|2014-12-31 23:25:...| AA| 1| 4| 1| 14.0| DFW| 569.0|2014-12-31| 1184| ABQ| 2.0|(646,[53,324,644,...|[3.66319242779629...|[0.18315962138981...| 1.0|\n", "| 36.0|2015-01-01 03:45:...|2015-01-01 01:00:...| AA| 1| 4| 1| -2.0| DFW| 569.0|2014-12-31| 336| ABQ| 3.0|(646,[53,324,644,...|[3.85744835938781...|[0.19287241796939...| 1.0|\n", "| -21.0|2015-01-01 11:30:...|2015-01-01 09:55:...| AA| 1| 4| 1| -1.0| DFW| 731.0|2014-12-31| 125| ATL| 0.0|(646,[0,324,644,6...|[4.16869700315495...|[0.20843485015774...| 1.0|\n", "| -14.0|2015-01-01 02:25:...|2015-01-01 00:55:...| AA| 1| 4| 1| -4.0| DFW| 731.0|2014-12-31| 1455| ATL| 1.0|(646,[0,324,644,6...|[4.16869700315495...|[0.20843485015774...| 1.0|\n", "| 16.0|2015-01-01 07:15:...|2015-01-01 05:45:...| AA| 1| 4| 1| 15.0| DFW| 731.0|2014-12-31| 1473| ATL| 2.0|(646,[0,324,644,6...|[4.16869700315495...|[0.20843485015774...| 1.0|\n", "| -7.0|2015-01-01 04:15:...|2015-01-01 02:45:...| AA| 1| 4| 1| -2.0| DFW| 731.0|2014-12-31| 1513| ATL| 1.0|(646,[0,324,644,6...|[4.16869700315495...|[0.20843485015774...| 1.0|\n", "| 13.0|2015-01-01 08:50:...|2015-01-01 07:25:...| AA| 1| 4| 1| 9.0| DFW| 731.0|2014-12-31| 194| ATL| 2.0|(646,[0,324,644,6...|[4.16869700315495...|[0.20843485015774...| 1.0|\n", "| 25.0|2015-01-01 12:30:...|2015-01-01 11:00:...| AA| 1| 4| 1| -2.0| DFW| 731.0|2014-12-31| 232| ATL| 2.0|(646,[0,324,644,6...|[4.16869700315495...|[0.20843485015774...| 1.0|\n", "| 58.0|2015-01-01 13:40:...|2015-01-01 12:15:...| AA| 1| 4| 1| 14.0| DFW| 731.0|2014-12-31| 276| ATL| 3.0|(646,[0,324,644,6...|[4.16869700315495...|[0.20843485015774...| 1.0|\n", "| 14.0|2015-01-01 05:25:...|2015-01-01 03:55:...| AA| 1| 4| 1| 15.0| DFW| 731.0|2014-12-31| 314| ATL| 2.0|(646,[0,324,644,6...|[4.16869700315495...|[0.20843485015774...| 1.0|\n", "| 1.0|2015-01-01 10:05:...|2015-01-01 08:40:...| AA| 1| 4| 1| -5.0| DFW| 731.0|2014-12-31| 356| ATL| 2.0|(646,[0,324,644,6...|[4.16869700315495...|[0.20843485015774...| 1.0|\n", "| -29.0|2015-01-01 02:12:...|2015-01-01 00:15:...| AA| 1| 4| 1| -9.0| MIA| 594.0|2014-12-31| 1652| ATL| 0.0|(646,[0,346,644,6...|[3.68497926035629...|[0.18424896301781...| 1.0|\n", "| -10.0|2015-01-01 00:52:...|2014-12-31 23:00:...| AA| 1| 4| 1| -4.0| MIA| 594.0|2014-12-31| 17| ATL| 1.0|(646,[0,346,644,6...|[3.68497926035629...|[0.18424896301781...| 1.0|\n", "| -3.0|2015-01-01 15:02:...|2015-01-01 13:10:...| AA| 1| 4| 1| -7.0| MIA| 594.0|2014-12-31| 349| ATL| 1.0|(646,[0,346,644,6...|[3.68497926035629...|[0.18424896301781...| 1.0|\n", "| -8.0|2015-01-01 06:35:...|2015-01-01 05:30:...| AA| 1| 4| 1| -2.0| DFW| 190.0|2014-12-31| 1023| AUS| 1.0|(646,[34,324,644,...|[3.78344522743971...|[0.18917226137198...| 1.0|\n", "| -1.0|2014-12-31 22:50:...|2014-12-31 21:50:...| AA| 1| 4| 1| -2.0| DFW| 190.0|2014-12-31| 1178| AUS| 1.0|(646,[34,324,644,...|[3.78344522743971...|[0.18917226137198...| 1.0|\n", "| -14.0|2015-01-01 01:40:...|2015-01-01 00:30:...| AA| 1| 4| 1| -6.0| DFW| 190.0|2014-12-31| 1296| AUS| 1.0|(646,[34,324,644,...|[3.78344522743971...|[0.18917226137198...| 1.0|\n", "| -16.0|2015-01-01 02:15:...|2015-01-01 01:05:...| AA| 1| 4| 1| -4.0| DFW| 190.0|2014-12-31| 1356| AUS| 0.0|(646,[34,324,644,...|[3.78344522743971...|[0.18917226137198...| 1.0|\n", "| 18.0|2015-01-01 08:55:...|2015-01-01 07:55:...| AA| 1| 4| 1| 3.0| DFW| 190.0|2014-12-31| 1365| AUS| 2.0|(646,[34,324,644,...|[3.78344522743971...|[0.18917226137198...| 1.0|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+--------------------+--------------------+--------------------+----------+\n", "only showing top 20 rows\n", "\n", "+----------+-------+\n", "|Prediction| count|\n", "+----------+-------+\n", "| 1.0|5713778|\n", "| 3.0| 230|\n", "+----------+-------+\n", "\n" ] } ], "source": [ "predictions = model.transform(final_vectorized_features)\n", "predictions.groupBy(\"Prediction\").count().show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Try Just StringIndexer\n", "The internet says not to use `OneHotEncoder`, so I'll try that." ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+-----------+\n", "|ArrDelay| CRSArrTime| CRSDepTime|Carrier|DayOfMonth|DayOfWeek|DayOfYear|DepDelay|Dest|Distance|FlightDate|FlightNum|Origin|ArrDelayBucket| Route|Route_index|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+-----------+\n", "| 13.0|2015-01-01 10:10:...|2015-01-01 07:30:...| AA| 1| 4| 1| 14.0| DFW| 569.0|2014-12-31| 1024| ABQ| 2.0|ABQ-DFW| 938.0|\n", "| 17.0|2015-01-01 02:15:...|2014-12-31 23:25:...| AA| 1| 4| 1| 14.0| DFW| 569.0|2014-12-31| 1184| ABQ| 2.0|ABQ-DFW| 938.0|\n", "| 36.0|2015-01-01 03:45:...|2015-01-01 01:00:...| AA| 1| 4| 1| -2.0| DFW| 569.0|2014-12-31| 336| ABQ| 3.0|ABQ-DFW| 938.0|\n", "| -21.0|2015-01-01 11:30:...|2015-01-01 09:55:...| AA| 1| 4| 1| -1.0| DFW| 731.0|2014-12-31| 125| ATL| 0.0|ATL-DFW| 37.0|\n", "| -14.0|2015-01-01 02:25:...|2015-01-01 00:55:...| AA| 1| 4| 1| -4.0| DFW| 731.0|2014-12-31| 1455| ATL| 1.0|ATL-DFW| 37.0|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+-----------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "from pyspark.ml import Pipeline\n", "from pyspark.ml.feature import StringIndexer, OneHotEncoder, VectorIndexer\n", "\n", "route_string_indexer = StringIndexer(\n", " inputCol='Route',\n", " outputCol='Route' + \"_index\"\n", ")\n", "route_string_indexer_model = route_string_indexer.fit(features_with_route)\n", "\n", "# Fit/transform to apply to the data\n", "features_with_route_index = route_string_indexer_model.transform(features_with_route)\n", "features_with_route_index.show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now compose the `Route_index` with the `DepDelay` and `Distance` columns" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Handle continuous, numeric fields by combining them into one feature vector\n", "new_numeric_columns = [\"DepDelay\", \"Distance\", \"Route_index\"]\n", "route_index_vector_assembler = VectorAssembler(\n", " inputCols=new_numeric_columns,\n", " outputCol=\"Features_vec\"\n", ")\n", "final_route_index_features = route_index_vector_assembler.transform(features_with_route_index)\n", "\n", "# Save the numeric vector assembler\n", "route_index_vector_assembler_path = \"{}/models/numeric_vector_assembler_route_index.bin\".format(base_path)\n", "route_index_vector_assembler.write().overwrite().save(route_index_vector_assembler_path)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+-----------+--------------------+\n", "|ArrDelay| CRSArrTime| CRSDepTime|Carrier|DayOfMonth|DayOfWeek|DayOfYear|DepDelay|Dest|Distance|FlightDate|FlightNum|Origin|ArrDelayBucket| Route|Route_index| Features_vec|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+-----------+--------------------+\n", "| -19.0|2015-01-01 13:00:...|2015-01-01 04:45:...| AA| 1| 4| 1| 0.0| BOS| 2611.0|2014-12-31| 202| LAX| 0.0|LAX-BOS| 206.0| [0.0,2611.0,206.0]|\n", "| -7.0|2015-01-01 07:14:...|2015-01-01 05:30:...| AS| 1| 4| 1| -6.0| SEA| 605.0|2014-12-31| 359| SMF| 1.0|SMF-SEA| 280.0| [-6.0,605.0,280.0]|\n", "| 0.0|2015-01-01 10:18:...|2015-01-01 07:55:...| B6| 1| 4| 1| 7.0| DTW| 632.0|2014-12-31| 1237| BOS| 2.0|BOS-DTW| 299.0| [7.0,632.0,299.0]|\n", "| -27.0|2015-01-01 03:30:...|2014-12-31 23:45:...| B6| 1| 4| 1| -10.0| SJU| 1189.0|2014-12-31| 1033| MCO| 0.0|MCO-SJU| 281.0|[-10.0,1189.0,281.0]|\n", "| -15.0|2015-01-01 06:29:...|2015-01-01 04:12:...| DL| 1| 4| 1| 3.0| JFK| 760.0|2014-12-31| 1218| ATL| 1.0|ATL-JFK| 852.0| [3.0,760.0,852.0]|\n", "+--------+--------------------+--------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+--------------+-------+-----------+--------------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "final_route_index_features.sample(False,0.001,88).show(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Train a new model on this new set of features." ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model trained on 5.7MM items successfully!\n" ] } ], "source": [ "# Instantiate and fit random forest classifier on all the data\n", "from pyspark.ml.classification import RandomForestClassifier, RandomForestClassificationModel\n", "rfc = RandomForestClassifier(featuresCol=\"Features_vec\", labelCol=\"ArrDelayBucket\", predictionCol=\"Prediction\", maxBins=4657)\n", "model = rfc.fit(final_route_index_features)\n", "# Save the new model over the old one\n", "model_output_path = \"{}/models/spark_random_forest_classifier.flight_delays_simplified_with_route_index.bin\".format(\n", " base_path\n", ")\n", "model.write().overwrite().save(model_output_path)\n", "print(\"Model trained on 5.7MM items successfully!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And make predictions on the training data and inspect their distribution." ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+----------+-------+\n", "|Prediction| count|\n", "+----------+-------+\n", "| 0.0| 1165|\n", "| 1.0|4143264|\n", "| 3.0| 565640|\n", "| 2.0|1003939|\n", "+----------+-------+\n", "\n" ] } ], "source": [ "predictions = model.transform(final_route_index_features)\n", "predictions.groupBy(\"Prediction\").count().show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It works!" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------+-------+\n", "|ArrDelayBucket| count|\n", "+--------------+-------+\n", "| 0.0|1098212|\n", "| 1.0|2402687|\n", "| 3.0| 650604|\n", "| 2.0|1562505|\n", "+--------------+-------+\n", "\n" ] } ], "source": [ "predictions.groupBy(\"ArrDelayBucket\").count().show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solution! We Sovled the Problem!\n", "\n", "We were mistaken in using `OneHotEncoder` on the values of `StringIndexerModel`, we should have just used `VectorAssembler` directly on the string indexes. After changing the `maxBins` parameter of our `RandomForestClassifier`, it works!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
Adobe-Marketing-Cloud/python-pandas-lab
.ipynb_checkpoints/Lesson #03ea -- Exercise Answers Manipulate Data-checkpoint.ipynb
1
1841
{ "metadata": { "name": "", "signature": "sha256:9ec1ff1d790d7cffcc6348d0fed674f97724768886dd6d5dc079f1172c836718" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Lesson #03 Exercise " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Time :10 min** \n", "\n", "In this exercise I want you to play with the dataframes. \n", "\n", "Do the Following\n", "* Run the cells below to pull back some data\n", "* print out the 1st 10 records of the dataframe\n", "* Find the mean and std deviation for page views\n", "* Print out a list of just the `page` on the site\n", "* plot the dataframe\n", "\n", "Optional (Advanced)\n", "* Filter down to the top 10 results\n", "* Plot the top 10 results in a bar chart \n", "* Plot a scatter chart of pageview and visits to each page\n", "* Create a new metric of pageivews per visit (which one has the highest?)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import omniture\n", "\n", "an = omniture.authenticate(\"labuser:Lab L721\",\"5b00fe37eb2659d0f3a1231cb3d803f9\")\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "suite = an.suites['omniture.devportal']\n", "report = suite.report.element('page', top=50).metric('pageviews').metric('visits').range('2015-01-31','2015-03-15').run()" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
apache-2.0
mcocdawc/chemcoord
Tutorial/Advanced_customisation.ipynb
1
4519
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import chemcoord as cc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Settings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Settings can be seen here:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'defaults': {'atomic_radius_data': 'atomic_radius_cc',\n", " 'use_lookup': False,\n", " 'viewer': 'gv.exe'}}" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cc.configuration.settings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A configuration file can be written with:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cc.configuration.write_configuration_file('./example_configuration_file', overwrite=True)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%less example_configuration_file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is read automatically during startup from ``'~/.chemcoordrc'``.\n", "Otherwise it is possible to explicitly call ``cc.configuration.read_configuration_file(...)``" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "!rm example_configuration_file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Inheritance\n", "\n", "You can safely inherit from the classes in this module" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class my_tailored_class(cc.Cartesian):\n", " def my_number_one_method(self):\n", " return 1" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "chemcoord.cartesian_coordinates.cartesian_class_main.Cartesian" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "molecule = cc.Cartesian.read_xyz('MIL53_small.xyz')\n", "type(molecule)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice how all old methods from Cartesian return an object of your tailored class" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "__main__.my_tailored_class" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_molecule = my_tailored_class.read_xyz('MIL53_small.xyz')\n", "type(my_molecule)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "__main__.my_tailored_class" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(my_molecule.get_inertia()['transformed_Cartesian'])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_molecule.get_inertia()['transformed_Cartesian'].my_number_one_method()" ] }, { "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": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }
lgpl-3.0
deepmind/neural-processes
attentive_neural_process.ipynb
1
544117
{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "opensource.ipynb", "version": "0.3.2", "provenance": [], "collapsed_sections": [], "last_runtime": { "build_target": "//learning/deepmind/dm_python:dm_notebook", "kind": "private" } }, "kernelspec": { "name": "python2", "display_name": "Python 2" } }, "cells": [ { "metadata": { "id": "eQ7K9blSUKTs", "colab_type": "text" }, "cell_type": "markdown", "source": [ "Copyright 2019 Google LLC\n", "\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", "\n", "https://www.apache.org/licenses/LICENSE-2.0\n", "\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.\n", "\n", "# (Attentive) Neural Processes for 1D regression\n", "\n", "Regression is usually cast as modelling the distribution of output **y** given input **x** via a deterministic function, such as a neural network, taking **x** as input. In this setting, the model is trained on a dataset of input-output pairs, and predictions of the outputs are independent of each other given the inputs. An alternative approach to regression involves using the training data to compute a distribution over functions that map inputs to outputs, and using draws from that distribution to make predictions on test inputs. This approach allows for reasoning about multiple functions consistent with the data, and can capture the co-variability in outputs given inputs. In the Bayesian machine learning literature, non-parametric models such as Gaussian Processes (GPs) are popular choices of this approach.\n", "\n", "[Neural Processes](https://arxiv.org/abs/1807.01622) (NPs) also approach regression by modelling a distribution over regression functions. Each function models the distribution of the output given an input, conditioning on some observed input-output pairs, which we call the context. Modelling this distribution over functions was made possible by incorporating a latent variable to the [Conditional Neural Process](https://arxiv.org/abs/1807.01613) (CNP). \n", "\n", "However NPs suffer from underfitting, giving inaccurate predictions at the inputs of the observed data they condition on. Hence [Attentive Neural Processes](https://arxiv.org/abs/1901.05761) (ANPs) were introduced, addressing this issue by incorporating attention into NPs. We share the implementation of ANPs (and NPs, which is a special case of ANP with uniform attention) for a 1D regression task where (A)NPs are trained on random 1D functions." ] }, { "metadata": { "id": "UMJxjfsTa08h", "colab_type": "text" }, "cell_type": "markdown", "source": [ "## Imports" ] }, { "metadata": { "id": "Ncb7M0FpNfix", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "import tensorflow as tf\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import collections" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "ARGYAmEsa5K7", "colab_type": "text" }, "cell_type": "markdown", "source": [ "## Data generator\n", "Instead of training using observations from a single function (as in classic regression tasks), we would like to train on a dataset that comes from multiple functions with shared characteristics. Hence for training, we use data that comes from a Gaussian Process (GP) with randomly varying kernel parameters. At each training iteration, we sample a batch of random kernel parameters, and for each parameter setting we sample a curve (a realisation) from the corresponding GP. We select random points on each curve to be the targets and a subset to be the contexts for optimising the training loss. The data generation is almost the same as for the [implementation of CNPs](https://github.com/deepmind/conditional-neural-process/blob/master/conditional_neural_process.ipynb), but with kernel parameters varying randomly at each iteration." ] }, { "metadata": { "id": "Px-atGEfNnWT", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "# The (A)NP takes as input a `NPRegressionDescription` namedtuple with fields:\n", "# `query`: a tuple containing ((context_x, context_y), target_x)\n", "# `target_y`: a tensor containing the ground truth for the targets to be\n", "# predicted\n", "# `num_total_points`: A vector containing a scalar that describes the total\n", "# number of datapoints used (context + target)\n", "# `num_context_points`: A vector containing a scalar that describes the number\n", "# of datapoints used as context\n", "# The GPCurvesReader returns the newly sampled data in this format at each\n", "# iteration\n", "\n", "NPRegressionDescription = collections.namedtuple(\n", " \"NPRegressionDescription\",\n", " (\"query\", \"target_y\", \"num_total_points\", \"num_context_points\"))\n", "\n", "\n", "class GPCurvesReader(object):\n", " \"\"\"Generates curves using a Gaussian Process (GP).\n", "\n", " Supports vector inputs (x) and vector outputs (y). Kernel is\n", " mean-squared exponential, using the x-value l2 coordinate distance scaled by\n", " some factor chosen randomly in a range. Outputs are independent gaussian\n", " processes.\n", " \"\"\"\n", "\n", " def __init__(self,\n", " batch_size,\n", " max_num_context,\n", " x_size=1,\n", " y_size=1,\n", " l1_scale=0.6,\n", " sigma_scale=1.0,\n", " random_kernel_parameters=True,\n", " testing=False):\n", " \"\"\"Creates a regression dataset of functions sampled from a GP.\n", "\n", " Args:\n", " batch_size: An integer.\n", " max_num_context: The max number of observations in the context.\n", " x_size: Integer >= 1 for length of \"x values\" vector.\n", " y_size: Integer >= 1 for length of \"y values\" vector.\n", " l1_scale: Float; typical scale for kernel distance function.\n", " sigma_scale: Float; typical scale for variance.\n", " random_kernel_parameters: If `True`, the kernel parameters (l1 and sigma) \n", " will be sampled uniformly within [0.1, l1_scale] and [0.1, sigma_scale].\n", " testing: Boolean that indicates whether we are testing. If so there are\n", " more targets for visualization.\n", " \"\"\"\n", " self._batch_size = batch_size\n", " self._max_num_context = max_num_context\n", " self._x_size = x_size\n", " self._y_size = y_size\n", " self._l1_scale = l1_scale\n", " self._sigma_scale = sigma_scale\n", " self._random_kernel_parameters = random_kernel_parameters\n", " self._testing = testing\n", "\n", " def _gaussian_kernel(self, xdata, l1, sigma_f, sigma_noise=2e-2):\n", " \"\"\"Applies the Gaussian kernel to generate curve data.\n", "\n", " Args:\n", " xdata: Tensor of shape [B, num_total_points, x_size] with\n", " the values of the x-axis data.\n", " l1: Tensor of shape [B, y_size, x_size], the scale\n", " parameter of the Gaussian kernel.\n", " sigma_f: Tensor of shape [B, y_size], the magnitude\n", " of the std.\n", " sigma_noise: Float, std of the noise that we add for stability.\n", "\n", " Returns:\n", " The kernel, a float tensor of shape\n", " [B, y_size, num_total_points, num_total_points].\n", " \"\"\"\n", " num_total_points = tf.shape(xdata)[1]\n", "\n", " # Expand and take the difference\n", " xdata1 = tf.expand_dims(xdata, axis=1) # [B, 1, num_total_points, x_size]\n", " xdata2 = tf.expand_dims(xdata, axis=2) # [B, num_total_points, 1, x_size]\n", " diff = xdata1 - xdata2 # [B, num_total_points, num_total_points, x_size]\n", "\n", " # [B, y_size, num_total_points, num_total_points, x_size]\n", " norm = tf.square(diff[:, None, :, :, :] / l1[:, :, None, None, :])\n", "\n", " norm = tf.reduce_sum(\n", " norm, -1) # [B, data_size, num_total_points, num_total_points]\n", "\n", " # [B, y_size, num_total_points, num_total_points]\n", " kernel = tf.square(sigma_f)[:, :, None, None] * tf.exp(-0.5 * norm)\n", "\n", " # Add some noise to the diagonal to make the cholesky work.\n", " kernel += (sigma_noise**2) * tf.eye(num_total_points)\n", "\n", " return kernel\n", "\n", " def generate_curves(self):\n", " \"\"\"Builds the op delivering the data.\n", "\n", " Generated functions are `float32` with x values between -2 and 2.\n", " \n", " Returns:\n", " A `NPRegressionDescription` namedtuple.\n", " \"\"\"\n", " num_context = tf.random_uniform(\n", " shape=[], minval=3, maxval=self._max_num_context, dtype=tf.int32)\n", "\n", " # If we are testing we want to have more targets and have them evenly\n", " # distributed in order to plot the function.\n", " if self._testing:\n", " num_target = 400\n", " num_total_points = num_target\n", " x_values = tf.tile(\n", " tf.expand_dims(tf.range(-2., 2., 1. / 100, dtype=tf.float32), axis=0),\n", " [self._batch_size, 1])\n", " x_values = tf.expand_dims(x_values, axis=-1)\n", " # During training the number of target points and their x-positions are\n", " # selected at random\n", " else:\n", " num_target = tf.random_uniform(shape=(), minval=0, \n", " maxval=self._max_num_context - num_context,\n", " dtype=tf.int32)\n", " num_total_points = num_context + num_target\n", " x_values = tf.random_uniform(\n", " [self._batch_size, num_total_points, self._x_size], -2, 2)\n", "\n", " # Set kernel parameters\n", " # Either choose a set of random parameters for the mini-batch\n", " if self._random_kernel_parameters:\n", " l1 = tf.random_uniform([self._batch_size, self._y_size,\n", " self._x_size], 0.1, self._l1_scale)\n", " sigma_f = tf.random_uniform([self._batch_size, self._y_size],\n", " 0.1, self._sigma_scale)\n", " # Or use the same fixed parameters for all mini-batches\n", " else:\n", " l1 = tf.ones(shape=[self._batch_size, self._y_size,\n", " self._x_size]) * self._l1_scale\n", " sigma_f = tf.ones(shape=[self._batch_size,\n", " self._y_size]) * self._sigma_scale\n", "\n", " # Pass the x_values through the Gaussian kernel\n", " # [batch_size, y_size, num_total_points, num_total_points]\n", " kernel = self._gaussian_kernel(x_values, l1, sigma_f)\n", "\n", " # Calculate Cholesky, using double precision for better stability:\n", " cholesky = tf.cast(tf.cholesky(tf.cast(kernel, tf.float64)), tf.float32)\n", "\n", " # Sample a curve\n", " # [batch_size, y_size, num_total_points, 1]\n", " y_values = tf.matmul(\n", " cholesky,\n", " tf.random_normal([self._batch_size, self._y_size, num_total_points, 1]))\n", "\n", " # [batch_size, num_total_points, y_size]\n", " y_values = tf.transpose(tf.squeeze(y_values, 3), [0, 2, 1])\n", "\n", " if self._testing:\n", " # Select the targets\n", " target_x = x_values\n", " target_y = y_values\n", "\n", " # Select the observations\n", " idx = tf.random_shuffle(tf.range(num_target))\n", " context_x = tf.gather(x_values, idx[:num_context], axis=1)\n", " context_y = tf.gather(y_values, idx[:num_context], axis=1)\n", "\n", " else:\n", " # Select the targets which will consist of the context points as well as\n", " # some new target points\n", " target_x = x_values[:, :num_target + num_context, :]\n", " target_y = y_values[:, :num_target + num_context, :]\n", "\n", " # Select the observations\n", " context_x = x_values[:, :num_context, :]\n", " context_y = y_values[:, :num_context, :]\n", "\n", " query = ((context_x, context_y), target_x)\n", "\n", " return NPRegressionDescription(\n", " query=query,\n", " target_y=target_y,\n", " num_total_points=tf.shape(target_x)[1],\n", " num_context_points=num_context)" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "GhMwui0VfmNn", "colab_type": "text" }, "cell_type": "markdown", "source": [ "## Attentive Neural Processes: a short introduction\n", "\n", "Here below are the model diagrams for the **NP** (left) and **ANP** (right).\n", "\n", "![](https://i.ibb.co/Js1B7RB/model-figure-new-1-page-001.jpg)\n", "\n", "In the NP, the **context points** $(x_C,y_C)=(x_i, y_i)_{i \\in C}$ are passed through the encoder that consists of two paths, a **deterministic path** and and a **latent path**. \n", "\n", "In the **deterministic path**, each context pair $(x_i,y_i)$ is passed through an MLP (shared parameters across the contexts) to produce representation $r_i$. These are aggregated by taking the mean to produce the deterministic code $r_C$. \n", "\n", "In the **latent path**, a code $s_C$ is computed in a similar manner from the representations $s_i$, and is used to parameterise the distribution of the latent variable $z$, giving the latent distribution $q(z|s_C)$.\n", "\n", "In the decoder, the $r_C$ and $z$ are concatenated alongside $x_*$ and passed through an MLP to produce the parameters of the distribution $p(y_*|x_*,r_C,z)$. \n", "\n", "The motivation for having a global latent is to model different realisations of the data generating stochastic process - each sample of $z$ would correspond to one realisation of the stochastic process. One can define the model using just the deterministic path, just the latent path, or both.\n", "\n", "One problem of the NP is that the **mean-aggregation step in the encoder acts as a bottleneck**: since taking the mean across context representations gives the same weight to each context point, it is difficult for the decoder to learn which context points provide relevant information for a given target prediction. \n", "\n", "This is addressed by ANPs, where the mean-aggregation is replaced by a **cross-attention mechanism** - the target query $x_*$ attends to the key-value pairs $(x_i,r_i)_{i \\in C}$ and assigns weights $w_i$ to each pair to form a query-specific representation $r_*=\\sum_i w_i r_i$. This is precisely where the model allows each query to attend more closely to the context points that it deems relevant for the prediction. Note that if we use uniform attention (all $w_i$ equal), then we revert to the NP.\n", "\n", "Another change is the **self-attention mechanism** that replaces the MLPs in the encoder, used in order to model interactions between the context points. However for the 1D regression task here, we do not use self-attention and resort to the MLP setting as it is shown to be sufficient, and just use cross-attention.\n", "\n", "Learning for both the NP and ANP is done by optimising the ELBO to the log predictive likelihood:\n", "\n", "$$\\log p(y_T|x_T,x_C,y_C) \\geq\n", "\\mathbb{E}_{q(z|s_T)} [\\log p(y_T|x_T,r_C,z)] - KL ( q(z|s_T) \\Vert q(z|s_C) )$$\n", "where $C$ represents contexts and $T$ represents targets." ] }, { "metadata": { "id": "ps97odopnvkv", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "# utility methods\n", "def batch_mlp(input, output_sizes, variable_scope):\n", " \"\"\"Apply MLP to the final axis of a 3D tensor (reusing already defined MLPs).\n", " \n", " Args:\n", " input: input tensor of shape [B,n,d_in].\n", " output_sizes: An iterable containing the output sizes of the MLP as defined \n", " in `basic.Linear`.\n", " variable_scope: String giving the name of the variable scope. If this is set\n", " to be the same as a previously defined MLP, then the weights are reused.\n", " \n", " Returns:\n", " tensor of shape [B,n,d_out] where d_out=output_sizes[-1]\n", " \"\"\"\n", " # Get the shapes of the input and reshape to parallelise across observations\n", " batch_size, _, filter_size = input.shape.as_list()\n", " output = tf.reshape(input, (-1, filter_size))\n", " output.set_shape((None, filter_size))\n", "\n", " # Pass through MLP\n", " with tf.variable_scope(variable_scope, reuse=tf.AUTO_REUSE):\n", " for i, size in enumerate(output_sizes[:-1]):\n", " output = tf.nn.relu(\n", " tf.layers.dense(output, size, name=\"layer_{}\".format(i)))\n", "\n", " # Last layer without a ReLu\n", " output = tf.layers.dense(\n", " output, output_sizes[-1], name=\"layer_{}\".format(i + 1))\n", "\n", " # Bring back into original shape\n", " output = tf.reshape(output, (batch_size, -1, output_sizes[-1]))\n", " return output" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "kli9cfXqt2Jf", "colab_type": "text" }, "cell_type": "markdown", "source": [ "## Encoder: Deterministic Path\n", "\n", "The encoder in the deterministic path is shared between all context pairs and consists of an MLP and an attention module. Each context $x_i$ and $y_i$ are concatenated and passed through the MLP (with relu non-linearities) to output a representation $r_i$. These $(r_i)_{i \\in C}$ and $x_i$ are fed into the cross-attention module, along with query $x_*$ to output a query-specific representation $r_*$. The MLP architecture is given by the `output_sizes` argument, a list of hidden layer sizes, and the `attention` argument which is the cross-attention module defined later on." ] }, { "metadata": { "id": "yXdB68gkPwy2", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "class DeterministicEncoder(object):\n", " \"\"\"The Deterministic Encoder.\"\"\"\n", "\n", " def __init__(self, output_sizes, attention):\n", " \"\"\"(A)NP deterministic encoder.\n", "\n", " Args:\n", " output_sizes: An iterable containing the output sizes of the encoding MLP.\n", " attention: The attention module.\n", " \"\"\"\n", " self._output_sizes = output_sizes\n", " self._attention = attention\n", "\n", " def __call__(self, context_x, context_y, target_x):\n", " \"\"\"Encodes the inputs into one representation.\n", "\n", " Args:\n", " context_x: Tensor of shape [B,observations,d_x]. For this 1D regression\n", " task this corresponds to the x-values.\n", " context_y: Tensor of shape [B,observations,d_y]. For this 1D regression\n", " task this corresponds to the y-values.\n", " target_x: Tensor of shape [B,target_observations,d_x]. \n", " For this 1D regression task this corresponds to the x-values.\n", "\n", " Returns:\n", " The encoded representation. Tensor of shape [B,target_observations,d]\n", " \"\"\"\n", "\n", " # Concatenate x and y along the filter axes\n", " encoder_input = tf.concat([context_x, context_y], axis=-1)\n", "\n", " # Pass final axis through MLP\n", " hidden = batch_mlp(encoder_input, self._output_sizes, \n", " \"deterministic_encoder\")\n", "\n", " # Apply attention\n", " with tf.variable_scope(\"deterministic_encoder\", reuse=tf.AUTO_REUSE):\n", " hidden = self._attention(context_x, target_x, hidden)\n", "\n", " return hidden\n" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "1944scGst6fS", "colab_type": "text" }, "cell_type": "markdown", "source": [ "## Encoder: Latent Path\n", "\n", "The encoder in the latent path is again shared by all context pairs and consists of an MLP. After the MLP (with relu non-linearities) is applied the resulting representation is aggregated by taking the mean across the contexts and a further MLP is applied to compute the mean and variance of the Gaussian $q(z|s_C)$. Again the initial MLP's architecture is given by the `output_sizes` argument, and the `num_latent` argument sets the latent dimensionality." ] }, { "metadata": { "id": "gufhxfGSRCs9", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "class LatentEncoder(object):\n", " \"\"\"The Latent Encoder.\"\"\"\n", "\n", " def __init__(self, output_sizes, num_latents):\n", " \"\"\"(A)NP latent encoder.\n", "\n", " Args:\n", " output_sizes: An iterable containing the output sizes of the encoding MLP.\n", " num_latents: The latent dimensionality.\n", " \"\"\"\n", " self._output_sizes = output_sizes\n", " self._num_latents = num_latents\n", "\n", " def __call__(self, x, y):\n", " \"\"\"Encodes the inputs into one representation.\n", "\n", " Args:\n", " x: Tensor of shape [B,observations,d_x]. For this 1D regression\n", " task this corresponds to the x-values.\n", " y: Tensor of shape [B,observations,d_y]. For this 1D regression\n", " task this corresponds to the y-values.\n", "\n", " Returns:\n", " A normal distribution over tensors of shape [B, num_latents]\n", " \"\"\"\n", "\n", " # Concatenate x and y along the filter axes\n", " encoder_input = tf.concat([x, y], axis=-1)\n", "\n", " # Pass final axis through MLP\n", " hidden = batch_mlp(encoder_input, self._output_sizes, \"latent_encoder\")\n", " \n", " # Aggregator: take the mean over all points\n", " hidden = tf.reduce_mean(hidden, axis=1)\n", " \n", " # Have further MLP layers that map to the parameters of the Gaussian latent\n", " with tf.variable_scope(\"latent_encoder\", reuse=tf.AUTO_REUSE):\n", " # First apply intermediate relu layer \n", " hidden = tf.nn.relu(\n", " tf.layers.dense(hidden, \n", " (self._output_sizes[-1] + self._num_latents)/2, \n", " name=\"penultimate_layer\"))\n", " # Then apply further linear layers to output latent mu and log sigma\n", " mu = tf.layers.dense(hidden, self._num_latents, name=\"mean_layer\")\n", " log_sigma = tf.layers.dense(hidden, self._num_latents, name=\"std_layer\")\n", " \n", " # Compute sigma\n", " sigma = 0.1 + 0.9 * tf.sigmoid(log_sigma)\n", "\n", " return tf.contrib.distributions.Normal(loc=mu, scale=sigma)\n" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "roGKUH3Nt9Xg", "colab_type": "text" }, "cell_type": "markdown", "source": [ "## Decoder\n", "\n", "The context representation (either $z$ or $z$ and $r_C$ concatenated) and the target inputs $x_T$ are fed into the decoder. First they are concatenated and passed through an MLP, whose architecture is given by the `output_sizes` argument. The MLP outputs the mean and variance of the Gaussian $p(y_T|x_T,r_C,z)$. " ] }, { "metadata": { "id": "RLhx-J8ALyST", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "class Decoder(object):\n", " \"\"\"The Decoder.\"\"\"\n", "\n", " def __init__(self, output_sizes):\n", " \"\"\"(A)NP decoder.\n", "\n", " Args:\n", " output_sizes: An iterable containing the output sizes of the decoder MLP \n", " as defined in `basic.Linear`.\n", " \"\"\"\n", " self._output_sizes = output_sizes\n", "\n", " def __call__(self, representation, target_x):\n", " \"\"\"Decodes the individual targets.\n", "\n", " Args:\n", " representation: The representation of the context for target predictions. \n", " Tensor of shape [B,target_observations,?].\n", " target_x: The x locations for the target query.\n", " Tensor of shape [B,target_observations,d_x].\n", "\n", " Returns:\n", " dist: A multivariate Gaussian over the target points. A distribution over\n", " tensors of shape [B,target_observations,d_y].\n", " mu: The mean of the multivariate Gaussian.\n", " Tensor of shape [B,target_observations,d_x].\n", " sigma: The standard deviation of the multivariate Gaussian.\n", " Tensor of shape [B,target_observations,d_x].\n", " \"\"\"\n", " # concatenate target_x and representation\n", " hidden = tf.concat([representation, target_x], axis=-1)\n", " \n", " # Pass final axis through MLP\n", " hidden = batch_mlp(hidden, self._output_sizes, \"decoder\")\n", "\n", " # Get the mean an the variance\n", " mu, log_sigma = tf.split(hidden, 2, axis=-1)\n", "\n", " # Bound the variance\n", " sigma = 0.1 + 0.9 * tf.nn.softplus(log_sigma)\n", "\n", " # Get the distribution\n", " dist = tf.contrib.distributions.MultivariateNormalDiag(\n", " loc=mu, scale_diag=sigma)\n", "\n", " return dist, mu, sigma" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "bOfbRLHpt_KK", "colab_type": "text" }, "cell_type": "markdown", "source": [ "## Model\n", "\n", "We can put the encoders and the decoder together to form the ANP model." ] }, { "metadata": { "id": "n5XJ6gr7ZDPl", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "class LatentModel(object):\n", " \"\"\"The (A)NP model.\"\"\"\n", "\n", " def __init__(self, latent_encoder_output_sizes, num_latents,\n", " decoder_output_sizes, use_deterministic_path=True, \n", " deterministic_encoder_output_sizes=None, attention=None):\n", " \"\"\"Initialises the model.\n", "\n", " Args:\n", " latent_encoder_output_sizes: An iterable containing the sizes of hidden \n", " layers of the latent encoder.\n", " num_latents: The latent dimensionality.\n", " decoder_output_sizes: An iterable containing the sizes of hidden layers of\n", " the decoder. The last element should correspond to d_y * 2\n", " (it encodes both mean and variance concatenated)\n", " use_deterministic_path: a boolean that indicates whether the deterministic\n", " encoder is used or not.\n", " deterministic_encoder_output_sizes: An iterable containing the sizes of \n", " hidden layers of the deterministic encoder. The last one is the size \n", " of the deterministic representation r.\n", " attention: The attention module used in the deterministic encoder.\n", " Only relevant when use_deterministic_path=True.\n", " \"\"\"\n", " self._latent_encoder = LatentEncoder(latent_encoder_output_sizes, \n", " num_latents)\n", " self._decoder = Decoder(decoder_output_sizes)\n", " self._use_deterministic_path = use_deterministic_path\n", " if use_deterministic_path:\n", " self._deterministic_encoder = DeterministicEncoder(\n", " deterministic_encoder_output_sizes, attention)\n", " \n", "\n", " def __call__(self, query, num_targets, target_y=None):\n", " \"\"\"Returns the predicted mean and variance at the target points.\n", "\n", " Args:\n", " query: Array containing ((context_x, context_y), target_x) where:\n", " context_x: Tensor of shape [B,num_contexts,d_x]. \n", " Contains the x values of the context points.\n", " context_y: Tensor of shape [B,num_contexts,d_y]. \n", " Contains the y values of the context points.\n", " target_x: Tensor of shape [B,num_targets,d_x]. \n", " Contains the x values of the target points.\n", " num_targets: Number of target points.\n", " target_y: The ground truth y values of the target y. \n", " Tensor of shape [B,num_targets,d_y].\n", "\n", " Returns:\n", " log_p: The log_probability of the target_y given the predicted\n", " distribution. Tensor of shape [B,num_targets].\n", " mu: The mean of the predicted distribution. \n", " Tensor of shape [B,num_targets,d_y].\n", " sigma: The variance of the predicted distribution.\n", " Tensor of shape [B,num_targets,d_y].\n", " \"\"\"\n", "\n", " (context_x, context_y), target_x = query\n", "\n", " # Pass query through the encoder and the decoder\n", " prior = self._latent_encoder(context_x, context_y)\n", " \n", " # For training, when target_y is available, use targets for latent encoder.\n", " # Note that targets contain contexts by design.\n", " if target_y is None:\n", " latent_rep = prior.sample()\n", " # For testing, when target_y unavailable, use contexts for latent encoder.\n", " else:\n", " posterior = self._latent_encoder(target_x, target_y)\n", " latent_rep = posterior.sample()\n", " latent_rep = tf.tile(tf.expand_dims(latent_rep, axis=1),\n", " [1, num_targets, 1])\n", " if self._use_deterministic_path:\n", " deterministic_rep = self._deterministic_encoder(context_x, context_y,\n", " target_x)\n", " representation = tf.concat([deterministic_rep, latent_rep], axis=-1)\n", " else:\n", " representation = latent_rep\n", " \n", " dist, mu, sigma = self._decoder(representation, target_x)\n", " \n", " # If we want to calculate the log_prob for training we will make use of the\n", " # target_y. At test time the target_y is not available so we return None.\n", " if target_y is not None:\n", " log_p = dist.log_prob(target_y)\n", " posterior = self._latent_encoder(target_x, target_y)\n", " kl = tf.reduce_sum(\n", " tf.contrib.distributions.kl_divergence(posterior, prior), \n", " axis=-1, keepdims=True)\n", " kl = tf.tile(kl, [1, num_targets])\n", " loss = - tf.reduce_mean(log_p - kl / tf.cast(num_targets, tf.float32))\n", " else:\n", " log_p = None\n", " kl = None\n", " loss = None\n", "\n", " return mu, sigma, log_p, kl, loss\n" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "ULaa6hZEuHvU", "colab_type": "text" }, "cell_type": "markdown", "source": [ "## Cross-Attention Module\n", "Given a set of key-value pairs $(k_i,v_i)_{i \\in I}$ and query $q$, an attention module computes weights for each key and aggregates the values with these weights to form the value corresponding to the query.\n", "\n", "\n", "**`rep`** determines whether the raw inputs to the module will be used as the keys and queries, or whether you will pass them through an MLP and use the output instead. One of 'identity', 'mlp'.\n", "\n", "**`output_sizes`** determines the architecture of the MLP used to obtain the keys/queries if `rep` is 'mlp'.\n", "\n", "**`att_type`** is a string argument that determines the type of attention used. Valid choices of attention are: uniform, laplace, dot product, multihead.\n", "\n", "* **Uniform** $((k_i,v_i)_{i\\in I}, q)= \\frac{1}{|I|} \\sum_i v_i$\n", "\n", "* **Laplace** $((k_i,v_i)_{i\\in I}, q)= \\sum_i w_i v_i, \\hspace{2mm} w_i \\propto \\exp(-\\frac{||q - k_i||_1}{l})$\n", "\n", "* **DotProduct** $((k_i,v_i)_{i\\in I}, q)= \\sum_i w_i v_i, \\hspace{2mm} w_i \\propto \\exp(q^\\top k_i / \\sqrt{d_k})$ where $k_i \\in \\mathbb{R}^{d_k}$.\n", "\n", "* **Multihead** $((k_i,v_i)_{i\\in I}, q)= \\mathcal{L}^O(\\text{concat}(\\text{head}_1, \\ldots, \\text{head}_H))$, $\\text{head}_h = \\text{DotProduct}((\\mathcal{L}^K_h(k_i),\\mathcal{L}^V_h(v_i))_{i \\in I}, \\mathcal{L}^Q_h(q))$ \n", "\n", " where $\\mathcal{L}$ are linear maps with trainable parameters.\n", "\n", "**`scale`**: length scale $l$ in Laplace attention.\n", "\n", "**`normalise`**: whether to use a softmax so that weights sum to 1 or not.\n", "\n", "**`num_heads`**: $H$, the number of heads for multihead attention." ] }, { "metadata": { "id": "DImJP8HfhmmM", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "def uniform_attention(q, v):\n", " \"\"\"Uniform attention. Equivalent to np.\n", "\n", " Args:\n", " q: queries. tensor of shape [B,m,d_k].\n", " v: values. tensor of shape [B,n,d_v].\n", " \n", " Returns:\n", " tensor of shape [B,m,d_v].\n", " \"\"\"\n", " total_points = tf.shape(q)[1]\n", " rep = tf.reduce_mean(v, axis=1, keepdims=True) # [B,1,d_v]\n", " rep = tf.tile(rep, [1, total_points, 1])\n", " return rep\n", "\n", "def laplace_attention(q, k, v, scale, normalise):\n", " \"\"\"Computes laplace exponential attention.\n", "\n", " Args:\n", " q: queries. tensor of shape [B,m,d_k].\n", " k: keys. tensor of shape [B,n,d_k].\n", " v: values. tensor of shape [B,n,d_v].\n", " scale: float that scales the L1 distance.\n", " normalise: Boolean that determines whether weights sum to 1.\n", " \n", " Returns:\n", " tensor of shape [B,m,d_v].\n", " \"\"\"\n", " k = tf.expand_dims(k, axis=1) # [B,1,n,d_k]\n", " q = tf.expand_dims(q, axis=2) # [B,m,1,d_k]\n", " unnorm_weights = - tf.abs((k - q) / scale) # [B,m,n,d_k]\n", " unnorm_weights = tf.reduce_sum(unnorm_weights, axis=-1) # [B,m,n]\n", " if normalise:\n", " weight_fn = tf.nn.softmax\n", " else:\n", " weight_fn = lambda x: 1 + tf.tanh(x)\n", " weights = weight_fn(unnorm_weights) # [B,m,n]\n", " rep = tf.einsum('bik,bkj->bij', weights, v) # [B,m,d_v]\n", " return rep\n", "\n", "\n", "def dot_product_attention(q, k, v, normalise):\n", " \"\"\"Computes dot product attention.\n", "\n", " Args:\n", " q: queries. tensor of shape [B,m,d_k].\n", " k: keys. tensor of shape [B,n,d_k].\n", " v: values. tensor of shape [B,n,d_v].\n", " normalise: Boolean that determines whether weights sum to 1.\n", " \n", " Returns:\n", " tensor of shape [B,m,d_v].\n", " \"\"\"\n", " d_k = tf.shape(q)[-1]\n", " scale = tf.sqrt(tf.cast(d_k, tf.float32))\n", " unnorm_weights = tf.einsum('bjk,bik->bij', k, q) / scale # [B,m,n]\n", " if normalise:\n", " weight_fn = tf.nn.softmax\n", " else:\n", " weight_fn = tf.sigmoid\n", " weights = weight_fn(unnorm_weights) # [B,m,n]\n", " rep = tf.einsum('bik,bkj->bij', weights, v) # [B,m,d_v]\n", " return rep\n", "\n", "\n", "def multihead_attention(q, k, v, num_heads=8):\n", " \"\"\"Computes multi-head attention.\n", "\n", " Args:\n", " q: queries. tensor of shape [B,m,d_k].\n", " k: keys. tensor of shape [B,n,d_k].\n", " v: values. tensor of shape [B,n,d_v].\n", " num_heads: number of heads. Should divide d_v.\n", " \n", " Returns:\n", " tensor of shape [B,m,d_v].\n", " \"\"\"\n", " d_k = q.get_shape().as_list()[-1]\n", " d_v = v.get_shape().as_list()[-1]\n", " head_size = d_v / num_heads\n", " key_initializer = tf.random_normal_initializer(stddev=d_k**-0.5)\n", " value_initializer = tf.random_normal_initializer(stddev=d_v**-0.5)\n", " rep = tf.constant(0.0)\n", " for h in range(num_heads):\n", " o = dot_product_attention(\n", " tf.layers.Conv1D(head_size, 1, kernel_initializer=key_initializer,\n", " name='wq%d' % h, use_bias=False, padding='VALID')(q),\n", " tf.layers.Conv1D(head_size, 1, kernel_initializer=key_initializer,\n", " name='wk%d' % h, use_bias=False, padding='VALID')(k),\n", " tf.layers.Conv1D(head_size, 1, kernel_initializer=key_initializer,\n", " name='wv%d' % h, use_bias=False, padding='VALID')(v),\n", " normalise=True)\n", " rep += tf.layers.Conv1D(d_v, 1, kernel_initializer=value_initializer,\n", " name='wo%d' % h, use_bias=False, padding='VALID')(o)\n", " return rep\n", "\n", "class Attention(object):\n", " \"\"\"The Attention module.\"\"\"\n", "\n", " def __init__(self, rep, output_sizes, att_type, scale=1., normalise=True,\n", " num_heads=8):\n", " \"\"\"Create attention module.\n", "\n", " Takes in context inputs, target inputs and\n", " representations of each context input/output pair\n", " to output an aggregated representation of the context data.\n", " Args:\n", " rep: transformation to apply to contexts before computing attention. \n", " One of: ['identity','mlp'].\n", " output_sizes: list of number of hidden units per layer of mlp.\n", " Used only if rep == 'mlp'.\n", " att_type: type of attention. One of the following:\n", " ['uniform','laplace','dot_product','multihead']\n", " scale: scale of attention.\n", " normalise: Boolean determining whether to:\n", " 1. apply softmax to weights so that they sum to 1 across context pts or\n", " 2. apply custom transformation to have weights in [0,1].\n", " num_heads: number of heads for multihead.\n", " \"\"\"\n", " self._rep = rep\n", " self._output_sizes = output_sizes\n", " self._type = att_type\n", " self._scale = scale\n", " self._normalise = normalise\n", " if self._type == 'multihead':\n", " self._num_heads = num_heads\n", "\n", " def __call__(self, x1, x2, r):\n", " \"\"\"Apply attention to create aggregated representation of r.\n", "\n", " Args:\n", " x1: tensor of shape [B,n1,d_x].\n", " x2: tensor of shape [B,n2,d_x].\n", " r: tensor of shape [B,n1,d].\n", " \n", " Returns:\n", " tensor of shape [B,n2,d]\n", "\n", " Raises:\n", " NameError: The argument for rep/type was invalid.\n", " \"\"\"\n", " if self._rep == 'identity':\n", " k, q = (x1, x2)\n", " elif self._rep == 'mlp':\n", " # Pass through MLP\n", " k = batch_mlp(x1, self._output_sizes, \"attention\")\n", " q = batch_mlp(x2, self._output_sizes, \"attention\")\n", " else:\n", " raise NameError(\"'rep' not among ['identity','mlp']\")\n", "\n", " if self._type == 'uniform':\n", " rep = uniform_attention(q, r)\n", " elif self._type == 'laplace':\n", " rep = laplace_attention(q, k, r, self._scale, self._normalise)\n", " elif self._type == 'dot_product':\n", " rep = dot_product_attention(q, k, r, self._normalise)\n", " elif self._type == 'multihead':\n", " rep = multihead_attention(q, k, r, self._num_heads)\n", " else:\n", " raise NameError((\"'att_type' not among ['uniform','laplace','dot_product'\"\n", " \",'multihead']\"))\n", "\n", " return rep" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "zYcTQDLltFmA", "colab_type": "text" }, "cell_type": "markdown", "source": [ "## Ploting function\n", "\n", "Same plotting function as for the [implementation of CNPs](https://github.com/deepmind/conditional-neural-process/blob/master/conditional_neural_process.ipynb) that plots the intermediate predictions every so often during training." ] }, { "metadata": { "id": "AFlT3lJQTM_m", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "def plot_functions(target_x, target_y, context_x, context_y, pred_y, std):\n", " \"\"\"Plots the predicted mean and variance and the context points.\n", " \n", " Args: \n", " target_x: An array of shape [B,num_targets,1] that contains the\n", " x values of the target points.\n", " target_y: An array of shape [B,num_targets,1] that contains the\n", " y values of the target points.\n", " context_x: An array of shape [B,num_contexts,1] that contains \n", " the x values of the context points.\n", " context_y: An array of shape [B,num_contexts,1] that contains \n", " the y values of the context points.\n", " pred_y: An array of shape [B,num_targets,1] that contains the\n", " predicted means of the y values at the target points in target_x.\n", " std: An array of shape [B,num_targets,1] that contains the\n", " predicted std dev of the y values at the target points in target_x.\n", " \"\"\"\n", " # Plot everything\n", " plt.plot(target_x[0], pred_y[0], 'b', linewidth=2)\n", " plt.plot(target_x[0], target_y[0], 'k:', linewidth=2)\n", " plt.plot(context_x[0], context_y[0], 'ko', markersize=10)\n", " plt.fill_between(\n", " target_x[0, :, 0],\n", " pred_y[0, :, 0] - std[0, :, 0],\n", " pred_y[0, :, 0] + std[0, :, 0],\n", " alpha=0.2,\n", " facecolor='#65c9f7',\n", " interpolate=True)\n", "\n", " # Make the plot pretty\n", " plt.yticks([-2, 0, 2], fontsize=16)\n", " plt.xticks([-2, 0, 2], fontsize=16)\n", " plt.ylim([-2, 2])\n", " plt.grid('off')\n", " ax = plt.gca()\n", " plt.show()" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "Z69Ham1nteeD", "colab_type": "text" }, "cell_type": "markdown", "source": [ "## Training the (A)NP\n", "\n", "We can now start training. First we need to define some variables:\n", "\n", "**`TRAINING_ITERATIONS`**: Number of iterations used for trianing. At each iteration we sample a new batch of sample curves from GPs and pick a random set of points on each curve to be the target and a subset to be the context. We optimise the ELBO on the log predictive likelihood of the target given context.\n", "\n", "**`MAX_CONTEXT_POINTS`**: Maximum number of context points used during training. This is also set to be the upper bound on the number of target points.\n", "\n", "**`PLOT_AFTER`**: The number of iterations between the intermediate plots.\n", "\n", "**`HIDDEN_SIZE`**: Master parameter that governs the hidden layer size of all MLPs in the model and also the latent dimensionality. \n", "\n", "**`MODEL_TYPE`**: 'NP' or 'ANP'.\n", "\n", "**`ATTENTION_TYPE`**: The type of attention used for ANP. One of `uniform`, `laplace` `dot_product` or `multihead`\n", "\n", "**`random_kernel_parameters`**: Boolean to determine whether the GP kernel parameters are sample randomly for each iteration or fixed.\n" ] }, { "metadata": { "id": "yHqnv8FP4Rtu", "colab_type": "text" }, "cell_type": "markdown", "source": [ "### NP training\n", "First we train the NP. Notice from the plots that the predictions after 1e5 iterations do not go through the context points perfectly - i.e. underfits." ] }, { "metadata": { "id": "c6FcSLfnLD9_", "colab_type": "code", "outputId": "8b1ecc7c-5218-4f7f-b596-a833ecfbd8b7", "executionInfo": { "status": "ok", "timestamp": 1551095030039, "user_tz": 0, "elapsed": 493150, "user": { "displayName": "Hyunjik Kim", "photoUrl": "https://lh5.googleusercontent.com/-oMZCZLTka34/AAAAAAAAAAI/AAAAAAAAAAc/ENlUKunKSqo/s64/photo.jpg", "userId": "01465310974685628261" } }, "colab": { "height": 2897 } }, "cell_type": "code", "source": [ "TRAINING_ITERATIONS = 100000 #@param {type:\"number\"}\n", "MAX_CONTEXT_POINTS = 50 #@param {type:\"number\"}\n", "PLOT_AFTER = 10000 #@param {type:\"number\"}\n", "HIDDEN_SIZE = 128 #@param {type:\"number\"}\n", "MODEL_TYPE = 'NP' #@param ['NP','ANP']\n", "ATTENTION_TYPE = 'uniform' #@param ['uniform','laplace','dot_product','multihead']\n", "random_kernel_parameters=True #@param {type:\"boolean\"}\n", "\n", "tf.reset_default_graph()\n", "# Train dataset\n", "dataset_train = GPCurvesReader(\n", " batch_size=16, max_num_context=MAX_CONTEXT_POINTS, random_kernel_parameters=random_kernel_parameters)\n", "data_train = dataset_train.generate_curves()\n", "\n", "# Test dataset\n", "dataset_test = GPCurvesReader(\n", " batch_size=1, max_num_context=MAX_CONTEXT_POINTS, testing=True, random_kernel_parameters=random_kernel_parameters)\n", "data_test = dataset_test.generate_curves()\n", "\n", "\n", "# Sizes of the layers of the MLPs for the encoders and decoder\n", "# The final output layer of the decoder outputs two values, one for the mean and\n", "# one for the variance of the prediction at the target location\n", "latent_encoder_output_sizes = [HIDDEN_SIZE]*4\n", "num_latents = HIDDEN_SIZE\n", "deterministic_encoder_output_sizes= [HIDDEN_SIZE]*4\n", "decoder_output_sizes = [HIDDEN_SIZE]*2 + [2]\n", "use_deterministic_path = True\n", "\n", "\n", "# ANP with multihead attention\n", "if MODEL_TYPE == 'ANP':\n", " attention = Attention(rep='mlp', output_sizes=[HIDDEN_SIZE]*2, \n", " att_type=ATTENTION_TYPE)\n", "# NP - equivalent to uniform attention\n", "elif MODEL_TYPE == 'NP':\n", " attention = Attention(rep='identity', output_sizes=None, att_type='uniform')\n", "else:\n", " raise NameError(\"MODEL_TYPE not among ['ANP,'NP']\")\n", "\n", "# Define the model\n", "model = LatentModel(latent_encoder_output_sizes, num_latents,\n", " decoder_output_sizes, use_deterministic_path, \n", " deterministic_encoder_output_sizes, attention)\n", "\n", "# Define the loss\n", "_, _, log_prob, _, loss = model(data_train.query, data_train.num_total_points,\n", " data_train.target_y)\n", "\n", "# Get the predicted mean and variance at the target points for the testing set\n", "mu, sigma, _, _, _ = model(data_test.query, data_test.num_total_points)\n", "\n", "# Set up the optimizer and train step\n", "optimizer = tf.train.AdamOptimizer(1e-4)\n", "train_step = optimizer.minimize(loss)\n", "init = tf.initialize_all_variables()\n", "\n", "# Train and plot\n", "with tf.Session() as sess:\n", " sess.run(init)\n", "\n", " for it in range(TRAINING_ITERATIONS):\n", " sess.run([train_step])\n", "\n", " # Plot the predictions in `PLOT_AFTER` intervals\n", " if it % PLOT_AFTER == 0:\n", " loss_value, pred_y, std_y, target_y, whole_query = sess.run(\n", " [loss, mu, sigma, data_test.target_y, \n", " data_test.query])\n", "\n", " (context_x, context_y), target_x = whole_query\n", " print('Iteration: {}, loss: {}'.format(it, loss_value))\n", "\n", " # Plot the prediction and the context\n", " plot_functions(target_x, target_y, context_x, context_y, pred_y, std_y)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Iteration: 0, loss: 1.0364459753\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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U0NLm+vvvc/D29kbHjp1Eub8hW7b8ilOnTmDatJktio9lWUyY8ALO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\nbtw4rWMDBgxAZGQk1q5di48//liI2xL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"text/plain": [ "<matplotlib.figure.Figure at 0x7f90285a7ad0>" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Iteration: 10000, loss: 0.254928678274\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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AURQ2jhHVIPXNldi8cS3mLHgB190wNNDJaTQc04lnHD8GWfJuOu1rEwdj4dLX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\nIzFixEj/fNEgx5JBFcxmM/L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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8f5cfbf490>" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Iteration: 20000, loss: 0.0929502248764\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8f5cfe5490>" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Iteration: 30000, loss: 0.0790428519249\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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F3UqBxQCFOu4p/tiTvIWaJ3mNouaJHkDXXs6qa//Zkq+c1v6AEokmylqTBMs4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\nY2O8NykrRaNR+Hw+aDSapNOhX0hSDLxeLz59+oS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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8f5b87ee50>" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Iteration: 40000, loss: 0.438342481852\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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TcoTxiWbHIt5u4HbYai9qWqJmsZ2zhmaR1bkDPn+IooNV7NryFZvXrWDDnhB/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\njkF45SMH4b78h67mw6uo2Qj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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8f5b8dee50>" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Iteration: 50000, loss: 0.274243146181\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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qqKisb/e2bBgwKCGKpIRobDaD+FgXCXFuEuLcxAd/JsS6iYlxYm9xgwu1BezavhVfU+eB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\n1aq/sXHjhrBDRDo24EdThOb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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8f5b68e3d0>" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Iteration: 60000, loss: 0.0882532298565\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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tLyTTGQKBZWnyCspZs/kQn/9nF4eDXMknieoEAFOmPMA33/wroHMn3XI39zz0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\nqJlHsEd4sjtLGZT5NJWe1i8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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8f5c776810>" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Iteration: 70000, loss: -0.293868511915\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8f58e26f50>" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Iteration: 80000, loss: -0.406084805727\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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oDHUnnbW3ve6mMijw6UZ3qHrwjh9z6GAmf5n+3yZtTQP/z/b+n99Q711MxpDh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\nNoPmr2KlKNL+7cdVlZeVsXP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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8f58d9ed90>" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Iteration: 90000, loss: 1.34461963177\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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Nkt2k7srFbDJw/ZSh9EwObIz92tRD/LRqHwKYetEARgwMbKizQUCMRcGkqVRU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\n46effoTZbGb8+AlERrqzxHVFUOJ0+7Y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UcRMBuLqRhisypIIAAAZd0R7iC1ICEYqEw6Jc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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f8f577b5d90>" ] }, "metadata": { "tags": [] } } ] }, { "metadata": { "id": "pudKobiUJbbT", "colab_type": "text" }, "cell_type": "markdown", "source": [ "### ANP training\n", "\n", "Next, we train the ANP with multihead attention, using the same hyperparameter setting as NPs. Note that the predictions are now much more accurate for the observed context data." ] }, { "metadata": { "id": "q6wQwAwjTEc_", "colab_type": "code", "outputId": "79adcd77-3af4-41ca-d01c-7ded9ef6fa4e", "executionInfo": { "status": "ok", "timestamp": 1548700486631, "user_tz": 0, "elapsed": 2728390, "user": { "displayName": "Hyunjik Kim", "photoUrl": "https://lh5.googleusercontent.com/-oMZCZLTka34/AAAAAAAAAAI/AAAAAAAAAAc/ENlUKunKSqo/s64/photo.jpg", "userId": "01465310974685628261" } }, "colab": { "height": 2897 } }, "cell_type": "code", "source": [ "TRAINING_ITERATIONS = 100000 #@param {type:\"number\"}\n", "MAX_CONTEXT_POINTS = 50 #@param {type:\"number\"}\n", "PLOT_AFTER = 10000 #@param {type:\"number\"}\n", "HIDDEN_SIZE = 128 #@param {type:\"number\"}\n", "MODEL_TYPE = 'ANP' #@param ['NP','ANP']\n", "ATTENTION_TYPE = 'multihead' #@param ['uniform','laplace','dot_product','multihead']\n", "random_kernel_parameters=True #@param {type:\"boolean\"}\n", "\n", "tf.reset_default_graph()\n", "# Train dataset\n", "dataset_train = GPCurvesReader(\n", " batch_size=16, max_num_context=MAX_CONTEXT_POINTS)\n", "data_train = dataset_train.generate_curves()\n", "\n", "# Test dataset\n", "dataset_test = GPCurvesReader(\n", " batch_size=1, max_num_context=MAX_CONTEXT_POINTS, testing=True)\n", "data_test = dataset_test.generate_curves()\n", "\n", "\n", "# Sizes of the layers of the MLPs for the encoders and decoder\n", "# The final output layer of the decoder outputs two values, one for the mean and\n", "# one for the variance of the prediction at the target location\n", "latent_encoder_output_sizes = [HIDDEN_SIZE]*4\n", "num_latents = HIDDEN_SIZE\n", "deterministic_encoder_output_sizes= [HIDDEN_SIZE]*4\n", "decoder_output_sizes = [HIDDEN_SIZE]*2 + [2]\n", "use_deterministic_path = True\n", "\n", "# ANP with multihead attention\n", "if MODEL_TYPE == 'ANP':\n", " attention = Attention(rep='mlp', output_sizes=[HIDDEN_SIZE]*2, \n", " att_type='multihead')\n", "# NP - equivalent to uniform attention\n", "elif MODEL_TYPE == 'NP':\n", " attention = Attention(rep='identity', output_sizes=None, att_type='uniform')\n", "else:\n", " raise NameError(\"MODEL_TYPE not among ['ANP,'NP']\")\n", "\n", "# Define the model\n", "model = LatentModel(latent_encoder_output_sizes, num_latents,\n", " decoder_output_sizes, use_deterministic_path, \n", " deterministic_encoder_output_sizes, attention)\n", "\n", "# Define the loss\n", "_, _, log_prob, _, loss = model(data_train.query, data_train.num_total_points,\n", " data_train.target_y)\n", "\n", "# Get the predicted mean and variance at the target points for the testing set\n", "mu, sigma, _, _, _ = model(data_test.query, data_test.num_total_points)\n", "\n", "# Set up the optimizer and train step\n", "optimizer = tf.train.AdamOptimizer(1e-4)\n", "train_step = optimizer.minimize(loss)\n", "init = tf.initialize_all_variables()\n", "\n", "# Train and plot\n", "with tf.train.MonitoredSession() as sess:\n", " sess.run(init)\n", "\n", " for it in range(TRAINING_ITERATIONS):\n", " sess.run([train_step])\n", "\n", " # Plot the predictions in `PLOT_AFTER` intervals\n", " if it % PLOT_AFTER == 0:\n", " loss_value, pred_y, std_y, target_y, whole_query = sess.run(\n", " [loss, mu, sigma, data_test.target_y, \n", " data_test.query])\n", "\n", " (context_x, context_y), target_x = whole_query\n", " print('Iteration: {}, loss: {}'.format(it, loss_value))\n", "\n", " # Plot the prediction and the context\n", " plot_functions(target_x, target_y, context_x, context_y, pred_y, std_y)" ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ "Iteration: 0, loss: 1.42949461937\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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kpPYyh0FBQdi3b1+9r/v+F98gNzsTTZt7NyQ8Qdhqo1ptooaPxp2b1+Hk5CLI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\nkpXOCgoK4OnpqbGtuLgYERERiIiIwNK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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8f55529190>" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Iteration: 10000, loss: 1.30306947231\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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UxES5cDoCf/82rPmGF37/GIcPH27Z+VuJ0lYwjVAmURWPSGU9j7Nr4pxGpw0I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\nMBSJTmt+alCUoMivqL/p6jK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mmmvYuHFjrWVz5szh7rvvrrUsZMFg+fLl3HfffcyYMYMn\nnngiqH0C5QzS09N57rnneP7550lJSQlFUoVokbS0NK677jreeecd+W6KDictLY0HHniABx98kOTk\n5FrrAuUMQtLpbP369Tz44IMMHjyYqVOnsnnz5qp1TqeToUOHBtwvUAIBNm7cWC+bI0R78/v9HD58\nWL6bokPy+/1s3LiR5ORkevTo0eT2IQkGa9euxev1sn37dq699tpa67p168aKFStCcVohhBAtFJJg\nMGfOHObMmROKQwshhAgBGbVUCCEEtrlz585t70Q0xeVycdppp+Fyudo7KULUIt9N0ZE15/sZ0qal\nQgghOgcpJhJCCCHBQAghRIhaE4WKzKAmOoL09HTmzZvH6tWr0Vozfvx4Hn30Uel4JtrdsmXLWLp0\nKVu3biU7O5uUlBSmTJnC7bffTkRERKP7dqo6g3feeYf333+fadOm1ZpBbdu2bTKDmmgTZWVlXH75\n5bhcLu6//34AXnjhBcrLy/noo49wu93tnEJxIps5cybdunVj0qRJJCcns23bNubPn0///v1ZvHhx\n4zvrTiQ3N7fessLCQj1u3Dj9q1/9qh1SJE40Cxcu1MOGDdMHDx6sWnbo0CE9bNgw/cYbb7RfwoTQ\nWufk5NRb9sEHH+ghQ4bo7777rtF9O1WdgcygJtrbV199xahRo2oNxd6jRw/GjBkjPetFu4uLi6u3\nbMSIEWitm7xGdqpgEIjMoCba0u7duxk4cGC95QMGDGDPnj3tkCIhGrdu3TqUUk1eIzt9MJAZ1ERb\nysvLIyYmpt7ymJiYeiPuCtHeMjIymD9/PuPHj2f48OGNbtuurYlkBjXRGSlVf6Il3XnaYYgTRElJ\nCbNnz8bhcDBv3rwmt2/XYNBWM6gJ0VpiYmLIy8urt7ygoCDg8OtCtAePx8Mdd9zB4cOHeeedd0hK\nSmpyn3YNBi6Xi759+zZ7vyVLlvDkk08ya9YsbrvtthCkTIjABgwYwO7du+st3717t9RbiQ7B5/Mx\nZ84ctm7dysKFCxkwYEBQ+3W6OoPly5fzm9/8hquuuiqoqTSFaE0TJ05k8+bNpKamVi1LTU1l06ZN\nTJo0qR1TJoRVXPnggw+ydu1a/vSnPzFy5Mig9+1Unc7Wr1/PrFmzGDBgAI899hiGUR3LGptBTYjW\nUlpaytSpU3G5XNx7770AvPjii5SWlvLhhx8GLNIUoq08/vjjvPfee8yePZvzzjuv1rrk5ORGi4s6\nVTBYsGABL730UsB1MoOaaCuBhqN45JFH6NatW3snTZzgJk6cSFpaWsB1d911V6OTjnWqYCCEECI0\nOl2dgRBCiNYnwUAIIYQEAyGEEBIMhBBCIMFACCEEEgyEEEIgwUAIIQQSDIQQQiDBQAghBPD/AXfw\nGlcM4y1RAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f8f5bc3e0d0>" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Iteration: 20000, loss: 0.436478257179\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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nn2Xu3F8yePDQ6u35+XnMmzeb9PS0Oi/tr77aytKlr7K4mQSDqgQMHYFXmkkD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\nekWEXM0CIgodXCYKED0klKt7Ag8NTYSUEkvm/wY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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8f4d040090>" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Iteration: 30000, loss: -0.564530730247\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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wSbBbuOPesbz90Vo2bStg0fIdXDW0J25t4IrgbKd9+/YRDNZ8aExKSiIpKanG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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f8f4d16b2d0>" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Iteration: 40000, loss: -1.08188056946\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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RFHhD0dXgSgSOCmEAMHr0GM4882wGDcrv76nEBSFExAgCEYVjtVG3zC/hHL3d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\nBfmYikKtReoV0aLWKlZcSrWuUMCiohU3tlYFWRT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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8f4de86f10>" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Iteration: 50000, loss: 0.485689043999\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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efXI8rVYZ3PvI46AzcvYlVxIRGVyz9dCBfcx9+lEGDB3BVTfdidXmJK+gnIzs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\n398fw4ZJN4ioYxjcUPGfBY3cpdfrkTLjf3DhbMdbl4eGD8L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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8f56b880d0>" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Iteration: 60000, loss: 0.134729295969\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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OxpBhjI+Q112I46EU5OZXBoPoulZGjjmZ5e+/yV8ff47tWzbw0TuvE/J7rc1nps1g4sRJ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\nnoKBM7+FAAkG4ja4exO1vPm+u3ydPQfP8tG2QqJ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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8f4e5da1d0>" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Iteration: 70000, loss: -1.29039406776\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8f4dca71d0>" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Iteration: 80000, loss: -0.624202787876\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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tErEU9U2tuyE89thDnHnmXHbu2olK8cYSAoJCUhmCSr9FXdDCF1aE2yz6yfBF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\nK+oOe1QvD5UYEJIEAyFEq2Sp/safXj3+f3ZjL21/w8icgRC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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8f4dcd2e50>" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Iteration: 90000, loss: -1.33056414127\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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GgsmT/8mNN17L0aNHGu2aLSYYJHeN4ci+n/l1764699u1K40VK5Y1UqmE8I1S3vHuulK4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\nBAMhhBBIMBBCCIEEAyGEEEgwEEIIAfx/8oGm9tmJj/YAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f8f4c9dbe90>" ] }, "metadata": { "tags": [] } } ] } ] }
apache-2.0
rohinkumar/galsurveystudy
DR12G/DR12G_CMASS1_correl_V01_LCDM_rand.ipynb
1
446055
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Correlation function of DR12G SDSS CMASS Catalog" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First import all the modules such as healpy and astropy needed for analyzing the structure" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "import healpix_util as hu\n", "import astropy as ap\n", "import numpy as np\n", "from astropy.io import fits\n", "from astropy.table import Table\n", "import astropy.io.ascii as ascii\n", "from astropy.io import fits\n", "from astropy.constants import c\n", "import matplotlib.pyplot as plt\n", "import math as m\n", "from math import pi\n", "#from scipy.constants import c\n", "import scipy.special as sp\n", "from astroML.decorators import pickle_results\n", "from scipy import integrate\n", "import warnings\n", "from sklearn.neighbors import BallTree\n", "import pickle\n", "import multiprocessing as mp\n", "import time\n", "from cython_metric import *\n", "from lcdmmetric import *\n", "from progressbar import *\n", "from tqdm import *\n", "from functools import partial\n", "import pymangle\n", "import pyfits\n", "#from astroML.datasets import fetch_sdss_specgals\n", "#from astroML.correlation import bootstrap_two_point_angular\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read the data file (taken from http://cosmo.nyu.edu/~eak306/SDSS-LRG.html ) converted to ascii with comoving distance etc. in V01 reading from pkl files for faster read" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data=ascii.read(\"../output/dr12gcmnsrarf.dat\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/html": [ "&lt;Table length=618806&gt;\n", "<table id=\"table4604757264\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>z</th><th>ra</th><th>dec</th><th>s</th><th>rar</th><th>decr</th></tr></thead>\n", "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n", "<tr><td>0.54253</td><td>129.176188</td><td>48.946499</td><td>0.473078</td><td>2.25455</td><td>0.854278</td></tr>\n", "<tr><td>0.399682</td><td>117.416963</td><td>39.276759</td><td>0.36202</td><td>2.049313</td><td>0.685509</td></tr>\n", "<tr><td>0.537702</td><td>116.912724</td><td>39.443311</td><td>0.469476</td><td>2.040512</td><td>0.688416</td></tr>\n", "<tr><td>0.519172</td><td>116.950172</td><td>39.490769</td><td>0.455552</td><td>2.041166</td><td>0.689244</td></tr>\n", "<tr><td>0.543191</td><td>117.528471</td><td>40.176493</td><td>0.473571</td><td>2.051259</td><td>0.701212</td></tr>\n", "<tr><td>0.589608</td><td>123.816159</td><td>46.636784</td><td>0.50767</td><td>2.161</td><td>0.813965</td></tr>\n", "<tr><td>0.548197</td><td>127.601277</td><td>49.775937</td><td>0.477293</td><td>2.227062</td><td>0.868754</td></tr>\n", "<tr><td>0.555224</td><td>122.816418</td><td>43.940864</td><td>0.482501</td><td>2.143551</td><td>0.766913</td></tr>\n", "<tr><td>0.380021</td><td>122.937804</td><td>44.033184</td><td>0.345996</td><td>2.145669</td><td>0.768524</td></tr>\n", "<tr><td>0.562535</td><td>123.132378</td><td>44.200812</td><td>0.487896</td><td>2.149065</td><td>0.77145</td></tr>\n", "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n", "<tr><td>0.482233</td><td>225.499984</td><td>58.361607</td><td>0.427336</td><td>3.935717</td><td>1.018602</td></tr>\n", "<tr><td>0.476738</td><td>172.852073</td><td>60.085187</td><td>0.423085</td><td>3.016838</td><td>1.048684</td></tr>\n", "<tr><td>0.474484</td><td>173.448708</td><td>60.332544</td><td>0.421339</td><td>3.027251</td><td>1.053002</td></tr>\n", "<tr><td>0.456862</td><td>173.667282</td><td>60.181836</td><td>0.407597</td><td>3.031066</td><td>1.050371</td></tr>\n", "<tr><td>0.471463</td><td>174.140124</td><td>60.81971</td><td>0.418992</td><td>3.039319</td><td>1.061504</td></tr>\n", "<tr><td>0.526395</td><td>213.817287</td><td>64.462038</td><td>0.460998</td><td>3.731816</td><td>1.125075</td></tr>\n", "<tr><td>0.479835</td><td>215.483846</td><td>63.98142</td><td>0.425483</td><td>3.760903</td><td>1.116686</td></tr>\n", "<tr><td>0.448259</td><td>218.302727</td><td>63.158747</td><td>0.400837</td><td>3.810101</td><td>1.102328</td></tr>\n", "<tr><td>0.473205</td><td>216.816829</td><td>64.036989</td><td>0.420346</td><td>3.784168</td><td>1.117656</td></tr>\n", "<tr><td>0.485066</td><td>215.166779</td><td>65.041442</td><td>0.429522</td><td>3.755369</td><td>1.135187</td></tr>\n", "</table>" ], "text/plain": [ "<Table length=618806>\n", " z ra dec s rar decr \n", "float64 float64 float64 float64 float64 float64 \n", "-------- ---------- --------- -------- -------- --------\n", " 0.54253 129.176188 48.946499 0.473078 2.25455 0.854278\n", "0.399682 117.416963 39.276759 0.36202 2.049313 0.685509\n", "0.537702 116.912724 39.443311 0.469476 2.040512 0.688416\n", "0.519172 116.950172 39.490769 0.455552 2.041166 0.689244\n", "0.543191 117.528471 40.176493 0.473571 2.051259 0.701212\n", "0.589608 123.816159 46.636784 0.50767 2.161 0.813965\n", "0.548197 127.601277 49.775937 0.477293 2.227062 0.868754\n", "0.555224 122.816418 43.940864 0.482501 2.143551 0.766913\n", "0.380021 122.937804 44.033184 0.345996 2.145669 0.768524\n", "0.562535 123.132378 44.200812 0.487896 2.149065 0.77145\n", " ... ... ... ... ... ...\n", "0.482233 225.499984 58.361607 0.427336 3.935717 1.018602\n", "0.476738 172.852073 60.085187 0.423085 3.016838 1.048684\n", "0.474484 173.448708 60.332544 0.421339 3.027251 1.053002\n", "0.456862 173.667282 60.181836 0.407597 3.031066 1.050371\n", "0.471463 174.140124 60.81971 0.418992 3.039319 1.061504\n", "0.526395 213.817287 64.462038 0.460998 3.731816 1.125075\n", "0.479835 215.483846 63.98142 0.425483 3.760903 1.116686\n", "0.448259 218.302727 63.158747 0.400837 3.810101 1.102328\n", "0.473205 216.816829 64.036989 0.420346 3.784168 1.117656\n", "0.485066 215.166779 65.041442 0.429522 3.755369 1.135187" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "mask=pymangle.Mangle(\"../masks/mask_DR12v5_CMASS_North.ply\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.8 s, sys: 19.8 ms, total: 1.82 s\n", "Wall time: 1.82 s\n" ] } ], "source": [ "%%time\n", "rar,decr=mask.genrand(len(data))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "zr=data['z']" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 249.3145, 151.7149, 222.89044, ..., 116.63463, 156.69369,\n", " 256.05447], dtype=float128)" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rar" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 41.271022, 17.698841, 61.11093, ..., 46.655516, 58.653583,\n", " 27.601141], dtype=float128)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "decr" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Ez = lambda x: 1/m.sqrt(0.3*(1+x)**3+0.7)\n", "\n", "np.vectorize(Ez)\n", "#Calculate comoving distance of a data point using the Redshift - This definition is based on the cosmology model we take. Here the distance for E-dS universe is considered. Also note that c/H0 ratio is cancelled in the equations and hence not taken.\n", "\n", "def DC_LCDM(z):\n", " return integrate.quad(Ez, 0, z)[0]\n", "DC_LCDM=np.vectorize(DC_LCDM)with open('../pkl/BTR16MdatsLCDM.pkl') as f:\n", " BTR = pickle.load(f)\n", " \n", "BTR" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "dr12gcmn = open(\"../output/rdr12gcmnsrarf.dat\",'w')\n", "dr12gcmn.write(\"z\\t ra\\t dec\\t s\\t rar\\t decr \\n\")\n", "\n", "for i in range(0,len(zr)):\n", " dr12gcmn.write(\"%f\\t \" %zr[i])\n", " dr12gcmn.write(\"%f\\t %f\\t \" %(rar[i],decr[i]))\n", " dr12gcmn.write(\"%f\\t \" %DC_LCDM(zr[i]))\n", " dr12gcmn.write(\"%f\\t %f\\n \" %(rar[i]*pi/180.0,decr[i]*pi/180.0))\n", "dr12gcmn.close()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dr12gcmndat=ascii.read(\"../output/rdr12gcmnsrarf.dat\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "NSIDE=512\n", "dr12gcmnpix=hu.HealPix(\"ring\",NSIDE)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = open(\"../output/rpixdatadr12gcmn.dat\",'w')\n", "pixdata.write(\"z\\t pix \\n\")\n", "\n", "for i in range(0,len(dr12gcmndat)):\n", " pixdata.write(\"%f\\t\" %dr12gcmndat['z'][i])\n", " pixdata.write(\"%d\\n\" %dr12gcmnpix.eq2pix(dr12gcmndat['ra'][i],dr12gcmndat['dec'][i]))\n", "pixdata.close()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = ascii.read(\"../output/rpixdatadr12gcmn.dat\")\n", "hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n", "for j in range(len(pixdata)):\n", " hpixdata[pixdata[j]['pix']]+=1" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0., 0., 0., ..., 0., 0., 0.])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hpixdata" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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TEzbOLqqakJKYUzO2zNbXou4hQjB8bDj1xMu5yKklrDzpmfho66Jccb6b5vhA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"text/plain": [ "<matplotlib.figure.Figure at 0x112f0dd50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hu.mollview(hpixdata)\n", "plt.savefig(\"../plots/rdr12gcmnmoll.pdf\")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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F2g7U5GmQdG8H30wMokHuJ5x6G3qfDOKeAGxafpQFw8eJLBBmZDx6bFJmvCnw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\n85cAACNPHcKu5Y9w9/TxKu3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mNUcqDSxPy2haKLerMLjZoCY0ddXQeUEBSseMugK42TPl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\nI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cVwTrh19Xyceqs/dto07yCi8/53vqd0zTjlK08Jb11eG0lX3u2kpvgJXzwTMdxBpLRF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\nAAAgIlAMAQAAAGAAi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"text/plain": [ "<matplotlib.figure.Figure at 0x114e75fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hu.orthview(hpixdata)\n", "plt.savefig(\"../plots/rdr12gcmnorth.pdf\")" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data=ascii.read(\"../output/rdr12gcmnsrarf.dat\")" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data.remove_column('z')\n", "data.remove_column('ra')\n", "data.remove_column('dec')" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/html": [ "&lt;Table length=618806&gt;\n", "<table id=\"table4604660240\" class=\"table-striped table-bordered table-condensed\">\n", "<thead><tr><th>s</th><th>rar</th><th>decr</th></tr></thead>\n", "<thead><tr><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n", "<tr><td>0.473078</td><td>4.351359</td><td>0.720315</td></tr>\n", "<tr><td>0.36202</td><td>2.647925</td><td>0.308903</td></tr>\n", "<tr><td>0.469475</td><td>3.890172</td><td>1.066587</td></tr>\n", "<tr><td>0.455552</td><td>3.856244</td><td>0.233592</td></tr>\n", "<tr><td>0.473571</td><td>3.659573</td><td>0.840978</td></tr>\n", "<tr><td>0.50767</td><td>2.228806</td><td>0.891138</td></tr>\n", "<tr><td>0.477294</td><td>2.078801</td><td>0.335469</td></tr>\n", "<tr><td>0.482501</td><td>2.372468</td><td>0.600558</td></tr>\n", "<tr><td>0.345996</td><td>3.458542</td><td>1.119782</td></tr>\n", "<tr><td>0.487896</td><td>5.937181</td><td>-0.03823</td></tr>\n", "<tr><td>...</td><td>...</td><td>...</td></tr>\n", "<tr><td>0.427336</td><td>0.220193</td><td>0.050788</td></tr>\n", "<tr><td>0.423086</td><td>2.699902</td><td>0.702053</td></tr>\n", "<tr><td>0.421338</td><td>0.095472</td><td>-0.12676</td></tr>\n", "<tr><td>0.407597</td><td>2.504155</td><td>-0.030693</td></tr>\n", "<tr><td>0.418993</td><td>3.711406</td><td>0.206414</td></tr>\n", "<tr><td>0.460998</td><td>0.120757</td><td>0.406888</td></tr>\n", "<tr><td>0.425483</td><td>3.927697</td><td>0.107129</td></tr>\n", "<tr><td>0.400837</td><td>2.035658</td><td>0.814292</td></tr>\n", "<tr><td>0.420346</td><td>2.734821</td><td>1.023698</td></tr>\n", "<tr><td>0.429522</td><td>4.468994</td><td>0.481731</td></tr>\n", "</table>" ], "text/plain": [ "<Table length=618806>\n", " s rar decr \n", "float64 float64 float64 \n", "-------- -------- ---------\n", "0.473078 4.351359 0.720315\n", " 0.36202 2.647925 0.308903\n", "0.469475 3.890172 1.066587\n", "0.455552 3.856244 0.233592\n", "0.473571 3.659573 0.840978\n", " 0.50767 2.228806 0.891138\n", "0.477294 2.078801 0.335469\n", "0.482501 2.372468 0.600558\n", "0.345996 3.458542 1.119782\n", "0.487896 5.937181 -0.03823\n", " ... ... ...\n", "0.427336 0.220193 0.050788\n", "0.423086 2.699902 0.702053\n", "0.421338 0.095472 -0.12676\n", "0.407597 2.504155 -0.030693\n", "0.418993 3.711406 0.206414\n", "0.460998 0.120757 0.406888\n", "0.425483 3.927697 0.107129\n", "0.400837 2.035658 0.814292\n", "0.420346 2.734821 1.023698\n", "0.429522 4.468994 0.481731" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rs=np.array(data['s'])\n", "rrar=np.array(data['rar'])\n", "rdecr=np.array(data['decr'])" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dat=np.array([rs,rrar,rdecr])" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.473078, 0.36202 , 0.469475, ..., 0.400837, 0.420346,\n", " 0.429522],\n", " [ 4.351359, 2.647925, 3.890172, ..., 2.035658, 2.734821,\n", " 4.468994],\n", " [ 0.720315, 0.308903, 1.066587, ..., 0.814292, 1.023698,\n", " 0.481731]])" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dat" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.473078, 0.36202 , 0.469475, ..., 0.400837, 0.420346,\n", " 0.429522],\n", " [ 4.351359, 2.647925, 3.890172, ..., 2.035658, 2.734821,\n", " 4.468994],\n", " [ 0.720315, 0.308903, 1.066587, ..., 0.814292, 1.023698,\n", " 0.481731]])" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dat.reshape(3,len(data))" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dat=dat.transpose()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.473078, 4.351359, 0.720315],\n", " [ 0.36202 , 2.647925, 0.308903],\n", " [ 0.469475, 3.890172, 1.066587],\n", " ..., \n", " [ 0.400837, 2.035658, 0.814292],\n", " [ 0.420346, 2.734821, 1.023698],\n", " [ 0.429522, 4.468994, 0.481731]])" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dat" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Saving the objects:\n", "with open('../pkl/rdr12gcmnLCDM.pkl', 'w') as f: # Python 3: open(..., 'wb')\n", " pickle.dump(dat, f)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.473078, 4.351359, 0.720315],\n", " [ 0.36202 , 2.647925, 0.308903],\n", " [ 0.469475, 3.890172, 1.066587],\n", " ..., \n", " [ 0.400837, 2.035658, 0.814292],\n", " [ 0.420346, 2.734821, 1.023698],\n", " [ 0.429522, 4.468994, 0.481731]])" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Getting back the objects:\n", "with open('../pkl/rdr12gcmnLCDM.pkl') as f: # Python 3: open(..., 'rb')\n", " dat = pickle.load(f)\n", "dat" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.3186079774712368" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "LCDMmetricsq(dat[0],dat[1])" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 32.9 s, sys: 297 ms, total: 33.2 s\n", "Wall time: 33.6 s\n" ] } ], "source": [ "%%time\n", "BT_D = BallTree(dat,metric='pyfunc',func=LCDMmetricsq) \n", "\n", "with open('../pkl/rBTDdr12gcmnsLCDM.pkl', 'w') as f:\n", " pickle.dump(BT_D,f)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<sklearn.neighbors.ball_tree.BinaryTree at 0x7fdb41b50e90>" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with open('../pkl/rBTDdr12gcmnsLCDM.pkl') as f:\n", " BTD = pickle.load(f)\n", " \n", "BTD" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.004, 0.008, 0.012, 0.016, 0.02 , 0.024, 0.028, 0.032,\n", " 0.036, 0.04 , 0.044, 0.048, 0.052, 0.056, 0.06 , 0.064,\n", " 0.068, 0.072, 0.076, 0.08 ])" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bins=np.arange(0.004,0.084,0.004)\n", "bins" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1.60000000e-05, 6.40000000e-05, 1.44000000e-04,\n", " 2.56000000e-04, 4.00000000e-04, 5.76000000e-04,\n", " 7.84000000e-04, 1.02400000e-03, 1.29600000e-03,\n", " 1.60000000e-03, 1.93600000e-03, 2.30400000e-03,\n", " 2.70400000e-03, 3.13600000e-03, 3.60000000e-03,\n", " 4.09600000e-03, 4.62400000e-03, 5.18400000e-03,\n", " 5.77600000e-03, 6.40000000e-03])" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "binsq=bins**2\n", "binsq" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1358459 6273635 19012588 42810633 80777592 136780874\n", " 215684501 321583256 457878569 627472414 832603166 1075487654\n", " 1358410654 1682811804 2050435215 2463042572 2922567634 3430652477\n", " 3987726170 4593614344]\n", "Total run time:\n", "27841.8716681\n", "CPU times: user 7h 33min 59s, sys: 1min 17s, total: 7h 35min 17s\n", "Wall time: 7h 44min 2s\n" ] } ], "source": [ "%%time\n", "start_time=time.time()\n", "counts_DD=BTD.two_point_correlation(dat,binsq)\n", "print counts_DD\n", "end_time=time.time()\n", "tottime=end_time-start_time\n", "print \"Total run time:\"\n", "print tottime\n", "\n", "with open('../pkl/rBTDdr12gcmnsDDLCDM.pkl', 'w') as f:\n", " pickle.dump(counts_DD,f)\n", "\n", "with open('../pkl/rBTDdr12gcmnsDDLCDM.pkl') as f:\n", " counts_DD = pickle.load(f)\n", " \n", "counts_DD" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1358459, 6273635, 19012588, 42810633, 80777592,\n", " 136780874, 215684501, 321583256, 457878569, 627472414,\n", " 832603166, 1075487654, 1358410654, 1682811804, 2050435215,\n", " 2463042572, 2922567634, 3430652477, 3987726170, 4593614344])" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "counts_DD" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true }, "outputs": [], "source": [ "DD=np.diff(counts_DD)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 4915176, 12738953, 23798045, 37966959, 56003282, 78903627,\n", " 105898755, 136295313, 169593845, 205130752, 242884488, 282923000,\n", " 324401150, 367623411, 412607357, 459525062, 508084843, 557073693,\n", " 605888174])" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "RR=DD\n", "RR" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1249d13d0>]" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11270f5d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(bins[1:len(bins)],RR,'bo-')" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "RR_zero = (RR == 0)\n", "RR[RR_zero] = 1" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ColDefs(\n", " name = 'RA'; format = 'D'\n", " name = 'DEC'; format = 'D'\n", " name = 'Z'; format = 'D'\n", " name = 'WEIGHT_FKP'; format = 'D'\n", " name = 'NZ'; format = 'D'\n", " name = 'IPOLY'; format = 'I'\n", " name = 'ISECT'; format = 'I'\n", " name = 'ZINDX'; format = 'J'\n", " name = 'SKYFLUX'; format = '5E'\n", " name = 'IMAGE_DEPTH'; format = '5E'\n", " name = 'AIRMASS'; format = 'E'\n", " name = 'EB_MINUS_V'; format = 'E'\n", " name = 'PSF_FWHM'; format = '5E'\n", ")" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataRf=pyfits.open(\"/Users/rohin/Downloads/random0_DR12v5_CMASS_North.fits\")\n", "dataRf=dataRf[1].data\n", "dataRf.columns" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": true }, "outputs": [], "source": [ "z=dataRf['Z']\n", "ra=dataRf['RA']\n", "dec=dataRf['DEC']" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.66511017, 0.46273524, 0.48828444, ..., 0.53592575,\n", " 0.57767165, 0.47802863])" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "32151741" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(z)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Ez = lambda x: 1/m.sqrt(0.3*(1+x)**3+0.7)\n", "\n", "np.vectorize(Ez)\n", "#Calculate comoving distance of a data point using the Redshift - This definition is based on the cosmology model we take. Here the distance for E-dS universe is considered. Also note that c/H0 ratio is cancelled in the equations and hence not taken.\n", "\n", "def DC_LCDM(z):\n", " return integrate.quad(Ez, 0, z)[0]\n", "DC_LCDM=np.vectorize(DC_LCDM)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dr12gcmnr = open(\"../output/DR12gcmnsrandf32M.dat\",'w')\n", "dr12gcmnr.write(\"z\\t ra\\t dec\\t s\\t rar\\t decr \\n\")\n", "\n", "for i in range(0,len(z)):\n", " dr12gcmnr.write(\"%f\\t \" %z[i])\n", " dr12gcmnr.write(\"%f\\t %f\\t \" %(ra[i],dec[i]))\n", " dr12gcmnr.write(\"%f\\t \" %DC_LCDM(z[i]))\n", " dr12gcmnr.write(\"%f\\t %f\\n \" %(ra[i]*pi/180.0,dec[i]*pi/180.0))\n", "dr12gcmnr.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR=ascii.read(\"../output/DR12gcmnsrandf32M.dat\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR.remove_column('z')\n", "dataR.remove_column('ra')\n", "dataR.remove_column('dec')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rs=np.array(dataR['s'])\n", "rrar=np.array(dataR['rar'])\n", "rdecr=np.array(dataR['decr'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "datR=np.array([rs,rrar,rdecr])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "datR" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "datR.reshape(3,len(dataR))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "datR=datR.transpose()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "datR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Saving the objects:\n", "with open('../pkl/datrdr12cgnRs32MLCDM.pkl', 'w') as f: # Python 3: open(..., 'wb')\n", " pickle.dump(datR, f)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 49min 28s, sys: 38.9 s, total: 50min 7s\n", "Wall time: 52min 44s\n", "Parser : 21 s\n" ] } ], "source": [ "%%time\n", "BT_R2 = BallTree(datR,metric='pyfunc',func=LCDMmetricsq,leaf_size=5) \n", "\n", "with open('../pkl/BTR32MdatsLCDM.pkl', 'w') as f:\n", " pickle.dump(BT_R2,f)\n", "\n", "with open('../pkl/BTR32MdatsLCDM.pkl') as f:\n", " BTR = pickle.load(f)\n", " \n", "BTR" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1664948" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(datR)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 34442877 277519979 935955176 2205713756 4300579243\n", " 7396350872 11666242173 17312825240 24528343692 33484624025\n", " 44339349187 57219540836 72246431853 89501176009 109070262735\n", " 131085371685 155620842410 182738236771 212477380050 244905674541]\n", "Total run time:\n", "5172.29826498\n", "CPU times: user 1h 25min 33s, sys: 17.3 s, total: 1h 25min 51s\n", "Wall time: 1h 26min 12s\n" ] } ], "source": [ "%%time\n", "start_time=time.time()\n", "counts_DR=BTR.two_point_correlation(dat,binsq)\n", "print counts_DR\n", "end_time=time.time()\n", "tottime=end_time-start_time\n", "print \"Total run time:\"\n", "print tottime\n", "\n", "with open('../pkl/BTR32MRRLCDM.pkl', 'w') as f:\n", " pickle.dump(counts_DR,f)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 34442877, 277519979, 935955176, 2205713756,\n", " 4300579243, 7396350872, 11666242173, 17312825240,\n", " 24528343692, 33484624025, 44339349187, 57219540836,\n", " 72246431853, 89501176009, 109070262735, 131085371685,\n", " 155620842410, 182738236771, 212477380050, 244905674541])" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with open('../pkl/BTR32MRRLCDM.pkl') as f:\n", " counts_DR = pickle.load(f)\n", " \n", "counts_DR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "DR=np.diff(counts_DR)\n", "DR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "correl=4.0*DD/RR-1.0" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1.50498415, 0.26478836, 0.08084476, 0.03974161, 0.02578027,\n", " 0.02243945, 0.02628759, 0.02791146, 0.02888038, 0.03076744,\n", " 0.03112052, 0.03297988, 0.03271199, 0.03416311, 0.0350347 ])" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "correl" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x113c4c5d0>]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x113b56f50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(bins[1:len(bins)],correl,'go-')" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x113d5fed0>]" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11057d390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(bins[1:len(bins)],bins[1:len(bins)]*bins[1:len(bins)]*correl,'go-')" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x113e8ba90>]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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DsTWP5b3B73HOieeEHZJUQ7qgL1KFPLPoGbqldiMxIZFPbvxEiUVCo5GLSIyK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\nfbyZLTaz2RUX9YFzH83f+mo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"text/plain": [ "<matplotlib.figure.Figure at 0x113d86fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(bins[2:len(bins)],bins[2:len(bins)]*bins[2:len(bins)]*correl[1:len(bins)],'go-')" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x11054ac90>]" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x113eeaf90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(bins[2:len(bins)],correl[1:len(bins)],'go-')" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x114fe6110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(bins[2:len(bins)],correl[1:len(bins)],'go-')\n", "plt.savefig(\"correl2x.pdf\")" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x114498b50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(bins[2:len(bins)]*c/1e5,correl[1:len(bins)],'bo-')\n", "plt.savefig(\"correl2x1.pdf\")" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x10f65f690>]" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10f65f6d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.yscale('log')\n", "plt.plot(bins[1:len(bins)],correl,'bo-')" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1142e1f10>]" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1142e1f90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.yscale('log')\n", "plt.plot(bins[2:len(bins)]*c/1e5,correl[1:len(bins)],'ro-')" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0 236152 1835676 6024256 13930163 26635863 45156095\n", " 70428809 103344608 144719046 195306049 255767295 326645884 408522145\n", " 501808240 606822439]\n", "Total run time:\n", "5362.83386302\n", "CPU times: user 1h 29min 5s, sys: 7.79 s, total: 1h 29min 13s\n", "Wall time: 1h 29min 23s\n" ] } ], "source": [ "%%time\n", "start_time=time.time()\n", "counts_DR=BTR.two_point_correlation(dat,bins)\n", "print counts_DR\n", "end_time=time.time()\n", "tottime=end_time-start_time\n", "print \"Total run time:\"\n", "print tottime\n", "\n", "with open('BTR200kcDRLCDM.pkl', 'w') as f:\n", " pickle.dump(counts_DR,f)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0, 236152, 1835676, 6024256, 13930163, 26635863,\n", " 45156095, 70428809, 103344608, 144719046, 195306049, 255767295,\n", " 326645884, 408522145, 501808240, 606822439])" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with open('BTR200kcDRLCDM.pkl') as f:\n", " counts_DR = pickle.load(f)\n", " \n", "counts_DR" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "DR=np.diff(counts_DR)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": true }, "outputs": [], "source": [ "corrells=(4.0 * DD - 4.0 * DR + RR) / RR" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 236152, 1599524, 4188580, 7905907, 12705700, 18520232,\n", " 25272714, 32915799, 41374438, 50587003, 60461246, 70878589,\n", " 81876261, 93286095, 105014199])" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DR" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1.7052646 , 0.29978504, 0.10500558, 0.05797354, 0.03719729,\n", " 0.02859312, 0.02948462, 0.02686032, 0.02325382, 0.02064449,\n", " 0.01721195, 0.01703756, 0.01286895, 0.01164703, 0.01089049])" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corrells" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x114929c10>]" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1144a3e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(bins[1:len(bins)],corrells,'go-')" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1143fea50>]" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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UtRwDbHDbfaOrza+ESAjXnH0N83+Zb903AeTD1R+yee9muwhrTCV4VOhVtURV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\nkz9nBZoDnV3LDYDVR+7rZ3k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"text/plain": [ "<matplotlib.figure.Figure at 0x11494f190>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(bins[1:len(bins)],bins[1:len(bins)]*bins[1:len(bins)]*corrells*(c*1e-5)**2,'go-')" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x113f16490>]" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1144dd290>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(bins[2:len(bins)],bins[2:len(bins)]*bins[2:len(bins)]*corrells[1:len(bins)]*(c*1e-5)**2,'go-')" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1144d4690>]" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x114fafe10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(bins[2:len(bins)],corrells[1:len(bins)],'go-')" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x115337d50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(bins[2:len(bins)],corrells[1:len(bins)],'go-')\n", "plt.savefig(\"correl2xls.pdf\")" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11543b810>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(bins[2:len(bins)]*c/1e5,corrells[1:len(bins)],'bo-')\n", "plt.savefig(\"correl2x1ls.pdf\")" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11535a210>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.yscale('log')\n", "plt.plot(bins[1:len(bins)]*c/1e5,corrells,'bo-')\n", "plt.savefig(\"correllsfiglog.pdf\")" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1150b4e90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.yscale('log')\n", "plt.plot(bins[2:len(bins)]*c/1e5,corrells[1:len(bins)],'ro-')\n", "plt.savefig(\"correllslog2x.pdf\")" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1155bd610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.yscale('log')\n", "plt.xscale('log')\n", "plt.plot(bins[1:len(bins)]*c/1e5,corrells,'bo-')\n", "plt.savefig(\"correllsloglog.pdf\")" ] }, { "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": [ "from functools import partial\n", "\n", "def harvester(text, case):\n", " X = case[0]\n", " return text + str(X)\n", "\n", "\n", "partial_harvester = partial(harvester, case=RAW_DATASET)\n", "\n", "partial_qr=partial(BTD.query_radius,count_only=True)\n", "\n", "if __name__ == '__main__':\n", " pool = multiprocessing.Pool(processes=6)\n", " case_data = RAW_DATASET\n", " pool.map(partial_harvester, case_data, 1)\n", " pool.close()\n", " pool.join()\n", "\n", "mapfunc = partial(BTD.query_radius, count_only=True)\n", "map(mapfunc, volume_ids)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#ascii.write(\"DR72DDbinned.dat\",(bins[1:len(bins)],DDresult))\n", "start_time=time.time()\n", "@pickle_results(\"DR72DDmp1.pkl\")\n", "def ddcal(BTD,dat,bins,Nbins):\n", " counts_DD=np.zeros(Nbins)\n", " for i in tqdm(range(Nbins)):\n", " counts_DD[i]=np.sum(BTD.query_radius(dat, bins[i],count_only=True))\n", " DD = np.diff(counts_DD)\n", " print counts_DD\n", " print DD\n", " return DD\n", "\n", "def mf_wrap(args):\n", " return ddcal(*args)\n", "\n", "pool=mp.Pool(8)\n", "\n", "arg=[(BTD,dat,bins,Nbins)]\n", "%timeit DDresult=pool.map(mf_wrap,arg) \n", "#DDresult = ddcal(BTD,dat,bins,Nbins)\n", "end_time=time.time()\n", "tottime=end_time-start_time\n", "print \"Total run time:\"\n", "print tottime" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%timeit dat" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "DDresult[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "DDresult[1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[1:len(bins)],DDresult[0],'ro')" ] }, { "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": [ "def myfun(a,b):\n", " print a + b\n", " return a+b\n", "\n", "def mf_wrap(args):\n", " return myfun(*args)\n", "\n", "p = mp.Pool(4)\n", "\n", "fl = [(a,b) for a in range(3) for b in range(2)]\n", "\n", "p.map(mf_wrap, fl)" ] }, { "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": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "counts_DD=np.zeros(Nbins)\n", "\n", "for i in range(Nbins):\n", " counts_DD[i]=np.sum(BTD.query_radius(dat, bins[i],count_only=True))\n", "DD = np.diff(counts_DD)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print counts_DD\n", "print DD" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[1:len(bins)],DD,'ro')" ] }, { "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": [] }, { "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": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR=fits.open(\"/Users/rohin/Downloads/random-DR7-Full.fits\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR=dataR[1].data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "len(dataR)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata=np.array(data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "type(tdata[4])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata=np.atleast_d(tdata)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata.reshape(len(tdata),3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tdata=np.asarray(data)\n", "tdata=tdata.transpose()" ] }, { "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": [ "tdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "len(tdata)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "BTD.two_point_correlationpoint_correlationpoint_correlationpoint_correlationtime\n", "stime=time.time()\n", "tpcf=BTD.two_point_correlation(dat,bins)\n", "print time.time()-stime\n", "print tpcf\n", "plt.plot(bins,tpcf)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "stime=time.time()\n", "tpcfd=BTD.two_point_correlation(dat,bins,dualtree=True)\n", "print time.time()-stime\n", "print tpcfd\n", "plt.plot(bins,tpcfd)" ] }, { "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": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.random.seed(0)\n", "X = np.random.random((30,3))\n", "r = np.linspace(0, 1, 10)\n", "tree = BallTree(X,metric='pyfunc',func=LCDMmetric) \n", "s = pickle.dumps(tree) \n", "treedump = pickle.loads(s) \n", "treedump.two_point_correlation(X,r)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "BT_D = BallTree(data)\n", " BT_R = BallTree(data_R)\n", "\n", " counts_DD = np.zeros(Nbins + 1)\n", " counts_RR = np.zeros(Nbins + 1)\n", "\n", " for i in range(Nbins + 1):\n", " counts_DD[i] = np.sum(BT_D.query_radius(data, bins[i],\n", " count_only=True))\n", " counts_RR[i] = np.sum(BT_R.query_radius(data_R, bins[i],\n", " count_only=True))\n", "\n", " DD = np.diff(counts_DD)\n", " RR = np.diff(counts_RR)\n", "\n", " # check for zero in the denominator\n", " RR_zero = (RR == 0)\n", " RR[RR_zero] = 1\n", "\n", " if method == 'standard':\n", " corr = factor ** 2 * DD / RR - 1\n", " elif method == 'landy-szalay':\n", " if sklearn_has_two_point:\n", " counts_DR = KDT_R.two_point_correlation(data, bins)\n", " else:\n", " counts_DR = np.zeros(Nbins + 1)\n", " for i in range(Nbins + 1):\n", " counts_DR[i] = np.sum(BT_R.query_radius(data, bins[i],\n", " count_only=True))\n", " DR = np.diff(counts_DR)\n", "\n", " corr = (factor ** 2 * DD - 2 * factor * DR + RR) / RR\n", "\n", " corr[RR_zero] = np.nan\n", "\n", " return corr" ] }, { "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": [] }, { "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": [ "dr7fdat=np.array([data['s'][0:300] data['rar'][0:300] data['decr'][0:300]])\n", "dr7fdat" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dr7fdat[2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def LCDMmetric(p1,p2):\n", " costheta=m.sin(dec1)*m.sin(dec2)+m.cos(dec1)*m.cos(dec2)*m.cos(ra1-ra2)\n", " s1=DC_LCDM(z1)\n", " s2=DC_LCDM(z2)\n", " return np.sqrt(s1**2+s2**2-2.0*s1*s2*costheta)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#fdata=fits.open(\"/Users/rohin/Downloads/DR7-Full.fits\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#fdata.writeto(\"./output/DR7fulltrim.fits\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdata=fits.open(\"./output/DR7fulltrim.fits\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cols=fdata[1].columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cols.del_col('ZTYPE')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cols.del_col('SECTOR')\n", "cols.del_col('FGOTMAIN')\n", "cols.del_col('QUALITY')\n", "cols.del_col('ISBAD')\n", "cols.del_col('M')\n", "cols.del_col('MMAX')\n", "cols.del_col('ILSS')\n", "cols.del_col('ICOMB')\n", "cols.del_col('VAGC_SELECT')\n", "cols.del_col('LSS_INDEX')\n", "cols.del_col('FIBERWEIGHT')\n", "cols.del_col('PRIMTARGET')\n", "cols.del_col('MG')\n", "cols.del_col('SECTOR_COMPLETENESS')\n", "cols.del_col('COMOV_DENSITY')\n", "cols.del_col('RADIAL_WEIGHT')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdata[1].columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdata.writeto(\"./output/DR7fullzradec.fits\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat=fits.open(\"./output/DR7fullzradec.fits\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].data['Z']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].data['RA']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "comovlcdm=DC_LCDM(fdat[1].data['Z'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].data['Z']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "comovlcdm" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "comovlcdm.dtype" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#cols=fdat[1].columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nc=fits.Column(name='COMOV',format='D',array=comovlcdm)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nc1=fits.Column(name='COMOV',format='D')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdata[1].data['Z']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdata[1].data['RA']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nc" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nc.dtype" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#cols.add_col(nc)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].columns.info()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].columns.add_col(nc1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].data['COMOV']=comovlcdm" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "comovlcdm" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].data['Z']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].data['COMOV']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].data['RA']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fdat[1].data['RA']=fdat[1].data['RA']*pi/180.0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "comovlcdm=DC_LCDM(fdat[1].data['Z'])\n", "comovlcdm" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Random catalog created based on the survey limitations also taken from http://cosmo.nyu.edu/~eak306/SDSS-LRG.html" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR=fits.open(\"/Users/rohin/Downloads/random-DR7-Full.fits\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataR=dataR[1].data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "len(dataR)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "NSIDE=512\n", "dr72hpix=hu.HealPix(\"ring\",NSIDE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = open(\"./output/pixdatadr72VAGCfullrand.dat\",'w')\n", "pixdata.write(\"z\\t pix \\n\")\n", "\n", "for i in range(0,len(data)-1):\n", " pixdata.write(\"%f\\t\" %data['z'][i])\n", " pixdata.write(\"%d\\n\" %dr72hpix.eq2pix(dataR['ra'][i],dataR['dec'][i]))\n", "pixdata.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = ascii.read(\"./output/pixdatadr72VAGCfullrand.dat\")\n", "hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n", "for j in range(len(pixdata)):\n", " hpixdata[pixdata[j]['pix']]+=1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hpixdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.mollview(hpixdata,rot=180)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.orthview(hpixdata)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "Tools for computing two-point correlation functions.\n", "\"\"\"\n", "\n", "#from .utils import check_random_state\n", "# From scikit-learn utilities:\n", "def check_random_state(seed):\n", " \"\"\"Turn seed into a np.random.RandomState instance\n", "\n", " If seed is None, return the RandomState singleton used by np.random.\n", " If seed is an int, return a new RandomState instance seeded with seed.\n", " If seed is already a RandomState instance, return it.\n", " Otherwise raise ValueError.\n", " \"\"\"\n", " if seed is None or seed is np.random:\n", " return np.random.mtrand._rand\n", " if isinstance(seed, (int, np.integer)):\n", " return np.random.RandomState(seed)\n", " if isinstance(seed, np.random.RandomState):\n", " return seed\n", " raise ValueError('%r cannot be used to seed a numpy.random.RandomState'\n", " ' instance' % seed)\n", "\n", "# Check if scikit-learn's two-point functionality is available.\n", "# This was added in scikit-learn version 0.14\n", "try:\n", " from sklearn.neighbors import KDTree\n", " sklearn_has_two_point = True\n", "except ImportError:\n", " import warnings\n", " sklearn_has_two_point = False\n", "\n", "\n", "def uniform_sphere(RAlim, DEClim, size=1):\n", " \"\"\"Draw a uniform sample on a sphere\n", "\n", " Parameters\n", " ----------\n", " RAlim : tuple\n", " select Right Ascension between RAlim[0] and RAlim[1]\n", " units are degrees\n", " DEClim : tuple\n", " select Declination between DEClim[0] and DEClim[1]\n", " size : int (optional)\n", " the size of the random arrays to return (default = 1)\n", "\n", " Returns\n", " -------\n", " RA, DEC : ndarray\n", " the random sample on the sphere within the given limits.\n", " arrays have shape equal to size.\n", " \"\"\"\n", " zlim = np.sin(np.pi * np.asarray(DEClim) / 180.)\n", "\n", " z = zlim[0] + (zlim[1] - zlim[0]) * np.random.random(size)\n", " DEC = (180. / np.pi) * np.arcsin(z)\n", " RA = RAlim[0] + (RAlim[1] - RAlim[0]) * np.random.random(size)\n", "\n", " return RA, DEC\n", "\n", "\n", "def ra_dec_to_xyz(ra, dec):\n", " \"\"\"Convert ra & dec to Euclidean points\n", "\n", " Parameters\n", " ----------\n", " ra, dec : ndarrays\n", "\n", " Returns\n", " x, y, z : ndarrays\n", " \"\"\"\n", " sin_ra = np.sin(ra * np.pi / 180.)\n", " cos_ra = np.cos(ra * np.pi / 180.)\n", "\n", " sin_dec = np.sin(np.pi / 2. - dec * np.pi / 180.)\n", " cos_dec = np.cos(np.pi / 2. - dec * np.pi / 180.)\n", "\n", " return (cos_ra * sin_dec,\n", " sin_ra * sin_dec,\n", " cos_dec)\n", "\n", "\n", "def angular_dist_to_euclidean_dist(D, r=1):\n", " \"\"\"convert angular distances to euclidean distances\"\"\"\n", " return 2 * r * np.sin(0.5 * D * np.pi / 180.)\n", "\n", "\n", "def two_point(data, bins, method='standard',\n", " data_R=None, random_state=None):\n", " \"\"\"Two-point correlation function\n", "\n", " Parameters\n", " ----------\n", " data : array_like\n", " input data, shape = [n_samples, n_features]\n", " bins : array_like\n", " bins within which to compute the 2-point correlation.\n", " shape = Nbins + 1\n", " method : string\n", " \"standard\" or \"landy-szalay\".\n", " data_R : array_like (optional)\n", " if specified, use this as the random comparison sample\n", " random_state : integer, np.random.RandomState, or None\n", " specify the random state to use for generating background\n", "\n", " Returns\n", " -------\n", " corr : ndarray\n", " the estimate of the correlation function within each bin\n", " shape = Nbins\n", " \"\"\"\n", " data = np.asarray(data)\n", " bins = np.asarray(bins)\n", " rng = check_random_state(random_state)\n", "\n", " if method not in ['standard', 'landy-szalay']:\n", " raise ValueError(\"method must be 'standard' or 'landy-szalay'\")\n", "\n", " if bins.ndim != 1:\n", " raise ValueError(\"bins must be a 1D array\")\n", "\n", " if data.ndim == 1:\n", " data = data[:, np.newaxis]\n", " elif data.ndim != 2:\n", " raise ValueError(\"data should be 1D or 2D\")\n", "\n", " n_samples, n_features = data.shape\n", " Nbins = len(bins) - 1\n", "\n", " # shuffle all but one axis to get background distribution\n", " if data_R is None:\n", " data_R = data.copy()\n", " for i in range(n_features - 1):\n", " rng.shuffle(data_R[:, i])\n", " else:\n", " data_R = np.asarray(data_R)\n", " if (data_R.ndim != 2) or (data_R.shape[-1] != n_features):\n", " raise ValueError('data_R must have same n_features as data')\n", "\n", " factor = len(data_R) * 1. / len(data)\n", "\n", " if sklearn_has_two_point:\n", " # Fast two-point correlation functions added in scikit-learn v. 0.14\n", " KDT_D = KDTree(data)\n", " KDT_R = KDTree(data_R)\n", "\n", " counts_DD = KDT_D.two_point_correlation(data, bins)\n", " counts_RR = KDT_R.two_point_correlation(data_R, bins)\n", "\n", " else:\n", " warnings.warn(\"Version 0.3 of astroML will require scikit-learn \"\n", " \"version 0.14 or higher for correlation function \"\n", " \"calculations. Upgrade to sklearn 0.14+ now for much \"\n", " \"faster correlation function calculations.\")\n", "\n", " BT_D = BallTree(data)\n", " BT_R = BallTree(data_R)\n", "\n", " counts_DD = np.zeros(Nbins + 1)\n", " counts_RR = np.zeros(Nbins + 1)\n", "\n", " for i in range(Nbins + 1):\n", " counts_DD[i] = np.sum(BT_D.query_radius(data, bins[i],\n", " count_only=True))\n", " counts_RR[i] = np.sum(BT_R.query_radius(data_R, bins[i],\n", " count_only=True))\n", "\n", " DD = np.diff(counts_DD)\n", " RR = np.diff(counts_RR)\n", "\n", " # check for zero in the denominator\n", " RR_zero = (RR == 0)\n", " RR[RR_zero] = 1\n", "\n", " if method == 'standard':\n", " corr = factor ** 2 * DD / RR - 1\n", " elif method == 'landy-szalay':\n", " if sklearn_has_two_point:\n", " counts_DR = KDT_R.two_point_correlation(data, bins)\n", " else:\n", " counts_DR = np.zeros(Nbins + 1)\n", " for i in range(Nbins + 1):\n", " counts_DR[i] = np.sum(BT_R.query_radius(data, bins[i],\n", " count_only=True))\n", " DR = np.diff(counts_DR)\n", "\n", " corr = (factor ** 2 * DD - 2 * factor * DR + RR) / RR\n", "\n", " corr[RR_zero] = np.nan\n", "\n", " return corr\n", "\n", "\n", "def bootstrap_two_point(data, bins, Nbootstrap=10,\n", " method='standard', return_bootstraps=False,\n", " random_state=None):\n", " \"\"\"Bootstrapped two-point correlation function\n", "\n", " Parameters\n", " ----------\n", " data : array_like\n", " input data, shape = [n_samples, n_features]\n", " bins : array_like\n", " bins within which to compute the 2-point correlation.\n", " shape = Nbins + 1\n", " Nbootstrap : integer\n", " number of bootstrap resamples to perform (default = 10)\n", " method : string\n", " \"standard\" or \"landy-szalay\".\n", " return_bootstraps: bool\n", " if True, return full bootstrapped samples\n", " random_state : integer, np.random.RandomState, or None\n", " specify the random state to use for generating background\n", "\n", " Returns\n", " -------\n", " corr, corr_err : ndarrays\n", " the estimate of the correlation function and the bootstrap\n", " error within each bin. shape = Nbins\n", " \"\"\"\n", " data = np.asarray(data)\n", " bins = np.asarray(bins)\n", " rng = check_random_state(random_state)\n", "\n", " if method not in ['standard', 'landy-szalay']:\n", " raise ValueError(\"method must be 'standard' or 'landy-szalay'\")\n", "\n", " if bins.ndim != 1:\n", " raise ValueError(\"bins must be a 1D array\")\n", "\n", " if data.ndim == 1:\n", " data = data[:, np.newaxis]\n", " elif data.ndim != 2:\n", " raise ValueError(\"data should be 1D or 2D\")\n", "\n", " if Nbootstrap < 2:\n", " raise ValueError(\"Nbootstrap must be greater than 1\")\n", "\n", " n_samples, n_features = data.shape\n", "\n", " # get the baseline estimate\n", " corr = two_point(data, bins, method=method, random_state=rng)\n", "\n", " bootstraps = np.zeros((Nbootstrap, len(corr)))\n", "\n", " for i in range(Nbootstrap):\n", " indices = rng.randint(0, n_samples, n_samples)\n", " bootstraps[i] = two_point(data[indices, :], bins, method=method,\n", " random_state=rng)\n", "\n", " # use masked std dev in case of NaNs\n", " corr_err = np.asarray(np.ma.masked_invalid(bootstraps).std(0, ddof=1))\n", "\n", " if return_bootstraps:\n", " return corr, corr_err, bootstraps\n", " else:\n", " return corr, corr_err\n", "\n", "\n", "def two_point_angular(ra, dec, bins, method='standard', random_state=None):\n", " \"\"\"Angular two-point correlation function\n", "\n", " A separate function is needed because angular distances are not\n", " euclidean, and random sampling needs to take into account the\n", " spherical volume element.\n", "\n", " Parameters\n", " ----------\n", " ra : array_like\n", " input right ascention, shape = (n_samples,)\n", " dec : array_like\n", " input declination\n", " bins : array_like\n", " bins within which to compute the 2-point correlation.\n", " shape = Nbins + 1\n", " method : string\n", " \"standard\" or \"landy-szalay\".\n", " random_state : integer, np.random.RandomState, or None\n", " specify the random state to use for generating background\n", "\n", " Returns\n", " -------\n", " corr : ndarray\n", " the estimate of the correlation function within each bin\n", " shape = Nbins\n", " \"\"\"\n", " ra = np.asarray(ra)\n", " dec = np.asarray(dec)\n", " rng = check_random_state(random_state)\n", "\n", " if method not in ['standard', 'landy-szalay']:\n", " raise ValueError(\"method must be 'standard' or 'landy-szalay'\")\n", "\n", " if bins.ndim != 1:\n", " raise ValueError(\"bins must be a 1D array\")\n", "\n", " if (ra.ndim != 1) or (dec.ndim != 1) or (ra.shape != dec.shape):\n", " raise ValueError('ra and dec must be 1-dimensional '\n", " 'arrays of the same length')\n", "\n", " n_features = len(ra)\n", " Nbins = len(bins) - 1\n", "\n", " # draw a random sample with N points\n", " ra_R, dec_R = uniform_sphere((min(ra), max(ra)),\n", " (min(dec), max(dec)),\n", " 2 * len(ra))\n", "\n", " data = np.asarray(ra_dec_to_xyz(ra, dec), order='F').T\n", " data_R = np.asarray(ra_dec_to_xyz(ra_R, dec_R), order='F').T\n", "\n", " # convert spherical bins to cartesian bins\n", " bins_transform = angular_dist_to_euclidean_dist(bins)\n", "\n", " return two_point(data, bins_transform, method=method,\n", " data_R=data_R, random_state=rng)\n", "\n", "\n", "def bootstrap_two_point_angular(ra, dec, bins, method='standard',\n", " Nbootstraps=10, random_state=None):\n", " # type: (object, object, object, object, object, object) -> object\n", " \"\"\"Angular two-point correlation function\n", "\n", " A separate function is needed because angular distances are not\n", " euclidean, and random sampling needs to take into account the\n", " spherical volume element.\n", "\n", " Parameters\n", " ----------\n", " ra : array_like\n", " input right ascention, shape = (n_samples,)\n", " dec : array_like\n", " input declination\n", " bins : array_like\n", " bins within which to compute the 2-point correlation.\n", " shape = Nbins + 1\n", " method : string\n", " \"standard\" or \"landy-szalay\".\n", " Nbootstraps : int\n", " number of bootstrap resamples\n", " random_state : integer, np.random.RandomState, or None\n", " specify the random state to use for generating background\n", "\n", " Returns\n", " -------\n", " corr : ndarray\n", " the estimate of the correlation function within each bin\n", " shape = Nbins\n", " dcorr : ndarray\n", " error estimate on dcorr (sample standard deviation of\n", " bootstrap resamples)\n", " bootstraps : ndarray\n", " The full sample of bootstraps used to compute corr and dcorr\n", " \"\"\"\n", " ra = np.asarray(ra)\n", " dec = np.asarray(dec)\n", " rng = check_random_state(random_state)\n", "\n", " if method not in ['standard', 'landy-szalay']:\n", " raise ValueError(\"method must be 'standard' or 'landy-szalay'\")\n", "\n", " if bins.ndim != 1:\n", " raise ValueError(\"bins must be a 1D array\")\n", "\n", " if (ra.ndim != 1) or (dec.ndim != 1) or (ra.shape != dec.shape):\n", " raise ValueError('ra and dec must be 1-dimensional '\n", " 'arrays of the same length')\n", "\n", " n_features = len(ra)\n", " Nbins = len(bins) - 1\n", " data = np.asarray(ra_dec_to_xyz(ra, dec), order='F').T\n", "\n", " # convert spherical bins to cartesian bins\n", " bins_transform = angular_dist_to_euclidean_dist(bins)\n", "\n", " bootstraps = []\n", "\n", " for i in range(Nbootstraps):\n", " # draw a random sample with N points\n", " ra_R, dec_R = uniform_sphere((min(ra), max(ra)),\n", " (min(dec), max(dec)),\n", " 2 * len(ra))\n", "\n", " data_R = np.asarray(ra_dec_to_xyz(ra_R, dec_R), order='F').T\n", "\n", " if i > 0:\n", " # random sample of the data\n", " ind = np.random.randint(0, data.shape[0], data.shape[0])\n", " data_b = data[ind]\n", " else:\n", " data_b = data\n", "\n", " bootstraps.append(two_point(data_b, bins_transform, method=method,\n", " data_R=data_R, random_state=rng))\n", "\n", " bootstraps = np.asarray(bootstraps)\n", " corr = np.mean(bootstraps, 0)\n", " corr_err = np.std(bootstraps, 0, ddof=1)\n", "\n", " return corr, corr_err, bootstraps" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sklearn_has_two_point" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "help(KDTree)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyz=ra_dec_to_xyz(data['ra'],data['dec'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyz=np.asarray(dataxyz)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyz=dataxyz.transpose()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyz" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyzR=ra_dec_to_xyz(dataR['ra'],dataR['dec'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyzR=np.asarray(dataxyzR)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyzR=dataxyzR.transpose()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyzR" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins=np.arange(0.0,1.05,0.05)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#@pickle_results(\"tpcf_std.pkl\")\n", "tpcf=two_point(dataxyz,bins,method='standard',data_R=dataxyzR, random_state=None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tpcf " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[1:],tpcf,'ro')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tpcfam=two_point(dataxyz,bins,method='standard',data_R=None, random_state=None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tpcfam" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[1:],tpcfam,'bo')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins2=np.arange(0.2,0.6,0.02)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tpcfamb2=two_point(dataxyz,bins2,method='standard',data_R=None, random_state=None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins2[1:],tpcfamb2,'go')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above doesn't show any BAO feature... It used inbuilt astroML method to generate random catalog... by shuffling the original data's content... That way all of the random points fall in the same survey area and will adhere to all the filtering criteria... the factor or ratio of data pts vs. random pts will be 1... instead of large no. in case if we take existing random catalog or create one" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rng = check_random_state(None)\n", "\n", "n_samples, n_features = dataxyz.shape\n", "Nbins = len(bins) - 1\n", "\n", "# shuffle all but one axis to get background distribution\n", "data_Rxyz = dataxyz.copy()\n", "print data_Rxyz\n", "for i in range(n_features - 1):\n", " rng.shuffle(data_Rxyz[:, i])\n", "print data_Rxyz" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets see how it looks with a healpix map" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "NSIDE=512\n", "dr72hpix=hu.HealPix(\"ring\",NSIDE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import math as m\n", "\n", "def cart2sph(x,y,z):\n", " XsqPlusYsq = x**2 + y**2\n", " r = m.sqrt(XsqPlusYsq + z**2) # r\n", " elev = m.atan2(z,m.sqrt(XsqPlusYsq)) # theta\n", " az = m.atan2(y,x) # phi\n", " return r, elev, az\n", "\n", "def cart2sphA(pts):\n", " return np.array([cart2sph(x,y,z) for x,y,z in pts])\n", "\n", "def appendSpherical(xyz):\n", " np.hstack((xyz, cart2sphA(xyz)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ang=cart2sphA(data_Rxyz)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ang" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ang.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#ang.resize((105831, 2))\n", "np.squeeze(ang, axis=None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "help(ang.squeeze)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ang2=ang[:,1:]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ang2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ang2.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ang2[2,0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = open(\"./output/pixdatadr72VAGCfullrandam.dat\",'w')\n", "pixdata.write(\"pix \\n\")\n", "for i in range(0,len(ang2)-1):\n", " #pixdata.write(\"%f\\t\" %data['z'][i])\n", " pixdata.write(\"%d\\n\" %dr72hpix.ang2pix(ang2[i,0],ang2[i,1]))\n", "pixdata.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = ascii.read(\"./output/pixdatadr72VAGCfullrandam.dat\")\n", "hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n", "for j in range(len(pixdata)):\n", " hpixdata[pixdata[j]['pix']]+=1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hpixdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.mollview(hpixdata,rot=180)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.orthview(hpixdata)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This method doesnt seem to produce right random catalogs...doing it with ra and dec as follows" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data['z'],data['ra'],data['dec']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "datzradec=np.array([data['z'], data['ra'], data['dec']])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "datzradec" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "rng = check_random_state(None)\n", "\n", "n_features, n_samples = datzradec.shape\n", "\n", "# shuffle all but one axis to get background distribution\n", "data_Rzradec = datzradec.copy()\n", "print data_Rzradec\n", "for i in range(1,n_features):\n", " rng.shuffle(data_Rzradec[:, i])\n", "print data_Rzradec" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "min(data_Rzradec[:, 1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "max(data_Rzradec[:, 1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "min(data_Rzradec[:, 2])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "max(data_Rzradec[:, 2])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "min(datzradec[:, 1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "max(datzradec[:, 1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "min(datzradec[:, 2])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "max(datzradec[:, 2])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "range(1,3)" ] }, { "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": [ "help(rng.shuffle)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n_samples" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n_features" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_Rzradec" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_Rzradec[0][2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "len(data_Rzradec[0][:])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_Rzradec[0][:]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = open(\"./output/pixdatadr72VAGCfullrandamrd.dat\",'w')\n", "pixdata.write(\"z\\t pix \\n\")\n", "for i in range(0,len(data_Rzradec[0][:])-1):\n", " pixdata.write(\"%f\\t\" %data_Rzradec[0][i])\n", " pixdata.write(\"%d\\n\" %dr72hpix.eq2pix(data_Rzradec[1][i],data_Rzradec[2][i]))\n", "pixdata.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = ascii.read(\"./output/pixdatadr72VAGCfullrandamrd.dat\")\n", "hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n", "for j in range(len(pixdata)):\n", " hpixdata[pixdata[j]['pix']]+=1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hpixdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.mollview(hpixdata,rot=180)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.orthview(hpixdata)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyz" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyzR1=ra_dec_to_xyz(data_Rzradec[1][:],data_Rzradec[2][:])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_Rzradec[1][:]" ] }, { "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": [ "dataxyzR1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyzR1=np.asarray(dataxyzR1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyzR1=dataxyzR1.transpose()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dataxyzR1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins=np.arange(0.025,1.025,0.025)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#@pickle_results(\"tpcf_std.pkl\")\n", "tpcf=two_point(dataxyz,bins,method='standard',data_R=dataxyzR1, random_state=None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tpcf " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[1:],tpcf,'ro')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins=np.arange(0.0,1.05,0.05)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#@pickle_results(\"tpcf_std.pkl\")\n", "tpcf=two_point(dataxyz,bins,method='standard',data_R=dataxyzR1, random_state=None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tpcf " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.plot(bins[1:],tpcf,'ro')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "btpcf=bootstrap_two_point(dataxyz, bins, Nbootstrap=10,\n", " method='standard', return_bootstraps=False,\n", " random_state=None)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "btpcf" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.errorbar(bins[1:],btpcf[0],yerr=btpcf[1],fmt='ro-')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "help(plt.errorbar)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "@pickle_results(\"tpcf_ls.pkl\")\n", "tpcfls=two_point(dataxyz,bins,method='landy-szalay',\n", " data_R=dataxyzR, random_state=None)" ] }, { "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": [] }, { "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": [] }, { "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": [ "#------------------------------------------------------------\n", "# Set up correlation function computation\n", "# This calculation takes a long time with the bootstrap resampling,\n", "# so we'll save the results.\n", "@pickle_results(\"correlation_functionsdr72.pkl\")\n", "def compute_results(Nbins=16, Nbootstraps=10, method='landy-szalay', rseed=0):\n", " np.random.seed(rseed)\n", " bins = 10 ** np.linspace(np.log10(1. / 60.), np.log10(6), 16)\n", "\n", " results = [bins]\n", " for D in [data]:\n", " results += bootstrap_two_point_angular(D['ra'],\n", " D['dec'],\n", " bins=bins,\n", " method=method,\n", " Nbootstraps=Nbootstraps)\n", "\n", " return results\n", "\n", "(bins, r_corr, r_corr_err, r_bootstraps) = compute_results()\n", "\n", "bin_centers = 0.5 * (bins[1:] + bins[:-1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_corr" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_corr_err" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_bootstraps" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#------------------------------------------------------------\n", "# Plot the results\n", "\n", "label = '$0.15<z<0.25$\\n$N=33813$' \n", "\n", "fig = plt.figure(figsize=(6, 6))\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.errorbar(bin_centers, r_corr, r_corr_err,fmt='.k', ecolor='gray', lw=1)\n", "fig.text(0.8, 0.8, label, ha='right', va='top')\n", "plt.xlabel(r'$\\theta\\ (deg)$')\n", "plt.ylabel(r'$w(\\theta)$')\n", "plt.show()\n", "\n", "plt.show()\n", "fig.savefig(\"wth_dr72015025.pdf\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data=ascii.read('./input/sdssdr72_sorted_z.dat')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#m_max = 19\n", "\n", "# redshift and magnitude cuts\n", "data = data[data['z'] > 0.05]\n", "data = data[data['z'] < 0.15]\n", "#data = data[data['petroMag_r'] < m_max]\n", "\n", "# RA/DEC cuts\n", "RAmin, RAmax = 140, 220\n", "DECmin, DECmax = 5, 45\n", "data = data[data['ra'] < RAmax]\n", "data = data[data['ra'] > RAmin]\n", "data = data[data['dec'] < DECmax]\n", "data = data[data['dec'] > DECmin]\n", "\n", "#ur = data['modelMag_u'] - data['modelMag_r']\n", "#flag_red = (ur > 2.22)\n", "#flag_blue = ~flag_red\n", "\n", "#datag \n", "\n", "print \"data size:\"\n", "print \" total gals: \", len(data)\n", "#print \" blue gals:\", len(data_blue)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "NSIDE=512\n", "dr72hpix=hu.HealPix(\"ring\",NSIDE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = open(\"./output/pixdatadr72005015.dat\",'w')\n", "pixdata.write(\"z\\t pix \\n\")\n", "\n", "for i in range(0,len(data)-1):\n", " pixdata.write(\"%f\\t\" %data['z'][i])\n", " pixdata.write(\"%d\\n\" %dr72hpix.eq2pix(data['ra'][i],data['dec'][i]))\n", "pixdata.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = ascii.read(\"./output/pixdatadr72005015.dat\")\n", "hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n", "for j in range(len(pixdata)):\n", " hpixdata[pixdata[j]['pix']]+=1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hpixdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.mollview(hpixdata,rot=180)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.orthview(hpixdata)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#------------------------------------------------------------\n", "# Set up correlation function computation\n", "# This calculation takes a long time with the bootstrap resampling,\n", "# so we'll save the results.\n", "@pickle_results(\"correlation_functionsdr720515.pkl\")\n", "def compute_results(Nbins=16, Nbootstraps=10, method='landy-szalay', rseed=0):\n", " np.random.seed(rseed)\n", " bins = 10 ** np.linspace(np.log10(1. / 60.), np.log10(6), 16)\n", "\n", " results = [bins]\n", " for D in [data]:\n", " results += bootstrap_two_point_angular(D['ra'],\n", " D['dec'],\n", " bins=bins,\n", " method=method,\n", " Nbootstraps=Nbootstraps)\n", "\n", " return results\n", "\n", "(bins, r_corr, r_corr_err, r_bootstraps) = compute_results()\n", "\n", "bin_centers = 0.5 * (bins[1:] + bins[:-1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_corr" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_corr_err" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_bootstraps" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#------------------------------------------------------------\n", "# Plot the results\n", "label = '$0.05<z<0.15$\\n$N=138051$'\n", "\n", "fig = plt.figure(figsize=(6, 6))\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.errorbar(bin_centers, r_corr, r_corr_err,fmt='.k', ecolor='gray', lw=1)\n", "fig.text(0.8, 0.8, label, ha='right', va='top')\n", "plt.xlabel(r'$\\theta\\ (deg)$')\n", "plt.ylabel(r'$w(\\theta)$')\n", "plt.show()\n", "fig.savefig(\"wth_dr72005015.pdf\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.errorbar(bins[0:len(bins)-1],r_corr,r_corr_err)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data=ascii.read('./input/sdssdr72_sorted_z.dat')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data['z']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#m_max = 19\n", "\n", "# redshift and magnitude cuts\n", "data = data[data['z'] > 0.05]\n", "data = data[data['z'] <= 0.10]\n", "#data = data[data['petroMag_r'] < m_max]\n", "\n", "# RA/DEC cuts\n", "RAmin, RAmax = 140, 220\n", "DECmin, DECmax = 5, 45\n", "data = data[data['ra'] < RAmax]\n", "data = data[data['ra'] > RAmin]\n", "data = data[data['dec'] < DECmax]\n", "data = data[data['dec'] > DECmin]\n", "\n", "#ur = data['modelMag_u'] - data['modelMag_r']\n", "#flag_red = (ur > 2.22)\n", "#flag_blue = ~flag_red\n", "\n", "#datag \n", "\n", "print \"data size:\"\n", "print \" total gals: \", len(data)\n", "#print \" blue gals:\", len(data_blue)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "NSIDE=512\n", "dr72hpix=hu.HealPix(\"ring\",NSIDE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = open(\"./output/pixdatadr7200501.dat\",'w')\n", "pixdata.write(\"z\\t pix \\n\")\n", "\n", "for i in range(0,len(data)-1):\n", " pixdata.write(\"%f\\t\" %data['z'][i])\n", " pixdata.write(\"%d\\n\" %dr72hpix.eq2pix(data['ra'][i],data['dec'][i]))\n", "pixdata.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = ascii.read(\"./output/pixdatadr7200501.dat\")\n", "hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n", "for j in range(len(pixdata)):\n", " hpixdata[pixdata[j]['pix']]+=1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hpixdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.mollview(hpixdata,rot=180)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.orthview(hpixdata)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#------------------------------------------------------------\n", "# Set up correlation function computation\n", "# This calculation takes a long time with the bootstrap resampling,\n", "# so we'll save the results.\n", "@pickle_results(\"correlation_functionsdr720501.pkl\")\n", "def compute_results(Nbins=16, Nbootstraps=10, method='landy-szalay', rseed=0):\n", " np.random.seed(rseed)\n", " bins = 10 ** np.linspace(np.log10(1. / 60.), np.log10(6), 16)\n", "\n", " results = [bins]\n", " for D in [data]:\n", " results += bootstrap_two_point_angular(D['ra'],\n", " D['dec'],\n", " bins=bins,\n", " method=method,\n", " Nbootstraps=Nbootstraps)\n", "\n", " return results\n", "\n", "(bins, r_corr, r_corr_err, r_bootstraps) = compute_results()\n", "\n", "bin_centers = 0.5 * (bins[1:] + bins[:-1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_corr" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_corr_err" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_bootstraps" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#------------------------------------------------------------\n", "# Plot the results\n", "\n", "label = '$0.05<z<0.10$\\n$N=78939$'\n", "\n", "fig = plt.figure(figsize=(6, 6))\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.errorbar(bin_centers, r_corr, r_corr_err,fmt='.k', ecolor='gray', lw=1)\n", "fig.text(0.8, 0.8, label, ha='right', va='top')\n", "plt.xlabel(r'$\\theta\\ (deg)$')\n", "plt.ylabel(r'$w(\\theta)$')\n", "plt.show()\n", "fig.savefig(\"wth_dr720501.pdf\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.errorbar(bins[0:len(bins)-1],r_corr,r_corr_err)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data=ascii.read('./input/sdssdr72_sorted_z.dat')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data['z']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#m_max = 19\n", "\n", "# redshift and magnitude cuts\n", "data = data[data['z'] > 0.10]\n", "data = data[data['z'] <= 0.15]\n", "#data = data[data['petroMag_r'] < m_max]\n", "\n", "# RA/DEC cuts\n", "RAmin, RAmax = 140, 220\n", "DECmin, DECmax = 5, 45\n", "data = data[data['ra'] < RAmax]\n", "data = data[data['ra'] > RAmin]\n", "data = data[data['dec'] < DECmax]\n", "data = data[data['dec'] > DECmin]\n", "\n", "#ur = data['modelMag_u'] - data['modelMag_r']\n", "#flag_red = (ur > 2.22)\n", "#flag_blue = ~flag_red\n", "\n", "#datag \n", "\n", "print \"data size:\"\n", "print \" total gals: \", len(data)\n", "#print \" blue gals:\", len(data_blue)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "NSIDE=512\n", "dr72hpix=hu.HealPix(\"ring\",NSIDE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = open(\"./output/pixdatadr72001015.dat\",'w')\n", "pixdata.write(\"z\\t pix \\n\")\n", "\n", "for i in range(0,len(data)-1):\n", " pixdata.write(\"%f\\t\" %data['z'][i])\n", " pixdata.write(\"%d\\n\" %dr72hpix.eq2pix(data['ra'][i],data['dec'][i]))\n", "pixdata.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = ascii.read(\"./output/pixdatadr72001015.dat\")\n", "hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n", "for j in range(len(pixdata)):\n", " hpixdata[pixdata[j]['pix']]+=1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hpixdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.mollview(hpixdata,rot=180)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.orthview(hpixdata)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#------------------------------------------------------------\n", "# Set up correlation function computation\n", "# This calculation takes a long time with the bootstrap resampling,\n", "# so we'll save the results.\n", "@pickle_results(\"correlation_functionsdr72001015.pkl\")\n", "def compute_results(Nbins=16, Nbootstraps=10, method='landy-szalay', rseed=0):\n", " np.random.seed(rseed)\n", " bins = 10 ** np.linspace(np.log10(1. / 60.), np.log10(6), 16)\n", "\n", " results = [bins]\n", " for D in [data]:\n", " results += bootstrap_two_point_angular(D['ra'],\n", " D['dec'],\n", " bins=bins,\n", " method=method,\n", " Nbootstraps=Nbootstraps)\n", "\n", " return results\n", "\n", "(bins, r_corr, r_corr_err, r_bootstraps) = compute_results()\n", "\n", "bin_centers = 0.5 * (bins[1:] + bins[:-1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bins" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_corr" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_corr_err" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r_bootstraps" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#------------------------------------------------------------\n", "# Plot the results\n", "\n", "label = '$0.10<z<0.15$\\n$N=59112$'\n", "fig = plt.figure(figsize=(6, 6))\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "plt.errorbar(bin_centers, r_corr, r_corr_err,fmt='.k', ecolor='gray', lw=1)\n", "fig.text(0.8, 0.8, label, ha='right', va='top')\n", "plt.xlabel(r'$\\theta\\ (deg)$')\n", "plt.ylabel(r'$w(\\theta)$')\n", "plt.show()\n", "fig.savefig(\"wth_dr7201015.pdf\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.errorbar(bins[0:len(bins)-1],r_corr,r_corr_err)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.mollview(hpixdatab,rot=180)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.orthview(hpixdatab)" ] }, { "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": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "help(hu.mollview)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "from astroML.datasets import fetch_sdss_specgals\n", "from astroML.correlation import bootstrap_two_point_angular" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "help(bootstrap_two_point_angular)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "help(astroML.correlation)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import astroML.correlation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sklearn.neighbors" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "help(sklearn.neighbors)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sorted and reduced column set data can now be 'read' to reduce RAM requirements of the table reading. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sdssdr72=ascii.read('./input/dssdr72_sorted_z.dat')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a healpix map with NSIDE=64 (no. of pixels = 49152 as $NPIX=12\\times NSIDE^2$) because the no. of galaxies in the survey are less. For higher resolution (later for dr12) we will consider NSIDE=512 or even 1024. For now, we will create a 64 NSIDE map." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "NSIDE=64\n", "dt72hpix=hu.HealPix(\"ring\",NSIDE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have data of galaxies with redshifts between 0 and 0.5 ($0<z<0.5$). To look at a time slice/at a certain epoch we need to choose the list of galaxies within a redshift window. As, measurement of redshift has $\\pm 0.05$ error. We can bin all the data into redshifts with range limited to 0.05 variation each. So, we have 10 databins with almost identical redshifts. We save each databin in a different file. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "j=0\n", "for i in range(1,17):\n", " pixdata = open(\"/home/rohin/Desktop/healpix/binned1/pixdata%d_%d.dat\"%(NSIDE,i),'w')\n", " pixdata.write(\"ra\\t dec\\t z\\t pix \\n\")\n", " #for j in range(len(sdssdr72)):\n", " try:\n", " while sdssdr72[j]['z']<0.03*i:\n", " pixdata.write(\"%f\\t\" %sdssdr72[j]['ra'])\n", " pixdata.write(\"%f\\t\" %sdssdr72[j]['dec'])\n", " pixdata.write(\"%f\\t\" %sdssdr72[j]['z'])\n", " pixdata.write(\"%d\\n\" %dt72hpix.eq2pix(sdssdr72[j]['ra'],sdssdr72[j]['dec']))\n", " #print dt72hpix.eq2pix(sdssdr72[j]['ra'],sdssdr72[j]['dec'])\n", " j=j+1\n", " except:\n", " pass\n", " pixdata.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for i in range(1,17):\n", " pixdata = ascii.read(\"/home/rohin/Desktop/healpix/binned1/pixdata%d_%d.dat\"%(NSIDE,i))\n", " mpixdata = open(\"/home/rohin/Desktop/healpix/binned1/masked/pixdata%d_%d.dat\"%(NSIDE,i),'w')\n", " mpixdata.write(\"ra\\t dec\\t z\\t pix \\n\")\n", " for j in range((len(pixdata)-1)):\n", " if 100<pixdata[j]['ra']<250:\n", " mpixdata.write(\"%f\\t\" %pixdata[j]['ra'])\n", " mpixdata.write(\"%f\\t\" %pixdata[j]['dec'])\n", " mpixdata.write(\"%f\\t\" %pixdata[j]['z'])\n", " mpixdata.write(\"%d\\n\" %pixdata[j]['pix'])\n", " #pixdata.write(\"/home/rohin/Desktop/healpix/binned1/masked/pixdata_%d.dat\"%i,format='ascii')\n", " \n", " \n", " #print dt72hpix.eq2pix(sdssdr72[j]['ra'],sdssdr72[j]['dec'])\n", " mpixdata.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now, take each databin and assign the total no. of galaxies as the value of each pixel. The following routine will calculate the no. of galaxies by couting the occurence of pixel numbers in the file." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixdata = ascii.read(\"/home/rohin/Desktop/healpix/binned1/masked/pixdata%d_2.dat\"%NSIDE)\n", "hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n", "for j in range(len(pixdata)):\n", " hpixdata[pixdata[j]['pix']]+=1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hpixdata" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.orthview(hpixdata,rot=180)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixcl=hu.anafast(hpixdata,lmax=300)\n", "ell = np.arange(len(pixcl))\n", "plt.figure()\n", "plt.plot(ell,np.log(pixcl))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pixcl=hu.anafast(hpixdata,lmax=300)\n", "ell = np.arange(len(pixcl))\n", "plt.figure()\n", "plt.plot(ell,np.sqrt(ell*(ell+1)*pixcl/(4*math.pi)))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "theta=np.arange(0,np.pi,0.001)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "correldat = np.polynomial.legendre.legval(np.cos(theta),(2*ell+1)*np.absolute(pixcl))/(4*math.pi)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.figure()\n", "plt.plot(theta[0:600]*180/math.pi,correldat[0:600])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.figure()\n", "plt.plot(theta*180/math.pi,correldat)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "randra,randdec=hu.randsphere(2200000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "randhp=hu.HealPix(\"RING\",NSIDE)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "randhppix=randhp.eq2pix(randra,randdec)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "randpixdat=np.array(np.zeros(hu.nside2npix(NSIDE)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for j in range(len(randhppix)):\n", " randpixdat[randhppix[j]]+=1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "randmaphp=hu.mollview(randpixdat)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "randcl=hu.anafast(randpixdat,lmax=300)\n", "ell = np.arange(len(randcl))\n", "plt.figure()\n", "plt.plot(ell,np.sqrt(ell*(ell+1)*randcl/(4*math.pi)))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "correlrand = np.polynomial.legendre.legval(np.cos(theta),(2*ell+1)*np.absolute(randcl))/(4*math.pi)\n", "plt.figure()\n", "plt.plot(theta[0:600]*180/math.pi,correlrand[0:600])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "finalcorrel=correldat-correlrand\n", "plt.figure()\n", "plt.plot(theta[0:600]*180/math.pi,finalcorrel[0:600])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "finalpix=hpixdata-randpixdat" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hu.mollview(finalpix,rot=180)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cl=hu.anafast(finalpix,lmax=300)\n", "ell = np.arange(len(cl))\n", "plt.figure()\n", "plt.plot(ell,np.sqrt(ell*(ell+1)*cl/(4*math.pi)))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "correlrand = np.polynomial.legendre.legval(np.cos(theta),(2*ell+1)*np.absolute(cl))/(4*math.pi)\n", "plt.figure()\n", "plt.plot(theta[0:600]*180/math.pi,correlrand[0:600])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "finalcl=pixcl-randcl\n", "correlrand = np.polynomial.legendre.legval(np.cos(theta),(2*ell+1)*np.absolute(finalcl))/(4*math.pi)\n", "plt.figure()\n", "plt.plot(theta[0:600]*180/math.pi,correlrand[0:600])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "help(fits)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data[1].data['z']" ] }, { "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 }
mit
parambharat/ML-Programs
P2:_student_intervention/Grapher.ipynb
1
31575
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "from matplotlib import pylab\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X1 = np.linspace(0,2,10)\n", "X2 = X1*np.random.rand(10)\n", "\n", "X3 = np.linspace(4,6,10)\n", "X4 = X3*np.random.rand(10)\n", "a = np.ones(10)*3" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/bharat/anaconda2/lib/python2.7/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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LzQ2mJh8yJNjmzGk+U5Pn5ARTreflBdvcucExEUkpBY8YaKyBnMkEhlNPDcZr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\nKqgAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fa23ff5f490>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pylab.scatter(X1,X2, color=\"b\", marker=\"o\", label=\"Neg\")\n", "pylab.scatter(X3,X4, color=\"r\", marker=\"x\", label=\"Pos\")\n", "pylab.plot(a,np.arange(-1,9),label=\"Decision Boundary\")\n", "pylab.xlabel(\"feature X1\")\n", "pylab.ylabel(\"feature X2\")\n", "pylab.legend(loc=\"upper right\", bbox_to_anchor=(1.5,1))\n", "pylab.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "z = np.linspace(-5,5)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def sigmoid(x):\n", " return 1./(1. + np.exp(-x))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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4CmjyZGjRIu6IRGRrqCQgW2TdOrjnHvj976Fr19ATWAlAJH2pJCAV9tpr8Mc/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\n3lcI33NVBVWQhpKWlGJhBrJ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mit
eds-uga/csci1360-fa16
lectures/L5.ipynb
1
24347
{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Lecture 5: Loops\n", "\n", "CSCI 1360: Foundations for Informatics and Analytics" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Overview and Objectives\n", "\n", "In this lecture, we'll go over the basics of looping in Python. By the end of this lecture, you should be able to" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " - Perform basic arithmetic operations using arbitrary-length collections\n", " - Use \"unpacking\" as a shortcut for iterating through dictionaries\n", " - Describe the differences between the separate kinds of loops" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Part 1: `for` Loops" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Looping, like lists, is a critical component in programming and data science. When we're training models on data, we'll need to loop over each data point, examining it in turn and adjusting our model accordingly *regardless of how many data points there are*. This kind of repetitive task is ideal for looping." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Let's define for ourselves the following list:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "ages = [21, 22, 19, 19, 22, 21, 22, 31]" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "This is a list containing the ages of some group of students, and we want to compute the average. How do we compute averages?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "We know an average is some *total quantity* divided by *number of elements*. Well, the latter is easy enough to compute:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8\n" ] } ], "source": [ "number_of_elements = len(ages)\n", "print(number_of_elements)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "The total quantity is a bit trickier. You could certainly sum them all manually--" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "age_sum = ages[0] + ages[1] + ages[2] # + ... and so on" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "...but that seems really, really tedious. Plus, how do you even know how many elements your list has?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Loop structure" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "The structure itself is pretty simple:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " - some collection of \"things\" to iterate over" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " - a placeholder for the current \"thing\"" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " - a chunk of code describing what to do with the current \"thing\"" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Let's start simple: looping through a list, printing out each item one at a time." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "5\n", "7\n", "9\n" ] } ], "source": [ "for N in [2, 5, 7, 9]: # Header\n", " print(N) # Body" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "There are two main parts to the loop: the **header** and the **body**." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " - The **header** contains 1) the collection we're iterating over (in this example, the list), and 2) the \"placeholder\" we're using to hold the current value (in this example, `N`)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " - The **body** is the chunk of code under the header (indented!) that executes on each iteration." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Back, then, to computing an average:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average age: 22.12\n" ] } ], "source": [ "age_sum = 0\n", "ages = [21, 22, 19, 19, 22, 21, 22, 31]\n", "\n", "for age in ages:\n", " age_sum += age\n", "\n", "avg = age_sum / number_of_elements # Compute the average using the formula we know and love!\n", "print(\"Average age: {:.2f}\".format(avg))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "You can loop through sets and tuples the same way." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "5\n" ] } ], "source": [ "s = set([1, 1, 2, 3, 5])\n", "for item in s:\n", " print(item)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "1\n", "2\n", "3\n", "5\n" ] } ], "source": [ "t = tuple([1, 1, 2, 3, 5])\n", "for item in t:\n", " print(item)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Iterators" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "The unifying theme with all these collections you can loop through is that they're all examples of *iterators*." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Easily the most common iterator you'll use (aside from lists, sets, and tuples) is the `range` function:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 1 2 3 4 5 6 7 8 9 " ] } ], "source": [ "for i in range(10):\n", " print(i, end = \" \") # Prints everything on 1 line." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Note, again, that the range of numbers goes from 0 (inclusive) to the specified end (exclusive)! The critical point is that the argument to `range` specifies the *length* of the returned iterator." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "A few more examples of `range` before we get back to loops:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 1 2 3 4 " ] } ], "source": [ "for i in range(5): # One argument: specifies the \"end\"\n", " print(i, end = \" \")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5 6 7 8 9 " ] } ], "source": [ "for i in range(5, 10): # Two arguments: first is \"start\" (inclusive), second is \"end\" (exclusive)\n", " print(i, end = \" \")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 2 4 6 8 " ] } ], "source": [ "for i in range(0, 10, 2): # Three arguments: start, end, and increment\n", " print(i, end = \" \")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**IMPORTANT: INDENTATION MATTERS**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "You'll notice in these loops that the *loop body* is distinctly indented relative to the *loop header*. This is intentional and is indeed how it works! If you fail to indent the body of the loop, Python will complain:" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "ename": "IndentationError", "evalue": "expected an indented block (<ipython-input-48-e6ab552bd1f0>, line 3)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-48-e6ab552bd1f0>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m print(item)\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n" ] } ], "source": [ "some_list = [3.14159, \"random stuff\", 4200]\n", "for item in some_list:\n", "print(item)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "With loops, whitespace in Python *really* starts to matter. If you want many things to happen inside of a loop, you'll need to indent every line!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Let's say in some future homework assignment, I ask you to write a loop computing the squares of the numbers 1-10. How would you do it?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Well, you could manually write it out, I suppose..." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "squares = [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "...but that's awfully boring." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Instead, let's use the `range` function we were just discussing:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n" ] } ], "source": [ "squares = [] # Empty list for all our squares\n", "\n", "for num in range(10):\n", " squared_number = num ** 2 # Exponent operation!\n", " squares.append(squared_number) # Add to our list.\n", "\n", "print(squares)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Looping through dictionaries" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "This gets its own subsection because it pulls together pretty much all the concepts we've discussed so far: lists, tuples, dictionaries, and looping." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Let's start by defining a dictionary. In this case, we'll set up a dictionary that maps people to their favorite programming language." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "favorite_languages = {\n", " 'jen': 'python',\n", " 'sarah': 'c',\n", " 'edward': 'ruby',\n", " 'shannon': 'python'\n", "}\n", "# Notice the indentation, if you decide to define a dictionary this way!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Remember the super-useful methods for iterating through dictionaries? `keys` gives you a list of all the keys, `values` a list of all the values, and `items` a list of *tuples* of the key-value pairs. Here's the loop:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sarah prefers c.\n", "shannon prefers python.\n", "jen prefers python.\n", "edward prefers ruby.\n" ] } ], "source": [ "for key, value in favorite_languages.items(): # 1\n", " print(\"{} prefers {}.\".format(key, value))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "1: Notice how `key, value` are just out there floating! This is called *unpacking* and is a very useful technique in Python. If I have a list of a few items, and (critically) *I know how many items there are*, I can do this" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "some_list = ['a', 'b']\n", "a, b = some_list" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "instead of this" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "some_list = ['a', 'b']\n", "a = some_list[0]\n", "b = some_list[1]" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "In the same vein, I could have just as easily written the loop like this:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sarah prefers c.\n", "shannon prefers python.\n", "jen prefers python.\n", "edward prefers ruby.\n" ] } ], "source": [ "for keyvalue in favorite_languages.items(): # 1\n", " key = keyvalue[0]\n", " value = keyvalue[1]\n", " print(\"{} prefers {}.\".format(key, value)) # 2" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "and indeed, if that is easier for you to understand, by all means do it! This is to illustrate all the concepts at play at once:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " - the loop header iterates through a list provided by `favorite_languages.items()`\n", " - each iteration, `items()` provides a tuple: a key-value pair from the dictionary\n", " - we can \"unpack\" these variables using shorthand, but it's also perfectly valid to do it the \"regular\" way" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "That's pretty much `for` loops!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "What about the case where you don't know ahead of time how many iterations your loop will take?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Part 2: `while` Loops" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "\"While\" loops go back yet again to the concept of boolean logic we introduced in an earlier lecture: loop until some condition is reached." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "The structure here is a little different than `for` loops. Instead of explicitly looping over an iterator, you'll set some condition that evaluates to either `True` or `False`; as long as the condition is `True`, Python executes another loop." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 11 12 13 14 " ] } ], "source": [ "x = 10\n", "\n", "while x < 15:\n", " print(x, end = \" \")\n", " x += 1" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "`x < 15` is a boolean statement: it is either `True` or `False`, depending on the value of `x`. Initially, this number is 10, which is certainly `< 15`, so the loop executes. 10 is printed, `x` is incremented, and the condition is checked again." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "A potential downside of `while` loops: **forgetting to update the condition inside the loop.**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "It's easy to take for granted; `for` loops implicitly handle this for us!" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 11 12 13 14 " ] } ], "source": [ "for i in range(10, 15):\n", " print(i, end = \" \")\n", " # No update needed!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Use `for` loops frequently enough, and when you occasionally use a `while` loop, you'll forget you need to update the loop condition." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Review Questions" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Some questions to discuss and consider:\n", "\n", "1: Using the awful matrix construct of a \"list of lists,\" show how you could write loops that double the value of each element of the matrix.\n", "\n", "2: `for` and `while` loops may have different syntax and different use cases, but you can often translate the same task between the two types of loops. Show how you could use a `while` loop to iterate through a list of numbers from `range()`.\n", "\n", "3: Let's say you have two lists, `K` and `V`, that are both the same length. Show how, using only 1 loop, you can loop through both of them simultaneously.\n", "\n", "4: Now let's say your lists `K` and `V` are *not* the same length. Using 1 loop, iterate through them, stopping when you reach the end of the shorter list." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Course Administrivia" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "**A1 is out!**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "**Even though A0 isn't \"graded\", please submit by 11:59 tonight.**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "**Next week: generators and comprehensions and `enumerate`, oh my!**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Additional Resources\n", "\n", " 1. Matthes, Eric. *Python Crash Course*. 2016. ISBN-13: 978-1593276034\n", " 2. Grus, Joel. *Data Science from Scratch*. 2015. ISBN-13: 978-1491901427" ] } ], "metadata": { "celltoolbar": "Slideshow", "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
mclaughlin6464/pearce
notebooks/Make Data for Redmagic HOD Fit.ipynb
1
30708
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "from astropy.cosmology import LambdaCDM\n", "from astropy.io import fits" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.04166667 0.05245523 0.06603722 0.08313593 0.10466193 0.13176157\n", " 0.16587799 0.20882801 0.26289889 0.3309701 0.41666667 0.52455225\n", " 0.66037216 0.8313593 1.04661935 1.31761569 1.65877988 2.08828014\n", " 2.62898894 3.30970098 4.16666667]\n" ] } ], "source": [ "theta_bins = np.logspace(np.log10(2.5), np.log10(250), 21)/60\n", "print theta_bins" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fname = '/u/ki/jderose/public_html/bcc/measurement/y3/3x2pt/buzzard/flock/buzzard-2/tpt_Y3_v0.fits'\n", "hdulist = fits.open(fname)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Filename: /u/ki/jderose/public_html/bcc/measurement/y3/3x2pt/buzzard/flock/buzzard-2/tpt_Y3_v0.fits\n", "No. Name Type Cards Dimensions Format\n", " 0 PRIMARY PrimaryHDU 4 () \n", " 1 xip BinTableHDU 32 200R x 5C [K, K, K, D, D] \n", " 2 xim BinTableHDU 32 200R x 5C [K, K, K, D, D] \n", " 3 gammat BinTableHDU 32 400R x 5C [K, K, K, D, D] \n", " 4 wtheta BinTableHDU 32 300R x 5C [K, K, K, D, D] \n", " 5 nz_shear_bpz BinTableHDU 32 250R x 7C [D, D, D, D, D, D, D] \n", " 6 nz_shear_true BinTableHDU 32 250R x 7C [D, D, D, D, D, D, D] \n", " 7 nz_pos_zrm BinTableHDU 31 400R x 8C [D, D, D, D, D, D, D, D] \n", " 8 nz_pos_zspec BinTableHDU 31 400R x 8C [D, D, D, D, D, D, D, D] \n" ] } ], "source": [ "hdulist.info()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "z_bins = np.array([0.15, 0.3, 0.45, 0.6, 0.75, 0.9])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1, 1, 0, 0.45695746584854985, 2.823621287743753)\n", "(1, 1, 1, 0.36215490680577156, 3.557146025674375)\n", "(1, 1, 2, 0.29041139011879313, 4.4823793795798936)\n", "(1, 1, 3, 0.23118317670343497, 5.6400730550718423)\n", "(1, 1, 4, 0.19100328415347961, 7.1046610696008692)\n", "(1, 1, 5, 0.15100299741973047, 8.9441097105123593)\n", "(1, 1, 6, 0.13108268339458876, 11.262638241945165)\n", "(1, 1, 7, 0.10749353219459415, 14.177008926475571)\n", "(1, 1, 8, 0.086800552571491038, 17.847698728733025)\n", "(1, 1, 9, 0.075283022559461496, 22.471916045640072)\n", "(1, 1, 10, 0.062911948852758512, 28.288981962942596)\n", "(1, 1, 11, 0.051891487004727226, 35.614102833654002)\n", "(1, 1, 12, 0.041154875101075204, 44.834108999916808)\n", "(1, 1, 13, 0.030233398732561853, 56.447535720056472)\n", "(1, 1, 14, 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141.78439737665724)\n", "(3, 4, 18, 0.00030105088701182643, 178.48491599669507)\n", "(3, 4, 19, 0.00021999674000596265, 224.67564715642703)\n", "(3, 5, 0, 0.0049090151357742929, 2.8298305989271815)\n", "(3, 5, 1, 0.0011535736151568302, 3.5618042733490829)\n", "(3, 5, 2, 0.0026685773082431798, 4.4837958014153765)\n", "(3, 5, 3, 0.00099534673116967193, 5.6457735472808706)\n", "(3, 5, 4, -0.00051450675754333066, 7.1073140582202239)\n", "(3, 5, 5, 0.0026955965344334378, 8.9483861353007175)\n", "(3, 5, 6, 0.00068645615772977803, 11.263062802327864)\n", "(3, 5, 7, 0.002105088410889236, 14.181274924178155)\n", "(3, 5, 8, 0.00056288712410386833, 17.853176920070933)\n", "(3, 5, 9, 0.00039456598460191019, 22.473654872253892)\n", "(3, 5, 10, 0.00044328222607363814, 28.296258113202562)\n", "(3, 5, 11, -0.00015086515594202457, 35.622917224640524)\n", "(3, 5, 12, 0.00021235751853919505, 44.845335308688995)\n", "(3, 5, 13, -0.00063488310844303671, 56.455281400912583)\n", "(3, 5, 14, -0.00016012746799158961, 71.071994262327962)\n", "(3, 5, 15, -1.7119701655608174e-05, 89.471042685048829)\n", "(3, 5, 16, 0.0002998088129945503, 112.63363474205515)\n", "(3, 5, 17, -0.00012287504010779907, 141.78281039483767)\n", "(3, 5, 18, 0.00020278637935601551, 178.48306987248662)\n", "(3, 5, 19, 2.9988158449982913e-05, 224.67513503843836)\n", "(4, 4, 0, 0.4977577202543142, 2.8253563952893201)\n", "(4, 4, 1, 0.38686650327976574, 3.5586385362986075)\n", "(4, 4, 2, 0.3146338515944534, 4.4814757002664267)\n", "(4, 4, 3, 0.26243109599924991, 5.6400758580894825)\n", "(4, 4, 4, 0.21364762964600742, 7.1023438716826792)\n", "(4, 4, 5, 0.17483390560442674, 8.9411794822614485)\n", "(4, 4, 6, 0.14916900082099729, 11.257939649691856)\n", "(4, 4, 7, 0.11882015474795091, 14.174236986442191)\n", "(4, 4, 8, 0.092226480069477798, 17.845632763592679)\n", "(4, 4, 9, 0.073330114587813244, 22.469009358428966)\n", "(4, 4, 10, 0.057160508981274727, 28.287419850654732)\n", "(4, 4, 11, 0.044176912207443859, 35.614297436075738)\n", "(4, 4, 12, 0.032000819135871557, 44.835405954882368)\n", "(4, 4, 13, 0.019189757900171821, 56.446261753609349)\n", "(4, 4, 14, 0.011406198953262951, 71.064499696279839)\n", "(4, 4, 15, 0.0067631469773993614, 89.468320922360391)\n", "(4, 4, 16, 0.0033608553061440978, 112.62571649979526)\n", "(4, 4, 17, 0.00093822191151461968, 141.78668436600051)\n", "(4, 4, 18, 0.00062074717807965806, 178.49062627628948)\n", "(4, 4, 19, 7.6409194132451969e-05, 224.67401255972001)\n", "(4, 5, 0, 0.02283217764535557, 2.8285452603478398)\n", "(4, 5, 1, 0.027143838640308272, 3.5619017821749299)\n", "(4, 5, 2, 0.018387630495069491, 4.4851765197834679)\n", "(4, 5, 3, 0.02159128222451609, 5.644020869597604)\n", "(4, 5, 4, 0.021017377400627923, 7.1068701182331786)\n", "(4, 5, 5, 0.009376205078539538, 8.950406337944111)\n", "(4, 5, 6, 0.012756989313495386, 11.263437019739532)\n", "(4, 5, 7, 0.0080085891061448637, 14.178886618292546)\n", "(4, 5, 8, 0.004974304982678839, 17.854663316574698)\n", "(4, 5, 9, 0.0057847285280170305, 22.473770669749332)\n", "(4, 5, 10, 0.0040973703756086253, 28.292746240223153)\n", "(4, 5, 11, 0.0056567484389360902, 35.621423299637279)\n", "(4, 5, 12, 0.0038003356881214983, 44.843195490948439)\n", "(4, 5, 13, 0.0017221442691083275, 56.451678056046802)\n", "(4, 5, 14, 0.0012566804751472302, 71.071150410008499)\n", "(4, 5, 15, 0.00051253492338500998, 89.469962288494003)\n", "(4, 5, 16, 0.00049177902382400562, 112.6313291792479)\n", "(4, 5, 17, -8.1911068198835247e-05, 141.78235674330736)\n", "(4, 5, 18, -0.0001310947899328966, 178.48471254612707)\n", "(4, 5, 19, -0.00013983307587455429, 224.68041785477422)\n", "(5, 5, 0, 0.2931836225473517, 2.8292919298921726)\n", "(5, 5, 1, 0.31276971520235042, 3.5610325719402027)\n", "(5, 5, 2, 0.22125858742370993, 4.4823911146670552)\n", "(5, 5, 3, 0.22558600334517268, 5.6437938455310235)\n", "(5, 5, 4, 0.17879430286481829, 7.104666248052407)\n", "(5, 5, 5, 0.15007387991959195, 8.9462448717272913)\n", "(5, 5, 6, 0.1198475414172141, 11.256639676708279)\n", "(5, 5, 7, 0.092632477839272309, 14.17089431008857)\n", "(5, 5, 8, 0.074423451276596581, 17.846801005157428)\n", "(5, 5, 9, 0.063349562008801388, 22.469167238787875)\n", "(5, 5, 10, 0.045179037099225224, 28.293220720317631)\n", "(5, 5, 11, 0.033846345339259266, 35.616195479448244)\n", "(5, 5, 12, 0.023904136471663152, 44.835232624377085)\n", "(5, 5, 13, 0.01620018253437381, 56.442361953352027)\n", "(5, 5, 14, 0.0093013319703546954, 71.063864692850544)\n", "(5, 5, 15, 0.005390211563153998, 89.472011493360071)\n", "(5, 5, 16, -0.00054851205580428125, 112.62485839271983)\n", "(5, 5, 17, 0.00054618728605118822, 141.78431939427128)\n", "(5, 5, 18, 0.00072401109893683441, 178.47309233145495)\n", "(5, 5, 19, -0.001100387317727151, 224.67519354803539)\n", "[ 0.45695747 0.36215491 0.29041139 0.23118318 0.19100328 0.151003\n", " 0.13108268 0.10749353 0.08680055 0.07528302 0.06291195 0.05189149\n", " 0.04115488 0.0302334 0.02223498 0.015794 0.01055951 0.0067024\n", " 0.00453897 0.00266568]\n" ] } ], "source": [ "wtheta = hdulist[4]\n", "# includes cross, make sure to only take autos\n", "zbin = 1\n", "y = []\n", "for row in wtheta.data:\n", " print row\n", " if row[0] == zbin and row[1] == zbin:\n", " y.append(row[3])\n", " \n", "y = np.array(y)\n", "print y" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.savetxt('/u/ki/swmclau2/Git/pearce/bin/mcmc/buzzard2_wt_%d%d.npy'%(zbin,zbin), y)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Filename: /u/ki/jderose/public_html/bcc/measurement/y3/3x2pt/buzzard/flock/buzzard-2/tpt_Y3_v0.fits\n", "No. Name Type Cards Dimensions Format\n", " 0 PRIMARY PrimaryHDU 4 () \n", " 1 xip BinTableHDU 32 200R x 5C [K, K, K, D, D] \n", " 2 xim BinTableHDU 32 200R x 5C [K, K, K, D, D] \n", " 3 gammat BinTableHDU 32 400R x 5C [K, K, K, D, D] \n", " 4 wtheta BinTableHDU 32 300R x 5C ['K', 'K', 'K', 'D', 'D'] \n", " 5 nz_shear_bpz BinTableHDU 32 250R x 7C [D, D, D, D, D, D, D] \n", " 6 nz_shear_true BinTableHDU 32 250R x 7C [D, D, D, D, D, D, D] \n", " 7 nz_pos_zrm BinTableHDU 31 400R x 8C [D, D, D, D, D, D, D, D] \n", " 8 nz_pos_zspec BinTableHDU 31 400R x 8C [D, D, D, D, D, D, D, D] \n" ] } ], "source": [ "hdulist.info()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "216485.0\n" ] } ], "source": [ "nz_zspec = hdulist[8]\n", "N = 0#np.zeros((5,))\n", "for row in nz_zspec.data:\n", " N+=row[2+zbin]\n", " #for idx, n in enumerate(row[3:]):\n", " # N[idx]+=n\n", "print N" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cat_filename = '/afs/slac/u/ki/jderose/public_html/bcc/catalog/redmagic/y3/buzzard/flock/buzzard-0/a/buzzard-0_1.6_y3_run_redmapper_v6.4.20_redmagic_highdens_0.5-10.fit'" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hdulist = fits.open(cat_filename)\n", "catalog = hdulist[1].data" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "N = np.sum(np.logical_and(z_bins[zbin-1] < catalog['ZREDMAGIC'], catalog['ZREDMAGIC'] < z_bins[zbin] ))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "226994" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "N" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "area = 5063 #sq degrees\n", "full_sky = 41253 #sq degrees" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.000298352370804 1 / Mpc3\n" ] } ], "source": [ "buzzard = LambdaCDM(H0=70, Om0=0.286, Ode0=0.714, Tcmb0=2.725, Neff=3.04)\n", "#volIn, volOut = buzzard.comoving_volume(z_bins[zbin-1]), buzzard.comoving_volume(z_bins[zbin])\n", "volIn, volOut = buzzard.comoving_volume(z_bins[zbin-1]), buzzard.comoving_volume(z_bins[zbin])\n", "\n", "fullsky_volume = volOut-volIn\n", "survey_volume = fullsky_volume*area/full_sky\n", "nd = N/survey_volume\n", "print nd" ] }, { "cell_type": "raw", "metadata": { "collapsed": true }, "source": [ "np.savetxt('/u/ki/swmclau2/Git/pearce/bin/mcmc/buzzard2_nd_%d%d.npy'%(zbin, zbin),np.array(nd.value).reshape(1, 1))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mbins = np.logspace(np.log10(5e12), 15, 21)\n", "mbc = mbins[1:]+mbins[:-1]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hod_measurement_fname = '/u/ki/jderose/notebooks/des/redmagic_hod_buzzard1_v1.3_mean_5e12.npy'\n", "hod = np.load(hod_measurement_fname)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(20, 5, 3)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hod.shape" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(21,)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mbins.shape" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'plt' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-20-f79abad96f61>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0msat_hod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhod\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mhod\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmbc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcen_hod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#+sat_hod)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxscale\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'log'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5e12\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1e15\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined" ] } ], "source": [ "cen_hod = hod[:,0,1]/hod[:,0,0]\n", "sat_hod = hod[:,0,2]/hod[:,0,0]\n", "\n", "plt.plot(mbc, cen_hod)#+sat_hod)\n", "plt.xscale('log')\n", "plt.xlim(5e12, 1e15)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:hodemulator]", "language": "python", "name": "conda-env-hodemulator-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
mattwaite/JOUR491-Data-Visualization
Assignments/12_BubbleCharts/BubbleCharts.ipynb
1
239273
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bubble Charts: An in-class challenge\n", "\n", "A bubble chart is a scatterplot where the size of the bubble is scaled by some value. Bubble charts were made small f famous by the late [Hans Rosling in one of the most watched Ted talks ever](https://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen).\n", "\n", "Your challenge in class today: Make a bubble chart out of the dataset of majors by race and sex. I'm interested in gender differences by majors. \n", "\n", "I'll help you set up the data." ] }, { "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)\n", "library(reshape2)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "enrollment <- read.csv(\"../../Data/collegeenrollment.csv\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th scope=col>College</th><th scope=col>Degree</th><th scope=col>MajorCode</th><th scope=col>MajorName</th><th scope=col>RaceGender</th><th scope=col>Race</th><th scope=col>Gender</th><th scope=col>Count</th><th scope=col>Total</th></tr></thead>\n", "<tbody>\n", "\t<tr><td>College of Agri Sci and Natl Resources</td><td>B1BC </td><td>BIOC </td><td>Biochemistry </td><td>NonResidentAlienMale </td><td>NonResidentAlien </td><td>Male </td><td>3 </td><td> 97 </td></tr>\n", "\t<tr><td>College of Agri Sci and Natl Resources</td><td>B1AS </td><td>ASCI </td><td>Animal Science </td><td>NonResidentAlienMale </td><td>NonResidentAlien </td><td>Male </td><td>0 </td><td>338 </td></tr>\n", "\t<tr><td>College of Agri Sci and Natl Resources</td><td>B1FW </td><td>FWL </td><td>Fisheries and Wildlife </td><td>NonResidentAlienMale </td><td>NonResidentAlien </td><td>Male </td><td>0 </td><td>191 </td></tr>\n", "\t<tr><td>College of Agri Sci and Natl Resources</td><td>B1AP </td><td>APSC </td><td>Applied Science </td><td>NonResidentAlienMale </td><td>NonResidentAlien </td><td>Male </td><td>1 </td><td> 71 </td></tr>\n", "\t<tr><td>College of Agri Sci and Natl Resources</td><td>B1HO </td><td>HORT </td><td>Horticulture </td><td>NonResidentAlienMale </td><td>NonResidentAlien </td><td>Male </td><td>1 </td><td> 52 </td></tr>\n", "\t<tr><td>College of Agri Sci and Natl Resources</td><td>B1ED </td><td>AEDU </td><td>Agricultural Education </td><td>NonResidentAlienMale </td><td>NonResidentAlien </td><td>Male </td><td>0 </td><td>103 </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lllllllll}\n", " College & Degree & MajorCode & MajorName & RaceGender & Race & Gender & Count & Total\\\\\n", "\\hline\n", "\t College of Agri Sci and Natl Resources & B1BC & BIOC & Biochemistry & NonResidentAlienMale & NonResidentAlien & Male & 3 & 97 \\\\\n", "\t College of Agri Sci and Natl Resources & B1AS & ASCI & Animal Science & NonResidentAlienMale & NonResidentAlien & Male & 0 & 338 \\\\\n", "\t College of Agri Sci and Natl Resources & B1FW & FWL & Fisheries and Wildlife & NonResidentAlienMale & NonResidentAlien & Male & 0 & 191 \\\\\n", "\t College of Agri Sci and Natl Resources & B1AP & APSC & Applied Science & NonResidentAlienMale & NonResidentAlien & Male & 1 & 71 \\\\\n", "\t College of Agri Sci and Natl Resources & B1HO & HORT & Horticulture & NonResidentAlienMale & NonResidentAlien & Male & 1 & 52 \\\\\n", "\t College of Agri Sci and Natl Resources & B1ED & AEDU & Agricultural Education & NonResidentAlienMale & NonResidentAlien & Male & 0 & 103 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "College | Degree | MajorCode | MajorName | RaceGender | Race | Gender | Count | Total | \n", "|---|---|---|---|---|---|\n", "| College of Agri Sci and Natl Resources | B1BC | BIOC | Biochemistry | NonResidentAlienMale | NonResidentAlien | Male | 3 | 97 | \n", "| College of Agri Sci and Natl Resources | B1AS | ASCI | Animal Science | NonResidentAlienMale | NonResidentAlien | Male | 0 | 338 | \n", "| College of Agri Sci and Natl Resources | B1FW | FWL | Fisheries and Wildlife | NonResidentAlienMale | NonResidentAlien | Male | 0 | 191 | \n", "| College of Agri Sci and Natl Resources | B1AP | APSC | Applied Science | NonResidentAlienMale | NonResidentAlien | Male | 1 | 71 | \n", "| College of Agri Sci and Natl Resources | B1HO | HORT | Horticulture | NonResidentAlienMale | NonResidentAlien | Male | 1 | 52 | \n", "| College of Agri Sci and Natl Resources | B1ED | AEDU | Agricultural Education | NonResidentAlienMale | NonResidentAlien | Male | 0 | 103 | \n", "\n", "\n" ], "text/plain": [ " College Degree MajorCode\n", "1 College of Agri Sci and Natl Resources B1BC BIOC \n", "2 College of Agri Sci and Natl Resources B1AS ASCI \n", "3 College of Agri Sci and Natl Resources B1FW FWL \n", "4 College of Agri Sci and Natl Resources B1AP APSC \n", "5 College of Agri Sci and Natl Resources B1HO HORT \n", "6 College of Agri Sci and Natl Resources B1ED AEDU \n", " MajorName RaceGender Race Gender Count\n", "1 Biochemistry NonResidentAlienMale NonResidentAlien Male 3 \n", "2 Animal Science NonResidentAlienMale NonResidentAlien Male 0 \n", "3 Fisheries and Wildlife NonResidentAlienMale NonResidentAlien Male 0 \n", "4 Applied Science NonResidentAlienMale NonResidentAlien Male 1 \n", "5 Horticulture NonResidentAlienMale NonResidentAlien Male 1 \n", "6 Agricultural Education NonResidentAlienMale NonResidentAlien Male 0 \n", " Total\n", "1 97 \n", "2 338 \n", "3 191 \n", "4 71 \n", "5 52 \n", "6 103 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(enrollment)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note we have data that is long -- data by race and gender. We just want it by gender. So we need to do some grouping together and later we need to make this wider. So we have to first group by College, Major and Gender and add them up. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "majors <- enrollment %>% \n", " group_by(College, MajorName, Gender) %>%\n", " summarize(\n", " Total=sum(Count)\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we need to make that long data wide, so Male and Female are on the same line." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using Total as value column: use value.var to override.\n" ] } ], "source": [ "majors_bubble <- dcast(majors, College + MajorName ~ Gender)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th scope=col>College</th><th scope=col>MajorName</th><th scope=col>Female</th><th scope=col>Male</th></tr></thead>\n", "<tbody>\n", "\t<tr><td>College of Agri Sci and Natl Resources</td><td>Agribusiness </td><td>66 </td><td>175 </td></tr>\n", "\t<tr><td>College of Agri Sci and Natl Resources </td><td>Agricultural &amp; Env Sci Comm </td><td>27 </td><td><span style=white-space:pre-wrap> 5</span></td></tr>\n", "\t<tr><td>College of Agri Sci and Natl Resources</td><td>Agricultural Economics </td><td>23 </td><td>109 </td></tr>\n", "\t<tr><td>College of Agri Sci and Natl Resources</td><td>Agricultural Education </td><td>80 </td><td> 23 </td></tr>\n", "\t<tr><td>College of Agri Sci and Natl Resources</td><td>Agricultural Journalism </td><td> 2 </td><td> 0 </td></tr>\n", "\t<tr><td>College of Agri Sci and Natl Resources</td><td>Agronomy </td><td>24 </td><td>167 </td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", " College & MajorName & Female & Male\\\\\n", "\\hline\n", "\t College of Agri Sci and Natl Resources & Agribusiness & 66 & 175 \\\\\n", "\t College of Agri Sci and Natl Resources & Agricultural \\& Env Sci Comm & 27 & 5 \\\\\n", "\t College of Agri Sci and Natl Resources & Agricultural Economics & 23 & 109 \\\\\n", "\t College of Agri Sci and Natl Resources & Agricultural Education & 80 & 23 \\\\\n", "\t College of Agri Sci and Natl Resources & Agricultural Journalism & 2 & 0 \\\\\n", "\t College of Agri Sci and Natl Resources & Agronomy & 24 & 167 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "College | MajorName | Female | Male | \n", "|---|---|---|---|---|---|\n", "| College of Agri Sci and Natl Resources | Agribusiness | 66 | 175 | \n", "| College of Agri Sci and Natl Resources | Agricultural & Env Sci Comm | 27 | 5 | \n", "| College of Agri Sci and Natl Resources | Agricultural Economics | 23 | 109 | \n", "| College of Agri Sci and Natl Resources | Agricultural Education | 80 | 23 | \n", "| College of Agri Sci and Natl Resources | Agricultural Journalism | 2 | 0 | \n", "| College of Agri Sci and Natl Resources | Agronomy | 24 | 167 | \n", "\n", "\n" ], "text/plain": [ " College MajorName Female\n", "1 College of Agri Sci and Natl Resources Agribusiness 66 \n", "2 College of Agri Sci and Natl Resources Agricultural & Env Sci Comm 27 \n", "3 College of Agri Sci and Natl Resources Agricultural Economics 23 \n", "4 College of Agri Sci and Natl Resources Agricultural Education 80 \n", "5 College of Agri Sci and Natl Resources Agricultural Journalism 2 \n", "6 College of Agri Sci and Natl Resources Agronomy 24 \n", " Male\n", "1 175 \n", "2 5 \n", "3 109 \n", "4 23 \n", "5 0 \n", "6 167 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(majors_bubble)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have enough to do a scatterplot, but what are we lacking for a bubble chart? Some kind of weighting. So let's create a couple. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bubble <- majors_bubble %>% \n", " mutate(\n", " Total = Male+Female,\n", " Difference = abs(Male-Female)\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `abs()` bits there mean give me the absolute value -- so everything is above zero, regardless of which is larger. \n", "\n", "So let's try a plot:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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9kivo2bxbdZv7ZtFynR4EgDIwmF9qH4tGNWAyM3EBC3ZQsJt28noXbtvO/h9u3F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\nVaz+Ah0K3jwzs2xrZ50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3LhhReKBUdWBgwY4AVAkydP9p5bcNWrVy8vOLINaWlp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anLz784Sqn/MWAc4wMHjeW/jH+uijj5qH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\nk3asXb5F77doPHuZjULm1eQIbSV1oiZnYXMZUwPXWQOp+xVc50Gm1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c5kkW7Vq4HUAREjyCSE8H/BDdSKup6upYpJM9Qf2VevkTikB/44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eTzqH7YzkDYERG7Cf7vSqFfAxUpzW76wcqVzupA/3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\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Ff5MgA3CDcoShXj8vLuX+xOX7K9GMqwPwNIAgqGWVYv0d7uJQZHrIrYFViNcAlS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\niyTw/2a48o2zklRxAAAAAElFTkSuQmCC", "text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ggplot(bubble, aes(x = Male, y = Female, size=Difference)) +\n", "geom_point(alpha=0.4, color='red') +\n", "scale_size_continuous(range=c(.1, 20)) +\n", "scale_colour_continuous(guide = FALSE) +\n", "geom_text(data=bubble, aes(label=MajorName, size=10), check_overlap=TRUE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Your challenge: Make this better. [Here's some guidance on things you can change](https://www.r-graph-gallery.com/320-the-basis-of-bubble-plot/). [Here's more](http://t-redactyl.io/blog/2016/02/creating-plots-in-r-using-ggplot2-part-6-weighted-scatterplots.html). We'll talk at the end of class. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "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": "3.4.1" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
ClaudioVZ/Teoria-FEM-Python
Matrices 3D/Multiplicacion.ipynb
1
94920
{ "metadata": { "name": "Matrices 3D" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Matrices 3D" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Definici\u00f3n:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La multiplicaci\u00f3n se define como:\n", "\n", "\\begin{equation}\n", "(AB)C\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Ejemplo 1**:Multiplicar:\n", "\n", "\\begin{equation}\n", "\\left [\n", "\\begin{matrix}\n", "1 \\\\\\\n", "2 \\\\\\\n", "3\n", "\\end{matrix}\n", "\\right ]\n", "\\left [\n", "\\begin{matrix}\n", "4 & 5 & 6\n", "\\end{matrix}\n", "\\right ]\n", "\\left [\n", "\\begin{matrix}\n", "7 & 8 & 9\n", "\\end{matrix}\n", "\\right ]\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Primero se multiplican $A$ y $B$:\n", "\n", "\\begin{equation}\n", "\\left [\n", "\\begin{matrix}\n", "1 \\\\\\\n", "2 \\\\\\\n", "3\n", "\\end{matrix}\n", "\\right ]\n", "\\left [\n", "\\begin{matrix}\n", "4 & 5 & 6\n", "\\end{matrix}\n", "\\right ] = \\left [\n", "\\begin{matrix}\n", "4 & 5 & 6 \\\\\\\n", "8 & 10 & 12 \\\\\\\n", "12 & 15 & 18\n", "\\end{matrix}\n", "\\right ]\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Luego se multiplican todas las columnas de $AB$ con $C$.\n", "\n", "\\begin{equation}\n", "\\left [\n", "\\begin{matrix}\n", "4 \\\\\\\n", "8 \\\\\\\n", "12\n", "\\end{matrix}\n", "\\right ]\n", "\\left [\n", "\\begin{matrix}\n", "7 & 8 & 9\n", "\\end{matrix}\n", "\\right ] = \\left [\n", "\\begin{matrix}\n", "28 & 32 & 36 \\\\\\\n", "56 & 64 & 72 \\\\\\\n", "84 & 96 & 108\n", "\\end{matrix}\n", "\\right ]\n", "\\end{equation}\n", " \n", "\\begin{equation}\n", "\\left [\n", "\\begin{matrix}\n", "5 \\\\\\\n", "10 \\\\\\\n", "15\n", "\\end{matrix}\n", "\\right ]\n", "\\left [\n", "\\begin{matrix}\n", "7 & 8 & 9\n", "\\end{matrix}\n", "\\right ] = \\left [\n", "\\begin{matrix}\n", "35 & 40 & 45 \\\\\\\n", "70 & 80 & 90 \\\\\\\n", "105 & 120 & 135\n", "\\end{matrix}\n", "\\right ]\n", "\\end{equation}\n", " \n", "\\begin{equation}\n", "\\left [\n", "\\begin{matrix}\n", "6 \\\\\\\n", "12 \\\\\\\n", "18\n", "\\end{matrix}\n", "\\right ]\n", "\\left [\n", "\\begin{matrix}\n", "7 & 8 & 9\n", "\\end{matrix}\n", "\\right ] = \\left [\n", "\\begin{matrix}\n", "42 & 48 & 54 \\\\\\\n", "84 & 96 & 108 \\\\\\\n", "126 & 144 & 162\n", "\\end{matrix}\n", "\\right ]\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "El resultado es:\n", " \n", "\\begin{equation}\n", "\\left [\n", "\\begin{matrix}\n", "1 \\\\\\\n", "2 \\\\\\\n", "3\\\n", "\\end{matrix}\n", "\\right ]\n", "\\left [\n", "\\begin{matrix}\n", "4 & 5 & 6\n", "\\end{matrix}\n", "\\right ]\n", "\\left [\n", "\\begin{matrix}\n", "7 & 8 & 9\n", "\\end{matrix}\n", "\\right ] = \\left [\n", "\\left [\n", "\\begin{matrix}\n", "28 & 32 & 36 \\\\\\\n", "56 & 64 & 72 \\\\\\\n", "84 & 96 & 108\n", "\\end{matrix}\n", "\\right ]\n", "\\left [\n", "\\begin{matrix}\n", "35 & 40 & 45 \\\\\\\n", "70 & 80 & 90 \\\\\\\n", "105 & 120 & 135\n", "\\end{matrix}\n", "\\right ]\n", "\\left [\n", "\\begin{matrix}\n", "42 & 48 & 54 \\\\\\\n", "84 & 96 & 108 \\\\\\\n", "126 & 144 & 162\n", "\\end{matrix}\n", "\\right ]\n", "\\right ]\n", "\\end{equation}" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Ejemplo gr\u00e1fico:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from mpl_toolkits.mplot3d import Axes3D\n", "import matplotlib.pyplot as plt\n", "\n", "fig = plt.figure()\n", "ax = fig.gca(projection='3d')\n", "\n", "ax.text(1, 0, 0, \"1\", color='red')\n", "ax.text(2, 0, 0, \"2\", color='red')\n", "ax.text(3, 0, 0, \"3\", color='red')\n", "\n", "ax.text(0, 1, 0, \"4\", color='blue')\n", "ax.text(0, 2, 0, \"5\", color='blue')\n", "ax.text(0, 3, 0, \"6\", color='blue')\n", "\n", "ax.text(0, 0, 1, \"7\", color='green')\n", "ax.text(0, 0, 2, \"8\", color='green')\n", "ax.text(0, 0, 3, \"9\", color='green')\n", "\n", "ax.set_xlim3d(0, 5)\n", "ax.set_ylim3d(0, 5)\n", "ax.set_zlim3d(0, 5)\n", "\n", "ax.set_xlabel('Eje x')\n", "ax.set_ylabel('Eje y')\n", "ax.set_zlabel('Eje z')\n", "\n", "ax.view_init(17, 24)\n", "\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": 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eHIeVSfXGm401reXeYG+iABCNRhNa0/Esaxpbtpg9l/LFAEoVy4iuKIoYM2YM+vXrh5de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\nMcfocBTmY71beAbk4zLMdkYQBLjdbvT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} ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "from mpl_toolkits.mplot3d import Axes3D\n", "import matplotlib.pyplot as plt\n", "\n", "fig = plt.figure()\n", "ax = fig.gca(projection='3d')\n", "\n", "ax.text(1, 0, 0, \"1\", color='red')\n", "ax.text(2, 0, 0, \"2\", color='red')\n", "ax.text(3, 0, 0, \"3\", color='red')\n", "\n", "ax.text(0, 1, 0, \"4\", color='blue')\n", "ax.text(0, 2, 0, \"5\", color='blue')\n", "ax.text(0, 3, 0, \"6\", color='blue')\n", "\n", "ax.text(0, 0, 1, \"7\", color='green')\n", "ax.text(0, 0, 2, \"8\", color='green')\n", "ax.text(0, 0, 3, \"9\", color='green')\n", "\n", "## 4 5 6\n", "## 8 10 12\n", "##12 15 18\n", "\n", "ax.text(1, 1, 0, \"4\", color='magenta')\n", "ax.text(2, 1, 0, \"8\", color='magenta')\n", "ax.text(3, 1, 0, \"12\", color='magenta')\n", "\n", "ax.text(1, 2, 0, \"5\", color='orange')\n", "ax.text(2, 2, 0, \"10\", color='orange')\n", "ax.text(3, 2, 0, \"15\", color='orange')\n", "\n", "ax.text(1, 3, 0, \"6\", color='purple')\n", "ax.text(2, 3, 0, \"12\", color='purple')\n", "ax.text(3, 3, 0, \"18\", color='purple')\n", "\n", "ax.set_xlim3d(0, 5)\n", "ax.set_ylim3d(0, 5)\n", "ax.set_zlim3d(0, 5)\n", "\n", "ax.set_xlabel('Eje x')\n", "ax.set_ylabel('Eje y')\n", "ax.set_zlabel('Eje z')\n", "\n", "ax.view_init(17, 24)\n", "\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": 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ms8nuA5/PlzfHYWaSvfFmYk1ruTeUN1EACAaDca3pWJY1GVum5Hou5YsBlCymEV1BEHDW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\nVHQpFArFQKjoUigUioFQ0aVQKBQD+f+BxxIksi/GNQAAAABJRU5ErkJggg==\n" } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "from mpl_toolkits.mplot3d import Axes3D\n", "import matplotlib.pyplot as plt\n", "\n", "fig = plt.figure()\n", "ax = fig.gca(projection='3d')\n", "\n", "ax.text(1, 0, 0, \"1\", color='red')\n", "ax.text(2, 0, 0, \"2\", color='red')\n", "ax.text(3, 0, 0, \"3\", color='red')\n", "\n", "ax.text(0, 1, 0, \"4\", color='blue')\n", "ax.text(0, 2, 0, \"5\", color='blue')\n", "ax.text(0, 3, 0, \"6\", color='blue')\n", "\n", "ax.text(0, 0, 1, \"7\", color='green')\n", "ax.text(0, 0, 2, \"8\", color='green')\n", "ax.text(0, 0, 3, \"9\", color='green')\n", "\n", "##28 32 36\n", "##56 64 72\n", "##84 96 108\n", "##\n", "## 35 40 45\n", "## 70 80 90\n", "##105 120 135\n", "##\n", "## 42 48 54\n", "## 84 96 108\n", "##126 144 162\n", "\n", "ax.text(1, 1, 0, \"28\", color='magenta')\n", "ax.text(2, 1, 0, \"56\", color='magenta')\n", "ax.text(3, 1, 0, \"84\", color='magenta')\n", "ax.text(1, 1, 1, \"32\", color='magenta')\n", "ax.text(2, 1, 1, \"64\", color='magenta')\n", "ax.text(3, 1, 1, \"96\", color='magenta')\n", "ax.text(1, 1, 2, \"36\", color='magenta')\n", "ax.text(2, 1, 2, \"72\", color='magenta')\n", "ax.text(3, 1, 2, \"108\", color='magenta')\n", "\n", "ax.text(1, 2, 0, \"35\", color='orange')\n", "ax.text(2, 2, 0, \"70\", color='orange')\n", "ax.text(3, 2, 0, \"105\", color='orange')\n", "ax.text(1, 2, 1, \"40\", color='orange')\n", "ax.text(2, 2, 1, \"80\", color='orange')\n", "ax.text(3, 2, 1, \"120\", color='orange')\n", "ax.text(1, 2, 2, \"45\", color='orange')\n", "ax.text(2, 2, 2, \"90\", color='orange')\n", "ax.text(3, 2, 2, \"135\", color='orange')\n", "\n", "ax.text(1, 3, 0, \"42\", color='purple')\n", "ax.text(2, 3, 0, \"84\", color='purple')\n", "ax.text(3, 3, 0, \"126\", color='purple')\n", "ax.text(1, 3, 1, \"48\", color='purple')\n", "ax.text(2, 3, 1, \"96\", color='purple')\n", "ax.text(3, 3, 1, \"144\", color='purple')\n", "ax.text(1, 3, 2, \"54\", color='purple')\n", "ax.text(2, 3, 2, \"108\", color='purple')\n", "ax.text(3, 3, 2, \"162\", color='purple')\n", "\n", "\n", "ax.set_xlim3d(0, 5)\n", "ax.set_ylim3d(0, 5)\n", "ax.set_zlim3d(0, 5)\n", "\n", "ax.set_xlabel('Eje x')\n", "ax.set_ylabel('Eje y')\n", "ax.set_zlabel('Eje z')\n", "\n", "ax.view_init(17, 24)\n", "\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": 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IY6aLiKkWEFsr0N2aIMuybBftFj+LfT525JttOdPhRXfy5Mn897//pbq6mlNPPdW2bttL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\n" } ], "prompt_number": 3 } ], "metadata": {} } ] }
artistic-2.0
mtmarsh2/vislab
image_style_experiments/ava style sklearn.ipynb
4
166331
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "df = dataset['val_df']\n", "yt = (df['label'] > 0).astype(int)\n", "yt_pred = np.zeros_like(yt).astype(float)\n", "#yt_pred = np.random.rand(len(yt))\n", "vislab.results.get_pr_curve(yt, yt_pred)\n", "import sklearn.metrics\n", "print sklearn.metrics.average_precision_score(yt, yt_pred)\n", "print sklearn.metrics.accuracy_score(yt, yt_pred)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.75\n", "0.5\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x118150810>" ] } ], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "df = dataset['test_df']\n", "yt = (df['label'] > 0).astype(int)\n", "yt_pred = np.zeros_like(yt).astype(float)\n", "#yt_pred = np.random.rand(len(yt))\n", "vislab.results.get_pr_curve(yt, yt_pred)\n", "import sklearn.metrics\n", "print sklearn.metrics.average_precision_score(yt, yt_pred)\n", "print sklearn.metrics.accuracy_score(yt, yt_pred)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.542185831257\n", "0.915628337487\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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kyBAcOXIEHh4eUodFJDn2XElj9u/fj3nz5mHfvn3w9fWVOhyiJmPPlWRlwoQJSE5ORlhY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\ngD1XIqI/Yc+ViEimRC2umZm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"text": [ "<matplotlib.figure.Figure at 0x11814c290>" ] } ], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "%load_ext autoreload\n", "%autoreload 2\n", "\n", "import vislab.vw3\n", "import vislab.datasets\n", "import vislab.predict\n", "\n", "feat_dirname = '/Users/sergeyk/work/aphrodite/data/feats/ava_style'\n", "feat_names = ['lab_hist']\n", "\n", "label_df = vislab.datasets.ava.get_style_df()\n", "all_styles = [x for x in label_df.columns if not x.startswith('_')]\n", "dataset = vislab.predict.get_multiclass_dataset(\n", " label_df, 'ava_style', 'all_styles', all_styles,\n", " .2, False, 42)\n", "\n", "vw = vislab.vw3.VW('/Users/sergeyk/work/vislab/_temp' + '/vw_ava_style_all_styles')\n", "pred_df, test_score, val_score, train_score = vw.fit_and_predict(\n", " dataset, feat_names, feat_dirname, force=True)\n", "print test_score, val_score, train_score" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/Users/sergeyk/work/vislab/vislab/results.py:71: UserWarning: The sum of true positives and false positives are equal to zero for some labels. Precision is ill defined for those labels [ True]. The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [ True]. \n", " pred_df['label'], pred_df['pred_bin'])\n", "/Users/sergeyk/work/vislab/vislab/results.py:71: UserWarning: The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [ True]. \n", " pred_df['label'], pred_df['pred_bin'])\n", "/Users/sergeyk/work/vislab/vislab/results.py:258: UserWarning: The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [3]. \n", " y_true, y_pred)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/Users/sergeyk/work/vislab/vislab/results.py:258: UserWarning: The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [10]. \n", " y_true, y_pred)\n", "/Users/sergeyk/work/vislab/vislab/results.py:71: UserWarning: The sum of true positives and false positives are equal to zero for some labels. Precision is ill defined for those labels [False]. The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [False]. \n", " pred_df['label'], pred_df['pred_bin'])\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/Users/sergeyk/work/vislab/vislab/results.py:258: UserWarning: The sum of true positives and false positives are equal to zero for some labels. Precision is ill defined for those labels [0 4]. The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [ 0 2 4 8 9 10 11 13]. \n", " y_true, y_pred)\n", "/Users/sergeyk/work/vislab/vislab/results.py:258: UserWarning: The sum of true positives and false positives are equal to zero for some labels. Precision is ill defined for those labels [ 0 4 10 11]. The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [ 0 2 4 7 8 9 10 11]. \n", " y_true, y_pred)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/Users/sergeyk/work/vislab/vislab/results.py:258: UserWarning: The sum of true positives and false positives are equal to zero for some labels. Precision is ill defined for those labels [ 0 4 10]. The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [ 0 2 3 4 8 9 10 11 13]. \n", " y_true, y_pred)\n", "/Users/sergeyk/work/vislab/vislab/results.py:258: UserWarning: The sum of true positives and false positives are equal to zero for some labels. Precision is ill defined for those labels [0 4]. The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [ 0 2 3 4 8 9 10 11 13]. \n", " y_true, y_pred)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/Users/sergeyk/work/vislab/vislab/results.py:258: UserWarning: The sum of true positives and false positives are equal to zero for some labels. Precision is ill defined for those labels [ 0 4 10 11]. The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [ 0 2 4 8 10 11]. \n", " y_true, y_pred)\n", "/Users/sergeyk/work/vislab/vislab/results.py:258: UserWarning: The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [ 3 13]. \n", " y_true, y_pred)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/Users/sergeyk/work/vislab/vislab/results.py:258: UserWarning: The sum of true positives and false positives are equal to zero for some labels. Precision is ill defined for those labels [ 0 7 8 11 13]. The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [ 0 6 7 8 9 10 11 12 13]. \n", " y_true, y_pred)\n", "/Users/sergeyk/work/vislab/vislab/results.py:258: UserWarning: The sum of true positives and false positives are equal to zero for some labels. Precision is ill defined for those labels [ 0 11 13]. The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [ 0 6 7 8 10 11 12 13]. \n", " y_true, y_pred)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/Users/sergeyk/work/vislab/vislab/results.py:258: UserWarning: The sum of true positives and false positives are equal to zero for some labels. Precision is ill defined for those labels [ 0 6 7 8 11 13]. The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [ 0 6 7 8 10 11 12 13]. \n", " y_true, y_pred)\n", "/Users/sergeyk/work/vislab/vislab/results.py:258: UserWarning: The sum of true positives and false positives are equal to zero for some labels. Precision is ill defined for those labels [ 0 6 7 8 11 12 13]. The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [ 0 6 7 8 10 11 12 13]. \n", " y_true, y_pred)\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/Users/sergeyk/work/vislab/vislab/results.py:258: UserWarning: The sum of true positives and false positives are equal to zero for some labels. Precision is ill defined for those labels [ 0 7 8 11 12 13]. The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [ 0 7 8 10 11 12 13]. \n", " y_true, y_pred)\n", "/Users/sergeyk/work/vislab/vislab/results.py:258: UserWarning: The sum of true positives and false positives are equal to zero for some labels. Precision is ill defined for those labels [ 0 6 8 11 12 13]. The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [ 0 3 6 7 8 9 10 11 12 13]. \n", " y_true, y_pred)\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Running VW training for 18 param settings, 6 at a time\n", " l1 l2 loss num_passes val_score test_score\n", "0 0 0 hinge 25 0.207 0.219\n", "1 0 1e-6 hinge 25 0.198 0.223\n", "2 0 1e-9 hinge 25 0.196 0.226\n", "3 1e-6 0 hinge 25 0.046 0.056\n", "4 1e-6 1e-6 hinge 25 0.048 0.041\n", "5 1e-6 1e-9 hinge 25 0.047 0.054\n", "6 1e-9 0 hinge 25 0.135 0.136\n", "7 1e-9 1e-6 hinge 25 0.178 0.197\n", "8 1e-9 1e-9 hinge 25 0.150 0.146\n", "9 0 0 logistic 25 0.242 0.277\n", "10 0 1e-6 logistic 25 0.243 0.294\n", "11 0 1e-9 logistic 25 0.261 0.280\n", "12 1e-6 0 logistic 25 0.088 0.109\n", "13 1e-6 1e-6 logistic 25 0.084 0.094\n", "14 1e-6 1e-9 logistic 25 0.102 0.086\n", "15 1e-9 0 logistic 25 0.206 0.224\n", "16 1e-9 1e-6 logistic 25 0.213 0.220\n", "17 1e-9 1e-9 logistic 25 0.214 0.240" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Best setting: {'loss': 'logistic', 'l2': '1e-9', 'num_passes': 25, 'l1': '0'}\n", "Best score: 0.261\n", "Updating best VW model with validation data\n", "Running VW prediction on all splits." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "0.288571428571" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 0.265780730897 0.352195423624\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/Users/sergeyk/work/vislab/vislab/results.py:258: UserWarning: The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [6]. \n", " y_true, y_pred)\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import vislab.results\n", "metrics = vislab.results.multiclass_metrics(pred_df[pred_df['split'] == 'test'], 'pred')\n", "metrics['binary_metrics_df']['ap'].plot(kind='bar')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "<matplotlib.axes.AxesSubplot at 0x11922b310>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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avmRnZ6Nz587ctoGBAapVqyZI8/nz5wgLC8OdO3dgYGAAS0tLuLu7o0GDBrw1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\nqktJdO/encXGxnIpPwUFBaJOqxV7RZbKQm4pUen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"text": [ "<matplotlib.figure.Figure at 0x10b196850>" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "pdf = pred_df[pred_df['split'] == 'test']\n", "pdf = pdf.join(dataset['test_df'])\n", "_ = vislab.results.binary_metrics(pdf, balanced=False, with_print=True, with_plot=True)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "------------------------------------------------------------\n", "Classification metrics on the {} full full\n", "ap_sklearn: 0.229261539704\n", "mcc: 0.250574431586\n", "ap: 0.242722363914\n", " precision recall f1-score support\n", "False 0.950989 0.827141 0.884752 2557\n", "True 0.221831 0.536170 0.313823 235\n", "accuracy: 0.802650429799\n", "\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x11c72edd0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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gS5cutbqgNulzcbW1tUWvXr2wefNmNGrUiOtwCGiha/I/OnmGVmRkJCIiItC8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\nhXCLWq5EK/Lz89GvXz+4ubnB0dERQUF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"text": [ "<matplotlib.figure.Figure at 0x11762f050>" ] } ], "prompt_number": 100 }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "feat_df = pd.read_hdf(\n", " '/Users/sergeyk/work/aphrodite/data/feats/ava_style/lab_hist.h5',\n", " 'df')\n", "feat_df" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<pre>\n", "&lt;class 'pandas.core.frame.DataFrame'&gt;\n", "Index: 13994 entries, 1187 to 97009\n", "Data columns (total 1 columns):\n", "0 13994 non-null values\n", "dtypes: object(1)\n", "</pre>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 83, "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Index: 13994 entries, 1187 to 97009\n", "Data columns (total 1 columns):\n", "0 13994 non-null values\n", "dtypes: object(1)" ] } ], "prompt_number": 83 }, { "cell_type": "code", "collapsed": false, "input": [ "X = np.array([row[0] for row in feat_df.values])\n", "X[X < 1e-4] = 0\n", "feat_df = pd.DataFrame([(row,) for row in X], feat_df.index)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "feat_df.to_hdf(\n", " '/Users/sergeyk/work/aphrodite/data/feats/ava_style/lab_hist.h5',\n", " 'df', mode='w')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/Users/sergeyk/anaconda/python.app/Contents/lib/python2.7/site-packages/pandas-0.12.0_856_gd686154-py2.7-macosx-10.5-x86_64.egg/pandas/io/pytables.py:2303: PerformanceWarning: \n", "your performance may suffer as PyTables will pickle object types that it cannot\n", "map directly to c-types [inferred_type->mixed,key->block0_values] [items->[0]]\n", "\n", " warnings.warn(ws, PerformanceWarning)\n" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "feat = feat_df.values[80][0]\n", "print np.unique(feat)[:10]\n", "matshow(feat.reshape(49, 16).T)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 0. 0.04837961 0.30261752 2.90482926 3.40419579\n", " 7.60864496 16.43554497 38.28302383]\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 84, "text": [ "<matplotlib.image.AxesImage at 0x11a432e90>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x11c401b50>" ] } ], "prompt_number": 84 }, { "cell_type": "code", "collapsed": false, "input": [ "import vislab.datasets\n", "import vislab.predict\n", "\n", "label_df = vislab.datasets.ava.get_style_df()\n", "dataset = vislab.predict.get_binary_or_regression_dataset(\n", " label_df, 'ava_style', 'style_Complementary_Colors',\n", " .2, -1, 42)\n", "print(dataset['train_df'].shape)\n", "print(dataset['train_df']['label'].iloc[:3])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(10966, 2)\n", "image_id\n", "1187 -1\n", "1270 -1\n", "1279 -1\n", "Name: label, dtype: int64\n" ] } ], "prompt_number": 85 }, { "cell_type": "code", "collapsed": false, "input": [ "feat_df_train = feat_df.ix[dataset['train_df'].index].dropna()\n", "feat_df_test = feat_df.ix[dataset['test_df'].index].dropna()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 86 }, { "cell_type": "code", "collapsed": false, "input": [ "X = np.array([row[0] for row in feat_df_train.values])\n", "X[X < 1e-4] = 0\n", "y = dataset['train_df']['label'].ix[feat_df_train.index].values > 0\n", "Xt = np.vstack([row[0] for row in feat_df_test.values])\n", "Xt[Xt < 1e-4] = 0\n", "yt = dataset['test_df']['label'].ix[feat_df_test.index].values > 0\n", "\n", "for arr in [X, y, Xt, yt]:\n", " print arr.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(10901, 784)\n", "(10901,)\n", "(2792, 784)\n", "(2792,)\n" ] } ], "prompt_number": 87 }, { "cell_type": "code", "collapsed": false, "input": [ "import sklearn.linear_model\n", "import sklearn.grid_search\n", "\n", "clf = sklearn.linear_model.SGDClassifier(loss='log', n_iter=20, shuffle=True)\n", "grid_clf = sklearn.grid_search.GridSearchCV(\n", " clf,\n", " {'alpha': [1e-1, 1e-2, 1e-3, 1e-4, 1e-5, 1e-6, 1e-7, 1e-8]},\n", " n_jobs=1\n", ")\n", "grid_clf.fit(X, y)\n", "print grid_clf.best_params_" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{'alpha': 0.1}\n" ] } ], "prompt_number": 89 }, { "cell_type": "code", "collapsed": false, "input": [ "import vislab.results\n", "import sklearn.metrics\n", "\n", "yt_pred = grid_clf.predict_proba(Xt)\n", "vislab.results.get_pr_curve(yt, yt_pred[:, 1])\n", "print sklearn.metrics.average_precision_score(yt, yt_pred[:, 1])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.26694060153\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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rVmdBPY7OnTujYcOG+OCDDx5p/+TkZFy9etVkz9EhovqpxuKq1WoV/1ntgoKCkJiY+Mj7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\n", "text": [ "<matplotlib.figure.Figure at 0x11c1e3a90>" ] } ], "prompt_number": 90 }, { "cell_type": "code", "collapsed": false, "input": [ "vw = VW('temp_vw_ava_style')\n", "vw.fit(X, y)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Running command\n", "vw -d temp_vw_ava_style/train_data.txt -k --cache_file temp_vw_ava_style/train_cache.vw --noop\n", "Running command\n", "vw --cache_file temp_vw_ava_style/train_cache.vw -f temp_vw_ava_style/model.vw --passes 100 --bit_precision=18 --l1=0 --l2=0 --loss_function=hinge --holdout_off\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 47, "text": [ "<__main__.VW at 0x117b5fa10>" ] } ], "prompt_number": 47 }, { "cell_type": "code", "collapsed": false, "input": [ "yt_pred = vw.predict_proba(Xt)\n", "vislab.results.get_pr_curve(yt, yt_pred)\n", "print sklearn.metrics.average_precision_score(yt, yt_pred)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Running command\n", "vw -d temp_vw_ava_style/test_data.txt -k -t --cache_file temp_vw_ava_style/test_cache.vw -i temp_vw_ava_style/model.vw -p temp_vw_ava_style/test_pred.txt -r temp_vw_ava_style/test_raw_pred.txt\n", "0.120523937182" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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2.41142236e+00, 2.47654444e+00, 2.54166652e+00,\n", " 2.60678860e+00, 2.67191068e+00, 2.73703276e+00,\n", " 2.80215484e+00, 2.86727692e+00, 2.93239900e+00]),\n", " <a list of 50 Patch objects>)" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x1185be390>" ] } ], "prompt_number": 45 } ], "metadata": {} } ] }
bsd-2-clause
mldbai/mldb
container_files/tutorials/SELECT Tutorial.ipynb
1
114744
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SELECT Tutorial\n", "\n", "\n", "MLDB comes with a powerful implementation of [SQL's `SELECT` Queries](../../../../doc/#builtin/sql/Sql.md.html). This tutorial will walk you through the basics of `SELECT`, and some MLDB-specific features.\n", "\n", "The notebook cells below use `pymldb`'s `.query()` method; you can check out the [Using `pymldb` Tutorial](../../../../doc/nblink.html#_tutorials/Using pymldb Tutorial) for more details." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pymldb import Connection\n", "mldb = Connection()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# `SELECT`\n", "\n", "All queries start with the keyword `SELECT`. Here is the simplest possible query: we ask for 1 and we get a very short result set consisting of one row with one column named 1 and the single cell in it also contains 1." ] }, { "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>1</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th></th>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 1\n", "_rowName \n", " 1" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select 1\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course we can ask for more: the query below does a little math and shows how you can rename your columns with the `as` keyword. Note that single-quotes (`'`) are used to denote strings and double-quotes (`\"`) denote column names, both of which can contain any Unicode character." ] }, { "cell_type": "code", "execution_count": 3, "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>1+1</th>\n", " <th>var</th>\n", " <th>hello, François</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th></th>\n", " <td>2</td>\n", " <td>7</td>\n", " <td>UTF8 striñg</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 1+1 var hello, François\n", "_rowName \n", " 2 7 UTF8 striñg" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select 1+1, 3+4 as var, 'UTF8 striñg' as \"hello, François\"\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use a variety of operators in a `SELECT` expression, like this:" ] }, { "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>1 between 0 and 2</th>\n", " <th>2 in (1,2,3)</th>\n", " <th>3 is integer</th>\n", " <th>(case when 4&lt;5 then 'yes' else 'no' end)</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th></th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>yes</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 1 between 0 and 2 2 in (1,2,3) 3 is integer \\\n", "_rowName \n", " 1 1 1 \n", "\n", " (case when 4<5 then 'yes' else 'no' end) \n", "_rowName \n", " yes " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select \n", " 1 between 0 and 2,\n", " 2 in (1,2,3),\n", " 3 is integer,\n", " (case when 4<5 then 'yes' else 'no' end)\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# `FROM` and `LIMIT`\n", "\n", "Queries are mostly useful when run on actual datasets, so let's import part of the passenger manifest from the Titanic." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<Response [201]>\n" ] } ], "source": [ "print mldb.put('/v1/procedures/import_titanic', { \n", " \"type\": \"import.text\",\n", " \"params\": { \n", " \"dataFileUrl\": \"file://mldb/mldb_test_data/titanic_train.csv\",\n", " \"outputDataset\": \"titanic\",\n", " \"runOnCreation\": True\n", " } \n", "})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's query all columns with the star (`*`) operator `FROM` our `titanic` dataset, using the `LIMIT` keyword to avoid getting too much output." ] }, { "cell_type": "code", "execution_count": 6, "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>PassengerId</th>\n", " <th>label</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>97</th>\n", " <td>96</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>ShorneyMr.CharlesJoseph</td>\n", " <td>male</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>374910</td>\n", " <td>8.0500</td>\n", " <td>None</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>273</th>\n", " <td>272</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>TornquistMr.WilliamHenry</td>\n", " <td>male</td>\n", " <td>25</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>LINE</td>\n", " <td>0.0000</td>\n", " <td>None</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>524</th>\n", " <td>523</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>LahoudMr.Sarkis</td>\n", " <td>male</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2624</td>\n", " <td>7.2250</td>\n", " <td>None</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>278</th>\n", " <td>277</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>LindblomMiss.AugustaCharlotta</td>\n", " <td>female</td>\n", " <td>45</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>347073</td>\n", " <td>7.7500</td>\n", " <td>None</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>211</th>\n", " <td>210</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>BlankMr.Henry</td>\n", " <td>male</td>\n", " <td>40</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>112277</td>\n", " <td>31.0000</td>\n", " <td>A31</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>210</th>\n", " <td>209</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>CarrMiss.Helen\"Ellen\"</td>\n", " <td>female</td>\n", " <td>16</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>367231</td>\n", " <td>7.7500</td>\n", " <td>None</td>\n", " <td>Q</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>10</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>NasserMrs.Nicholas(AdeleAchem)</td>\n", " <td>female</td>\n", " <td>14</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>237736</td>\n", " <td>30.0708</td>\n", " <td>None</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>281</th>\n", " <td>280</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>AbbottMrs.Stanton(RosaHunt)</td>\n", " <td>female</td>\n", " <td>35</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>C.A.2673</td>\n", " <td>20.2500</td>\n", " <td>None</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>698</th>\n", " <td>697</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>KellyMr.James</td>\n", " <td>male</td>\n", " <td>44</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>363592</td>\n", " <td>8.0500</td>\n", " <td>None</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>460</th>\n", " <td>459</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>ToomeyMiss.Ellen</td>\n", " <td>female</td>\n", " <td>50</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>F.C.C.13531</td>\n", " <td>10.5000</td>\n", " <td>None</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId label Pclass Name Sex \\\n", "_rowName \n", "97 96 0 3 ShorneyMr.CharlesJoseph male \n", "273 272 1 3 TornquistMr.WilliamHenry male \n", "524 523 0 3 LahoudMr.Sarkis male \n", "278 277 0 3 LindblomMiss.AugustaCharlotta female \n", "211 210 1 1 BlankMr.Henry male \n", "210 209 1 3 CarrMiss.Helen\"Ellen\" female \n", "11 10 1 2 NasserMrs.Nicholas(AdeleAchem) female \n", "281 280 1 3 AbbottMrs.Stanton(RosaHunt) female \n", "698 697 0 3 KellyMr.James male \n", "460 459 1 2 ToomeyMiss.Ellen female \n", "\n", " Age SibSp Parch Ticket Fare Cabin Embarked \n", "_rowName \n", "97 NaN 0 0 374910 8.0500 None S \n", "273 25 0 0 LINE 0.0000 None S \n", "524 NaN 0 0 2624 7.2250 None C \n", "278 45 0 0 347073 7.7500 None S \n", "211 40 0 0 112277 31.0000 A31 C \n", "210 16 0 0 367231 7.7500 None Q \n", "11 14 1 0 237736 30.0708 None C \n", "281 35 1 1 C.A.2673 20.2500 None S \n", "698 44 0 0 363592 8.0500 None S \n", "460 50 0 0 F.C.C.13531 10.5000 None S " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select *\n", "from titanic\n", "limit 10\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also ask for just certain columns by name." ] }, { "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>Name</th>\n", " <th>Age</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>97</th>\n", " <td>ShorneyMr.CharlesJoseph</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>273</th>\n", " <td>TornquistMr.WilliamHenry</td>\n", " <td>25</td>\n", " </tr>\n", " <tr>\n", " <th>524</th>\n", " <td>LahoudMr.Sarkis</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>278</th>\n", " <td>LindblomMiss.AugustaCharlotta</td>\n", " <td>45</td>\n", " </tr>\n", " <tr>\n", " <th>211</th>\n", " <td>BlankMr.Henry</td>\n", " <td>40</td>\n", " </tr>\n", " <tr>\n", " <th>210</th>\n", " <td>CarrMiss.Helen\"Ellen\"</td>\n", " <td>16</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>NasserMrs.Nicholas(AdeleAchem)</td>\n", " <td>14</td>\n", " </tr>\n", " <tr>\n", " <th>281</th>\n", " <td>AbbottMrs.Stanton(RosaHunt)</td>\n", " <td>35</td>\n", " </tr>\n", " <tr>\n", " <th>698</th>\n", " <td>KellyMr.James</td>\n", " <td>44</td>\n", " </tr>\n", " <tr>\n", " <th>460</th>\n", " <td>ToomeyMiss.Ellen</td>\n", " <td>50</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Name Age\n", "_rowName \n", "97 ShorneyMr.CharlesJoseph NaN\n", "273 TornquistMr.WilliamHenry 25\n", "524 LahoudMr.Sarkis NaN\n", "278 LindblomMiss.AugustaCharlotta 45\n", "211 BlankMr.Henry 40\n", "210 CarrMiss.Helen\"Ellen\" 16\n", "11 NasserMrs.Nicholas(AdeleAchem) 14\n", "281 AbbottMrs.Stanton(RosaHunt) 35\n", "698 KellyMr.James 44\n", "460 ToomeyMiss.Ellen 50" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select Name, Age\n", "from titanic\n", "limit 10\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# `ORDER BY`\n", "\n", "When we've used the `LIMIT` keyword above, we were just getting an arbitrary set of 10 rows. Using the `ORDER BY` keyword we can ask for the 'top 10' according to some criterion, for example `Age`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Name</th>\n", " <th>Age</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>632</th>\n", " <td>BarkworthMr.AlgernonHenryWilson</td>\n", " <td>80.0</td>\n", " </tr>\n", " <tr>\n", " <th>853</th>\n", " <td>SvenssonMr.Johan</td>\n", " <td>74.0</td>\n", " </tr>\n", " <tr>\n", " <th>98</th>\n", " <td>GoldschmidtMr.GeorgeB</td>\n", " <td>71.0</td>\n", " </tr>\n", " <tr>\n", " <th>495</th>\n", " <td>ArtagaveytiaMr.Ramon</td>\n", " <td>71.0</td>\n", " </tr>\n", " <tr>\n", " <th>118</th>\n", " <td>ConnorsMr.Patrick</td>\n", " <td>70.5</td>\n", " </tr>\n", " <tr>\n", " <th>674</th>\n", " <td>MitchellMr.HenryMichael</td>\n", " <td>70.0</td>\n", " </tr>\n", " <tr>\n", " <th>747</th>\n", " <td>CrosbyCapt.EdwardGifford</td>\n", " <td>70.0</td>\n", " </tr>\n", " <tr>\n", " <th>35</th>\n", " <td>WheadonMr.EdwardH</td>\n", " <td>66.0</td>\n", " </tr>\n", " <tr>\n", " <th>282</th>\n", " <td>DuaneMr.Frank</td>\n", " <td>65.0</td>\n", " </tr>\n", " <tr>\n", " <th>458</th>\n", " <td>MilletMr.FrancisDavis</td>\n", " <td>65.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Name Age\n", "_rowName \n", "632 BarkworthMr.AlgernonHenryWilson 80.0\n", "853 SvenssonMr.Johan 74.0\n", "98 GoldschmidtMr.GeorgeB 71.0\n", "495 ArtagaveytiaMr.Ramon 71.0\n", "118 ConnorsMr.Patrick 70.5\n", "674 MitchellMr.HenryMichael 70.0\n", "747 CrosbyCapt.EdwardGifford 70.0\n", "35 WheadonMr.EdwardH 66.0\n", "282 DuaneMr.Frank 65.0\n", "458 MilletMr.FrancisDavis 65.0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select Name, Age\n", "from titanic\n", "order by Age desc \n", "limit 10\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# `WHERE`\n", "\n", "Beyond limiting the number of records, sometimes we want to look at records which match certain criteria, which we can do with the `WHERE` keyword. You can use the same operators in the `WHERE` clause as in the `SELECT` clause." ] }, { "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>Name</th>\n", " <th>Age</th>\n", " <th>Pclass</th>\n", " <th>Sex</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>label</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</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>535</th>\n", " <td>PeterMrs.Catherine(CatherineRizk)</td>\n", " <td>None</td>\n", " <td>3</td>\n", " <td>female</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Name Age Pclass Sex SibSp \\\n", "_rowName \n", "535 PeterMrs.Catherine(CatherineRizk) None 3 female 0 \n", "\n", " Parch label \n", "_rowName \n", "535 2 1 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select Name, Age, Pclass, Sex, SibSp, Parch, label\n", "from titanic\n", "where Pclass in (1,3) and Sex='female' and (SibSp>3 or Parch=2) and label=1 and Age is null\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the query above we used the special operator `is` to retrieve only rows where `Age is null`. This is worth pointing out because `null` is a special value in SQL: it means \"unknown\". `null` has some strange properties, as you can see below: any comparison between `Age` and 1 returns `null`. This makes sense because if, say, `Age` is unknown, then we don't know if `Age` is less than, equal to or greater than anything else. SQL works according to [3-valued logic](https://en.wikipedia.org/wiki/Null_(SQL)).\n", "\n", "The only reliable way to check if a value is null is with the `is null` operator." ] }, { "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>Age</th>\n", " <th>Age = 1</th>\n", " <th>Age &lt; 1</th>\n", " <th>Age &gt; 1</th>\n", " <th>Age + 1</th>\n", " <th>Age / 1</th>\n", " <th>Age is null</th>\n", " <th>Age is not null</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</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>97</th>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>None</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Age Age = 1 Age < 1 Age > 1 Age + 1 Age / 1 Age is null \\\n", "_rowName \n", "97 None None None None None None 1 \n", "\n", " Age is not null \n", "_rowName \n", "97 0 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select Age, Age = 1, Age < 1, Age > 1, Age + 1, Age / 1, Age is null, Age is not null\n", "from titanic\n", "where Age is null\n", "limit 1\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Functions and Aggregate Functions\n", "\n", "MLDB comes with a number of builtin functions to operate on your data. Here's an example where we convert a string to uppercase and lowercase." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Name</th>\n", " <th>upper(Name)</th>\n", " <th>lower(Name)</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>632</th>\n", " <td>BarkworthMr.AlgernonHenryWilson</td>\n", " <td>BARKWORTHMR.ALGERNONHENRYWILSON</td>\n", " <td>barkworthmr.algernonhenrywilson</td>\n", " </tr>\n", " <tr>\n", " <th>853</th>\n", " <td>SvenssonMr.Johan</td>\n", " <td>SVENSSONMR.JOHAN</td>\n", " <td>svenssonmr.johan</td>\n", " </tr>\n", " <tr>\n", " <th>98</th>\n", " <td>GoldschmidtMr.GeorgeB</td>\n", " <td>GOLDSCHMIDTMR.GEORGEB</td>\n", " <td>goldschmidtmr.georgeb</td>\n", " </tr>\n", " <tr>\n", " <th>495</th>\n", " <td>ArtagaveytiaMr.Ramon</td>\n", " <td>ARTAGAVEYTIAMR.RAMON</td>\n", " <td>artagaveytiamr.ramon</td>\n", " </tr>\n", " <tr>\n", " <th>118</th>\n", " <td>ConnorsMr.Patrick</td>\n", " <td>CONNORSMR.PATRICK</td>\n", " <td>connorsmr.patrick</td>\n", " </tr>\n", " <tr>\n", " <th>674</th>\n", " <td>MitchellMr.HenryMichael</td>\n", " <td>MITCHELLMR.HENRYMICHAEL</td>\n", " <td>mitchellmr.henrymichael</td>\n", " </tr>\n", " <tr>\n", " <th>747</th>\n", " <td>CrosbyCapt.EdwardGifford</td>\n", " <td>CROSBYCAPT.EDWARDGIFFORD</td>\n", " <td>crosbycapt.edwardgifford</td>\n", " </tr>\n", " <tr>\n", " <th>35</th>\n", " <td>WheadonMr.EdwardH</td>\n", " <td>WHEADONMR.EDWARDH</td>\n", " <td>wheadonmr.edwardh</td>\n", " </tr>\n", " <tr>\n", " <th>282</th>\n", " <td>DuaneMr.Frank</td>\n", " <td>DUANEMR.FRANK</td>\n", " <td>duanemr.frank</td>\n", " </tr>\n", " <tr>\n", " <th>458</th>\n", " <td>MilletMr.FrancisDavis</td>\n", " <td>MILLETMR.FRANCISDAVIS</td>\n", " <td>milletmr.francisdavis</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Name upper(Name) \\\n", "_rowName \n", "632 BarkworthMr.AlgernonHenryWilson BARKWORTHMR.ALGERNONHENRYWILSON \n", "853 SvenssonMr.Johan SVENSSONMR.JOHAN \n", "98 GoldschmidtMr.GeorgeB GOLDSCHMIDTMR.GEORGEB \n", "495 ArtagaveytiaMr.Ramon ARTAGAVEYTIAMR.RAMON \n", "118 ConnorsMr.Patrick CONNORSMR.PATRICK \n", "674 MitchellMr.HenryMichael MITCHELLMR.HENRYMICHAEL \n", "747 CrosbyCapt.EdwardGifford CROSBYCAPT.EDWARDGIFFORD \n", "35 WheadonMr.EdwardH WHEADONMR.EDWARDH \n", "282 DuaneMr.Frank DUANEMR.FRANK \n", "458 MilletMr.FrancisDavis MILLETMR.FRANCISDAVIS \n", "\n", " lower(Name) \n", "_rowName \n", "632 barkworthmr.algernonhenrywilson \n", "853 svenssonmr.johan \n", "98 goldschmidtmr.georgeb \n", "495 artagaveytiamr.ramon \n", "118 connorsmr.patrick \n", "674 mitchellmr.henrymichael \n", "747 crosbycapt.edwardgifford \n", "35 wheadonmr.edwardh \n", "282 duanemr.frank \n", "458 milletmr.francisdavis " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select Name, upper(Name), lower(Name)\n", "from titanic\n", "order by Age desc limit 10\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The functions below are special: they're aggregate functions, so they operate on multiple rows and give you a single output. They operate only on non-`null` values of their input." ] }, { "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>count(Age)</th>\n", " <th>sum(Age)</th>\n", " <th>sum(Age)/count(Age)</th>\n", " <th>avg(Age)</th>\n", " <th>min(Age)</th>\n", " <th>max(Age)</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</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>[]</th>\n", " <td>714</td>\n", " <td>21205.17</td>\n", " <td>29.699118</td>\n", " <td>29.699118</td>\n", " <td>0.42</td>\n", " <td>80</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " count(Age) sum(Age) sum(Age)/count(Age) avg(Age) min(Age) \\\n", "_rowName \n", "[] 714 21205.17 29.699118 29.699118 0.42 \n", "\n", " max(Age) \n", "_rowName \n", "[] 80 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select count(Age), sum(Age), sum(Age)/count(Age), avg(Age), min(Age), max(Age)\n", "from titanic\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `count` aggregate function is special in that it accepts `*` as an input, in which case it will return the count of all rows:" ] }, { "cell_type": "code", "execution_count": 13, "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>count(*)</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>[]</th>\n", " <td>891</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " count(*)\n", "_rowName \n", "[] 891" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select count(*)\n", "from titanic\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# GROUP BY & HAVING\n", "\n", " You can get aggregate functions to return multiple rows by grouping the input according to some criteria with the `GROUP BY` keyword. If you use an aggregate function in your `SELECT` clause, then you cannot use any non-aggregate expressions unless they appear in a `GROUP BY` clause." ] }, { "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>Pclass</th>\n", " <th>avg(Age)</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>[1]</th>\n", " <td>1</td>\n", " <td>38.233441</td>\n", " </tr>\n", " <tr>\n", " <th>[2]</th>\n", " <td>2</td>\n", " <td>29.877630</td>\n", " </tr>\n", " <tr>\n", " <th>[3]</th>\n", " <td>3</td>\n", " <td>25.140620</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Pclass avg(Age)\n", "_rowName \n", "[1] 1 38.233441\n", "[2] 2 29.877630\n", "[3] 3 25.140620" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select Pclass, avg(Age)\n", "from titanic\n", "group by Pclass\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You cannot use aggregate functions in a `WHERE` clause. The `HAVING` clause is a little bit like a `WHERE` clause which is applied after `GROUP BY`, and in which you can use aggregate functions." ] }, { "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>Pclass</th>\n", " <th>avg(Age)</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>[1]</th>\n", " <td>1</td>\n", " <td>38.233441</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Pclass avg(Age)\n", "_rowName \n", "[1] 1 38.233441" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select Pclass, avg(Age)\n", "from titanic\n", "group by Pclass\n", "having avg(Age) > 30\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Advanced `FROM` with subqueries\n", "\n", "SQL allows you to use the output of one query as the input to another by putting queries in the `FROM` clause, at which point they become \"subqueries\". The following example shows how to emulate the `HAVING` example above with a subquery, although it should be noted that the `HAVING` form will be faster in this case." ] }, { "cell_type": "code", "execution_count": 16, "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>Pclass</th>\n", " <th>mean_age</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>[1]</th>\n", " <td>1</td>\n", " <td>38.233441</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Pclass mean_age\n", "_rowName \n", "[1] 1 38.233441" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select *\n", "from (\n", " select Pclass, avg(Age) as mean_age\n", " from titanic\n", " group by Pclass\n", ")\n", "where mean_age > 30\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# `INTO`: supported via `transform` Procedures \n", "\n", "Standard SQL defines an `INTO` clause to create new datasets from the output of queries. MLDB `SELECT` queries are idempotent (they do not modify anything) so `INTO` is not supported directly. You can accomplish the same task with a `transform` procedure, however:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<Response [201]>\n" ] } ], "source": [ "not_supported = \"\"\"\n", " select Pclass, avg(Age) as mean_age\n", " into class_stats\n", " from titanic\n", " group by Pclass\n", "\"\"\"\n", "\n", "supported = mldb.post('/v1/procedures', { \n", " \"type\": \"transform\",\n", " \"params\": { \n", " \"inputData\": \"\"\"\n", " select Pclass, avg(Age) as mean_age\n", " from titanic\n", " group by Pclass\n", " \"\"\",\n", " \"outputDataset\": \"class_stats\",\n", " \"runOnCreation\": True\n", " } \n", "})\n", "\n", "print supported" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now query our new dataset!" ] }, { "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>Pclass</th>\n", " <th>mean_age</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>[1]</th>\n", " <td>1</td>\n", " <td>38.233441</td>\n", " </tr>\n", " <tr>\n", " <th>[3]</th>\n", " <td>3</td>\n", " <td>25.140620</td>\n", " </tr>\n", " <tr>\n", " <th>[2]</th>\n", " <td>2</td>\n", " <td>29.877630</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Pclass mean_age\n", "_rowName \n", "[1] 1 38.233441\n", "[3] 3 25.140620\n", "[2] 2 29.877630" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select *\n", "from class_stats\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Advanced `FROM` with `JOIN`\n", "\n", "You can run queries across multiple datasets with the `JOIN` keyword, using the `ON` keyword to define how to combine the datasets." ] }, { "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>titanic.Name</th>\n", " <th>titanic.Pclass</th>\n", " <th>class_stats.Pclass</th>\n", " <th>class_stats.mean_age</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>[835]-[[3]]</th>\n", " <td>AugustssonMr.Albert</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>25.14062</td>\n", " </tr>\n", " <tr>\n", " <th>[60]-[[2]]</th>\n", " <td>WestMiss.ConstanceMirium</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>29.87763</td>\n", " </tr>\n", " <tr>\n", " <th>[78]-[[3]]</th>\n", " <td>StaneffMr.Ivan</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>25.14062</td>\n", " </tr>\n", " <tr>\n", " <th>[356]-[[3]]</th>\n", " <td>YousifMr.Wazli</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>25.14062</td>\n", " </tr>\n", " <tr>\n", " <th>[134]-[[3]]</th>\n", " <td>RobinsMrs.AlexanderA(GraceCharityLaury)</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>25.14062</td>\n", " </tr>\n", " <tr>\n", " <th>[740]-[[3]]</th>\n", " <td>IvanoffMr.Kanio</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>25.14062</td>\n", " </tr>\n", " <tr>\n", " <th>[652]-[[3]]</th>\n", " <td>MitkoffMr.Mito</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>25.14062</td>\n", " </tr>\n", " <tr>\n", " <th>[10]-[[3]]</th>\n", " <td>JohnsonMrs.OscarW(ElisabethVilhelminaBerg)</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>25.14062</td>\n", " </tr>\n", " <tr>\n", " <th>[249]-[[2]]</th>\n", " <td>HamalainenMrs.William(Anna)</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>29.87763</td>\n", " </tr>\n", " <tr>\n", " <th>[280]-[[3]]</th>\n", " <td>RiceMaster.Eric</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>25.14062</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " titanic.Name titanic.Pclass \\\n", "_rowName \n", "[835]-[[3]] AugustssonMr.Albert 3 \n", "[60]-[[2]] WestMiss.ConstanceMirium 2 \n", "[78]-[[3]] StaneffMr.Ivan 3 \n", "[356]-[[3]] YousifMr.Wazli 3 \n", "[134]-[[3]] RobinsMrs.AlexanderA(GraceCharityLaury) 3 \n", "[740]-[[3]] IvanoffMr.Kanio 3 \n", "[652]-[[3]] MitkoffMr.Mito 3 \n", "[10]-[[3]] JohnsonMrs.OscarW(ElisabethVilhelminaBerg) 3 \n", "[249]-[[2]] HamalainenMrs.William(Anna) 2 \n", "[280]-[[3]] RiceMaster.Eric 3 \n", "\n", " class_stats.Pclass class_stats.mean_age \n", "_rowName \n", "[835]-[[3]] 3 25.14062 \n", "[60]-[[2]] 2 29.87763 \n", "[78]-[[3]] 3 25.14062 \n", "[356]-[[3]] 3 25.14062 \n", "[134]-[[3]] 3 25.14062 \n", "[740]-[[3]] 3 25.14062 \n", "[652]-[[3]] 3 25.14062 \n", "[10]-[[3]] 3 25.14062 \n", "[249]-[[2]] 2 29.87763 \n", "[280]-[[3]] 3 25.14062 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select titanic.Name, titanic.Pclass, class_stats.*\n", "from titanic JOIN class_stats ON titanic.Pclass = class_stats.Pclass \n", "order by Age desc limit 10\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----------\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# MLDB extensions to conventional SQL" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "MLDB has some notable differences with more conventional SQL databases like PostgreSQL, MySQL, Oracle or SQLServer. For example, MLDB datasets are not SQL tables: \n", "\n", "* datasets have no fixed schema\n", "* datasets can have a variable number of columns, numbering into the millions\n", "* columns can contain mixed types (i.e. both numbers and strings in the same column)\n", "* both rows and columns have names\n", "\n", "In order to accomodate this, MLDB provides a number of extensions to standard SQL. Examples are provided below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Selecting columns based on a prefix:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Pclass</th>\n", " <th>Parch</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>97</th>\n", " <td>96</td>\n", " <td>3</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Pclass Parch\n", "_rowName \n", "97 96 3 0" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select P*\n", "from titanic\n", "limit 1\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Renaming columns based on a prefix pattern:" ] }, { "cell_type": "code", "execution_count": 21, "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>xassengerId</th>\n", " <th>xclass</th>\n", " <th>xarch</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>97</th>\n", " <td>96</td>\n", " <td>3</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " xassengerId xclass xarch\n", "_rowName \n", "97 96 3 0" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select P* as x*\n", "from titanic\n", "limit 1\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Excluding columns from a selection:" ] }, { "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>label</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</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>97</th>\n", " <td>0</td>\n", " <td>ShorneyMr.CharlesJoseph</td>\n", " <td>male</td>\n", " <td>None</td>\n", " <td>0</td>\n", " <td>374910</td>\n", " <td>8.05</td>\n", " <td>None</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " label Name Sex Age SibSp Ticket Fare \\\n", "_rowName \n", "97 0 ShorneyMr.CharlesJoseph male None 0 374910 8.05 \n", "\n", " Cabin Embarked \n", "_rowName \n", "97 None S " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select * excluding(P*)\n", "from titanic\n", "limit 1\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NOTE: Selecting a column which is not in the dataset will not cause an error, instead it will return `NULL`." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>nothing</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>97</th>\n", " <td>None</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " nothing\n", "_rowName \n", "97 None" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select nothing\n", "from titanic\n", "limit 1\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "MLDB supports JSON-like objects in queries." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>{a: 1, b:2, c: 'hello'}.a</th>\n", " <th>{a: 1, b:2, c: 'hello'}.b</th>\n", " <th>{a: 1, b:2, c: 'hello'}.c</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th></th>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>hello</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " {a: 1, b:2, c: 'hello'}.a {a: 1, b:2, c: 'hello'}.b \\\n", "_rowName \n", " 1 2 \n", "\n", " {a: 1, b:2, c: 'hello'}.c \n", "_rowName \n", " hello " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select {a: 1, b:2, c: 'hello'}\n", "\n", "\"\"\")" ] }, { "cell_type": "code", "execution_count": 25, "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>obj.a</th>\n", " <th>obj.b</th>\n", " <th>obj.c</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th></th>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>hello</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " obj.a obj.b obj.c\n", "_rowName \n", " 1 2 hello" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select {a: 1, b:2, c: 'hello'} as obj\n", "\n", "\"\"\")" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>a</th>\n", " <th>b.x</th>\n", " <th>c</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th></th>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>hello</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " a b.x c\n", "_rowName \n", " 1 2 hello" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select {a: 1, b:{x:2}, c: 'hello'} as *\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is especially useful for tokenizing text into bags of words, or importing semi-structured JSON data." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>world</th>\n", " <th>Hello</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th></th>\n", " <td>1</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " world Hello\n", "_rowName \n", " 1 2" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select tokenize('Hello world, Hello!', {splitChars: ' ,!'}) as *\n", "\n", "\"\"\")" ] }, { "cell_type": "code", "execution_count": 28, "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>hello</th>\n", " <th>list.0</th>\n", " <th>list.1</th>\n", " <th>list.2</th>\n", " <th>list.3</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</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></th>\n", " <td>world</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " hello list.0 list.1 list.2 list.3\n", "_rowName \n", " world 1 2 3 4" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select parse_json('{\"hello\":\"world\",\"list\":[1,2,3,4]}') as *\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "MLDB's object notation also allows you to run aggregate functions on multiple columns at once, with the special `{*}` notation, which refers to all fields in the current row as an object." ] }, { "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>Pclass</th>\n", " <th>count(*)</th>\n", " <th>count({*}).Age</th>\n", " <th>count({*}).Cabin</th>\n", " <th>count({*}).Embarked</th>\n", " <th>count({*}).Fare</th>\n", " <th>count({*}).Name</th>\n", " <th>count({*}).Parch</th>\n", " <th>count({*}).PassengerId</th>\n", " <th>count({*}).Pclass</th>\n", " <th>count({*}).Sex</th>\n", " <th>count({*}).SibSp</th>\n", " <th>count({*}).Ticket</th>\n", " <th>count({*}).label</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>[1]</th>\n", " <td>1</td>\n", " <td>216</td>\n", " <td>186</td>\n", " <td>176</td>\n", " <td>214</td>\n", " <td>216</td>\n", " <td>216</td>\n", " <td>216</td>\n", " <td>216</td>\n", " <td>216</td>\n", " <td>216</td>\n", " <td>216</td>\n", " <td>216</td>\n", " <td>216</td>\n", " </tr>\n", " <tr>\n", " <th>[2]</th>\n", " <td>2</td>\n", " <td>184</td>\n", " <td>173</td>\n", " <td>16</td>\n", " <td>184</td>\n", " <td>184</td>\n", " <td>184</td>\n", " <td>184</td>\n", " <td>184</td>\n", " <td>184</td>\n", " <td>184</td>\n", " <td>184</td>\n", " <td>184</td>\n", " <td>184</td>\n", " </tr>\n", " <tr>\n", " <th>[3]</th>\n", " <td>3</td>\n", " <td>491</td>\n", " <td>355</td>\n", " <td>12</td>\n", " <td>491</td>\n", " <td>491</td>\n", " <td>491</td>\n", " <td>491</td>\n", " <td>491</td>\n", " <td>491</td>\n", " <td>491</td>\n", " <td>491</td>\n", " <td>491</td>\n", " <td>491</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Pclass count(*) count({*}).Age count({*}).Cabin \\\n", "_rowName \n", "[1] 1 216 186 176 \n", "[2] 2 184 173 16 \n", "[3] 3 491 355 12 \n", "\n", " count({*}).Embarked count({*}).Fare count({*}).Name \\\n", "_rowName \n", "[1] 214 216 216 \n", "[2] 184 184 184 \n", "[3] 491 491 491 \n", "\n", " count({*}).Parch count({*}).PassengerId count({*}).Pclass \\\n", "_rowName \n", "[1] 216 216 216 \n", "[2] 184 184 184 \n", "[3] 491 491 491 \n", "\n", " count({*}).Sex count({*}).SibSp count({*}).Ticket \\\n", "_rowName \n", "[1] 216 216 216 \n", "[2] 184 184 184 \n", "[3] 491 491 491 \n", "\n", " count({*}).label \n", "_rowName \n", "[1] 216 \n", "[2] 184 \n", "[3] 491 " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select Pclass, count(*), count({*})\n", "from titanic\n", "group by Pclass\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "MLDB's flexible output model also enables powerful aggregate functions like `pivot()` to operate." ] }, { "cell_type": "code", "execution_count": 30, "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>Pclass</th>\n", " <th>female</th>\n", " <th>male</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>[1]</th>\n", " <td>1</td>\n", " <td>94</td>\n", " <td>122</td>\n", " </tr>\n", " <tr>\n", " <th>[2]</th>\n", " <td>2</td>\n", " <td>76</td>\n", " <td>108</td>\n", " </tr>\n", " <tr>\n", " <th>[3]</th>\n", " <td>3</td>\n", " <td>144</td>\n", " <td>347</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Pclass female male\n", "_rowName \n", "[1] 1 94 122\n", "[2] 2 76 108\n", "[3] 3 144 347" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select Pclass, pivot(Sex, \"count(*)\") as *\n", "from (\n", " select Pclass, Sex, count(*) from titanic group by Pclass, Sex\n", ")\n", "group by Pclass\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "MLDB supports multi-dimensional arrays called embeddings, also known as tensors." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>embedding.0</th>\n", " <th>embedding.1</th>\n", " <th>embedding.2</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th></th>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " embedding.0 embedding.1 embedding.2\n", "_rowName \n", " 1 2 3" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select [1,2,3] as embedding\n", "\n", "\"\"\")" ] }, { "cell_type": "code", "execution_count": 32, "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>n.0</th>\n", " <th>n.1</th>\n", " <th>n.2</th>\n", " <th>d.0</th>\n", " <th>d.1</th>\n", " <th>d.2</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</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></th>\n", " <td>0.166667</td>\n", " <td>0.333333</td>\n", " <td>0.5</td>\n", " <td>0.166667</td>\n", " <td>0.333333</td>\n", " <td>0.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " n.0 n.1 n.2 d.0 d.1 d.2\n", "_rowName \n", " 0.166667 0.333333 0.5 0.166667 0.333333 0.5" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select normalize([1,2,3], 1) as n, [1,2,3] / norm([1,2,3] ,1) as d\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "MLDB datasets have named rows as well as columns, and the `NAMED` keyword allows you to control the names of your output rows." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>label</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>BarkworthMr.AlgernonHenryWilson aged 80</th>\n", " <td>631</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>BarkworthMr.AlgernonHenryWilson</td>\n", " <td>male</td>\n", " <td>80.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>27042</td>\n", " <td>30.0000</td>\n", " <td>A23</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>SvenssonMr.Johan aged 74</th>\n", " <td>852</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>SvenssonMr.Johan</td>\n", " <td>male</td>\n", " <td>74.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>347060</td>\n", " <td>7.7750</td>\n", " <td>None</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>GoldschmidtMr.GeorgeB aged 71</th>\n", " <td>97</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>GoldschmidtMr.GeorgeB</td>\n", " <td>male</td>\n", " <td>71.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>PC17754</td>\n", " <td>34.6542</td>\n", " <td>A5</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>ArtagaveytiaMr.Ramon aged 71</th>\n", " <td>494</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>ArtagaveytiaMr.Ramon</td>\n", " <td>male</td>\n", " <td>71.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>PC17609</td>\n", " <td>49.5042</td>\n", " <td>None</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>ConnorsMr.Patrick aged 70.5</th>\n", " <td>117</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>ConnorsMr.Patrick</td>\n", " <td>male</td>\n", " <td>70.5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>370369</td>\n", " <td>7.7500</td>\n", " <td>None</td>\n", " <td>Q</td>\n", " </tr>\n", " <tr>\n", " <th>MitchellMr.HenryMichael aged 70</th>\n", " <td>673</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>MitchellMr.HenryMichael</td>\n", " <td>male</td>\n", " <td>70.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>C.A.24580</td>\n", " <td>10.5000</td>\n", " <td>None</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>CrosbyCapt.EdwardGifford aged 70</th>\n", " <td>746</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>CrosbyCapt.EdwardGifford</td>\n", " <td>male</td>\n", " <td>70.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>WE/P5735</td>\n", " <td>71.0000</td>\n", " <td>B22</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>WheadonMr.EdwardH aged 66</th>\n", " <td>34</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>WheadonMr.EdwardH</td>\n", " <td>male</td>\n", " <td>66.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>C.A.24579</td>\n", " <td>10.5000</td>\n", " <td>None</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>DuaneMr.Frank aged 65</th>\n", " <td>281</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>DuaneMr.Frank</td>\n", " <td>male</td>\n", " <td>65.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>336439</td>\n", " <td>7.7500</td>\n", " <td>None</td>\n", " <td>Q</td>\n", " </tr>\n", " <tr>\n", " <th>MilletMr.FrancisDavis aged 65</th>\n", " <td>457</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>MilletMr.FrancisDavis</td>\n", " <td>male</td>\n", " <td>65.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>13509</td>\n", " <td>26.5500</td>\n", " <td>E38</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId label Pclass \\\n", "_rowName \n", "BarkworthMr.AlgernonHenryWilson aged 80 631 1 1 \n", "SvenssonMr.Johan aged 74 852 0 3 \n", "GoldschmidtMr.GeorgeB aged 71 97 0 1 \n", "ArtagaveytiaMr.Ramon aged 71 494 0 1 \n", "ConnorsMr.Patrick aged 70.5 117 0 3 \n", "MitchellMr.HenryMichael aged 70 673 0 2 \n", "CrosbyCapt.EdwardGifford aged 70 746 0 1 \n", "WheadonMr.EdwardH aged 66 34 0 2 \n", "DuaneMr.Frank aged 65 281 0 3 \n", "MilletMr.FrancisDavis aged 65 457 0 1 \n", "\n", " Name \\\n", "_rowName \n", "BarkworthMr.AlgernonHenryWilson aged 80 BarkworthMr.AlgernonHenryWilson \n", "SvenssonMr.Johan aged 74 SvenssonMr.Johan \n", "GoldschmidtMr.GeorgeB aged 71 GoldschmidtMr.GeorgeB \n", "ArtagaveytiaMr.Ramon aged 71 ArtagaveytiaMr.Ramon \n", "ConnorsMr.Patrick aged 70.5 ConnorsMr.Patrick \n", "MitchellMr.HenryMichael aged 70 MitchellMr.HenryMichael \n", "CrosbyCapt.EdwardGifford aged 70 CrosbyCapt.EdwardGifford \n", "WheadonMr.EdwardH aged 66 WheadonMr.EdwardH \n", "DuaneMr.Frank aged 65 DuaneMr.Frank \n", "MilletMr.FrancisDavis aged 65 MilletMr.FrancisDavis \n", "\n", " Sex Age SibSp Parch Ticket \\\n", "_rowName \n", "BarkworthMr.AlgernonHenryWilson aged 80 male 80.0 0 0 27042 \n", "SvenssonMr.Johan aged 74 male 74.0 0 0 347060 \n", "GoldschmidtMr.GeorgeB aged 71 male 71.0 0 0 PC17754 \n", "ArtagaveytiaMr.Ramon aged 71 male 71.0 0 0 PC17609 \n", "ConnorsMr.Patrick aged 70.5 male 70.5 0 0 370369 \n", "MitchellMr.HenryMichael aged 70 male 70.0 0 0 C.A.24580 \n", "CrosbyCapt.EdwardGifford aged 70 male 70.0 1 1 WE/P5735 \n", "WheadonMr.EdwardH aged 66 male 66.0 0 0 C.A.24579 \n", "DuaneMr.Frank aged 65 male 65.0 0 0 336439 \n", "MilletMr.FrancisDavis aged 65 male 65.0 0 0 13509 \n", "\n", " Fare Cabin Embarked \n", "_rowName \n", "BarkworthMr.AlgernonHenryWilson aged 80 30.0000 A23 S \n", "SvenssonMr.Johan aged 74 7.7750 None S \n", "GoldschmidtMr.GeorgeB aged 71 34.6542 A5 C \n", "ArtagaveytiaMr.Ramon aged 71 49.5042 None C \n", "ConnorsMr.Patrick aged 70.5 7.7500 None Q \n", "MitchellMr.HenryMichael aged 70 10.5000 None S \n", "CrosbyCapt.EdwardGifford aged 70 71.0000 B22 S \n", "WheadonMr.EdwardH aged 66 10.5000 None S \n", "DuaneMr.Frank aged 65 7.7500 None Q \n", "MilletMr.FrancisDavis aged 65 26.5500 E38 S " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select *\n", "named Name + ' aged ' + cast(Age as string)\n", "from titanic\n", "order by Age desc limit 10\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Having named rows as well as columns allows us to easily operate on the transpose of a dataset" ] }, { "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>632</th>\n", " <th>853</th>\n", " <th>98</th>\n", " <th>495</th>\n", " <th>118</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</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>Cabin</th>\n", " <td>A23</td>\n", " <td>None</td>\n", " <td>A5</td>\n", " <td>None</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>Fare</th>\n", " <td>30</td>\n", " <td>7.775</td>\n", " <td>34.6542</td>\n", " <td>49.5042</td>\n", " <td>7.75</td>\n", " </tr>\n", " <tr>\n", " <th>SibSp</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>Ticket</th>\n", " <td>27042</td>\n", " <td>347060</td>\n", " <td>PC17754</td>\n", " <td>PC17609</td>\n", " <td>370369</td>\n", " </tr>\n", " <tr>\n", " <th>PassengerId</th>\n", " <td>631</td>\n", " <td>852</td>\n", " <td>97</td>\n", " <td>494</td>\n", " <td>117</td>\n", " </tr>\n", " <tr>\n", " <th>label</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>Age</th>\n", " <td>80</td>\n", " <td>74</td>\n", " <td>71</td>\n", " <td>71</td>\n", " <td>70.5</td>\n", " </tr>\n", " <tr>\n", " <th>Pclass</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>Name</th>\n", " <td>BarkworthMr.AlgernonHenryWilson</td>\n", " <td>SvenssonMr.Johan</td>\n", " <td>GoldschmidtMr.GeorgeB</td>\n", " <td>ArtagaveytiaMr.Ramon</td>\n", " <td>ConnorsMr.Patrick</td>\n", " </tr>\n", " <tr>\n", " <th>Sex</th>\n", " <td>male</td>\n", " <td>male</td>\n", " <td>male</td>\n", " <td>male</td>\n", " <td>male</td>\n", " </tr>\n", " <tr>\n", " <th>Parch</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>Embarked</th>\n", " <td>S</td>\n", " <td>S</td>\n", " <td>C</td>\n", " <td>C</td>\n", " <td>Q</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 632 853 \\\n", "_rowName \n", "Cabin A23 None \n", "Fare 30 7.775 \n", "SibSp 0 0 \n", "Ticket 27042 347060 \n", "PassengerId 631 852 \n", "label 1 0 \n", "Age 80 74 \n", "Pclass 1 3 \n", "Name BarkworthMr.AlgernonHenryWilson SvenssonMr.Johan \n", "Sex male male \n", "Parch 0 0 \n", "Embarked S S \n", "\n", " 98 495 118 \n", "_rowName \n", "Cabin A5 None None \n", "Fare 34.6542 49.5042 7.75 \n", "SibSp 0 0 0 \n", "Ticket PC17754 PC17609 370369 \n", "PassengerId 97 494 117 \n", "label 0 0 0 \n", "Age 71 71 70.5 \n", "Pclass 1 1 3 \n", "Name GoldschmidtMr.GeorgeB ArtagaveytiaMr.Ramon ConnorsMr.Patrick \n", "Sex male male male \n", "Parch 0 0 0 \n", "Embarked C C Q " ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select * from transpose(\n", " (select * from titanic order by Age desc limit 5)\n", ")\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "MLDB supports inline Javascript application via the `jseval()` function." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Name</th>\n", " <th>processed_name</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>632</th>\n", " <td>BarkworthMr.AlgernonHenryWilson</td>\n", " <td>Barkworth Mr. Algernon Henry Wilson</td>\n", " </tr>\n", " <tr>\n", " <th>853</th>\n", " <td>SvenssonMr.Johan</td>\n", " <td>Svensson Mr. Johan</td>\n", " </tr>\n", " <tr>\n", " <th>98</th>\n", " <td>GoldschmidtMr.GeorgeB</td>\n", " <td>Goldschmidt Mr. George B</td>\n", " </tr>\n", " <tr>\n", " <th>495</th>\n", " <td>ArtagaveytiaMr.Ramon</td>\n", " <td>Artagaveytia Mr. Ramon</td>\n", " </tr>\n", " <tr>\n", " <th>118</th>\n", " <td>ConnorsMr.Patrick</td>\n", " <td>Connors Mr. Patrick</td>\n", " </tr>\n", " <tr>\n", " <th>674</th>\n", " <td>MitchellMr.HenryMichael</td>\n", " <td>Mitchell Mr. Henry Michael</td>\n", " </tr>\n", " <tr>\n", " <th>747</th>\n", " <td>CrosbyCapt.EdwardGifford</td>\n", " <td>Crosby Capt. Edward Gifford</td>\n", " </tr>\n", " <tr>\n", " <th>35</th>\n", " <td>WheadonMr.EdwardH</td>\n", " <td>Wheadon Mr. Edward H</td>\n", " </tr>\n", " <tr>\n", " <th>282</th>\n", " <td>DuaneMr.Frank</td>\n", " <td>Duane Mr. Frank</td>\n", " </tr>\n", " <tr>\n", " <th>458</th>\n", " <td>MilletMr.FrancisDavis</td>\n", " <td>Millet Mr. Francis Davis</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Name \\\n", "_rowName \n", "632 BarkworthMr.AlgernonHenryWilson \n", "853 SvenssonMr.Johan \n", "98 GoldschmidtMr.GeorgeB \n", "495 ArtagaveytiaMr.Ramon \n", "118 ConnorsMr.Patrick \n", "674 MitchellMr.HenryMichael \n", "747 CrosbyCapt.EdwardGifford \n", "35 WheadonMr.EdwardH \n", "282 DuaneMr.Frank \n", "458 MilletMr.FrancisDavis \n", "\n", " processed_name \n", "_rowName \n", "632 Barkworth Mr. Algernon Henry Wilson \n", "853 Svensson Mr. Johan \n", "98 Goldschmidt Mr. George B \n", "495 Artagaveytia Mr. Ramon \n", "118 Connors Mr. Patrick \n", "674 Mitchell Mr. Henry Michael \n", "747 Crosby Capt. Edward Gifford \n", "35 Wheadon Mr. Edward H \n", "282 Duane Mr. Frank \n", "458 Millet Mr. Francis Davis " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select Name,\n", " jseval(\n", " 'return Name.replace(/([A-Z])/g, function(m, p) { return \" \"+p; });',\n", " 'Name', Name\n", " ) as processed_name\n", "from titanic\n", "order by Age desc limit 10\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "MLDB datasets handle millions of columns, and deal very well with sparse datasets, making them ideal for operating on bags of words." ] }, { "cell_type": "code", "execution_count": 36, "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>Henry</th>\n", " <th>Algernon</th>\n", " <th>Mr</th>\n", " <th>Wilson</th>\n", " <th>Barkworth</th>\n", " <th>Johan</th>\n", " <th>Svensson</th>\n", " <th>B</th>\n", " <th>George</th>\n", " <th>Goldschmidt</th>\n", " <th>...</th>\n", " <th>Edward</th>\n", " <th>Capt</th>\n", " <th>Crosby</th>\n", " <th>H</th>\n", " <th>Wheadon</th>\n", " <th>Frank</th>\n", " <th>Duane</th>\n", " <th>Davis</th>\n", " <th>Francis</th>\n", " <th>Millet</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>632</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</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>853</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</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>98</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</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>495</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>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>118</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>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>674</th>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>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>747</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <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>35</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>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>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</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>282</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>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>1</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>458</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>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>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>10 rows × 27 columns</p>\n", "</div>" ], "text/plain": [ " Henry Algernon Mr Wilson Barkworth Johan Svensson B George \\\n", "_rowName \n", "632 1 1 1 1 1 NaN NaN NaN NaN \n", "853 NaN NaN 1 NaN NaN 1 1 NaN NaN \n", "98 NaN NaN 1 NaN NaN NaN NaN 1 1 \n", "495 NaN NaN 1 NaN NaN NaN NaN NaN NaN \n", "118 NaN NaN 1 NaN NaN NaN NaN NaN NaN \n", "674 1 NaN 1 NaN NaN NaN NaN NaN NaN \n", "747 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "35 NaN NaN 1 NaN NaN NaN NaN NaN NaN \n", "282 NaN NaN 1 NaN NaN NaN NaN NaN NaN \n", "458 NaN NaN 1 NaN NaN NaN NaN NaN NaN \n", "\n", " Goldschmidt ... Edward Capt Crosby H Wheadon Frank \\\n", "_rowName ... \n", "632 NaN ... NaN NaN NaN NaN NaN NaN \n", "853 NaN ... NaN NaN NaN NaN NaN NaN \n", "98 1 ... NaN NaN NaN NaN NaN NaN \n", "495 NaN ... NaN NaN NaN NaN NaN NaN \n", "118 NaN ... NaN NaN NaN NaN NaN NaN \n", "674 NaN ... NaN NaN NaN NaN NaN NaN \n", "747 NaN ... 1 1 1 NaN NaN NaN \n", "35 NaN ... 1 NaN NaN 1 1 NaN \n", "282 NaN ... NaN NaN NaN NaN NaN 1 \n", "458 NaN ... NaN NaN NaN NaN NaN NaN \n", "\n", " Duane Davis Francis Millet \n", "_rowName \n", "632 NaN NaN NaN NaN \n", "853 NaN NaN NaN NaN \n", "98 NaN NaN NaN NaN \n", "495 NaN NaN NaN NaN \n", "118 NaN NaN NaN NaN \n", "674 NaN NaN NaN NaN \n", "747 NaN NaN NaN NaN \n", "35 NaN NaN NaN NaN \n", "282 1 NaN NaN NaN \n", "458 NaN 1 1 1 \n", "\n", "[10 rows x 27 columns]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select tokenize(\n", " jseval('\n", " return Name.replace(/([A-Z])/g, function(m, p) { return \" \"+p; });\n", " ', 'Name', Name),\n", " {splitChars: ' .()\"', quoteChar:''}) as *\n", "from titanic\n", "order by Age desc\n", "limit 10\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Putting it all together, here are the top 20 tokens present in the names of Titanic passengers." ] }, { "cell_type": "code", "execution_count": 37, "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>counts</th>\n", " </tr>\n", " <tr>\n", " <th>_rowName</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Mr</th>\n", " <td>521</td>\n", " </tr>\n", " <tr>\n", " <th>Miss</th>\n", " <td>182</td>\n", " </tr>\n", " <tr>\n", " <th>Mrs</th>\n", " <td>128</td>\n", " </tr>\n", " <tr>\n", " <th>William</th>\n", " <td>64</td>\n", " </tr>\n", " <tr>\n", " <th>John</th>\n", " <td>44</td>\n", " </tr>\n", " <tr>\n", " <th>Master</th>\n", " <td>40</td>\n", " </tr>\n", " <tr>\n", " <th>Henry</th>\n", " <td>35</td>\n", " </tr>\n", " <tr>\n", " <th>George</th>\n", " <td>24</td>\n", " </tr>\n", " <tr>\n", " <th>James</th>\n", " <td>24</td>\n", " </tr>\n", " <tr>\n", " <th>Charles</th>\n", " <td>24</td>\n", " </tr>\n", " <tr>\n", " <th>Thomas</th>\n", " <td>22</td>\n", " </tr>\n", " <tr>\n", " <th>Mary</th>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>Edward</th>\n", " <td>18</td>\n", " </tr>\n", " <tr>\n", " <th>Anna</th>\n", " <td>17</td>\n", " </tr>\n", " <tr>\n", " <th>Joseph</th>\n", " <td>16</td>\n", " </tr>\n", " <tr>\n", " <th>Frederick</th>\n", " <td>15</td>\n", " </tr>\n", " <tr>\n", " <th>Johan</th>\n", " <td>15</td>\n", " </tr>\n", " <tr>\n", " <th>Elizabeth</th>\n", " <td>15</td>\n", " </tr>\n", " <tr>\n", " <th>Richard</th>\n", " <td>14</td>\n", " </tr>\n", " <tr>\n", " <th>Samuel</th>\n", " <td>13</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " counts\n", "_rowName \n", "Mr 521\n", "Miss 182\n", "Mrs 128\n", "William 64\n", "John 44\n", "Master 40\n", "Henry 35\n", "George 24\n", "James 24\n", "Charles 24\n", "Thomas 22\n", "Mary 20\n", "Edward 18\n", "Anna 17\n", "Joseph 16\n", "Frederick 15\n", "Johan 15\n", "Elizabeth 15\n", "Richard 14\n", "Samuel 13" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mldb.query(\"\"\"\n", "\n", "select * from transpose((\n", " select sum(\n", " tokenize(\n", " jseval(\n", " 'return Name.replace(/([A-Z])/g, function(m, p) { return \" \"+p; });', \n", " 'Name', Name\n", " ),\n", " {splitChars: ' .()\"', quoteChar:''}\n", " )\n", " ) as *\n", " named 'counts'\n", " from titanic\n", "))\n", "order by counts desc limit 20\n", "\n", "\"\"\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Where to next?\n", "\n", "Check out the other [Tutorials and Demos](../../../../doc/#builtin/Demos.md.html)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
mirjalil/DataScience
notebooks/clustering/hierarchical-clustering.ipynb
2
1997
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Hierarchical Clustering\n", "===" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example\n", "\n", "||p1 | p2 | p3|p4|p5|p6|\n", "|:--:|:--:|:--:|:--:|:--:|:--:|:--:|\n", "|**p1**|0.00||||||\n", "|**p2**||0.00|||||\n", "|**p3**|||0.00||||\n", "|**p4**||||0.00|||\n", "|**p5**|||||0.00||\n", "|**p6**||||||0.00|\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Inter-cluster proximity\n", "\n", " * Min distance (single link)\n", " * Max distance (complete link)\n", " * Average distance (average link)\n", " \n", "**Single link** \n", " * handle non-spherical shaped clusters\n", " * prevents breaking large clusters (k-means breaks large clusters) \n", " * very sensitive to noise **limitation**\n", "\n", "**Complete link**\n", " * more robust to noise\n", " * similar porblem with k-means: breaking large clusters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Summary\n", "\n", "**K-means** \n", " * Storage $O((N+k)d)$\n", " * Computation $O(Nkd \\ Iterations)$\n", " \n", "**Hierarchical**\n", " * Storage: $O(N^2)$\n", " * Computation $O(N^3)$ but using " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
ReidAtcheson/laplace_screencast
.ipynb_checkpoints/ISPC enhanced Julia Compared to C-checkpoint.ipynb
1
98281
{ "metadata": { "language": "Julia", "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "using PyPlot\n", "include(\"julia/laplace1D_ispc.jl\")" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ "laplace_matrix (generic function with 1 method)" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "#Boundary conditions.\n", "a=0.0;\n", "b=1.0;\n", "#Number of grid points.\n", "N=1000;\n", "#Richardson iteration parameter.\n", "alpha=1.0/2.0;\n", "#Number of Richardson iterations.\n", "iter=1000*N;\n", "#Output vectors\n", "u_c=zeros(N);\n", "u_jl=zeros(N);\n", "#Discretized domain\n", "x=linspace(0,1,N);" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "#Time the C code\n", "time_c = time()\n", "ccall( (:driver,\"./c/laplace1D.so\"), # 2-tuple containing symbol and shared library path\n", "Void, #Return type.\n", "(Float64,Int64,Int64,Float64,Float64,Ptr{Float64}), #Function signature.\n", "alpha,iter,N,a,b,u_c); #Arguments to C function. (Julia handles marshalling for you.) \n", "time_c = time() - time_c;\n", "plot(x,u_c);" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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mit
bicepjai/Deep-Survey-Text-Classification
deep_models/paper_01_cnn_sent_class/models.ipynb
1
121555
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:23:01.898998Z", "start_time": "2017-11-07T04:23:00.657322Z" } }, "outputs": [], "source": [ "import sys\n", "import os\n", "\n", "import re\n", "import collections\n", "import itertools\n", "import bcolz\n", "import pickle\n", "sys.path.append('../../lib')\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import gc\n", "import random\n", "import smart_open\n", "import h5py\n", "import csv\n", "import json\n", "import functools\n", "import time\n", "import string\n", "\n", "import datetime as dt\n", "from tqdm import tqdm_notebook as tqdm\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "import global_utils\n", "\n", "random_state_number = 967898" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:23:02.379870Z", "start_time": "2017-11-07T04:23:01.900248Z" } }, "outputs": [ { "data": { "text/plain": [ "['/gpu:0', '/gpu:1']" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import tensorflow as tf\n", "from tensorflow.python.client import device_lib\n", "def get_available_gpus():\n", " local_device_protos = device_lib.list_local_devices()\n", " return [x.name for x in local_device_protos if x.device_type == 'GPU']\n", "\n", "config = tf.ConfigProto()\n", "config.gpu_options.allow_growth=True\n", "sess = tf.Session(config=config)\n", "get_available_gpus()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:23:02.542383Z", "start_time": "2017-11-07T04:23:02.380926Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using matplotlib backend: Qt5Agg\n", "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/bicepjai/Programs/anaconda3/envs/dsotc-c3/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['random']\n", "`%matplotlib` prevents importing * from pylab and numpy\n", " \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n" ] } ], "source": [ "%pylab\n", "%matplotlib inline\n", "%load_ext line_profiler\n", "%load_ext memory_profiler\n", "%load_ext autoreload" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:23:02.546392Z", "start_time": "2017-11-07T04:23:02.543671Z" } }, "outputs": [], "source": [ "pd.options.mode.chained_assignment = None\n", "pd.options.display.max_columns = 999\n", "color = sns.color_palette()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:23:20.650863Z", "start_time": "2017-11-07T04:23:02.547357Z" } }, "outputs": [], "source": [ "store = pd.HDFStore('../../data_prep/processed/stage1/data_frames.h5')\n", "train_df = store['train_df']\n", "test_df = store['test_df']" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:23:20.963869Z", "start_time": "2017-11-07T04:23:20.652089Z" } }, "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>ID</th>\n", " <th>Gene</th>\n", " <th>Variation</th>\n", " <th>Class</th>\n", " <th>Sentences</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>[fam58a]</td>\n", " <td>[truncating, mutations]</td>\n", " <td>1</td>\n", " <td>[[cyclin-dependent, kinases, , cdks, , regulat...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>[cbl]</td>\n", " <td>[w802*]</td>\n", " <td>2</td>\n", " <td>[[abstract, background, non-small, cell, lung,...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>[cbl]</td>\n", " <td>[q249e]</td>\n", " <td>2</td>\n", " <td>[[abstract, background, non-small, cell, lung,...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>[cbl]</td>\n", " <td>[n454d]</td>\n", " <td>3</td>\n", " <td>[[recent, evidence, has, demonstrated, that, a...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>[cbl]</td>\n", " <td>[l399v]</td>\n", " <td>4</td>\n", " <td>[[oncogenic, mutations, in, the, monomeric, ca...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ID Gene Variation Class \\\n", "0 0 [fam58a] [truncating, mutations] 1 \n", "1 1 [cbl] [w802*] 2 \n", "2 2 [cbl] [q249e] 2 \n", "3 3 [cbl] [n454d] 3 \n", "4 4 [cbl] [l399v] 4 \n", "\n", " Sentences \n", "0 [[cyclin-dependent, kinases, , cdks, , regulat... \n", "1 [[abstract, background, non-small, cell, lung,... \n", "2 [[abstract, background, non-small, cell, lung,... \n", "3 [[recent, evidence, has, demonstrated, that, a... \n", "4 [[oncogenic, mutations, in, the, monomeric, ca... 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<td>[l333f]</td>\n", " <td>[[vascular, endothelial, growth, factor, recep...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>[ing1]</td>\n", " <td>[a148d]</td>\n", " <td>[[inflammatory, myofibroblastic, tumor, , imt,...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>[tmem216]</td>\n", " <td>[g77a]</td>\n", " <td>[[abstract, retinoblastoma, is, a, pediatric, ...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ID Gene Variation Sentences\n", "0 0 [acsl4] [r570s] [[2, this, mutation, resulted, in, a, myelopro...\n", "1 1 [naglu] [p521l] [[abstract, the, large, tumor, suppressor, 1, ...\n", "2 2 [pah] [l333f] [[vascular, endothelial, growth, factor, recep...\n", "3 3 [ing1] [a148d] [[inflammatory, myofibroblastic, tumor, , imt,...\n", "4 4 [tmem216] [g77a] [[abstract, retinoblastoma, is, a, pediatric, ..." ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(train_df.head())\n", "display(test_df.head())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:23:21.066663Z", "start_time": "2017-11-07T04:23:20.965010Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "352220 352220\n" ] } ], "source": [ "corpus_vocab_list, corpus_vocab_wordidx = None, None\n", "with open('../../data_prep/processed/stage1/vocab_words_wordidx.pkl', 'rb') as f:\n", " (corpus_vocab_list, corpus_wordidx) = pickle.load(f)\n", "print(len(corpus_vocab_list), len(corpus_wordidx))" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2017-08-14T08:20:17.449244Z", "start_time": "2017-08-14T08:20:15.593136Z" }, "collapsed": true }, "source": [ "# Data Prep" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To control the vocabulary pass in updated corpus_wordidx" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:28:34.856451Z", "start_time": "2017-11-07T04:28:34.799259Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2988, 5)\n", "(333, 5)\n" ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", "x_train_df, x_val_df = train_test_split(train_df,\n", " test_size=0.10, random_state=random_state_number,\n", " stratify=train_df.Class)\n", "\n", "print(x_train_df.shape)\n", "print(x_val_df.shape)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:28:35.048380Z", "start_time": "2017-11-07T04:28:35.042006Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7 858\n", "4 617\n", "1 511\n", "2 407\n", "6 247\n", "5 218\n", "3 80\n", "9 33\n", "8 17\n", "Name: Class, dtype: int64\n", "7 95\n", "4 69\n", "1 57\n", "2 45\n", "6 28\n", "5 24\n", "3 9\n", "9 4\n", "8 2\n", "Name: Class, dtype: int64\n" ] } ], "source": [ "print(x_train_df.Class.value_counts())\n", "print(x_val_df.Class.value_counts())" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:28:35.287941Z", "start_time": "2017-11-07T04:28:35.284599Z" } }, "outputs": [], "source": [ "from tensorflow.contrib.keras.python.keras.utils import np_utils\n", "from keras.preprocessing.sequence import pad_sequences\n", "from keras.utils.np_utils import to_categorical" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:28:36.375218Z", "start_time": "2017-11-07T04:28:36.370582Z" } }, "outputs": [], "source": [ "vocab_size=len(corpus_vocab_list)" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## T:sent_words" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "### generate data" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2017-09-25T06:25:05.352459Z", "start_time": "2017-09-25T06:25:05.349721Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "custom_unit_dict = {\n", " \"gene_unit\" : \"words\",\n", " \"variation_unit\" : \"words\",\n", " # text transformed to sentences attribute\n", " \"doc_unit\" : \"words\",\n", " \"doc_form\" : \"sentences\",\n", " \"divide_document\": \"multiple_unit\"\n", " }" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2017-09-25T06:26:20.210322Z", "start_time": "2017-09-25T06:25:05.353617Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "%autoreload\n", "import global_utils\n", "gen_data = global_utils.GenerateDataset(x_train_df, corpus_wordidx)\n", "x_train_21_T, x_train_21_G, x_train_21_V, x_train_21_C = gen_data.generate_data(custom_unit_dict, \n", " has_class=True,\n", " add_start_end_tag=True)\n", "del gen_data" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2017-09-25T06:26:21.998936Z", "start_time": "2017-09-25T06:26:20.211666Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train data\n", "(1086419,) [352216, 252037, 202038, 70974, 86431, 164788, 109857, 338562, 123191, 209585, 221967, 49123, 331220, 140212, 209585, 229015, 140770, 182848, 111721, 8208, 0, 352217]\n", "(1086419, 3) [352216, 164788, 352217]\n", "(1086419,) [352216, 86196, 352217]\n", "(1086419,) 4\n" ] } ], "source": [ "print(\"Train data\")\n", "print(np.array(x_train_21_T).shape, x_train_21_T[0])\n", "print(np.array(x_train_21_G).shape, x_train_21_G[0])\n", "print(np.array(x_train_21_V).shape, x_train_21_V[0])\n", "print(np.array(x_train_21_C).shape, x_train_21_C[0])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2017-09-25T06:26:29.776970Z", "start_time": "2017-09-25T06:26:22.000201Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "gen_data = global_utils.GenerateDataset(x_val_df, corpus_wordidx)\n", "x_val_21_T, x_val_21_G, x_val_21_V, x_val_21_C = gen_data.generate_data(custom_unit_dict, \n", " has_class=True,\n", " add_start_end_tag=True)\n", "del gen_data" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2017-09-25T06:26:29.999447Z", "start_time": "2017-09-25T06:26:29.778292Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Val data\n", "text (128341,)\n", "gene (128341, 3) [352216, 217983, 352217]\n", "variation (128341,) [352216, 41934, 352217]\n", "classes (128341,) 4\n" ] } ], "source": [ "print(\"Val data\")\n", "print(\"text\",np.array(x_val_21_T).shape)\n", "print(\"gene\",np.array(x_val_21_G).shape, x_val_21_G[0])\n", "print(\"variation\",np.array(x_val_21_V).shape, x_val_21_V[0])\n", "print(\"classes\",np.array(x_val_21_C).shape, x_val_21_C[0])" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "### format data" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2017-09-25T06:26:30.003257Z", "start_time": "2017-09-25T06:26:30.000708Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "word_unknown_tag_idx = corpus_wordidx[\"<UNK>\"]\n", "char_unknown_tag_idx = global_utils.char_unknown_tag_idx" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2017-09-25T06:26:30.006725Z", "start_time": "2017-09-25T06:26:30.004479Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "MAX_SENT_LEN = 60" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2017-09-25T06:26:37.079748Z", "start_time": "2017-09-25T06:26:30.007947Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1086419, 60) (128341, 60)\n" ] } ], "source": [ "x_train_21_T = pad_sequences(x_train_21_T, maxlen=MAX_SENT_LEN, value=word_unknown_tag_idx,\n", " padding=\"post\",truncating=\"post\")\n", "x_val_21_T = pad_sequences(x_val_21_T, maxlen=MAX_SENT_LEN, value=word_unknown_tag_idx,\n", " padding=\"post\",truncating=\"post\")\n", "print(x_train_21_T.shape, x_val_21_T.shape)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "keras np_utils.to_categorical expects zero index categorical variables\n", "\n", "https://github.com/fchollet/keras/issues/570" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2017-09-25T06:26:37.170342Z", "start_time": "2017-09-25T06:26:37.081558Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "x_train_21_C = np.array(x_train_21_C) - 1\n", "x_val_21_C = np.array(x_val_21_C) - 1" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2017-09-25T06:26:37.196926Z", "start_time": "2017-09-25T06:26:37.171834Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1086419, 9) (128341, 9)\n" ] } ], "source": [ "x_train_21_C = np_utils.to_categorical(np.array(x_train_21_C), 9)\n", "x_val_21_C = np_utils.to_categorical(np.array(x_val_21_C), 9)\n", "print(x_train_21_C.shape, x_val_21_C.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## T:text_words" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### generate data" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:28:38.848466Z", "start_time": "2017-11-07T04:28:38.844619Z" } }, "outputs": [], "source": [ "custom_unit_dict = {\n", " \"gene_unit\" : \"words\",\n", " \"variation_unit\" : \"words\",\n", " # text transformed to sentences attribute\n", " \"doc_unit\" : \"words\",\n", " \"doc_form\" : \"text\",\n", " \"divide_document\": \"single_unit\"\n", " }" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:28:49.319904Z", "start_time": "2017-11-07T04:28:39.119185Z" } }, "outputs": [], "source": [ "%autoreload\n", "import global_utils\n", "gen_data = global_utils.GenerateDataset(x_train_df, corpus_wordidx)\n", "x_train_22_T, x_train_22_G, x_train_22_V, x_train_22_C = gen_data.generate_data(custom_unit_dict, \n", " has_class=True,\n", " add_start_end_tag=True)\n", "del gen_data" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:28:50.426965Z", "start_time": "2017-11-07T04:28:49.321123Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train data\n", "text (2988,)\n", "gene (2988, 3) [352216, 164788, 352217]\n", "variation (2988,) [352216, 86196, 352217]\n", "classes (2988,) 4\n", "[1 2 3 4 5 6 7 8 9]\n" ] } ], "source": [ "print(\"Train data\")\n", "print(\"text\",np.array(x_train_22_T).shape)\n", "print(\"gene\",np.array(x_train_22_G).shape, x_train_22_G[0])\n", "print(\"variation\",np.array(x_train_22_V).shape, x_train_22_V[0])\n", "print(\"classes\",np.array(x_train_22_C).shape, x_train_22_C[0])\n", "print(unique(x_train_22_C))" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:28:51.909055Z", "start_time": "2017-11-07T04:28:50.428145Z" } }, "outputs": [], "source": [ "gen_data = global_utils.GenerateDataset(x_val_df, corpus_wordidx)\n", "x_val_22_T, x_val_22_G, x_val_22_V, x_val_22_C = gen_data.generate_data(custom_unit_dict, \n", " has_class=True,\n", " add_start_end_tag=True)\n", "del gen_data" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:28:52.047669Z", "start_time": "2017-11-07T04:28:51.910177Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Val data\n", "text (333,)\n", "gene (333, 3) [352216, 217983, 352217]\n", "variation (333,) [352216, 41934, 352217]\n", "classes (333,) 4\n", "[1 2 3 4 5 6 7 8 9]\n" ] } ], "source": [ "print(\"Val data\")\n", "print(\"text\",np.array(x_val_22_T).shape)\n", "print(\"gene\",np.array(x_val_22_G).shape, x_val_22_G[0])\n", "print(\"variation\",np.array(x_val_22_V).shape, x_val_22_V[0])\n", "print(\"classes\",np.array(x_val_22_C).shape, x_val_22_C[0])\n", "print(unique(x_val_22_C))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### format data" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:28:52.052729Z", "start_time": "2017-11-07T04:28:52.049256Z" } }, "outputs": [], "source": [ "word_unknown_tag_idx = corpus_wordidx[\"<UNK>\"]\n", "char_unknown_tag_idx = global_utils.char_unknown_tag_idx" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:28:52.057420Z", "start_time": "2017-11-07T04:28:52.054149Z" } }, "outputs": [], "source": [ "MAX_TEXT_LEN = 5000" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:28:53.193409Z", "start_time": "2017-11-07T04:28:52.058563Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2988, 5000) (333, 5000)\n" ] } ], "source": [ "x_train_22_T = pad_sequences(x_train_22_T, maxlen=MAX_TEXT_LEN, value=word_unknown_tag_idx,\n", " padding=\"post\",truncating=\"post\")\n", "x_val_22_T = pad_sequences(x_val_22_T, maxlen=MAX_TEXT_LEN, value=word_unknown_tag_idx,\n", " padding=\"post\",truncating=\"post\")\n", "print(x_train_22_T.shape, x_val_22_T.shape)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:28:53.238060Z", "start_time": "2017-11-07T04:28:53.194569Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2988, 1) (2988, 4)\n", "(333, 1) (333, 4)\n" ] } ], "source": [ "MAX_GENE_LEN = 1\n", "MAX_VAR_LEN = 4\n", "x_train_22_G = pad_sequences(x_train_22_G, maxlen=MAX_GENE_LEN, value=word_unknown_tag_idx)\n", "x_train_22_V = pad_sequences(x_train_22_V, maxlen=MAX_VAR_LEN, value=word_unknown_tag_idx)\n", "\n", "x_val_22_G = pad_sequences(x_val_22_G, maxlen=MAX_GENE_LEN, value=word_unknown_tag_idx)\n", "x_val_22_V = pad_sequences(x_val_22_V, maxlen=MAX_VAR_LEN, value=word_unknown_tag_idx)\n", "\n", "print(x_train_22_G.shape, x_train_22_V.shape)\n", "print(x_val_22_G.shape, x_val_22_V.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "keras np_utils.to_categorical expects zero index categorical variables\n", "\n", "https://github.com/fchollet/keras/issues/570" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:28:53.245708Z", "start_time": "2017-11-07T04:28:53.239272Z" } }, "outputs": [], "source": [ "x_train_22_C = np.array(x_train_22_C) - 1\n", "x_val_22_C = np.array(x_val_22_C) - 1\n", "x_train_22_Cp = x_train_22_C\n", "x_val_22_Cp = x_val_22_C" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:28:53.251889Z", "start_time": "2017-11-07T04:28:53.246846Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1 2 3 4 5 6 7 8]\n", "[0 1 2 3 4 5 6 7 8]\n" ] } ], "source": [ "print(unique(x_val_22_Cp))\n", "print(unique(x_train_22_Cp))" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:28:53.257067Z", "start_time": "2017-11-07T04:28:53.253172Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2988, 9) (333, 9)\n" ] } ], "source": [ "x_train_22_C = np_utils.to_categorical(np.array(x_train_22_C), 9)\n", "x_val_22_C = np_utils.to_categorical(np.array(x_val_22_C), 9)\n", "print(x_train_22_C.shape, x_val_22_C.shape)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:28:53.261836Z", "start_time": "2017-11-07T04:28:53.258154Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 1.]\n", "[ 0. 1.]\n" ] } ], "source": [ "print(unique(x_train_22_C))\n", "print(unique(x_val_22_C))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CV setup" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:23:32.726283Z", "start_time": "2017-11-07T04:23:21.071774Z" } }, "outputs": [], "source": [ "gen_data = global_utils.GenerateDataset(train_df, corpus_wordidx)\n", "x_22_T, x_22_G, x_22_V, x_22_C = gen_data.generate_data(custom_unit_dict,\n", " has_class=True,\n", " add_start_end_tag=True)\n", "del gen_data" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:25:24.842796Z", "start_time": "2017-11-07T04:25:23.654056Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(3321, 5000)\n" ] } ], "source": [ "x_22_T = pad_sequences(x_22_T, maxlen=MAX_TEXT_LEN, value=word_unknown_tag_idx,\n", " padding=\"post\",truncating=\"post\")\n", "print(x_22_T.shape)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:25:24.846580Z", "start_time": "2017-11-07T04:25:24.843851Z" } }, "outputs": [], "source": [ "x_22_C = np.array(x_22_C) - 1\n", "x_22_Cp = x_22_C" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:25:24.855164Z", "start_time": "2017-11-07T04:25:24.847722Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(3321, 9)\n" ] } ], "source": [ "x_22_C = np_utils.to_categorical(np.array(x_22_C), 9)\n", "print(x_22_C.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### test Data setup" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:25:44.190647Z", "start_time": "2017-11-07T04:25:25.794302Z" } }, "outputs": [], "source": [ "gen_data = global_utils.GenerateDataset(test_df, corpus_wordidx)\n", "x_test_22_T, x_test_22_G, x_test_22_V, _ = gen_data.generate_data(custom_unit_dict, \n", " has_class=False,\n", " add_start_end_tag=True)\n", "del gen_data" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:25:46.090854Z", "start_time": "2017-11-07T04:25:44.191806Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test data\n", "text (5668,)\n", "gene (5668, 3) [352216, 136191, 352217]\n", "variation (5668,) [352216, 327792, 352217]\n" ] } ], "source": [ "print(\"Test data\")\n", "print(\"text\",np.array(x_test_22_T).shape)\n", "print(\"gene\",np.array(x_test_22_G).shape, x_test_22_G[0])\n", "print(\"variation\",np.array(x_test_22_V).shape, x_test_22_V[0])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:25:48.069601Z", "start_time": "2017-11-07T04:25:46.091946Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(5668, 5000)\n" ] } ], "source": [ "x_test_22_T = pad_sequences(x_test_22_T, maxlen=MAX_TEXT_LEN, value=word_unknown_tag_idx,\n", " padding=\"post\",truncating=\"post\")\n", "print(x_test_22_T.shape)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:25:48.138294Z", "start_time": "2017-11-07T04:25:48.070916Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(5668, 1) (5668, 4)\n" ] } ], "source": [ "MAX_GENE_LEN = 1\n", "MAX_VAR_LEN = 4\n", "x_test_22_G = pad_sequences(x_test_22_G, maxlen=MAX_GENE_LEN, value=word_unknown_tag_idx)\n", "x_test_22_V = pad_sequences(x_test_22_V, maxlen=MAX_VAR_LEN, value=word_unknown_tag_idx)\n", "\n", "print(x_test_22_G.shape, x_test_22_V.shape)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:25:48.158390Z", "start_time": "2017-11-07T04:25:48.140054Z" } }, "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>ID</th>\n", " <th>class1</th>\n", " <th>class2</th>\n", " <th>class3</th>\n", " <th>class4</th>\n", " <th>class5</th>\n", " <th>class6</th>\n", " <th>class7</th>\n", " <th>class8</th>\n", " <th>class9</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ID class1 class2 class3 class4 class5 class6 class7 class8 class9\n", "0 0 0 0 1 0 0 0 0 0 0\n", "1 1 0 0 0 0 0 0 0 0 1\n", "2 2 0 0 0 0 0 0 1 0 0\n", "3 3 0 1 0 0 0 0 0 0 0\n", "4 4 0 0 0 0 0 0 0 0 1" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s1_solution_df = pd.read_csv(\"../../data_prep/dataset/stage2/stage1_solution.csv\")\n", "s1_solution_df.head()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:25:48.175595Z", "start_time": "2017-11-07T04:25:48.159655Z" } }, "outputs": [], "source": [ "test_matrix = s1_solution_df[['class1', 'class2', 'class3', 'class4', 'class5', 'class6', 'class7', 'class8', 'class9']]\n", "x_test_22_Cp = [np.where(r==1)[0][0] for r in test_matrix.values ]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:25:48.181387Z", "start_time": "2017-11-07T04:25:48.176757Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(5668, 9)\n" ] } ], "source": [ "x_test_22_C = np.array(x_test_22_Cp) - 1\n", "x_test_22_C = np_utils.to_categorical(np.array(x_test_22_C), 9)\n", "print(x_test_22_C.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Embedding layer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### for words" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:25:48.184796Z", "start_time": "2017-11-07T04:25:48.182558Z" } }, "outputs": [], "source": [ "WORD_EMB_SIZE = 200" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:26:11.234716Z", "start_time": "2017-11-07T04:25:48.185918Z" } }, "outputs": [ { "data": { "text/plain": [ "(352220, 200)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%autoreload\n", "import global_utils\n", "ft_file_path = \"/home/bicepjai/Projects/Deep-Survey-Text-Classification/data_prep/processed/stage1/pretrained_word_vectors/ft_sg_200d_50e.vec\"\n", "trained_embeddings = global_utils.get_embeddings_from_ft(ft_file_path, WORD_EMB_SIZE, corpus_vocab_list)\n", "trained_embeddings.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### for characters" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:26:11.237544Z", "start_time": "2017-11-07T04:26:11.235777Z" } }, "outputs": [], "source": [ "CHAR_EMB_SIZE = 100" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:26:11.246607Z", "start_time": "2017-11-07T04:26:11.238506Z" } }, "outputs": [ { "data": { "text/plain": [ "(75, 100)" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "char_embeddings = np.random.randn(global_utils.CHAR_ALPHABETS_LEN, CHAR_EMB_SIZE)\n", "char_embeddings.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## prep" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:26:11.281792Z", "start_time": "2017-11-07T04:26:11.247791Z" } }, "outputs": [], "source": [ "import tensorflow.contrib.keras as keras\n", "import tensorflow as tf\n", "\n", "from keras import backend as K\n", "\n", "from keras.engine import Layer, InputSpec, InputLayer\n", "\n", "from keras.models import Model, Sequential\n", "\n", "from keras.layers import Dropout, Embedding, concatenate\n", "from keras.layers import Conv1D, MaxPool1D, Conv2D, MaxPool2D, ZeroPadding1D\n", "from keras.layers import Dense, Input, Flatten, BatchNormalization\n", "from keras.layers import Concatenate, Dot, Merge, Multiply, RepeatVector\n", "from keras.layers import Bidirectional, TimeDistributed\n", "from keras.layers import SimpleRNN, LSTM, GRU, Lambda, Permute\n", "\n", "from keras.layers.core import Reshape, Activation\n", "from keras.optimizers import Adam\n", "from keras.callbacks import ModelCheckpoint,EarlyStopping,TensorBoard\n", "from keras.constraints import maxnorm\n", "from keras.regularizers import l2\n", "\n", "%autoreload" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2017-08-24T06:58:17.661183Z", "start_time": "2017-08-24T06:58:17.655020Z" } }, "source": [ "## model_1: paper" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T03:33:20.907296Z", "start_time": "2017-11-07T03:33:20.245039Z" } }, "outputs": [], "source": [ "text_seq_input = Input(shape=(MAX_TEXT_LEN,), dtype='int32')\n", "text_embedding = Embedding(vocab_size, WORD_EMB_SIZE, input_length=MAX_TEXT_LEN,\n", " weights=[trained_embeddings], trainable=True)(text_seq_input)\n", "\n", "filter_sizes = [3,4,5]\n", "convs = []\n", "for filter_size in filter_sizes:\n", " l_conv = Conv1D(filters=128, kernel_size=filter_size, padding='same', activation='relu')(text_embedding)\n", " l_pool = MaxPool1D(filter_size)(l_conv)\n", " convs.append(l_pool)\n", "\n", "l_merge = Concatenate(axis=1)(convs)\n", "l_cov1= Conv1D(128, 5, activation='relu')(l_merge)\n", "# since the text is too long we are maxpooling over 100\n", "# and not GlobalMaxPool1D\n", "l_pool1 = MaxPool1D(100)(l_cov1)\n", "l_flat = Flatten()(l_pool1)\n", "l_dense = Dense(128, activation='relu')(l_flat)\n", "l_out = Dense(9, activation='softmax')(l_dense)\n", "model_1 = Model(inputs=[text_seq_input], outputs=l_out)\n" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T03:33:20.935035Z", "start_time": "2017-11-07T03:33:20.908462Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "____________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "====================================================================================================\n", "input_1 (InputLayer) (None, 5000) 0 \n", "____________________________________________________________________________________________________\n", "embedding_1 (Embedding) (None, 5000, 200) 70444000 input_1[0][0] \n", "____________________________________________________________________________________________________\n", "conv1d_1 (Conv1D) (None, 5000, 128) 76928 embedding_1[0][0] \n", "____________________________________________________________________________________________________\n", "conv1d_2 (Conv1D) (None, 5000, 128) 102528 embedding_1[0][0] \n", "____________________________________________________________________________________________________\n", "conv1d_3 (Conv1D) (None, 5000, 128) 128128 embedding_1[0][0] \n", "____________________________________________________________________________________________________\n", "max_pooling1d_1 (MaxPooling1D) (None, 1666, 128) 0 conv1d_1[0][0] \n", "____________________________________________________________________________________________________\n", "max_pooling1d_2 (MaxPooling1D) (None, 1250, 128) 0 conv1d_2[0][0] \n", "____________________________________________________________________________________________________\n", "max_pooling1d_3 (MaxPooling1D) (None, 1000, 128) 0 conv1d_3[0][0] \n", "____________________________________________________________________________________________________\n", "concatenate_1 (Concatenate) (None, 3916, 128) 0 max_pooling1d_1[0][0] \n", " max_pooling1d_2[0][0] \n", " max_pooling1d_3[0][0] \n", "____________________________________________________________________________________________________\n", "conv1d_4 (Conv1D) (None, 3912, 128) 82048 concatenate_1[0][0] \n", "____________________________________________________________________________________________________\n", "max_pooling1d_4 (MaxPooling1D) (None, 39, 128) 0 conv1d_4[0][0] \n", "____________________________________________________________________________________________________\n", "flatten_1 (Flatten) (None, 4992) 0 max_pooling1d_4[0][0] \n", "____________________________________________________________________________________________________\n", "dense_1 (Dense) (None, 128) 639104 flatten_1[0][0] \n", "____________________________________________________________________________________________________\n", "dense_2 (Dense) (None, 9) 1161 dense_1[0][0] \n", "====================================================================================================\n", "Total params: 71,473,897\n", "Trainable params: 71,473,897\n", "Non-trainable params: 0\n", "____________________________________________________________________________________________________\n" ] } ], "source": [ "model_1.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['categorical_accuracy'])\n", "model_1.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### training" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T03:37:04.282110Z", "start_time": "2017-11-07T03:37:04.277386Z" } }, "outputs": [], "source": [ "tb_callback = keras.callbacks.TensorBoard(log_dir='./tb_graphs', histogram_freq=0, write_graph=True, write_images=True)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T03:37:04.853479Z", "start_time": "2017-11-07T03:37:04.849453Z" } }, "outputs": [], "source": [ "checkpointer = ModelCheckpoint(filepath=\"model_1_weights.hdf5\", \n", " verbose=1,\n", " monitor=\"val_categorical_accuracy\",\n", " save_best_only=True,\n", " mode=\"max\")" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T03:37:05.162020Z", "start_time": "2017-11-07T03:37:05.158745Z" } }, "outputs": [], "source": [ "earlystopping = EarlyStopping(monitor='val_categorical_accuracy', \n", " min_delta=0, patience=5, \n", " verbose=0, mode='auto')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### no CV" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T03:13:56.094108Z", "start_time": "2017-11-07T03:12:07.772842Z" }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "no checkpoints available !\n", "Train on 2988 samples, validate on 333 samples\n", "Epoch 1/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 1.7705 - categorical_accuracy: 0.3346Epoch 00000: val_categorical_accuracy improved from -inf to 0.45045, saving model to model_1_weights.hdf5\n", "2988/2988 [==============================] - 10s - loss: 1.7673 - categorical_accuracy: 0.3377 - val_loss: 1.6119 - val_categorical_accuracy: 0.4505\n", "Epoch 2/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 1.3092 - categorical_accuracy: 0.5673Epoch 00001: val_categorical_accuracy improved from 0.45045 to 0.58859, saving model to model_1_weights.hdf5\n", "2988/2988 [==============================] - 11s - loss: 1.3063 - categorical_accuracy: 0.5683 - val_loss: 1.3341 - val_categorical_accuracy: 0.5886\n", "Epoch 3/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.9138 - categorical_accuracy: 0.7272Epoch 00002: val_categorical_accuracy improved from 0.58859 to 0.60961, saving model to model_1_weights.hdf5\n", "2988/2988 [==============================] - 11s - loss: 0.9135 - categorical_accuracy: 0.7269 - val_loss: 1.2460 - val_categorical_accuracy: 0.6096\n", "Epoch 4/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.7357 - categorical_accuracy: 0.7514Epoch 00003: val_categorical_accuracy did not improve\n", "2988/2988 [==============================] - 9s - loss: 0.7339 - categorical_accuracy: 0.7517 - val_loss: 1.2656 - val_categorical_accuracy: 0.5946\n", "Epoch 5/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.6520 - categorical_accuracy: 0.7622Epoch 00004: val_categorical_accuracy improved from 0.60961 to 0.62763, saving model to model_1_weights.hdf5\n", "2988/2988 [==============================] - 11s - loss: 0.6537 - categorical_accuracy: 0.7610 - val_loss: 1.2111 - val_categorical_accuracy: 0.6276\n", "Epoch 6/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.6101 - categorical_accuracy: 0.7741Epoch 00005: val_categorical_accuracy did not improve\n", "2988/2988 [==============================] - 9s - loss: 0.6103 - categorical_accuracy: 0.7741 - val_loss: 1.2082 - val_categorical_accuracy: 0.6066\n", "Epoch 7/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.5625 - categorical_accuracy: 0.7796Epoch 00006: val_categorical_accuracy did not improve\n", "2988/2988 [==============================] - 9s - loss: 0.5627 - categorical_accuracy: 0.7798 - val_loss: 1.1969 - val_categorical_accuracy: 0.5886\n", "Epoch 8/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.5450 - categorical_accuracy: 0.7846Epoch 00007: val_categorical_accuracy improved from 0.62763 to 0.62763, saving model to model_1_weights.hdf5\n", "2988/2988 [==============================] - 11s - loss: 0.5466 - categorical_accuracy: 0.7845 - val_loss: 1.2106 - val_categorical_accuracy: 0.6276\n", "Epoch 9/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.5325 - categorical_accuracy: 0.7846Epoch 00008: val_categorical_accuracy did not improve\n", "2988/2988 [==============================] - 9s - loss: 0.5313 - categorical_accuracy: 0.7855 - val_loss: 1.2050 - val_categorical_accuracy: 0.6036\n", "Epoch 10/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.5120 - categorical_accuracy: 0.7833Epoch 00009: val_categorical_accuracy did not improve\n", "2988/2988 [==============================] - 9s - loss: 0.5108 - categorical_accuracy: 0.7841 - val_loss: 1.2568 - val_categorical_accuracy: 0.6066\n" ] } ], "source": [ "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " try:\n", " model_1.load_weights(\"model_11_weights.hdf5\")\n", " except IOError as ioe:\n", " print(\"no checkpoints available !\")\n", " \n", " model_1.fit(x_train_22_T, x_train_22_C, \n", " validation_data=(x_val_22_T, x_val_22_C),\n", " epochs=10, batch_size=64,shuffle=True,\n", " callbacks=[tb_callback,checkpointer])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### prediction" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T03:14:52.800428Z", "start_time": "2017-11-07T03:14:47.627602Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 67 28 2 54 16 9 245 0 1]\n", " [ 85 71 2 87 8 13 473 0 2]\n", " [ 76 48 3 64 8 6 441 0 4]\n", " [ 79 57 1 101 16 9 447 0 1]\n", " [ 77 52 1 70 27 10 451 0 0]\n", " [ 90 56 2 65 14 21 454 0 6]\n", " [ 77 75 2 56 19 7 503 0 0]\n", " [ 72 47 3 72 11 6 456 0 2]\n", " [ 24 32 0 32 10 4 232 0 6]]\n", " precision recall f1-score support\n", "\n", " 0 0.10 0.16 0.13 422\n", " 1 0.15 0.10 0.12 741\n", " 2 0.19 0.00 0.01 650\n", " 3 0.17 0.14 0.15 711\n", " 4 0.21 0.04 0.07 688\n", " 5 0.25 0.03 0.05 708\n", " 6 0.14 0.68 0.23 739\n", " 7 0.00 0.00 0.00 669\n", " 8 0.27 0.02 0.03 340\n", "\n", "avg / total 0.16 0.14 0.09 5668\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/bicepjai/Programs/anaconda3/envs/dsotc-c3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: 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": [ "from sklearn.metrics import classification_report,confusion_matrix\n", "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " try:\n", " model_1.load_weights(\"model_11_weights.hdf5\")\n", " except IOError as ioe:\n", " print(\"no checkpoints available !\")\n", " \n", " y_pred = model_1.predict(x_test_22_T)\n", " y_classes = y_pred.argmax(axis=-1)\n", "\n", "print(confusion_matrix(x_test_22_Cp, y_classes))\n", "print(classification_report(x_test_22_Cp, y_classes))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CV" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T03:37:11.850736Z", "start_time": "2017-11-07T03:37:11.846710Z" } }, "outputs": [], "source": [ "from sklearn.model_selection import StratifiedKFold\n", "from sklearn.metrics import accuracy_score" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:04:33.606246Z", "start_time": "2017-11-07T04:04:33.602367Z" } }, "outputs": [], "source": [ "checkpointer = ModelCheckpoint(filepath=\"model_12_chk.hdf5\", \n", " verbose=1,\n", " monitor=\"val_categorical_accuracy\",\n", " save_best_only=True,\n", " mode=\"max\")" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:12:46.464990Z", "start_time": "2017-11-07T04:04:49.007880Z" }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "fold 0 =*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", "no checkpoints available !\n", "Train on 2653 samples, validate on 668 samples\n", "Epoch 1/10\n", "2624/2653 [============================>.] - ETA: 0s - loss: 1.7498 - categorical_accuracy: 0.3453Epoch 00000: val_categorical_accuracy improved from -inf to 0.52545, saving model to model_12_chk.hdf5\n", "2653/2653 [==============================] - 11s - loss: 1.7488 - categorical_accuracy: 0.3464 - val_loss: 1.6020 - val_categorical_accuracy: 0.5254\n", "Epoch 2/10\n", "2624/2653 [============================>.] - ETA: 0s - loss: 1.2979 - categorical_accuracy: 0.5945Epoch 00001: val_categorical_accuracy improved from 0.52545 to 0.54790, saving model to model_12_chk.hdf5\n", "2653/2653 [==============================] - 10s - loss: 1.2945 - categorical_accuracy: 0.5956 - val_loss: 1.3016 - val_categorical_accuracy: 0.5479\n", "Epoch 3/10\n", "2624/2653 [============================>.] - ETA: 0s - loss: 0.8691 - categorical_accuracy: 0.7412Epoch 00002: val_categorical_accuracy did not improve\n", "2653/2653 [==============================] - 9s - loss: 0.8735 - categorical_accuracy: 0.7395 - val_loss: 1.2846 - val_categorical_accuracy: 0.4970\n", "Epoch 4/10\n", "2624/2653 [============================>.] - ETA: 0s - loss: 0.7099 - categorical_accuracy: 0.7626Epoch 00003: val_categorical_accuracy did not improve\n", "2653/2653 [==============================] - 9s - loss: 0.7130 - categorical_accuracy: 0.7614 - val_loss: 1.2404 - val_categorical_accuracy: 0.5434\n", "Epoch 5/10\n", "2624/2653 [============================>.] - ETA: 0s - loss: 0.6389 - categorical_accuracy: 0.7748Epoch 00004: val_categorical_accuracy improved from 0.54790 to 0.56886, saving model to model_12_chk.hdf5\n", "2653/2653 [==============================] - 10s - loss: 0.6434 - categorical_accuracy: 0.7731 - val_loss: 1.3267 - val_categorical_accuracy: 0.5689\n", "Epoch 6/10\n", "2624/2653 [============================>.] - ETA: 0s - loss: 0.6217 - categorical_accuracy: 0.7790Epoch 00005: val_categorical_accuracy did not improve\n", "2653/2653 [==============================] - 9s - loss: 0.6245 - categorical_accuracy: 0.7776 - val_loss: 1.2580 - val_categorical_accuracy: 0.5479\n", "Epoch 7/10\n", "2624/2653 [============================>.] - ETA: 0s - loss: 0.5589 - categorical_accuracy: 0.7889Epoch 00006: val_categorical_accuracy did not improve\n", "2653/2653 [==============================] - 9s - loss: 0.5592 - categorical_accuracy: 0.7874 - val_loss: 1.2553 - val_categorical_accuracy: 0.5659\n", "Epoch 8/10\n", "2624/2653 [============================>.] - ETA: 0s - loss: 0.5459 - categorical_accuracy: 0.7851Epoch 00007: val_categorical_accuracy improved from 0.56886 to 0.57036, saving model to model_12_chk.hdf5\n", "2653/2653 [==============================] - 11s - loss: 0.5463 - categorical_accuracy: 0.7851 - val_loss: 1.2700 - val_categorical_accuracy: 0.5704\n", "Epoch 9/10\n", "2624/2653 [============================>.] - ETA: 0s - loss: 0.5181 - categorical_accuracy: 0.7915Epoch 00008: val_categorical_accuracy did not improve\n", "2653/2653 [==============================] - 9s - loss: 0.5194 - categorical_accuracy: 0.7912 - val_loss: 1.2886 - val_categorical_accuracy: 0.5359\n", "Epoch 10/10\n", "2624/2653 [============================>.] - ETA: 0s - loss: 0.5034 - categorical_accuracy: 0.7976Epoch 00009: val_categorical_accuracy improved from 0.57036 to 0.57485, saving model to model_12_chk.hdf5\n", "2653/2653 [==============================] - 10s - loss: 0.5020 - categorical_accuracy: 0.7980 - val_loss: 1.2252 - val_categorical_accuracy: 0.5749\n", "fold 1 =*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", "Train on 2654 samples, validate on 667 samples\n", "Epoch 1/10\n", "2624/2654 [============================>.] - ETA: 0s - loss: 0.6851 - categorical_accuracy: 0.7405Epoch 00000: val_categorical_accuracy improved from 0.57485 to 0.78561, saving model to model_12_chk.hdf5\n", "2654/2654 [==============================] - 11s - loss: 0.6853 - categorical_accuracy: 0.7408 - val_loss: 0.5595 - val_categorical_accuracy: 0.7856\n", "Epoch 2/10\n", "2624/2654 [============================>.] - ETA: 0s - loss: 0.5265 - categorical_accuracy: 0.7950Epoch 00001: val_categorical_accuracy did not improve\n", "2654/2654 [==============================] - 9s - loss: 0.5299 - categorical_accuracy: 0.7935 - val_loss: 0.6224 - val_categorical_accuracy: 0.7511\n", "Epoch 3/10\n", "2624/2654 [============================>.] - ETA: 0s - loss: 0.4930 - categorical_accuracy: 0.7995Epoch 00002: val_categorical_accuracy did not improve\n", "2654/2654 [==============================] - 9s - loss: 0.4968 - categorical_accuracy: 0.7977 - val_loss: 0.6817 - val_categorical_accuracy: 0.7496\n", "Epoch 4/10\n", "2624/2654 [============================>.] - ETA: 0s - loss: 0.4862 - categorical_accuracy: 0.7931Epoch 00003: val_categorical_accuracy did not improve\n", "2654/2654 [==============================] - 9s - loss: 0.4863 - categorical_accuracy: 0.7935 - val_loss: 0.7085 - val_categorical_accuracy: 0.7511\n", "Epoch 5/10\n", "2624/2654 [============================>.] - ETA: 0s - loss: 0.4636 - categorical_accuracy: 0.8095Epoch 00004: val_categorical_accuracy did not improve\n", "2654/2654 [==============================] - 9s - loss: 0.4645 - categorical_accuracy: 0.8097 - val_loss: 0.7526 - val_categorical_accuracy: 0.7556\n", "Epoch 6/10\n", "2624/2654 [============================>.] - ETA: 0s - loss: 0.4448 - categorical_accuracy: 0.8018Epoch 00005: val_categorical_accuracy did not improve\n", "2654/2654 [==============================] - 9s - loss: 0.4436 - categorical_accuracy: 0.8026 - val_loss: 0.7705 - val_categorical_accuracy: 0.7616\n", "Epoch 7/10\n", "2624/2654 [============================>.] - ETA: 0s - loss: 0.4425 - categorical_accuracy: 0.8079Epoch 00006: val_categorical_accuracy did not improve\n", "2654/2654 [==============================] - 9s - loss: 0.4410 - categorical_accuracy: 0.8090 - val_loss: 0.7882 - val_categorical_accuracy: 0.7496\n", "Epoch 8/10\n", "2624/2654 [============================>.] - ETA: 0s - loss: 0.4312 - categorical_accuracy: 0.8072Epoch 00007: val_categorical_accuracy did not improve\n", "2654/2654 [==============================] - 9s - loss: 0.4311 - categorical_accuracy: 0.8078 - val_loss: 0.7901 - val_categorical_accuracy: 0.7646\n", "Epoch 9/10\n", "2624/2654 [============================>.] - ETA: 0s - loss: 0.4216 - categorical_accuracy: 0.8095Epoch 00008: val_categorical_accuracy did not improve\n", "2654/2654 [==============================] - 9s - loss: 0.4239 - categorical_accuracy: 0.8082 - val_loss: 0.8149 - val_categorical_accuracy: 0.7631\n", "Epoch 10/10\n", "2624/2654 [============================>.] - ETA: 0s - loss: 0.4052 - categorical_accuracy: 0.8148Epoch 00009: val_categorical_accuracy did not improve\n", "2654/2654 [==============================] - 9s - loss: 0.4104 - categorical_accuracy: 0.8131 - val_loss: 0.8808 - val_categorical_accuracy: 0.7361\n", "fold 2 =*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", "Train on 2657 samples, validate on 664 samples\n", "Epoch 1/10\n", "2624/2657 [============================>.] - ETA: 0s - loss: 0.5289 - categorical_accuracy: 0.7919Epoch 00000: val_categorical_accuracy improved from 0.78561 to 0.80271, saving model to model_12_chk.hdf5\n", "2657/2657 [==============================] - 10s - loss: 0.5288 - categorical_accuracy: 0.7919 - val_loss: 0.4491 - val_categorical_accuracy: 0.8027\n", "Epoch 2/10\n", "2624/2657 [============================>.] - ETA: 0s - loss: 0.4494 - categorical_accuracy: 0.8011Epoch 00001: val_categorical_accuracy did not improve\n", "2657/2657 [==============================] - 9s - loss: 0.4493 - categorical_accuracy: 0.8017 - val_loss: 0.4931 - val_categorical_accuracy: 0.7892\n", "Epoch 3/10\n", "2624/2657 [============================>.] - ETA: 0s - loss: 0.4281 - categorical_accuracy: 0.8011Epoch 00002: val_categorical_accuracy did not improve\n", "2657/2657 [==============================] - 9s - loss: 0.4278 - categorical_accuracy: 0.8009 - val_loss: 0.5374 - val_categorical_accuracy: 0.7696\n", "Epoch 4/10\n", "2624/2657 [============================>.] - ETA: 0s - loss: 0.4234 - categorical_accuracy: 0.8121Epoch 00003: val_categorical_accuracy did not improve\n", "2657/2657 [==============================] - 9s - loss: 0.4242 - categorical_accuracy: 0.8114 - val_loss: 0.6054 - val_categorical_accuracy: 0.7741\n", "Epoch 5/10\n", "2624/2657 [============================>.] - ETA: 0s - loss: 0.4132 - categorical_accuracy: 0.8037Epoch 00004: val_categorical_accuracy did not improve\n", "2657/2657 [==============================] - 9s - loss: 0.4132 - categorical_accuracy: 0.8035 - val_loss: 0.6082 - val_categorical_accuracy: 0.7500\n", "Epoch 6/10\n", "2624/2657 [============================>.] - ETA: 0s - loss: 0.4010 - categorical_accuracy: 0.8152Epoch 00005: val_categorical_accuracy did not improve\n", "2657/2657 [==============================] - 9s - loss: 0.4010 - categorical_accuracy: 0.8148 - val_loss: 0.6385 - val_categorical_accuracy: 0.7786\n", "Epoch 7/10\n", "2624/2657 [============================>.] - ETA: 0s - loss: 0.4009 - categorical_accuracy: 0.8129Epoch 00006: val_categorical_accuracy did not improve\n", "2657/2657 [==============================] - 9s - loss: 0.4009 - categorical_accuracy: 0.8133 - val_loss: 0.6459 - val_categorical_accuracy: 0.7681\n", "Epoch 8/10\n", "2624/2657 [============================>.] - ETA: 0s - loss: 0.3928 - categorical_accuracy: 0.8091Epoch 00007: val_categorical_accuracy did not improve\n", "2657/2657 [==============================] - 9s - loss: 0.3949 - categorical_accuracy: 0.8096 - val_loss: 0.7110 - val_categorical_accuracy: 0.7696\n", "Epoch 9/10\n", "2624/2657 [============================>.] - ETA: 0s - loss: 0.3875 - categorical_accuracy: 0.8095Epoch 00008: val_categorical_accuracy did not improve\n", "2657/2657 [==============================] - 9s - loss: 0.3863 - categorical_accuracy: 0.8103 - val_loss: 0.6911 - val_categorical_accuracy: 0.7711\n", "Epoch 10/10\n", "2624/2657 [============================>.] - ETA: 0s - loss: 0.3828 - categorical_accuracy: 0.8148Epoch 00009: val_categorical_accuracy did not improve\n", "2657/2657 [==============================] - 9s - loss: 0.3812 - categorical_accuracy: 0.8160 - val_loss: 0.7392 - val_categorical_accuracy: 0.7741\n", "fold 3 =*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", "Train on 2659 samples, validate on 662 samples\n", "Epoch 1/10\n", "2624/2659 [============================>.] - ETA: 0s - loss: 0.4776 - categorical_accuracy: 0.8079Epoch 00000: val_categorical_accuracy improved from 0.80271 to 0.81269, saving model to model_12_chk.hdf5\n", "2659/2659 [==============================] - 10s - loss: 0.4764 - categorical_accuracy: 0.8074 - val_loss: 0.4080 - val_categorical_accuracy: 0.8127\n", "Epoch 2/10\n", "2624/2659 [============================>.] - ETA: 0s - loss: 0.4081 - categorical_accuracy: 0.8213Epoch 00001: val_categorical_accuracy did not improve\n", "2659/2659 [==============================] - 9s - loss: 0.4068 - categorical_accuracy: 0.8225 - val_loss: 0.4649 - val_categorical_accuracy: 0.7915\n", "Epoch 3/10\n", "2624/2659 [============================>.] - ETA: 0s - loss: 0.3904 - categorical_accuracy: 0.8186Epoch 00002: val_categorical_accuracy did not improve\n", "2659/2659 [==============================] - 9s - loss: 0.3909 - categorical_accuracy: 0.8184 - val_loss: 0.5519 - val_categorical_accuracy: 0.7855\n", "Epoch 4/10\n", "2624/2659 [============================>.] - ETA: 0s - loss: 0.3860 - categorical_accuracy: 0.8117Epoch 00003: val_categorical_accuracy did not improve\n", "2659/2659 [==============================] - 9s - loss: 0.3855 - categorical_accuracy: 0.8120 - val_loss: 0.5712 - val_categorical_accuracy: 0.7810\n", "Epoch 5/10\n", "2624/2659 [============================>.] - ETA: 0s - loss: 0.3764 - categorical_accuracy: 0.8197Epoch 00004: val_categorical_accuracy did not improve\n", "2659/2659 [==============================] - 9s - loss: 0.3773 - categorical_accuracy: 0.8195 - val_loss: 0.6462 - val_categorical_accuracy: 0.7795\n", "Epoch 6/10\n", "2624/2659 [============================>.] - ETA: 0s - loss: 0.3752 - categorical_accuracy: 0.8247Epoch 00005: val_categorical_accuracy did not improve\n", "2659/2659 [==============================] - 9s - loss: 0.3746 - categorical_accuracy: 0.8247 - val_loss: 0.6767 - val_categorical_accuracy: 0.7840\n", "Epoch 7/10\n", "2624/2659 [============================>.] - ETA: 0s - loss: 0.3736 - categorical_accuracy: 0.8190Epoch 00006: val_categorical_accuracy did not improve\n", "2659/2659 [==============================] - 9s - loss: 0.3730 - categorical_accuracy: 0.8191 - val_loss: 0.7115 - val_categorical_accuracy: 0.7825\n", "Epoch 8/10\n", "2624/2659 [============================>.] - ETA: 0s - loss: 0.3706 - categorical_accuracy: 0.8228Epoch 00007: val_categorical_accuracy did not improve\n", "2659/2659 [==============================] - 9s - loss: 0.3699 - categorical_accuracy: 0.8225 - val_loss: 0.7410 - val_categorical_accuracy: 0.7915\n", "Epoch 9/10\n", "2624/2659 [============================>.] - ETA: 0s - loss: 0.3690 - categorical_accuracy: 0.8277Epoch 00008: val_categorical_accuracy did not improve\n", "2659/2659 [==============================] - 9s - loss: 0.3685 - categorical_accuracy: 0.8274 - val_loss: 0.7296 - val_categorical_accuracy: 0.7870\n", "Epoch 10/10\n", "2624/2659 [============================>.] - ETA: 0s - loss: 0.3586 - categorical_accuracy: 0.8274Epoch 00009: val_categorical_accuracy did not improve\n", "2659/2659 [==============================] - 9s - loss: 0.3604 - categorical_accuracy: 0.8263 - val_loss: 0.7143 - val_categorical_accuracy: 0.7810\n", "fold 4 =*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", "Train on 2661 samples, validate on 660 samples\n", "Epoch 1/10\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.4543 - categorical_accuracy: 0.8098Epoch 00000: val_categorical_accuracy improved from 0.81269 to 0.81970, saving model to model_12_chk.hdf5\n", "2661/2661 [==============================] - 10s - loss: 0.4511 - categorical_accuracy: 0.8113 - val_loss: 0.3918 - val_categorical_accuracy: 0.8197\n", "Epoch 2/10\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.3834 - categorical_accuracy: 0.8186Epoch 00001: val_categorical_accuracy did not improve\n", "2661/2661 [==============================] - 9s - loss: 0.3843 - categorical_accuracy: 0.8185 - val_loss: 0.4175 - val_categorical_accuracy: 0.8045\n", "Epoch 3/10\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.3778 - categorical_accuracy: 0.8262Epoch 00002: val_categorical_accuracy did not improve\n", "2661/2661 [==============================] - 9s - loss: 0.3792 - categorical_accuracy: 0.8253 - val_loss: 0.4932 - val_categorical_accuracy: 0.8015\n", "Epoch 4/10\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.3756 - categorical_accuracy: 0.8209Epoch 00003: val_categorical_accuracy did not improve\n", "2661/2661 [==============================] - 9s - loss: 0.3760 - categorical_accuracy: 0.8204 - val_loss: 0.5261 - val_categorical_accuracy: 0.7970\n", "Epoch 5/10\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.3666 - categorical_accuracy: 0.8175Epoch 00004: val_categorical_accuracy did not improve\n", "2661/2661 [==============================] - 9s - loss: 0.3662 - categorical_accuracy: 0.8177 - val_loss: 0.5771 - val_categorical_accuracy: 0.7894\n", "Epoch 6/10\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.3629 - categorical_accuracy: 0.8255Epoch 00005: val_categorical_accuracy did not improve\n", "2661/2661 [==============================] - 9s - loss: 0.3626 - categorical_accuracy: 0.8260 - val_loss: 0.6609 - val_categorical_accuracy: 0.7924\n", "Epoch 7/10\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.3646 - categorical_accuracy: 0.8186Epoch 00006: val_categorical_accuracy did not improve\n", "2661/2661 [==============================] - 9s - loss: 0.3623 - categorical_accuracy: 0.8207 - val_loss: 0.6431 - val_categorical_accuracy: 0.7833\n", "Epoch 8/10\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.3620 - categorical_accuracy: 0.8232Epoch 00007: val_categorical_accuracy did not improve\n", "2661/2661 [==============================] - 9s - loss: 0.3608 - categorical_accuracy: 0.8238 - val_loss: 0.6936 - val_categorical_accuracy: 0.7788\n", "Epoch 9/10\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.3571 - categorical_accuracy: 0.8258Epoch 00008: val_categorical_accuracy did not improve\n", "2661/2661 [==============================] - 9s - loss: 0.3594 - categorical_accuracy: 0.8249 - val_loss: 0.7796 - val_categorical_accuracy: 0.7909\n", "Epoch 10/10\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.3593 - categorical_accuracy: 0.8277Epoch 00009: val_categorical_accuracy did not improve\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2661/2661 [==============================] - 9s - loss: 0.3613 - categorical_accuracy: 0.8264 - val_loss: 0.7249 - val_categorical_accuracy: 0.7879\n" ] } ], "source": [ "cv_kfold = StratifiedKFold(n_splits=5, shuffle=True)\n", "\n", "model_12 = None\n", "model_acc = 0\n", "for index, (train_indices, val_indices) in enumerate(cv_kfold.split(x_22_T, x_22_Cp)):\n", " \n", " print(\"fold\",index,\"=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\")\n", " xtrain, xval = x_22_T[train_indices], x_22_T[val_indices]\n", " ytrain, yval = x_22_Cp[train_indices], x_22_Cp[val_indices]\n", " ytrain = np_utils.to_categorical(np.array(ytrain), 9)\n", " yval = np_utils.to_categorical(np.array(yval), 9)\n", " \n", " with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " try:\n", " model_1.load_weights(\"model_12_weights.hdf5\")\n", " except IOError as ioe:\n", " print(\"no checkpoints available !\")\n", " \n", " model_1.fit(xtrain, ytrain, \n", " validation_data=(xval, yval),\n", " epochs=10, batch_size=64,shuffle=True,\n", " callbacks=[tb_callback,checkpointer])\n", " \n", " loss, acc = model_1.evaluate(xval, yval, verbose=0)\n", " if model_acc < acc:\n", " model_12 = model_1\n", " model_12.save_weights(\"model_12_weights.hdf5\")\n", " model_acc = acc\n", " \n", " " ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2017-11-07T01:51:00.622994Z", "start_time": "2017-11-07T01:51:00.619822Z" } }, "source": [ "#### predictions" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:14:31.338294Z", "start_time": "2017-11-07T04:14:26.298845Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[155 63 2 45 8 10 135 1 3]\n", " [222 134 4 71 7 19 278 2 4]\n", " [194 122 4 54 4 12 253 3 4]\n", " [202 132 1 91 9 17 254 1 4]\n", " [186 116 2 69 20 15 276 2 2]\n", " [220 123 3 55 6 28 261 2 10]\n", " [193 134 8 55 18 7 315 5 4]\n", " [194 110 5 56 11 6 275 4 8]\n", " [ 77 59 1 27 4 8 155 1 8]]\n", " precision recall f1-score support\n", "\n", " 0 0.09 0.37 0.15 422\n", " 1 0.13 0.18 0.15 741\n", " 2 0.13 0.01 0.01 650\n", " 3 0.17 0.13 0.15 711\n", " 4 0.23 0.03 0.05 688\n", " 5 0.23 0.04 0.07 708\n", " 6 0.14 0.43 0.21 739\n", " 7 0.19 0.01 0.01 669\n", " 8 0.17 0.02 0.04 340\n", "\n", "avg / total 0.17 0.13 0.10 5668\n", "\n" ] } ], "source": [ "from sklearn.metrics import classification_report,confusion_matrix\n", "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " try:\n", " model_1.load_weights(\"model_12_weights.hdf5\")\n", " except IOError as ioe:\n", " print(\"no checkpoints available !\")\n", " \n", " y_pred = model_1.predict(x_test_22_T)\n", " y_classes = y_pred.argmax(axis=-1)\n", "\n", "print(confusion_matrix(x_test_22_Cp, y_classes))\n", "print(classification_report(x_test_22_Cp, y_classes))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2017-08-24T06:58:17.661183Z", "start_time": "2017-08-24T06:58:17.655020Z" } }, "source": [ "## model_2: refined" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:26:23.411780Z", "start_time": "2017-11-07T04:26:22.642308Z" } }, "outputs": [], "source": [ "text_seq_input = Input(shape=(MAX_TEXT_LEN,), dtype='int32')\n", "text_embedding = Embedding(vocab_size, WORD_EMB_SIZE, input_length=MAX_TEXT_LEN,\n", " weights=[trained_embeddings], trainable=True)(text_seq_input)\n", "\n", "filter_sizes = [3, 4, 5, 10, 30, 50]\n", "convs = []\n", "for filter_size in filter_sizes:\n", " l_conv = Conv1D(filters=128, kernel_size=filter_size, padding='same', activation='relu')(text_embedding)\n", " l_pool = MaxPool1D(filter_size)(l_conv)\n", " convs.append(l_pool)\n", "\n", "l_merge = Concatenate(axis=1)(convs)\n", "l_cov1= Conv1D(128, 5, activation='relu', kernel_regularizer= l2(0.01))(l_merge)\n", "l_pool1 = MaxPool1D(128)(l_cov1)\n", "\n", "l_flat = Flatten()(l_pool1)\n", "l_dense = Dense(128, activation='relu')(l_flat)\n", "l_out = Dense(9, activation='softmax')(l_dense)\n", "model_2 = Model(inputs=[text_seq_input], outputs=l_out)\n" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:26:27.154196Z", "start_time": "2017-11-07T04:26:27.090531Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "____________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "====================================================================================================\n", "input_2 (InputLayer) (None, 5000) 0 \n", "____________________________________________________________________________________________________\n", "embedding_1 (Embedding) (None, 5000, 200) 70444000 input_2[0][0] \n", "____________________________________________________________________________________________________\n", "conv1d_1 (Conv1D) (None, 5000, 128) 76928 embedding_1[0][0] \n", "____________________________________________________________________________________________________\n", "conv1d_2 (Conv1D) (None, 5000, 128) 102528 embedding_1[0][0] \n", "____________________________________________________________________________________________________\n", "conv1d_3 (Conv1D) (None, 5000, 128) 128128 embedding_1[0][0] \n", "____________________________________________________________________________________________________\n", "conv1d_4 (Conv1D) (None, 5000, 128) 256128 embedding_1[0][0] \n", "____________________________________________________________________________________________________\n", "conv1d_5 (Conv1D) (None, 5000, 128) 768128 embedding_1[0][0] \n", "____________________________________________________________________________________________________\n", "conv1d_6 (Conv1D) (None, 5000, 128) 1280128 embedding_1[0][0] \n", "____________________________________________________________________________________________________\n", "max_pooling1d_1 (MaxPooling1D) (None, 1666, 128) 0 conv1d_1[0][0] \n", "____________________________________________________________________________________________________\n", "max_pooling1d_2 (MaxPooling1D) (None, 1250, 128) 0 conv1d_2[0][0] \n", "____________________________________________________________________________________________________\n", "max_pooling1d_3 (MaxPooling1D) (None, 1000, 128) 0 conv1d_3[0][0] \n", "____________________________________________________________________________________________________\n", "max_pooling1d_4 (MaxPooling1D) (None, 500, 128) 0 conv1d_4[0][0] \n", "____________________________________________________________________________________________________\n", "max_pooling1d_5 (MaxPooling1D) (None, 166, 128) 0 conv1d_5[0][0] \n", "____________________________________________________________________________________________________\n", "max_pooling1d_6 (MaxPooling1D) (None, 100, 128) 0 conv1d_6[0][0] \n", "____________________________________________________________________________________________________\n", "concatenate_1 (Concatenate) (None, 4682, 128) 0 max_pooling1d_1[0][0] \n", " max_pooling1d_2[0][0] \n", " max_pooling1d_3[0][0] \n", " max_pooling1d_4[0][0] \n", " max_pooling1d_5[0][0] \n", " max_pooling1d_6[0][0] \n", "____________________________________________________________________________________________________\n", "conv1d_7 (Conv1D) (None, 4678, 128) 82048 concatenate_1[0][0] \n", "____________________________________________________________________________________________________\n", "max_pooling1d_7 (MaxPooling1D) (None, 36, 128) 0 conv1d_7[0][0] \n", "____________________________________________________________________________________________________\n", "flatten_1 (Flatten) (None, 4608) 0 max_pooling1d_7[0][0] \n", "____________________________________________________________________________________________________\n", "dense_1 (Dense) (None, 128) 589952 flatten_1[0][0] \n", "____________________________________________________________________________________________________\n", "dense_2 (Dense) (None, 9) 1161 dense_1[0][0] \n", "====================================================================================================\n", "Total params: 73,729,129\n", "Trainable params: 73,729,129\n", "Non-trainable params: 0\n", "____________________________________________________________________________________________________\n" ] } ], "source": [ "model_2.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['categorical_accuracy'])\n", "model_2.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### training" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:26:31.044229Z", "start_time": "2017-11-07T04:26:31.039298Z" } }, "outputs": [], "source": [ "tb_callback = keras.callbacks.TensorBoard(log_dir='./tb_graphs', histogram_freq=0, write_graph=True, write_images=True)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:28:02.231553Z", "start_time": "2017-11-07T04:28:02.226897Z" } }, "outputs": [], "source": [ "checkpointer = ModelCheckpoint(filepath=\"model_21_weights.hdf5\", \n", " verbose=1,\n", " monitor=\"val_categorical_accuracy\",\n", " save_best_only=True,\n", " mode=\"max\")" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:26:33.596497Z", "start_time": "2017-11-07T04:26:33.591708Z" } }, "outputs": [], "source": [ "earlystopping = EarlyStopping(monitor='val_categorical_accuracy', \n", " min_delta=0, patience=5, \n", " verbose=1, mode='auto')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### no CV" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:33:41.349085Z", "start_time": "2017-11-07T04:28:54.581471Z" }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "no checkpoints available !\n", "Train on 2988 samples, validate on 333 samples\n", "Epoch 1/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 2.4043 - categorical_accuracy: 0.3685Epoch 00000: val_categorical_accuracy improved from -inf to 0.44144, saving model to model_21_weights.hdf5\n", "2988/2988 [==============================] - 31s - loss: 2.3980 - categorical_accuracy: 0.3691 - val_loss: 1.8602 - val_categorical_accuracy: 0.4414\n", "Epoch 2/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 1.5442 - categorical_accuracy: 0.5187Epoch 00001: val_categorical_accuracy improved from 0.44144 to 0.59159, saving model to model_21_weights.hdf5\n", "2988/2988 [==============================] - 29s - loss: 1.5407 - categorical_accuracy: 0.5194 - val_loss: 1.3750 - val_categorical_accuracy: 0.5916\n", "Epoch 3/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 1.1770 - categorical_accuracy: 0.6311Epoch 00002: val_categorical_accuracy did not improve\n", "2988/2988 [==============================] - 27s - loss: 1.1761 - categorical_accuracy: 0.6315 - val_loss: 1.2786 - val_categorical_accuracy: 0.5766\n", "Epoch 4/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.9931 - categorical_accuracy: 0.6967Epoch 00003: val_categorical_accuracy improved from 0.59159 to 0.63664, saving model to model_21_weights.hdf5\n", "2988/2988 [==============================] - 29s - loss: 0.9922 - categorical_accuracy: 0.6971 - val_loss: 1.1795 - val_categorical_accuracy: 0.6366\n", "Epoch 5/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.8791 - categorical_accuracy: 0.7405Epoch 00004: val_categorical_accuracy did not improve\n", "2988/2988 [==============================] - 27s - loss: 0.8765 - categorical_accuracy: 0.7420 - val_loss: 1.2330 - val_categorical_accuracy: 0.6066\n", "Epoch 6/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.8097 - categorical_accuracy: 0.7595Epoch 00005: val_categorical_accuracy did not improve\n", "2988/2988 [==============================] - 27s - loss: 0.8120 - categorical_accuracy: 0.7584 - val_loss: 1.1753 - val_categorical_accuracy: 0.6306\n", "Epoch 7/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.7477 - categorical_accuracy: 0.7629Epoch 00006: val_categorical_accuracy improved from 0.63664 to 0.64264, saving model to model_21_weights.hdf5\n", "2988/2988 [==============================] - 29s - loss: 0.7531 - categorical_accuracy: 0.7614 - val_loss: 1.1824 - val_categorical_accuracy: 0.6426\n", "Epoch 8/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.7089 - categorical_accuracy: 0.7673Epoch 00007: val_categorical_accuracy improved from 0.64264 to 0.64865, saving model to model_21_weights.hdf5\n", "2988/2988 [==============================] - 29s - loss: 0.7110 - categorical_accuracy: 0.7657 - val_loss: 1.1630 - val_categorical_accuracy: 0.6486\n", "Epoch 9/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.6771 - categorical_accuracy: 0.7711Epoch 00008: val_categorical_accuracy did not improve\n", "2988/2988 [==============================] - 27s - loss: 0.6765 - categorical_accuracy: 0.7707 - val_loss: 1.1500 - val_categorical_accuracy: 0.6126\n", "Epoch 10/10\n", "2944/2988 [============================>.] - ETA: 0s - loss: 0.6502 - categorical_accuracy: 0.7711Epoch 00009: val_categorical_accuracy did not improve\n", "2988/2988 [==============================] - 27s - loss: 0.6489 - categorical_accuracy: 0.7714 - val_loss: 1.1513 - val_categorical_accuracy: 0.6096\n" ] } ], "source": [ "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " try:\n", " model_2.load_weights(\"model_21_weights.hdf5\")\n", " except IOError as ioe:\n", " print(\"no checkpoints available !\")\n", " \n", " model_2.fit(x_train_22_T, x_train_22_C, \n", " validation_data=(x_val_22_T, x_val_22_C),\n", " epochs=10, batch_size=64,shuffle=True,\n", " callbacks=[tb_callback,checkpointer])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### prediction" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:37:55.451853Z", "start_time": "2017-11-07T04:37:45.747849Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[173 35 1 55 30 6 122 0 0]\n", " [226 91 1 76 41 11 295 0 0]\n", " [214 69 4 61 38 8 256 0 0]\n", " [218 81 0 97 49 6 260 0 0]\n", " [216 67 1 67 62 8 267 0 0]\n", " [246 76 1 62 51 21 251 0 0]\n", " [222 105 4 59 39 3 307 0 0]\n", " [208 77 3 61 41 9 269 0 1]\n", " [ 87 37 0 37 25 5 147 0 2]]\n", " precision recall f1-score support\n", "\n", " 0 0.10 0.41 0.16 422\n", " 1 0.14 0.12 0.13 741\n", " 2 0.27 0.01 0.01 650\n", " 3 0.17 0.14 0.15 711\n", " 4 0.16 0.09 0.12 688\n", " 5 0.27 0.03 0.05 708\n", " 6 0.14 0.42 0.21 739\n", " 7 0.00 0.00 0.00 669\n", " 8 0.67 0.01 0.01 340\n", "\n", "avg / total 0.19 0.13 0.10 5668\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/bicepjai/Programs/anaconda3/envs/dsotc-c3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1135: 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": [ "from sklearn.metrics import classification_report,confusion_matrix\n", "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " try:\n", " model_2.load_weights(\"model_21_weights.hdf5\")\n", " except IOError as ioe:\n", " print(\"no checkpoints available !\")\n", " \n", " y_pred = model_2.predict(x_test_22_T)\n", " y_classes = y_pred.argmax(axis=-1)\n", "\n", "print(confusion_matrix(x_test_22_Cp, y_classes))\n", "print(classification_report(x_test_22_Cp, y_classes))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### CV" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:38:00.736648Z", "start_time": "2017-11-07T04:38:00.732462Z" } }, "outputs": [], "source": [ "from sklearn.model_selection import StratifiedKFold\n", "from sklearn.metrics import accuracy_score" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:38:01.752762Z", "start_time": "2017-11-07T04:38:01.747414Z" } }, "outputs": [], "source": [ "checkpointer = ModelCheckpoint(filepath=\"model_22_chk.hdf5\", \n", " verbose=1,\n", " monitor=\"val_categorical_accuracy\",\n", " save_best_only=True,\n", " mode=\"max\")" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T05:04:27.907239Z", "start_time": "2017-11-07T04:43:24.874500Z" }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "fold 0 =*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", "no checkpoints available !\n", "Train on 2653 samples, validate on 668 samples\n", "Epoch 1/10\n", "2624/2653 [============================>.] - ETA: 0s - loss: 2.4730 - categorical_accuracy: 0.3445Epoch 00000: val_categorical_accuracy did not improve\n", "2653/2653 [==============================] - 24s - loss: 2.4671 - categorical_accuracy: 0.3449 - val_loss: 1.9488 - val_categorical_accuracy: 0.3338\n", "Epoch 2/10\n", "2624/2653 [============================>.] - ETA: 0s - loss: 1.6751 - categorical_accuracy: 0.4840Epoch 00001: val_categorical_accuracy did not improve\n", "2653/2653 [==============================] - 24s - loss: 1.6717 - categorical_accuracy: 0.4847 - val_loss: 1.5826 - val_categorical_accuracy: 0.4970\n", "Epoch 3/10\n", "2624/2653 [============================>.] - ETA: 0s - loss: 1.2800 - categorical_accuracy: 0.6044Epoch 00002: val_categorical_accuracy did not improve\n", "2653/2653 [==============================] - 24s - loss: 1.2792 - categorical_accuracy: 0.6057 - val_loss: 1.3578 - val_categorical_accuracy: 0.5689\n", "Epoch 4/10\n", "2624/2653 [============================>.] - ETA: 0s - loss: 1.0418 - categorical_accuracy: 0.6875Epoch 00003: val_categorical_accuracy improved from 0.60629 to 0.60778, saving model to model_22_chk.hdf5\n", "2653/2653 [==============================] - 26s - loss: 1.0446 - categorical_accuracy: 0.6871 - val_loss: 1.3145 - val_categorical_accuracy: 0.6078\n", "Epoch 5/10\n", "2624/2653 [============================>.] - ETA: 0s - loss: 0.8911 - categorical_accuracy: 0.7416Epoch 00004: val_categorical_accuracy did not improve\n", "2653/2653 [==============================] - 24s - loss: 0.8912 - categorical_accuracy: 0.7410 - val_loss: 1.3518 - val_categorical_accuracy: 0.5778\n", "Epoch 6/10\n", "2624/2653 [============================>.] - ETA: 0s - loss: 0.8344 - categorical_accuracy: 0.7485Epoch 00005: val_categorical_accuracy did not improve\n", "2653/2653 [==============================] - 24s - loss: 0.8373 - categorical_accuracy: 0.7475 - val_loss: 1.2809 - val_categorical_accuracy: 0.5853\n", "Epoch 7/10\n", "2624/2653 [============================>.] - ETA: 0s - loss: 0.7899 - categorical_accuracy: 0.7595Epoch 00006: val_categorical_accuracy did not improve\n", "2653/2653 [==============================] - 24s - loss: 0.7893 - categorical_accuracy: 0.7591 - val_loss: 1.3969 - val_categorical_accuracy: 0.5898\n", "Epoch 8/10\n", "2624/2653 [============================>.] - ETA: 0s - loss: 0.7258 - categorical_accuracy: 0.7633Epoch 00007: val_categorical_accuracy did not improve\n", "2653/2653 [==============================] - 24s - loss: 0.7273 - categorical_accuracy: 0.7622 - val_loss: 1.2452 - val_categorical_accuracy: 0.6078\n", "Epoch 9/10\n", "2624/2653 [============================>.] - ETA: 0s - loss: 0.6835 - categorical_accuracy: 0.7744Epoch 00008: val_categorical_accuracy did not improve\n", "2653/2653 [==============================] - 24s - loss: 0.6810 - categorical_accuracy: 0.7746 - val_loss: 1.3094 - val_categorical_accuracy: 0.5958\n", "Epoch 10/10\n", "2624/2653 [============================>.] - ETA: 0s - loss: 0.6618 - categorical_accuracy: 0.7725Epoch 00009: val_categorical_accuracy improved from 0.60778 to 0.61976, saving model to model_22_chk.hdf5\n", "2653/2653 [==============================] - 26s - loss: 0.6643 - categorical_accuracy: 0.7708 - val_loss: 1.2167 - val_categorical_accuracy: 0.6198\n", "fold 1 =*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", "Train on 2654 samples, validate on 667 samples\n", "Epoch 1/10\n", "2624/2654 [============================>.] - ETA: 0s - loss: 0.7994 - categorical_accuracy: 0.7283Epoch 00000: val_categorical_accuracy improved from 0.61976 to 0.77511, saving model to model_22_chk.hdf5\n", "2654/2654 [==============================] - 28s - loss: 0.7984 - categorical_accuracy: 0.7287 - val_loss: 0.6808 - val_categorical_accuracy: 0.7751\n", "Epoch 2/10\n", "2624/2654 [============================>.] - ETA: 0s - loss: 0.6539 - categorical_accuracy: 0.7691Epoch 00001: val_categorical_accuracy did not improve\n", "2654/2654 [==============================] - 24s - loss: 0.6528 - categorical_accuracy: 0.7698 - val_loss: 0.6930 - val_categorical_accuracy: 0.7646\n", "Epoch 3/10\n", "2624/2654 [============================>.] - ETA: 0s - loss: 0.6004 - categorical_accuracy: 0.7839Epoch 00002: val_categorical_accuracy did not improve\n", "2654/2654 [==============================] - 24s - loss: 0.6016 - categorical_accuracy: 0.7841 - val_loss: 0.7073 - val_categorical_accuracy: 0.7436\n", "Epoch 4/10\n", "2624/2654 [============================>.] - ETA: 0s - loss: 0.5859 - categorical_accuracy: 0.7900Epoch 00003: val_categorical_accuracy did not improve\n", "2654/2654 [==============================] - 24s - loss: 0.5838 - categorical_accuracy: 0.7909 - val_loss: 0.7475 - val_categorical_accuracy: 0.7676\n", "Epoch 5/10\n", "2624/2654 [============================>.] - ETA: 0s - loss: 0.5574 - categorical_accuracy: 0.7934Epoch 00004: val_categorical_accuracy did not improve\n", "2654/2654 [==============================] - 24s - loss: 0.5581 - categorical_accuracy: 0.7924 - val_loss: 0.7455 - val_categorical_accuracy: 0.7646\n", "Epoch 6/10\n", "2624/2654 [============================>.] - ETA: 0s - loss: 0.5325 - categorical_accuracy: 0.7946Epoch 00005: val_categorical_accuracy did not improve\n", "2654/2654 [==============================] - 24s - loss: 0.5337 - categorical_accuracy: 0.7943 - val_loss: 0.7550 - val_categorical_accuracy: 0.7451\n", "Epoch 7/10\n", "2624/2654 [============================>.] - ETA: 0s - loss: 0.5303 - categorical_accuracy: 0.7942Epoch 00006: val_categorical_accuracy did not improve\n", "2654/2654 [==============================] - 24s - loss: 0.5310 - categorical_accuracy: 0.7946 - val_loss: 0.7947 - val_categorical_accuracy: 0.7526\n", "Epoch 8/10\n", "2624/2654 [============================>.] - ETA: 0s - loss: 0.5115 - categorical_accuracy: 0.7992Epoch 00007: val_categorical_accuracy did not improve\n", "2654/2654 [==============================] - 24s - loss: 0.5144 - categorical_accuracy: 0.7969 - val_loss: 0.8074 - val_categorical_accuracy: 0.7541\n", "Epoch 9/10\n", "2624/2654 [============================>.] - ETA: 0s - loss: 0.4916 - categorical_accuracy: 0.8007Epoch 00008: val_categorical_accuracy did not improve\n", "2654/2654 [==============================] - 24s - loss: 0.4903 - categorical_accuracy: 0.8011 - val_loss: 0.8387 - val_categorical_accuracy: 0.7391\n", "Epoch 10/10\n", "2624/2654 [============================>.] - ETA: 0s - loss: 0.4934 - categorical_accuracy: 0.7988Epoch 00009: val_categorical_accuracy did not improve\n", "2654/2654 [==============================] - 24s - loss: 0.4910 - categorical_accuracy: 0.7995 - val_loss: 0.8857 - val_categorical_accuracy: 0.7376\n", "fold 2 =*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", "Train on 2657 samples, validate on 664 samples\n", "Epoch 1/10\n", "2624/2657 [============================>.] - ETA: 0s - loss: 0.5986 - categorical_accuracy: 0.7843Epoch 00000: val_categorical_accuracy improved from 0.77511 to 0.79217, saving model to model_22_chk.hdf5\n", "2657/2657 [==============================] - 28s - loss: 0.5988 - categorical_accuracy: 0.7836 - val_loss: 0.5248 - val_categorical_accuracy: 0.7922\n", "Epoch 2/10\n", "2624/2657 [============================>.] - ETA: 0s - loss: 0.5126 - categorical_accuracy: 0.8022Epoch 00001: val_categorical_accuracy did not improve\n", "2657/2657 [==============================] - 24s - loss: 0.5156 - categorical_accuracy: 0.8009 - val_loss: 0.5648 - val_categorical_accuracy: 0.7756\n", "Epoch 3/10\n", "2624/2657 [============================>.] - ETA: 0s - loss: 0.4944 - categorical_accuracy: 0.8102Epoch 00002: val_categorical_accuracy did not improve\n", "2657/2657 [==============================] - 24s - loss: 0.4952 - categorical_accuracy: 0.8092 - val_loss: 0.5844 - val_categorical_accuracy: 0.7651\n", "Epoch 4/10\n", "2624/2657 [============================>.] - ETA: 0s - loss: 0.4750 - categorical_accuracy: 0.8045Epoch 00003: val_categorical_accuracy did not improve\n", "2657/2657 [==============================] - 24s - loss: 0.4754 - categorical_accuracy: 0.8043 - val_loss: 0.5905 - val_categorical_accuracy: 0.7726\n", "Epoch 5/10\n", "2624/2657 [============================>.] - ETA: 0s - loss: 0.4652 - categorical_accuracy: 0.8129Epoch 00004: val_categorical_accuracy did not improve\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2657/2657 [==============================] - 24s - loss: 0.4656 - categorical_accuracy: 0.8126 - val_loss: 0.6405 - val_categorical_accuracy: 0.7801\n", "Epoch 6/10\n", "2624/2657 [============================>.] - ETA: 0s - loss: 0.4513 - categorical_accuracy: 0.8087Epoch 00005: val_categorical_accuracy did not improve\n", "2657/2657 [==============================] - 24s - loss: 0.4507 - categorical_accuracy: 0.8092 - val_loss: 0.6611 - val_categorical_accuracy: 0.7666\n", "Epoch 7/10\n", "2624/2657 [============================>.] - ETA: 0s - loss: 0.4423 - categorical_accuracy: 0.8152Epoch 00006: val_categorical_accuracy did not improve\n", "2657/2657 [==============================] - 24s - loss: 0.4461 - categorical_accuracy: 0.8141 - val_loss: 0.6571 - val_categorical_accuracy: 0.7861\n", "Epoch 8/10\n", "2624/2657 [============================>.] - ETA: 0s - loss: 0.4404 - categorical_accuracy: 0.8095Epoch 00007: val_categorical_accuracy did not improve\n", "2657/2657 [==============================] - 24s - loss: 0.4405 - categorical_accuracy: 0.8092 - val_loss: 0.6686 - val_categorical_accuracy: 0.7741\n", "Epoch 9/10\n", "2624/2657 [============================>.] - ETA: 0s - loss: 0.4320 - categorical_accuracy: 0.8102Epoch 00008: val_categorical_accuracy did not improve\n", "2657/2657 [==============================] - 24s - loss: 0.4356 - categorical_accuracy: 0.8084 - val_loss: 0.6883 - val_categorical_accuracy: 0.7711\n", "Epoch 10/10\n", "2624/2657 [============================>.] - ETA: 0s - loss: 0.4355 - categorical_accuracy: 0.8129Epoch 00009: val_categorical_accuracy did not improve\n", "2657/2657 [==============================] - 24s - loss: 0.4325 - categorical_accuracy: 0.8145 - val_loss: 0.7109 - val_categorical_accuracy: 0.7636\n", "fold 3 =*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", "Train on 2659 samples, validate on 662 samples\n", "Epoch 1/10\n", "2624/2659 [============================>.] - ETA: 0s - loss: 0.5042 - categorical_accuracy: 0.8018Epoch 00000: val_categorical_accuracy improved from 0.79217 to 0.79909, saving model to model_22_chk.hdf5\n", "2659/2659 [==============================] - 28s - loss: 0.5087 - categorical_accuracy: 0.7999 - val_loss: 0.4921 - val_categorical_accuracy: 0.7991\n", "Epoch 2/10\n", "2624/2659 [============================>.] - ETA: 0s - loss: 0.4419 - categorical_accuracy: 0.8148Epoch 00001: val_categorical_accuracy did not improve\n", "2659/2659 [==============================] - 24s - loss: 0.4423 - categorical_accuracy: 0.8142 - val_loss: 0.5427 - val_categorical_accuracy: 0.7764\n", "Epoch 3/10\n", "2624/2659 [============================>.] - ETA: 0s - loss: 0.4264 - categorical_accuracy: 0.8129Epoch 00002: val_categorical_accuracy did not improve\n", "2659/2659 [==============================] - 24s - loss: 0.4237 - categorical_accuracy: 0.8146 - val_loss: 0.5743 - val_categorical_accuracy: 0.7704\n", "Epoch 4/10\n", "2624/2659 [============================>.] - ETA: 0s - loss: 0.4149 - categorical_accuracy: 0.8209Epoch 00003: val_categorical_accuracy did not improve\n", "2659/2659 [==============================] - 24s - loss: 0.4119 - categorical_accuracy: 0.8225 - val_loss: 0.6180 - val_categorical_accuracy: 0.7447\n", "Epoch 5/10\n", "2624/2659 [============================>.] - ETA: 0s - loss: 0.4014 - categorical_accuracy: 0.8133Epoch 00004: val_categorical_accuracy did not improve\n", "2659/2659 [==============================] - 24s - loss: 0.4024 - categorical_accuracy: 0.8127 - val_loss: 0.6729 - val_categorical_accuracy: 0.7568\n", "Epoch 6/10\n", "2624/2659 [============================>.] - ETA: 0s - loss: 0.3952 - categorical_accuracy: 0.8224Epoch 00005: val_categorical_accuracy did not improve\n", "2659/2659 [==============================] - 24s - loss: 0.3960 - categorical_accuracy: 0.8214 - val_loss: 0.6611 - val_categorical_accuracy: 0.7462\n", "Epoch 7/10\n", "2624/2659 [============================>.] - ETA: 0s - loss: 0.3939 - categorical_accuracy: 0.8201Epoch 00006: val_categorical_accuracy did not improve\n", "2659/2659 [==============================] - 24s - loss: 0.3951 - categorical_accuracy: 0.8195 - val_loss: 0.6758 - val_categorical_accuracy: 0.7477\n", "Epoch 8/10\n", "2624/2659 [============================>.] - ETA: 0s - loss: 0.3960 - categorical_accuracy: 0.8209Epoch 00007: val_categorical_accuracy did not improve\n", "2659/2659 [==============================] - 24s - loss: 0.3963 - categorical_accuracy: 0.8210 - val_loss: 0.7850 - val_categorical_accuracy: 0.7447\n", "Epoch 9/10\n", "2624/2659 [============================>.] - ETA: 0s - loss: 0.3918 - categorical_accuracy: 0.8182Epoch 00008: val_categorical_accuracy did not improve\n", "2659/2659 [==============================] - 24s - loss: 0.3918 - categorical_accuracy: 0.8184 - val_loss: 0.7055 - val_categorical_accuracy: 0.7492\n", "Epoch 10/10\n", "2624/2659 [============================>.] - ETA: 0s - loss: 0.3837 - categorical_accuracy: 0.8296Epoch 00009: val_categorical_accuracy did not improve\n", "2659/2659 [==============================] - 24s - loss: 0.3846 - categorical_accuracy: 0.8285 - val_loss: 0.7014 - val_categorical_accuracy: 0.7432\n", "fold 4 =*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n", "Train on 2661 samples, validate on 660 samples\n", "Epoch 1/10\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.5093 - categorical_accuracy: 0.7980Epoch 00000: val_categorical_accuracy did not improve\n", "2661/2661 [==============================] - 26s - loss: 0.5074 - categorical_accuracy: 0.7989 - val_loss: 0.4850 - val_categorical_accuracy: 0.7788\n", "Epoch 2/10\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.4418 - categorical_accuracy: 0.8072Epoch 00001: val_categorical_accuracy did not improve\n", "2661/2661 [==============================] - 24s - loss: 0.4412 - categorical_accuracy: 0.8076 - val_loss: 0.5466 - val_categorical_accuracy: 0.7758\n", "Epoch 3/10\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.4302 - categorical_accuracy: 0.8171Epoch 00002: val_categorical_accuracy did not improve\n", "2661/2661 [==============================] - 24s - loss: 0.4303 - categorical_accuracy: 0.8162 - val_loss: 0.6032 - val_categorical_accuracy: 0.7712\n", "Epoch 4/10\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.4173 - categorical_accuracy: 0.8129Epoch 00003: val_categorical_accuracy did not improve\n", "2661/2661 [==============================] - 24s - loss: 0.4158 - categorical_accuracy: 0.8136 - val_loss: 0.6016 - val_categorical_accuracy: 0.7500\n", "Epoch 5/10\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.4055 - categorical_accuracy: 0.8190Epoch 00004: val_categorical_accuracy did not improve\n", "2661/2661 [==============================] - 24s - loss: 0.4073 - categorical_accuracy: 0.8189 - val_loss: 0.6425 - val_categorical_accuracy: 0.7470\n", "Epoch 6/10\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.4049 - categorical_accuracy: 0.8205Epoch 00005: val_categorical_accuracy did not improve\n", "2661/2661 [==============================] - 24s - loss: 0.4019 - categorical_accuracy: 0.8219 - val_loss: 0.6947 - val_categorical_accuracy: 0.7576\n", "Epoch 7/10\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.4021 - categorical_accuracy: 0.8266Epoch 00006: val_categorical_accuracy did not improve\n", "2661/2661 [==============================] - 24s - loss: 0.4013 - categorical_accuracy: 0.8264 - val_loss: 0.7055 - val_categorical_accuracy: 0.7530\n", "Epoch 8/10\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.3949 - categorical_accuracy: 0.8213Epoch 00007: val_categorical_accuracy did not improve\n", "2661/2661 [==============================] - 24s - loss: 0.3951 - categorical_accuracy: 0.8211 - val_loss: 0.7124 - val_categorical_accuracy: 0.7545\n", "Epoch 9/10\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.3938 - categorical_accuracy: 0.8224Epoch 00008: val_categorical_accuracy did not improve\n", "2661/2661 [==============================] - 24s - loss: 0.3927 - categorical_accuracy: 0.8215 - val_loss: 0.7809 - val_categorical_accuracy: 0.7576\n", "Epoch 10/10\n", "2624/2661 [============================>.] - ETA: 0s - loss: 0.3863 - categorical_accuracy: 0.8232Epoch 00009: val_categorical_accuracy did not improve\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "2661/2661 [==============================] - 24s - loss: 0.3877 - categorical_accuracy: 0.8226 - val_loss: 0.7656 - val_categorical_accuracy: 0.7606\n" ] } ], "source": [ "cv_kfold = StratifiedKFold(n_splits=5, shuffle=True)\n", "\n", "model_22 = None\n", "model_acc = 0\n", "for index, (train_indices, val_indices) in enumerate(cv_kfold.split(x_22_T, x_22_Cp)):\n", " \n", " print(\"fold\",index,\"=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\")\n", " xtrain, xval = x_22_T[train_indices], x_22_T[val_indices]\n", " ytrain, yval = x_22_Cp[train_indices], x_22_Cp[val_indices]\n", " ytrain = np_utils.to_categorical(np.array(ytrain), 9)\n", " yval = np_utils.to_categorical(np.array(yval), 9)\n", " \n", " with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " try:\n", " model_2.load_weights(\"model_22_weights.hdf5\")\n", " except IOError as ioe:\n", " print(\"no checkpoints available !\")\n", " \n", " model_2.fit(xtrain, ytrain, \n", " validation_data=(xval, yval),\n", " epochs=10, batch_size=64,shuffle=True,\n", " callbacks=[tb_callback,checkpointer])\n", " \n", " loss, acc = model_2.evaluate(xval, yval, verbose=0)\n", " if model_acc < acc:\n", " model_22 = model_2\n", " model_22.save_weights(\"model_22_weights.hdf5\")\n", " model_acc = acc\n", " \n", " " ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2017-11-07T01:51:00.622994Z", "start_time": "2017-11-07T01:51:00.619822Z" } }, "source": [ "#### predictions" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T05:04:37.647775Z", "start_time": "2017-11-07T05:04:27.909589Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[148 46 2 86 5 8 123 1 3]\n", " [174 110 3 134 6 12 296 2 4]\n", " [153 86 5 117 5 6 269 2 7]\n", " [165 85 1 154 6 11 283 3 3]\n", " [174 93 3 125 21 10 258 3 1]\n", " [188 88 5 125 7 23 261 2 9]\n", " [170 99 7 116 16 4 321 3 3]\n", " [164 102 3 107 10 8 263 5 7]\n", " [ 70 41 1 59 6 7 146 2 8]]\n", " precision recall f1-score support\n", "\n", " 0 0.11 0.35 0.16 422\n", " 1 0.15 0.15 0.15 741\n", " 2 0.17 0.01 0.01 650\n", " 3 0.15 0.22 0.18 711\n", " 4 0.26 0.03 0.05 688\n", " 5 0.26 0.03 0.06 708\n", " 6 0.14 0.43 0.22 739\n", " 7 0.22 0.01 0.01 669\n", " 8 0.18 0.02 0.04 340\n", "\n", "avg / total 0.18 0.14 0.10 5668\n", "\n" ] } ], "source": [ "from sklearn.metrics import classification_report,confusion_matrix\n", "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " try:\n", " model_2.load_weights(\"model_22_weights.hdf5\")\n", " except IOError as ioe:\n", " print(\"no checkpoints available !\")\n", " \n", " y_pred = model_2.predict(x_test_22_T)\n", " y_classes = y_pred.argmax(axis=-1)\n", "\n", "print(confusion_matrix(x_test_22_Cp, y_classes))\n", "print(classification_report(x_test_22_Cp, y_classes))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" }, "toc": { "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "toc_cell": false, "toc_position": { "height": "827px", "left": "0px", "right": "1192px", "top": "52px", "width": "300px" }, "toc_section_display": "block", "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 2 }
mit
CalculatedContent/tsvm
tsvm_gmm.ipynb
1
569085
{ "metadata": { "name": "", "signature": "sha256:55890931132935325af6dacb302669a7f6a6eba130d4fd63070df8a7a8460689" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import pandas as pd\n", "import scipy\n", "\n", "import sklearn\n", "import sklearn.datasets\n", "\n", "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "def sample_from_gaussian(mean, covariance, n_samples):\n", " return scipy.random.multivariate_normal(mean=mean, cov=covariance, size=(n_samples,))\n", " \n", "def sample_unit_variance_gaussian(mean, n_samples):\n", " mean = np.array(mean)\n", " return sample_from_gaussian(mean=mean, n_samples=n_samples, covariance=np.identity(mean.shape[0]))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "N_labeled = 1000\n", "labeled_data = []\n", "labeled_labels = []\n", "\n", "N_unlabeled = 10000\n", "unlabeled_data = []\n", "unlabeled_labels = []\n", "\n", "unlabeled_data.extend(sample_unit_variance_gaussian((0,5), N_unlabeled))\n", "unlabeled_labels.extend([1]*N_unlabeled)\n", "unlabeled_data.extend(sample_unit_variance_gaussian((0,-5), N_unlabeled))\n", "unlabeled_labels.extend([-1]*N_unlabeled)\n", "\n", "labeled_data.extend(sample_unit_variance_gaussian((5,0), N_labeled))\n", "labeled_labels.extend([1]*N_labeled)\n", "labeled_data.extend(sample_unit_variance_gaussian((-5,0), N_labeled))\n", "labeled_labels.extend([-1]*N_labeled)\n", "\n", "unlabeled_data = np.array(unlabeled_data)\n", "labeled_data = np.array(labeled_data)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "scatter(*labeled_data.T)\n", "scatter(*unlabeled_data.T)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "<matplotlib.collections.PathCollection at 0x7f521f833bd0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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KLNMovoIJ2vYq2pE0pnRojSiplDvQCi2NsfCSfg9ROqUllE4xWa/7Ykp4ZlWK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\nW+uzr9t6o9adQMnJY7bxJa2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f2l4/ZRIWKWmAQxRfwQdaRkvtWPpQ0g8nUFaYukWPjVKS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7AAAg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\nQFIS8eKLTyE5+X1Ic347pOleC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"text": [ "<matplotlib.figure.Figure at 0x7f521f93bb10>" ] } ], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "sklearn.datasets.dump_svmlight_file(unlabeled_data, unlabeled_labels, 'gmm_degenerate.svm.t', zero_based=False)\n", "sklearn.datasets.dump_svmlight_file(labeled_data, labeled_labels, 'gmm_degenerate.svm', zero_based=False)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### View decision boundary of linear SVM trained on labeled data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import sklearn\n", "import sklearn.svm" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "svm = sklearn.svm.LinearSVC(loss='l2', dual=True)\n", "svm.fit(labeled_data, labeled_labels)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n", " intercept_scaling=1, loss='l2', multi_class='ovr', penalty='l2',\n", " random_state=None, tol=0.0001, verbose=0)" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "# create a mesh to plot in\n", "X = np.concatenate([labeled_data, unlabeled_data])\n", "y = np.concatenate([labeled_labels, unlabeled_labels])\n", "\n", "h = 0.02\n", "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n", "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n", "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n", " np.arange(y_min, y_max, h))\n", "\n", "plt.figure(figsize=(12,12))\n", "\n", "# Plot the decision boundary. For that, we will assign a color to each\n", "# point in the mesh [x_min, m_max]x[y_min, y_max].\n", "plt.subplots_adjust(wspace=0.4, hspace=0.4)\n", "\n", "Z = svm.predict(np.c_[xx.ravel(), yy.ravel()])\n", "\n", "# Put the result into a color plot\n", "Z = Z.reshape(xx.shape)\n", "plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8)\n", "\n", "# Plot also the training points\n", "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)\n", "plt.xlabel('Sepal length')\n", "plt.ylabel('Sepal width')\n", "plt.xlim(xx.min(), xx.max())\n", "plt.ylim(yy.min(), yy.max())\n", "plt.xticks(())\n", "plt.yticks(())\n", " \n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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+LKY8+i6RiWntYxRFIRD6PkYwBFqdgdPueA6/142iaNAZ2l4gi8/sSs9JU7lp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TfsfB6Xk00fTOHMge1EaWWIYgiCIGB1efF6RSQyGWkDxjDjvy9zdz0XJqeXtbl2jIlp/GfN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bKyCpje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\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k8e1r7J75CR/M3kRYk854hVUnN/klVbuPxsk78L1/T4W6LXFw9yH54Q3U3uW4fvoIV47t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\n8ySVyycO0WfOpr80h9+D3WalrCAHpbvXbzbKeImX+L+CdZtO0jfOg84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jq9Dvk+1k3LuK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9cEIPrZIyrWaUjvcTM5uWsTdw59Q7ORCwFw8S/G5R++x794KS4e20+34V9w+fgBBJsNRCK0Hj50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BbWdiTGvkdLV4+Daxbxw9rd2Lu5o1QqmdWnLQnRkQycthiHSlXx37OJDTPH4D1gJEKhCDWR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sHq3Iz8nCpWp1dDodOekplCmVPAsNwqdZa/z7DuXFvRBkv5UhCzl1EB3AX2y20mo1iMRi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Wnf+PT2FcYW1pTmZWNibk5pUSGO7l6sOHET0DcQGNncj+6jJhM0aDQAUc8esXn2OKRG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"text": [ "<matplotlib.figure.Figure at 0x7f521f712b10>" ] } ], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
Pritam-N/Linx
Test_Classification.ipynb
1
129290
{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Test_Classification.ipynb", "version": "0.3.2", "provenance": [], "collapsed_sections": [], "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "<a href=\"https://colab.research.google.com/github/Pritam-N/Linx/blob/master/Test_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" ] }, { "metadata": { "id": "d1e3Atcm_9yY", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "import tensorflow as tf\n", "from tensorflow import keras\n", "import numpy as np" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "WOnQria9AwJ8", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "dataset = keras.datasets.imdb" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "0X2BLf9AAzMr", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "(train_data,train_labels), (test_data,test_labels) = dataset.load_data()" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "AnN_ORcNA6e_", "colab_type": "code", "outputId": "2fb5da43-7f70-4c6d-a5e4-2bf7adea217c", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "cell_type": "code", "source": [ "train_data.shape" ], "execution_count": 15, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(25000,)" ] }, "metadata": { "tags": [] }, "execution_count": 15 } ] }, { "metadata": { "id": "3MP6WoJsBA-J", "colab_type": "code", "outputId": "942f1ebf-a780-4d6e-f056-5affbd58fd49", "colab": { "base_uri": "https://localhost:8080/", "height": 34 } }, "cell_type": "code", "source": [ "test_data.shape" ], "execution_count": 16, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(25000,)" ] }, "metadata": { "tags": [] }, "execution_count": 16 } ] }, { "metadata": { "id": "-M3QHv6oBDuY", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "word_index = dataset.get_word_index()" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "NKqm0Nw3BPSn", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 17812 }, "outputId": "cd32e1ab-682a-4c95-babe-792736537d86" }, "cell_type": "code", "source": [ "word_index" ], "execution_count": 18, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'fawn': 34701,\n", " 'tsukino': 52006,\n", " 'nunnery': 52007,\n", " 'sonja': 16816,\n", " 'vani': 63951,\n", " 'woods': 1408,\n", " 'spiders': 16115,\n", " 'hanging': 2345,\n", " 'woody': 2289,\n", " 'trawling': 52008,\n", " \"hold's\": 52009,\n", " 'comically': 11307,\n", " 'localized': 40830,\n", " 'disobeying': 30568,\n", " \"'royale\": 52010,\n", " \"harpo's\": 40831,\n", " 'canet': 52011,\n", " 'aileen': 19313,\n", " 'acurately': 52012,\n", " \"diplomat's\": 52013,\n", " 'rickman': 25242,\n", " 'arranged': 6746,\n", " 'rumbustious': 52014,\n", " 'familiarness': 52015,\n", " \"spider'\": 52016,\n", " 'hahahah': 68804,\n", " \"wood'\": 52017,\n", " 'transvestism': 40833,\n", " \"hangin'\": 34702,\n", " 'bringing': 2338,\n", " 'seamier': 40834,\n", " 'wooded': 34703,\n", " 'bravora': 52018,\n", " 'grueling': 16817,\n", " 'wooden': 1636,\n", " 'wednesday': 16818,\n", " \"'prix\": 52019,\n", " 'altagracia': 34704,\n", " 'circuitry': 52020,\n", " 'crotch': 11585,\n", " 'busybody': 57766,\n", " \"tart'n'tangy\": 52021,\n", " 'burgade': 14129,\n", " 'thrace': 52023,\n", " \"tom's\": 11038,\n", " 'snuggles': 52025,\n", " 'francesco': 29114,\n", " 'complainers': 52027,\n", " 'templarios': 52125,\n", " '272': 40835,\n", " '273': 52028,\n", " 'zaniacs': 52130,\n", " '275': 34706,\n", " 'consenting': 27631,\n", " 'snuggled': 40836,\n", " 'inanimate': 15492,\n", " 'uality': 52030,\n", " 'bronte': 11926,\n", " 'errors': 4010,\n", " 'dialogs': 3230,\n", " \"yomada's\": 52031,\n", " \"madman's\": 34707,\n", " 'dialoge': 30585,\n", " 'usenet': 52033,\n", " 'videodrome': 40837,\n", " \"kid'\": 26338,\n", " 'pawed': 52034,\n", " \"'girlfriend'\": 30569,\n", " \"'pleasure\": 52035,\n", " \"'reloaded'\": 52036,\n", " \"kazakos'\": 40839,\n", " 'rocque': 52037,\n", " 'mailings': 52038,\n", " 'brainwashed': 11927,\n", " 'mcanally': 16819,\n", " \"tom''\": 52039,\n", " 'kurupt': 25243,\n", " 'affiliated': 21905,\n", " 'babaganoosh': 52040,\n", " \"noe's\": 40840,\n", " 'quart': 40841,\n", " 'kids': 359,\n", " 'uplifting': 5034,\n", " 'controversy': 7093,\n", " 'kida': 21906,\n", " 'kidd': 23379,\n", " \"error'\": 52041,\n", " 'neurologist': 52042,\n", " 'spotty': 18510,\n", " 'cobblers': 30570,\n", " 'projection': 9878,\n", " 'fastforwarding': 40842,\n", " 'sters': 52043,\n", " \"eggar's\": 52044,\n", " 'etherything': 52045,\n", " 'gateshead': 40843,\n", " 'airball': 34708,\n", " 'unsinkable': 25244,\n", " 'stern': 7180,\n", " \"cervi's\": 52046,\n", " 'dnd': 40844,\n", " 'dna': 11586,\n", " 'insecurity': 20598,\n", " \"'reboot'\": 52047,\n", " 'trelkovsky': 11037,\n", " 'jaekel': 52048,\n", " 'sidebars': 52049,\n", " \"sforza's\": 52050,\n", " 'distortions': 17633,\n", " 'mutinies': 52051,\n", " 'sermons': 30602,\n", " '7ft': 40846,\n", " 'boobage': 52052,\n", " \"o'bannon's\": 52053,\n", " 'populations': 23380,\n", " 'chulak': 52054,\n", " 'mesmerize': 27633,\n", " 'quinnell': 52055,\n", " 'yahoo': 10307,\n", " 'meteorologist': 52057,\n", " 'beswick': 42577,\n", " 'boorman': 15493,\n", " 'voicework': 40847,\n", " \"ster'\": 52058,\n", " 'blustering': 22922,\n", " 'hj': 52059,\n", " 'intake': 27634,\n", " 'morally': 5621,\n", " 'jumbling': 40849,\n", " 'bowersock': 52060,\n", " \"'porky's'\": 52061,\n", " 'gershon': 16821,\n", " 'ludicrosity': 40850,\n", " 'coprophilia': 52062,\n", " 'expressively': 40851,\n", " \"india's\": 19500,\n", " \"post's\": 34710,\n", 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'stoicism': 23382,\n", " \"guys'\": 14896,\n", " \"beroemd'\": 52077,\n", " 'butcher': 6703,\n", " \"melfi's\": 40857,\n", " 'aargh': 30623,\n", " 'playhouse': 20599,\n", " 'wickedly': 11308,\n", " 'fit': 1180,\n", " 'labratory': 52078,\n", " 'lifeline': 40859,\n", " 'screaming': 1927,\n", " 'fix': 4287,\n", " 'cineliterate': 52079,\n", " 'fic': 52080,\n", " 'fia': 52081,\n", " 'fig': 34714,\n", " 'fmvs': 52082,\n", " 'fie': 52083,\n", " 'reentered': 52084,\n", " 'fin': 30574,\n", " 'doctresses': 52085,\n", " 'fil': 52086,\n", " 'zucker': 12606,\n", " 'ached': 31931,\n", " 'counsil': 52088,\n", " 'paterfamilias': 52089,\n", " 'songwriter': 13885,\n", " 'shivam': 34715,\n", " 'hurting': 9654,\n", " 'effects': 299,\n", " 'slauther': 52090,\n", " \"'flame'\": 52091,\n", " 'sommerset': 52092,\n", " 'interwhined': 52093,\n", " 'whacking': 27638,\n", " 'bartok': 52094,\n", " 'barton': 8775,\n", " 'frewer': 21909,\n", " \"fi'\": 52095,\n", " 'ingrid': 6192,\n", " 'stribor': 30575,\n", " 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'massacring': 42584,\n", " 'bicenntinial': 52160,\n", " 'skaal': 40887,\n", " 'droning': 14360,\n", " 'lds': 8776,\n", " 'jaguar': 21912,\n", " \"cale's\": 34727,\n", " 'nicely': 1777,\n", " 'mummy': 4588,\n", " \"lot's\": 18513,\n", " 'patch': 10086,\n", " 'kerkhof': 50202,\n", " \"leader's\": 52161,\n", " \"'movie\": 27644,\n", " 'uncomfirmed': 52162,\n", " 'heirloom': 40888,\n", " 'wrangle': 47360,\n", " 'emotion\\x85': 52163,\n", " \"'stargate'\": 52164,\n", " 'pinoy': 40889,\n", " 'conchatta': 40890,\n", " 'broeke': 41128,\n", " 'advisedly': 40891,\n", " \"barker's\": 17636,\n", " 'descours': 52166,\n", " 'lots': 772,\n", " 'lotr': 9259,\n", " 'irs': 9879,\n", " 'lott': 52167,\n", " 'xvi': 40892,\n", " 'irk': 34728,\n", " 'irl': 52168,\n", " 'ira': 6887,\n", " 'belzer': 21913,\n", " 'irc': 52169,\n", " 'ire': 27645,\n", " 'requisites': 40893,\n", " 'discipline': 7693,\n", " 'lyoko': 52961,\n", " 'extend': 11310,\n", " 'nature': 873,\n", " \"'dickie'\": 52170,\n", " 'optimist': 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'tonino': 40904,\n", " 'unleavened': 52187,\n", " 'fry': 11587,\n", " \"'tv'\": 40905,\n", " 'toning': 40906,\n", " 'obese': 14361,\n", " 'sensationalized': 30589,\n", " 'spiv': 40907,\n", " 'spit': 6259,\n", " 'arkin': 7364,\n", " 'charleton': 21915,\n", " 'jeon': 16823,\n", " 'boardroom': 21916,\n", " 'doubts': 4989,\n", " 'spin': 3084,\n", " 'hepo': 53083,\n", " 'wildcat': 27649,\n", " 'venoms': 10584,\n", " 'misconstrues': 52191,\n", " 'mesmerising': 18514,\n", " 'misconstrued': 40908,\n", " 'rescinds': 52192,\n", " 'prostrate': 52193,\n", " 'majid': 40909,\n", " 'climbed': 16479,\n", " 'canoeing': 34731,\n", " 'majin': 52195,\n", " 'animie': 57804,\n", " 'sylke': 40910,\n", " 'conditioned': 14899,\n", " 'waddell': 40911,\n", " '3\\x85': 52196,\n", " 'hyperdrive': 41188,\n", " 'conditioner': 34732,\n", " 'bricklayer': 53153,\n", " 'hong': 2576,\n", " 'memoriam': 52198,\n", " 'inventively': 30592,\n", " \"levant's\": 25249,\n", " 'portobello': 20638,\n", " 'remand': 52200,\n", " 'mummified': 19504,\n", " 'honk': 27650,\n", " 'spews': 19505,\n", " 'visitations': 40912,\n", " 'mummifies': 52201,\n", " 'cavanaugh': 25250,\n", " 'zeon': 23385,\n", " \"jungle's\": 40913,\n", " 'viertel': 34733,\n", " 'frenchmen': 27651,\n", " 'torpedoes': 52202,\n", " 'schlessinger': 52203,\n", " 'torpedoed': 34734,\n", " 'blister': 69876,\n", " 'cinefest': 52204,\n", " 'furlough': 34735,\n", " 'mainsequence': 52205,\n", " 'mentors': 40914,\n", " 'academic': 9094,\n", " 'stillness': 20602,\n", " 'academia': 40915,\n", " 'lonelier': 52206,\n", " 'nibby': 52207,\n", " \"losers'\": 52208,\n", " 'cineastes': 40916,\n", " 'corporate': 4449,\n", " 'massaging': 40917,\n", " 'bellow': 30593,\n", " 'absurdities': 19506,\n", " 'expetations': 53241,\n", " 'nyfiken': 40918,\n", " 'mehras': 75638,\n", " 'lasse': 52209,\n", " 'visability': 52210,\n", " 'militarily': 33946,\n", " \"elder'\": 52211,\n", " 'gainsbourg': 19023,\n", " 'hah': 20603,\n", " 'hai': 13420,\n", " 'haj': 34736,\n", " 'hak': 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\"britian's\": 52276,\n", " 'childs': 34744,\n", " \"portrait's\": 52277,\n", " 'chain': 3626,\n", " 'whoever': 2497,\n", " 'puttered': 52278,\n", " 'childe': 52279,\n", " 'maywether': 52280,\n", " 'chair': 3036,\n", " \"rance's\": 52281,\n", " 'machu': 34745,\n", " 'ballet': 4517,\n", " 'grapples': 34746,\n", " 'summerize': 76152,\n", " 'freelance': 30603,\n", " \"andrea's\": 52283,\n", " '\\x91very': 52284,\n", " 'coolidge': 45879,\n", " 'mache': 18518,\n", " 'balled': 52285,\n", " 'grappled': 40937,\n", " 'macha': 18519,\n", " 'underlining': 21921,\n", " 'macho': 5623,\n", " 'oversight': 19507,\n", " 'machi': 25257,\n", " 'verbally': 11311,\n", " 'tenacious': 21922,\n", " 'windshields': 40938,\n", " 'paychecks': 18557,\n", " 'jerk': 3396,\n", " \"good'\": 11931,\n", " 'prancer': 34748,\n", " 'prances': 21923,\n", " 'olympus': 52286,\n", " 'lark': 21924,\n", " 'embark': 10785,\n", " 'gloomy': 7365,\n", " 'jehaan': 52287,\n", " 'turaqui': 52288,\n", " \"child'\": 20607,\n", " 'locked': 2894,\n", " 'pranced': 52289,\n", " 'exact': 2588,\n", " 'unattuned': 52290,\n", " 'minute': 783,\n", " 'skewed': 16118,\n", " 'hodgins': 40940,\n", " 'skewer': 34749,\n", " 'think\\x85': 52291,\n", " 'rosenstein': 38765,\n", " 'helmit': 52292,\n", " 'wrestlemanias': 34750,\n", " 'hindered': 16826,\n", " \"martha's\": 30604,\n", " 'cheree': 52293,\n", " \"pluckin'\": 52294,\n", " 'ogles': 40941,\n", " 'heavyweight': 11932,\n", " 'aada': 82190,\n", " 'chopping': 11312,\n", " 'strongboy': 61534,\n", " 'hegemonic': 41342,\n", " 'adorns': 40942,\n", " 'xxth': 41346,\n", " 'nobuhiro': 34751,\n", " 'capitães': 52298,\n", " 'kavogianni': 52299,\n", " 'antwerp': 13422,\n", " 'celebrated': 6538,\n", " 'roarke': 52300,\n", " 'baggins': 40943,\n", " 'cheeseburgers': 31270,\n", " 'matras': 52301,\n", " \"nineties'\": 52302,\n", " \"'craig'\": 52303,\n", " 'celebrates': 12999,\n", " 'unintentionally': 3383,\n", " 'drafted': 14362,\n", " 'climby': 52304,\n", " '303': 52305,\n", " 'oldies': 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'alastair': 16827,\n", " 'wvs': 52319,\n", " 'ooooooh': 52320,\n", " 'approving': 34756,\n", " 'consoles': 40949,\n", " 'disparagement': 52064,\n", " 'futureistic': 52322,\n", " 'rebounding': 52323,\n", " \"'date\": 52324,\n", " 'gregoire': 52325,\n", " 'rutherford': 21927,\n", " 'americanised': 34757,\n", " 'novikov': 82196,\n", " 'following': 1042,\n", " 'munroe': 34758,\n", " \"morita'\": 52326,\n", " 'christenssen': 52327,\n", " 'oatmeal': 23106,\n", " 'fossey': 25260,\n", " 'livered': 40950,\n", " 'listens': 13000,\n", " \"'marci\": 76164,\n", " \"otis's\": 52330,\n", " 'thanking': 23387,\n", " 'maude': 16019,\n", " 'extensions': 34759,\n", " 'ameteurish': 52332,\n", " \"commender's\": 52333,\n", " 'agricultural': 27661,\n", " 'convincingly': 4518,\n", " 'fueled': 17639,\n", " 'mahattan': 54014,\n", " \"paris's\": 40952,\n", " 'vulkan': 52336,\n", " 'stapes': 52337,\n", " 'odysessy': 52338,\n", " 'harmon': 12259,\n", " 'surfing': 4252,\n", " 'halloran': 23494,\n", " 'unbelieveably': 49580,\n", " \"'offed'\": 52339,\n", " 'quadrant': 30607,\n", " 'inhabiting': 19510,\n", " 'nebbish': 34760,\n", " 'forebears': 40953,\n", " 'skirmish': 34761,\n", " 'ocassionally': 52340,\n", " \"'resist\": 52341,\n", " 'impactful': 21928,\n", " 'spicier': 52342,\n", " 'touristy': 40954,\n", " \"'football'\": 52343,\n", " 'webpage': 40955,\n", " 'exurbia': 52345,\n", " 'jucier': 52346,\n", " 'professors': 14901,\n", " 'structuring': 34762,\n", " 'jig': 30608,\n", " 'overlord': 40956,\n", " 'disconnect': 25261,\n", " 'sniffle': 82201,\n", " 'slimeball': 40957,\n", " 'jia': 40958,\n", " 'milked': 16828,\n", " 'banjoes': 40959,\n", " 'jim': 1237,\n", " 'workforces': 52348,\n", " 'jip': 52349,\n", " 'rotweiller': 52350,\n", " 'mundaneness': 34763,\n", " \"'ninja'\": 52351,\n", " \"dead'\": 11040,\n", " \"cipriani's\": 40960,\n", " 'modestly': 20608,\n", " \"professor'\": 52352,\n", " 'shacked': 40961,\n", " 'bashful': 34764,\n", " 'sorter': 23388,\n", " 'overpowering': 16120,\n", " 'workmanlike': 18521,\n", " 'henpecked': 27662,\n", " 'sorted': 18522,\n", " \"jōb's\": 52354,\n", " \"'always\": 52355,\n", " \"'baptists\": 34765,\n", " 'dreamcatchers': 52356,\n", " \"'silence'\": 52357,\n", " 'hickory': 21929,\n", " 'fun\\x97yet': 52358,\n", " 'breakumentary': 52359,\n", " 'didn': 15496,\n", " 'didi': 52360,\n", " 'pealing': 52361,\n", " 'dispite': 40962,\n", " \"italy's\": 25262,\n", " 'instability': 21930,\n", " 'quarter': 6539,\n", " 'quartet': 12608,\n", " 'padmé': 52362,\n", " \"'bleedmedry\": 52363,\n", " 'pahalniuk': 52364,\n", " 'honduras': 52365,\n", " 'bursting': 10786,\n", " \"pablo's\": 41465,\n", " 'irremediably': 52367,\n", " 'presages': 40963,\n", " 'bowlegged': 57832,\n", " 'dalip': 65183,\n", " 'entering': 6260,\n", " 'newsradio': 76172,\n", " 'presaged': 54150,\n", " \"giallo's\": 27663,\n", " 'bouyant': 40964,\n", " 'amerterish': 52368,\n", " 'rajni': 18523,\n", " 'leeves': 30610,\n", " 'macauley': 34767,\n", " 'seriously': 612,\n", " 'sugercoma': 52369,\n", " 'grimstead': 52370,\n", " \"'fairy'\": 52371,\n", " 'zenda': 30611,\n", " \"'twins'\": 52372,\n", " 'realisation': 17640,\n", " 'highsmith': 27664,\n", " 'raunchy': 7817,\n", " 'incentives': 40965,\n", " 'flatson': 52374,\n", " 'snooker': 35097,\n", " 'crazies': 16829,\n", " 'crazier': 14902,\n", " 'grandma': 7094,\n", " 'napunsaktha': 52375,\n", " 'workmanship': 30612,\n", " 'reisner': 52376,\n", " \"sanford's\": 61306,\n", " '\\x91doña': 52377,\n", " 'modest': 6108,\n", " \"everything's\": 19153,\n", " 'hamer': 40966,\n", " \"couldn't'\": 52379,\n", " 'quibble': 13001,\n", " 'socking': 52380,\n", " 'tingler': 21931,\n", " 'gutman': 52381,\n", " 'lachlan': 40967,\n", " 'tableaus': 52382,\n", " 'headbanger': 52383,\n", " 'spoken': 2847,\n", " 'cerebrally': 34768,\n", " \"'road\": 23490,\n", " 'tableaux': 21932,\n", " \"proust's\": 40968,\n", " 'periodical': 40969,\n", " \"shoveller's\": 52385,\n", " 'tamara': 25263,\n", " 'affords': 17641,\n", " 'concert': 3249,\n", " \"yara's\": 87955,\n", " 'someome': 52386,\n", " 'lingering': 8424,\n", " \"abraham's\": 41511,\n", " 'beesley': 34769,\n", " 'cherbourg': 34770,\n", " 'kagan': 28624,\n", " 'snatch': 9097,\n", " \"miyazaki's\": 9260,\n", " 'absorbs': 25264,\n", " \"koltai's\": 40970,\n", " 'tingled': 64027,\n", " 'crossroads': 19511,\n", " 'rehab': 16121,\n", " 'falworth': 52389,\n", " 'sequals': 52390,\n", " ...}" ] }, "metadata": { "tags": [] }, "execution_count": 18 } ] }, { "metadata": { "id": "gtSyAKxnBRsh", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "10sjaLhfBlez", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "reverse_word_index" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "v--nwd4QBqNB", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "word_index = {k:(v+3) for k,v in word_index.items()}" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "g7TIL1RqBw3i", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "word_index[\"<PAD>\"] = 0" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "jHOU17z0CABT", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "word_index[\"<PAD>\"]" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "7bRKMIugCGHm", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "train_data[0]" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "_fYRAriZCQFV", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "word_index[\"<START>\"] = 1\n", "word_index[\"<UNK>\"] = 2 # unknown\n", "word_index[\"<UNUSED>\"] = 3\n", "reverse_word_index = dict([(value,key) for (key,value) in word_index.items()])" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "e6pl6H5yCYWr", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "def decode_review(text):\n", " return ' '.join([reverse_word_index.get(i,'?') for i in text])" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "z6uqDCf4Cp0G", "colab_type": "code", "outputId": "12bc19aa-a17a-4a0d-c0a5-14bb574f4573", "colab": { "base_uri": "https://localhost:8080/", "height": 54 } }, "cell_type": "code", "source": [ "decode_review(train_data[0])" ], "execution_count": 22, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "\"<START> this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert redford's is an amazing actor and now the same being director norman's father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for retail and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also congratulations to the two little boy's that played the part's of norman and paul they were just brilliant children are often left out of the praising list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\"" ] }, "metadata": { "tags": [] }, "execution_count": 22 } ] }, { "metadata": { "id": "EjVGe4OBCtEq", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "# A dictionary mapping words to an integer index\n", "word_index = dataset.get_word_index()\n", "\n", "# The first indices are reserved\n", "word_index = {k:(v+3) for k,v in word_index.items()} \n", "word_index[\"<PAD>\"] = 0\n", "word_index[\"<START>\"] = 1\n", "word_index[\"<UNK>\"] = 2 # unknown\n", "word_index[\"<UNUSED>\"] = 3\n", "\n", "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\n", "\n", "def decode_review(text):\n", " return ' '.join([reverse_word_index.get(i, '?') for i in text])" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "UJaiGD9OC6Je", "colab_type": "code", "outputId": "8b972884-e10a-4ffd-c9ee-28bd55d7ff67", "colab": { "base_uri": "https://localhost:8080/", "height": 54 } }, "cell_type": "code", "source": [ "decode_review(train_data[0])" ], "execution_count": 0, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "\"<START> this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert redford's is an amazing actor and now the same being director norman's father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for retail and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also congratulations to the two little boy's that played the part's of norman and paul they were just brilliant children are often left out of the praising list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\"" ] }, "metadata": { "tags": [] }, "execution_count": 26 } ] }, { "metadata": { "id": "mC_LhIbdDBQn", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "train_data = keras.preprocessing.sequence.pad_sequences(train_data,\n", " value=word_index[\"<PAD>\"],\n", " padding='post',\n", " maxlen=256)\n", "\n", "test_data = keras.preprocessing.sequence.pad_sequences(test_data,\n", " value=word_index[\"<PAD>\"],\n", " padding='post',\n", " maxlen=256)" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "ZPPQnW3GQ1ER", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 283 }, "outputId": "c41d4405-c828-4afc-e1ee-9a1eec7bd597" }, "cell_type": "code", "source": [ "vocab_size = 100000\n", "\n", "model = keras.Sequential()\n", "model.add(keras.layers.Embedding(vocab_size, 16))\n", "model.add(keras.layers.GlobalAveragePooling1D())\n", "model.add(keras.layers.Dense(16, activation=tf.nn.relu))\n", "model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))\n", "\n", "model.summary()" ], "execution_count": 39, "outputs": [ { "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "embedding_3 (Embedding) (None, None, 16) 1600000 \n", "_________________________________________________________________\n", "global_average_pooling1d_3 ( (None, 16) 0 \n", "_________________________________________________________________\n", "dense_6 (Dense) (None, 16) 272 \n", "_________________________________________________________________\n", "dense_7 (Dense) (None, 1) 17 \n", "=================================================================\n", "Total params: 1,600,289\n", "Trainable params: 1,600,289\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ], "name": "stdout" } ] }, { "metadata": { "id": "glMwZ_-TQ4SM", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "model.compile(optimizer=tf.train.AdamOptimizer(),\n", " loss='binary_crossentropy',\n", " metrics=['accuracy'])" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "vj9nkiM8Q675", "colab_type": "code", "colab": {} }, "cell_type": "code", "source": [ "x_val = train_data[:10000]\n", "partial_x_train = train_data[10000:]\n", "\n", "y_val = train_labels[:10000]\n", "partial_y_train = train_labels[10000:]" ], "execution_count": 0, "outputs": [] }, { "metadata": { "id": "WEP5NMR9Q817", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 1456 }, "outputId": "447c8b14-186b-422c-837d-b98d3b7a6ad9" }, "cell_type": "code", "source": [ "history = model.fit(partial_x_train,\n", " partial_y_train,\n", " epochs=40,\n", " batch_size=1024,\n", " validation_data=(x_val, y_val),\n", " verbose=1)" ], "execution_count": 42, "outputs": [ { "output_type": "stream", "text": [ "Train on 15000 samples, validate on 10000 samples\n", "Epoch 1/40\n", "15000/15000 [==============================] - 1s 93us/step - loss: 0.6924 - acc: 0.5355 - val_loss: 0.6911 - val_acc: 0.6015\n", "Epoch 2/40\n", "15000/15000 [==============================] - 1s 70us/step - loss: 0.6891 - acc: 0.6336 - val_loss: 0.6874 - val_acc: 0.7201\n", "Epoch 3/40\n", "15000/15000 [==============================] - 1s 71us/step - loss: 0.6838 - acc: 0.7521 - val_loss: 0.6818 - val_acc: 0.7514\n", "Epoch 4/40\n", "15000/15000 [==============================] - 1s 71us/step - loss: 0.6760 - acc: 0.7896 - val_loss: 0.6740 - val_acc: 0.7690\n", "Epoch 5/40\n", "15000/15000 [==============================] - 1s 72us/step - loss: 0.6652 - acc: 0.8043 - val_loss: 0.6631 - val_acc: 0.7681\n", "Epoch 6/40\n", "15000/15000 [==============================] - 1s 71us/step - loss: 0.6506 - acc: 0.8088 - val_loss: 0.6491 - val_acc: 0.7821\n", "Epoch 7/40\n", "15000/15000 [==============================] - 1s 71us/step - loss: 0.6321 - acc: 0.8187 - val_loss: 0.6317 - val_acc: 0.7820\n", "Epoch 8/40\n", "15000/15000 [==============================] - 1s 69us/step - loss: 0.6098 - acc: 0.8264 - val_loss: 0.6115 - val_acc: 0.7936\n", "Epoch 9/40\n", "15000/15000 [==============================] - 1s 71us/step - loss: 0.5840 - acc: 0.8362 - val_loss: 0.5890 - val_acc: 0.7991\n", "Epoch 10/40\n", "15000/15000 [==============================] - 1s 70us/step - loss: 0.5559 - acc: 0.8431 - val_loss: 0.5651 - val_acc: 0.8109\n", "Epoch 11/40\n", "15000/15000 [==============================] - 1s 71us/step - loss: 0.5263 - acc: 0.8525 - val_loss: 0.5406 - val_acc: 0.8191\n", "Epoch 12/40\n", "15000/15000 [==============================] - 1s 71us/step - loss: 0.4963 - acc: 0.8631 - val_loss: 0.5160 - val_acc: 0.8222\n", "Epoch 13/40\n", "15000/15000 [==============================] - 1s 71us/step - loss: 0.4661 - acc: 0.8737 - val_loss: 0.4923 - val_acc: 0.8316\n", "Epoch 14/40\n", "15000/15000 [==============================] - 1s 71us/step - loss: 0.4372 - acc: 0.8817 - val_loss: 0.4698 - val_acc: 0.8385\n", "Epoch 15/40\n", "15000/15000 [==============================] - 1s 72us/step - loss: 0.4094 - acc: 0.8905 - val_loss: 0.4488 - val_acc: 0.8456\n", "Epoch 16/40\n", "15000/15000 [==============================] - 1s 73us/step - loss: 0.3832 - acc: 0.8973 - val_loss: 0.4300 - val_acc: 0.8493\n", "Epoch 17/40\n", "15000/15000 [==============================] - 1s 70us/step - loss: 0.3590 - acc: 0.9053 - val_loss: 0.4118 - val_acc: 0.8551\n", "Epoch 18/40\n", "15000/15000 [==============================] - 1s 71us/step - loss: 0.3362 - acc: 0.9120 - val_loss: 0.3960 - val_acc: 0.8589\n", "Epoch 19/40\n", "15000/15000 [==============================] - 1s 70us/step - loss: 0.3155 - acc: 0.9158 - val_loss: 0.3819 - val_acc: 0.8626\n", "Epoch 20/40\n", "15000/15000 [==============================] - 1s 72us/step - loss: 0.2964 - acc: 0.9211 - val_loss: 0.3696 - val_acc: 0.8651\n", "Epoch 21/40\n", "15000/15000 [==============================] - 1s 72us/step - loss: 0.2789 - acc: 0.9247 - val_loss: 0.3583 - val_acc: 0.8694\n", "Epoch 22/40\n", "15000/15000 [==============================] - 1s 72us/step - loss: 0.2629 - acc: 0.9287 - val_loss: 0.3480 - val_acc: 0.8725\n", "Epoch 23/40\n", "15000/15000 [==============================] - 1s 71us/step - loss: 0.2483 - acc: 0.9337 - val_loss: 0.3391 - val_acc: 0.8747\n", "Epoch 24/40\n", "15000/15000 [==============================] - 1s 71us/step - loss: 0.2343 - acc: 0.9383 - val_loss: 0.3313 - val_acc: 0.8762\n", "Epoch 25/40\n", "15000/15000 [==============================] - 1s 71us/step - loss: 0.2214 - acc: 0.9427 - val_loss: 0.3243 - val_acc: 0.8789\n", "Epoch 26/40\n", "15000/15000 [==============================] - 1s 71us/step - loss: 0.2095 - acc: 0.9447 - val_loss: 0.3181 - val_acc: 0.8784\n", "Epoch 27/40\n", "15000/15000 [==============================] - 1s 70us/step - loss: 0.1987 - acc: 0.9482 - val_loss: 0.3127 - val_acc: 0.8790\n", "Epoch 28/40\n", "15000/15000 [==============================] - 1s 70us/step - loss: 0.1884 - acc: 0.9525 - val_loss: 0.3077 - val_acc: 0.8801\n", "Epoch 29/40\n", "15000/15000 [==============================] - 1s 72us/step - loss: 0.1787 - acc: 0.9552 - val_loss: 0.3037 - val_acc: 0.8812\n", "Epoch 30/40\n", "15000/15000 [==============================] - 1s 71us/step - loss: 0.1697 - acc: 0.9578 - val_loss: 0.2998 - val_acc: 0.8826\n", "Epoch 31/40\n", "15000/15000 [==============================] - 1s 70us/step - loss: 0.1611 - acc: 0.9609 - val_loss: 0.2962 - val_acc: 0.8832\n", "Epoch 32/40\n", "15000/15000 [==============================] - 1s 70us/step - loss: 0.1533 - acc: 0.9634 - val_loss: 0.2934 - val_acc: 0.8841\n", "Epoch 33/40\n", "15000/15000 [==============================] - 1s 71us/step - loss: 0.1458 - acc: 0.9663 - val_loss: 0.2908 - val_acc: 0.8850\n", "Epoch 34/40\n", "15000/15000 [==============================] - 1s 72us/step - loss: 0.1388 - acc: 0.9685 - val_loss: 0.2883 - val_acc: 0.8854\n", "Epoch 35/40\n", "15000/15000 [==============================] - 1s 70us/step - loss: 0.1321 - acc: 0.9701 - val_loss: 0.2871 - val_acc: 0.8862\n", "Epoch 36/40\n", "15000/15000 [==============================] - 1s 70us/step - loss: 0.1259 - acc: 0.9725 - val_loss: 0.2845 - val_acc: 0.8855\n", "Epoch 37/40\n", "15000/15000 [==============================] - 1s 70us/step - loss: 0.1201 - acc: 0.9743 - val_loss: 0.2843 - val_acc: 0.8859\n", "Epoch 38/40\n", "15000/15000 [==============================] - 1s 70us/step - loss: 0.1146 - acc: 0.9767 - val_loss: 0.2834 - val_acc: 0.8853\n", "Epoch 39/40\n", "15000/15000 [==============================] - 1s 71us/step - loss: 0.1093 - acc: 0.9779 - val_loss: 0.2815 - val_acc: 0.8866\n", "Epoch 40/40\n", "15000/15000 [==============================] - 1s 69us/step - loss: 0.1043 - acc: 0.9801 - val_loss: 0.2804 - val_acc: 0.8878\n" ], "name": "stdout" } ] }, { "metadata": { "id": "73LqBRTRQ-kT", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "outputId": "c35bec87-9c62-43c9-f5ab-c043e9346636" }, "cell_type": "code", "source": [ "results = model.evaluate(test_data, test_labels)" ], "execution_count": 32, "outputs": [ { "output_type": "stream", "text": [ "25000/25000 [==============================] - 1s 43us/step\n" ], "name": "stdout" } ] }, { "metadata": { "id": "89aUii2eRdCB", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "outputId": "d3690a7a-604e-4835-a60a-6150aa9710f5" }, "cell_type": "code", "source": [ "print(results)" ], "execution_count": 33, "outputs": [ { "output_type": "stream", "text": [ "[0.3344138513469696, 0.87244]\n" ], "name": "stdout" } ] }, { "metadata": { "id": "VQrT7zOpRiCx", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "outputId": "e237fccd-e91f-4769-8522-2e59a62a2104" }, "cell_type": "code", "source": [ "history_dict = history.history\n", "history_dict.keys()" ], "execution_count": 34, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])" ] }, "metadata": { "tags": [] }, "execution_count": 34 } ] }, { "metadata": { "id": "aWkEY0YSRk_5", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 375 }, "outputId": "5cdfdaff-2e41-4d4a-9d2d-41d8bf1f8a53" }, "cell_type": "code", "source": [ "import matplotlib.pyplot as plt\n", "\n", "acc = history.history['acc']\n", "val_acc = history.history['val_acc']\n", "loss = history.history['loss']\n", "val_loss = history.history['val_loss']\n", "\n", "epochs = range(1, len(acc) + 1)\n", "\n", "# \"bo\" is for \"blue dot\"\n", "plt.plot(epochs, loss, 'bo', label='Training loss')\n", "# b is for \"solid blue line\"\n", "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n", "plt.title('Training and validation loss')\n", "plt.xlabel('Epochs')\n", "plt.ylabel('Loss')\n", "plt.legend()\n", "\n", "plt.show()" ], "execution_count": 43, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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ynKFNRKSyUvAWjxQaCgsXZtOokYVZswJ54w3/ii6SiIjHUPAWj7NsmYl27UK4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\nREQso/AWERGxjMJbRETEMl8GQf0Fp60sGAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb62f688668>" ] }, "metadata": { "tags": [] } } ] }, { "metadata": { "id": "lqzRsB54RuCY", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 375 }, "outputId": "a4bc28f7-947f-4127-9c8b-3dd467dc98bf" }, "cell_type": "code", "source": [ "plt.clf() # clear figure\n", "acc_values = history_dict['acc']\n", "val_acc_values = history_dict['val_acc']\n", "\n", "plt.plot(epochs, acc, 'bo', label='Training acc')\n", "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n", "plt.title('Training and validation accuracy')\n", "plt.xlabel('Epochs')\n", "plt.ylabel('Accuracy')\n", "plt.legend()\n", "\n", "plt.show()" ], "execution_count": 44, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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R6HdQkLPftKBVVrildu6SdHa2s8+2IIgLAtqTS7Pni4x0XnatV89OlSrOy7DV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\n++67jzVr1ni1UFKyefOCePJ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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb62f67ad68>" ] }, "metadata": { "tags": [] } } ] }, { "metadata": { "id": "jRbwfYK5RwYB", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 1841 }, "outputId": "e06ea8b1-ec57-4f4d-fd79-85ef534b7d6e" }, "cell_type": "code", "source": [ "history = model.fit(partial_x_train,\n", " partial_y_train,\n", " epochs=40,\n", " batch_size=1024,\n", " validation_data=(x_val, y_val),\n", " verbose=1)" ], "execution_count": 38, "outputs": [ { "output_type": "stream", "text": [ "Train on 15000 samples, validate on 10000 samples\n", "Epoch 1/40\n", "15000/15000 [==============================] - 1s 60us/step - loss: 3.7544e-07 - acc: 1.0000 - val_loss: 1.1327 - val_acc: 0.8629\n", "Epoch 2/40\n", "15000/15000 [==============================] - 1s 62us/step - loss: 3.8036e-07 - acc: 1.0000 - val_loss: 1.1329 - val_acc: 0.8627\n", "Epoch 3/40\n", "15000/15000 [==============================] - 1s 63us/step - loss: 3.7371e-07 - acc: 1.0000 - val_loss: 1.1325 - val_acc: 0.8623\n", "Epoch 4/40\n", "15000/15000 [==============================] - 1s 70us/step - loss: 3.6858e-07 - acc: 1.0000 - val_loss: 1.1326 - val_acc: 0.8623\n", "Epoch 5/40\n", "15000/15000 [==============================] - 1s 68us/step - loss: 3.6539e-07 - acc: 1.0000 - val_loss: 1.1332 - val_acc: 0.8625\n", "Epoch 6/40\n", "15000/15000 [==============================] - 1s 68us/step - loss: 3.6334e-07 - acc: 1.0000 - val_loss: 1.1334 - val_acc: 0.8625\n", "Epoch 7/40\n", "15000/15000 [==============================] - 1s 68us/step - loss: 3.6110e-07 - acc: 1.0000 - val_loss: 1.1336 - val_acc: 0.8626\n", "Epoch 8/40\n", "15000/15000 [==============================] - 1s 67us/step - loss: 3.5959e-07 - acc: 1.0000 - val_loss: 1.1339 - val_acc: 0.8624\n", "Epoch 9/40\n", "15000/15000 [==============================] - 1s 68us/step - loss: 3.5732e-07 - acc: 1.0000 - val_loss: 1.1343 - val_acc: 0.8624\n", "Epoch 10/40\n", "15000/15000 [==============================] - 1s 68us/step - loss: 3.5602e-07 - acc: 1.0000 - val_loss: 1.1346 - val_acc: 0.8624\n", "Epoch 11/40\n", "15000/15000 [==============================] - 1s 68us/step - loss: 3.5433e-07 - acc: 1.0000 - val_loss: 1.1348 - val_acc: 0.8622\n", "Epoch 12/40\n", "15000/15000 [==============================] - 1s 69us/step - loss: 3.5248e-07 - acc: 1.0000 - val_loss: 1.1352 - val_acc: 0.8624\n", "Epoch 13/40\n", "15000/15000 [==============================] - 1s 69us/step - loss: 3.5083e-07 - acc: 1.0000 - val_loss: 1.1355 - val_acc: 0.8624\n", "Epoch 14/40\n", "15000/15000 [==============================] - 1s 68us/step - loss: 3.4880e-07 - acc: 1.0000 - val_loss: 1.1358 - val_acc: 0.8624\n", "Epoch 15/40\n", "15000/15000 [==============================] - 1s 68us/step - loss: 3.4733e-07 - acc: 1.0000 - val_loss: 1.1361 - val_acc: 0.8622\n", "Epoch 16/40\n", "15000/15000 [==============================] - 1s 68us/step - loss: 3.4512e-07 - acc: 1.0000 - val_loss: 1.1367 - val_acc: 0.8623\n", "Epoch 17/40\n", " 8192/15000 [===============>..............] - ETA: 0s - loss: 3.7739e-07 - acc: 1.0000" ], "name": "stdout" }, { "output_type": "error", "ename": "KeyboardInterrupt", "evalue": "ignored", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-38-b6957959e5ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1024\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_val\u001b[0m\u001b[0;34m,\u001b[0m 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validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m 1640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1641\u001b[0m def evaluate(self,\n", "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m 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gpl-2.0
mayank-johri/LearnSeleniumUsingPython
Section 1 - Core Python/Chapter 01 - Introduction/01_Introduction.ipynb
1
28023
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Chapter 1 - Introduction to Python\n", "__________" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Python](http://www.python.org) is a very **High Level, object-oriented, dynamic<sup>1</sup>, strong typing<sup>2</sup>, interpreted<sup>4</sup>** & **interactive<sup>5</sup>** programming language." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> 1. **Dynamic Programming language**: It at runtime executes many common programming tasks which static programming languages perform during compilation such as \n", " - Computation of code at runtime and late binding\n", " - alteration of Objects at runtime \n", " - Assembling of code at runtime based on the class of instances\n", "- **Strongly Typed**: Typing errors are prevented at runtime using the implicit type conversion, also it don't have static type checking, i.e. compiler don't check or enforce type constraint rules. The term **duck typing<sup>3</sup>** is now used to describe the dynamic typing paradigm.\n", "- Duck typing (https://en.wikipedia.org/wiki/Duck_typing) is an application of the duck test in type safety. It means that type checking of variables and data are done at runtime only, and is implemented by use of dynamic typing or by reflection.\n", "- **Interpreted**: Instead of compiled, the source code is interpreted by Python at runtime and then executed. \n", "- **Interactive**: Python provide an interactive shell, where you can run one line at a time and update the code as need be. More details about the shell will be provide later in this section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> It is also an open source language (with license compatible with the *General Public License (GPL)*, but less restrictive, allowing Python to be even incorporated into proprietary products). Its specification is maintained by the [Python Software Foundation](http://www.python.org/psf/) (PSF)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Key Features" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python has been my choice of programming language since 2006 due to following reasons:\n", "\n", "* It uses an simple & elegant syntax which makes the code easier to read and maintain\n", "* Due to its simplicity, maintainability & fast development: its ideal language for prototype development and ad-hoc programming \n", "* Default installation of Python contains huge about of standard library supporting most of the common programming tasks, such as developing REST clients, websites, socket programming, searching text with regular expressions, reading and modifying files such as XML, json and yaml etc.\n", "* Python's interactive mode, makes it easy to validate snippets of code. \n", "* Is easily extended by adding new modules implemented in a compiled language such as C or C++.\n", "* Can also be embedded into an application to provide a programmable interface.\n", "* It can be executed on almost all Operating Systems including Mac OS X, Windows, Linux, and Unix.\n", "* Is free software in two senses\n", " - It doesn't cost anything to download or use Python, or to include it in your application. \n", " - It can be freely modified and re-distributed, because while the language is copyrighted it's available under an open source license.\n", "* Bundled development environment called IDLE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Some programming-language features of Python are:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Many basic data types: \n", " - numbers (floating point, complex, and unlimited-length long integers)\n", " - strings (both ASCII and Unicode) \n", " - Collections (lists, dictionaries)\n", "* Python supports object-oriented programming with classes and multiple inheritance.\n", "* Code can be grouped into modules and packages.\n", "* The language supports raising and catching exceptions, resulting in cleaner error handling.\n", "* Data types are strongly and dynamically typed. Mixing incompatible types (e.g. attempting to add a string and a number) causes an exception to be raised, so errors are caught sooner.\n", "* Python contains advanced programming features such as generators and list comprehensions.\n", "* Python's automatic memory management frees you from having to manually allocate and free memory in your code. \n", "\n", "It is possible to integrate Python with other languages such as C and Fortran. In general terms, it has many similarities with other dynamic languages such as Perl and Ruby." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## History" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "The language was created in 1990 by **Guido van Rossum**, at National Research Institute for Mathematics and Computer Science in the Netherlands (CWI) and had originally focused on users as physicists and engineers. Python was designed from another existing language at the time, called ABC.\n", "![Guido van Rossum](files/GuidoVanRossum.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Versions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The official implementation of Python is maintained by the PSF and written in C, and therefore is also known as CPython. The latest stable version is available for download at:\n", "\n", "[http://www.python.org/download/](http://www.python.org/download/)\n", "\n", "For Windows platforms, simply run the installer. For other platforms, such as Linux, Python should already be part of the system, in rare cases you might have to install it from the system package management, but in very rare cases, you may wish to compile and install the interpreter from the source files.\n", "\n", "There are also implementations of Python for. NET (IronPython), JVM (Jython) and Python (PyPy)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running programs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What code looks like" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example of Python program:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Drums\n", "Flute\n", "Harmonium\n", "Guitar\n", "Hello\n" ] } ], "source": [ "# the character \"#\" indicate that rest of the line is a comment \n", "# and will be ignored by the interpreter\n", "\n", "# A list of musical instruments\n", "instruments = ['Drums', 'Flute', 'Harmonium', \"Guitar\"]\n", "\n", "# for each instrument in the list of instruments\n", "for instrument in instruments:\n", " print(instrument)\n", "print(\"Hello\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In above example, `instruments` is a list containing the items \"Drums\", \"Flute\", \"Harmonium\" and \"Guitar\" and as the `for` loop is executed `instrument` corresponds to, an item from items on the list, one at a time." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Executing the code" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As the python is an interpreted language, source code have to executed by running the command `python` and passing the main source file as a paramter.\n", "\n", "To understand it better lets save the above code in a text file and name it `ap1.py`. This will be our main source file. The source files are usually identified by the extension \".py\" and can be run directly by the interpreter. Now lets \n", "- open a command terminal (`cmd.exe` for `Windows` and respective terminal command for *inx's such as xterm, mate-terminal, terminology [https://www.enlightenment.org/about-terminology]). \n", "- Navigate to the directory where the `apl.py` file is stored.\n", "- execute the following command\n", "\n", "```sh\n", "python apl.py\n", "```\n", "\n", "Thus `apl.py` will run. On Windows, the file extensions \".py\", \". pyw\", \". pyc\" and \". pyo\" are associated with Python automatically during installation, so just click a the file to run it. The \". pyw\" files run with an alternate version of the interpreter that does not open the console window." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dynamic Typing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Python uses dynamic typing, which means that the type of a variable is inferred by the interpreter at runtime (this is known as *Duck Typing*). By the time a variable is created by attribution the interpreter defines the type of a variable, along with the operations that can be applied.\n", "\n", "Typing of Python is strong, ie, the interpreter checks whether the transactions are valid and does automatic coercions between incompatible types. In Python, coercions are performed automatically only between types that are clearly related, as integer and long integer. To perform the operation between non-compatible types, you must explicitly convert the type of the variable or variables before the operation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compilation and interpretation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The source code is translated by Python to bytecode, which is a binary format with instructions for the interpreter. The bytecode is cross platform and can be distributed and run without the original source.\n", "\n", "<img src=\"files/bpyfd_diags1.png\" alt=\"Compilation, interpretation and packing\" width=500>\n", "\n", "By default, the parser compiles the code and stores the bytecode on disk, so the next time you run it, there is no need to recompile the program, reducing the load time of execution. If the source files are changed, the interpreter will be responsible for regenerating the bytecode automatically, even using the *interactive shell*. When a program or a module is invoked, the interpreter performs the analysis of the code, converts to symbols, compiles (if there is no updated bytecode on disk) and runs it in the Python virtual machine.\n", "\n", "The bytecode is stored in files with the extension \". pyc\" (normal bytecode) or \". pyo\" (optimized bytecode). The bytecode can also be packaged along with an executable interpreter, to facilitate the distribution of the application, eliminating the need to install Python on each computer." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interactive Mode" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Python interpreter can be used interactively, where lines of code are typed into a *prompt* (command line) *shell* similar to the operating system.\n", "\n", "`python`\n", "\n", "It is ready to receive commands after the appearance of the signal `>>>` on the screen:\n", "\n", "`Python 2.6.4 (r264:75706, Nov 3 2009, 13:20:47)`<br/>\n", "`[GCC 4.4.1] on linux2`<br/>\n", "`Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.`<br/>\n", "`>>>`\n", "\n", "On Windows, the interactive mode is also available via the icon \"Python (command line)\".\n", "\n", "The interactive mode is a distinguishing feature of the language, as it is possible to test and modify code snippets before inclusion in programs, to extract and convert data or even analyze the state of the objects in memory, among other possibilities.\n", "\n", "Besides the traditional interactive mode of Python, there are other programs that act as alternatives to more sophisticated interfaces (such as <span class=\"note\" title=\"PyCrust is part of wxPython project (http://www.wxpython.org/\">PyCrust</span>):\n", "<img src=\"files/pycrust.png\" alt=\"pycrust\" width=700>\n", "![PyCrust] (files/pycrust.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Common IDE & Tools" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many development tools for Python, such as IDEs, editors and shells (that take advantage of the interactive capabilities of Python).\n", "\n", "*Integrated Development Environments* (IDEs) are software packages that integrate various development tools in an environment consistent with the goal of increasing developer productivity. Generally, IDEs include such features as syntax highlighting (colorized source code according to the syntax of the language), source browsers, integrated shell and *code completion* (the editor presents possible ways to complete the text it can identify while typing).\n", "Among Python IDEs, there are most popular ones:\n", "\n", "+ [Atom](https://atom.io)\n", "+ [PyDev](http://pydev.org/) (plug-in for Eclipse IDE)\n", "+ [vim](http://vim.org)\n", "+ [Sublime Text](http://www.sublimetext.com/)\n", "+ [PyScripter](http://code.google.com/p/pyscripter/)\n", "+ [SPE](http://pythonide.blogspot.com/) (Stani's Python Editor)\n", "+ [Eric](http://eric-ide.python-projects.org/)\n", "\n", "![PyScripter](files/pyscripter.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Entire list " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "(from https://wiki.python.org/moin/IntegratedDevelopmentEnvironments?action=show&redirect=IDE) \n", "\n", "\n", "**Name**|**Platform**|**Updated**|**Notes**\n", "-----|-----|-----|-----\n", "Thonny|Windows, Linux, Mac OS X, more|2016|For teaching/learning programming. Focused on program runtime visualization. Provides stepping both in statements and expressions, no-hassle variables view, separate mode for explaining references etc.\n", "Komodo|Windows/Linux/Mac OS X|2012|Multi-language IDE with support for Python 2.x and Python 3. Available as Komodo IDE (commercial).\n", "LiClipse|Linux/Mac OS X/Windows|2015|Commercial Eclipse-based IDE which provides a standalone bundling PyDev, Workspace Mechanic, Eclipse Color Theme, StartExplorer and AnyEdit, along with lightweigth support for other languages, and other usability enhancements (such as multi-caret-edition).\n", "NetBeans|Linux, Mac, Solaris, Windows|2009|Python/Jython support in NetBeans -- Open source, allows Python and Jython Editing, code-completion, debugger, refactoring, templates, syntax analysis, etc.; see also http://wiki.netbeans.org/Python. UPDATE: Netbeans 7.0 released without Python support. Check http://wiki.netbeans.org/Python70Roadmap for upcoming Python support.\n", "PyCharm|Linux/Mac OS X/Windows|2014|Free open-source IDE with a smart Python editor providing quick code navigation, code completion, refactoring, unit testing and debugger. Has a commercial Professional edition that fully supports Web development with Django, Flask, Mako and Web2Py and allows to develop remotely. Free PyCharm professional licenses for open-source projects.\n", "Python for VS Code|Linux/Mac OS X/Windows|2016|Free open-source extension for Visual Studio Code. Supports syntax highlighting, debugging, code completion, code navigation, refactoring, with support for Django, multi threaded, local and remote debugging.\n", "KDevelop|Linux/Mac OS X/(Windows)|2014|Free open-source IDE with a focus on static analysis-based code completion, navigation and highlighting. Also features a VI emulation mode.\n", "PyDev|Eclipse|2015|Free, open-source plugin for Eclipse -- Allows Python, Jython, and IronPython editing, code-completion, debugger, refactoring, quick navigation, templates, code analysis, unittest integration, Django integration, etc.\n", "Wing IDE|Windows, Linux, Mac OS X|2016|Commercial Python IDE with advanced debugger, editor with vi, emacs, visual studio and other key bindings, auto-completion, auto-editing, snippets, goto-definition, find uses, refactoring, unit testing, source browser, and much more. There are several product levels, including free and paid versions with a fully functional trial with up to three 10 day trial periods. See product features and pricing for details.\n", "PyScripter|Windows|2012|MIT licensed IDE written in Delphi with debugger, integrated unit testing, source browser, code navigation and syntax coloring/auto-completing editor.\n", "Pyshield|Windows, Linux|2010|Commercial IDE tool used to edit, debug Python script, publish encrypted scripts, build a standalone executable file, manage more files by project view, and make installation in various forms(.msi, .tar.gz, .rpm, .zip, .tar.bz2). It includes an editor simulating Emacs python-mode, a GUI debugger simulating GDB, a project view used to manage scripts, modules, extensions, packages, platform specific data files, and GUI interface to make installation.\n", "Spyder|Windows/Linux/Mac OS X|2012|Free open-source scientific Python development environment providing MATLAB-like features: console with variable browser, sys.path browser, environment variables browser, integrated plotting features, autocompletion and tooltips - editor with syntax highlighting, class/function browser, pyflakes/pylint code analysis, inline find/replace and search in files features, code completion and tooltips. 100% pure Python, part of Python(x,y) distribution (Windows/Linux).\n", "IDLE|Windows/Linux/Mac OS X/All Tk Platforms|2009|Multi-window colorized source browser, autoindent, autocompletion, tool tips, code context panel, search in files, class and path browsers, debugger, executes code in clean separate subprocess with one keystroke. 100% pure Python, part of Python 2.x and 3.x distributions.\n", "IdleX|Windows/Linux/Mac OS X/All Tk Platforms|2012|IdleX is a collection of over twenty extensions and plugins that provide additional functionality to IDLE, a Python IDE provided in the standard library. It transforms IDLE into a more useful tool for academic research and development as well as exploratory programming.\n", "µ.dev|Windows (needs to be compiled manually for other platforms)|2010|An open-source IDE, created using Lazarus. It's only for Python. include syntax highlighting, project manager, and uses pdb for debugging.\n", "Pyzo (formerly IEP)|Windows/Linux/Mac OS X|2016|Open-source Python IDE focused on interactivity and introspection, which makes it very suitable for scientific computing. Its practical design is aimed at simplicity and efficiency. Pyzo consists of two main components, the editor and the shell, and uses a set of pluggable tools to help the programmer in various ways: e.g. source structure, interactive help, workspace, file browser (with functionality for searching). Also includes a post-mortem debugger.\n", "PythonToolkit (PTK)|Windows/Linux/Mac OS X|2011|An interactive environment for python built around a matlab style console window and editor. It was designed to provide a python based environment similiar to Matlab for scientists and engineers however it can also be used as a general purpose interactive python environment especially for interactive GUI programming. Features include: Multiple independent python interpreters. Interactively program with different GUI toolkits (wxPython, TkInter, pyGTK, pyQT4 and PySide). Matlab style namespace/workspace browser. Object auto-completions, calltips and multi-line command editing in the console. Object inspection and python path management. Simple code editor and integrated debugger.\n", "PyStudio|Windows/Linux/Mac OS X|2012|Open-source plugin that adds syntax checking, integrated debugger and module search to Editra, a general purpose developer's text editor that supports python syntax highlighting, auto-indent, auto-completion, classbrowser, and can run scripts from inside the editor.\n", "Python Tools for Visual Studio|Windows|2013|Open-source plugin for Visual Studio 2010, 2012 and 2013. Supports syntax highlighting, debugging and rich intellisense, refactoring, object browser, MPI cluster debugging, Django intellisense and debugging, development REPL window and a debugging REPL window. Supports mixed-mode Python/C/C++ debugging.\n", "Exedore|Mac OS X|2013|Commercial with feature-limited free trial. A Mac-native, single-window IDE inspired by Xcode. Features integrated debugger, tabs, code completion with tab triggers, syntax highlighting themes, search and replace with regex, integrated REPL sessions, goto definition, file browser, integrated documentation browser. As of June 2015, does not support input() meaning any console input using this function is not supported.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### IDE Configurations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I will be using either vim/nvim/emacs or atom during this course. Few tips to use them are as follows\n", "\n", "- vim/nvim: \n", " - https://realpython.com/blog/python/vim-and-python-a-match-made-in-heaven/, \n", " - https://www.fullstackpython.com/vim.html\n", "- emacs: emacs-for-python, \n", " - http://www.jesshamrick.com/2012/09/18/emacs-as-a-python-ide/,\n", " - https://emacswiki.org/emacs/PythonProgrammingInEmacs\n", "- atom: \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Text Editors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are also text editors specialized in programming code, which have features like syntax colorization, export to other formats and convert text encoding.\n", "\n", "These editors support multiple programming languages​​, Python among them:\n", "\n", "+ [SciTE](http://www.scintilla.org/SciTE.html)\n", "+ [Notepad++](http://notepad-plus.sourceforge.net/br/site.htm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Shell** is the name given to interactive environments for executing commands that can be used to test small pieces of code and for activities like data crunching (extraction of information of interest in masses of data and subsequent translation to other formats).\n", "\n", "Beyond the standard Python **Shell**, there are others available:\n", "\n", "+ PyCrust \n", "+ IPython \n", "+ Reinteract\n", "+ bpython\n", "+ PyroShell" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Packers** are utilities that are used to build executables that comprise the bytecode, the interpreter and other dependencies, allowing the application to run on machines without Python installed, which facilitates program distribution.\n", "\n", "Among packers for Python, are available:\n", "\n", "+ py2exe (Windows only)\n", "+ cx_Freeze (portable)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Frameworks** are collections of software components (libraries, utilities and others) that have been designed to be used by other systems.\n", "\n", "Some of the most known *frameworks* availble are:\n", "\n", "+ Web: Django, TurboGears, Zope and web2py.\n", "+ Graphic interface: wxPython, PyGTK and PyQt.\n", "+ Scientific processing: NumPy and SciPy.\n", "+ Image processing: PIL.\n", "+ 2D: Matplotlib and SVGFig.\n", "+ 3D: Visual Python, PyOpenGL and Python Ogre.\n", "+ Object-relational mapping: SQLAlchemy, SQLObject." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting Help" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Python home page -- http://www.python.org\n", "- Python standard documentation -- http://www.python.org/doc/.\n", "- tutorials:\n", " - FAQs -- http://www.python.org/doc/faq/.\n", " - The Python Wiki -- http://wiki.python.org/\n", " - The Python Package Index -- Lots of Python packages -- https://pypi.python.org/pypi\n", " - Special interest groups (SIGs) -- http://www.python.org/sigs/\n", " - The Python tutor email list -- http://mail.python.org/mailman/listinfo/tutor\n", "- Open source projects (Lots of projects. Search for \"python\")\n", " - http://sourceforge.net\n", " - https://github.com/\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Culture" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The name Python was taken by Guido van Rossum from british TV program *Monty Python's Flying Circus*, and there are many references to the show in its documentation. For instance, Python's oficial package repository was called Cheese Shop, the name of one of the frames of the program. Currently, the repository name is [Python Package Index](http://pypi.python.org/pypi) (PYPI).\n", "\n", "The goals of the project are summarized by Tim Peters in a text called *Zen of Python*, which can be found in Python itself using the command:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import this" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Zen of Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 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!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The chapters in this book are enhanced on https://github.com/ricardoduarte/python-for-developers. " ] } ], "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": "3.4.6" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
jhillairet/ICRH
Reflectometry/E-plane bend with hyperbolic secant curvature.ipynb
2
85090
{ "metadata": { "name": "", "signature": "sha256:c63448ee57f1b69ae9a4a9f2f781c3eddb4c0c7b5af0b143f17d451666ae6d63" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "We remind here the expression of the curvature for a given set of parametric equations $(x(t), y(t))$ :\n", "$$ c(t) = \\frac{x'(t) y''(t) - y'(t) x''(t) }{\\left(x'(t)^2 + y'(t)^2 \\right)^{3/2}}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let :\n", "$$ x(t) = t R_0 \\cos\\frac{\\pi}{4}$$\n", "$$ y(t) = 2 R_0 \\mathrm{sech}\\left( \\frac{x(t)}{R_0} \\right) = \\frac{2 R_0}{\\cosh\\left( \\frac{x(t)}{R_0} \\right)}$$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sympy import *" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a nice $\\LaTeX$ printing :" ] }, { "cell_type": "code", "collapsed": false, "input": [ "init_printing()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Defining the symbolic notations :" ] }, { "cell_type": "code", "collapsed": false, "input": [ "t, r, a, b = symbols('t r a b')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "x = -r * sin(t*pi/4)\n", "y = a/cosh(sin(t*pi/4))+b" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "(diff(y,t)+1).subs(t,-1)" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$\\frac{\\sqrt{2} \\pi a \\sinh{\\left (\\frac{\\sqrt{2}}{2} \\right )}}{8 \\cosh^{2}{\\left (\\frac{\\sqrt{2}}{2} \\right )}} + 1$$" ], "metadata": {}, "output_type": "pyout", "png": "iVBORw0KGgoAAAANSUhEUgAAALgAAABRBAMAAACTVnG+AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n", "prompt_number": 25, "text": [ " \u239b ___\u239e \n", " ___ \u239c\u2572\u2571 2 \u239f \n", "\u2572\u2571 2 \u22c5\u03c0\u22c5a\u22c5sinh\u239c\u2500\u2500\u2500\u2500\u2500\u239f \n", " \u239d 2 \u23a0 \n", "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 + 1\n", " \u239b ___\u239e \n", " 2\u239c\u2572\u2571 2 \u239f \n", " 8\u22c5cosh \u239c\u2500\u2500\u2500\u2500\u2500\u239f \n", " \u239d 2 \u23a0 " ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "solve(_, a)" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$\\begin{bmatrix}- \\frac{4 \\sqrt{2} \\cosh^{2}{\\left (\\frac{\\sqrt{2}}{2} \\right )}}{\\pi \\sinh{\\left (\\frac{\\sqrt{2}}{2} \\right )}}\\end{bmatrix}$$" ], "metadata": {}, "output_type": "pyout", "png": "iVBORw0KGgoAAAANSUhEUgAAAJgAAAA3BAMAAAACpp4pAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nAAAAAElFTkSuQmCC\n", "prompt_number": 26, "text": [ "\u23a1 \u239b ___\u239e \u23a4\n", "\u23a2 ___ 2\u239c\u2572\u2571 2 \u239f \u23a5\n", "\u23a2-4\u22c5\u2572\u2571 2 \u22c5cosh \u239c\u2500\u2500\u2500\u2500\u2500\u239f \u23a5\n", "\u23a2 \u239d 2 \u23a0 \u23a5\n", "\u23a2\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u23a5\n", "\u23a2 \u239b ___\u239e \u23a5\n", "\u23a2 \u239c\u2572\u2571 2 \u239f \u23a5\n", "\u23a2 \u03c0\u22c5sinh\u239c\u2500\u2500\u2500\u2500\u2500\u239f \u23a5\n", "\u23a3 \u239d 2 \u23a0 \u23a6" ] } ], "prompt_number": 26 }, { "cell_type": "raw", "metadata": {}, "source": [ "x,y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculating the differential expressions" ] }, { "cell_type": "code", "collapsed": false, "input": [ "xp = diff(x, t)\n", "yp = diff(y, t).simplify()\n", "xpp = diff(xp, t)\n", "ypp = diff(yp, t).simplify()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 49 }, { "cell_type": "code", "collapsed": false, "input": [ "xp, yp, xpp, ypp" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$\\begin{pmatrix}- \\frac{\\pi r}{4} \\cos{\\left (\\frac{\\pi t}{4} \\right )}, & - \\frac{\\pi a \\cos{\\left (\\frac{\\pi t}{4} \\right )} \\sinh{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )}}{4 \\cosh^{2}{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )}}, & \\frac{\\pi^{2} r}{16} \\sin{\\left (\\frac{\\pi t}{4} \\right )}, & \\frac{\\pi^{2} a}{16 \\cosh{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )}} \\left(\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\tanh{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )} + \\cos^{2}{\\left (\\frac{\\pi t}{4} \\right )} - \\frac{2 \\cos^{2}{\\left (\\frac{\\pi t}{4} \\right )}}{\\cosh^{2}{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )}}\\right)\\end{pmatrix}$$" ], "metadata": {}, "output_type": "pyout", "prompt_number": 50, "text": [ "\u239b \u239b \n", "\u239c \u239c \n", "\u239c 2 \u239c \u239b\u03c0\u22c5t\u239e \n", "\u239c \u03c0 \u22c5a\u22c5\u239csin\u239c\u2500\u2500\u2500\u239f\u22c5\n", "\u239c \u239b\u03c0\u22c5t\u239e \u239b\u03c0\u22c5t\u239e \u239b \u239b\u03c0\u22c5t\u239e\u239e 2 \u239b\u03c0\u22c5t\u239e \u239c \u239d 4 \u23a0 \n", "\u239c-\u03c0\u22c5r\u22c5cos\u239c\u2500\u2500\u2500\u239f -\u03c0\u22c5a\u22c5cos\u239c\u2500\u2500\u2500\u239f\u22c5sinh\u239csin\u239c\u2500\u2500\u2500\u239f\u239f \u03c0 \u22c5r\u22c5sin\u239c\u2500\u2500\u2500\u239f \u239c \n", "\u239c \u239d 4 \u23a0 \u239d 4 \u23a0 \u239d \u239d 4 \u23a0\u23a0 \u239d 4 \u23a0 \u239d \n", "\u239c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500, \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500, \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500, \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", "\u239c 4 2\u239b \u239b\u03c0\u22c5t\u239e\u239e 16 \n", "\u239c 4\u22c5cosh \u239csin\u239c\u2500\u2500\u2500\u239f\u239f \n", "\u239d \u239d \u239d 4 \u23a0\u23a0 \n", "\n", " 2\u239b\u03c0\u22c5t\u239e \u239e\u239e\n", " 2\u22c5cos \u239c\u2500\u2500\u2500\u239f \u239f\u239f\n", " \u239b \u239b\u03c0\u22c5t\u239e\u239e 2\u239b\u03c0\u22c5t\u239e \u239d 4 \u23a0 \u239f\u239f\n", "tanh\u239csin\u239c\u2500\u2500\u2500\u239f\u239f + cos \u239c\u2500\u2500\u2500\u239f - \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u239f\u239f\n", " \u239d \u239d 4 \u23a0\u23a0 \u239d 4 \u23a0 2\u239b \u239b\u03c0\u22c5t\u239e\u239e\u239f\u239f\n", " cosh \u239csin\u239c\u2500\u2500\u2500\u239f\u239f\u239f\u239f\n", " \u239d \u239d 4 \u23a0\u23a0\u23a0\u239f\n", "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u239f\n", " \u239b \u239b\u03c0\u22c5t\u239e\u239e \u239f\n", " 16\u22c5cosh\u239csin\u239c\u2500\u2500\u2500\u239f\u239f \u239f\n", " \u239d \u239d 4 \u23a0\u23a0 \u23a0" ] } ], "prompt_number": 50 }, { "cell_type": "code", "collapsed": false, "input": [ "curvature = (xp * ypp - yp * xpp) / (xp**2 + yp**2)**Rational(3,2)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 51 }, { "cell_type": "code", "collapsed": false, "input": [ "curvature" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$\\frac{1}{\\left(\\frac{\\pi^{2} a^{2} \\cos^{2}{\\left (\\frac{\\pi t}{4} \\right )} \\sinh^{2}{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )}}{16 \\cosh^{4}{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )}} + \\frac{\\pi^{2} r^{2}}{16} \\cos^{2}{\\left (\\frac{\\pi t}{4} \\right )}\\right)^{\\frac{3}{2}}} \\left(- \\frac{\\pi^{3} a r \\cos{\\left (\\frac{\\pi t}{4} \\right )}}{64 \\cosh{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )}} \\left(\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\tanh{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )} + \\cos^{2}{\\left (\\frac{\\pi t}{4} \\right )} - \\frac{2 \\cos^{2}{\\left (\\frac{\\pi t}{4} \\right )}}{\\cosh^{2}{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )}}\\right) + \\frac{\\pi^{3} a r \\sin{\\left (\\frac{\\pi t}{4} \\right )} \\cos{\\left (\\frac{\\pi t}{4} \\right )}}{64 \\cosh^{2}{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )}} \\sinh{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )}\\right)$$" ], "metadata": {}, "output_type": "pyout", "png": 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"prompt_number": 52, "text": [ " \u239b 2\u239b\u03c0\u22c5t\u239e \u239e \n", " \u239c 2\u22c5cos \u239c\u2500\u2500\u2500\u239f \u239f \n", " 3 \u239c \u239b\u03c0\u22c5t\u239e \u239b \u239b\u03c0\u22c5t\u239e\u239e 2\u239b\u03c0\u22c5t\u239e \u239d 4 \u23a0 \u239f \u239b\u03c0\u22c5t\u239e \n", " \u03c0 \u22c5a\u22c5r\u22c5\u239csin\u239c\u2500\u2500\u2500\u239f\u22c5tanh\u239csin\u239c\u2500\u2500\u2500\u239f\u239f + cos \u239c\u2500\u2500\u2500\u239f - \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u239f\u22c5cos\u239c\u2500\u2500\u2500\u239f \n", " \u239c \u239d 4 \u23a0 \u239d \u239d 4 \u23a0\u23a0 \u239d 4 \u23a0 2\u239b \u239b\u03c0\u22c5t\u239e\u239e\u239f \u239d 4 \u23a0 3\n", " \u239c cosh \u239csin\u239c\u2500\u2500\u2500\u239f\u239f\u239f \u03c0 \n", " \u239d \u239d \u239d 4 \u23a0\u23a0\u23a0 \n", "- \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 + \u2500\u2500\n", " \u239b \u239b\u03c0\u22c5t\u239e\u239e \n", " 64\u22c5cosh\u239csin\u239c\u2500\u2500\u2500\u239f\u239f \n", " \u239d \u239d 4 \u23a0\u23a0 \n", "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", " \n", " \u239b 2 2 2\u239b\u03c0\u22c5t\u239e 2\u239b \u239b\u03c0\u22c5t\u239e\u239e 2 2 2\u239b\u03c0\n", " \u239c\u03c0 \u22c5a \u22c5cos \u239c\u2500\u2500\u2500\u239f\u22c5sinh \u239csin\u239c\u2500\u2500\u2500\u239f\u239f \u03c0 \u22c5r \u22c5cos \u239c\u2500\n", " \u239c \u239d 4 \u23a0 \u239d \u239d 4 \u23a0\u23a0 \u239d \n", " \u239c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 + \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", " \u239c 4\u239b \u239b\u03c0\u22c5t\u239e\u239e 16 \n", " \u239c 16\u22c5cosh \u239csin\u239c\u2500\u2500\u2500\u239f\u239f \n", " \u239d \u239d \u239d 4 \u23a0\u23a0 \n", "\n", " \n", " \n", " \n", " \n", " \u239b\u03c0\u22c5t\u239e \u239b\u03c0\u22c5t\u239e \u239b \u239b\u03c0\u22c5t\u239e\u239e\n", "\u22c5a\u22c5r\u22c5sin\u239c\u2500\u2500\u2500\u239f\u22c5cos\u239c\u2500\u2500\u2500\u239f\u22c5sinh\u239csin\u239c\u2500\u2500\u2500\u239f\u239f\n", " \u239d 4 \u23a0 \u239d 4 \u23a0 \u239d \u239d 4 \u23a0\u23a0\n", "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", " 2\u239b \u239b\u03c0\u22c5t\u239e\u239e \n", " 64\u22c5cosh \u239csin\u239c\u2500\u2500\u2500\u239f\u239f \n", " \u239d \u239d 4 \u23a0\u23a0 \n", "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", " 3/2 \n", "\u22c5t\u239e\u239e \n", "\u2500\u2500\u239f\u239f \n", "4 \u23a0\u239f \n", "\u2500\u2500\u2500\u239f \n", " \u239f \n", " \u239f \n", " \u23a0 " ] } ], "prompt_number": 52 }, { "cell_type": "code", "collapsed": false, "input": [ "curvature.simplify()" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$- \\frac{a r \\left(1 - \\frac{2}{\\cosh^{2}{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )}}\\right) \\cos^{3}{\\left (\\frac{\\pi t}{4} \\right )}}{\\left(\\left(\\frac{a^{2} \\sinh^{2}{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )}}{\\cosh^{4}{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )}} + r^{2}\\right) \\cos^{2}{\\left (\\frac{\\pi t}{4} \\right )}\\right)^{\\frac{3}{2}} \\cosh{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )}}$$" ], "metadata": {}, "output_type": "pyout", "png": 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"prompt_number": 53, "text": [ " \u239b 2 \u239e 3\u239b\u03c0\u22c5t\u239e \n", " -a\u22c5r\u22c5\u239c1 - \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u239f\u22c5cos \u239c\u2500\u2500\u2500\u239f \n", " \u239c 2\u239b \u239b\u03c0\u22c5t\u239e\u239e\u239f \u239d 4 \u23a0 \n", " \u239c cosh \u239csin\u239c\u2500\u2500\u2500\u239f\u239f\u239f \n", " \u239d \u239d \u239d 4 \u23a0\u23a0\u23a0 \n", "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", " 3/2 \n", "\u239b\u239b 2 2\u239b \u239b\u03c0\u22c5t\u239e\u239e \u239e \u239e \n", "\u239c\u239ca \u22c5sinh \u239csin\u239c\u2500\u2500\u2500\u239f\u239f \u239f \u239f \n", "\u239c\u239c \u239d \u239d 4 \u23a0\u23a0 2\u239f 2\u239b\u03c0\u22c5t\u239e\u239f \u239b \u239b\u03c0\u22c5t\u239e\u239e\n", "\u239c\u239c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 + r \u239f\u22c5cos \u239c\u2500\u2500\u2500\u239f\u239f \u22c5cosh\u239csin\u239c\u2500\u2500\u2500\u239f\u239f\n", "\u239c\u239c 4\u239b \u239b\u03c0\u22c5t\u239e\u239e \u239f \u239d 4 \u23a0\u239f \u239d \u239d 4 \u23a0\u23a0\n", "\u239c\u239c cosh \u239csin\u239c\u2500\u2500\u2500\u239f\u239f \u239f \u239f \n", "\u239d\u239d \u239d \u239d 4 \u23a0\u23a0 \u23a0 \u23a0 " ] } ], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [ "equa = r*cosh(a*t) - 1/curvature" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "equa.simplify()" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$\\frac{a r^{2} \\left(- \\left(\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\tanh{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )} + \\cos^{2}{\\left (\\frac{\\pi t}{4} \\right )}\\right) \\cosh^{2}{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )} + \\sin{\\left (\\frac{\\pi t}{4} \\right )} \\sinh{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )} \\cosh{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )} + 2 \\cos^{2}{\\left (\\frac{\\pi t}{4} \\right )}\\right) \\cos{\\left (\\frac{\\pi t}{4} \\right )} \\cosh{\\left (a t \\right )} - \\left(\\left(\\frac{a^{2} \\sinh^{2}{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )}}{\\cosh^{4}{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )}} + r^{2}\\right) \\cos^{2}{\\left (\\frac{\\pi t}{4} \\right )}\\right)^{\\frac{3}{2}} \\cosh^{3}{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )}}{a r \\left(- \\left(\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\tanh{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )} + \\cos^{2}{\\left (\\frac{\\pi t}{4} \\right )}\\right) \\cosh^{2}{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )} + \\sin{\\left (\\frac{\\pi t}{4} \\right )} \\sinh{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )} \\cosh{\\left (\\sin{\\left (\\frac{\\pi t}{4} \\right )} \\right )} + 2 \\cos^{2}{\\left (\\frac{\\pi t}{4} \\right )}\\right) \\cos{\\left (\\frac{\\pi t}{4} \\right )}}$$" ], "metadata": {}, "output_type": "pyout", "png": 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YLuku+zIW5DHmN8AmzvZUzLASc130fXh03nxGeh8tov8G0WV7em+DwG4GZlAc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\n", "prompt_number": 55, "text": [ " \n", " \n", " \n", " 2 \u239b \u239b \u239b\u03c0\u22c5t\u239e \u239b \u239b\u03c0\u22c5t\u239e\u239e 2\u239b\u03c0\u22c5t\u239e\u239e 2\u239b \u239b\u03c0\u22c5t\u239e\u239e \u239b\u03c0\u22c5t\u239e \u239b\n", "a\u22c5r \u22c5\u239c- \u239csin\u239c\u2500\u2500\u2500\u239f\u22c5tanh\u239csin\u239c\u2500\u2500\u2500\u239f\u239f + cos \u239c\u2500\u2500\u2500\u239f\u239f\u22c5cosh \u239csin\u239c\u2500\u2500\u2500\u239f\u239f + sin\u239c\u2500\u2500\u2500\u239f\u22c5sinh\u239c\n", " \u239d \u239d \u239d 4 \u23a0 \u239d \u239d 4 \u23a0\u23a0 \u239d 4 \u23a0\u23a0 \u239d \u239d 4 \u23a0\u23a0 \u239d 4 \u23a0 \u239d\n", " \n", " \n", "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", " \u239b \u239b \u239b\u03c0\u22c5t\u239e \u239b \u239b\u03c0\u22c5t\u239e\u239e 2\u239b\u03c0\u22c5t\u239e\n", " a\u22c5r\u22c5\u239c- \u239csin\u239c\u2500\u2500\u2500\u239f\u22c5tanh\u239csin\u239c\u2500\u2500\u2500\u239f\u239f + cos \u239c\u2500\u2500\u2500\u239f\n", " \u239d \u239d \u239d 4 \u23a0 \u239d \u239d 4 \u23a0\u23a0 \u239d 4 \u23a0\n", "\n", " \n", " \u239b\u239b 2 2\u239b \u239b\u03c0\u22c5\n", " \u239c\u239ca \u22c5sinh \u239csin\u239c\u2500\u2500\n", " \u239b\u03c0\u22c5t\u239e\u239e \u239b \u239b\u03c0\u22c5t\u239e\u239e 2\u239b\u03c0\u22c5t\u239e\u239e \u239b\u03c0\u22c5t\u239e \u239c\u239c \u239d \u239d 4\n", "sin\u239c\u2500\u2500\u2500\u239f\u239f\u22c5cosh\u239csin\u239c\u2500\u2500\u2500\u239f\u239f + 2\u22c5cos \u239c\u2500\u2500\u2500\u239f\u239f\u22c5cos\u239c\u2500\u2500\u2500\u239f\u22c5cosh(a\u22c5t) - \u239c\u239c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", " \u239d 4 \u23a0\u23a0 \u239d \u239d 4 \u23a0\u23a0 \u239d 4 \u23a0\u23a0 \u239d 4 \u23a0 \u239c\u239c 4\u239b \u239b\u03c0\u22c5t\u239e\n", " \u239c\u239c cosh \u239csin\u239c\u2500\u2500\u2500\u239f\n", " \u239d\u239d \u239d \u239d 4 \u23a0\n", "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", "\u239e 2\u239b \u239b\u03c0\u22c5t\u239e\u239e \u239b\u03c0\u22c5t\u239e \u239b \u239b\u03c0\u22c5t\u239e\u239e \u239b \u239b\u03c0\u22c5t\u239e\u239e 2\u239b\u03c0\u22c5t\u239e\u239e \u239b\n", "\u239f\u22c5cosh \u239csin\u239c\u2500\u2500\u2500\u239f\u239f + sin\u239c\u2500\u2500\u2500\u239f\u22c5sinh\u239csin\u239c\u2500\u2500\u2500\u239f\u239f\u22c5cosh\u239csin\u239c\u2500\u2500\u2500\u239f\u239f + 2\u22c5cos \u239c\u2500\u2500\u2500\u239f\u239f\u22c5cos\u239c\n", "\u23a0 \u239d \u239d 4 \u23a0\u23a0 \u239d 4 \u23a0 \u239d \u239d 4 \u23a0\u23a0 \u239d \u239d 4 \u23a0\u23a0 \u239d 4 \u23a0\u23a0 \u239d\n", "\n", " 3/2 \n", "t\u239e\u239e \u239e \u239e \n", "\u2500\u239f\u239f \u239f \u239f \n", " \u23a0\u23a0 2\u239f 2\u239b\u03c0\u22c5t\u239e\u239f 3\u239b \u239b\u03c0\u22c5t\u239e\u239e\n", "\u2500\u2500\u2500 + r \u239f\u22c5cos \u239c\u2500\u2500\u2500\u239f\u239f \u22c5cosh \u239csin\u239c\u2500\u2500\u2500\u239f\u239f\n", "\u239e \u239f \u239d 4 \u23a0\u239f \u239d \u239d 4 \u23a0\u23a0\n", "\u239f \u239f \u239f \n", "\u23a0 \u23a0 \u23a0 \n", "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", "\u03c0\u22c5t\u239e \n", "\u2500\u2500\u2500\u239f \n", " 4 \u23a0 " ] } ], "prompt_number": 55 }, { "cell_type": "code", "collapsed": false, "input": [ "u = symbols('u')\n", "diff(1/cosh(a*u), u)" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$- \\frac{a \\sinh{\\left (a u \\right )}}{\\cosh^{2}{\\left (a u \\right )}}$$" ], "metadata": {}, "output_type": "pyout", "png": 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"prompt_number": 56, "text": [ "-a\u22c5sinh(a\u22c5u) \n", "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", " 2 \n", " cosh (a\u22c5u) " ] } ], "prompt_number": 56 }, { "cell_type": "code", "collapsed": false, "input": [ "sinh(-u)" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$- \\sinh{\\left (u \\right )}$$" ], "metadata": {}, "output_type": "pyout", "png": 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"prompt_number": 23, "text": [ "-sinh(u)" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "cosh(-u)" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$\\cosh{\\left (u \\right )}$$" ], "metadata": {}, "output_type": "pyout", "png": 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"prompt_number": 27, "text": [ "cosh(u)" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "1/cosh(-sqrt(2)/2)" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$\\frac{1}{\\cosh{\\left (\\frac{\\sqrt{2}}{2} \\right )}}$$" ], "metadata": {}, "output_type": "pyout", "png": 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"prompt_number": 58, "text": [ " 1 \n", "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", " \u239b ___\u239e\n", " \u239c\u2572\u2571 2 \u239f\n", "cosh\u239c\u2500\u2500\u2500\u2500\u2500\u239f\n", " \u239d 2 \u23a0" ] } ], "prompt_number": 58 }, { "cell_type": "code", "collapsed": false, "input": [ "f = Function('f')(t)\n", "diff(a/cosh(f/r)+b, t)" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$- \\frac{a \\sinh{\\left (\\frac{1}{r} f{\\left (t \\right )} \\right )} \\frac{d}{d t} f{\\left (t \\right )}}{r \\cosh^{2}{\\left (\\frac{1}{r} f{\\left (t \\right )} \\right )}}$$" ], "metadata": {}, "output_type": "pyout", "png": 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"prompt_number": 64, "text": [ " \u239bf(t)\u239e d \n", "-a\u22c5sinh\u239c\u2500\u2500\u2500\u2500\u239f\u22c5\u2500\u2500(f(t)) \n", " \u239d r \u23a0 dt \n", "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", " 2\u239bf(t)\u239e \n", " r\u22c5cosh \u239c\u2500\u2500\u2500\u2500\u239f \n", " \u239d r \u23a0 " ] } ], "prompt_number": 64 }, { "cell_type": "code", "collapsed": false, "input": [ "diff(-r*sin(t*pi/4),t)" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$- \\frac{\\pi r}{4} \\cos{\\left (\\frac{\\pi t}{4} \\right )}$$" ], "metadata": {}, "output_type": "pyout", "png": 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"prompt_number": 70, "text": [ " \u239b\u03c0\u22c5t\u239e \n", "-\u03c0\u22c5r\u22c5cos\u239c\u2500\u2500\u2500\u239f \n", " \u239d 4 \u23a0 \n", "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", " 4 " ] } ], "prompt_number": 70 }, { "cell_type": "code", "collapsed": false, "input": [ "cosh(a)**2/sinh(a)" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$\\frac{\\cosh^{2}{\\left (a \\right )}}{\\sinh{\\left (a \\right )}}$$" ], "metadata": {}, "output_type": "pyout", "png": 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"prompt_number": 71, "text": [ " 2 \n", "cosh (a)\n", "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", "sinh(a) " ] } ], "prompt_number": 71 }, { "cell_type": "code", "collapsed": false, "input": [ "expand(_)" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$\\frac{\\cosh^{2}{\\left (a \\right )}}{\\sinh{\\left (a \\right )}}$$" ], "metadata": {}, "output_type": "pyout", "png": 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"prompt_number": 74, "text": [ " 2 \n", "cosh (a)\n", "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", "sinh(a) " ] } ], "prompt_number": 74 }, { "cell_type": "code", "collapsed": false, "input": [ "cosh(sqrt(2)/2)" ], "language": "python", "metadata": {}, "outputs": [ { "latex": [ "$$\\cosh{\\left (\\frac{\\sqrt{2}}{2} \\right )}$$" ], "metadata": {}, "output_type": "pyout", "png": 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"prompt_number": 77, "text": [ " \u239b ___\u239e\n", " \u239c\u2572\u2571 2 \u239f\n", "cosh\u239c\u2500\u2500\u2500\u2500\u2500\u239f\n", " \u239d 2 \u23a0" ] } ], "prompt_number": 77 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
alepoydes/introduction-to-numerical-simulation
practice/matrix/schur.ipynb
1
10957
{"cells":[{"source":["# Разложение Шура\n","\n","Любую квадратную матрицу $A$ можно преобразованием $U$ подобия привести к верхнетреугольной матрице $R$,\n","причем преобразование $U$ можно выбрать унитарным (ортогональным для вещественных матриц):\n","$$A=URU^*,\\quad U^*AU=R,\\quad UU^*=U^*U=1,$$\n","такое представление матрицы называется [разложением Шура](https://en.wikipedia.org/wiki/Schur_decomposition).\n","Здесь и далее мы будем обозначать единичную матрицу подходящего размера через $1$,\n","а сопряженную матрицу к $A$ через $A^*$.\n","Элементы сопряженной матрицы получаются транспонирование матрицы с последующим комплексным сопряжением:\n","$$\n","A^*=\\bar A^T,\\quad (A^*)_{nk}=\\overline{A_{kn}}. \n","$$ \n","Столбцы матрицы преобразования $U$ называются векторами Шура."],"cell_type":"markdown","metadata":{}},{"source":["### Задания.\n","\n","1. Какой смысл имеют диагональные элементы матрицы $R$?\n","\n","2. В каком случае вектора Шура оказываются собственными векторами?\n","\n","3. Покажите, что сумма квадратов абсолютных значений всех недиагональных элементов матрицы $R$ не зависит от выбора $U$ и определяется только матрицей $A$:\n","$$ N = \\sum_{n}\\sum_{k>n} |R_{nk}|^2.$$\n","Какой смысл вы можете придать этой сумме?"],"cell_type":"markdown","metadata":{}},{"source":["Первый вектор Шура всегда является собственным, поэтому мы можем построить наивную процедуру\n","вычисления разложения Шура, имея способ вычисления собственных чисел и векторов.\n","Проще всего оказывается вычислить самое большое по модулю собственное значение матрицы (спектральный радиус),\n","для этого можно воспользоваться [методом степеней](https://en.wikipedia.org/wiki/Power_iteration).\n","Суть метода заключается в вычислении последовательности:\n","$$e_{n+1}=\\frac{Ae_n}{\\|Ae_n\\|},$$\n","которая при определенных условиях сходится к собственному вектору, отвечающему максимальному по модулю собственому значению $A$. "],"cell_type":"markdown","metadata":{}},{"source":["## Задания.\n","\n","4. Предложите достаточные условия сходимости степенного метода.\n","\n","5. Реализуйте степенной метод. Для проверки результата воспользуйтесь функцией \n","[scipy.linalg.norm(A, ord=2)](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.norm.html),\n","которая на квадратной матрице $A$ возвращает ее спектральных радиус.\n","\n","6. Первый вектор Шура находится степенным методом, как можно найти второй вектор Шура? \n","__Указание:__ Рассмотрите подматрицу $A_{2\\colon,2\\colon}$.\n","\n","7. Реализуйте функцию для построения разложения Шура с помощью степенного метода. В каких случаях алгоритм сойдется? В каких случаях сойдется к разложению Шура? С какой скоростью итерации сходятся в случае сходимости?\n","\n","8. Обобщите степенной метод так, чтобы одновременно вычислялось несколько собственных векторов. \n","Реализуйте эту модификацию. Какие условия являются достаточными для сходимости вашего метода."],"cell_type":"markdown","metadata":{}},{"source":["Задача вычисления собственных чисел может быть плохо обусловлена для матрицы общего вида. \n","Наибольшую трудность представляют близкие собственные значения и вырожденные собственные значения. \n","Проведите несколько экспериментов, используя библиотечную функцию для вычисления разложения Шура \n","[scipy.linalg.schur](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.schur.html).\n"],"cell_type":"markdown","metadata":{}},{"source":["## Задания.\n","\n","9. Предложите матрицу, у которой левый и правый собственные вектра для одного собственного значения почти ортогональны. Добавляя малое возмущение к матрице (можно воспользоваться [numpy.random.randn](https://numpy.org/doc/stable/reference/random/generated/numpy.random.randn.html)) и находя собственные значения для возмущенной матрицы через `scipy.linalg.schur` оцените число обусловленности для вычисления собственного числа. Сравните с теорией.\n","\n","10. Рассмотрите малое возмущение $\\epsilon$ для матрицы \n","$$A=\\begin{pmatrix}1&a\\\\\\epsilon&1\\end{pmatrix},$$\n","где $a$ - параметр. Насколько сильно возущение изменяет собственные значения?\n","Собственные вектора (`scipy.linalg.eig`)?"],"cell_type":"markdown","metadata":{}},{"source":["На практике для вычисления разложения Шура как правило используется [QR алгоритм](https://en.wikipedia.org/wiki/QR_algorithm) и его варианты. \n","Мы ограничимся изучением этого метода только для симметричных матриц.\n","\n","Пусть матрица $A$ имеет только вещественные коэффициенты и симметрична, т.е. $A^T=A$.\n","В этом случае матрица $R$ в разложение Шура для матрица $A$ оказывается диагональным, т.е. выполняется спектральное разложение.\n","\n","Перед выполнение QR алгоритма матрица $A$ приводится преобразованием подобия к более простому виду $A_0=VAV^T$,\n","как правило к виду [матрицы Хессенберга](https://en.wikipedia.org/wiki/Hessenberg_matrix).\n","Преобразование $V$ можно представить, например, в виде цепочки [вращений Гивенса](https://en.wikipedia.org/wiki/Givens_rotation).\n","На одном шаге QR алгоритма строится QR разложение матрицы $A_n=Q_nR_n$,\n","затем матрицы из разложения перемножаются в обратном порядке, формируя новый член последовательности:\n","$A_{n+1}=R_nQ_n$.\n","Все матрицы в последовательности подобны: $A_{n+1}=Q_n^TA_nQ_n$.\n","Итерации повторяются до тех пор, пока матрица $A_n$ не станет достаточно треугольной.\n","\n","В наивном варианте QR алгоритм не всегда сходится, однако ситуацию можно исправить, добавив в сдвиги.\n","На каждом шаге алгоритма будем строить QR разложение для $A_n-\\zeta_n=Q_nR_n$ с подходящим $\\zeta_n$.\n","Следующий член последовательности определим так $A_{n+1}=R_nQ_n+\\zeta_n$.\n","Последовательность $\\zeta_n$ выбирается так, чтобы $\\zeta_n$ сходилось к минимальному собственному числу,\n","например, полагая $\\zeta_n$ равным элементу $R$ из последнего столбца и строки.\n","В этому случае итерации почти всегда сходятся и дают кубическую скорость сходимости."],"cell_type":"markdown","metadata":{}},{"source":["## Задания.\n","\n","11. Реализуйте QR алгоритм со сдвигами для симметричной матрицы $A$. \n","Экспериментально проверьте скорость сходимости. \n","Сравните со скорость сходимости степенного метода.\n","\n","12. **(повышенная сложность)** Реализуйте неявный QR алгоритм. Сравните его работу с работой явного метода.\n","\n","13. Предложите и реализуйте метод вычисления [сингулярного (SVD) разложения](https://en.wikipedia.org/wiki/Singular_value_decomposition), используя разложение Шура. Постарайтесь избежать вычисления матриц $AA^T$ и $A^TA$."],"cell_type":"markdown","metadata":{}},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}],"nbformat":4,"nbformat_minor":2,"metadata":{"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.3-final"},"orig_nbformat":2,"kernelspec":{"name":"python39264bit912f2bb8ffcf47089065dd5cad2767e2","display_name":"Python 3.9.2 64-bit"}}}
mit
RyanAlberts/Springbaord-Capstone-Project
Statistics_Exercises/sliderule_dsi_inferential_statistics_exercise_1.ipynb
1
50544
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# What is the True Normal Human Body Temperature? \n", "\n", "#### Background\n", "\n", "The mean normal body temperature was held to be 37$^{\\circ}$C or 98.6$^{\\circ}$F for more than 120 years since it was first conceptualized and reported by Carl Wunderlich in a famous 1868 book. But, is this value statistically correct?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"span5 alert alert-info\">\n", "<h3>Exercises</h3>\n", "\n", "<p>In this exercise, you will analyze a dataset of human body temperatures and employ the concepts of hypothesis testing, confidence intervals, and statistical significance.</p>\n", "\n", "<p>Answer the following questions <b>in this notebook below and submit to your Github account</b>.</p> \n", "\n", "<ol>\n", "<li> Is the distribution of body temperatures normal? \n", " <ul>\n", " <li> Although this is not a requirement for CLT to hold (read CLT carefully), it gives us some peace of mind that the population may also be normally distributed if we assume that this sample is representative of the population.\n", " </ul>\n", "<li> Is the sample size large? Are the observations independent?\n", " <ul>\n", " <li> Remember that this is a condition for the CLT, and hence the statistical tests we are using, to apply.\n", " </ul>\n", "<li> Is the true population mean really 98.6 degrees F?\n", " <ul>\n", " <li> Would you use a one-sample or two-sample test? Why?\n", " <li> In this situation, is it appropriate to use the $t$ or $z$ statistic? \n", " <li> Now try using the other test. How is the result be different? Why?\n", " </ul>\n", "<li> At what temperature should we consider someone's temperature to be \"abnormal\"?\n", " <ul>\n", " <li> Start by computing the margin of error and confidence interval.\n", " </ul>\n", "<li> Is there a significant difference between males and females in normal temperature?\n", " <ul>\n", " <li> What test did you use and why?\n", " <li> Write a story with your conclusion in the context of the original problem.\n", " </ul>\n", "</ol>\n", "\n", "You can include written notes in notebook cells using Markdown: \n", " - In the control panel at the top, choose Cell > Cell Type > Markdown\n", " - Markdown syntax: http://nestacms.com/docs/creating-content/markdown-cheat-sheet\n", "\n", "#### Resources\n", "\n", "+ Information and data sources: http://www.amstat.org/publications/jse/datasets/normtemp.txt, http://www.amstat.org/publications/jse/jse_data_archive.htm\n", "+ Markdown syntax: http://nestacms.com/docs/creating-content/markdown-cheat-sheet\n", "\n", "****\n", "</div>" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import re\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import scipy as sp\n", "import seaborn as sns\n", "sns.set_style(\"whitegrid\")\n", "import calendar\n", "\n", "df = pd.read_csv('data/human_body_temperature.csv')" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "With .25 p-value, we fail to reject the null hypothesis that the distribution comes from a normal distribution:\n", "\n", "NormaltestResult(statistic=2.7038014333192031, pvalue=0.2587479863488254)\n", "\n", "Standard error of measurement: 0.0643044168379\n", "\n", "Standard Deviation: 0.730357778905\n", "\n", "DescribeResult(nobs=130, minmax=(96.299999999999997, 100.8), mean=98.249230769230749, variance=0.53755754323196159, skewness=-0.004367976879081625, kurtosis=0.7049597854114693)\n" ] }, { "data": { "image/png": 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GICtrosf7XHvuGH/ZWYwhIsl9JIuvfD8GS8ahniv94vE4tZKTk4PBYGDZsmW88MIL/Pzn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\nfpJfrm4Q4ROkyEe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"text/plain": [ "<matplotlib.figure.Figure at 0x117e09690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 1. The distribution is roughly normal according the below visualization, \n", "# with the follow qualifications:\n", "\n", "# Let's see how much it differs from the norm\n", "print '\\nWith .25 p-value, we fail to reject the null hypothesis that the distribution comes from a normal distribution:'\n", "print '\\n', sp.stats.normaltest(df['temperature'])\n", "print '\\nStandard error of measurement: ', sp.stats.sem(df['temperature'])\n", "print '\\nStandard Deviation: ', np.std(df['temperature'])\n", "print '\\n', sp.stats.describe(df['temperature'])\n", "\n", "sns.distplot(df['temperature']);" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The sample size is: 130\n", "\n", "The same size is greater than 30, which for CTL means that the sample mean should be a good approximation of the population mean\n" ] } ], "source": [ "# 2. Is the sample size large? Are the observations independent?\n", "\n", "print 'The sample size is: ', len(df)\n", "print '\\nThe same size is greater than 30, which for CTL means that the sample mean should be a good approximation of the population mean'\n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The true population mean is not 98.6. The statistical mean of the population can be approximated using the data: 98.2492307692\n" ] } ], "source": [ "# 3. Is the true population mean really 98.6 degrees F?\n", "\n", "print 'The true population mean is not 98.6. The statistical mean of the population can be approximated using the data: ', df['temperature'].mean()\n" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abnormal body temperatures fall outside the following interval: \n", "(96.804200016139845, 99.69426152232171)\n" ] } ], "source": [ "# 4. At what temperature should we consider someone's temperature to be \"abnormal\"?\n", "\n", "# For general Bayesian statistics:\n", "sp.stats.bayes_mvs(df['temperature'])\n", "\n", "# For confidence intervals \n", "print 'abnormal body temperatures fall outside the following interval: \\n', \\\n", "sp.stats.t.interval(0.95, len(df)-1, loc=np.mean(df['temperature']), scale=np.std(df['temperature']))" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean of Male samples : 98.1046153846\n", "Mean of Female samples: 98.3938461538\n", "\n", "Male : NormaltestResult(statistic=0.89119996669505019, pvalue=0.64043990745691226)\n", "Female: NormaltestResult(statistic=4.7354381129084002, pvalue=0.093694193898563982)\n", "\n", "Standard error of measurement (Male) : 0.0866699855229\n", "Standard error of measurement (Female): 0.0922183060804\n", "\n", "Male : DescribeResult(nobs=65, minmax=(96.299999999999997, 99.5), mean=98.104615384615371, variance=0.48825961538461526, skewness=-0.20841723175963575, kurtosis=-0.43415936141781497)\n", "\n", "Female: DescribeResult(nobs=65, minmax=(96.400000000000006, 100.8), mean=98.393846153846141, variance=0.55277403846153739, skewness=0.09590698007442927, kurtosis=1.4676242642332449)\n" ] }, { "data": { "image/png": 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OxIbau1eyCrLckEx4Kynk4gKvvALV1XDdddDBSVOh7K2WbpWzOgSV02ugkdwD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\nvw9FUdwdSbiQw0vrpKQkNmz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"text/plain": [ "<matplotlib.figure.Figure at 0x11874e9d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 5. Is there a significant difference between males and females in normal temperature?\n", "\n", "Male = df[df['gender'] == 'M']['temperature']\n", "Female = df[df['gender'] == 'F']['temperature']\n", "\n", "\n", "print 'Mean of Male samples : ', df[df['gender'] == 'M']['temperature'].mean()\n", "print 'Mean of Female samples: ', df[df['gender'] == 'F']['temperature'].mean()\n", "\n", "print '\\nMale : ', sp.stats.normaltest(Male)\n", "print 'Female: ', sp.stats.normaltest(Female)\n", "print '\\nStandard error of measurement (Male) : ', sp.stats.sem(Male)\n", "print 'Standard error of measurement (Female): ', sp.stats.sem(Female)\n", "\n", "print '\\nMale : ', sp.stats.describe(Male)\n", "print '\\nFemale: ', sp.stats.describe(Female)\n", "sns.distplot(Male, label='Male', color='blue')\n", "sns.distplot(Female, color='green');" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Ks_2sampResult(statistic=0.18461538461538457, pvalue=0.19539014047941772)" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scipy.stats import ks_2samp\n", "# Kolmogorov-Smirnov Statistic\n", "ks_2samp(Male, Female)\n", "\n", "# We CANNOT reject the null hypothesis that the samples are drawen from the same distribution \n", "# Because the p-value is high and the K-S (Kolmogorov-Smirnov) statistic is high\n", "\n", "# Thus there is no significant difference between male and female normal temperature." ] } ], "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 }
mit
eaton-lab/toytree
sandbox/ancient.ipynb
1
181611
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## IPCOAL WITH ANCIENT SAMPLES" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import ipcoal\n", "import toytree\n", "import msprime as ms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Species tree" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div class=\"toyplot\" id=\"t116ef3d292a444c0b7bf95ccb4bd54be\" style=\"text-align:center\"><svg class=\"toyplot-canvas-Canvas\" height=\"260.0px\" id=\"t2eb72782bdb84f95ade1d69c4f775979\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;border-color:#292724;border-style:none;border-width:1.0;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" viewBox=\"0 0 350.0 260.0\" width=\"350.0px\" 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" var module = {};\n", " module.save_file = function(mime_type, charset, data, filename)\n", " {\n", " var uri = \"data:\" + mime_type + \";charset=\" + charset + \",\" + data;\n", " uri = encodeURI(uri);\n", "\n", " var link = document.createElement(\"a\");\n", " if(typeof link.download != \"undefined\")\n", " {\n", " link.href = uri;\n", " link.style = \"visibility:hidden\";\n", " link.download = filename;\n", "\n", " document.body.appendChild(link);\n", " link.click();\n", " document.body.removeChild(link);\n", " }\n", " else\n", " {\n", " window.open(uri);\n", " }\n", " };\n", " return module;\n", " })();\n", "modules[\"toyplot.coordinates.Axis\"] = (\n", " function(canvas)\n", " {\n", " function sign(x)\n", " {\n", " return x < 0 ? -1 : x > 0 ? 1 : 0;\n", " }\n", "\n", " function mix(a, b, amount)\n", " {\n", " return ((1.0 - amount) * a) + (amount * b);\n", " }\n", "\n", " function log(x, base)\n", " {\n", " return Math.log(Math.abs(x)) / Math.log(base);\n", " }\n", "\n", " function in_range(a, x, b)\n", " {\n", " var left = Math.min(a, b);\n", " var right = Math.max(a, b);\n", " return left <= x && x <= right;\n", " }\n", "\n", " function inside(range, projection)\n", " {\n", " for(var i = 0; i != projection.length; ++i)\n", " {\n", " var segment = projection[i];\n", " if(in_range(segment.range.min, range, segment.range.max))\n", " return true;\n", " }\n", " return false;\n", " }\n", "\n", " function to_domain(range, projection)\n", " {\n", " for(var i = 0; i != projection.length; ++i)\n", " {\n", " var segment = projection[i];\n", " if(in_range(segment.range.bounds.min, range, segment.range.bounds.max))\n", " {\n", " if(segment.scale == \"linear\")\n", " {\n", " var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n", " return mix(segment.domain.min, segment.domain.max, amount)\n", " }\n", " else if(segment.scale[0] == \"log\")\n", " {\n", " var amount = (range - segment.range.min) / (segment.range.max - 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tables.get_csv(owner_id, key), filename + \".csv\");\n", " }\n", "\n", " context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n", " })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t12932dfb9f3d4f059f86d5128984e8cc\",\"edge_data\",\"graph edge data\",[\"source\", \"target\"],[[9, 10, 11, 12, 13, 14, 15, 16, 8, 8, 7, 7, 6, 6, 5, 5], [4, 7, 3, 6, 2, 5, 1, 0, 9, 10, 11, 12, 13, 14, 15, 16]],\"toyplot\");\n", "(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n", " {\n", " tables.set(owner_id, key, names, columns);\n", "\n", " var owner = document.querySelector(\"#\" + owner_id);\n", " function show_item(e)\n", " {\n", " return owner.contains(e.target);\n", " }\n", "\n", " function choose_item()\n", " {\n", " io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n", " }\n", "\n", " context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n", " })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t5259f02c29df4b3da5191aa2de06bf54\",\"data\",\"point\",[\"x\", \"y0\"],[[0.0, 1.0, 2.0, 3.0, 4.0, 0.5, 1.25, 2.125, 3.0625], [186631.26694688178, 0.0, 687318.0270802841, 694068.4351877805, 718233.776959995, 357082.16217276256, 744004.1312124364, 832711.1165651656, 1000000.0]],\"toyplot\");\n", "(function(axis, axis_id, projection)\n", " {\n", " axis.show_coordinates(axis_id, projection);\n", " })(modules[\"toyplot.coordinates.Axis\"],\"t9a135b3064d54380929ad8e980fc7afc\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 1000000.0, \"min\": -100000.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 160.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n", "})();</script></div></div>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tre = toytree.rtree.rtree(5, treeheight=1e6, seed=33333)\n", "tre.draw(ts='p', edge_type='p');" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "wrote concat locus (10 x 15000bp) to /tmp/9143.phy\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>locus</th>\n", " <th>start</th>\n", " <th>end</th>\n", " <th>nbps</th>\n", " <th>nsnps</th>\n", " <th>tidx</th>\n", " <th>genealogy</th>\n", " <th>inferred_tree</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2151</td>\n", " <td>2151</td>\n", " <td>38</td>\n", " <td>0</td>\n", " <td>((r0-0:8129,r0-1:8129):3...</td>\n", " <td>(r0-1:0.0000666703231137...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>2151</td>\n", " <td>8420</td>\n", " <td>6269</td>\n", " <td>117</td>\n", " <td>1</td>\n", " <td>((r0-0:8129,r0-1:8129):3...</td>\n", " <td>(r0-1:0.0000666703231137...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>8420</td>\n", " <td>8519</td>\n", " <td>99</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>((r0-0:8129,r0-1:8129):2...</td>\n", " <td>(r0-1:0.0000666703231137...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>8519</td>\n", " <td>15000</td>\n", " <td>6481</td>\n", " <td>128</td>\n", " <td>3</td>\n", " <td>((r0-0:8129,r0-1:8129):2...</td>\n", " <td>(r0-1:0.0000666703231137...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " locus start end nbps nsnps tidx genealogy \\\n", "0 0 0 2151 2151 38 0 ((r0-0:8129,r0-1:8129):3... \n", "1 0 2151 8420 6269 117 1 ((r0-0:8129,r0-1:8129):3... \n", "2 0 8420 8519 99 2 2 ((r0-0:8129,r0-1:8129):2... \n", "3 0 8519 15000 6481 128 3 ((r0-0:8129,r0-1:8129):2... \n", "\n", " inferred_tree \n", "0 (r0-1:0.0000666703231137... \n", "1 (r0-1:0.0000666703231137... \n", "2 (r0-1:0.0000666703231137... \n", "3 (r0-1:0.0000666703231137... " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "self = ipcoal.Model(tre, Ne=1e4, nsamples=2)\n", "self.sim_loci(1, 15000)\n", "self.infer_gene_trees()\n", "self.df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Genealogy" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div class=\"toyplot\" id=\"t04a1a1ab4b684e28b9965cf9a449b15d\" style=\"text-align:center\"><svg class=\"toyplot-canvas-Canvas\" height=\"270.0px\" id=\"t0c2a13ab85ac4e90ab645b1cae720c5b\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;border-color:#292724;border-style:none;border-width:1.0;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" viewBox=\"0 0 350.0 270.0\" width=\"350.0px\" xmlns=\"http://www.w3.org/2000/svg\" 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1041060.0]],\"toyplot\");\n", "(function(axis, axis_id, projection)\n", " {\n", " axis.show_coordinates(axis_id, projection);\n", " })(modules[\"toyplot.coordinates.Axis\"],\"tbe17c6463d6c442f8eb772c08ff6fdde\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 1041060.0, \"min\": -208212.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 170.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n", "})();</script></div></div>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gtre = toytree.tree(self.df.genealogy[0]).root(regex=\"r0\")\n", "gtre.draw(ts='c', tip_labels=True, edge_type='p');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inferred" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div class=\"toyplot\" id=\"t7dd1a87557674c14a2d1ae8a653966c3\" style=\"text-align:center\"><svg class=\"toyplot-canvas-Canvas\" height=\"270.0px\" 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"metadata": {}, "source": [ "### PROBLEMS WITH ADMIXTURE" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div class=\"toyplot\" id=\"t957c1192a56e463aab5c752de0de2798\" style=\"text-align:center\"><svg class=\"toyplot-canvas-Canvas\" height=\"260.0px\" id=\"t9db7d38895bc43a482d725fa519ee542\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;border-color:#292724;border-style:none;border-width:1.0;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" viewBox=\"0 0 350.0 260.0\" width=\"350.0px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:toyplot=\"http://www.sandia.gov/toyplot\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g class=\"toyplot-coordinates-Cartesian\" id=\"t79a461174c9844af83818ad642bee6ae\"><clipPath id=\"t4c9fd96044c042bebe6a3e4235190d8c\"><rect height=\"200.0\" width=\"290.0\" 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edge_type='p');" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'NoneType' object is not subscriptable", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-7-b8217f841066>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mself\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mipcoal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtre\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNe\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madmixture_edges\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msim_trees\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m15000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Documents/ipcoal/ipcoal/Model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, tree, Ne, admixture_edges, admixture_type, nsamples, mut, recomb, recomb_map, seed, seed_mutations, substitution_model, debug, **kwargs)\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 238\u001b[0m \u001b[0;31m# get migration time, rate {mrates: [], mtimes: []}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 239\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_migration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 241\u001b[0m \u001b[0;31m# get .ms_demography dict for msprime input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Documents/ipcoal/ipcoal/Model.py\u001b[0m in \u001b[0;36m_get_migration\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 509\u001b[0m \u001b[0mdnode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtreenode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch_nodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0miedge\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 510\u001b[0m \u001b[0mival\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mintervals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midx\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--> 511\u001b[0;31m \u001b[0mdist_ival\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mival\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mival\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[0m\n\u001b[0m\u001b[1;32m 512\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 513\u001b[0m \u001b[0;31m# intervals mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable" ] } ], "source": [ "self = ipcoal.Model(tre, Ne=1e4, admixture_edges=[(0, 2, 0.5, 0.5)])\n", "self.sim_trees(1, 15000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 4 }
bsd-3-clause
eblur/dust
examples/mie_vs_RG_scattering.ipynb
1
41727
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare the RG-Drude and Mie scattering cross-sections" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from astrodust import distlib\n", "from astrodust.extinction import sigma_scat as ss\n", "import astrodust.constants as c" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "NH, d2g = 1.e21, 0.009\n", "MDUST = NH * c.m_p * d2g \n", "ERANGE = np.logspace(-0.6,1.0,20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Set up grain size distributions and materials" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "RHO_SIL, RHO_GRA, RHO_AVG = 3.8, 2.2, 3.0 # g cm^-3; see Draine's ISM book" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "AMIN, AMAX = 0.005, 0.25 # micron (limits on grain size distribution)\n", "PMRN = 3.5" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "MRN_sil = distlib.MRN_dist(AMIN, AMAX, p=PMRN, rho=RHO_SIL, md=MDUST)\n", "MRN_gra = distlib.MRN_dist(AMIN, AMAX, p=PMRN, rho=RHO_GRA, md=MDUST)\n", "MRN_avg = distlib.MRN_dist(AMIN, AMAX, p=PMRN, rho=RHO_AVG, md=MDUST)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Set up the three grain scattering models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The <code>ss.makeScatModel()</code> function is a short cut for producing a scattering model superclass.\n", "\n", "This class contains a model from the <code>extinction.scatmodels</code> module and a complex index of refraction from the <code>distlib.composition.cmindex</code> module." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "RGdrude = ss.makeScatModel('RG','Drude')\n", "Mie_Sil = ss.makeScatModel('Mie','Silicate')\n", "Mie_Gra = ss.makeScatModel('Mie','Graphite')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['cmodel', 'cmtype', 'smodel', 'stype']\n", "<class 'astrodust.extinction.scatmodels.RGscat'>\n", "<class 'astrodust.distlib.composition.cmindex.CmDrude'>\n" ] } ], "source": [ "print(RGdrude.__dict__.keys())\n", "print(type(RGdrude.smodel))\n", "print(type(RGdrude.cmodel))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compute the scattering cross-sections\n", "\n", "$$\\kappa = \\frac{1}{M_d}\\ \\int \\sigma\\ \\frac{dn}{da}\\ da $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The <code>extinction.sigma_scat.KappaScat</code> object is a container for all of the previous objects. \n", "\n", "In future iterations of the code, I'm going to change this to a function. There is also a long history as to why the dust mass is used. These are things that will be simplified in future iterations of the code." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 7.68 ms, sys: 1.42 ms, total: 9.1 ms\n", "Wall time: 14.1 ms\n" ] } ], "source": [ "%%time\n", "RGD_kappa = ss.KappaScat(E=ERANGE, dist=MRN_avg, scatm=RGdrude)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1min 35s, sys: 188 ms, total: 1min 35s\n", "Wall time: 1min 35s\n" ] } ], "source": [ "%%time\n", "Sil_kappa = ss.KappaScat(E=ERANGE, dist=MRN_sil, scatm=Mie_Sil)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3min 9s, sys: 350 ms, total: 3min 9s\n", "Wall time: 3min 9s\n" ] } ], "source": [ "%%time\n", "Gra_kappa = ss.KappaScat(E=ERANGE, dist=MRN_gra, scatm=Mie_Gra)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot it" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Multiply through by the dust mass to get the total opacity." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_kappa(ax, kappa_obj, **kwargs):\n", " ax.plot(kappa_obj.E, kappa_obj.kappa * kappa_obj.dist.md, **kwargs)\n", " ax.tick_params(labelsize=12)\n", " ax.set_xlabel('Energy (keV)', size=14)\n", " ax.set_ylabel('Scattering Opacity ($\\tau$ per 10$^{21}$ H cm$^{-2}$)', size=14)\n", " return" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(0.0001, 1.0)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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SAh9/DJs2waVLRXcbPowQgk6dOvHNN9/w5ptv4uTkxNGjRxk5ciRz587llhbp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\nbA4ccMft2sGVV0K5cpCamkpqampkcpeJyP3AX4FanJylJrg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"text/plain": [ "<matplotlib.figure.Figure at 0x10f2e00d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = plt.subplot(111)\n", "plot_kappa(ax, RGD_kappa, color='k', lw=2, alpha=0.8, label='RG-Drude')\n", "plot_kappa(ax, Sil_kappa, marker='o', color='g', lw=2, alpha=0.5, label='Mie-Silicate')\n", "plot_kappa(ax, Gra_kappa, marker='o', color='b', lw=2, alpha=0.5, label='Mie-Graphite')\n", "plt.legend(loc='upper right', frameon=False)\n", "plt.loglog()\n", "plt.xlim(0.2, 10.0)\n", "plt.ylim(1.e-4, 1.0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-2-clause
cbare/Etudes
notebooks/stock-trading-example.ipynb
1
1732760
null
apache-2.0
DTUWindEnergy/Python4WindEnergy
lesson 2/lecture-02.ipynb
1
29048
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#Python Course, lesson 2\n", "#Advanced pure python" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "###Contents:\n", "- Programming paradigmes in Python\n", " - Imperative programming\n", " - Structural programming\n", " - Functional programming\n", " - Object oriented programming\n", "- Error handling\n", "\n", "\n", "\n", "\n", " " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "##Programming paradigmes in Python\n", "###Overview\n", "- __Imperative programming__\n", " - Use sequentially ordered statements to change program state\n", "- __Structural programming__\n", " - Structure your code in modules\n", "- __Functional programming__\n", " - Write you code as functions.\n", " - Pure functional programming: No statements only functions without side effects\n", "- __Object- oriented programming__\n", " - Distribute you code into classes" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "##Programming paradigmes in Python\n", "###Imperative programming\n", "Use sequentially ordered statements to change program state, as in iPython\n", "\n", "__Pro:__\n", "\n", "- Fast\n", "- Easy for small programs\n", "\n", "__Con:__\n", "\n", "- Chaotic for larger programs\n", "- Difficult to test`\n", "- Difficult to reuse code between programs\n", "- Difficult to reuse code in program -> Code duplication -> BAD!!!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "##Programming paradigmes in Python\n", "###Example Prime list generator\n", "To simplify the example, 2 is ignored." ] }, { "cell_type": "code", "collapsed": false, "input": [ "n = 100\n", "primes = []\n", "for p in range(3,n):\n", "\n", " \n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "n = 100\n", "primes = []\n", "for p in range(3,n):\n", " for x in range(2,p):\n", " if p%x==0:\n", " break\n", " else:\n", " primes.append(p)\n", "print primes\n" ], "language": "python", "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "n\n" ], "language": "python", "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "##Programming paradigmes in Python\n", "###Structural programming\n", "Structure your code in modules and packages\n", "\n", "__Pro:__\n", "\n", "- Easy to reuse code between programs\n", "- Easier to reuse code in program\n", "\n", "\n", "__Con:__\n", "\n", "- You need to create and switch between multiple files(modules)\n", "- Difficult to test`\n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "import prime_module" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "print prime_module.primes\n", "print prime_module.__file__" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "##Programming paradigmes in Python\n", "###Structural programming - packages\n", "package = folder containing (possibly empty) `__init__.py`\n", "\n", "Current working directory (cwd):\n", "\n", "- file: `prime_module.py` [function: \"def prime_list(n)...\"]\n", "- folder: `my_package`\n", " - file: `__init__.py` [empty]\n", " - file: `prime_module.py` [function: \"def prime_list(n)...\"]\n", "- folder: `my_folder`\n", " - file: `prime_module.py` [function: \"def prime_list(n)...\"]\n", " " ] }, { "cell_type": "code", "collapsed": false, "input": [ "from my_package import prime_module\n", "print prime_module.primes\n", "print prime_module.__file__" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "from my_folder import prime_module" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "##Programming paradigmes in Python\n", "###Functional programming\n", "Write you code as functions.\n", "Pure functional programming: No statements only functions without side effects\n", "\n", "__Pro:__\n", "\n", "- Easy to reuse code between programs\n", "- Easy to reuse code in program\n", "- Very easy to test as a function given the same input always produces the same output\n", "\n", "__Con:__\n", "\n", "- Chaotic for large programs if not structured into modules\n", "- No state -> results cannot be reused -> slow (depending on intelligence of compiler/interpreter)\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "n = 100\n", "primes = []\n", "for p in range(3,n):\n", " for x in range(2,p):\n", " if p%x==0:\n", " break\n", " else:\n", " primes.append(p)\n", "print primes" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def prime_list(n):\n", " primes = []\n", " for p in range(3,n):\n", " for x in range(2,p):\n", " if p%x==0:\n", " break\n", " else:\n", " primes.append(p)\n", " return primes\n", "print prime_list(100)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def prime_list(n):\n", " primes = []\n", " for p in range(3,n):\n", " if is_prime(p):\n", " primes.append(p)\n", " return primes\n", "\n", "def is_prime(p):\n", " for x in range(2,p):\n", " if p%x==0:\n", " return False\n", " else:\n", " return True\n", "print prime_list(100)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def prime_list(n):\n", " return [p for p in range(3,n) if is_prime(p)]\n", "\n", "def is_prime(p):\n", " return all([p%x for x in xrange(2,p)])\n", " \n", "print prime_list(100)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def prime_list(n):\n", " return [p for p in range(3,n) if all([p%x for x in xrange(2,p)])]\n", "\n", " \n", "print prime_list(100)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#Programming paradigmes in Python\n", "##Functional programming - functions\n", "- __map__(*function, sequence*) -> list (python 3: generator)\n", " - Apply function to each element in sequence\n", "- __filter__(*function, sequence*) -> list (python 3: generator)\n", " - Filter sequence to contain elements where function(element)==True only\n", "- __reduce__(*function, sequence*) -> object\n", " - Reduce sequence to single value by a function f(x,y) -> z, i.e.\n", " f(s[0], f(s[1], f(s[3], s[4])))\n", " - Ex: `reduce(sum, [2,4,6])` -> sum(2, sum(4, 6)) -> sum(2,10) -> 12" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def prime_list(n):\n", " return [p for p in range(3,n) if is_prime(p)]\n", "\n", "def is_prime(p):\n", " return all([p%x for x in range(2,p)])\n", " \n", "print prime_list(100)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def prime_list(n):\n", " def add(a,b):\n", " return a+b\n", " return reduce(add, filter(is_prime, range(3,n)))\n", "\n", "def is_prime(p):\n", " def not_div(x):\n", " return p%x>0\n", " return all(map(not_div,range(2,p)))\n", " \n", "print prime_list(100)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#list vs generator\n", "- __List__\n", " - All elements generated at once\n", "- __Generator__ (lazy list)\n", " - Elements generated when needed" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "###List" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def my_list(n):\n", " l = []\n", " i = 0\n", " while i<n:\n", " l.append(i)\n", " i+=1\n", " return l\n", "\n", "print my_list(10)\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def my_list(n):\n", " l = []\n", " for i in range(n):\n", " l.append(i)\n", " return l\n", "\n", "print my_list(10)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def my_list(n):\n", " return [x for x in range(n)]\n", "\n", "print my_list(10)\n" ], "language": "python", "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def my_list(n):\n", " return range(n)\n", "\n", "print my_list(10)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "my_list = range\n", "print my_list(10)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "###Generator" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def my_generator(n):\n", " i = 0\n", " while i<n:\n", " yield(i)\n", " i+=1\n", " \n", "print my_generator(10) \n", "\n", "\n", "\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "for i in my_generator(5):\n", " print i" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def my_generator(n):\n", " for in xrange(n):\n", " yield(i)\n", " \n", "print my_generator(10) " ], "language": "python", "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def my_generator(n):\n", " return (x for x in xrange(n))\n", "print my_generator(10)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "my_generator = xrange\n", "print my_generator(10)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def test(range_, exp=30):\n", " for x in range_(2**exp):\n", " if x==3:\n", " break" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#test with generator\n", "%timeit test(xrange)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#test with list\n", "%timeit test(range,30)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "`[x for x in ...]` -> List\n", "\n", "`(x for x in ...)` -> Generator\n", "\n", "##Python 2:\n", "- __xrange__: Generator\n", "- __range__: List\n", "\n", "##Python 3 (+ python 2 when using template.py)\n", "- __xrange__: Gone!!!\n", "- __range__: Generator\n", "- __list(range(x))__: List" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#Programming paradigmes in Python\n", "##Functional programming - Anonyous lambda functions\n", "\n", "`lambda arg_0,...,arg_n : <function body>`\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def pow(x,e=2):\n", " return x**e\n", "print pow(4)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#refactor\n", "pow = lambda x: x**2\n", "print pow(4)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#refactor\n", "print (lambda x: x**2)(5)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "#example\n", "print map(lambda x: x**2, range(4))" ], "language": "python", "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#Exercise: Lazy prime list generator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Modify `generator_map` to be a generator version of the of the built-in `map` function" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def generator_map(f,seq):\n", " return map(f,seq) #Modify this line\n", "\n", "#Test\n", "assert not isinstance(generator_map(lambda x : x**2, [1,2,3,4]), list)\n", "for a,b in zip(generator_map(lambda x : x**2, [1,2,3,4]), [1,4,9,16]):\n", " assert a==b\n", "assert a==b==16\n", "print \"Yeah!\" " ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to benefit from `generator_map` being a generator, `is_primes` must return as soon as _any_ number that is divisible by x is found.\n", "\n", "- Modify `is_prime_any` to use `any` instead of `all`\n", "- Note how it is tested without code duplication!!!" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def is_prime_all(p):\n", " return all(map(lambda x : p%x, xrange(2,p)))\n", "\n", "def is_prime_any(p,map_=map):\n", " return all(map_(lambda x : p%x, xrange(2,p))) #Modify this line\n", "\n", "for p in range(3,100):\n", " for map_ in [map, generator_map]:\n", " assert is_prime_all(p)==is_prime_any(p, map_)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Finish `is_prime_generator2` to be a lambda function analougue to `is_prime_generator2`\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def prime_list(n, is_prime_func=is_prime_all):\n", " return map(is_prime_func, range(3,n))\n", "\n", "\n", "def is_prime_generator1(p):\n", " return is_prime_any(p, generator_map)\n", "\n", "#Your generator lambda function\n", "is_prime_generator2 = lambda p ...\n", "\n", "\n", "\n", "assert prime_list(100)==prime_list(100, is_prime_generator1)\n", "assert prime_list(100)==prime_list(100, is_prime_generator2)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Speed test \n", "- What do you expect\n", "- Run test below\n", "- Was the result as expected\n", "- Try other values of n\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "n=1000\n", "%timeit prime_list(n)\n", "%timeit prime_list(n, lambda p : is_prime_any(p, generator_map))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "##Programming paradigmes in Python\n", "###Object-oriented programming\n", "Organize code in classes\n", "\n", "__Pro:__\n", "\n", "- Cohession: State and method can be organized in coherrent objects\n", "- Encapsulation: Implementation details can be hidden for users by private fields and methods (Limited support)\n", "- Decoupling: Programs can be split into decoupled classes, that interacts via a simple interface (Limited support)\n", "- Inherritance: Extend and change existing classes (instead of copy/paste/modify = code duplication = Bad!!!)\n", "\n", "- Testing may be easy\n", "\n", "__Con:__\n", "\n", "- Some overhead for simple programs\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class Prime(object):\n", " def __init__(self, n, last_number):\n", " self.n = n\n", " self.last_number = last_number\n", "\n", " def last_prime(self):\n", " return self.n\n", "\n", " def __str__(self):\n", " return \"I am: %2d, prime: %s,\\tprev prime: %s\" % (self.n, \n", " bool(isinstance(self, Prime)), \n", " self.last_number.last_prime())\n", "\n", "\n", "class NonPrime(object):\n", " def __init__(self, n, last_number):\n", " self.n = n\n", " self.last_number = last_number\n", "\n", " def last_prime(self):\n", " return self.last_number.last_prime()\n", "\n", " def __str__(self):\n", " return \"I am: %2d, prime: %s,\\tprev prime: %s\" % (self.n, \n", " bool(isinstance(self, Prime)), \n", " self.last_number.last_prime())\n", "\n", "class Two(object):\n", " def __init__(self):\n", " self.n = 2\n", " self.last_number = None\n", "\n", " def last_prime(self):\n", " return self.n\n", "\n", " def __str__(self):\n", " return \"I am: 2, prime: True,\\tprev prime: --\"\n", "\n", "\n", "\n", "\n", "is_prime = lambda n : all([n%x for x in range(2,n)])\n", "\n", "numbers = [Two()]\n", "\n", "for n in range(3,13):\n", " if is_prime(n):\n", " numbers.append(Prime(n, numbers[-1]))\n", " else:\n", " numbers.append(NonPrime(n, numbers[-1]))\n", "\n", "for n in numbers:\n", " print n" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "I am: 2, prime: True,\tprev prime: --\n", "I am: 3, prime: True,\tprev prime: 2\n", "I am: 4, prime: False,\tprev prime: 3\n", "I am: 5, prime: True,\tprev prime: 3\n", "I am: 6, prime: False,\tprev prime: 5\n", "I am: 7, prime: True,\tprev prime: 5\n", "I am: 8, prime: False,\tprev prime: 7\n", "I am: 9, prime: False,\tprev prime: 7\n", "I am: 10, prime: False,\tprev prime: 7\n", "I am: 11, prime: True,\tprev prime: 7\n", "I am: 12, prime: False,\tprev prime: 11\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "#refactor\n", "class Number(object):\n", " def __init__(self, n, last_number):\n", " self.n = n\n", " self.last_number = last_number\n", "\n", " def info(self, prev_prime):\n", " return \"I am: %2d, prime: %s,\\tprevious prime: %s\" % (self.n, \n", " bool(isinstance(self, Prime)), \n", " prev_prime)\n", " \n", " def __str__(self):\n", " return self.info(self.last_number.last_prime())\n", " \n", "class Prime(Number):\n", " def last_prime(self):\n", " return self.n\n", "\n", "class NonPrime(Number):\n", " def last_prime(self):\n", " return self.last_number.last_prime()\n", "\n", " \n", "class Two(Prime):\n", " def __init__(self):\n", " Prime.__init__(self, 2,None)\n", "\n", " def __str__(self):\n", " return self.info(\"--\")\n", "\n", "\n", "\n", "is_prime = lambda n : all([n%x for x in range(2,n)])\n", "\n", "numbers = [Two()]\n", "\n", "for n in range(3,13):\n", " if is_prime(n):\n", " numbers.append(Prime(n, numbers[-1]))\n", " else:\n", " numbers.append(NonPrime(n, numbers[-1]))\n", "\n", "for n in numbers:\n", " print n" ], "language": "python", "metadata": { "slideshow": { "slide_type": "notes" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "I am: 2, prime: True,\tprevious prime: --\n", "I am: 3, prime: True,\tprevious prime: 2\n", "I am: 4, prime: False,\tprevious prime: 3\n", "I am: 5, prime: True,\tprevious prime: 3\n", "I am: 6, prime: False,\tprevious prime: 5\n", "I am: 7, prime: True,\tprevious prime: 5\n", "I am: 8, prime: False,\tprevious prime: 7\n", "I am: 9, prime: False,\tprevious prime: 7\n", "I am: 10, prime: False,\tprevious prime: 7\n", "I am: 11, prime: True,\tprevious prime: 7\n", "I am: 12, prime: False,\tprevious prime: 11\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "##Exercise Magic number\n", "Make a class `MagicInt` that satisfies the assert statements, i.e:\n", "\n", "- Is a int\n", "- Makes 2 + 2 = 5\n", "- Result of addition is also a MagicInt object\n", "- Lets len(magicInt) return the number of digits\n", "- Has a property `prime` that returns `True` the if it is a prime.\n", "- Allows the user to set the `prime` property manually" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class MagicInt(...\n", " \n", " \n", "#Do not modify code below\n", "two = MagicInt(2)\n", "m127 = MagicInt(127)\n", "\n", "# a int\n", "assert isinstance(two, int)\n", "\n", "# 2 + 2 = 5\n", "assert two + two == 5\n", "assert two + 3 == 5\n", "\n", "#Result of addition is also a MInt object\n", "assert isinstance(two + two, MagicInt)\n", "assert isinstance(two + 3, MagicInt)\n", "\n", "# len() return the number of digits\n", "assert len(two)==1\n", "assert len(m127) == 3\n", "assert len(MagicInt(-127)) == 3\n", "\n", "# property prime that returns True the if it is a prime\n", "assert m127.prime\n", "assert (two+m127).prime==False\n", "\n", "# Allow the user to set the 'prime' property manually\n", "m127.prime = False\n", "assert m127.prime==False\n", "\n", "print \"Yeah!!! All asserts satisfied\"\n", "\n", "\n", " \n" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
apache-2.0
GoogleCloudPlatform/mlops-on-gcp
immersion/tfx_pipelines/04-metadata/labs/lab-04.ipynb
1
22108
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Inspecting TFX metadata\n", "\n", "\n", "## Learning Objectives\n", "\n", "1. Use a GRPC server to access and analyze pipeline artifacts stored in the ML Metadata service of your AI Platform Pipelines instance.\n", "\n", "In this lab, you will explore TFX pipeline metadata including pipeline and run artifacts. A hosted **AI Platform Pipelines** instance includes the [ML Metadata](https://github.com/google/ml-metadata) service. In **AI Platform Pipelines**, ML Metadata uses *MySQL* as a database backend and can be accessed using a GRPC server." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "import json\n", "\n", "import ml_metadata\n", "import tensorflow_data_validation as tfdv\n", "import tensorflow_model_analysis as tfma\n", "\n", "\n", "from ml_metadata.metadata_store import metadata_store\n", "from ml_metadata.proto import metadata_store_pb2\n", "\n", "from tfx.orchestration import metadata\n", "from tfx.types import standard_artifacts\n", "\n", "from tensorflow.python.lib.io import file_io" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!python -c \"import tfx; print('TFX version: {}'.format(tfx.__version__))\"\n", "!python -c \"import kfp; print('KFP version: {}'.format(kfp.__version__))\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Option 1: Explore metadata from existing TFX pipeline runs from AI Pipelines instance created in `lab-02` or `lab-03`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 1.1 Configure Kubernetes port forwarding\n", "\n", "To enable access to the ML Metadata GRPC server, configure Kubernetes port forwarding.\n", "\n", "From a JupyterLab terminal, execute the following commands:\n", "\n", "```\n", "gcloud container clusters get-credentials [YOUR CLUSTER] --zone [YOUR CLUSTER ZONE] \n", "kubectl port-forward service/metadata-grpc-service --namespace [YOUR NAMESPACE] 7000:8080\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Proceed to the next step, \"Connecting to ML Metadata\"." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Option 2: Create new AI Pipelines instance and evaluate metadata on newly triggered pipeline runs.\n", "\n", "Hosted AI Pipelines incurs cost for the duration your Kubernetes cluster is running. If you deleted your previous lab instance, proceed with the 6 steps below to deploy a new TFX pipeline and triggers runs to inspect its metadata." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import yaml\n", "\n", "# Set `PATH` to include the directory containing TFX CLI.\n", "PATH=%env PATH\n", "%env PATH=/home/jupyter/.local/bin:{PATH}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The pipeline source can be found in the `pipeline` folder. Switch to the `pipeline` folder and compile the pipeline." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%cd pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2.1 Create AI Platform Pipelines cluster\n", "\n", "Navigate to [AI Platform Pipelines](https://console.cloud.google.com/ai-platform/pipelines/clusters) page in the Google Cloud Console.\n", "\n", "Create or select an existing Kubernetes cluster (GKE) and deploy AI Platform. Make sure to select `\"Allow access to the following Cloud APIs https://www.googleapis.com/auth/cloud-platform\"` to allow for programmatic access to your pipeline by the Kubeflow SDK for the rest of the lab. Also, provide an `App instance name` such as \"TFX-lab-04\"." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2.2 Configure environment settings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Update the below constants with the settings reflecting your lab environment.\n", "\n", "- `GCP_REGION` - the compute region for AI Platform Training and Prediction\n", "- `ARTIFACT_STORE` - the GCS bucket created during installation of AI Platform Pipelines. The bucket name starts with the `kubeflowpipelines-` prefix. Alternatively, you can specify create a new storage bucket to write pipeline artifacts to." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!gsutil ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* `CUSTOM_SERVICE_ACCOUNT` - In the gcp console Click on the Navigation Menu. Navigate to `IAM & Admin`, then to `Service Accounts` and use the service account starting with prifix - `'tfx-tuner-caip-service-account'`. This enables CloudTuner and the Google Cloud AI Platform extensions Tuner component to work together and allows for distributed and parallel tuning backed by AI Platform Vizier's hyperparameter search algorithm. Please see the lab setup `README` for setup instructions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- `ENDPOINT` - set the `ENDPOINT` constant to the endpoint to your AI Platform Pipelines instance. The endpoint to the AI Platform Pipelines instance can be found on the [AI Platform Pipelines](https://console.cloud.google.com/ai-platform/pipelines/clusters) page in the Google Cloud Console.\n", "\n", "1. Open the *SETTINGS* for your instance\n", "2. Use the value of the `host` variable in the *Connect to this Kubeflow Pipelines instance from a Python client via Kubeflow Pipelines SKD* section of the *SETTINGS* window." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#TODO: Set your environment resource settings here for GCP_REGION, ARTIFACT_STORE_URI, ENDPOINT, and CUSTOM_SERVICE_ACCOUNT.\n", "GCP_REGION = 'us-central1'\n", "ARTIFACT_STORE_URI = 'gs://dougkelly-sandbox-kubeflowpipelines-default' #Change\n", "ENDPOINT = '60ff837483ecde05-dot-us-central2.pipelines.googleusercontent.com' #Change\n", "CUSTOM_SERVICE_ACCOUNT = 'tfx-tuner-caip-service-account@dougkelly-sandbox.iam.gserviceaccount.com' #Change\n", "\n", "PROJECT_ID = !(gcloud config get-value core/project)\n", "PROJECT_ID = PROJECT_ID[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Set your resource settings as environment variables. These override the default values in pipeline/config.py.\n", "%env GCP_REGION={GCP_REGION}\n", "%env ARTIFACT_STORE_URI={ARTIFACT_STORE_URI}\n", "%env CUSTOM_SERVICE_ACCOUNT={CUSTOM_SERVICE_ACCOUNT}\n", "%env PROJECT_ID={PROJECT_ID}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2.3 Compile pipeline" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "PIPELINE_NAME = 'tfx_covertype_lab_04'\n", "MODEL_NAME = 'tfx_covertype_classifier'\n", "DATA_ROOT_URI = 'gs://workshop-datasets/covertype/small'\n", "CUSTOM_TFX_IMAGE = 'gcr.io/{}/{}'.format(PROJECT_ID, PIPELINE_NAME)\n", "RUNTIME_VERSION = '2.3'\n", "PYTHON_VERSION = '3.7'\n", "USE_KFP_SA=False\n", "ENABLE_TUNING=True" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%env PIPELINE_NAME={PIPELINE_NAME}\n", "%env MODEL_NAME={MODEL_NAME}\n", "%env DATA_ROOT_URI={DATA_ROOT_URI}\n", "%env KUBEFLOW_TFX_IMAGE={CUSTOM_TFX_IMAGE}\n", "%env RUNTIME_VERSION={RUNTIME_VERSION}\n", "%env PYTHON_VERIONS={PYTHON_VERSION}\n", "%env USE_KFP_SA={USE_KFP_SA}\n", "%env ENABLE_TUNING={ENABLE_TUNING}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!tfx pipeline compile --engine kubeflow --pipeline_path runner.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2.4 Deploy pipeline to AI Platform" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!tfx pipeline create \\\n", "--pipeline_path=runner.py \\\n", "--endpoint={ENDPOINT} \\\n", "--build_target_image={CUSTOM_TFX_IMAGE}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(optional) If you make local changes to the pipeline, you can update the deployed package on AI Platform with the following command:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!tfx pipeline update --pipeline_path runner.py --endpoint {ENDPOINT}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2.5 Create and monitor pipeline run" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!tfx run create --pipeline_name={PIPELINE_NAME} --endpoint={ENDPOINT}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2.6 Configure Kubernetes port forwarding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To enable access to the ML Metadata GRPC server, configure Kubernetes port forwarding.\n", "\n", "From a JupyterLab terminal, execute the following commands:\n", "\n", "```\n", "gcloud container clusters get-credentials [YOUR CLUSTER] --zone [YOURE CLUSTER ZONE] \n", "kubectl port-forward service/metadata-grpc-service --namespace [YOUR NAMESPACE] 7000:8080\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Connecting to ML Metadata " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Configure ML Metadata GRPC client" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "grpc_host = 'localhost'\n", "grpc_port = 7000\n", "connection_config = metadata_store_pb2.MetadataStoreClientConfig()\n", "connection_config.host = grpc_host\n", "connection_config.port = grpc_port" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Connect to ML Metadata service" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "store = metadata_store.MetadataStore(connection_config)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Important\n", "\n", "A full pipeline run without tuning takes about 40-45 minutes to complete. You need to wait until a pipeline run is complete before proceeding with the steps below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exploring ML Metadata \n", "\n", "The Metadata Store uses the following data model:\n", "\n", "- `ArtifactType` describes an artifact's type and its properties that are stored in the Metadata Store. These types can be registered on-the-fly with the Metadata Store in code, or they can be loaded in the store from a serialized format. Once a type is registered, its definition is available throughout the lifetime of the store.\n", "- `Artifact` describes a specific instances of an ArtifactType, and its properties that are written to the Metadata Store.\n", "- `ExecutionType` describes a type of component or step in a workflow, and its runtime parameters.\n", "- `Execution` is a record of a component run or a step in an ML workflow and the runtime parameters. An Execution can be thought of as an instance of an ExecutionType. Every time a developer runs an ML pipeline or step, executions are recorded for each step.\n", "- `Event` is a record of the relationship between an Artifact and Executions. When an Execution happens, Events record every Artifact that was used by the Execution, and every Artifact that was produced. These records allow for provenance tracking throughout a workflow. By looking at all Events MLMD knows what Executions happened, what Artifacts were created as a result, and can recurse back from any Artifact to all of its upstream inputs.\n", "- `ContextType` describes a type of conceptual group of Artifacts and Executions in a workflow, and its structural properties. For example: projects, pipeline runs, experiments, owners.\n", "- `Context` is an instances of a ContextType. It captures the shared information within the group. For example: project name, changelist commit id, experiment annotations. It has a user-defined unique name within its ContextType.\n", "- `Attribution` is a record of the relationship between Artifacts and Contexts.\n", "- `Association` is a record of the relationship between Executions and Contexts." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "List the registered artifact types." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for artifact_type in store.get_artifact_types():\n", " print(artifact_type.name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Display the registered execution types." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for execution_type in store.get_execution_types():\n", " print(execution_type.name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "List the registered context types." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for context_type in store.get_context_types():\n", " print(context_type.name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing TFX artifacts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Retrieve data analysis and validation artifacts" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with metadata.Metadata(connection_config) as store:\n", " schema_artifacts = store.get_artifacts_by_type(standard_artifacts.Schema.TYPE_NAME) \n", " stats_artifacts = store.get_artifacts_by_type(standard_artifacts.ExampleStatistics.TYPE_NAME)\n", " anomalies_artifacts = store.get_artifacts_by_type(standard_artifacts.ExampleAnomalies.TYPE_NAME)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "schema_file = os.path.join(schema_artifacts[-1].uri, 'schema.pbtxt')\n", "print(\"Generated schame file:{}\".format(schema_file))\n", "\n", "stats_path = stats_artifacts[-1].uri\n", "train_stats_file = os.path.join(stats_path, 'train', 'stats_tfrecord')\n", "eval_stats_file = os.path.join(stats_path, 'eval', 'stats_tfrecord')\n", "print(\"Train stats file:{}, Eval stats file:{}\".format(\n", " train_stats_file, eval_stats_file))\n", "\n", "anomalies_path = anomalies_artifacts[-1].uri\n", "train_anomalies_file = os.path.join(anomalies_path, 'train', 'anomalies.pbtxt')\n", "eval_anomalies_file = os.path.join(anomalies_path, 'eval', 'anomalies.pbtxt')\n", "\n", "print(\"Train anomalies file:{}, Eval anomalies file:{}\".format(\n", " train_anomalies_file, eval_anomalies_file))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualize schema" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "schema = tfdv.load_schema_text(schema_file)\n", "tfdv.display_schema(schema=schema)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualize statistics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercise: looking at the features visualized below, answer the following questions:\n", "\n", "- Which feature transformations would you apply to each feature with TF Transform?\n", "- Are there data quality issues with certain features that may impact your model performance? How might you deal with it?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train_stats = tfdv.load_statistics(train_stats_file)\n", "eval_stats = tfdv.load_statistics(eval_stats_file)\n", "tfdv.visualize_statistics(lhs_statistics=eval_stats, rhs_statistics=train_stats,\n", " lhs_name='EVAL_DATASET', rhs_name='TRAIN_DATASET')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualize anomalies" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train_anomalies = tfdv.load_anomalies_text(train_anomalies_file)\n", "tfdv.display_anomalies(train_anomalies)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "eval_anomalies = tfdv.load_anomalies_text(eval_anomalies_file)\n", "tfdv.display_anomalies(eval_anomalies)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Retrieve model artifacts" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with metadata.Metadata(connection_config) as store:\n", " model_eval_artifacts = store.get_artifacts_by_type(standard_artifacts.ModelEvaluation.TYPE_NAME)\n", " hyperparam_artifacts = store.get_artifacts_by_type(standard_artifacts.HyperParameters.TYPE_NAME)\n", " \n", "model_eval_path = model_eval_artifacts[-1].uri\n", "print(\"Generated model evaluation result:{}\".format(model_eval_path))\n", "best_hparams_path = os.path.join(hyperparam_artifacts[-1].uri, 'best_hyperparameters.txt')\n", "print(\"Generated model best hyperparameters result:{}\".format(best_hparams_path))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Return best hyperparameters" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Latest pipeline run Tuner search space.\n", "json.loads(file_io.read_file_to_string(best_hparams_path))['space']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Latest pipeline run Tuner searched best_hyperparameters artifacts.\n", "json.loads(file_io.read_file_to_string(best_hparams_path))['values']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualize model evaluations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercise: review the model evaluation results below and answer the following questions:\n", "\n", "- Which Wilderness Area had the highest accuracy?\n", "- Which Wilderness Area had the lowest performance? Why do you think that is? What are some steps you could take to improve your next model runs?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "eval_result = tfma.load_eval_result(model_eval_path)\n", "tfma.view.render_slicing_metrics(\n", " eval_result, slicing_column='Wilderness_Area')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Debugging tip**: If the TFMA visualization of the Evaluator results do not render, try switching to view in a Classic Jupyter Notebook. You do so by clicking `Help > Launch Classic Notebook` and re-opening the notebook and running the above cell to see the interactive TFMA results." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## License" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<font size=-1>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 [https://www.apache.org/licenses/LICENSE-2.0](https://www.apache.org/licenses/LICENSE-2.0)\n", "\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.</font>\n" ] } ], "metadata": { "environment": { "name": "tf2-gpu.2-3.m61", "type": "gcloud", "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-3:m61" }, "kernelspec": { "display_name": "Python [conda env: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": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 4 }
apache-2.0
theandygross/HIV_Methylation
Setup/Read_HIV_Data.ipynb
1
95670
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Read HIV Data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "if os.getcwd().endswith('Setup'):\n", " os.chdir('..')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "importing IPython notebook from <a href='./Setup/Imports.ipynb' target='_blank'>Setup/Imports</a>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "import NotebookImport\n", "from Setup.Imports import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read in Clinical Data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "c1 = pd.read_excel(ucsd_path + 'DESIGN_Fox_v2_Samples-ChipLAyout-Clinical UNMC-UCSD methylomestudy.xlsx', \n", " 'HIV- samples from OldStudy', index_col=0)\n", "c2 = pd.read_excel(ucsd_path + 'DESIGN_Fox_v2_Samples-ChipLAyout-Clinical UNMC-UCSD methylomestudy.xlsx', \n", " 'HIV+ samples', index_col=0)\n", "clinical = c1.append(c2)\n", "clinical['Sentrix_Position'] = clinical['Sentrix_Position\\\\'].map(lambda s: s[:-1])\n", "del clinical['Sentrix_Position\\\\']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Update clinical data with new data provided by Howard Fox" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "age_new = pd.read_csv(ucsd_path + 'UpdatesAges-Infection.csv', index_col=0)\n", "age = age_new.age.combine_first(clinical.age)\n", "age.name= 'age'\n", "clinical['age'] = age\n", "l = 'estimated duration hiv (months)'\n", "clinical[l] = age_new['Estimated Duration HIV+ (months)'].combine_first(clinical[l])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clean up diabetes across annotation files" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "no 192\n", "dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diabetes = clinical['diabetes'].combine_first(clinical['Diabetes @ 000'])\n", "diabetes = diabetes.replace('N','no')\n", "clinical['diabetes'] = diabetes\n", "del clinical['Diabetes @ 000']\n", "diabetes.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All of the patients are white or Caucasian" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "white 192\n", "dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ethnicity = clinical.ethnicity\n", "ethnicity = ethnicity.replace('wht','white')\n", "ethnicity = ethnicity.replace('Caucasian - European','white')\n", "clinical['ethnicity'] = ethnicity\n", "ethnicity.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sex is not recorded for the cases but they are all HIV+ men." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "clinical['sex'] = clinical['sex'].fillna('M')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fix BMI to unified labels" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bmi = clinical['bmi'].combine_first(clinical['BMI'])\n", "clinical['BMI'] = bmi\n", "clinical = clinical[clinical.columns.difference(['bmi'])]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7f0b7015c290>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0b701441d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Do not import\n", "bmi.hist()" ] }, { "cell_type": "code", "execution_count": 10, "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>NO</th>\n", " <th>YES</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Current 'Other' dx</th>\n", " <td> 142</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>Current Alcohol dx</th>\n", " <td> 140</td>\n", " <td> 2</td>\n", " </tr>\n", " <tr>\n", " <th>Current Bipolar I</th>\n", " <td> 142</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>Current Bipolar II</th>\n", " <td> 142</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>Current Cannabis dx</th>\n", " <td> 141</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>Current Cocaine dx</th>\n", " <td> 142</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>Current Dysthymia</th>\n", " <td> 142</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>Current Halucinogen dx</th>\n", " <td> 142</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>Current Inhalant dx</th>\n", " <td> 142</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>Current MDD</th>\n", " <td> 120</td>\n", " <td> 22</td>\n", " </tr>\n", " <tr>\n", " <th>Current Methamphetamine dx</th>\n", " <td> 142</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>Current Opioid dx</th>\n", " <td> 142</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>Current PCP dx</th>\n", " <td> 142</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>Current Sedative dx</th>\n", " <td> 142</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>Any Current Substance dx</th>\n", " <td> 139</td>\n", " <td> 3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NO YES\n", "Current 'Other' dx 142 0\n", "Current Alcohol dx 140 2\n", "Current Bipolar I 142 0\n", "Current Bipolar II 142 0\n", "Current Cannabis dx 141 1\n", "Current Cocaine dx 142 0\n", "Current Dysthymia 142 0\n", "Current Halucinogen dx 142 0\n", "Current Inhalant dx 142 0\n", "Current MDD 120 22\n", "Current Methamphetamine dx 142 0\n", "Current Opioid dx 142 0\n", "Current PCP dx 142 0\n", "Current Sedative dx 142 0\n", "Any Current Substance dx 139 3" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "current_usage = [\"Current 'Other' dx\", 'Current Alcohol dx',\n", " 'Current Bipolar I', 'Current Bipolar II',\n", " 'Current Cannabis dx', 'Current Cocaine dx',\n", " 'Current Dysthymia', 'Current Halucinogen dx',\n", " 'Current Inhalant dx', 'Current MDD',\n", " 'Current Methamphetamine dx', 'Current Opioid dx',\n", " 'Current PCP dx', 'Current Sedative dx',\n", " 'Any Current Substance dx']\n", "current_usage = clinical[current_usage]\n", "current_usage.dropna(how='all').apply(pd.value_counts).fillna(0).T" ] }, { "cell_type": "code", "execution_count": 11, "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>NO</th>\n", " <th>YES</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>LT 'Other' dx</th>\n", " <td> 141</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>LT Alcohol dx</th>\n", " <td> 50</td>\n", " <td> 92</td>\n", " </tr>\n", " <tr>\n", " <th>LT Bipolar I</th>\n", " <td> 142</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>LT Bipolar II</th>\n", " <td> 142</td>\n", " <td> 0</td>\n", " </tr>\n", " <tr>\n", " <th>LT Cannabis dx</th>\n", " <td> 99</td>\n", " <td> 43</td>\n", " </tr>\n", " <tr>\n", " <th>LT Cocaine dx</th>\n", " <td> 112</td>\n", " <td> 30</td>\n", " </tr>\n", " <tr>\n", " <th>LT Dysthymia</th>\n", " <td> 141</td>\n", " <td> 1</td>\n", " </tr>\n", " <tr>\n", " <th>LT Halucinogen dx</th>\n", " <td> 123</td>\n", " <td> 19</td>\n", " </tr>\n", " <tr>\n", " <th>LT Inhalant dx</th>\n", " <td> 134</td>\n", " <td> 8</td>\n", " </tr>\n", " <tr>\n", " <th>LT MDD</th>\n", " <td> 58</td>\n", " <td> 84</td>\n", " </tr>\n", " <tr>\n", " <th>LT Methamphetamine dx</th>\n", " <td> 104</td>\n", " <td> 38</td>\n", " </tr>\n", " <tr>\n", " <th>LT Opioid dx</th>\n", " <td> 131</td>\n", " <td> 11</td>\n", " </tr>\n", " <tr>\n", " <th>LT PCP dx</th>\n", " <td> 138</td>\n", " <td> 4</td>\n", " </tr>\n", " <tr>\n", " <th>LT Sedative dx</th>\n", " <td> 132</td>\n", " <td> 10</td>\n", " </tr>\n", " <tr>\n", " <th>Any LT Substance dx</th>\n", " <td> 36</td>\n", " <td> 106</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " NO YES\n", "LT 'Other' dx 141 1\n", "LT Alcohol dx 50 92\n", "LT Bipolar I 142 0\n", "LT Bipolar II 142 0\n", "LT Cannabis dx 99 43\n", "LT Cocaine dx 112 30\n", "LT Dysthymia 141 1\n", "LT Halucinogen dx 123 19\n", "LT Inhalant dx 134 8\n", "LT MDD 58 84\n", "LT Methamphetamine dx 104 38\n", "LT Opioid dx 131 11\n", "LT PCP dx 138 4\n", "LT Sedative dx 132 10\n", "Any LT Substance dx 36 106" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "past_usage = [\"LT 'Other' dx\", 'LT Alcohol dx', 'LT Bipolar I', \n", " 'LT Bipolar II', 'LT Cannabis dx', 'LT Cocaine dx',\n", " 'LT Dysthymia', 'LT Halucinogen dx', 'LT Inhalant dx',\n", " 'LT MDD', 'LT Methamphetamine dx', 'LT Opioid dx',\n", " 'LT PCP dx', 'LT Sedative dx', 'Any LT Substance dx']\n", "past_usage = clinical[past_usage]\n", "past_usage.dropna(how='all').apply(pd.value_counts).fillna(0).T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Trimming the clinical dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "None of the patients are diabetic" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "no 192\n", "dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clinical.diabetes.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All of the patients are hepatitis C negative" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Neg 142\n", "dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clinical['HCV'].dropna(0).value_counts(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All patients are currently using anti-retoviral therepy, but 5 patients treatment is not classified as HAART" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Currently Using 142\n", "dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clinical['ARV History'].value_counts()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "HAART 137\n", "non-HAART 5\n", "dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clinical['ARV Status'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All patients are reported as adhererent, but a few are not 100% adherent" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Adherent 142\n", "dtype: int64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clinical['adherent'].value_counts()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7f0b415c20d0>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0b4162fcd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Do not import\n", "clinical['adherence %'].hist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have a wide varienty of regimens with 81 unique combinations of 35 drugs" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "EVG 1\n", "TPV 1\n", "DDC 1\n", "DRV 2\n", "SQV 2\n", "APV 2\n", "BDT 2\n", "BSD 3\n", "SQV2 3\n", "DLV 4\n", "FPV 5\n", "T20 8\n", "NFV 8\n", "D4T 9\n", "DDI 15\n", "NVP 26\n", "ABV 30\n", "LPV 30\n", "EFV 31\n", "ZDV 35\n", "ATV 46\n", "FTC 52\n", "3TC 63\n", "RTV 82\n", "TFV 91\n", "dtype: int64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reg = clinical['Current Regimen'].dropna().str.split('/').map(sorted)\n", "drugs = {r for s in reg for r in s}\n", "drug_mat = pd.DataFrame({i: {d: d in s for d in drugs} for i,s in \n", " reg.iteritems()}).T\n", "drug_mat.sum().order()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These can be broken down into 8 regimen types" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "PI/NRTI Based 69\n", "NNRTI/NRTI Based 43\n", "3-class 22\n", "NRTI Based 3\n", "PI/NNRTI Based 2\n", "4+ class 1\n", "NNRTI Based 1\n", "PI Based 1\n", "dtype: int64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clinical['Regimen Type'].value_counts()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "kill_list = ['zhang id', 'diabetes', 'Methylation ID', \n", " 'Sentrix_ID','Sample_Plate','Sample_Well','Sentrix_Position',\n", " ]\n", "drugs = ['ARV History', 'ARV Status', 'Current Regimen', 'Regimen Type',\n", " 'adherence %' ,'adherent']" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "left = [c for c in clinical if c not in past_usage and c not in current_usage\n", " and c not in drugs]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "age = clinical.age" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 91\n", "1 51\n", "dtype: int64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clinical['BDI > 17'].value_counts()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Currently Using 142\n", "dtype: int64" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clinical['ARV History'].value_counts()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "C3 57\n", "B3 24\n", "A2 20\n", "A3 17\n", "B2 14\n", "C2 6\n", "A1 4\n", "dtype: int64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clinical['CDC stage'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "IADL = Instrumental activities of daily living" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "indep 109\n", "dep 25\n", "missing 8\n", "dtype: int64" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iadl = clinical.IADL\n", "iadl.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Global imparement" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "nml 74\n", "imp 68\n", "dtype: int64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clinical['global impairment'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Patient's Assessment of Own Functioning Inventory (PAOFI)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7f0b40fb7450>" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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1iiRJfaGrAKnC4zrgrcB5mTnasXgEWDZp/cXAQmC4pjrnnfvvv5/nnnuu12W8xPj4OK1W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\n4JnUrf+zfgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f0b4101e710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Do not import\n", "paofi = clinical['paofi total']\n", "paofi.hist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "RPR (Rapid plasma reagin) is a diagnostic used to detect syphilis" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Neg 136\n", "Pos 6\n", "dtype: int64" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clinical.RPR.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Batches" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "UCSD 73\n", "VNDRBLT 69\n", "dtype: int64" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "site = clinical.Site\n", "site.value_counts()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "utox neg 130\n", "utox pos 12\n", "dtype: int64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clinical.Utox.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Beck Depression Inventory is a questionarre measuring depression levels (from [Wikipedia](http://en.wikipedia.org/wiki/Beck_Depression_Inventory))\n", "* 0–13: minimal depression \n", "* 14–19: mild depression \n", "* 20–28: moderate depression \n", "* 29–63: severe depression." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7f0b40ebe850>" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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JGtYtSWqoOg6ib6mjEEnSYPFIsiSpiAEiSSpigEiSihggkqQiBogkqYgBIkkqYoBIkooY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\nqixC/PeOAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f0b40f24890>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Do not import\n", "beck = clinical['beck total'].dropna()\n", "beck.hist()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes.AxesSubplot at 0x7f0b410a2d90>" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0b40e7f390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Do not import\n", "clinical['paofi total'].hist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Read in lab blood work data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* abstract exam date onto exam year for anonymity" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "labs = pd.read_excel(ucsd_path + 'fox_methylation_labdata_073014.xlsx',\n", " index_col=0)\n", "labs['nb exam year']= labs['nb exam date'].map(lambda s: s.year)\n", "del labs['nb exam date']\n", "labs = labs.dropna(axis=1, how='all')\n", "labs = labs.ix[labs.index.intersection(clinical.index)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Dropping five patients because they don't look Kosher \n", "* Renormalizing cell percentages because some don't sum to 100%" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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/nkRBjoiISAtbvXI5k6+4j/4Dh8bZX2cH1184jra2tij7qyYFOSJNqhp3ZznN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\nM5tACBhvIXzWNwCXmNkZefs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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0b415f2ad0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Do not import\n", "fig, axs = subplots(1,2, figsize=(9,4))\n", "clinical.age.hist(ax=axs[0])\n", "clinical['estimated duration hiv (months)'].hist(ax=axs[1])\n", "axs[0].set_xlabel('Age')\n", "axs[1].set_xlabel('estimated duration hiv (months)')\n", "for ax in axs:\n", " ax.set_ylabel('# of Patients')\n", " prettify_ax(ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As can be seen above, we have two groups of patients with respect to HIV duration, we don't really have the sample size to tease apart any differences other than this main distiction so for now I am just treating duration of HIV as a categorical variable (e.g. controls, short exposure and long exposure)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "HIV Long 105\n", "Control 50\n", "HIV Short 37\n", "dtype: int64" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "duration = clinical['estimated duration hiv (months)']\n", "duration = (1.*duration.notnull()) + (1.*duration > 100)\n", "duration = duration.map({0:'Control',1:'HIV Short',2:'HIV Long'})\n", "duration.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Patient Selection Criteria\n", "* There are a couple of female patients in the controls, we are going to get rid of those as all of the cases are males \n", "* Most of the HIV patients are under HAART therepy, there are a few that are not and we are going to filter those out for now and possibly look at them after the primary analysis" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "HIV Long 104\n", "Control 48\n", "HIV Short 33\n", "dtype: int64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "duration = duration.ix[ti(clinical['ARV Status'] != 'non-HAART')]\n", "duration = duration.ix[ti(clinical.sex != 'F')].dropna()\n", "duration.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read in HIV Methylation data " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read in quantile-normalized data, adjusted for cellular compositions and then normalized agin using BMIQ." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_hiv = pd.read_hdf(HDFS_DIR + 'methylation_norm.h5', \n", " 'quant_BMIQ_adj')" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_hiv = pd.read_hdf(HDFS_DIR + '/methylation_norm.h5', \n", " 'quant_BMIQ_adj')\n", "df_hiv = df_hiv.ix[:, duration.index]\n", "df_hiv = df_hiv.dropna(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read in data processed with BMIQ using Horvath's gold standard." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_hiv_n = pd.read_hdf(HDFS_DIR + 'methylation_norm.h5', \n", " 'BMIQ_Horvath')\n", "df_hiv_n = df_hiv_n.ix[:, duration.index]\n", "df_hiv_n = df_hiv_n.dropna(1)\n", "df_hiv_n = df_hiv_n.groupby(level=0).first()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adjust this data for cellular composition. This is done after the normalization to not mess around with Horvath's pipeline too much. " ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "flow_sorted_data = pd.read_hdf(HDFS_DIR + 'methylation_annotation.h5',\n", " 'flow_sorted_data_horvath_norm')\n", "cell_type = pd.read_hdf(HDFS_DIR + 'methylation_annotation.h5', 'label_map')\n", "cell_counts = pd.read_hdf(HDFS_DIR + 'dx_methylation.h5', 'cell_counts')\n", "cell_counts = cell_counts.groupby(level=0, axis=0).first()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [], "source": [ "n2 = flow_sorted_data.groupby(cell_type, axis=1).mean()\n", "avg = n2[cell_counts.columns].dot(cell_counts.ix[df_hiv.columns].T)\n", "d2 = df_hiv_n.ix[avg.index, df_hiv.columns].dropna(axis=[0,1], how='all')\n", "cc = avg.ix[:, ti(duration=='Control')].mean(1)\n", "df_hiv_n = (d2 - avg).add(cc, axis=0).dropna(how='all')" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "keepers = duration.index.intersection(df_hiv.columns.intersection(df_hiv_n.columns))\n", "duration = duration.ix[keepers]\n", "duration = duration.groupby(level=0).first()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [], "source": [ "consent = c1['zhang id'].isin([12373001,12805001,14055003,15455001]) == False\n", "duration = duration.ix[duration.index.difference(ti(consent == False))]" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index([u'METI-29', u'METI-31', u'METI-41', u'METI-43'], dtype='object')" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ti(c1['zhang id'].isin([12373001,12805001,14055003,15455001]))" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "HIV Long 104\n", "Control 42\n", "HIV Short 33\n", "dtype: int64" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "duration.value_counts()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "store = pd.HDFStore(HDFS_DIR + 'dx_methylation.h5')\n", "study = store['study']\n", "age = store['age']\n", "gender = store['gender']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Set up Probe Filters" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [], "source": [ "detection_p = pd.read_hdf(HDFS_DIR + 'dx_methylation.h5', 'detection_p')\n", "#detection_p = detection_p[detection_p[0] > 10e-5]\n", "detection_p = detection_p[detection_p.Sample_Name.isin(duration.index)]" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False 33349\n", "True 12468\n", "dtype: int64" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ff = detection_p.groupby('level_0').size() > 3\n", "ff.value_counts()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [], "source": [ "STORE = HDFS_DIR + 'methylation_annotation.h5'\n", "snps = pd.read_hdf(STORE, 'snps')" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False 425620\n", "True 59892\n", "dtype: int64" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "snp_near = (snps.Probe_SNPs != '')\n", "snp_near.value_counts()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [], "source": [ "probe_idx = df_hiv.index.difference(ti(ff))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
endlesspint8/endlesspint8.github.io
code/lynda-building-deep-learning-apps/06_keras_visualize.ipynb
1
5504
{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/50\n", " - 0s - loss: 0.0588\n", "Epoch 2/50\n", " - 0s - loss: 0.0277\n", "Epoch 3/50\n", " - 0s - loss: 0.0247\n", "Epoch 4/50\n", " - 0s - loss: 0.0234\n", "Epoch 5/50\n", " - 0s - loss: 0.0227\n", "Epoch 6/50\n", " - 0s - loss: 0.0211\n", "Epoch 7/50\n", " - 0s - loss: 0.0191\n", "Epoch 8/50\n", " - 0s - loss: 0.0168\n", "Epoch 9/50\n", " - 0s - loss: 0.0139\n", "Epoch 10/50\n", " - 0s - loss: 0.0113\n", "Epoch 11/50\n", " - 0s - loss: 0.0088\n", "Epoch 12/50\n", " - 0s - loss: 0.0065\n", "Epoch 13/50\n", " - 0s - loss: 0.0048\n", "Epoch 14/50\n", " - 0s - loss: 0.0032\n", "Epoch 15/50\n", " - 0s - loss: 0.0024\n", "Epoch 16/50\n", " - 0s - loss: 0.0017\n", "Epoch 17/50\n", " - 0s - loss: 0.0014\n", "Epoch 18/50\n", " - 0s - loss: 0.0012\n", "Epoch 19/50\n", " - 0s - loss: 0.0010\n", "Epoch 20/50\n", " - 0s - loss: 8.8411e-04\n", "Epoch 21/50\n", " - 0s - loss: 8.2826e-04\n", "Epoch 22/50\n", " - 0s - loss: 7.6278e-04\n", "Epoch 23/50\n", " - 0s - loss: 6.5110e-04\n", "Epoch 24/50\n", " - 0s - loss: 6.3048e-04\n", "Epoch 25/50\n", " - 0s - loss: 5.7419e-04\n", "Epoch 26/50\n", " - 0s - loss: 5.7883e-04\n", "Epoch 27/50\n", " - 0s - loss: 5.1502e-04\n", "Epoch 28/50\n", " - 0s - loss: 4.3292e-04\n", "Epoch 29/50\n", " - 0s - loss: 4.5275e-04\n", "Epoch 30/50\n", " - 0s - loss: 3.9630e-04\n", "Epoch 31/50\n", " - 0s - loss: 3.7888e-04\n", "Epoch 32/50\n", " - 0s - loss: 3.9089e-04\n", "Epoch 33/50\n", " - 0s - loss: 3.2177e-04\n", "Epoch 34/50\n", " - 0s - loss: 3.0820e-04\n", "Epoch 35/50\n", " - 0s - loss: 2.7886e-04\n", "Epoch 36/50\n", " - 0s - loss: 2.6574e-04\n", "Epoch 37/50\n", " - 0s - loss: 2.6187e-04\n", "Epoch 38/50\n", " - 0s - loss: 2.3749e-04\n", "Epoch 39/50\n", " - 0s - loss: 2.4857e-04\n", "Epoch 40/50\n", " - 0s - loss: 2.4797e-04\n", "Epoch 41/50\n", " - 0s - loss: 2.0134e-04\n", "Epoch 42/50\n", " - 0s - loss: 2.0205e-04\n", "Epoch 43/50\n", " - 0s - loss: 2.0219e-04\n", "Epoch 44/50\n", " - 0s - loss: 1.9717e-04\n", "Epoch 45/50\n", " - 0s - loss: 1.7174e-04\n", "Epoch 46/50\n", " - 0s - loss: 1.7831e-04\n", "Epoch 47/50\n", " - 0s - loss: 1.9075e-04\n", "Epoch 48/50\n", " - 0s - loss: 1.6237e-04\n", "Epoch 49/50\n", " - 0s - loss: 1.7307e-04\n", "Epoch 50/50\n", " - 0s - loss: 1.6758e-04\n", "The mean squared error (MSE) for the test data set is: 0.00019516230269800872\n" ] } ], "source": [ "import pandas as pd\n", "import keras\n", "from keras.models import Sequential\n", "from keras.layers import *\n", "\n", "RUN_NAME = \"run 2 with 5 nodes\"\n", "\n", "training_data_df = pd.read_csv(\"Exercise Files/06/sales_data_training_scaled.csv\")\n", "\n", "X = training_data_df.drop('total_earnings', axis=1).values\n", "Y = training_data_df[['total_earnings']].values\n", "\n", "# Define the model\n", "model = Sequential()\n", "model.add(Dense(5, input_dim=9, activation='relu', name='layer_1'))\n", "model.add(Dense(100, activation='relu', name='layer_2'))\n", "model.add(Dense(50, activation='relu', name='layer_3'))\n", "model.add(Dense(1, activation='linear', name='output_layer'))\n", "model.compile(loss='mean_squared_error', optimizer='adam')\n", "\n", "# Create a TensorBoard logger\n", "logger = keras.callbacks.TensorBoard(\n", " log_dir='Exercise Files/06/logs/{}'.format(RUN_NAME),\n", "# histogram_freq=5,\n", " write_graph=True\n", ")\n", "\n", "# Train the model\n", "model.fit(\n", " X,\n", " Y,\n", " epochs=50,\n", " shuffle=True,\n", " verbose=2,\n", " callbacks=[logger]\n", ")\n", "\n", "# Load the separate test data set\n", "test_data_df = pd.read_csv(\"Exercise Files/06/sales_data_test_scaled.csv\")\n", "\n", "X_test = test_data_df.drop('total_earnings', axis=1).values\n", "Y_test = test_data_df[['total_earnings']].values\n", "\n", "test_error_rate = model.evaluate(X_test, Y_test, verbose=0)\n", "print(\"The mean squared error (MSE) for the test data set is: {}\".format(test_error_rate))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:keras]", "language": "python", "name": "conda-env-keras-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": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
flavio-casacurta/Nat2Py
Define Mapa.ipynb
1
1627
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from Util.DataPatterns import *" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "line = '01 #CAMPO-PE2-MULTIPLO (A003/00001:00005,00001:00005)'" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "match = DataPatterns.row_pattern.match(line.strip())" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'init': None,\n", " 'length': '003',\n", " 'level': '01',\n", " 'name': '#CAMPO-PE2-MULTIPLO',\n", " 'occurs': '00005',\n", " 'scale': None,\n", " 'two_dimension': '00005',\n", " 'type': 'A'}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "match = match.groupdict()\n", "match" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
msyriac/orphics
scripts/EnlibResampleBug.ipynb
1
40154
{ "cells": [ { "cell_type": "code", "execution_count": 8, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "from __future__ import print_function\n", "import matplotlib.pyplot as plt\n", "from enlib import enmap,resample\n", "import numpy as np\n", "import os,sys" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(60, 60) (60, 60)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 2 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "arc = 30.*np.pi/180./60. # 30 arcmin wide stamp\n", "px = 0.1*np.pi/180./60. # high resolution pixel size\n", "dpx = 0.5*np.pi/180./60. # low resolution pixel size\n", "\n", "# high res geometry\n", "shape,wcs = enmap.geometry(pos=[[-arc/2.,-arc/2.],[arc/2.,arc/2.]],res=px)\n", "modrmap = enmap.modrmap(shape,wcs)\n", "sigma = 2.0*np.pi/180./60.\n", "a = np.exp(-modrmap**2./2./sigma**2.) # test gaussian\n", "\n", "# downsampled shape (factor of 5)\n", "dshape0 = tuple((np.array(shape)*(px/dpx)).astype(np.int))\n", "\n", "# downsampled gaussian\n", "b = enmap.resample(a,dshape0,method=\"fft\")\n", "a1 = enmap.resample(b,shape,method=\"fft\")\n", "\n", "# downsampled geometry\n", "dshape,dwcs = enmap.geometry(pos=[[-arc/2.,-arc/2.],[arc/2.,arc/2.]],res=dpx)\n", "print(dshape0,dshape) # check shapes match\n", "\n", "# create low res gaussian native to this geometry\n", "dmodrmap = enmap.modrmap(dshape,dwcs)\n", "c = np.exp(-dmodrmap**2./2./sigma**2.)\n", "\n", "# plot difference of native to downsampled\n", "plt.imshow(c-b)\n", "plt.colorbar()\n", "plt.show()\n", "plt.imshow(a1-a)\n", "plt.colorbar()\n", "plt.show()" ] }, { "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": "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": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "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" }, "name": "DmeffXcorr.ipynb" }, "nbformat": 4, "nbformat_minor": 2 }
bsd-2-clause
szigyi/DAT210x
Module4/.ipynb_checkpoints/solution_pca-checkpoint.ipynb
1
55831
{ "cells": [ { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.decomposition import PCA\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np\n", "%matplotlib inline\n", "matplotlib.style.use(\"ggplot\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.DataFrame(np.random.randn(1000, 5), columns=['a', 'b', 'c', 'd', 'e'])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1000, 5)\n", "(1000, 2)\n" ] } ], "source": [ "pca = PCA(n_components=2, svd_solver=\"full\")\n", "pca.fit(df)\n", "T = pca.transform(df)\n", "\n", "print(df.shape)\n", "print(T.shape)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "explained_variance_ [ 1.09271881 0.99326585]\n", "explained_variance_ratio_ [ 0.22763862 0.20692027]\n", "components_ [[-0.40867203 -0.24175357 -0.31282186 0.52075977 0.63678422]\n", " [-0.45247747 0.32316253 -0.57093155 0.16314664 -0.58159303]]\n" ] } ], "source": [ "print(\"explained_variance_\", pca.explained_variance_)\n", "print(\"explained_variance_ratio_\", pca.explained_variance_ratio_)\n", "print(\"components_\", pca.components_)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x11aa23c50>" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Cj/2coylb+R1o6EW6LsxPPwUvFCBNE7xQoO2+p2XSiOJmmkAyCem6YGtrkIUC3L4+IBwm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"text/plain": [ "<matplotlib.figure.Figure at 0x11a8855f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(x=pca.components_[0], y=pca.components_[1], c=\"g\")\n", "plt.scatter(x=df.iloc[:,0], y=df.iloc[:,1], c=\"r\", alpha=0.2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
drublackberry/fantastic_demos
Titanic/Titanic.ipynb
1
587158
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Notebook to process Kaggle's Titanic dataset\n", "\n", "This notebook uses the dataset from Kaggle's Titanic Comptetition to train a logistic regression and provides results with the test dataset\n", "https://www.kaggle.com/c/titanic\n", "\n", "author: drublackberry (github)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Configuration\n", "User configuration parameters" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train_size = 80 # % of the training set used for training\n", "N_MonteCarlo = 50 # number of runs for the monte-carlo analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data pre-processing and exploratory data analysis\n", "Gather the train dataset, convert feature to numerical values and plot the values in stacked histograms to get a feeling of the importance of the features." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pydot\n", "from IPython.display import Image, display\n", " \n", "# Load into CSV\n", "myRawTrainDf = pd.read_csv('train.csv', index_col=0)\n", "myRawTestDf = pd.read_csv('test.csv', index_col=0)\n", "\n", "# Add survived column before merging\n", "myRawTestDf['Survived'] = np.nan\n", "myRawTestDf = myRawTestDf[myRawTrainDf.columns]\n", "\n", "# Merge\n", "myRawDf = myRawTrainDf.append(myRawTestDf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Feature exploration\n", "This chapter will explore the weight of the features wrt survival rate and will explore the possibilities of " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Features on the name\n", "The name itself can contain some features of interest, if we explore it closely we can see that the pattern of ', 'and '.' allows us to retrieve the title and the surname." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "PassengerId\n", "1 Braund, Mr. Owen Harris\n", "2 Cumings, Mrs. John Bradley (Florence Briggs Th...\n", "3 Heikkinen, Miss. Laina\n", "4 Futrelle, Mrs. Jacques Heath (Lily May Peel)\n", "5 Allen, Mr. William Henry\n", "6 Moran, Mr. James\n", "7 McCarthy, Mr. Timothy J\n", "8 Palsson, Master. Gosta Leonard\n", "9 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)\n", "10 Nasser, Mrs. Nicholas (Adele Achem)\n", "Name: Name, dtype: object" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Inspect the names to see if something can be done\n", "myRawDf['Name'].head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's create two extra columns with the title and the surname to be used as features." ] }, { "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>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " <th>Title</th>\n", " <th>Surname</th>\n", " </tr>\n", " <tr>\n", " <th>PassengerId</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>0.0</td>\n", " <td>3</td>\n", " <td>Braund, Mr. Owen Harris</td>\n", " <td>male</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>A/5 21171</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " <td>Mr</td>\n", " <td>Braund</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n", " <td>female</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>PC 17599</td>\n", " <td>71.2833</td>\n", " <td>C85</td>\n", " <td>C</td>\n", " <td>Mrs</td>\n", " <td>Cumings</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1.0</td>\n", " <td>3</td>\n", " <td>Heikkinen, Miss. Laina</td>\n", " <td>female</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>STON/O2. 3101282</td>\n", " <td>7.9250</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " <td>Miss</td>\n", " <td>Heikkinen</td>\n", " </tr>\n", " <tr>\n", " <th>1307</th>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>Saether, Mr. Simon Sivertsen</td>\n", " <td>male</td>\n", " <td>38.5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>SOTON/O.Q. 3101262</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " <td>Mr</td>\n", " <td>Saether</td>\n", " </tr>\n", " <tr>\n", " <th>1308</th>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>Ware, Mr. Frederick</td>\n", " <td>male</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>359309</td>\n", " <td>8.0500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " <td>Mr</td>\n", " <td>Ware</td>\n", " </tr>\n", " <tr>\n", " <th>1309</th>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>Peter, Master. Michael J</td>\n", " <td>male</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2668</td>\n", " <td>22.3583</td>\n", " <td>NaN</td>\n", " <td>C</td>\n", " <td>Master</td>\n", " <td>Peter</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Survived Pclass \\\n", "PassengerId \n", "1 0.0 3 \n", "2 1.0 1 \n", "3 1.0 3 \n", "1307 NaN 3 \n", "1308 NaN 3 \n", "1309 NaN 3 \n", "\n", " Name Sex Age \\\n", "PassengerId \n", "1 Braund, Mr. Owen Harris male 22.0 \n", "2 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \n", "3 Heikkinen, Miss. Laina female 26.0 \n", "1307 Saether, Mr. Simon Sivertsen male 38.5 \n", "1308 Ware, Mr. Frederick male NaN \n", "1309 Peter, Master. Michael J male NaN \n", "\n", " SibSp Parch Ticket Fare Cabin Embarked Title \\\n", "PassengerId \n", "1 1 0 A/5 21171 7.2500 NaN S Mr \n", "2 1 0 PC 17599 71.2833 C85 C Mrs \n", "3 0 0 STON/O2. 3101282 7.9250 NaN S Miss \n", "1307 0 0 SOTON/O.Q. 3101262 7.2500 NaN S Mr \n", "1308 0 0 359309 8.0500 NaN S Mr \n", "1309 1 1 2668 22.3583 NaN C Master \n", "\n", " Surname \n", "PassengerId \n", "1 Braund \n", "2 Cumings \n", "3 Heikkinen \n", "1307 Saether \n", "1308 Ware \n", "1309 Peter " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import re\n", "\n", "def getTitle (aName):\n", " '''Finds the title in the name'''\n", " myPosStart = aName.find(',')\n", " myPosEnd = aName.find('.')\n", " return re.sub('[^A-Za-z0-9]+', '', aName[myPosStart:myPosEnd])\n", "def getSurname (aName):\n", " '''Finds the title in the name'''\n", " myPos = aName.find(',')\n", " return re.sub('[^A-Za-z0-9]+', '', aName[:myPos])\n", "\n", "myInDf = myRawDf.copy()\n", "myInDf['Title'] = [getTitle(x) for x in myInDf['Name']]\n", "myInDf['Surname'] = [getSurname(x) for x in myInDf['Name']]\n", "\n", "# Get a sample\n", "myInDf.head(3).append(myInDf.tail(3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to be able to plot and perform regressions (if needed) one can assign a number to each string for each feature." ] }, { "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>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " <th>Title</th>\n", " <th>Surname</th>\n", " </tr>\n", " <tr>\n", " <th>PassengerId</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>0.0</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>71.2833</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1.0</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>7.9250</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>2</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1307</th>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>1304</td>\n", " <td>0</td>\n", " <td>38.5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>927</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>874</td>\n", " </tr>\n", " <tr>\n", " <th>1308</th>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>1305</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>928</td>\n", " <td>8.0500</td>\n", " <td>NaN</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>818</td>\n", " </tr>\n", " <tr>\n", " <th>1309</th>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>1306</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>119</td>\n", " <td>22.3583</td>\n", " <td>NaN</td>\n", " <td>1.0</td>\n", " <td>3</td>\n", " <td>117</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Survived Pclass Name Sex Age SibSp Parch Ticket Fare \\\n", "PassengerId \n", "1 0.0 3 0 0 22.0 1 0 0 7.2500 \n", "2 1.0 1 1 1 38.0 1 0 1 71.2833 \n", "3 1.0 3 2 1 26.0 0 0 2 7.9250 \n", "1307 NaN 3 1304 0 38.5 0 0 927 7.2500 \n", "1308 NaN 3 1305 0 NaN 0 0 928 8.0500 \n", "1309 NaN 3 1306 0 NaN 1 1 119 22.3583 \n", "\n", " Cabin Embarked Title Surname \n", "PassengerId \n", "1 NaN 0.0 0 0 \n", "2 1.0 1.0 1 1 \n", "3 NaN 0.0 2 2 \n", "1307 NaN 0.0 0 874 \n", "1308 NaN 0.0 0 818 \n", "1309 NaN 1.0 3 117 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def assignNumericalType (aSeries):\n", " '''Assigns a numerical type to string values'''\n", " val = aSeries.unique()\n", " myDict = {val[x]:x for x in range(len(val))}\n", " myDict[np.nan] = np.nan # Ensure nan stays nan\n", " aOut = [myDict[x] for x in aSeries]\n", " return aOut\n", "\n", "# Convert strings to numerical type\n", "for myCol in myInDf.columns:\n", " if type(myInDf[myCol].dropna().iloc[0])==str:\n", " myInDf[myCol] = assignNumericalType(myInDf[myCol])\n", " \n", "# Get a sample\n", "myInDf.head(3).append(myInDf.tail(3))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ISktL6dChA6eddlo+m1s9dejQoUHL4JuZWfOWeSCJiIk5b/8paTrwNnASyRmT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\n3gAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ff3f8619750>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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vwI41wTgu4vn1qnkL6BBgnyIZSEFEJFZ5wJEB96kgEj/6eokkJQUREYmZ5smISEspiIhI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\n0znkkENi36AkoSAiIiJJKy8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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff3d3d6f090>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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cdttt3HbbbfUaQ039+/enf//+q5RNnDiRnj17FvR7SiGQDAXGSroEuJ8kaPwI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\n5jIPJJIukTRe0nxJcyQ9JGm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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff3d39d2810>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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M/gt8AtwCfI4/CdU594GZvQw8aGaXA+2Au4Ey3TEjIiKSvtIiiACDgRl4k1Id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\nn4A5umMmecWEc3+IyE7ZGv5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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff3d397e7d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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r9p49e3LHHXdwxx13tKqGRGVlZZSVlTVqq6iooLS0NKnxsiF4TAbmmdmVwAME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\niYyCh4iIiERGwUNEREQio+AhIiIikVH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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff3d359c390>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff3d3348150>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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jIhIPqYaVPKCigfZKf5tI5GlNGBGReEg1rHwMnAjckdR+It4VQiKxke1rwoiI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\nDPyP/8iwY4DTA+5TYUVERLJ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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff3d34defd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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3cAIQQngUeHQjbS4GLt5A/WfA6EQHZmZmZi0uDZcSm5mZmdVzODEzM7NUcTgx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\ncFjme0mnAh8DJcALkroDpwM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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff3d3819bd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff3d39cb810>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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ROwADgYfrylJKS4BngL0KRaOBRXWBpeCPQAL2bO0XIEmSWqbU00OTgAUR8fOI\nGFXODjVjR+A0YDZwIPAz4McRcUJh+0Cy8LGw0X4LC9vq6vyjeGNKaTXwTlEdSZLUQZV6emgwcDhw\nEvBkRMwGJgM3p5T+Waa+FasAZqSUzi88/2tEfAz4T+CWVjheQw8CjceVdis8JEnq4qZOncrUqVMb\nlC1evLjsxykptKSUVgJ3AndGxCDgi8CXgB9GxAPAjcDvUkqpTP2cDzReM3gWcGTh+wVAAANoONoy\nAHi+qE7/4gYiohuwVWFb8z5DFtMkSdJaxo0bx7hx4xqUzZw5kxEjRpT1OC2+eiilNJ9sbsijZKdo\nRgJTgZcjYp+Wtl/wJDC0UdlQCpNxU0pzyYLHmLqNEbEF2VyVpwpFTwNbRsQni9oYQxZ2nilTPyVJ\nUispObRERFVE/FdE/JUsVPQnu1JnO2Ab4D7g5rL0MptDMzoizomID0fEccCXgZ8W1bkKOC8iPhsR\nuxWO/SbwG4CU0kvAQ8AvImKPiNgb+Akw1SuHJEnq+Eo6PRQR9wJjgbnADcBNjeayLI2Iy4BvtbyL\nkFL6c0QcAVwCnF847ukppV8V1bksInqSrbuyJfAn4ODCqaw6x5EFnT8Ca4C7yC6VliRJHVypE3GX\nAAeklP60jjr/BHYusf21pJR+B/xuPXUuAC5Yx/Z3AW//KUlSDpU6EffEDaiTgFdKaV+SJKmxUheX\nmxQRX2+i/GsRcUXLuyVJktRQqRNxjwamN1E+HTi29O5IkiQ1rdTQUkXRzQqLLC5skyRJKqtSQ8sr\nZDcfbOwgsit7JEmSyqrUq4euAq6KiH7AI4WyMcBZwLfL0TFJkqRipV499IvCXZ6/C3yvUPwm8M2U\n0i/L1TlJkqQ6pY60kFL6CfCTwr2H3i+sgSJJktQqSg4tdQr3HpIkSWpVpa7TsnVETI6I6oiojYiV\nxY9yd1KSJKnUkZYpwIeBy4H5ZHd3liRJajWlhpZ9gX1TSs+XszOSJEnNKXWdljdxdEWSJLWhUkPL\nGcDFEfGhcnZGkiSpOaWeHroF6A28HhFLgA+KN6aU+re0Y5IkScVKDS3fKWsvJEmS1qPUFXFvLHdH\nJEmS1qXUOS1ExPYRcUFE3BIR/QtlB0bEsPJ1T5IkKVPq4nL7AH8DPg0cA/QqbBoBXFierkmSJP1L\nqSMtlwIXpJT2B4pXwH0YGN3iXkmSJDVSamj5OHBXE+X/ALYuvTuSJElNKzW0LAYGNlH+CeCt0rsj\nSZLUtFLZm6USAAAQ7ElEQVRDyx3AJRGxNYWVcSNiT+AK4NYy9U2SJKleqaHlHOBVYB7ZJNy/A08B\nzwIXladrkiRJ/1LqOi0rgJMj4kJgN7LgMjOl9FI5OydJklSn1BVxAUgpzQXmlqkvkiRJzSoptETE\n9evanlL6amndkSRJalqpIy2DGj3fBPgo2U0Un2hRjyRJkppQ6pyWzzYui4juwHVkk3IlSZLKquR7\nDzWWUloFXA6cWa42JUmS6pQttBTsQHaqSJIkqaxKnYh7WeMisnkuh+HicpIkqRWUOhF3r0bP1wD/\nBL4D/KJFPZIkSWpCqRNx9yl3RyRJktal3HNaJEmSWkWpc1qepXCjxPVJKY0q5RiSJEnFSp3T8ihw\nKjAHeLpQNhoYCvwcWNHyrkmSJP1LqaFlS+CalNJ3iwsj4gfAgJTSl1vcM0mSpCKlzmk5BpjcRPkU\n4OiSeyNJktSMUkPLCrLTQY2NxlNDkiSpFZR6eujHwM8j4pPAjELZnsBXgIvL0TFJkqRipa7T8oOI\nmAucDtTNX5kFfDWldHu5OidJklSn1JEWCuHEgCJJktpEyYvLRcQWEXFSRFwYEX0LZZ+IiEHl654k\nSVKm1MXlPgb8EVgObEt21dAi4FhgG+DEMvVPkiQJKH2kZRLZqaEPA7VF5Q8A+7a0U5IkSY2VGlr2\nAK5NKTVeyv8twNNDkiSp7EoNLR8AvZoo3wmoKb07kiRJTSs1tPwWOD8i6ubEpIjYBrgEuKcsPZMk\nSSpSamj5b2ArYAGwGfAI8CrZ/JbvrmM/SZKkkpS6uNwiYP+I+DTwCbJTRTOBh5qY5yJJktRiGz3S\nEhGbRMRDEbFzSunxlNKPU0o/TCk92FaBJSK+ExFrIuLKorIeEXFNRNRExNKIuCsi+jfab9uIeCAi\nlkXEgoi4LCJKXqtGkiS1nY3+wE4pfQCMANplRCUi9gC+Cvy10aargEOAz5Nddj0YuLtovwrgd2Sj\nS6PJ1pI5Cbiw1TstSZJarNRRhtuAk8vZkQ0REb2AW8nud/RuUfkWwCnAGYXRn+cL/ds7IkYVqh0E\nfAQYn1J6MaX0EHA+8LWiCcWSJKmDKvXDOgFfj4gDgD8DyxpsTOmslnasGdcAv00pPRIR5xeVjyR7\nLQ8X9WF2RFQDe5HdiXo08GJKqfiS7IeAnwEfZe2RG0mS1IGUGlpGAC8Uvv94o22tctooIr4A7E4W\nUBobAKxMKS1pVL4QGFj4fmDheePtddsMLZIkdWAbFVoiYkdgbkppn1bqT3PH/RDZnJUDCnNq2taD\nQGWjst0KD0mSuripU6cyderUBmWLFy8u+3E2dqTlZbJl+v8BEBF3AN9MKTUewSi3EcDWwMyIiEJZ\nN2DfiPg68BmgR0Rs0Wi0ZQDZWjIUvu7RqN0BRdua9xmyab2SJGkt48aNY9y4cQ3KZs6cyYgRI8p6\nnI2diBuNno8FNi9TX9blj2TjGruTrQvzCbK5NLcWff8BMKa+oxFDgSHAU4Wip4HdIqKqqN0DgcXA\n31u5/5IkqYVycdVMSmkZjYJFRCwD3k4pzSo8vxG4MiIWAUuBHwNPppSeLezy+0Ibt0TE2WQjRhcB\nP22XU06SJGmjbGxoSaw90ba9VsBtfNwzgNXAXUAPspkoX6uvnNKaiDiU7Gqhp8iueJoCTGyLzkqS\npJbZ2NASwJSIWFF4XglcVxj1qJdSOrIcnVuXlNK/N3q+AvhG4dHcPm8Ah7Zy1yRJUivY2NByU6Pn\nt5arI5IkSeuyUaElpdTmq+BKkiRB6cv4S5IktSlDiyRJygVDiyRJygVDiyRJygVDiyRJygVDiyRJ\nygVDiyRJygVDiyRJygVDiyRJygVDiyRJygVDiyRJygVDiyRJygVDiyRJygVDiyRJygVDiyRJygVD\niyRJygVDiyRJygVDiyRJygVDiyRJygVDiyRJygVDiyRJygVDiyRJygVDiyRJygVDiyRJygVDiyRJ\nygVDiyRJygVDiyRJygVDiyRJygVDiyRJygVDiyRJygVDiyRJygVDiyRJyoXu7d2BXLgRiPI2+cGq\nD3jjjTfK26jUxVRXV1NTU9MqbVdVVTFkyJBWaVtSaQwtG2L1fsCWZW0y4mEGDBhQ1jalrqS6upph\nQ4eyvLa2VdrvWVnJrNmzDS5SB2Jo2SBXAMPL2mL37h9n0003LWubUldSU1PD8tpabgWGlbntWcDx\ntbXU1NQYWqQOxNAiKdeGUe7/UkjqqJyIK0mScsHQIkmScsHQIkmScsHQIkmScsHQIkmScsHQIkmS\ncsHQIkmScsHQIkmScsHQIkmScsHQIkmScsHQIkmScsHQIkmScsHQIkmSciEXoSUizomIGRGxJCIW\nRsS9EbFLozo9IuKaiKiJiKURcVdE9G9UZ9uIeCAilkXEgoi4LCJy8TOQJKmry8sH9j7AT4A9gQOA\nTYDfR8RmRXWuAg4BPg/sCwwG7q7bWAgnvwO6A6OBE4GTgAtbv/uSJKmlurd3BzZESmls8fOIOAn4\nBzACmBYRWwCnAF9IKT1eqHMyMCsiRqWUZgAHAR8B9k8p1QAvRsT5wCURcUFKaVXbvSJJkrSx8jLS\n0tiWQALeKTwfQRbAHq6rkFKaDVQDexWKRgMvFgJLnYeAPsBHW7vDkiSpZXIXWiIiyE4FTUsp/b1Q\nPBBYmVJa0qj6wsK2ujoLm9hOUR1JktRB5eL0UCPXArsC/9Z2hzyDbECm2LjCQ5Kkrm3q1KlMnTq1\nQdnixYvLfpxchZaI+CkwFtgnpTSvaNMCYNOI2KLRaMuAwra6Ons0anJA0bZ1mAQML7HXkiR1buPG\njWPcuIb/kZ85cyYjRowo63Fyc3qoEFgOJ5tIW91o83PAKmBMUf2hwBDgqULR08BuEVFVtN+BwGLg\n70iSpA4tFyMtEXEt2bmYw4BlEVE3QrI4pVSbUloSETcCV0bEImAp8GPgyZTSs4W6vycLJ7dExNnA\nIOAi4KcppQ/a8vVIkqSNl4vQAvwn2dVCjzUqPxm4ufD9GcBq4C6gB/Ag8LW6iimlNRFxKPAzstGX\nZcAUYGIr9luSJJVJLkJLSmm9p7FSSiuAbxQezdV5Azi0jF2TJEltJDdzWiRJUteWi5EWSWrOrJy0\nKanlDC2ScmnFihVUAMe3UvsVhWNI6jgMLZJyqUePHqwB2B/oW+bGF8GaR7NjSOo4DC2S8m1nsnu6\nl9M84NEytympxZyIK0mScsHQIkmScsHQIkmScsHQIkmScsHQIkmScsHQIkmScsHQIkmScsHQIkmS\ncsHQIkmScsHQIkmScsHQIkmScsHQIkmScsEbJkqS1EVVV1dTU1PTKm3PmjWr7G0aWiRJ6oKqq6sZ\nNnQoy2tr27srG8zQIklSF1RTU8Py2lpuBYa1Qvu/A84vc5uGFkmSurBhwPBWaLf8J4eciCtJknLC\n0CJJknLB0CJJknLBOS2S8q01rtZsnStAJbWQoUVSLq1YsQICuKeVDhCFY0jqMAwtknKpR48ekAAu\nAnYoc+tzIZ2fHUNSh2FokZRzYyn/BZszKf8KE1LH1BqXJgPMbYU2DS2SJHVBK1asoAI4vr07shEM\nLZIkdUE9evRgDcD+QN9WOEA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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff3d31f0c50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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I1ZJGE90l8wywBpgBTGyh+s3MzCxLqQeREELxNravA34Qv7bUZwkwOuHSzMzM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\nq7HNMHYesG7dumYY2czMbOs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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff3d3dcbb10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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HPBNge2mSj7eHCgOAICfDr4Tq16B169YBNioiItks1eSkB3AScA4wx8wWAJOA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\n17kNGAL8HFgN3AE87p8r25M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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff3d392cd90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Exploratory data analysis\n", "for myFeature in myInDf.columns:\n", " if myFeature != 'Survived' and (len(myInDf[myFeature]) > len(myInDf[myFeature].unique())):\n", " myInDf.pivot(columns='Survived', values=myFeature).plot(kind='hist', stacked=True, bins=20)\n", " plt.title(myFeature)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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VvihNuGn/E1+JZNki7c9NKJO+5yngAeAMfGnfqG/i/wHzA1/AQ4G/gT9V/ILihRmH4l+i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\nAAkpCTflKkIQSUjCJSEkAUye/vG+G9bZZ59z1t5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Kqibpq8BPuv0cZtZRh0bEFc1cIGkC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\nAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ff3d3b34fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Do a correlation plot\n", "cax = plt.matshow(myInDf.corr().abs())\n", "plt.colorbar(cax)\n", "plt.xticks(range(len(myInDf.columns)), myInDf.columns, rotation='vertical')\n", "plt.yticks(range(len(myInDf.columns)), myInDf.columns, rotation='horizontal')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusions of the exploratory data analysis\n", "\n", "* Passenger on the age 20-40 are more likely to die.\n", "* Babies and infants are more likely to survive.\n", "* Most passenger where in the range 20-40 with children (i.e. families).\n", "* Older people is more likely to survive\n", "* Lower fares are more likely to die\n", "* People with more than 3 siblings is likely to die\n", "* Travelling with no siblings meant a higher change of survival\n", "* People not related to children are more likely to die\n", "* There is a clear dependence on passenger class\n", "* Males are more likely to die\n", "* There is a certain dependence on the cabin, port and ticket\n", "* There is high correlation of hte survival with sex, pclass and . Weaker correlations with parch and embarked.\n", "* Title is a strong feature for survival" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Missing data\n", "A part of the dataset is missing, how many missing values do we have in the training set per feature?" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr>\n", " <th></th>\n", " <th colspan=\"2\" halign=\"left\">Training</th>\n", " <th colspan=\"2\" halign=\"left\">Test</th>\n", " </tr>\n", " <tr>\n", " <th></th>\n", " <th>Missing</th>\n", " <th>Total</th>\n", " <th>Missing</th>\n", " <th>Total</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Survived</th>\n", " <td>0</td>\n", " <td>891</td>\n", " <td>418</td>\n", " <td>418</td>\n", " </tr>\n", " <tr>\n", " <th>Pclass</th>\n", " <td>0</td>\n", " <td>891</td>\n", " <td>0</td>\n", " <td>418</td>\n", " </tr>\n", " <tr>\n", " <th>Name</th>\n", " <td>0</td>\n", " <td>891</td>\n", " <td>0</td>\n", " <td>418</td>\n", " </tr>\n", " <tr>\n", " <th>Sex</th>\n", " <td>0</td>\n", " <td>891</td>\n", " <td>0</td>\n", " <td>418</td>\n", " </tr>\n", " <tr>\n", " <th>Age</th>\n", " <td>177</td>\n", " <td>891</td>\n", " <td>86</td>\n", " <td>418</td>\n", " </tr>\n", " <tr>\n", " <th>SibSp</th>\n", " <td>0</td>\n", " <td>891</td>\n", " <td>0</td>\n", " <td>418</td>\n", " </tr>\n", " <tr>\n", " <th>Parch</th>\n", " <td>0</td>\n", " <td>891</td>\n", " <td>0</td>\n", " <td>418</td>\n", " </tr>\n", " <tr>\n", " <th>Ticket</th>\n", " <td>0</td>\n", " <td>891</td>\n", " <td>0</td>\n", " <td>418</td>\n", " </tr>\n", " <tr>\n", " <th>Fare</th>\n", " <td>0</td>\n", " <td>891</td>\n", " <td>1</td>\n", " <td>418</td>\n", " </tr>\n", " <tr>\n", " <th>Cabin</th>\n", " <td>687</td>\n", " <td>891</td>\n", " <td>327</td>\n", " <td>418</td>\n", " </tr>\n", " <tr>\n", " <th>Embarked</th>\n", " <td>2</td>\n", " <td>891</td>\n", " <td>0</td>\n", " <td>418</td>\n", " </tr>\n", " <tr>\n", " <th>Title</th>\n", " <td>0</td>\n", " <td>891</td>\n", " <td>0</td>\n", " <td>418</td>\n", " </tr>\n", " <tr>\n", " <th>Surname</th>\n", " <td>0</td>\n", " <td>891</td>\n", " <td>0</td>\n", " <td>418</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Training Test \n", " Missing Total Missing Total\n", "Survived 0 891 418 418\n", "Pclass 0 891 0 418\n", "Name 0 891 0 418\n", "Sex 0 891 0 418\n", "Age 177 891 86 418\n", "SibSp 0 891 0 418\n", "Parch 0 891 0 418\n", "Ticket 0 891 0 418\n", "Fare 0 891 1 418\n", "Cabin 687 891 327 418\n", "Embarked 2 891 0 418\n", "Title 0 891 0 418\n", "Surname 0 891 0 418" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myMind = pd.MultiIndex.from_product([['Training', 'Test',],['Missing', 'Total']])\n", "myMissingDf = pd.DataFrame(columns=myMind, index=myInDf.columns)\n", "myMissingDf['Test', 'Missing'] = myInDf[myInDf['Survived'].isnull()].isnull().sum()\n", "myMissingDf['Test', 'Total'] = myInDf[myInDf['Survived'].isnull()].isnull().count()\n", "myMissingDf['Training', 'Missing'] = myInDf[myInDf['Survived'].notnull()].isnull().sum()\n", "myMissingDf['Training', 'Total'] = myInDf[myInDf['Survived'].notnull()].isnull().count()\n", "myMissingDf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given the results one can conclude that\n", "* The age is missing for a number of passengers but it is still a usable feature given the amount of it that is missing\n", "* The cabin is missing for large part of the dataset, if used as feature it will have little weight\n", "* Two passenger are missing the port where they embarked in the training set, the feature should be still usable\n", "* One passenger is missing the fare in the test test\n", "\n", "The age feature is specially interesting given the high correlation with the survival" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Predicting age\n", "Age is a key feature that is missing in a considerable part of the dataset, however, the age can be infered from other features. The predicted age can be used to feed the decision tree." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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DmZmZlXLCYGZmZqWcMJiZmVkpJwxmZmZWygmDmZmZlXLCYGZmZqWcMJiZmVkp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\niKtu8gBbP42If+f/t9WPRkujqet3Kmk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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff3d3b56750>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "myInDf.corr()[[x for x in myInDf.columns if x != 'Age']].loc['Age'].plot(kind='bar')\n", "plt.title('Correlation coefficient of Age with other features')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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BI4AuwCTgb6ilXrJEVOvYgOY2EMl0m266KWvWrGlSXltb27C9uccBaT02U2RE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\ngL8BvYFXgUOcc2ubViciIiK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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff3d3b93550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Recall the distribution of the age\n", "myInDf.pivot(columns='Survived', values='Age').plot(kind='hist', stacked=True, bins=20)\n", "plt.title('Age before interpolation of nan')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Predict the age with a linear regression\n", "from sklearn import linear_model\n", "from sklearn.preprocessing import normalize\n", "\n", "def predictValueByLinearRegression (aInDf, aFeatureToUse, aFeatureToPredict):\n", " aFeatureToUse.append(aFeatureToPredict)\n", " myDf = aInDf[aFeatureToUse]\n", " # Train\n", " myX = myDf.dropna()[[x for x in myDf.columns if x != aFeatureToPredict]]\n", " myY = myDf.dropna()[aFeatureToPredict]\n", " myLR = linear_model.LinearRegression()\n", " myLR.fit(myX, myY)\n", " # Predict\n", " myX = myDf[myDf[aFeatureToPredict].isnull()][[x for x in myDf.columns if x != aFeatureToPredict]]\n", " return myLR.predict(myX)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Assign\n", "myInDf.loc[myInDf.isnull()['Age'], 'Age'] = predictValueByLinearRegression(myInDf, ['Sex', 'SibSp', 'Parch', 'Fare', 'Title'], 'Age')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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C7Qqofjj6a80UFia/xvw999yzxbVmnnjiCQ455JD6OmZGXl7DkxmdO3dm7ty5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\nBwhrtsJLIbWbbkqBkqiDaAOZ/nq2ZLZ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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff3d3cc6290>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Check the histogram again to see the distribution\n", "myInDf.pivot(columns='Survived', values='Age').plot(kind='hist', stacked=True, bins=20)\n", "plt.title('Age after linear regression')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Missing embarked values\n", "There are two values with nan for the embarked. Embarked is a feature that holds a certain correlation with the survival rate. It should be better to keep it.\n", "Let's work on the raw data to see the alphanumerical values" ] }, { "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>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " <tr>\n", " <th>PassengerId</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>62</th>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>Icard, Miss. Amelie</td>\n", " <td>female</td>\n", " <td>38.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>113572</td>\n", " <td>80.0</td>\n", " <td>B28</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>830</th>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n", " <td>female</td>\n", " <td>62.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>113572</td>\n", " <td>80.0</td>\n", " <td>B28</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Survived Pclass Name \\\n", "PassengerId \n", "62 1.0 1 Icard, Miss. Amelie \n", "830 1.0 1 Stone, Mrs. George Nelson (Martha Evelyn) \n", "\n", " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n", "PassengerId \n", "62 female 38.0 0 0 113572 80.0 B28 NaN \n", "830 female 62.0 0 0 113572 80.0 B28 NaN " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myRawDf[myRawDf['Embarked'].isnull()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both of them where at Cabin B28, on which port did the passengers at these cabin also board? Also it is OK to assume that the tickets were sold in order?" ] }, { "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>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " <tr>\n", " <th>PassengerId</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>55</th>\n", " <td>0.0</td>\n", " <td>1</td>\n", " <td>Ostby, Mr. Engelhart Cornelius</td>\n", " <td>male</td>\n", " <td>65.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>113509</td>\n", " <td>61.9792</td>\n", " <td>B30</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>62</th>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>Icard, Miss. Amelie</td>\n", " <td>female</td>\n", " <td>38.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>113572</td>\n", " <td>80.0000</td>\n", " <td>B28</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>167</th>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>Chibnall, Mrs. (Edith Martha Bowerman)</td>\n", " <td>female</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>113505</td>\n", " <td>55.0000</td>\n", " <td>E33</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>253</th>\n", " <td>0.0</td>\n", " <td>1</td>\n", " <td>Stead, Mr. William Thomas</td>\n", " <td>male</td>\n", " <td>62.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>113514</td>\n", " <td>26.5500</td>\n", " <td>C87</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>330</th>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>Hippach, Miss. Jean Gertrude</td>\n", " <td>female</td>\n", " <td>16.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>111361</td>\n", " <td>57.9792</td>\n", " <td>B18</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>352</th>\n", " <td>0.0</td>\n", " <td>1</td>\n", " <td>Williams-Lambert, Mr. Fletcher Fellows</td>\n", " <td>male</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>113510</td>\n", " <td>35.0000</td>\n", " <td>C128</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>357</th>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>Bowerman, Miss. Elsie Edith</td>\n", " <td>female</td>\n", " <td>22.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>113505</td>\n", " <td>55.0000</td>\n", " <td>E33</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>378</th>\n", " <td>0.0</td>\n", " <td>1</td>\n", " <td>Widener, Mr. Harry Elkins</td>\n", " <td>male</td>\n", " <td>27.0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>113503</td>\n", " <td>211.5000</td>\n", " <td>C82</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>524</th>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>Hippach, Mrs. Louis Albert (Ida Sophia Fischer)</td>\n", " <td>female</td>\n", " <td>44.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>111361</td>\n", " <td>57.9792</td>\n", " <td>B18</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>783</th>\n", " <td>0.0</td>\n", " <td>1</td>\n", " <td>Long, Mr. Milton Clyde</td>\n", " <td>male</td>\n", " <td>29.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>113501</td>\n", " <td>30.0000</td>\n", " <td>D6</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>830</th>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n", " <td>female</td>\n", " <td>62.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>113572</td>\n", " <td>80.0000</td>\n", " <td>B28</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>890</th>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>Behr, Mr. Karl Howell</td>\n", " <td>male</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>111369</td>\n", " <td>30.0000</td>\n", " <td>C148</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>918</th>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>Ostby, Miss. Helene Ragnhild</td>\n", " <td>female</td>\n", " <td>22.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>113509</td>\n", " <td>61.9792</td>\n", " <td>B36</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>966</th>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>Geiger, Miss. Amalie</td>\n", " <td>female</td>\n", " <td>35.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>113503</td>\n", " <td>211.5000</td>\n", " <td>C130</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>967</th>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>Keeping, Mr. Edwin</td>\n", " <td>male</td>\n", " <td>32.5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>113503</td>\n", " <td>211.5000</td>\n", " <td>C132</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>1110</th>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>Widener, Mrs. George Dunton (Eleanor Elkins)</td>\n", " <td>female</td>\n", " <td>50.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>113503</td>\n", " <td>211.5000</td>\n", " <td>C80</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>1299</th>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>Widener, Mr. George Dunton</td>\n", " <td>male</td>\n", " <td>50.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>113503</td>\n", " <td>211.5000</td>\n", " <td>C80</td>\n", " <td>C</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Survived Pclass \\\n", "PassengerId \n", "55 0.0 1 \n", "62 1.0 1 \n", "167 1.0 1 \n", "253 0.0 1 \n", "330 1.0 1 \n", "352 0.0 1 \n", "357 1.0 1 \n", "378 0.0 1 \n", "524 1.0 1 \n", "783 0.0 1 \n", "830 1.0 1 \n", "890 1.0 1 \n", "918 NaN 1 \n", "966 NaN 1 \n", "967 NaN 1 \n", "1110 NaN 1 \n", "1299 NaN 1 \n", "\n", " Name Sex Age \\\n", "PassengerId \n", "55 Ostby, Mr. Engelhart Cornelius male 65.0 \n", "62 Icard, Miss. Amelie female 38.0 \n", "167 Chibnall, Mrs. (Edith Martha Bowerman) female NaN \n", "253 Stead, Mr. William Thomas male 62.0 \n", "330 Hippach, Miss. Jean Gertrude female 16.0 \n", "352 Williams-Lambert, Mr. Fletcher Fellows male NaN \n", "357 Bowerman, Miss. Elsie Edith female 22.0 \n", "378 Widener, Mr. Harry Elkins male 27.0 \n", "524 Hippach, Mrs. Louis Albert (Ida Sophia Fischer) female 44.0 \n", "783 Long, Mr. Milton Clyde male 29.0 \n", "830 Stone, Mrs. George Nelson (Martha Evelyn) female 62.0 \n", "890 Behr, Mr. Karl Howell male 26.0 \n", "918 Ostby, Miss. Helene Ragnhild female 22.0 \n", "966 Geiger, Miss. Amalie female 35.0 \n", "967 Keeping, Mr. Edwin male 32.5 \n", "1110 Widener, Mrs. George Dunton (Eleanor Elkins) female 50.0 \n", "1299 Widener, Mr. George Dunton male 50.0 \n", "\n", " SibSp Parch Ticket Fare Cabin Embarked \n", "PassengerId \n", "55 0 1 113509 61.9792 B30 C \n", "62 0 0 113572 80.0000 B28 NaN \n", "167 0 1 113505 55.0000 E33 S \n", "253 0 0 113514 26.5500 C87 S \n", "330 0 1 111361 57.9792 B18 C \n", "352 0 0 113510 35.0000 C128 S \n", "357 0 1 113505 55.0000 E33 S \n", "378 0 2 113503 211.5000 C82 C \n", "524 0 1 111361 57.9792 B18 C \n", "783 0 0 113501 30.0000 D6 S \n", "830 0 0 113572 80.0000 B28 NaN \n", "890 0 0 111369 30.0000 C148 C \n", "918 0 1 113509 61.9792 B36 C \n", "966 0 0 113503 211.5000 C130 C \n", "967 0 0 113503 211.5000 C132 C \n", "1110 1 1 113503 211.5000 C80 C \n", "1299 1 1 113503 211.5000 C80 C " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get passengers with similar tickets\n", "myRawDf[myRawDf['Ticket'].map(lambda x: '1135' in x or '1136' in x)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One can see that a number of passenger with similar fare price, cabin on the same section and ticket number close enough to the missing ones embarked in 'C'. Let's assume that's their port of origin" ] }, { "cell_type": "code", "execution_count": 16, "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>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " <th>Title</th>\n", " <th>Surname</th>\n", " </tr>\n", " <tr>\n", " <th>PassengerId</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>62</th>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>61</td>\n", " <td>1</td>\n", " <td>38.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>60</td>\n", " <td>80.0</td>\n", " <td>13.0</td>\n", " <td>1.0</td>\n", " <td>2</td>\n", " <td>59</td>\n", " </tr>\n", " <tr>\n", " <th>830</th>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>829</td>\n", " <td>1</td>\n", " <td>62.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>60</td>\n", " <td>80.0</td>\n", " <td>13.0</td>\n", " <td>1.0</td>\n", " <td>1</td>\n", " <td>629</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Survived Pclass Name Sex Age SibSp Parch Ticket Fare \\\n", "PassengerId \n", "62 1.0 1 61 1 38.0 0 0 60 80.0 \n", "830 1.0 1 829 1 62.0 0 0 60 80.0 \n", "\n", " Cabin Embarked Title Surname \n", "PassengerId \n", "62 13.0 1.0 2 59 \n", "830 13.0 1.0 1 629 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Assign a numerical value\n", "myInDf.loc[myInDf['Embarked'].isnull(), 'Embarked'] = myInDf.loc[55]['Embarked']\n", "# Check\n", "myInDf.loc[[62,830]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Missing fare on test set\n", "One passenger has a missing fare on the test set" ] }, { "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>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " <tr>\n", " <th>PassengerId</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1044</th>\n", " <td>NaN</td>\n", " <td>3</td>\n", " <td>Storey, Mr. Thomas</td>\n", " <td>male</td>\n", " <td>60.5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3701</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Survived Pclass Name Sex Age SibSp Parch \\\n", "PassengerId \n", "1044 NaN 3 Storey, Mr. Thomas male 60.5 0 0 \n", "\n", " Ticket Fare Cabin Embarked \n", "PassengerId \n", "1044 3701 NaN NaN S " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myRawDf[myRawDf['Fare'].isnull()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at how the fare correlates with the pclass, sex, age and embarcation port." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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T3dcHcfz6NCUB/HZEfIoy6NgLKcfzTgP3Op+lJJYfr243/JByS2Mnyrlgb0r31kOjDE99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\nZ/V39za9K7CqwbhOpwym01u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HEZTu4ltRak0fAtyV0mPikAbjarM3A2+LMgFhI+b8LYnq\nnvzPgRMoo31d3XBIQHurY6tYtqCM1HYQcC7w2hZ009IsRItn+Ky+mycD3T00nkipbVvbDS9HPJdE\n2+ceiIi7Um6pPpaJ8zScCrw0My9tIq5uVfX6rpl5QfX3bpm5PCJ2BT6TmTs2HGLrRMQ5wD0p2/Qy\nemr+MnPhsGOY07ckokz880LgSy3sBtjK6tiIeA2lBuYqyiQ2692i0MYjyxTpu8ckM3wCT6MkEk34\nTJ+yz408iv5aO/dAVcP3H1V31HtRTjAXtewY92+gc+FzDeV4t5zymd6lqaBaron5ZiawhqE0+tqp\nDVl3t6ph1bbZNeVqNZzqzk3GWl1d3Uhpud57cllr1Fd90qhU34Ft29jocWMREd8Gjs3ME6rbmjtT\n2i0cAGyVmbs2GmDLVBe3DwN+lZmNzAUCc7yGoXIecA/KZDZtEsCxEdFdHTsf+EQ1mBPQyIn5ONp7\n/1YaBff/DfcG1g33/UbKceXjlMG4Gu0220aZubpKsnaiocnDwBoGIuJxlMZeb6b/EKVNTe50zHSW\ny8xnDzsWSetYw6AmRMQvKO3FGht634Rh4hCl3R9GK2arlKSbq4i4A+umZj6/+xasJmrDxa0JQwuH\nd5Wkm7OI2JwyA+8zWDcT6mrgC5SeHK1sUNqkNlzczvmEQZI0WhHxBeABlK7ZP66KdwOOBM5teAyc\nVmrDxe2cTxgiYvepns/M748qFkmaC6qG24/NzB/0lD8COCUzN2smMk3FXhJwZp+y7izKNgySNFh/\nof84FispQyCrRxsubk0YytCk3TalVJW9ndLdR5I0WO8ADo+IAzLzKoCI2BZ4H+XYq/Wd2adspBe3\nc/6WxGSq+0WHZ+aipmORpI1dNbRx9wlnO+BWwBXV43HKMOAXjWKY441NNRprtwkXt6PobmkNw+Su\nZl13H0nShml8aOON2SQ9R74TEf8CDgeGfnE752sYImLn3iLgjsDrgFtk5sNHH5UkSfUiYkfgF5l5\nm2G/lzUMZbbFZP057n+CQ5RK0lBFxG2ATbrLmhpht81qLm7PHUUMJgxw957Ha4A/Z+aqJoKRpJu7\niLg78BHgkZQ5ctY+RbmAs3fa+hq/uJ2zCUNE7AbcLjNP7Cp7FvBWYLOI+BpwUGb+c7J1SJJm5XOU\nE99zKO3F5va98elp/OJ2zrZhiIiTgTMz8z3V4/sBy4BjKfOyvxo4KjPf0lSMknRzFBHXA4sy84Km\nY2m7uotbSmPSkVzcblK/yM3WLkB3N5RnAD/NzOdn5uHAwcC+jUQmSTdvPwfu0nQQG4lDgft0HlQX\nt58CTgPeDTwReP0oApmztyQoAzZd3fV4D+Dkrsfu0JI0HM8DPhER/w84D/h395OZ+atGomqnXSgz\nVHasvbgFiIgrKbUNbxl2IHM5Ybiack/oyoi4JbAQOKzr+c3p2YklSQNxe+CewDFdZZ0GfTZ6nKg1\nF7dz+ZbEt4B3V5OdvAu4ATir6/mdgYubCEySbuY+DZxDmaHyHpSLt+7fWqdzcUvXxe1Pup4f2cXt\nXK5heDPwFeB7wPXAgZn5r67nnwN8u4nAJOlm7q7APpn5u6YD2Qh0Lm5fC/wXDV7cztmEITNXALtX\n43Nfn5mrexZ5GiWRkCQN1neB+wMmDPVac3E7Z7tVSpKaEREvAN5EuTXxa9Zv9PiNJuJqs8kubiNi\n66r8X/1fOcAYTBgkSaMUEWumeDoz00aPLWTCIEmSas3lXhKSpBGKiG9VVeudx6+LiC27Ht8uIn7b\nTHSqYw2DJGkkImI1cMfMvKZ6/Ddgl8y8pHq8DfBHb0m0kzUMkqRR6Z1psfexWsyEQZIk1TJhkCSN\nSrL+VNbeF99IzNmBmyRJIxfAsRHRmYp5PmUSqn9Uj2/VTFiaDhs9SpJGIiKOqV8KMvPZw45FM2fC\nIEmSatmGQZIk1TJhkCRJtUwYJElSLRMGSZJUy4RBkiTVMmGQJEm1TBgkSVKt/x8L9c4i5YKsLwAA\nAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ff3d36f0510>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "myInDf.corr()[[x for x in myInDf.columns if x != 'Fare']].loc['Fare'].plot(kind='bar')\n", "plt.title('Correlation coefficient of Age with other features')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Run a linear regression with the features that are most correlated\n", "myInDf.loc[myInDf['Fare'].isnull(), 'Fare'] = predictValueByLinearRegression(myInDf, ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Title'], 'Fare')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Survived NaN\n", "Pclass 3.000000\n", "Name 1041.000000\n", "Sex 0.000000\n", "Age 60.500000\n", "SibSp 0.000000\n", "Parch 0.000000\n", "Ticket 778.000000\n", "Fare 2.782652\n", "Cabin NaN\n", "Embarked 0.000000\n", "Title 0.000000\n", "Surname 757.000000\n", "Name: 1044, dtype: float64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Check the final \n", "myInDf.loc[1044]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Final check\n", "Is there any missing data left in our training set?" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Survived 418\n", "Pclass 0\n", "Name 0\n", "Sex 0\n", "Age 0\n", "SibSp 0\n", "Parch 0\n", "Ticket 0\n", "Fare 0\n", "Cabin 1014\n", "Embarked 0\n", "Title 0\n", "Surname 0\n", "dtype: int64" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myInDf.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The 'Cabin' feature is largely missing and difficult to be infered from other features, for the moment then let's just discard it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Classification using decision trees" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn import tree\n", "from sklearn.metrics import precision_score, recall_score, f1_score\n", " \n", "def splitDataset (aDf, aFrac):\n", " '''Splits a Df in a training and a validation dataset randomly'''\n", " aTrainDf = aDf.sample(frac=aFrac/100.)\n", " myValInd = [ind for ind in aDf.index if ind not in aTrainDf.index]\n", " aValDf = aDf.loc[myValInd]\n", " # Create X and Y datasets\n", " aXtrain = aTrainDf[[x for x in aTrainDf.columns if x!='Survived']]\n", " aYtrain = aTrainDf['Survived']\n", " aXval = aValDf[[x for x in aTrainDf.columns if x!='Survived']]\n", " aYval = aValDf['Survived']\n", " return aXtrain, aYtrain, aXval, aYval\n", "\n", "def assessPerformance (aX, aY, aClf):\n", " '''Prints the performance of a certain machine learning algorithm'''\n", " myYpred = aClf.predict(aX)\n", " aPrecision = precision_score(aY, myYpred)\n", " aRecall = recall_score(aY, myYpred)\n", " aF1score = f1_score(aY, myYpred)\n", " return aPrecision, aRecall, aF1score\n", "\n", "def trainPredictAndAnalyzeDecisionTree (aDf, aDepth=None, draw=False):\n", " # Build a decision tree classifier\n", " myXtrain, myYtrain, myXval, myYval = splitDataset (aDf, train_size)\n", " myClf = tree.DecisionTreeClassifier(max_depth=aDepth)\n", " myClf = myClf.fit(myXtrain, myYtrain)\n", " aTrainPrecision, aTrainRecall, aTrainF1 = assessPerformance(myXtrain, myYtrain, myClf)\n", " aValPrecision, aValRecall, aValF1 = assessPerformance(myXval, myYval, myClf)\n", " if draw:\n", " # Draw the decision tree\n", " myDotData = tree.export_graphviz(myClf, feature_names=myXtrain.columns, out_file='tree.dot' )\n", " (myGraph,) = pydot.graph_from_dot_file('tree.dot')\n", " myPlt = Image(myGraph.create_png())\n", " myGraph.write_png('tree.png')\n", " display(myPlt)\n", " return aTrainPrecision, aTrainRecall, aTrainF1, aValPrecision, aValRecall, aValF1\n", " \n", "def runMonteCarlo (aDf, aF, *args):\n", " myPerfDf = pd.DataFrame(columns=['Train Precision', 'Train Recall', 'Train F1', 'Val Precision', 'Val Recall', 'Val F1'])\n", " for i in range(N_MonteCarlo):\n", " myPerfDf.loc[i] = aF(aDf, *args)\n", " return myPerfDf" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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4+Hj2pqjs7OwdO3Y8evSILXdzc3N3d1+5cqWXl5e9vb2bm5uPj0+LnxcAgGTh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6+vL3W/AJJIRkbmzp07Pa84BNdegiCoPknF/loK4H4qEGqwko3o4qXuhg0b9sMPP3T2bA+rlQuh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\nXl5tbW0IoadPn3p6e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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Calling and drawing a precision tree with max_depth\n", "# Note that this call removes all the nan (large part of the dataset) and just displays a decision tree for illustration\n", "foo = trainPredictAndAnalyzeDecisionTree(myInDf.dropna(), aDepth=3, draw=True)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Train Precision</th>\n", " <th>Train Recall</th>\n", " <th>Train F1</th>\n", " <th>Val Precision</th>\n", " <th>Val Recall</th>\n", " <th>Val F1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>50.0</td>\n", " <td>50.0</td>\n", " <td>50.0</td>\n", " <td>50.000000</td>\n", " <td>50.000000</td>\n", " <td>50.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.689049</td>\n", " <td>0.690920</td>\n", " <td>0.688122</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.061491</td>\n", " <td>0.062419</td>\n", " <td>0.050817</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.531646</td>\n", " <td>0.553846</td>\n", " <td>0.586466</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.653646</td>\n", " <td>0.648566</td>\n", " <td>0.648951</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.684950</td>\n", " <td>0.692810</td>\n", " <td>0.694232</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.738650</td>\n", " <td>0.736111</td>\n", " <td>0.726936</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.815385</td>\n", " <td>0.797101</td>\n", " <td>0.779412</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Train Precision Train Recall Train F1 Val Precision Val Recall \\\n", "count 50.0 50.0 50.0 50.000000 50.000000 \n", "mean 1.0 1.0 1.0 0.689049 0.690920 \n", "std 0.0 0.0 0.0 0.061491 0.062419 \n", "min 1.0 1.0 1.0 0.531646 0.553846 \n", "25% 1.0 1.0 1.0 0.653646 0.648566 \n", "50% 1.0 1.0 1.0 0.684950 0.692810 \n", "75% 1.0 1.0 1.0 0.738650 0.736111 \n", "max 1.0 1.0 1.0 0.815385 0.797101 \n", "\n", " Val F1 \n", "count 50.000000 \n", "mean 0.688122 \n", "std 0.050817 \n", "min 0.586466 \n", "25% 0.648951 \n", "50% 0.694232 \n", "75% 0.726936 \n", "max 0.779412 " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Do not use features which have any value missing\n", "myCompleteFeatures = [x for x in myInDf.columns if myInDf.isnull().sum()[x] ==0]\n", "myCompleteFeatures.append('Survived')\n", "myStats = runMonteCarlo(myInDf[myCompleteFeatures].dropna(), trainPredictAndAnalyzeDecisionTree).describe() # capture for later\n", "myStats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Using random forests\n", "\n", "Random forests can be used in this dataset to boost performance and lower the variance (overfitting) of the classification." ] }, { "cell_type": "code", "execution_count": 25, "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>Train Precision</th>\n", " <th>Train Recall</th>\n", " <th>Train F1</th>\n", " <th>Val Precision</th>\n", " <th>Val Recall</th>\n", " <th>Val F1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>50.000000</td>\n", " <td>50.000000</td>\n", " <td>50.000000</td>\n", " <td>50.000000</td>\n", " <td>50.000000</td>\n", " <td>50.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>0.994336</td>\n", " <td>0.961128</td>\n", " <td>0.977415</td>\n", " <td>0.795430</td>\n", " <td>0.680868</td>\n", " <td>0.731252</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>0.004131</td>\n", " <td>0.011307</td>\n", " <td>0.006301</td>\n", " <td>0.051625</td>\n", " <td>0.064793</td>\n", " <td>0.042783</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>0.984556</td>\n", " <td>0.940299</td>\n", " <td>0.965517</td>\n", " <td>0.684932</td>\n", " <td>0.548387</td>\n", " <td>0.629630</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>0.992424</td>\n", " <td>0.954511</td>\n", " <td>0.973101</td>\n", " <td>0.755843</td>\n", " <td>0.632414</td>\n", " <td>0.698799</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>0.996132</td>\n", " <td>0.958877</td>\n", " <td>0.977442</td>\n", " <td>0.797086</td>\n", " <td>0.692810</td>\n", " <td>0.733041</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>0.996313</td>\n", " <td>0.967480</td>\n", " <td>0.981000</td>\n", " <td>0.827825</td>\n", " <td>0.717634</td>\n", " <td>0.763359</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>1.000000</td>\n", " <td>0.985714</td>\n", " <td>0.991023</td>\n", " <td>0.890909</td>\n", " <td>0.819672</td>\n", " <td>0.802817</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Train Precision Train Recall Train F1 Val Precision Val Recall \\\n", "count 50.000000 50.000000 50.000000 50.000000 50.000000 \n", "mean 0.994336 0.961128 0.977415 0.795430 0.680868 \n", "std 0.004131 0.011307 0.006301 0.051625 0.064793 \n", "min 0.984556 0.940299 0.965517 0.684932 0.548387 \n", "25% 0.992424 0.954511 0.973101 0.755843 0.632414 \n", "50% 0.996132 0.958877 0.977442 0.797086 0.692810 \n", "75% 0.996313 0.967480 0.981000 0.827825 0.717634 \n", "max 1.000000 0.985714 0.991023 0.890909 0.819672 \n", "\n", " Val F1 \n", "count 50.000000 \n", "mean 0.731252 \n", "std 0.042783 \n", "min 0.629630 \n", "25% 0.698799 \n", "50% 0.733041 \n", "75% 0.763359 \n", "max 0.802817 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", "def trainPredictAndAnalyzeRandomForest (aDf, *args):\n", " if len(args)>0:\n", " myNfeat = args[0]\n", " else:\n", " myNfeat = 'auto'\n", " # Build a decision tree classifier\n", " myXtrain, myYtrain, myXval, myYval = splitDataset (aDf, train_size)\n", " myClf = RandomForestClassifier(max_features=myNfeat)\n", " myClf = myClf.fit(myXtrain, myYtrain)\n", " aTrainPrecision, aTrainRecall, aTrainF1 = assessPerformance(myXtrain, myYtrain, myClf) \n", " aValPrecision, aValRecall, aValF1 = assessPerformance(myXval, myYval, myClf)\n", " return aTrainPrecision, aTrainRecall, aTrainF1, aValPrecision, aValRecall, aValF1\n", "\n", "runMonteCarlo(myInDf[myCompleteFeatures].dropna(), trainPredictAndAnalyzeRandomForest).describe()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Looking at these results it looks like we can get some better results by analyzing the cross-validation sets. Let's play with the number of features used in the random forest to see if we can boost performance" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create a MultiIndex dataframe to store the stats of all the different runs\n", "myNfeat = np.arange(2,11)\n", "myIndex = pd.MultiIndex.from_product([myNfeat, myStats.columns])\n", "myDf = pd.DataFrame(index=myIndex, columns=myStats.index)\n", "for myN in myNfeat:\n", " myDf.loc[myN,:].iloc[:] = runMonteCarlo(myInDf[myCompleteFeatures].dropna(), trainPredictAndAnalyzeRandomForest, myN).describe().iloc[:].transpose()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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QfnfY75hw/oQqM/9RxEHCzPoAo4ArgJnAEGCKmbV3zm0Ic8oI4GLgMuAH4CzgDTM70Tk3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\nI+I50tDyhZL+BvwDuAx4HpiXz3HKKpEgjbUG8LtW5WcAs7o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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff3c85158d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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JvLf8PQ4cO0DXOl3p16ofNzS/4ZT92RQkyL0gsXznch6Z/Qifrv6UllVa8ky3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\n0TSdjiQTt3YDVgDzgYEF/lRDlULshdiDZKx1F+BT4EWgf0R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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff3d3893350>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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g7U8AQ7yUQ0RERAJL99oQERERzxQkRERExDMFCREREfFMQUJEREQ8U5AQERER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\n9ZL+uVRZ0rmS9pO0laQvkYYkOoGr8uvrSrpE0m6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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff3d3b19450>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i in ['Val Precision', 'Val Recall', 'Val F1']:\n", " myDf.reset_index().set_index('level_1').loc[i][['level_0', 'min', 'max', 'mean']].set_index('level_0').plot()\n", " plt.title(i)\n", " plt.xlabel('max_features')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Seems that max_features does not have a huge effect and the data is not overfit. Let's assume the default value of sklearn (sqrt(num_features))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Using the full dataset and finding a solution\n", "Now we will use the full training dataset for training (no cross-validation) and we will train a random forest classifier" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Precision on full training set 0.987915407855\n", "Recall on full training set 0.956140350877\n", "F1 on full training set 0.97176820208\n" ] } ], "source": [ "# Train the classifier\n", "myUsedFeatures = [x for x in myCompleteFeatures if x != 'Survived']\n", "myXtrain = myInDf[myCompleteFeatures].dropna()[myUsedFeatures]\n", "myYtrain = myInDf['Survived'].dropna()\n", "myClf = RandomForestClassifier()\n", "myClf = myClf.fit(myXtrain, myYtrain)\n", "aTrainPrecision, aTrainRecall, aTrainF1 = assessPerformance(myXtrain, myYtrain, myClf) \n", "\n", "print 'Precision on full training set ' + str(aTrainPrecision)\n", "print 'Recall on full training set ' + str(aTrainRecall)\n", "print 'F1 on full training set ' + str(aTrainF1)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Run the prediction on the test set\n", "myXtest = myInDf.loc[myInDf['Survived'].isnull(), myUsedFeatures]\n", "myOut = pd.Series(myClf.predict(myXtest), index=myXtest.index)\n", "myOut = myOut.apply(np.int)\n", "myOut.to_csv('solution.csv', header=['Survived'])" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "PassengerId\n", "892 0\n", "893 0\n", "894 0\n", "895 0\n", "896 0\n", "dtype: int64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myOut.head(5)" ] }, { "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": 1 }
mit
vi3k6i5/pandas_basics
2_b_apply_a_function_row_wise.ipynb
1
5732
{ "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ "apply a function to dataframe rows" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "df = pd.DataFrame([['AA', 1],['BB', 2],['CC', 3]], columns = ['name','value'])" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>value</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>AA</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>BB</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>CC</td>\n", " <td>3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name value\n", "0 AA 1\n", "1 BB 2\n", "2 CC 3" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def function_1(val_1, val_2):\n", " return val_1 + str(val_2)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df['col_a'] = df.apply(lambda row: function_1(row['name'], row['value']), axis=1)" ] }, { "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>name</th>\n", " <th>value</th>\n", " <th>col_a</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>AA</td>\n", " <td>1</td>\n", " <td>AA1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>BB</td>\n", " <td>2</td>\n", " <td>BB2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>CC</td>\n", " <td>3</td>\n", " <td>CC3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name value col_a\n", "0 AA 1 AA1\n", "1 BB 2 BB2\n", "2 CC 3 CC3" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def function_2(row):\n", " return row['value'] * 2" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df['col_b'] = df.apply(lambda row: function_2(row), axis=1)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>value</th>\n", " <th>col_a</th>\n", " <th>col_b</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>AA</td>\n", " <td>1</td>\n", " <td>AA1</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>BB</td>\n", " <td>2</td>\n", " <td>BB2</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>CC</td>\n", " <td>3</td>\n", " <td>CC3</td>\n", " <td>6</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " name value col_a col_b\n", "0 AA 1 AA1 2\n", "1 BB 2 BB2 4\n", "2 CC 3 CC3 6" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
tiffanyj41/hermes
notebooks/CF - Bayes, Pearson Correlation, etc.ipynb
3
212838
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparing Collaborative Filtering Systems\n", "\n", "According to studies done by the article \"Comparing State-of-the-Art Collaborative Filtering Systems\" by Laurent Candillier, Frank Meyer, and Marc Boulle, the __best user based approach__ is based on __pearson similarity and 1500 neighbors__. \n", "\n", "The __best item based approach__ is based on __probabilistic similarity and 400 neighbors__. \n", "\n", "The __best model based approach__ is using __K-means with euclidean distance, 4 clusters and prediction scheme based on the nearest cluster and Bayes model minimizing MAE__. \n", "\n", "Lastly, the __best default approach__ is based on __Bayes rule minimizing MAE__. \n", "\n", "We will try to implement the studies done by this article and see if we will achieve the same results. \n", "\n", "What we have implemented so far:\n", "* Bayes\n", " * Bayes MAP\n", " * Bayes MSE\n", " * Bayes MAE\n", "* Pearson Correlation (partially)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Table of Contents:\n", "```\n", "0. Last updated\n", "1. Install and import libraries\n", "2. Load dataset\n", "3. Convert dataset to DataFrame (optional) \n", "4. Determine characteristics of data (optional)\n", "5. Splitting the data (optional)\n", "6. Calculate similarities and find nearest neighbors\n", "7. Develop Model\n", "8. Evaluate Metrics\n", "9. Compare different CF systems\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Explanation of Bayes:\n", "* https://www.countbayesie.com/blog/2015/2/18/bayes-theorem-with-lego\n", "* http://www.analyticsvidhya.com/blog/2015/09/naive-bayes-explained/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 0. Last updated" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2016-02-22 09:34:57\n" ] } ], "source": [ "import datetime, time\n", "\n", "# timestamp is not correct; it is 8 hours ahead\n", "print (datetime.datetime.now() - datetime.timedelta(hours=8)).strftime('%Y-%m-%d %H:%M:%S')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Install and import libraries" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting git+git://github.com/pymc-devs/pymc.git\n", " Cloning git://github.com/pymc-devs/pymc.git to /tmp/pip-cguRXH-build\n", " Requirement already satisfied (use --upgrade to upgrade): pymc==2.3.6 from git+git://github.com/pymc-devs/pymc.git in /home/cdh_data/anaconda/lib/python2.7/site-packages\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "You are using pip version 7.1.2, however version 8.0.2 is available.\n", "You should consider upgrading via the 'pip install --upgrade pip' command.\n" ] } ], "source": [ "import importlib\n", "import pip\n", "\n", "def _install(package):\n", " pip.main(['install', package])\n", "\n", "def _import(package):\n", " importlib.import_module(package)\n", " \n", "def install_and_import(package):\n", " try:\n", " _import(package)\n", " except ImportError:\n", " _install(package)\n", " \n", "# install PyMC\n", "install_and_import(\"git+git://github.com/pymc-devs/pymc.git\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "temporary directory: /tmp/tmpwlWr9J\n", "\n", "temporary directory for zip file: /tmp/tmpCu0tZV\n", "\n", "zip file's path: /tmp/tmpCu0tZV/hermes_src_2.zip\n", "\n", "current branch: master\n", "\n", "list all in /tmp/tmpwlWr9J:\n", "/tmp/tmpwlWr9J/.gitignore\n", "/tmp/tmpwlWr9J/README.md\n", "/tmp/tmpwlWr9J/LICENSE\n", "/tmp/tmpwlWr9J/.git/config\n", "/tmp/tmpwlWr9J/.git/packed-refs\n", "/tmp/tmpwlWr9J/.git/index\n", "/tmp/tmpwlWr9J/.git/description\n", "/tmp/tmpwlWr9J/.git/HEAD\n", "/tmp/tmpwlWr9J/.git/hooks/applypatch-msg.sample\n", "/tmp/tmpwlWr9J/.git/hooks/pre-rebase.sample\n", "/tmp/tmpwlWr9J/.git/hooks/update.sample\n", "/tmp/tmpwlWr9J/.git/hooks/post-commit.sample\n", "/tmp/tmpwlWr9J/.git/hooks/commit-msg.sample\n", "/tmp/tmpwlWr9J/.git/hooks/prepare-commit-msg.sample\n", "/tmp/tmpwlWr9J/.git/hooks/post-update.sample\n", "/tmp/tmpwlWr9J/.git/hooks/pre-applypatch.sample\n", "/tmp/tmpwlWr9J/.git/hooks/post-receive.sample\n", "/tmp/tmpwlWr9J/.git/hooks/pre-commit.sample\n", "/tmp/tmpwlWr9J/.git/refs/remotes/origin/HEAD\n", "/tmp/tmpwlWr9J/.git/refs/heads/master\n", "/tmp/tmpwlWr9J/.git/logs/HEAD\n", "/tmp/tmpwlWr9J/.git/logs/refs/heads/master\n", "/tmp/tmpwlWr9J/.git/info/exclude\n", "/tmp/tmpwlWr9J/.git/objects/pack/pack-ae2be7969f863ae6c9eef5c41b3da877f2a27a87.idx\n", "/tmp/tmpwlWr9J/.git/objects/pack/pack-ae2be7969f863ae6c9eef5c41b3da877f2a27a87.pack\n", "/tmp/tmpwlWr9J/src/hermes_run_script.py\n", "/tmp/tmpwlWr9J/src/__init__.py\n", "/tmp/tmpwlWr9J/src/utils/clean_links.py\n", "/tmp/tmpwlWr9J/src/utils/content_vector_tf_idf.py\n", "/tmp/tmpwlWr9J/src/utils/article_to_category.py\n", "/tmp/tmpwlWr9J/src/utils/glove.py\n", "/tmp/tmpwlWr9J/src/utils/remove_templates.py\n", "/tmp/tmpwlWr9J/src/utils/xml_to_json.py\n", "/tmp/tmpwlWr9J/src/utils/wiki_categories.py\n", "/tmp/tmpwlWr9J/src/utils/clean_categories.py\n", "/tmp/tmpwlWr9J/src/utils/save_load.py\n", "/tmp/tmpwlWr9J/src/utils/__init__.py\n", "/tmp/tmpwlWr9J/src/utils/osm_etl/osm.py\n", "/tmp/tmpwlWr9J/src/utils/osm_etl/__init__.py\n", "/tmp/tmpwlWr9J/src/utils/code_etl/git_manager.py\n", "/tmp/tmpwlWr9J/src/utils/code_etl/download_data.sh\n", "/tmp/tmpwlWr9J/src/utils/code_etl/cd.py\n", "/tmp/tmpwlWr9J/src/utils/code_etl/repo_to_json.py\n", "/tmp/tmpwlWr9J/src/utils/code_etl/blame_to_json.py\n", "/tmp/tmpwlWr9J/src/utils/code_etl/user_to_file_mapper.py\n", "/tmp/tmpwlWr9J/src/utils/code_etl/__init__.py\n", "/tmp/tmpwlWr9J/src/utils/movielens_etl/ml20m_to_json.py\n", "/tmp/tmpwlWr9J/src/utils/movielens_etl/ml1m_to_json.py\n", "/tmp/tmpwlWr9J/src/utils/movielens_etl/movielens.py\n", "/tmp/tmpwlWr9J/src/utils/movielens_etl/__init__.py\n", "/tmp/tmpwlWr9J/src/utils/movielens_etl/ml10m_to_json.py\n", "/tmp/tmpwlWr9J/src/utils/lastfm_etl/README.md\n", "/tmp/tmpwlWr9J/src/utils/lastfm_etl/lastfm.py\n", "/tmp/tmpwlWr9J/src/utils/lastfm_etl/__init__.py\n", "/tmp/tmpwlWr9J/src/utils/book_crossing_etl/README.md\n", "/tmp/tmpwlWr9J/src/utils/book_crossing_etl/bookcrossing.py\n", "/tmp/tmpwlWr9J/src/utils/book_crossing_etl/__init__.py\n", "/tmp/tmpwlWr9J/src/utils/jester_etl/README.md\n", "/tmp/tmpwlWr9J/src/utils/jester_etl/jester.py\n", "/tmp/tmpwlWr9J/src/utils/jester_etl/__init__.py\n", "/tmp/tmpwlWr9J/src/results/combined_results.csv\n", "/tmp/tmpwlWr9J/src/results/hermes_run_view.py\n", "/tmp/tmpwlWr9J/src/algorithms/simple_hybrid.py\n", "/tmp/tmpwlWr9J/src/algorithms/performance_metrics.py\n", "/tmp/tmpwlWr9J/src/algorithms/recommender_helpers.py\n", "/tmp/tmpwlWr9J/src/algorithms/cf.py\n", "/tmp/tmpwlWr9J/src/algorithms/dataset_stats.py\n", "/tmp/tmpwlWr9J/src/algorithms/content_based.py\n", "/tmp/tmpwlWr9J/src/algorithms/__init__.py\n", "/tmp/tmpwlWr9J/src/algorithms/random_recommender.py\n", "/tmp/tmpwlWr9J/src/algorithms/content_based_kmeans.py\n", "/tmp/tmpwlWr9J/src/examples/timer.py\n", "/tmp/tmpwlWr9J/src/examples/singleton.py\n", "/tmp/tmpwlWr9J/src/examples/cf_example.py\n", "/tmp/tmpwlWr9J/src/data_prep/wiki_vectorize.py\n", "/tmp/tmpwlWr9J/src/data_prep/book_vectorize.py\n", "/tmp/tmpwlWr9J/src/data_prep/movieLens_vectorize.py\n", "/tmp/tmpwlWr9J/src/data_prep/jester_vectorize.py\n", "/tmp/tmpwlWr9J/src/data_prep/git_vectorize.py\n", "/tmp/tmpwlWr9J/src/data_prep/last_fm_vectorize.py\n", "/tmp/tmpwlWr9J/src/data_prep/osm_vectorize.py\n", "/tmp/tmpwlWr9J/src/data_prep/__init__.py\n", "/tmp/tmpwlWr9J/notebooks/Schema save and load.ipynb\n", "/tmp/tmpwlWr9J/notebooks/CF Test.ipynb\n", "/tmp/tmpwlWr9J/notebooks/MovieLens 1M CF and CB test code.ipynb\n", "/tmp/tmpwlWr9J/notebooks/Wikipedia Summary Statistics.ipynb\n", "\n", "\n", "zipping: /tmp/tmpwlWr9J/src\n", "\n", "Adding package in /tmp/tmpwlWr9J/src as src\n", "Compiling /tmp/tmpwlWr9J/src/__init__.py\n", "Adding src/__init__.pyc\n", "Compiling /tmp/tmpwlWr9J/src/hermes_run_script.py\n", "Adding src/hermes_run_script.pyc\n", "Adding package in /tmp/tmpwlWr9J/src/utils as src/utils\n", "Compiling /tmp/tmpwlWr9J/src/utils/__init__.py\n", "Adding src/utils/__init__.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/clean_links.py\n", "Adding src/utils/clean_links.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/content_vector_tf_idf.py\n", "Adding src/utils/content_vector_tf_idf.pyc\n", "Adding package in /tmp/tmpwlWr9J/src/utils/osm_etl as src/utils/osm_etl\n", "Compiling /tmp/tmpwlWr9J/src/utils/osm_etl/__init__.py\n", "Adding src/utils/osm_etl/__init__.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/osm_etl/osm.py\n", "Adding src/utils/osm_etl/osm.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/article_to_category.py\n", "Adding src/utils/article_to_category.pyc\n", "Adding package in /tmp/tmpwlWr9J/src/utils/code_etl as src/utils/code_etl\n", "Compiling /tmp/tmpwlWr9J/src/utils/code_etl/__init__.py\n", "Adding src/utils/code_etl/__init__.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/code_etl/git_manager.py\n", "Adding src/utils/code_etl/git_manager.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/code_etl/cd.py\n", "Adding src/utils/code_etl/cd.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/code_etl/repo_to_json.py\n", "Adding src/utils/code_etl/repo_to_json.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/code_etl/blame_to_json.py\n", "Adding src/utils/code_etl/blame_to_json.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/code_etl/user_to_file_mapper.py\n", "Adding src/utils/code_etl/user_to_file_mapper.pyc\n", "Adding package in /tmp/tmpwlWr9J/src/utils/movielens_etl as src/utils/movielens_etl\n", "Compiling /tmp/tmpwlWr9J/src/utils/movielens_etl/__init__.py\n", "Adding src/utils/movielens_etl/__init__.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/movielens_etl/ml20m_to_json.py\n", "Adding src/utils/movielens_etl/ml20m_to_json.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/movielens_etl/ml1m_to_json.py\n", "Adding src/utils/movielens_etl/ml1m_to_json.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/movielens_etl/movielens.py\n", "Adding src/utils/movielens_etl/movielens.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/movielens_etl/ml10m_to_json.py\n", "Adding src/utils/movielens_etl/ml10m_to_json.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/glove.py\n", "Adding src/utils/glove.pyc\n", "Adding package in /tmp/tmpwlWr9J/src/utils/lastfm_etl as src/utils/lastfm_etl\n", "Compiling /tmp/tmpwlWr9J/src/utils/lastfm_etl/__init__.py\n", "Adding src/utils/lastfm_etl/__init__.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/lastfm_etl/lastfm.py\n", "Adding src/utils/lastfm_etl/lastfm.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/remove_templates.py\n", "Adding src/utils/remove_templates.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/xml_to_json.py\n", "Adding src/utils/xml_to_json.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/wiki_categories.py\n", "Adding src/utils/wiki_categories.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/clean_categories.py\n", "Adding src/utils/clean_categories.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/save_load.py\n", "Adding src/utils/save_load.pyc\n", "Adding package in /tmp/tmpwlWr9J/src/utils/book_crossing_etl as src/utils/book_crossing_etl\n", "Compiling /tmp/tmpwlWr9J/src/utils/book_crossing_etl/__init__.py\n", "Adding src/utils/book_crossing_etl/__init__.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/book_crossing_etl/bookcrossing.py\n", "Adding src/utils/book_crossing_etl/bookcrossing.pyc\n", "Adding package in /tmp/tmpwlWr9J/src/utils/jester_etl as src/utils/jester_etl\n", "Compiling /tmp/tmpwlWr9J/src/utils/jester_etl/__init__.py\n", "Adding src/utils/jester_etl/__init__.pyc\n", "Compiling /tmp/tmpwlWr9J/src/utils/jester_etl/jester.py\n", "Adding src/utils/jester_etl/jester.pyc\n", "Adding package in /tmp/tmpwlWr9J/src/algorithms as src/algorithms\n", "Compiling /tmp/tmpwlWr9J/src/algorithms/__init__.py\n", "Adding src/algorithms/__init__.pyc\n", "Compiling /tmp/tmpwlWr9J/src/algorithms/simple_hybrid.py\n", "Adding src/algorithms/simple_hybrid.pyc\n", "Compiling /tmp/tmpwlWr9J/src/algorithms/performance_metrics.py\n", "Adding src/algorithms/performance_metrics.pyc\n", "Compiling /tmp/tmpwlWr9J/src/algorithms/recommender_helpers.py\n", "Adding src/algorithms/recommender_helpers.pyc\n", "Compiling /tmp/tmpwlWr9J/src/algorithms/cf.py\n", "Adding src/algorithms/cf.pyc\n", "Compiling /tmp/tmpwlWr9J/src/algorithms/dataset_stats.py\n", "Adding src/algorithms/dataset_stats.pyc\n", "Compiling /tmp/tmpwlWr9J/src/algorithms/content_based.py\n", "Adding src/algorithms/content_based.pyc\n", "Compiling /tmp/tmpwlWr9J/src/algorithms/random_recommender.py\n", "Adding src/algorithms/random_recommender.pyc\n", "Compiling /tmp/tmpwlWr9J/src/algorithms/content_based_kmeans.py\n", "Adding src/algorithms/content_based_kmeans.pyc\n", "Adding package in /tmp/tmpwlWr9J/src/data_prep as src/data_prep\n", "Compiling /tmp/tmpwlWr9J/src/data_prep/__init__.py\n", "Adding src/data_prep/__init__.pyc\n", "Compiling /tmp/tmpwlWr9J/src/data_prep/wiki_vectorize.py\n", "Adding src/data_prep/wiki_vectorize.pyc\n", "Compiling /tmp/tmpwlWr9J/src/data_prep/book_vectorize.py\n", "Adding src/data_prep/book_vectorize.pyc\n", "Compiling /tmp/tmpwlWr9J/src/data_prep/movieLens_vectorize.py\n", "Adding src/data_prep/movieLens_vectorize.pyc\n", "Compiling /tmp/tmpwlWr9J/src/data_prep/jester_vectorize.py\n", "Adding src/data_prep/jester_vectorize.pyc\n", "Compiling /tmp/tmpwlWr9J/src/data_prep/git_vectorize.py\n", "Adding src/data_prep/git_vectorize.pyc\n", "Compiling /tmp/tmpwlWr9J/src/data_prep/last_fm_vectorize.py\n", "Adding src/data_prep/last_fm_vectorize.pyc\n", "Compiling /tmp/tmpwlWr9J/src/data_prep/osm_vectorize.py\n", "Adding src/data_prep/osm_vectorize.pyc\n", "Is zip file /tmp/tmpCu0tZV/hermes_src_2.zip valid? True\n", "\n", "add zip file /tmp/tmpCu0tZV/hermes_src_2.zip into spark context\n", "\n" ] } ], "source": [ "# zip up source_dir located in GitHub remote_url's remote_branch and add it to Spark's source context\n", "remote_url = \"https://github.com/lab41/hermes.git\"\n", "remote_branch = \"master\"\n", "source_dir = \"src\"\n", "debug = True\n", "\n", "# helper functions\n", "import os\n", "import functools\n", "\n", "def _list_all_in_dir(dir_path):\n", " for path, subdirs, files in os.walk(dir_path):\n", " for filename in files:\n", " print os.path.join(path, filename)\n", " \n", "def _zip_dir(srcdir_path, zipfile_handler):\n", " try:\n", " zipfile_handler.writepy(srcdir_path)\n", " finally:\n", " zipfile_handler.close()\n", " \n", "def trackcalls(func):\n", " @functools.wraps(func)\n", " def wrapper(*args, **kwargs):\n", " wrapper.has_been_called = True\n", " return func(*args, **kwargs)\n", " wrapper.has_been_called = False\n", " return wrapper\n", "\n", "@trackcalls\n", "def _add_zipfile_to_sc(zipfile_path):\n", " sc.addPyFile(zipfile_path) \n", " \n", "import git\n", "import os\n", "import tempfile\n", "import shutil\n", "import zipfile \n", "\n", "# create a temporary directory\n", "tmpdir_path = tempfile.mkdtemp()\n", "if debug: print \"temporary directory: %s\\n\" % tmpdir_path\n", "\n", "# ensure file is read/write by creator only\n", "saved_umask = os.umask(0077)\n", "\n", "# create a zipfile handler to zip the necessary files\n", "ziptmpdir_path = tempfile.mkdtemp()\n", "if debug: print \"temporary directory for zip file: %s\\n\" % ziptmpdir_path\n", "zipfile_path = ziptmpdir_path + \"/hermes_src_2.zip\"\n", "if debug: print \"zip file's path: %s\\n\" % zipfile_path\n", "zipfile_handler = zipfile.PyZipFile(zipfile_path, \"w\")\n", "\n", "# make zipfile handler verbose for debugging\n", "zipfile_handler.debug = 3\n", "\n", "try:\n", " # clone \"framework\" branch from GitHub into temporary directory\n", " local_branch = git.Repo.clone_from(remote_url, tmpdir_path, branch=remote_branch)\n", " if debug: print \"current branch: %s\\n\" % local_branch.head.ref\n", " if debug: print \"list all in %s:\" % tmpdir_path; _list_all_in_dir(tmpdir_path); print \"\\n\"\n", " \n", " # zip \"hermes\" directory\n", " if debug: print \"zipping: %s\\n\" % os.path.join(tmpdir_path, source_dir)\n", " _zip_dir(os.path.join(tmpdir_path, source_dir), zipfile_handler)\n", " \n", " # check zip file\n", " if debug: print \"Is zip file %s valid? %s\\n\" % (zipfile_path, zipfile.is_zipfile(zipfile_path))\n", " \n", " # add zip to SparkContext \n", " # note: you can only add zip to SparkContext one time\n", " if not _add_zipfile_to_sc.has_been_called:\n", " if debug: print \"add zip file %s into spark context\\n\" % zipfile_path\n", " _add_zipfile_to_sc(zipfile_path)\n", " else:\n", " if debug: print \"zip file %s is already added into spark context; will not re-add\\n\" % zipfile_path\n", " \n", "except IOError as e:\n", " raise e\n", "else:\n", " os.remove(zipfile_path)\n", "finally:\n", " os.umask(saved_umask)\n", " shutil.rmtree(tmpdir_path)\n", " shutil.rmtree(ziptmpdir_path)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RDFLib Version: 4.2.1\n" ] } ], "source": [ "# import the required modules from Hermes\n", "from src.algorithms import performance_metrics as pm\n", "from src.data_prep import movieLens_vectorize as mv\n", "from src.utils import save_load as sl\n", "\n", "# import other modules\n", "import os\n", "import time" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Timer(object):\n", " \"\"\" \n", " To time how long a particular function runs.\n", "\n", " Example:\n", " import Timer\n", " with Timer() as t:\n", " somefunction()\n", " print(\"somefunction() takes %s seconds\" % t.secs)\n", " print(\"somefunction() takes %s milliseconds\" % t.msecs)\n", " \"\"\"\n", "\n", " def __enter__(self):\n", " self.start = time.time()\n", " return self\n", "\n", " def __exit__(self, *args):\n", " self.end = time.time()\n", " self.secs = self.end - self.start\n", " self.msecs = self.secs * 1000" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Load dataset, in this case MovieLens data\n", "\n", "We are going to use MovieLens's 1M data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# ratings_json_path\n", "# movies_json_path" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Convert dataset to Dataframe\n", "Run this only when you load datasets from your home directory." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def convert_dataset_to_dataframe(dataset_path):\n", " df = sqlCtx.read.json(dataset_path, None)\n", " df = df.repartition(sc.defaultParallelism * 3)\n", " return df\n", "\n", "# obtaining ratings dataframe\n", "ratingsdf = convert_dataset_to_dataframe(ratings_json_path)\n", "\n", "# obtaining movies dataframe\n", "moviesdf = convert_dataset_to_dataframe(movies_json_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Determine characteristics of the MovieLens data (optional)\n", "Run this only when you load datasets from your home directory.\n", "\n", "Format: \n", "* ratings = [user_id, movie_id, rating, timestamp]\n", "* movies = [movie_id, title, genres]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------------------------------------------\n", "format: [(user_id, movie_id, rating)]\n", "\n", "umr:\n", "[(2, 1955, 4.0), (5, 2291, 5.0)]\n", "umr_weighted:\n", "[(2, 1955, 4.0), (5, 2291, 5.0)]\n", "--------------------------------------------------------------------------------\n", "\n", "To identify user-to-user similarity:\n", "format: [(movie_id, (user_id, rating))]\n", "\n", "m_ur:\n", "[(1955, (2, 4.0)), (2291, (5, 5.0))]\n", "m_ur_weighted (aka rating >=3):\n", "[(1955, (2, 4.0)), (2291, (5, 5.0))]\n", "--------------------------------------------------------------------------------\n", "\n", "To identify movie-to-movie similarity:\n", "format: [(user_id, (movie_id, rating))]\n", "\n", "um_r:\n", "[(2, (1955, 4.0)), (5, (2291, 5.0))]\n", "um_r_weighted (aka rating >=3):\n", "[(2, (1955, 4.0)), (5, (2291, 5.0))]\n", "--------------------------------------------------------------------------------\n" ] } ], "source": [ "# extract most commonly used vectors to be used later on\n", "\n", "# 1. using ratingsdf\n", "\n", "# a. [(user_id, movie_id, rating)]\n", "umr = ratingsdf.map(lambda row: (row.user_id, row.movie_id, row.rating))\n", "# b. [(user_id, movie_id, rating)] where rating >= 3\n", "umr_weighted = umr.filter(lambda (user_id, movie_id, rating): rating >= 3)\n", "\n", "print \"-\" * 80\n", "\n", "print \"format: [(user_id, movie_id, rating)]\\n\"\n", "print \"umr:\\n\", umr.take(2)\n", "print \"umr_weighted:\\n\", umr_weighted.take(2)\n", "\n", "print \"-\" * 80\n", "\n", "print \"\\nTo identify user-to-user similarity:\"\n", "print \"format: [(movie_id, (user_id, rating))]\\n\"\n", "# c. [(movie_id, (user_id, rating)] -> to identify user-to-user similarity\n", "m_ur = ratingsdf.map(lambda row: (row.movie_id, (row.user_id, row.rating)))\n", "# d. [(movie_id, (user_id, rating)] where rating >= 3\n", "m_ur_weighted = m_ur.filter(lambda (movie_id, (user_id, rating)): rating >= 3)\n", "print \"m_ur:\\n\", m_ur.take(2)\n", "print \"m_ur_weighted (aka rating >=3):\\n\", m_ur_weighted.take(2)\n", "\n", "print \"-\" * 80\n", "\n", "print \"\\nTo identify movie-to-movie similarity:\"\n", "print \"format: [(user_id, (movie_id, rating))]\\n\"\n", "# e. [(user_id, (movie_id, rating))] -> to identify movie-to-movie similarity\n", "u_mr = ratingsdf.map(lambda row: (row.user_id, (row.movie_id, row.rating)))\n", "# f. [(user_id, (movie_id, rating))] where rating >= 3\n", "u_mr_weighted = u_mr.filter(lambda (user_id, (movie_id, rating)): rating >= 3)\n", "print \"um_r:\\n\", u_mr.take(2)\n", "print \"um_r_weighted (aka rating >=3):\\n\", u_mr_weighted.take(2)\n", "\n", "print \"-\" * 80\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total number of distinct users = 6040\n", "total number of users = 1000209\n" ] } ], "source": [ "# total number of distinct users\n", "num_distinct_users = ratingsdf.map(lambda row: row.user_id).distinct().count()\n", "num_users = ratingsdf.map(lambda row: row.user_id).count()\n", "print \"total number of distinct users = \", num_distinct_users\n", "print \"total number of users = \", num_users" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total number of ratings = 1000209\n" ] } ], "source": [ "# total number of ratings\n", "# should be the same as num_users\n", "num_ratings = ratingsdf.map(lambda row: row.rating).count()\n", "print \"total number of ratings = \", num_ratings" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total number of distinct movies = 3883\n", "total number of movies = 3883\n" ] } ], "source": [ "# total number of distinct movies\n", "num_distinct_movies = moviesdf.map(lambda row: row.movie_id).distinct().count()\n", "num_movies = moviesdf.map(lambda row: row.movie_id).count()\n", "print \"total number of distinct movies = \", num_distinct_movies\n", "print \"total number of movies = \", num_movies" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "average number of ratings a user rates = 165.5975\n" ] } ], "source": [ "# what is the average number of ratings a user rates = number of ratings / number of users\n", "# round it to the fourth digit\n", "avg_num_ratings_per_user = round(float(num_ratings) / float(num_distinct_users), 4)\n", "print \"average number of ratings a user rates = \", avg_num_ratings_per_user" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "average number of ratings a movie receives = 257.5867\n" ] } ], "source": [ "# what is the average number of ratings a movie receives = number of ratings / number of movies\n", "avg_num_ratings_per_movie = round(float(num_ratings) / float(num_distinct_movies), 4)\n", "print \"average number of ratings a movie receives = \", avg_num_ratings_per_movie" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "completeness = 0.0426\n" ] } ], "source": [ "# completeness = number of ratings / (number of users * number of movies)\n", "completeness = round(float(num_ratings) / (float(num_distinct_users) * float(num_distinct_movies)), 4)\n", "print \"completeness = \", completeness" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mean rating = 3.58156445303\n" ] } ], "source": [ "# mean rating\n", "mean_rating = ratingsdf.map(lambda row: row.rating).mean()\n", "print \"mean rating = \", mean_rating" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(3072, 3.7871690427698574),\n", " (2304, 3.5714285714285716),\n", " (3120, 3.0153846153846153)]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# mean rating per movie\n", "\n", "# [(movie_id, rating)]\n", "movie_rating_pair = ratingsdf.map(lambda row: (row.movie_id, row.rating))\n", "\n", "\"\"\"\n", "combineByKey() requires 3 functions: \n", "* createCombiner: first aggregation step for each key\n", " -> lambda first_rating: (first_rating, 1)\n", "* mergeValue: what to do when a combiner is given a new value\n", " -> lambda x, first_rating: x[0] + first_rating, x[1] + 1\n", " -> lambda thisNewRating_thisNumRating, firstRating: thisNewRating + firstRating, thisNumRating + 1\n", "* mergeCombiner: how to merge two combiners\n", " -> lambda x, y: (x[0] + y[0], x[1] + y[1])\n", " -> lambda sumRating1_numRating1, sumRating2_numRating2: (sumRating1 + sumRating2, numRating1 + numRating2)\n", "\"\"\"\n", "\n", "# [(movie_id, (sum_rating, num_rating))]\n", "movie_sumRating_numRating_pair = movie_rating_pair.combineByKey(\n", " lambda first_rating: (first_rating, 1), \n", " lambda x, first_rating: (x[0] + first_rating, x[1] + 1),\n", " lambda x, y: (x[0] + y[0], x[1] + y[1]))\n", "\n", "# [(movie_id, mean_rating)]\n", "movie_meanRating_pair = movie_sumRating_numRating_pair.map(lambda (movie_id, (sum_rating, num_rating)): (movie_id, sum_rating/num_rating)) \n", "movie_meanRating_pair.take(3)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------------------+----+\n", "| _1| _2|\n", "+------------------+----+\n", "|3.7871690427698574| 982|\n", "|3.5714285714285716| 7|\n", "|3.0153846153846153| 130|\n", "| 3.697142857142857| 525|\n", "|2.1604938271604937| 81|\n", "|4.0739348370927315| 798|\n", "|3.1486486486486487| 222|\n", "| 3.0| 1|\n", "|3.1794871794871793| 624|\n", "|2.6666666666666665| 3|\n", "| 3.272727272727273| 33|\n", "| 4.5| 2|\n", "| 3.54| 50|\n", "|3.8797920727745288|1539|\n", "|3.2400756143667295| 529|\n", "| 3.201058201058201| 945|\n", "| 3.91864406779661| 590|\n", "|2.9764397905759163| 382|\n", "| 2.107476635514019| 214|\n", "|2.6826923076923075| 624|\n", "+------------------+----+\n", "\n" ] } ], "source": [ "# meanRating_numRating_pair will be used in plotting in the next cell\n", "# where _1 = mean rating of the movie\n", "# _2 = number of users who review the movie\n", "# [(mean_rating, num_rating)]\n", "meanRating_numRating_pair = movie_sumRating_numRating_pair.map(lambda (movie_id, (sum_rating, num_rating)): (sum_rating/num_rating, num_rating))\n", "meanRating_numRating_pair_df = meanRating_numRating_pair.toDF()\n", "meanRating_numRating_pair_df.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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YnOtJ91jTgsDyWqbkB33kea/q3KKGtHakDjLWZJsuV9VRdP2urMOWXS5aARyI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jAAAH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\niHTaSZIkdUQ67SRJkjoinXaSJEkd8TegfW5pPdGIqgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5271b4b110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot mean rating per movie\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "meanRating_numRating_pair = movie_sumRating_numRating_pair.map(lambda (movie_id, (sum_rating, num_rating)): (sum_rating/num_rating, num_rating))\n", "meanRating_numRating_pair_df = meanRating_numRating_pair.toDF()\n", "\n", "meanRating_numRating_pair_panda_df = meanRating_numRating_pair_df.toPandas()\n", "\n", "plot = meanRating_numRating_pair_panda_df.plot(\n", " x=\"_2\", \\\n", " y=\"_1\", \\\n", " kind=\"hexbin\", \\\n", " xscale=\"log\", \\\n", " cmap=\"YlGnBu\", \\\n", " gridsize=12, \\\n", " mincnt=1, \\\n", " title=\"Mean vs Number of Reviewers\")\n", "\n", "plot.set_xlabel(\"Number of Reviewers Per Movie\")\n", "plot.set_ylabel(\"Mean Rating Per Movie\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This figure shows that the average rating of a movie is actually slightly higher than 3. \n", "\n", "__Hypothesis__:\n", "* We can safely predict the mean rating after 100 reviews.\n", "* After 100 reviews, the average rating is approximately in between 3.0 and 4.0." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Splitting the data (optional)\n", "Run this only when you load datasets from your home directory.\n", "\n", "Default split data into:\n", "* 90% training\n", "* 10% test\n", "* 0% validation\n", "* seed = 41\n", "\n", "Remember that calling randomSplit when you restart the kernel will provide you with a different training, test, and validation data even though the weights and the seed are the same." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "weights = [0.9, 0.1, 0]\n", "seed = 41" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'umr' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-14-87717b2114ab>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# 1. using ratingsdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m# a. [(user_id, movie_id, rating)]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mumr_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mumr_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mumr_validation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mumr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandomSplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;31m# b. [(user_id, movie_id, rating)] where rating >= 3\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mumr_weighted_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mumr_weighted_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mumr_weighted_validation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mumr_weighted\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandomSplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'umr' is not defined" ] } ], "source": [ "# 1. using ratingsdf\n", "# a. [(user_id, movie_id, rating)]\n", "umr_train, umr_test, umr_validation = umr.randomSplit(weights, seed)\n", "# b. [(user_id, movie_id, rating)] where rating >= 3\n", "umr_weighted_train, umr_weighted_test, umr_weighted_validation = umr_weighted.randomSplit(weights, seed)\n", "# c. [(movie_id, (user_id, rating)]\n", "m_ur_train, m_ur_test, m_ur_validation = m_ur.randomSplit(weights, seed)\n", "# d. [(movie_id, (user_id, rating)] where rating >= 3\n", "m_ur_weighted_train, m_ur_weighted_test, m_ur_weighted_validation = m_ur_weighted.randomSplit(weights, seed)\n", "# e. [(user_id, (movie_id, rating)]\n", "u_mr_train, u_mr_test, u_mr_validation = u_mr.randomSplit(weights, seed)\n", "# f. [(user_id, (movie_id, rating)] where rating >= 3\n", "u_mr_weighted_train, u_mr_weighted_test, u_mr_weighted_validation = u_mr_weighted.randomSplit(weights, seed)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. Calculate similarity and find nearest neighbors\n", "\n", "These are the different similarity measurement implemented in the article:\n", "* pearson\n", "* cosine\n", "* constraint pearson: in the case of MovieLens data, it means any ratings greater than 3 (aka positive ratings)\n", "* adjusted cosine\n", "* probabilistic\n", "\n", "\"When implementing a user- or item-based approach, one may choose:\n", "* a similarity measure: pearson, cosine, constraint pearson, adjusted cosine, or probabilistic\n", "* a neighborhood size\n", "* and how to compute predictions: using a weighted sum of rating values or using a weighted sum of deviations from the mean.\"\n", "\n", "Table of Contents:\n", "```\n", "6.A.1. Calculate Pearson Correlation \n", " a. user-based: DONE except for prediction\n", " b. item-based\n", "6.A.2. Calculate Weighted Pearson Correlation \n", " a. user-based\n", " b. item-based\n", "6.A.3. Calculate Pearson Deviation\n", " a. user-based\n", " b. item-based\n", "6.B.1. Calculate Probabilistic Similarity \n", " a. user-based\n", " b. item-based\n", "6.B.2. Calculate Probabilistic Deviation\n", " a. user-based\n", " b. item-based\n", "6.C.1. Calculate Cosine Similarity\n", " a. user-based\n", " b. item-based\n", "6.C.2. Calculate Adjusted Cosine Similarity \n", " a. user-based\n", " b. item-based\n", "6.D. Comparing Similarities' Measurement\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6.A.1. Calculate Pearson Correlation " ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# helper functions\n", "\n", "from scipy.stats import pearsonr \n", "import math\n", "\n", "# filter out duplicate pairs\n", "# user-based approach: \n", "# input and output: [( movie_id, ((user_id_1, rating_1), (user_id_2, rating_2)) )]\n", "# item-based approach: \n", "# input and output: [( user_id, ((movie_id_1, rating_1), (movie_id_2, rating_2)) )]\n", "def removeDuplicates((key_id, ratings)):\n", " (value_id_1, rating_1) = ratings[0]\n", " (value_id_2, rating_2) = ratings[1]\n", " return value_id_1 < value_id_2\n", "\n", "# rearrange so that it will be in the format of pairs\n", "# user-based approach: \n", "# input: [( movie_id, ((user_id_1, rating_1), (user_id_2, rating_2)) )]\n", "# output: [( movie_id, ((user_id_1, user_id_2), (rating_1, rating_2)) )]\n", "# item-based approach: \n", "# input: [( user_id, ((movie_id_1, movie_id_2), (rating_1, rating2)) )]\n", "# output: [( user_id, ((movie_id_1, movie_id_2), (rating_1, rating2)) )]\n", "def createPairs((key_id, ratings)):\n", " (value_id_1, rating_1) = ratings[0]\n", " (value_id_2, rating_2) = ratings[1]\n", " return ((value_id_1, value_id_2), (rating_1, rating_2))\n", "\n", "# aggregate pairs using combineByKey() instead of groupByKey()\n", "# [( test_user_id, train_user_id), (test_rating_1, train_rating_1), (test_rating_2, train_rating_2), ...]\n", "def aggregatePairs(keyPairs):\n", " return keyPairs.combineByKey(\n", " lambda firstRatingPair: ((firstRatingPair),),\n", " lambda newRatingPair, firstRatingPair: newRatingPair + ((firstRatingPair),),\n", " lambda tupleRatingPairs1, tupleRatingPairs2: tupleRatingPairs1 + tupleRatingPairs2)\n", "\n", "# calculate pearson correlation when you passed in the values of\n", "# user-based approach: \n", "# input: values of [(user_id_1, user_id_2), ((rating_1, rating_2), (rating_1, rating_2)...)]\n", "# output: values of [(user_id_1, user_id_2), (pearson_correlation, num_rating_pairs, p_value)]\n", "# item-based approach:\n", "# input: values of [(movie_id_1, movie_id_2), ((rating_1, rating_2), (rating_1, rating_2)...)]\n", "# output: values of [(movie_id_1, movie_id_2), (pearson_correlation, num_rating_pairs, p_value)]\n", "# NOTE: ignore p_value\n", "def calculatePearson(ratingPairs):\n", " rating1s = [rating1 for (rating1, _) in ratingPairs] \n", " rating2s = [rating2 for (_, rating2) in ratingPairs] \n", " pearson_correlation, p_value = pearsonr(rating1s, rating2s)\n", " return (pearson_correlation, len(ratingPairs))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.A.1. Pearson's User-Based Approach: comparing USER similarities\n", "According to the article, this is supposed to be the best user-based approach. " ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[((1, 2), (3, 2)), ((2, 2), (4, 5))]" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#((user_id, movie_id), rating)\n", "a = sc.parallelize([ ((1, 2), 3), ((2, 2), 4) ])\n", "#((user_id, movie_id), predicted_rating)\n", "b = sc.parallelize([ ((1, 2), 2), ((2, 2), 5) ])\n", "#((user_id, movie_id), (rating, predicted_rating)\n", "c = a.join(b)\n", "c.collect()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "73388300\n" ] }, { "data": { "text/plain": [ "[(1920, ((731, 4.0), (746, 1.0))),\n", " (1920, ((731, 4.0), (791, 4.0))),\n", " (1920, ((731, 4.0), (1101, 3.0))),\n", " (1920, ((731, 4.0), (1333, 1.0))),\n", " (1920, ((731, 4.0), (1737, 5.0)))]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# combine test and train together so that\n", "# [movie_id, ( (test_user_id, test_rating), (train_user_id, train_rating) )]\n", "M_testUR_trainUR = m_ur_test.join(m_ur_train)\n", "print M_testUR_trainUR.count()\n", "M_testUR_trainUR.take(5)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "36722428\n" ] }, { "data": { "text/plain": [ "[(1920, ((731, 4.0), (746, 1.0))), (1920, ((731, 4.0), (791, 4.0)))]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# remove duplicates\n", "M_testUR_trainUR = M_testUR_trainUR.filter(removeDuplicates) \n", "print M_testUR_trainUR.count()\n", "M_testUR_trainUR.take(2)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "36722428\n" ] }, { "data": { "text/plain": [ "[((1264, 1519), (2.0, 2.0)), ((1264, 2020), (2.0, 3.0))]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# rearrange so that it will be in the format\n", "# [(test_user_id, train_user_id), (test_rating, train_rating)]\n", "userPairs = M_testUR_trainUR.map(createPairs)\n", "print userPairs.count()\n", "userPairs.take(2)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "aggregate user pairs: 0.0375311374664 seconds\n", "10728120\n" ] }, { "data": { "text/plain": [ "[((4978, 6024), ((5.0, 4.0), (4.0, 4.0))),\n", " ((1865, 3381), ((4.0, 4.0), (5.0, 4.0)))]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# congregate all ratings for each user pair so that it will be in the format of:\n", "# [( test_user_id, train_user_id), (test_rating_1, train_rating_1), (test_rating_2, train_rating_2), ...]\n", "\n", "# instead of using groupByKey(), use combineByKey() instead.\n", "\n", "\"\"\"\n", "# Implemented using groupByKey():\n", "with Timer() as t:\n", " aggUserPairs = userPairs.groupByKey()\n", "print \"aggregate user pairs approach #1: %s seconds\" % t.secs\n", "print aggUserPairs.count()\n", "aggUserPairs.take(5)\n", "-----------------------------------------------------------------\n", "# Output:\n", "aggregate user pairs: 0.0353801250458 seconds\n", "10728120\n", "Out[20]:\n", "[((1274, 2736), <pyspark.resultiterable.ResultIterable at 0x7f180eb55350>),\n", " ((2117, 5393), <pyspark.resultiterable.ResultIterable at 0x7f180eb55510>),\n", " ((1422, 3892), <pyspark.resultiterable.ResultIterable at 0x7f180eb55550>),\n", " ((1902, 5636), <pyspark.resultiterable.ResultIterable at 0x7f180eb55590>),\n", " ((3679, 5555), <pyspark.resultiterable.ResultIterable at 0x7f180eb555d0>)]\n", "-----------------------------------------------------------------\n", "output = aggUserPairs.mapValues(lambda iterable: tuple(iterable))\n", "output.take(2)\n", "-----------------------------------------------------------------\n", "# Output:\n", "[((3848, 4390), ((5.0, 5.0),)),\n", " ((897, 2621), ((4.0, 5.0), (4.0, 4.0), (2.0, 2.0)))]\n", "-----------------------------------------------------------------\n", "\"\"\"\n", "\n", "with Timer() as t:\n", " aggUserPairs = aggregatePairs(userPairs)\n", "print \"aggregate user pairs: %s seconds\" % t.secs\n", "print aggUserPairs.count()\n", "aggUserPairs.take(2)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10728120\n" ] }, { "data": { "text/plain": [ "[((1865, 3381), (nan, 2)),\n", " ((3848, 4390), (nan, 1)),\n", " ((897, 2621), (0.94491118252306805, 3)),\n", " ((1274, 2736), (-0.57735026918962584, 4)),\n", " ((1920, 5390), (0.64229374442338505, 10))]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# calculate pearson correlation to figure out user-to-user similarity in the format of:\n", "# [( (test_user_id, train_user_id), (pearson_correlation, num_rating_pairs) )]\n", "\n", "userPairSimilarities = aggUserPairs.mapValues(calculatePearson)\n", "userPairSimilarities.sortByKey()\n", "print userPairSimilarities.count()\n", "userPairSimilarities.take(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "find nearest neighbors\n", "1. select neighbors whose similarity correlation is greater than the threshold of 0.5\n", "2. select top n neighbors with the highest correlation" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "533394\n" ] }, { "data": { "text/plain": [ "[((1030, 2700), (0.63386569104638746, 5)),\n", " ((1920, 5390), (0.64229374442338505, 10)),\n", " ((2051, 5575), (0.55737040171315366, 5)),\n", " ((809, 3205), (0.58976782461958854, 8)),\n", " ((411, 3695), (0.55943092778551562, 7))]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. \n", "# a. select neighbors whose similarity correlation is greater than the threshold of 0.5\n", "# b. remove user pairs that do not share a minimum of 5 reviews\n", "\n", "# output: number of user pairs that passes minPearson = 1692207\n", "# number of user pairs that passes both minPearson and minSimilarReviews = 533407\n", "\n", "minPearson = 0.5\n", "minSimilarReviews = 5\n", "\n", "userPairPassThreshold = userPairSimilarities.filter(\n", " lambda (userPair, (pearson_correlation, num_rating_pairs)): \n", " pearson_correlation > minPearson and \n", " num_rating_pairs >= minSimilarReviews\n", " )\n", "print userPairPassThreshold.count()\n", "userPairPassThreshold.take(5)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "533416\n" ] }, { "data": { "text/plain": [ "[(1383, ((1383, 1755), (0.63439153512946889, 16))),\n", " (306, ((306, 3512), (0.5516772843673704, 8))),\n", " (1920, ((1920, 5390), (0.64229374442338505, 10))),\n", " (2051, ((2051, 5575), (0.55737040171315366, 5))),\n", " (809, ((809, 3205), (0.58976782461958854, 8)))]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 2. select top n neighbors for each test user \n", "\n", "from pyspark.rdd import RDD\n", "\n", "import heapq\n", "\n", "def takeOrderedByKey(self, topN, sortValueFn=None, ascending=False):\n", "\n", " def base(a):\n", " return [a]\n", "\n", " def combiner(agg, a):\n", " agg.append(a)\n", " return getTopN(agg)\n", "\n", " def merger(x, y):\n", " agg = x + y\n", " return getTopN(agg)\n", "\n", " def getTopN(agg):\n", " if ascending == True:\n", " return heapq.nsmallest(topN, agg, sortValueFn)\n", " else:\n", " return heapq.nlargest(topN, agg, sortValueFn) \n", "\n", " return self.combineByKey(base, combiner, merger)\n", " \n", "# add takeOrderedByKey() function to RDD class\n", "RDD.takeOrderedByKey = takeOrderedByKey\n", "\n", "# convert \n", "# [( (test_user_id, train_user_id), (pearson_correlation, num_rating_pairs) )]\n", "# to\n", "# [( test_user_id, [(test_user_id, train_user_id), (pearson_correlation, num_rating_pairs)] )]\n", "# so that you can sort by test_user_id after sorting the highest pearson correlation per test_user_id\n", "testU_testUtrainU_sim = userPairPassThreshold.map(\n", " lambda ((test_user_id, train_user_id), (pearson_correlation, num_rating_pairs)): \n", " (test_user_id, ((test_user_id, train_user_id), (pearson_correlation, num_rating_pairs)))\n", ")\n", "print testU_testUtrainU_sim.count()\n", "testU_testUtrainU_sim.take(5)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4206\n" ] }, { "data": { "text/plain": [ "[(4608,\n", " [((4608, 5397), (0.91287092917527679, 5)),\n", " ((4608, 4800), (0.91146543037530003, 6)),\n", " ((4608, 5387), (0.90971765229468415, 6)),\n", " ((4608, 5394), (0.85280286542244166, 6)),\n", " ((4608, 5522), (0.79056941504209477, 5)),\n", " ((4608, 5220), (0.79056941504209477, 5)),\n", " ((4608, 4728), (0.76376261582597327, 5)),\n", " ((4608, 5428), (0.68465319688145765, 5)),\n", " ((4608, 5759), (0.6670170091506028, 6)),\n", " ((4608, 5306), (0.66212219197173061, 5)),\n", " ((4608, 5106), (0.65938047339578698, 5)),\n", " ((4608, 5493), (0.64951905283832889, 6)),\n", " ((4608, 5501), (0.59761430466719678, 5)),\n", " ((4608, 5689), (0.58131835897618001, 6)),\n", " ((4608, 4980), (0.55901699437494745, 5)),\n", " ((4608, 5831), (0.54772255750516619, 6)),\n", " ((4608, 4932), (0.54232614454664041, 6)),\n", " ((4608, 5074), (0.54232614454664041, 6)),\n", " ((4608, 5636), (0.54006172486732162, 7)),\n", " ((4608, 5329), (0.53452248382484879, 5))]),\n", " (2304,\n", " [((2304, 2407), (1.0, 7)),\n", " ((2304, 3050), (1.0, 7)),\n", " ((2304, 5363), (1.0, 6)),\n", " ((2304, 3975), (1.0, 5)),\n", " ((2304, 3924), (0.96253342187962188, 5)),\n", " ((2304, 3984), (0.9074852129730302, 7)),\n", " ((2304, 3941), (0.89113278867900692, 6)),\n", " ((2304, 4274), (0.89113278867900692, 6)),\n", " ((2304, 3822), (0.88587956782829813, 11)),\n", " ((2304, 4605), (0.88388347648318455, 7)),\n", " ((2304, 3317), (0.87831006565367986, 6)),\n", " ((2304, 2754), (0.87499999999999989, 5)),\n", " ((2304, 4494), (0.87287156094396934, 5)),\n", " ((2304, 5515), (0.86692144686301087, 5)),\n", " ((2304, 2319), (0.86692144686301076, 8)),\n", " ((2304, 2701), (0.85280286542244133, 6)),\n", " ((2304, 4237), (0.85042006427076133, 9)),\n", " ((2304, 5703), (0.84515425472851657, 5)),\n", " ((2304, 2999), (0.84515425472851657, 5)),\n", " ((2304, 3417), (0.84515425472851657, 5))]),\n", " (5136,\n", " [((5136, 5767), (0.94345635304972641, 5)),\n", " ((5136, 5623), (0.875, 5)),\n", " ((5136, 5749), (0.87287156094396956, 5)),\n", " ((5136, 5442), (0.87287156094396956, 5)),\n", " ((5136, 5852), (0.87287156094396956, 5)),\n", " ((5136, 5366), (0.84515425472851657, 5)),\n", " ((5136, 5948), (0.80178372573727319, 5)),\n", " ((5136, 5664), (0.79056941504209477, 6)),\n", " ((5136, 5220), (0.77831178249415611, 7)),\n", " ((5136, 5269), (0.76553181582411123, 5)),\n", " ((5136, 6035), (0.75872122333360503, 7)),\n", " ((5136, 5543), (0.75, 6)),\n", " ((5136, 5539), (0.73192505471140001, 5)),\n", " ((5136, 5345), (0.73192505471139979, 5)),\n", " ((5136, 5412), (0.73029674334022154, 7)),\n", " ((5136, 5441), (0.7205766921228921, 5)),\n", " ((5136, 5468), (0.70710678118654746, 6)),\n", " ((5136, 5747), (0.68228823922101312, 6)),\n", " ((5136, 5657), (0.67936622048675732, 7)),\n", " ((5136, 5488), (0.66701700915060291, 6))]),\n", " (2592,\n", " [((2592, 4966), (0.97590007294853331, 5)),\n", " ((2592, 3048), (0.96308682468615359, 5)),\n", " ((2592, 2681), (0.95257934441568015, 5)),\n", " ((2592, 3415), (0.94868329805051377, 6)),\n", " ((2592, 5982), (0.94491118252306816, 6)),\n", " ((2592, 5363), (0.94491118252306816, 6)),\n", " ((2592, 5671), (0.92717264994553061, 6)),\n", " ((2592, 5963), (0.9046950831485675, 7)),\n", " ((2592, 4505), (0.89087080637474791, 8)),\n", " ((2592, 5099), (0.86859903621537937, 5)),\n", " ((2592, 4294), (0.86752761723570893, 5)),\n", " ((2592, 5765), (0.86602540378443871, 8)),\n", " ((2592, 5142), (0.86602540378443871, 6)),\n", " ((2592, 3718), (0.84274982807905263, 5)),\n", " ((2592, 5156), (0.84016805041680587, 5)),\n", " ((2592, 3487), (0.82495791138430541, 5)),\n", " ((2592, 4018), (0.81649658092772615, 5)),\n", " ((2592, 3741), (0.81348921681996067, 5)),\n", " ((2592, 5437), (0.81348921681996067, 5)),\n", " ((2592, 5654), (0.81127866785900216, 9))]),\n", " (5040,\n", " [((5040, 5831), (0.89442719099991586, 6)),\n", " ((5040, 5686), (0.86859903621537915, 5)),\n", " ((5040, 5720), (0.8660254037844386, 5)),\n", " ((5040, 6007), (0.82495791138430552, 8)),\n", " ((5040, 5954), (0.76376261582597327, 5)),\n", " ((5040, 5767), (0.74681604481493857, 7)),\n", " ((5040, 5387), (0.734930919740164, 7)),\n", " ((5040, 5283), (0.67419986246324215, 6)),\n", " ((5040, 5862), (0.66989384530323548, 5)),\n", " ((5040, 5653), (0.66666666666666663, 5)),\n", " ((5040, 5627), (0.66436383882991989, 6)),\n", " ((5040, 5854), (0.64285714285714279, 5)),\n", " ((5040, 5103), (0.61237243569579458, 5)),\n", " ((5040, 5620), (0.61237243569579447, 5)),\n", " ((5040, 5916), (0.58794473579213125, 7)),\n", " ((5040, 5367), (0.5837280385837107, 7)),\n", " ((5040, 5824), (0.58333333333333326, 5)),\n", " ((5040, 5536), (0.56011203361120387, 5)),\n", " ((5040, 5788), (0.53066185325791893, 7)),\n", " ((5040, 5433), (0.50564989684743156, 5))])]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# for each test user, take the top N neighbors and ordering with the highest pearson correlation first\n", "# [( test_user_id, [(test_user_id, train_user_id), (pearson_correlation, num_rating_pairs)] )]\n", "topN = 20\n", "testUserTopNeighbors = testU_testUtrainU_sim.takeOrderedByKey(\n", " topN,\n", " sortValueFn=lambda ((test_user_id, train_user_id), (pearson_correlation, num_rating_pairs)): (pearson_correlation, num_rating_pairs),\n", " ascending=False)\n", "# note: testUserTopNeighbors.count() should be less than the number of users\n", "print testUserTopNeighbors.count()\n", "testUserTopNeighbors.take(5)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "num_distinct_test_users = 5962\n", "num_distinct_test_users that passes the threshold check (aka pearson > 0.5, minReviews >= 5) = 4206\n", "num_test_users in testUserTopNeighbors = 4206\n" ] } ], "source": [ "num_distinct_test_users = m_ur_test.map(lambda (movie_id, (user_id, rating)): user_id).distinct().count()\n", "num_distinct_test_users_pass_threshold = userPairPassThreshold.map(lambda ((test_user_id, train_user_id), (pearson_correlation, num_rating_pairs)): test_user_id).distinct().count()\n", "num_test_users_in_top_neighbors = testUserTopNeighbors.count()\n", "\n", "print \"num_distinct_test_users = \", num_distinct_test_users\n", "print \"num_distinct_test_users that passes the threshold check (aka pearson > 0.5, minReviews >= 5) = \", num_distinct_test_users_pass_threshold\n", "print \"num_test_users in testUserTopNeighbors = \", num_test_users_in_top_neighbors" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "70218\n" ] }, { "data": { "text/plain": [ "[((3072, 4344), (1.0, 8)),\n", " ((3072, 4771), (1.0, 6)),\n", " ((3072, 5847), (1.0, 5)),\n", " ((3072, 5443), (1.0, 5)),\n", " ((3072, 3299), (1.0, 5))]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# flattened version, meaning\n", "# convert\n", "# [( test_user_id, [(test_user_id, train_user_id), (pearson_correlation, num_rating_pairs)] )]\n", "# to\n", "# [( (test_user_id, train_user_id), (pearson_correlation, num_rating_pairs) )]\n", "\n", "testUserTopNeighborsFlattened = testUserTopNeighbors.flatMap(lambda (test_user_id, rest): rest)\n", "print testUserTopNeighborsFlattened.count()\n", "testUserTopNeighborsFlattened.take(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compute Predictions \\#1: using weighted sum of rating values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__!!!!!! TODO !!!!!!!__ " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compute Predictions \\#2: using a weighted sum of deviations from the mean\n", "```\n", "P = predicted rating of user a on movie i\n", "M = mean rating of user a\n", "S = sum of ((pearson correlation of user a and each user u who rates movie i) * \n", " (rating of each user u on movie i - mean rating of user u))\n", "D = sum of (absolute value of pearson correlation of user a and each user u who rates movie i)\n", "\n", "P = M + S/D\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__!!!!!! TODO !!!!!!!__ " ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(1536, 4.222222222222222),\n", " (2304, 3.722794959908362),\n", " (2472, 3.6),\n", " (5592, 4.039370078740157),\n", " (4944, 4.363636363636363)]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# determine mean rating of each test user (aka find M)\n", "# output: [(user_id, mean_rating)]\n", "\n", "# convert to [(user_id, rating)]\n", "ur = m_ur.map(lambda (movie_id, (user_id, rating)): (user_id, rating))\n", "\n", "\n", "# [(user_id, (sum_rating, num_rating))]\n", "u_sumRating_numRating = ur.combineByKey(\n", " lambda first_rating: (first_rating, 1), \n", " lambda x, first_rating: (x[0] + first_rating, x[1] + 1),\n", " lambda x, y: (x[0] + y[0], x[1] + y[1]))\n", "\n", "# [(test_user_id, mean_rating)]\n", "u_meanRating = u_sumRating_numRating.map(\n", " lambda (user_id, (sum_rating, num_rating)): (user_id, sum_rating/num_rating)) \n", "u_meanRating.take(5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# for each movie i, \n", "# determine pearson correlation of user a and all other users who rates movie i\n", "# determine rating of each user u on movie i - mean rating of user u\n", "\n", "# testUserTopNeighborsFlattened == [( (test_user_id, train_user_id), (pearson_correlation, num_rating_pairs) )]\n", "# M_testUR_trainUR == # [movie_id, ( (test_user_id, test_rating), (train_user_id, train_rating) )]\n", "\n", "\n", "# movie_id, (for every users who rate movie_id, add all pearson correlation * rating of user u on movie i - mean rating of user u)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# compute predictions #2\n", "# using a weighted sum of deviations from the mean\n", "\n", "\"\"\"\n", "sum of user u has rated(pearson correlation of user a and user u) * (rating of user u on movie i - mean rating of user u)\n", "divided by\n", "\"\"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.A.1. Pearson's Item-Based Approach: comparing MOVIES similarities" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000209\n" ] }, { "data": { "text/plain": [ "[(1, (527, 5.0)),\n", " (1, (1207, 4.0)),\n", " (2, (1217, 3.0)),\n", " (2, (2490, 3.0)),\n", " (2, (1370, 5.0))]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# list all ratings in the format:\n", "# [user_id, (movie_id, rating)]\n", "print u_mr.count()\n", "u_mr.take(5)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "389989535\n" ] }, { "data": { "text/plain": [ "[(1536, ((1258, 5.0), (1955, 5.0))),\n", " (1536, ((1258, 5.0), (2054, 4.0))),\n", " (1536, ((1258, 5.0), (344, 4.0))),\n", " (1536, ((1258, 5.0), (585, 1.0))),\n", " (1536, ((1258, 5.0), (356, 5.0)))]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# list all combinations of movies rated by the same user in the format: \n", "# [user_id, ( (movie_id_1, rating_1), (movie_id_2, rating_2) )]\n", "# this is to find movie's similarity with each other\n", "sameUserRatingsCombo = u_mr.join(u_mr)\n", "print sameUserRatingsCombo.count()\n", "sameUserRatingsCombo.take(5)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "194494663\n" ] }, { "data": { "text/plain": [ "[(1536, ((788, 4.0), (3043, 1.0))),\n", " (1536, ((788, 4.0), (2797, 4.0))),\n", " (1536, ((788, 4.0), (1485, 4.0))),\n", " (1536, ((788, 4.0), (1193, 5.0))),\n", " (1536, ((788, 4.0), (1371, 1.0)))]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# filter out duplicate pairs\n", "def removeDuplicates((user_id, ratings)):\n", " (movie_id_1, rating_1) = ratings[0]\n", " (movie_id_2, rating_2) = ratings[1]\n", " return movie_id_1 < movie_id_2\n", "\n", "sameUserRatingsCombo = sameUserRatingsCombo.filter(removeDuplicates)\n", "print sameUserRatingsCombo.count()\n", "sameUserRatingsCombo.take(5)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "194494663\n" ] }, { "data": { "text/plain": [ "[((1193, 3100), (5.0, 4.0)),\n", " ((1193, 1955), (5.0, 5.0)),\n", " ((1193, 2054), (5.0, 4.0)),\n", " ((1193, 1198), (5.0, 4.0)),\n", " ((1193, 3043), (5.0, 1.0))]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# rearrange so that it will be in the format of movie pairs:\n", "# [(movie_id_1, movie_id_2), (rating_1, rating2)]\n", "def createMoviePairs((user_id, ratings)):\n", " (movie_id_1, rating_1) = ratings[0]\n", " (movie_id_2, rating_2) = ratings[1]\n", " return ((movie_id_1, movie_id_2), (rating_1, rating_2))\n", "\n", "moviePairs = sameUserRatingsCombo.map(createMoviePairs)\n", "print moviePairs.count()\n", "moviePairs.take(5)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5654974\n" ] }, { "data": { "text/plain": [ "[((199, 2859), <pyspark.resultiterable.ResultIterable at 0x7f527171e290>),\n", " ((441, 3941), <pyspark.resultiterable.ResultIterable at 0x7f527171e450>),\n", " ((2046, 3524), <pyspark.resultiterable.ResultIterable at 0x7f527171e490>),\n", " ((334, 1060), <pyspark.resultiterable.ResultIterable at 0x7f527171e4d0>),\n", " ((635, 2751), <pyspark.resultiterable.ResultIterable at 0x7f527171e510>)]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# congregate all ratings for each movie pair so that it will be in the format of:\n", "# [( movie_id_1, movie_id_2), (rating_1, rating_2), (rating_1, rating_2), ...]\n", "moviePairRatings = moviePairs.groupByKey()\n", "print moviePairRatings.count()\n", "moviePairRatings.take(5)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[((1050, 3408), (0.2803100687095229, 62)),\n", " ((535, 3867), (0.9999999999999998, 2)),\n", " ((609, 621), (-1.0, 2)),\n", " ((2676, 2946), (0.06537204504606135, 16)),\n", " ((561, 3789), (0, 1))]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# calculate pearson correlation approach #1\n", "# using udemy's approach\n", "# I prefer approach #2\n", "\n", "import math\n", "\n", "def computePearsonCorrelationCoefficient(ratingPairs):\n", " numPairs = 0\n", " if not ratingPairs:\n", " return (0, 0)\n", "\n", " muX = sum(1.*ratingX for (ratingX, _) in ratingPairs)/len(ratingPairs)\n", " muY = sum(1.*ratingY for (_, ratingY) in ratingPairs)/len(ratingPairs)\n", "\n", " cov = sum_sqdev_x = sum_sqdev_y = 0\n", " for ratingX, ratingY in ratingPairs:\n", " dev_x = ratingX - muX\n", " dev_y = ratingY - muY\n", " cov += dev_x * dev_y\n", " sum_sqdev_x += dev_x**2\n", " sum_sqdev_y += dev_y**2\n", " numPairs += 1\n", "\n", " numerator = cov\n", " denominator = math.sqrt(sum_sqdev_x) * math.sqrt(sum_sqdev_y)\n", "\n", " score = 0\n", " if (denominator):\n", " score = (numerator / (float(denominator)))\n", "\n", " return (score, numPairs)\n", " \n", "moviePairSimilarities = moviePairRatings.mapValues(computePearsonCorrelationCoefficient).cache()\n", "moviePairSimilarities.sortByKey()\n", "moviePairSimilarities.take(5)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5654974\n", "5654974\n" ] } ], "source": [ "print moviePairRatings.count()\n", "print moviePairSimilarities.count()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# calculate pearson correlation approach #2\n", "# using scipy\n", "# note: you cannot use pyspark.mllib.stat.Statistics's corr() function within the map function\n", "\n", "from scipy.stats import pearsonr \n", "\n", "def calculatePearson(ratingPairsPerMoviePairResultIterable):\n", " ratingPairsPerMoviePair = tuple(ratingPairsPerMoviePairResultIterable)\n", " rating1s = [rating1 for (rating1, _) in ratingPairsPerMoviePair] \n", " rating2s = [rating2 for (_, rating2) in ratingPairsPerMoviePair] \n", " pearson_correlation, p_value = pearsonr(rating1s, rating2s)\n", " return (pearson_correlation, len(ratingPairsPerMoviePair))\n", "\n", "moviePairSimilarities2 = moviePairRatings.mapValues(calculatePearson).cache()\n", "moviePairSimilarities2.sortByKey()\n", "moviePairSimilarities2.take(5)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5654974\n", "5654974\n" ] } ], "source": [ "print moviePairRatings.count()\n", "print moviePairSimilarities2.count()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## 6.A.2. Calculate Constraint Pearson Correlation\n", "In the case of MovieLens data, it means any ratings greater than 3 (aka positive ratings)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.A.2. Pearson's User-Based Approach: comparing USERS similarities\n", "This is the same as Pearson's User-Based Approach with the exception that it filters out ratings that are 2 or less." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.A.2. Constraint Pearson's Item-Based Approach: comparing MOVIES similarities\n", "This is the same as Pearson's Item-Based Approach with the exception that it filters out ratings that are 2 or less." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6.B. Calculate Probabilistic Similarity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.B. Probabilistic's Item-Based Approach: comparing MOVIES similarity\n", "According to the article, this is supposed to be the best item-based approach. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6.C.1. Calculate Cosine Similarity" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6.C.2. Calculate Adjusted Cosine Similarity" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6.D. Comparing Similarities' Measurement\n", "\n", "Graph user-based approaches using the deviation prediction scheme (MAE) and different neighborhood sizes (K)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7. Develop Model\n", "Comparing distance measures for model-based approaches using the mean item rating prediction scheme and different number of clusters (K)\n", "* Manhattan\n", "* Euclidian\n", "\n", "Comparing prediction schemes for model-based approaches using the euclidian distance and different numbers of clusters (K)\n", "* Mean Item\n", "* Bayes MAE\n", "\n", "Comparing different clustering algorithms for model-based approaches using the euclidian distance, the mean item rating prediction scheme, and different numbers of clusters (K)\n", "* K-Means\n", "* Bisecting\n", "* LAC\n", "* SSC" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# divide movielens data into 10 parts to perform 10-fold cross-validation\n", "# training model using 9 parts\n", "# test model using last part\n", "\n", "\n", "# results are better when default ratings are based on item information than when they are based on user information\n", "# using mean rating is better than using majority rating" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 7.A. Implement Bayes" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "What is Bayes?\n", "1. We have a prior belief in A \n", "2. We have a posterior probability X where X is the number of tests it passes\n", "3. Bayesian inference merely uses it to connect prior probabilities P(A) with an updated posterior probabilities P(A|X)\n", "\n", "```\n", "P(A|X) = P(X|A) * P(A) / P(X)\n", "P(A|X) = Posterior Probability: the posterior probability of class (A, target) given predictor(X, attributes)\n", "P(X|A) = Likelihood: the likelihood which is the probability of predictor given class\n", "P(A) = Class Prior Probability: prior probability of class\n", "P(X) = Predictor Prior Probability: prior probability of predictor\n", "```\n", "\n", "Types of Bayes: \n", "1. Maximum A Posteriori (MAP) : predict the most probably rating\n", "2. Mean Squared Error (MSE): compute the weighted sum of ratings that corresponds to minimizing the expectation of MSE\n", "3. Mean Absolute Error (MAE): select the rating that minimizes the expectation of Mean Absolute Error\n", "\n", "Table of Contents:\n", "```\n", "7.A. Implement Bayes\n", "7.A.1. Implement Naive Bayes using PySpark: DONE\n", "7.A.2. Implement Naive Bayes using PyMC\n", "7.A.3. Implement Naive Bayes manually: DONE\n", " * Implement Bayes MAP: DONE\n", " * Implement Bayes MSE: DONE\n", " * Implement Bayes MAE: DONE\n", "```\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 7.A.1. Implementing Naive Bayes using PySpark\n", "\n", "It does not support computation for Bayes MAP, MSE, and MAE because it does not provide a probability distribution over labels (aka rating) for the given featureset (aka user_id, movie_id). " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pyspark.mllib.classification import NaiveBayes\n", "from pyspark.mllib.regression import LabeledPoint\n", "\n", "# To use MLlib's Naive Bayes model, it requires the input to be in a format of a LabeledPoint\n", "# therefore, convert dataset so that it will be in the following format:\n", "# [(rating, (user_id, movie_id))]\n", "r_um = ratingsdf.map(lambda row: LabeledPoint(row.rating, (row.user_id, row.movie_id)))\n", "\n", "# split the data\n", "r_um_train, r_um_test, r_um_validation = r_um.randomSplit(weights, seed)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# train a Naive Bayes model\n", "naiveBayesModel = NaiveBayes.train(r_um_train, lambda_=1.0)\n", "\n", "# save this Naive Bayes model\n", "#naiveBayesModel.save(sc, \"NaiveBayes_MovieLens1M_UserUser\")\n", "# load this Naive Bayes model into the SparkContext\n", "#sameNaiveBayesModel = NaiveBayesModel.load(sc, \"NaiveBayes_MovieLens1M_UserUser\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[((2.0, 593.0), (1.0, 5.0)), ((2.0, 1955.0), (1.0, 4.0)), ((5.0, 3476.0), (1.0, 3.0)), ((5.0, 1093.0), (1.0, 2.0)), ((6.0, 3508.0), (1.0, 3.0))]\n" ] } ], "source": [ "# make prediction \n", "# [((test_user_id, test_movie_id), (predicted_rating, actual_rating))]\n", "r_um_predicted = r_um_test.map(\n", " lambda p: ( (p.features[0], p.features[1]), (naiveBayesModel.predict(p.features), p.label) ) \n", " )\n", "print r_um_predicted.take(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[((2.0, 593.0), (1.0, 5.0)), ((2.0, 1955.0), (1.0, 4.0)), ((5.0, 3476.0), (1.0, 3.0)), ((5.0, 1093.0), (1.0, 2.0)), ((6.0, 3508.0), (1.0, 3.0))]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "accuracy = (predicted_rating == actual_rating)/total_num_ratings = 0.165762116647\n" ] } ], "source": [ "# test accuracy\n", "sameRating = r_um_predicted.filter(\n", " lambda ((test_user_id, test_movie_id), (predicted_rating, actual_rating)): predicted_rating == actual_rating)\n", "accuracy = 1.0 * sameRating.count() / r_um_test.count()\n", "print \"accuracy = (predicted_rating == actual_rating)/total_num_ratings = \", accuracy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "accuracy = (predicted_rating == actual_rating)/total_num_ratings = 0.162442085039" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "actual:\n", "[(2.0, 593.0, 5.0), (2.0, 1955.0, 4.0), (5.0, 3476.0, 3.0), (5.0, 1093.0, 2.0), (6.0, 3508.0, 3.0)]\n", "predicted:\n", "[(2.0, 593.0, 1.0), (2.0, 1955.0, 1.0), (5.0, 3476.0, 1.0), (5.0, 1093.0, 1.0), (6.0, 3508.0, 1.0)]\n", "rmse = 2.25667970883\n", "mae = 1.87239858731\n" ] } ], "source": [ "# calculate RMSE and MAE\n", "\n", "# convert into two vectors where\n", "# one vector describes the actual ratings in the format [(user_id, movie_id, actual_rating)]\n", "# second vector describes the predicted ratings in the format [(user_id, movie_id, predicted_rating)]\n", "actual = r_um_predicted.map(\n", " lambda((test_user_id, test_movie_id), (predicted_rating, actual_rating)): (test_user_id, test_movie_id, actual_rating)\n", " )\n", "predicted = r_um_predicted.map(\n", " lambda((test_user_id, test_movie_id), (predicted_rating, actual_rating)): (test_user_id, test_movie_id, predicted_rating)\n", " )\n", "\n", "print \"actual:\\n\", actual.take(5)\n", "print \"predicted:\\n\", predicted.take(5)\n", "\n", "rmse = pm.calculate_rmse_using_rdd(actual, predicted)\n", "print \"rmse = \", rmse\n", "\n", "mae = pm.calculate_mae_using_rdd(actual, predicted)\n", "print \"mae = \", mae" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "actual:\n", "[(7.0, 3793.0, 3.0), (8.0, 2490.0, 2.0), (15.0, 1343.0, 3.0), (16.0, 2713.0, 2.0), (17.0, 457.0, 5.0)]\n", "\n", "predicted:\n", "[(7.0, 3793.0, 1.0), (8.0, 2490.0, 1.0), (15.0, 1343.0, 1.0), (16.0, 2713.0, 1.0), (17.0, 457.0, 1.0)]\n", "\n", "rmse = 2.26584476437\n", "\n", "mae = 1.88503067116" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Implementing Naive Bayes using PyMC" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Implementing Naive Bayes manually\n", "\n", "Probability of rating r for a given user u on a given item i can be defined as follows:\n", "\n", "$$P(r|u, i) = \\frac{P(r|u) * P(r|i)}{P(r)}*\\frac{P(u) * P(i)}{P(u, i)}$$\n", "\n", "We make the **assumption** that this is the same as:\n", "\n", "$$P(r|u, i) = \\frac{P(r|u) * P(r|i)}{P(r)}$$\n", "\n", "The last three probabilities P(u), P(i), and P(u, i) can be ignored since they are the same for all users and items.\n", "\n", "We will compute P(r|u), P(r|i), and P(r) individually before congregating them in a final computation." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "minRating = 1.0\n", "maxRating = 5.0\n" ] } ], "source": [ "# determine min and max of ratings\n", "minRating = ratingsdf.map(lambda row: row.rating).min()\n", "maxRating = ratingsdf.map(lambda row: row.rating).max()\n", "print \"minRating = \", minRating\n", "print \"maxRating = \", maxRating" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Output example:\n", "```\n", "minRating = 1.0\n", "maxRating = 5.0\n", "```" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 2, 3, 4, 5]\n", "5\n" ] } ], "source": [ "# create RDD for the range of ratings\n", "# [(1, 2, 3, 4, 5)]\n", "rangeOfRatings = sc.parallelize( list(range(int(minRating), int(maxRating + 1))) )\n", "print rangeOfRatings.collect()\n", "print rangeOfRatings.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Output example:\n", "```\n", "[1, 2, 3, 4, 5]\n", "5\n", "```" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# [(user_id, movie_id, rating)]\n", "umr = ratingsdf.map(lambda row: (row.user_id, row.movie_id, row.rating))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1000209" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "umr.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1000209" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(1.0, (1, 1197)), (1.0, (1, 938))]\n", "5001045\n" ] } ], "source": [ "# since we have to determine the probability of rating r for each user_id and movie_id,\n", "# we have to create a RDD with [(rating, (user_id, movie_id))] for each rating\n", "# ie. (rating_1, (user_id, movie_id)), (rating_2, (user_id, movie_id)), ..., (rating_5, (user_id, movie_id))\n", "um = umr.map(lambda (user_id, movie_id, rating): (user_id, movie_id))\n", "rCombo_um = rangeOfRatings.cartesian(um).map(lambda (rating, (user_id, movie_id)): (float(rating), (user_id, movie_id)))\n", "print rCombo_um.take(2)\n", "print rCombo_um.count() # == umr.count() * 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[(1.0, (1, 1197)), (1.0, (1, 938))]\n", "\n", "5001045" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(1, 1197, 1.0), (1, 938, 1.0)]\n", "5001045\n" ] } ], "source": [ "umrCombo = rCombo_um.map(lambda (rating, (user_id, movie_id)): (user_id, movie_id, rating))\n", "print umrCombo.take(2)\n", "print umrCombo.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[(1, 1197, 1.0), (1, 938, 1.0)]\n", "\n", "5001045" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(1.0, (287, 150)), (1.0, (287, 235))]\n", "500750\n" ] } ], "source": [ "# since we have to determine the probability of rating r for each user_id and movie_id,\n", "# we have to create a RDD with [(rating, (user_id, movie_id))] for each rating\n", "# ie. (rating_1, (user_id, movie_id)), (rating_2, (user_id, movie_id)), ..., (rating_5, (user_id, movie_id))\n", "um_test = umr_test.map(lambda (user_id, movie_id, rating): (user_id, movie_id))\n", "rCombo_um_test = rangeOfRatings.cartesian(um_test).map(lambda (rating, (user_id, movie_id)): (float(rating), (user_id, movie_id)))\n", "print rCombo_um_test.take(2)\n", "print rCombo_um_test.count() # == umr.count() * 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[(1.0, (2, 593)), (1.0, (2, 1955))]\n", "\n", "501170" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(58, 1205, 1.0), (58, 2019, 1.0)]\n", "500750\n" ] } ], "source": [ "umrCombo_test = rCombo_um_test.map(lambda (rating, (user_id, movie_id)): (user_id, movie_id, rating))\n", "print umrCombo_test.take(2)\n", "print umrCombo_test.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[(2, 593, 1.0), (2, 1955, 1.0)]\n", "\n", "501170" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Calculating P(r|u) , probability of rating r for user u\n", "$$ P(r|u) = { numberOfParticularRatingThatUserGives \\over totalNumberOfRatingsThatUserGives }$$\n", "\n", "```\n", "P(r|u) = (number of ratings r that user u gives) / (total number of ratings that user u gives)\n", "\n", "For example: \n", " r == 1\n", " P(r|u) = (number of ratings r == 1 that user u gives) / (total number of ratings that user u gives)\n", "```" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[((1, 3.0), 1), ((1, 4.0), 1)]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# [((user_id, rating), 1)]\n", "ur_1 = umr.map(lambda (user_id, movie_id, rating): ((user_id, rating), 1))\n", "ur_1.take(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[((1, 3.0), 1), ((1, 4.0), 1)]" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1000209" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ur_1.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1000209" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30200\n" ] } ], "source": [ "# [(((user_id, rating_1), 0), ((user_id, rating_2), 0), ..., ((user_id, rating_5), 0))]\n", "urCombo_0 = umrCombo.map(lambda (user_id, movie_id, rating): ((user_id, rating), 0)).distinct()\n", "#print urCombo_0.sortByKey().collect()\n", "print urCombo_0.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "30200" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[((1, 3.0), 1), ((1, 4.0), 1)]\n", "1030409\n" ] } ], "source": [ "ur_1Or0 = ur_1.union(urCombo_0)\n", "print ur_1Or0.take(2)\n", "print ur_1Or0.count()\n", "# ur_1Or0.count() == ur_1.count() + urCombo_0.count()\n", "# 1000209 + 30200 \n", "# 1030409 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[((1, 3.0), 1), ((1, 4.0), 1)]\n", "\n", "1030409" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[((1, 1.0), 0),\n", " ((1, 2.0), 0),\n", " ((1, 3.0), 1),\n", " ((1, 3.0), 1),\n", " ((1, 3.0), 1),\n", " ((1, 3.0), 1),\n", " ((1, 3.0), 1),\n", " ((1, 3.0), 1),\n", " ((1, 3.0), 1),\n", " ((1, 3.0), 1),\n", " ((1, 3.0), 0),\n", " ((1, 4.0), 1),\n", " ((1, 4.0), 1),\n", " ((1, 4.0), 1),\n", " ((1, 4.0), 1),\n", " ((1, 4.0), 1),\n", " ((1, 4.0), 1),\n", " ((1, 4.0), 1),\n", " ((1, 4.0), 1),\n", " ((1, 4.0), 1),\n", " ((1, 4.0), 1),\n", " ((1, 4.0), 1),\n", " ((1, 4.0), 1),\n", " ((1, 4.0), 1),\n", " 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1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 0),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 3.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ((10, 4.0), 1),\n", " ...]" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ur_1Or0.sortByKey().collect()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[((2001, 5.0), 13), ((5446, 4.0), 37)]\n", "30200\n" ] } ], "source": [ "from operator import add \n", "\n", "# [(user_id, rating), (num_rating)]\n", "ur_numRating = ur_1Or0.reduceByKey(add)\n", "print ur_numRating.take(2)\n", "print ur_numRating.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[((3577, 5.0), 29), ((1260, 2.0), 13)]\n", "\n", "30200" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(2001, (5.0, 13)), (5446, (4.0, 37))]\n", "30200\n" ] } ], "source": [ "# [(user_id, (rating, num_rating))]\n", "u_r_numRating = ur_numRating.map(lambda ((user_id, rating), num_rating): (user_id, (rating, num_rating)))\n", "print u_r_numRating.take(2)\n", "print u_r_numRating.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[(3577, (5.0, 29)), (1260, (2.0, 13))]\n", "\n", "30200" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(1, 53), (2, 129)]\n", "6040\n" ] } ], "source": [ "# [(user_id, total_rating)]\n", "u_totalRating = sc.parallelize(umr.map(lambda (user_id, movie_id, rating): (user_id, rating)).countByKey().items())\n", "print u_totalRating.take(2)\n", "print u_totalRating.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[(1, 53), (2, 129)]\n", "\n", "6040" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(6020, (43, (2.0, 0))), (6020, (43, (5.0, 24)))]\n", "30200\n" ] } ], "source": [ "# [(user_id, (total_rating, (rating, num_rating)))]\n", "u_componentsOfProb = u_totalRating.join(u_r_numRating)\n", "print u_componentsOfProb.take(2)\n", "print u_componentsOfProb.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[(2850, (43, (4.0, 12))), (2850, (43, (1.0, 5)))]\n", "\n", "30200" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(6020, 3.0, 0.16279069767441862), (6020, 4.0, 0.27906976744186046)]\n", "30200\n" ] } ], "source": [ "# [(user_id, rating, probRU)]\n", "probRU = u_componentsOfProb.map(lambda (user_id, (total_rating, (rating, num_rating))): \n", " (user_id, rating, float(num_rating)/float(total_rating))\n", " )\n", "print probRU.take(2)\n", "print probRU.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[(2850, 1.0, 0.11627906976744186), (2850, 3.0, 0.18604651162790697)]\n", "\n", "30200" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Calculating P(r|i) \n", "$$ P(r|i) = { numberOfParticularRatingThatItemReceives \\over totalNumberOfRatingsThatItemReceives }$$\n", "\n", "```\n", "P(r|i) = (number of ratings r that item i receives) / (total number of ratings that item i receives)\n", "\n", "For example: \n", " r == 1\n", " P(r|i) = (number of ratings r == 1 that movie i receives) / (total number of ratings that movie i receives)\n", "```" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[((1197, 3.0), 1), ((938, 4.0), 1)]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# [((movie_id, rating), 1)]\n", "mr_1 = umr.map(lambda (user_id, movie_id, rating): ((movie_id, rating), 1))\n", "mr_1.take(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[((1197, 3.0), 1), ((938, 4.0), 1)]" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1000209" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mr_1.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1000209" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "18530\n" ] } ], "source": [ "# [(((user_id, rating_1), 0), ((user_id, rating_2), 0), ..., ((user_id, rating_5), 0))]\n", "mrCombo_0 = umrCombo.map(lambda (user_id, movie_id, rating): ((movie_id, rating), 0)).distinct()\n", "#print mrCombo_0.sortByKey().collect()\n", "print mrCombo_0.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "18530" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[((1197, 3.0), 1), ((938, 4.0), 1)]\n", "1018739\n" ] } ], "source": [ "mr_1Or0 = mr_1.union(mrCombo_0)\n", "print mr_1Or0.take(2)\n", "print mr_1Or0.count()\n", "# ur_1Or0.count() == ur_1.count() + urCombo_0.count()\n", "# 1000209 + 18530\n", "# 1018739 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[((1197, 3.0), 1), ((938, 4.0), 1)]\n", "\n", "1018739" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[((2001, 5.0), 129), ((3654, 4.0), 266)]\n", "18530\n" ] } ], "source": [ "# [(movie_id, rating), (num_rating)]\n", "mr_numRating = mr_1Or0.reduceByKey(add)\n", "print mr_numRating.take(2)\n", "print mr_numRating.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[((3577, 5.0), 3), ((1260, 2.0), 6)]\n", "\n", "18530" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[((2001, 5.0), (129, 0)), ((3654, 4.0), (266, 0))]\n", "18530\n" ] } ], "source": [ "# OPTION instead of using union() and then reduceByKey()\n", "\"\"\"\n", "mr_1Or0 = mr_1.reduceByKey(add).rightOuterJoin(mrCombo_0)\n", "print mr_1Or0.take(2)\n", "print mr_1Or0.count()\n", "\"\"\"\n", "\"\"\"\n", "[((2001, 5.0), (129, 0)), ((3654, 4.0), (266, 0))]\n", "18530\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(2001, (5.0, 129)), (3654, (4.0, 266))]\n", "18530\n" ] } ], "source": [ "# [(movie_id, (rating, num_rating))]\n", "m_r_numRating = mr_numRating.map(lambda ((movie_id, rating), num_rating): (movie_id, (rating, num_rating)))\n", "print m_r_numRating.take(2)\n", "print m_r_numRating.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[(391, (3.0, 18)), (518, (4.0, 22))]\n", "\n", "18530" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(1, 2077), (2, 701)]\n", "3706\n" ] } ], "source": [ "# [(movie_id, total_rating)]\n", "m_totalRating = sc.parallelize(umr.map(lambda (user_id, movie_id, rating): (movie_id, rating)).countByKey().items())\n", "print m_totalRating.take(2)\n", "print m_totalRating.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[(1, 2077), (2, 701)]\n", "\n", "3706" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(3010, (35, (4.0, 13))), (3010, (35, (1.0, 1)))]\n", "18530\n" ] } ], "source": [ "# [(user_id, (total_rating, (rating, num_rating)))]\n", "m_componentsOfProb = m_totalRating.join(m_r_numRating)\n", "print m_componentsOfProb.take(2)\n", "print m_componentsOfProb.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[(3808, (44, (5.0, 17))), (3808, (44, (3.0, 8)))]\n", "\n", "18530" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(3010, 1.0, 0.02857142857142857), (3010, 4.0, 0.37142857142857144)]\n", "18530\n" ] } ], "source": [ "# [(movie_id, rating, probRI)]\n", "probRI = m_componentsOfProb.map(lambda (movie_id, (total_rating, (rating, num_rating))): \n", " (movie_id, rating, float(num_rating)/float(total_rating))\n", " )\n", "print probRI.take(2)\n", "print probRI.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[(3808, 5.0, 0.38636363636363635), (3808, 4.0, 0.36363636363636365)]\n", "\n", "18530" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#####P(r) = numRating / totalRatings \n", "\n", "ie. rating = 1\n", "\n", "P(r) = (number of rating == 1) / (total number of ratings)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000209\n" ] } ], "source": [ "totalRatings = umr.count()\n", "print totalRatings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1000209" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(1.0, 0.05616226208722377), (2.0, 0.1075345252842156)]" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# [(rating, 1)]\n", "r_1 = umr.map(lambda (user_id, movie_id, rating): (rating, 1))\n", "# [(rating, num_rating)]\n", "r_numRating = r_1.reduceByKey(add)\n", "# [(rating, probR)]\n", "probR = r_numRating.mapValues(lambda num_rating: float(num_rating)/float(totalRatings))\n", "probR.take(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[(1.0, 0.05616226208722377), (2.0, 0.1075345252842156)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### P(r | a, i) = (P(r|u) * P(r|i) / P(r)) * (P(u) * P(i) / P(u, i)) = P(r|u) * P(r|i) / P(r)\n" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(1.0, ((1, 1197), 0.05616226208722377)), (1.0, ((1, 938), 0.05616226208722377))]\n", "5001045\n" ] } ], "source": [ "# add probR to user_id, movie_id, rating\n", "components = rCombo_um.join(probR)\n", "print components.take(2)\n", "print components.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[(1.0, ((1, 914), 0.05616226208722377)), (1.0, ((1, 594), 0.05616226208722377))]\n", "\n", "5001045" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[((2034, 2.0), ((1446, 0.1075345252842156), 0.0)), ((2034, 2.0), ((3424, 0.1075345252842156), 0.0))]\n", "5001045\n" ] } ], "source": [ "# add probRU to user_id, movie_id, rating, probR\n", "tmp_a = components.map(lambda (rating, ((user_id, movie_id), prob_r)): ((user_id, rating), (movie_id, prob_r)))\n", "tmp_b = probRU.map(lambda (user_id, rating, prob_ru): ((user_id, rating), prob_ru))\n", "components = tmp_a.join(tmp_b)\n", "print components.take(2)\n", "print components.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[((327, 1.0), ((1248, 0.05616226208722377), 0.038135593220338986)), ((327, 1.0), ((1254, 0.05616226208722377), 0.038135593220338986))]\n", "\n", "5001045" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[((1516, 2.0), ((5530, 0.1075345252842156, 0.23466257668711657), 0.057692307692307696)), ((1516, 2.0), ((1671, 0.1075345252842156, 0.09717868338557993), 0.057692307692307696))]\n", "5001045\n" ] } ], "source": [ "# add probRI to user_id, movie_id, rating, probR, probRU\n", "tmp_a = components.map(lambda ( (user_id, rating), ((movie_id, prob_r), prob_ru) ): \n", " ( (movie_id, rating), (user_id, prob_r, prob_ru) ) \n", " )\n", "tmp_b = probRI.map(lambda (movie_id, rating, prob_ri): ((movie_id, rating), prob_ri))\n", "components = tmp_a.join(tmp_b)\n", "print components.take(2)\n", "print components.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[((1644, 5.0), ((1605, 0.22626271109338147, 0.038381742738589214), 0.056842105263157895)), ((1644, 5.0), ((1451, 0.22626271109338147, 0.3022636484687084), 0.056842105263157895))]\n", "\n", "5001045" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[((6036, 1516, 2.0), 0.08035421524402948), ((5082, 1516, 2.0), 0.0462500279600066)]\n" ] } ], "source": [ "# re-format\n", "# [((user_id, movie_id, rating), bayes_probability)]\n", "componentsReformat = components.map(lambda ((movie_id, rating), ((user_id, prob_r, prob_ru), prob_ri)):\n", " ((user_id, movie_id, rating), (prob_r, prob_ru, prob_ri))\n", " )\n", "# calculate bayes probability\n", "bayesProb = componentsReformat.mapValues(lambda (prob_r, prob_ru, prob_ri): prob_ru * prob_ri / prob_r)\n", "print bayesProb.take(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[((2168, 135, 4.0), 0.13697613242692502), ((4808, 135, 4.0), 0.12445827900425674)]" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "umr = 1000209\n", "probR = " ] }, { "ename": "AttributeError", "evalue": "'int' object has no attribute 'count'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-66-b5d41b87cd8e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"umr = \"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mumr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"probR = \"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprobR\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\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 3\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"probRU = \"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprobRU\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"probRI = \"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprobRI\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"bayesProb = \"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbayesProb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: 'int' object has no attribute 'count'" ] } ], "source": [ "print \"umr = \", umr.count()\n", "print \"probR = \", probR.count()\n", "print \"probRU = \", probRU.count()\n", "print \"probRI = \", probRI.count()\n", "print \"bayesProb = \", bayesProb.count()\n", "\n", "# note: bayesProb.count() = umr.count() * 5" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# bayesProb = umr_train * 5\n", "1000209 * 5" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " umrCombo_test.count() = 500750\n", "bayesProb.count() = 5001045\n", "[((15, 2501), (1.0, 0.0036157073023865916)), ((2129, 1345), (5.0, 0.1056989860131655))]\n", "500750\n" ] } ], "source": [ "# extract only user_id, movie_id in umr_test from bayes_prob\n", "# remember that we have to extract the bayes_prob for each rating too\n", "\n", "# [(user_id, movie_id, rating)]\n", "print \"umrCombo_test.count() = \", umrCombo_test.count()\n", "# [((user_id, movie_id, rating), bayes_prob)]\n", "print \"bayesProb.count() = \", bayesProb.count()\n", "\n", "# [((user_id, movie_id), (rating, bayes_prob))]\n", "tmp_a = umrCombo_test.map(lambda (user_id, movie_id, rating): ((user_id, movie_id, rating), 1))\n", "tmp_b = bayesProb\n", "bayesProb_test = tmp_a.join(tmp_b).map(\n", " lambda ((user_id, movie_id, rating), (_, bayes_prob)): ((user_id, movie_id), (rating, bayes_prob)))\n", "print bayesProb_test.take(2)\n", "print bayesProb_test.count() # == umrCombo_test.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "umrCombo_test.count() = 501170\n", "\n", "bayesProb.count() = 5001045\n", "\n", "[((5522, 2157), (3.0, 0.2209227724078584)), ((5786, 3210), (2.0, 0.08545729298368235))]\n", "\n", "501170" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[((255, 2571), [(4.0, 0.5746800859734277), (1.0, 0.0), (3.0, 0.04302813234864365), (2.0, 0.008755469773908795), (5.0, 0.4100315899159028)]), ((3336, 1784), [(2.0, 0.15385194938488958), (5.0, 0.04741139702767363), (4.0, 0.22600821260791448), (1.0, 0.03779289080632632), (3.0, 0.27444496083913184)])]\n", "100150\n" ] } ], "source": [ "# [((user_id, movie_id), [(rating_1, bayes_prob_1), (rating_2, bayes_prob_2), ..., (rating_5, bayes_prob_5)])]\n", "um_allBayesProb = bayesProb_test.mapValues(lambda value: [value]).reduceByKey(lambda a, b: a + b)\n", "print um_allBayesProb.take(2)\n", "print um_allBayesProb.count() # == bayesProb_test.count()/5 == umr_test.count() == 100234" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[((4335, 1588), [(3.0, 0.5498862085999521), (1.0, 0.016548382705956422), (2.0, 0.13615664002520045), (4.0, 0.32236074697317796), (5.0, 0.025783030822306607)]), ((4728, 1894), [(5.0, 0.01634723322124617), (3.0, 0.7342812664378788), (4.0, 0.256444827101078), (1.0, 0.005091044955245684), (2.0, 0.1289571243789302)])]\n", "\n", "100234" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[((255, 2571), [(1.0, 0.0), (2.0, 0.008755469773908795), (3.0, 0.04302813234864365), (4.0, 0.5746800859734277), (5.0, 0.4100315899159028)]), ((3336, 1784), [(1.0, 0.03779289080632632), (2.0, 0.15385194938488958), (3.0, 0.27444496083913184), (4.0, 0.22600821260791448), (5.0, 0.04741139702767363)])]\n", "100150\n" ] } ], "source": [ "um_allBayesProb = um_allBayesProb.mapValues(lambda value: sorted(value, key=lambda(rating, bayes_prob): rating))\n", "print um_allBayesProb.take(2)\n", "print um_allBayesProb.count()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### 7.A.1. Implementing Bayes MAP\n", "Maximum A Posteriori (MAP) : predict the most probably rating\n", "\n", "```\n", "Pai = predicted rating for user a on movie i\n", "P(r|a,i) = Naive Bayes that computes the probability of rating r for a given user a on movie i\n", "\n", "Pai = Argmax(r=1 to 5) P(r|a,i)\n", "```" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[((3336, 1784), 2), ((424, 1422), 2)]\n", "100150\n" ] } ], "source": [ "def calculate_bayes_map(value):\n", " # extract the bayes_prob \n", " bayesProbList = [x[1] for x in value]\n", " \n", " # define the argmax, return the index\n", " argmax = bayesProbList.index(max(bayesProbList))\n", " \n", " return argmax\n", " \n", "predicted_bayes_map = um_allBayesProb.mapValues(calculate_bayes_map)\n", "print predicted_bayes_map.take(2)\n", "print predicted_bayes_map.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[((4335, 1588), 0), ((4728, 1894), 2)]\n", "\n", "100234" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[((366, 3196), (5.0, 4)), ((1810, 3072), (4.0, 3))]\n", "100150\n" ] } ], "source": [ "# [(test_user_id, test_movie_id), (actual_rating, predicted_rating)]\n", "tmp_a = umr_test.map(lambda (user_id, movie_id, rating): ((user_id, movie_id), rating))\n", "tmp_b = predicted_bayes_map\n", "um_testBayesMap = tmp_a.join(tmp_b)\n", "print um_testBayesMap.take(2)\n", "print um_testBayesMap.count()\n", "\n", "# [(train_user_id, train_movie_id), (actual_rating, predicted_rating)]\n", "tmp_a = umr_train.map(lambda (user_id, movie_id, rating): ((user_id, movie_id), rating))\n", "tmp_b = predicted_bayes_map\n", "um_trainBayesMap = tmp_a.join(tmp_b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[((3491, 3699), (4.0, 0)), ((1120, 1654), (4.0, 0))]\n", "\n", "100234" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "899975\n", "100234\n", "0\n" ] } ], "source": [ "a, b, c = umr.randomSplit(weights, seed)\n", "print a.count()\n", "print b.count()\n", "print c.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "900042\n", "100167\n", "0" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(1, 1197, 3.0)]\n", "[(2, 593, 5.0)]\n" ] } ], "source": [ "print a.take(1)\n", "print b.take(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[(2, 1955, 4.0)]\n", "[(31, 3591, 3.0)]" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "actual:\n", "[(3039, 2937, 4), (1810, 3072, 3), (2718, 1610, 3), (5081, 3255, 3), (4448, 52, 3)]\n", "predicted:\n", "[(366, 3196, 5.0), (1810, 3072, 4.0), (2718, 1610, 2.0), (59, 943, 3.0), (4448, 52, 5.0)]\n", "rmse = 1.33451063018\n", "mae = 1.06974538193\n" ] } ], "source": [ "# calculate RMSE and MAE\n", "\n", "# convert into two vectors where\n", "# one vector describes the actual ratings in the format [(user_id, movie_id, actual_rating)]\n", "# second vector describes the predicted ratings in the format [(user_id, movie_id, predicted_rating)]\n", "actual = um_testBayesMap.map(\n", " lambda((test_user_id, test_movie_id), (actual_rating, predicted_rating)): (test_user_id, test_movie_id, actual_rating)\n", " )\n", "predicted = um_testBayesMap.map(\n", " lambda((test_user_id, test_movie_id), (actual_rating, predicted_rating)): (test_user_id, test_movie_id, predicted_rating)\n", " )\n", "\n", "print \"actual:\\n\", actual.take(5)\n", "print \"predicted:\\n\", predicted.take(5)\n", "\n", "rmse = pm.calculate_rmse_using_rdd(actual, predicted)\n", "print \"rmse = \", rmse\n", "\n", "mae = pm.calculate_mae_using_rdd(actual, predicted)\n", "print \"mae = \", mae" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "actual:\n", "[(3039, 2937, 4), (1810, 3072, 3), (2718, 1610, 3), (5081, 3255, 3), (4448, 52, 3)]\n", "predicted:\n", "[(366, 3196, 5.0), (1810, 3072, 4.0), (2718, 1610, 2.0), (59, 943, 3.0), (4448, 52, 5.0)]\n", "rmse = 1.33451063018\n", "mae = 1.06974538193\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# y_test\n", "y_test = um_testBayesMap.map(\n", " lambda((test_user_id, test_movie_id), (predicted_rating, actual_rating)): (test_user_id, test_movie_id, actual_rating)\n", ")\n", "\n", "# y_train\n", "y_train = um_trainBayesMap.map(\n", " lambda((test_user_id, test_movie_id), (predicted_rating, actual_rating)): (test_user_id, test_movie_id, actual_rating)\n", ")\n", "\n", "# y_predicted\n", "y_predicted = um_testBayesMap.map(\n", " lambda((test_user_id, test_movie_id), (predicted_rating, actual_rating)): (test_user_id, test_movie_id, predicted_rating)\n", ")\n", "\n", "pm_results_bayes_map = pm.get_perform_metrics(y_test, y_train, y_predicted, content_array, sqlCtx)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pprint import pprint\n", "pprint(pm_results_bayes_map)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### 7.A.2. Implementing Bayes MSE\n", "Mean Squared Error (MSE): compute the weighted sum of ratings that corresponds to minimizing the expectation of MSE\n", "\n", "```\n", "Pai = predicted rating for user a on movie i\n", "P(r|a,i) = Naive Bayes that computes the probability of rating r for a given user a on movie i\n", "\n", "Pai = Sum of (r * P(r|a,i)) from r=1 to 5\n", "```" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[((3336, 1784), 2.3099215076635273), ((2629, 1625), 4.746602171046973)]\n", "100150\n" ] } ], "source": [ "def calculate_bayes_mse(value):\n", " predicted = 0.\n", " for rating, bayes_prob in value:\n", " predicted += rating * bayes_prob\n", " return predicted\n", "\n", "predicted_bayes_mse = um_allBayesProb.mapValues(calculate_bayes_mse)\n", "print predicted_bayes_mse.take(2)\n", "print predicted_bayes_mse.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[((4335, 1588), 3.3568784305604584), ((4728, 1894), 3.5733645675372854)]\n", "\n", "100234" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[((366, 3196), (5.0, 5.648355331346897)), ((1810, 3072), (4.0, 4.023539470654143))]\n", "100150\n" ] } ], "source": [ "# [(test_user_id, test_movie_id), (predicted_rating, actual_rating)]\n", "tmp_a = umr_test.map(lambda (user_id, movie_id, rating): ((user_id, movie_id), rating))\n", "tmp_b = predicted_bayes_mse\n", "um_testBayesMse = tmp_a.join(tmp_b)\n", "print um_testBayesMse.take(2)\n", "print um_testBayesMse.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[((1120, 1654), (4.0, 3.44226621556054)), ((4439, 3005), (3.0, 3.360422113015879))]\n", "\n", "100234" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "actual:\n", "[(5302, 2294, 3.532715429601343), (2914, 1394, 6.48970175067121), (3108, 1358, 4.140766134805869), (881, 1183, 2.9152587564475585), (2415, 2475, 3.5870571391790125)]\n", "predicted:\n", "[(479, 2822, 4.0), (1645, 610, 5.0), (1912, 903, 2.0), (1209, 1196, 4.0), (1685, 1284, 3.0)]\n", "rmse = 1.17236436154\n", "mae = 0.915206301792\n" ] } ], "source": [ "# calculate RMSE and MAE\n", "\n", "# convert into two vectors where\n", "# one vector describes the actual ratings in the format [(user_id, movie_id, actual_rating)]\n", "# second vector describes the predicted ratings in the format [(user_id, movie_id, predicted_rating)]\n", "actual = um_testBayesMse.map(\n", " lambda((test_user_id, test_movie_id), (actual_rating, predicted_rating)): (test_user_id, test_movie_id, actual_rating)\n", " )\n", "predicted = um_testBayesMse.map(\n", " lambda((test_user_id, test_movie_id), (actual_rating, predicted_rating)): (test_user_id, test_movie_id, predicted_rating)\n", " )\n", "\n", "print \"actual:\\n\", actual.take(5)\n", "print \"predicted:\\n\", predicted.take(5)\n", "\n", "rmse = pm.calculate_rmse_using_rdd(actual, predicted)\n", "print \"rmse = \", rmse\n", "\n", "mae = pm.calculate_mae_using_rdd(actual, predicted)\n", "print \"mae = \", mae" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "actual:\n", "[(1120, 1654, 3.44226621556054), (4439, 3005, 3.360422113015879), (4271, 3671, 2.8494882459525477), (2259, 1213, 7.778718357130783), (1820, 3101, 3.7261784376809395)]\n", "\n", "predicted:\n", "[(1120, 1654, 4.0), (4439, 3005, 3.0), (4271, 3671, 5.0), (2259, 1213, 5.0), (1820, 3101, 5.0)]\n", "\n", "rmse = 1.17748775303\n", "\n", "mae = 0.918588835371" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# y_test\n", "y_test = um_testBayesMse.map(\n", " lambda((test_user_id, test_movie_id), (predicted_rating, actual_rating)): (test_user_id, test_movie_id, actual_rating)\n", ")\n", "\n", "# y_train\n", "tmp_a = umr_train.map(lambda (user_id, movie_id, rating): ((user_id, movie_id), rating))\n", "tmp_b = predicted_bayes_mse\n", "um_trainBayesMse = tmp_a.join(tmp_b)\n", "y_train = um_trainBayesMse.map(\n", " lambda((test_user_id, test_movie_id), (predicted_rating, actual_rating)): (test_user_id, test_movie_id, actual_rating)\n", ")\n", "\n", "# y_predicted\n", "y_predicted = um_testBayesMse.map(\n", " lambda((test_user_id, test_movie_id), (predicted_rating, actual_rating)): (test_user_id, test_movie_id, predicted_rating)\n", ")\n", "\n", "pm_results_bayes_mse = pm.get_perform_metrics(y_test, y_train, y_predicted, content_array, sqlCtx)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pprint import pprint\n", "pprint(pm_results_bayes_mse)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### 7.A.3. Implementing Bayes MAE\n", "Mean Absolute Error (MAE): select the rating that minimizes the expectation of Mean Absolute Error \n", "\n", "```\n", "Pai = predicted rating for user a on movie i\n", "P(r|a,i) = Naive Bayes that computes the probability of rating r for a given user a on movie i\n", "\n", "Pai = Argmin from r=1 to 5(Sum of (P(n|a,i) * |r-n|) from n=1 to 5)\n", "```" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[((255, 2571), 0), ((3336, 1784), 0)]\n", "100150\n" ] } ], "source": [ "# TODO: fix this the same as argmax\n", "\n", "def calculate_bayes_mae(value):\n", " sumOfProductList = []\n", " for rating, bayes_prob in value:\n", " sumOfProduct = 0.\n", " for i in range(1, 6):\n", " sumOfProduct += bayes_prob * abs(rating - i)\n", " sumOfProductList.append(sumOfProduct)\n", " \n", " argmin = sumOfProductList.index(min(sumOfProductList))\n", "\n", " return argmin\n", "\n", "predicted_bayes_mae = um_allBayesProb.mapValues(calculate_bayes_mae)\n", "print predicted_bayes_mae.take(2)\n", "print predicted_bayes_mae.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[((4335, 1588), 4), ((4728, 1894), 2)]\n", "\n", "100234" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[((1496, 3740), (4.0, 2)), ((1810, 3072), (4.0, 3))]\n", "100150\n" ] } ], "source": [ "# [(test_user_id, test_movie_id), (predicted_rating, actual_rating)]\n", "tmp_a = umr_test.map(lambda (user_id, movie_id, rating): ((user_id, movie_id), rating))\n", "tmp_b = predicted_bayes_map\n", "um_testBayesMae = tmp_a.join(tmp_b)\n", "print um_testBayesMae.take(2)\n", "print um_testBayesMae.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[((1120, 1654), (4.0, 2)), ((4169, 2723), (3.0, 3))]\n", "\n", "100234" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "actual:\n", "[(1496, 3740, 2), (1810, 3072, 3), (2485, 307, 3), (588, 1900, 4), (2718, 1610, 3)]\n", "predicted:\n", "[(366, 3196, 5.0), (1810, 3072, 4.0), (2485, 307, 3.0), (2718, 1610, 2.0), (474, 1580, 4.0)]\n", "rmse = 1.33451063018\n", "mae = 1.06974538193\n" ] } ], "source": [ "# calculate RMSE and MAE\n", "\n", "from src.algorithms import performance_metrics as pm\n", "\n", "# convert into two vectors where\n", "# one vector describes the actual ratings in the format [(user_id, movie_id, actual_rating)]\n", "# second vector describes the predicted ratings in the format [(user_id, movie_id, predicted_rating)]\n", "actual = um_testBayesMae.map(\n", " lambda((test_user_id, test_movie_id), (predicted_rating, actual_rating)): (test_user_id, test_movie_id, actual_rating)\n", " )\n", "predicted = um_testBayesMae.map(\n", " lambda((test_user_id, test_movie_id), (predicted_rating, actual_rating)): (test_user_id, test_movie_id, predicted_rating)\n", " )\n", "\n", "print \"actual:\\n\", actual.take(5)\n", "print \"predicted:\\n\", predicted.take(5)\n", "\n", "rmse = pm.calculate_rmse_using_rdd(actual, predicted)\n", "print \"rmse = \", rmse\n", "\n", "mae = pm.calculate_mae_using_rdd(actual, predicted)\n", "print \"mae = \", mae" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "actual:\n", "[(1120, 1654, 2), (4169, 2723, 3), (4271, 3671, 3), (2259, 1213, 1), (1820, 3101, 4)]\n", "\n", "predicted:\n", "[(1120, 1654, 4.0), (4439, 3005, 3.0), (4271, 3671, 5.0), (2259, 1213, 5.0), (1820, 3101, 5.0)]\n", "\n", "rmse = 2.39828498227\n", "\n", "mae = 1.95142366862" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# y_test\n", "y_test = um_testBayesMae.map(\n", " lambda((test_user_id, test_movie_id), (predicted_rating, actual_rating)): (test_user_id, test_movie_id, actual_rating)\n", ")\n", "\n", "# y_train\n", "tmp_a = umr_train.map(lambda (user_id, movie_id, rating): ((user_id, movie_id), rating))\n", "tmp_b = predicted_bayes_mae\n", "um_trainBayesMae = tmp_a.join(tmp_b)\n", "y_train = um_trainBayesMae.map(\n", " lambda((test_user_id, test_movie_id), (predicted_rating, actual_rating)): (test_user_id, test_movie_id, actual_rating)\n", ")\n", "\n", "# y_predicted\n", "y_predicted = um_testBayesMae.map(\n", " lambda((test_user_id, test_movie_id), (predicted_rating, actual_rating)): (test_user_id, test_movie_id, predicted_rating)\n", ")\n", "\n", "pm_results_bayes_mae = get_results(y_test, y_train, y_predicted, content_array, sqlCtx)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pprint(pm_results_bayes_mae)" ] }, { "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 }
apache-2.0
SHDShim/pytheos
examples/9_pv_eos_fit_scale_conversion.ipynb
1
847791
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Source and citation\n", "\n", "- This notebook is a part of the `pytheos` package.\n", "- Website: http://github.com/SHDShim/pytheos.\n", "- How to cite: S.-H. Shim (2017) Pytheos - a python tool set for equations of state. DOI: 10.5281/zenodo.802392\n" ] } ], "source": [ "%cat 0Source_Citation.txt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "# %matplotlib notebook # for interactive" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For high dpi displays." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%config InlineBackend.figure_format = 'retina' " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 0. General note" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* This notebook shows an example of EOS fitting for static compression, focusing on converting pressure scales when the volume data for pressure standard is not available. \n", "\n", "* The notebook demonstrates easy ways to change the EOS for pressure standard.\n", "\n", "* We use data from [Lundin et al. (2007, PEPI)](http://www.sciencedirect.com/science/article/pii/S0031920108000939)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Global setup" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "from uncertainties import unumpy as unp\n", "import pandas as pd\n", "import pytheos as eos" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Setup pressure scale and starting values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setup dictionaries for pressure standard `(au_eos)` and equation to use `(fit_model)`. This allows for eos fits with a wide range of different pressure scales." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "au_eos = {'Fei2007': eos.gold.Fei2007bm3(), 'Dorogokupets2007': eos.gold.Dorogokupets2007(),\n", " 'Yokoo2009': eos.gold.Yokoo2009(), 'Ye2017': eos.gold.Ye2017()}\n", "fit_model = {'Fei2007': eos.BM3Model(), 'Dorogokupets2007': eos.VinetModel(),\n", " 'Yokoo2009': eos.BM3Model(), 'Ye2017': eos.VinetModel()}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lundin et al. (2007) used the gold scale by Tsuchiya (2003). Since then many updates have been made by a few different authors." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "au_org = eos.gold.Tsuchiya2003()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Uncomment the following line to get some help." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#help(eos.gold.Yokoo2009)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We set initial values for the EOS parameters." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "v0 = {'en100': 162.30, 'en91': 163.18, 'en85': 163.30}\n", "k0 = {'en100': 260., 'en91': 260., 'en85': 260.}\n", "k0p = {'en100': 4.0, 'en91': 4.0, 'en85': 4.0}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Setup data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read data file. Data points are stored in `csv` files. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = pd.read_csv('./data/Lundin2007.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make error propagation possible." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "v = {'en100': unp.uarray(data['v(en100)'][~np.isnan(data['v(en100)'])],\n", " data['sv(en100)'][~np.isnan(data['v(en100)'])]),\n", " 'en91': unp.uarray(data['v(en91)'], data['sv(en91)']),\n", " 'en85': unp.uarray(data['v(en85)'], data['sv(en85)'])}\n", "p_org= {'en100': unp.uarray(data['p(en100)'][~np.isnan(data['v(en100)'])], \n", " data['sp(en100)'][~np.isnan(data['v(en100)'])]),\n", " 'en91': unp.uarray(data['p(en91)'], data['sp(en91)']),\n", " 'en85': unp.uarray(data['p(en85)'], data['sp(en85)'])}\n", "v_std= {}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. Fit with the original pressure scale" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to understand the cell below, please try the `8_pv_eos_fit_multi-scales.ipynb` file first." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "***en100\n", "[[Model]]\n", " Model(bm3_p)\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 4\n", " # data points = 16\n", " # variables = 1\n", " chi-square = 24.8394521\n", " reduced chi-square = 1.65596347\n", " Akaike info crit = 9.03751161\n", " Bayesian info crit = 9.81010034\n", "[[Variables]]\n", " v0: 162.3 (fixed)\n", " k0: 263.389033 +/- 1.45857729 (0.55%) (init = 260)\n", " k0p: 4 (fixed)\n", "\n", "***en91\n", "[[Model]]\n", " Model(bm3_p)\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 4\n", " # data points = 17\n", " # variables = 1\n", " chi-square = 6.73580735\n", " reduced chi-square = 0.42098796\n", " Akaike info crit = -13.7381864\n", " Bayesian info crit = -12.9049730\n", "[[Variables]]\n", " v0: 163.18 (fixed)\n", " k0: 257.839699 +/- 0.73597550 (0.29%) (init = 260)\n", " k0p: 4 (fixed)\n", "\n", "***en85\n", "[[Model]]\n", " Model(bm3_p)\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 4\n", " # data points = 17\n", " # variables = 1\n", " chi-square = 18.1022197\n", " reduced chi-square = 1.13138873\n", " Akaike info crit = 3.06796082\n", " Bayesian info crit = 3.90117416\n", "[[Variables]]\n", " v0: 163.3 (fixed)\n", " k0: 260.012624 +/- 1.01990065 (0.39%) (init = 260)\n", " k0p: 4 (fixed)\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 360x360 with 2 Axes>" ] }, "metadata": { "image/png": { "height": 351, "width": 350 } }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 360x360 with 2 Axes>" ] }, "metadata": { "image/png": { "height": 351, "width": 350 } }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 360x360 with 2 Axes>" ] }, "metadata": { "image/png": { "height": 351, "width": 350 } }, "output_type": "display_data" } ], "source": [ "for key, value in v.items():\n", " model = eos.BM3Model()\n", " params = model.make_params(v0=v0[key], k0=k0[key], k0p=k0p[key])\n", " params['v0'].vary = False\n", " params['k0p'].vary = False\n", " fitresult = model.fit(unp.nominal_values(p_org[key]), params, v=unp.nominal_values(v[key]))\n", " print('***'+key)\n", " print(fitresult.fit_report())\n", " eos.plot.static_fit_result(fitresult, title=key)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 5. Recover the volume of pressure standard" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to convert to different gold scale, we first invert the equation to get volume from pressure. The cell below iteratively perform this task for all three different compositions in Lundin et al." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for key, value in p_org.items():\n", " p_tempo = unp.nominal_values(value)\n", " # filter out empty cells\n", " p_norm = p_tempo[~np.isnan(p_tempo)]\n", " # now we get volume of gold\n", " v_std[key] = au_org.cal_v(p_norm,\n", " np.ones_like(p_norm) * 300.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 6. Fit for different pressure scales" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We fix `v0` and `k0p` in this fitting example" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "***Fei2007 en100\n", "[[Model]]\n", " Model(bm3_p)\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 4\n", " # data points = 16\n", " # variables = 1\n", " chi-square = 21.3661256\n", " reduced chi-square = 1.42440837\n", " Akaike info crit = 6.62748848\n", " Bayesian info crit = 7.40007721\n", "[[Variables]]\n", " v0: 162.3 (fixed)\n", " k0: 258.324402 +/- 1.35276181 (0.52%) (init = 260)\n", " k0p: 4 (fixed)\n", "\n", "***Dorogokupets2007 en100\n", "[[Model]]\n", " Model(vinet_p)\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 4\n", " # data points = 16\n", " # variables = 1\n", " chi-square = 22.0942768\n", " reduced chi-square = 1.47295178\n", " Akaike info crit = 7.16367812\n", " Bayesian info crit = 7.93626685\n", "[[Variables]]\n", " v0: 162.3 (fixed)\n", " k0: 260.178430 +/- 1.38729624 (0.53%) (init = 260)\n", " k0p: 4 (fixed)\n", "\n", "***Yokoo2009 en100\n", "[[Model]]\n", " Model(bm3_p)\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 4\n", " # data points = 16\n", " # variables = 1\n", " chi-square = 22.0678885\n", " reduced chi-square = 1.47119257\n", " Akaike info crit = 7.14455713\n", " Bayesian info crit = 7.91714585\n", "[[Variables]]\n", " v0: 162.3 (fixed)\n", " k0: 259.586895 +/- 1.37479781 (0.53%) (init = 260)\n", " k0p: 4 (fixed)\n", "\n", "***Ye2017 en100\n", "[[Model]]\n", " Model(vinet_p)\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 4\n", " # data points = 16\n", " # variables = 1\n", " chi-square = 21.9932649\n", " reduced chi-square = 1.46621766\n", " Akaike info crit = 7.09036066\n", " Bayesian info crit = 7.86294938\n", "[[Variables]]\n", " v0: 162.3 (fixed)\n", " k0: 260.098784 +/- 1.38412133 (0.53%) (init = 260)\n", " k0p: 4 (fixed)\n", "\n", "***Fei2007 en91\n", "[[Model]]\n", " Model(bm3_p)\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 5\n", " # data points = 17\n", " # variables = 1\n", " chi-square = 7.18584312\n", " reduced chi-square = 0.44911519\n", " Akaike info crit = -12.6387083\n", " Bayesian info crit = -11.8054949\n", "[[Variables]]\n", " v0: 163.18 (fixed)\n", " k0: 252.913356 +/- 0.76016418 (0.30%) (init = 260)\n", " k0p: 4 (fixed)\n", "\n", "***Dorogokupets2007 en91\n", "[[Model]]\n", " Model(vinet_p)\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 5\n", " # data points = 17\n", " # variables = 1\n", " chi-square = 6.49717476\n", " reduced chi-square = 0.40607342\n", " Akaike info crit = -14.3513805\n", " Bayesian info crit = -13.5181672\n", "[[Variables]]\n", " v0: 163.18 (fixed)\n", " k0: 254.800439 +/- 0.72842932 (0.29%) (init = 260)\n", " k0p: 4 (fixed)\n", "\n", "***Yokoo2009 en91\n", "[[Model]]\n", " Model(bm3_p)\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 5\n", " # data points = 17\n", " # variables = 1\n", " chi-square = 7.06237621\n", " reduced chi-square = 0.44139851\n", " Akaike info crit = -12.9333402\n", " Bayesian info crit = -12.1001268\n", "[[Variables]]\n", " v0: 163.18 (fixed)\n", " k0: 254.128793 +/- 0.75360532 (0.30%) (init = 260)\n", " k0p: 4 (fixed)\n", "\n", "***Ye2017 en91\n", "[[Model]]\n", " Model(vinet_p)\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 5\n", " # data points = 17\n", " # variables = 1\n", " chi-square = 6.52402296\n", " reduced chi-square = 0.40775143\n", " Akaike info crit = -14.2812764\n", " Bayesian info crit = -13.4480630\n", "[[Variables]]\n", " v0: 163.18 (fixed)\n", " k0: 254.726106 +/- 0.72993281 (0.29%) (init = 260)\n", " k0p: 4 (fixed)\n", "\n", "***Fei2007 en85\n", "[[Model]]\n", " Model(bm3_p)\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 5\n", " # data points = 17\n", " # variables = 1\n", " chi-square = 16.8949241\n", " reduced chi-square = 1.05593276\n", " Akaike info crit = 1.89459806\n", " Bayesian info crit = 2.72781140\n", "[[Variables]]\n", " v0: 163.3 (fixed)\n", " k0: 254.888121 +/- 0.98530362 (0.39%) (init = 260)\n", " k0p: 4 (fixed)\n", "\n", "***Dorogokupets2007 en85\n", "[[Model]]\n", " Model(vinet_p)\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 4\n", " # data points = 17\n", " # variables = 1\n", " chi-square = 17.0912331\n", " reduced chi-square = 1.06820207\n", " Akaike info crit = 2.09098920\n", " Bayesian info crit = 2.92420254\n", "[[Variables]]\n", " v0: 163.3 (fixed)\n", " k0: 256.875223 +/- 1.00146360 (0.39%) (init = 260)\n", " k0p: 4 (fixed)\n", "\n", "***Yokoo2009 en85\n", "[[Model]]\n", " Model(bm3_p)\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 5\n", " # data points = 17\n", " # variables = 1\n", " chi-square = 17.3707108\n", " reduced chi-square = 1.08566943\n", " Akaike info crit = 2.36672671\n", " Bayesian info crit = 3.19994005\n", "[[Variables]]\n", " v0: 163.3 (fixed)\n", " k0: 256.172153 +/- 0.99908110 (0.39%) (init = 260)\n", " k0p: 4 (fixed)\n", "\n", "***Ye2017 en85\n", "[[Model]]\n", " Model(vinet_p)\n", "[[Fit Statistics]]\n", " # fitting method = leastsq\n", " # function evals = 4\n", " # data points = 17\n", " # variables = 1\n", " chi-square = 17.0223455\n", " reduced chi-square = 1.06389659\n", " Akaike info crit = 2.02233078\n", " Bayesian info crit = 2.85554412\n", "[[Variables]]\n", " v0: 163.3 (fixed)\n", " k0: 256.789824 +/- 0.99944332 (0.39%) (init = 260)\n", " k0p: 4 (fixed)\n", "\n" ] }, { "data": { "image/png": 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"text/plain": [ "<Figure size 360x360 with 2 Axes>" ] }, "metadata": { "image/png": { "height": 351, "width": 350 } }, "output_type": "display_data" }, { "data": { "image/png": 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2m4Bj3P3tAm12AsMIktmqBO9b7NwoguEBiwkS0CMIxiKvJJhurL/vnj6uYbivxk//lgrbfjK8w4O5fDMJvph8RfDA21fhcUd3X0oBHszI0Ytgft/XwtcKQRJ8lrtfXbBNguPC/c+GO4hIcpi777mWiIjsNzP7E0Gv6UR3vyzicPbIzMYBfyIYTnF6xOGIiBww9fCKiBwgM5tgZnPNrH8h54ygJxXgxYLnRUQk+bTwhIjIgUsnWFq3pZl96sFDZLEZGm4hGKv7BZqCSkQkEkp4RUQO3C3AIOBI4HUz+4bgYbrDCP53dgtwvrtvKfoSIiKSLBrSICJygNz9S4I5cP9IsApYOsHsCB8RLHHbwd0XRRagiEgFp4fWRERERCSlqYdXRERERFKaEl4RERERSWlKeEVEREQkpSnhFREREZGUpoRXRERERFKa5uFNIjP7kGB99Y8iDkVERESkvGsKbHb3ZvvaUAlvctWsWrVqnVatWtVJ9o3Wrl0LQKtWrZJ9K4mAPt/Upc82tenzTW36fEvX2rVr2b59+361VcKbXB+1atWqzurVq5N+o8zMTABK415S+vT5pi59tqlNn29q0+dbujIzM1mzZs1H+9NWY3hFREREJKUp4RURERGRlKaEV0RERERSmhJeEREREUlpSnhFREREJKVploYUoSdEU5s+39Slzza16fNNbfp8yw/18IqIiIhISlMPr4iIiJRb+fn5fPfdd2zZsoXc3FzcPeqQZC+YGZUrV6ZGjRrUqVOHtLTk9sEq4RUREZFyKT8/nw0bNrBt27aoQ5F95O7s2LGDHTt2sHXrVho3bpzUpFcJr4iIiJRL3333Hdu2bSMjI4OGDRtSvXr1pPcUSsnIz89n69atfPnll2zbto3vvvuOevXqJe1++qsQERGRcmnLli0ANGzYkBo1aijZLUfS0tKoUaMGDRs2BHZ/lkm7X1KvLiIiIpIkubm5AFSvXj3iSGR/xT672GeZLEp4RUREpFyKPaCmnt3yy8wAkv6wof5CRERERCQSsYQ32ZTwioiIiEhKU8KbImbMmMEdd9wRdRgiIiIiZY4S3nIuPz+fcePGcfbZZ3Pdddcxa9asqEMSERGRUpaTk4OZ7XE7/fTTGTduHGbGzJkzow671Gge3nIuLy+PJUuWxI+HDBnCihUraNeuXYRRiYiISBQaNWrE2WefXeT5du3a0ahRI0aNGsUvf/nLeHlWVhaLFy9m48aNHHzwwaURaqlSwlvOVapUiWnTppGZmcnHH3/Mtm3bGDBgAK+88kpSJ3AWERGRsqdFixZMmDBhj/X69etXCtGUHRrSkALq1q3L3LlzqVatGgCffvopp512Gjt37ow4MhEREZHoKeFNEW3btuXJJ5+MT++xfPlyRo4cGXFUIiIiUtYkjuH96KOPMDMWL14MQO3atcnKyoo2wCRQwptCTj31VG666ab48cSJE7nvvvsijEhERETKspo1azJq1CgaNWoEwIgRI4odA1xeaQxvihk7diyvvfYa06dPB+D3v/89bdq04eSTT444MhEREUm29evXc+WVVxZ6rkWLFj/79bdOnTpMmDCBV199lc8++4zx48froTUp+8yMRx55hHfffZc33niDvLw8Tj/9dNasWUPTpk2jDk9ERKTUlNYqXiWhpJbW/eyzz/jHP/5R6Llu3bpV2OGOGtKQgqpVq8YzzzxD3bp1Adi4cSMDBgzghx9+iDgyERERSaZu3brh7oVuOTk5UYcXGSW8Kapx48bMnj2bjIygE3/t2rWcd9555OfnRxyZiIiISOlSwpvCjj/+eO6///748dy5c7nxxhsjjEhERKT0FNXTWRY3SS4lvCnukksu4Q9/+EP8+JZbbmHatGkRRiQiIiJSupTwVgB/+9vf6NGjR/z4N7/5DatXr44wIhERESlL0tKClHDXrl0RR5IcSngrgIyMDKZPn06zZs0AyM3NZcCAAXz22WcRRyYiIiJlQePGjQE4//zzueuuuyKOpuQp4a0gateuzbx586hZsyYAX3/9Nf3792fr1q0RRyYiIiJRu+aaa2jTpg0LFy5k0aJFUYdT4kwDpZPHzFZ36NChQ1kaPvD888/Tt29f8vLyABg4cCCzZs2K/5QhIiJSXqxduxaAVq1aRRyJHIi9/RwzMzNZs2bNGnfP3Nd7KMupYHr27Mm9994bP547dy7XX399hBGJiIiIJJcS3gro0ksv/cmyg3feeScPPfRQhBGJiIiIJI8S3grqzjvvpH///vHjESNGsHjx4ggjEhEREUkOJbwVVHp6Ov/6179o3bo1EExDctppp7F+/fqIIxMREREpWUp4K7AaNWowb9486tWrB8D3339P37592bhxY8SRiYiIiJQcJbwVXJMmTXjmmWeoXLkyAB988AGnnXYaO3fujDgyERERkZKRkgmvmV1hZm5mBxdx/kQzm29m35rZVjN7ycx+XUTdi8zslbDeZ2b2kJkdmtxXULo6derEo48+Gj9eunQpl112mdb2FhERkZSQcgmvmaUDlxRzfiCQAxwHzAXmAUcD/zSz0wrUvQGYDNQFHgfWAEOBlWZWPxnxR+Xcc8/lpptuih9PnjyZO++8M8KIREREREpGSiS8FuhgZpcQJLMdiqhXHZgCfA60c/eh7n42cBKQC9ycUPeXwDjgLaCtu1/q7oOA4UBj4LakvaCI3HDDDQwePDh+fP311zNjxowIIxIRERE5cCmR8ALVgdXAJODEYuoNJeitvdbdP40VuvurwDTgcDOrGRaPANKBP7v75oRrTCFImIeYWbUSewVlgJkxefJkunTpAoC7c/7557NixYqIIxMRERHZf6mS8G4HzknY3i6iXh9gBzCr4Al3/427105IbrOAfCC7QD0HlgKVgc4lEXxZUqVKFebMmUPz5s0ByM3NZcCAAbz//vsRRyYiIiKyf1Ii4XX3PHefHtuA/xZRtSvwjrvvMLPuZjbOzG41s7PMLKNA3WOADe6+pZDrrA33LUvoJZQp9erV47nnnqN27doAbNy4kV69evHtt99GHJmIiIjIviuY5KWsMKFtALxtZtOAswtUedvMBrn7B+GwhkrAV0VcLjZRbZ093Xft2rVkZmYWem716tV7FXsUWrRowTPPPEP37t3Jzc3lo48+YsCAASxevJgqVapEHZ6IiIikqKLyprVr1xZavjdSood3L9UO990IxvmeA9QEGhI8gNYamGlmaWE5BA+yFWZruE/pLwxdu3bl8ccfx8wAWLVqFeeffz75+fkRRyYiIiKJpkyZgpn9bMvIyODwww/nggsuYM2aNVGHGZmUTtgKOCjcGzDU3f8THm8BRptZFsGY3OOBdeG5qnu41rY93bRVq1Zluid3T8466yxuv/12rr32WgCeeuoprr/+eu64446IIxMREUkud493+qxbt47s7Gw2b95MzZo16devHy1btvxZvai1atWKPn36xI+3bdvG66+/zhNPPME///lP7rvvPoYPH75f127atCmbNm1i06ZNJRVuoYrKmzIzM/c7aa9ICe8P4X4HsKCQ8zMJEt5WwEogDyh04QqCmR4gmK0h5V199dW8//773H///QDceeedNG/enMsvvzziyERERJIjlsTm5ORw6623smDBgp8syGRm9OrVizFjxpCVlVVmkt5OnToxYcKEn5UvWrSI0047jREjRtCqVStOPLG4Sa1ST4UZ0uDu3xOMvd3u7oX9Jh8bpmDuvhN4H2gWzt1bUItw/2bJR1r2mBl33303/fv3j5eNHDmSZ555JsKoREREkiOWvE6ePJlevXoxf/78n60+6u7Mnz+fXr16xYcTlOUVSrt3786ECRPIz89n9OjRUYdT6ipMwht6EahtZi0KOXdcuH893D9PMA9vj8RK4UpuPQl6d99KUpxlTnp6OtOmTePYY48FID8/n3POOadCjwcSEZHUFOvZHT58OHl5ecXWzcvLY9iwYeTk5JSJHt7iXHjhhdSuXZtly5bx4YcfAsH0o3//+99p3749v/jFL6hbty6dOnXirrvuYteuXQDx1/bxxx/z/fffY2ZcdNFF8euuW7eOYcOG0bRpU6pUqULjxo0566yzWLp0aRQvs1AVLeF9MNz/n5nFx+eaWWdgMPAGwXAGgImAA39OrAuMBg4H7vGy/FUuCapXr868efM4/PDDAdi+fTt9+/blk08+iTgyERGRknXrrbfuMdmNycvLY/z48UmO6MBlZGRw0kknAcGD6ABDhgzh6quvJjc3l8GDBzNgwAA+/vhj/vCHPzBixAgADj/8cEaNGkWNGjU46KCDGDVqVHyc8IYNG+jYsSMPP/wwrVq14qKLLqJt27bMnj2brKwslixZEs2LLaBCJbzuPhN4COhPMA3ZI2Y2i2A54lzgolgSG66+dgfQHnjTzCaa2SKC5YdfBv4ewUuIXMOGDXnuueeoUaMGAN988w09e/bku+++izgyERGRkrFu3ToWLCjscZ+izZ8/n/Xr1ycpopITW1jq008/5auvvuKpp56iV69evP7660yaNInHHnuMt99+m9q1a/PPf/4TCKYqnTBhAnXq1KFq1apMmDCBIUOGAPDkk0/y/fff8/DDDzNv3jzuv/9+5s2bx8SJE8nPz+ff//53ZK81UYVKeEPDCZYN3kLQq3sCMBfo4u4/+X3e3a8HriB40O03wC+BCUBPd99RmkGXJa1bt2b27NlkZATPPK5fv57+/fuzffv2iCMTERE5cNnZ2fs8Htfdyc7O3nPFiFWvHjyatHPnTsyM8ePHc8stt8T/Px2gbt26HHbYYWzdurWoy8RlZmYyfvx4fv3rX/+kvF27dgD88MMPhTUrdSk5S4O7ZxVzzoEHwm1vrnUfcF/JRJY6srKyeOyxxzjvvPOA4KeRc845h1mzZpGenh5xdCIiIvtv8+bNpdquNMWmFGvQoAENGjRg9OjR7Ny5k1WrVrFu3To+/PBDXnnlFd56a+8eU+rZsyc9e/bkm2++YenSpXz44Yd88MEHzJkzJ5kvY59VxB5eKSGDBw/mb3/7W/z4mWeeYcSIEWX6KVUREZE9qVmz5p4rlWC70hR7WK1JkyZAMFa5Tp06dO7cmYsvvpipU6dSq1at+PM6e/L5558zcOBAGjRoQN++fbn++utZsmQJxx9/fNJew/5QwisH5H/+53+46qqr4seTJk3ipptuijAiERGRA9OvX799nnHBzOjXr1+SIioZubm5vPDCC/ziF7/gxBNP5JFHHmHs2LF06tSJNWvWsH37dt555x0efvhh6tSps1fXPP/883n22We5+eab+fzzz9m0aRMvvvgiv/vd75L8avaNEl45YHfeeSfnnntu/HjcuHFMmjQpwohERET2X8uWLenVq9c+tenduzctWhQ262nZMXHiRDZv3sypp55KlSpVmDt3LgD33Xcf7du3jw9J3LlzJxs2bNjj9X744QcWL15Mly5dGDNmDIceemj83AcffJCcF7GflPDKAUtLS+Oxxx4jKysrXjZixIj4f0giIiLlzZgxY/b6mZT09HTGjBmT5IgOTHZ2Ntdddx1VqlTh5ptvBuCggw4C+Mn0ojt37uSaa65h48aNP7tGWlpafG5eCF53WloaX331Fbm5ufHyjz/+mBtuuCFZL2W/KOGVEnHQQQcxa9Ys2rZtCwRzEp5zzjmsXLlyDy1FRETKFncnKyuLSZMm7THpTU9PZ9KkSXTr1q1MPMOyatUqrrzyyvh2xRVX0K1bN/r374+788QTT9CsWTMALr74YsyM008/nQsvvJChQ4fStGlT5syZw8knnwwE8/R+8803ADRu3JitW7dyzjnn8Pjjj1O1alXOO+88PvjgA4499lhGjBjBwIEDOfLII+nYsSNpaWk899xzP3neJzLuri1JG7C6Q4cOXpF88cUX3rhxYydYtMMPPvhgf/fdd6MOS0REUtDbb7/tb7/9dlKunZ+f7+7uixYt8j59+riZxf+/DXAz8z59+nhOTs5P6kdl8uTJP4kvtlWqVMmbNWvmQ4cO9TfeeONn7aZPn+6/+tWvvEqVKn7EEUf41Vdf7Rs3bvTly5f7oYce6jVr1vTPPvvM3d0XLlzozZo184yMDL/yyivd3f2HH37wK6+80hs1auSUavqNAAAgAElEQVTVqlXzzp07+9SpU93dffTo0V61alU/66yzio19bz/HDh06OLDa9yMnMy8D30ZSlZmt7tChQ4fVq1dHHUqpeu+99+jSpUv855DDDz+cl156iYYNG0YcmYiIpJK1a9cC0KpVq6Rc393jD6+tX7+e7OxsNm/eTM2aNenXr198zG5iPdl3e/s5ZmZmsmbNmjXunrmv90jJeXglWkceeSTz5s0jKyuLHTt28Omnn9K7d2+WLVtWLqZsERERAX6SxLZo0YKRI0fusZ6UTRrDK0nRuXNnpk+fHh/79OabbzJw4MCfDGoXERERKQ1KeCVpTjnlFCZOnBg/Xrp0Keeccw55eXkRRiUiIiIVjRJeSarf/va38elPAObMmcOwYcPKxJOsIiIiUjEo4ZWkGzNmDFdeeWX8eMqUKVx//fURRiQiIiIViRJeSToz429/+xsXXHBBvOyOO+7g9ttvjzAqERERqSiU8EqpSEtLY/LkyQwYMCBedv3112sJYhERkQqstIY4KuGVUpORkcH06dM54YQT4mWXXXYZTz/9dIRRiYhIeRWbDiw/Pz/iSGR/xRLeZE/tpoRXSlXVqlV55plnaNeuHRD8j9TgwYNZtGhRxJGJiEh5U7lyZQC2bt0acSSyv2KfXeyzTBYlvFLqatWqxYIFC+Jref/4448MGjSIirYinYiIHJgaNWoA8OWXX7Jlyxby8/M1C1A54O7k5+ezZcsWvvzyS2D3Z5ksWmlNItGgQQMWLlxIly5d+Oqrr9i6dSu9e/fmxRdf5Kijjoo6PBERKQfq1KnD1q1b2bZtG59++mnU4ch+qlatGnXq1EnqPdTDK5Fp2rQpzz//fHy54Y0bN9K9e3c2bNgQcWQiIlIepKWl0bhxY+rXr0+VKlW0xG85YmZUqVKF+vXr07hxY9LSkpuSqodXItWmTRuee+45evTowfbt2/niiy/o3r07L774IvXr1486PBERKePS0tKoV68e9erVizoUKcPUwyuR69KlCzNnziQjI/j+9f7779OjRw82bdoUcWQiIiKSCpTwSpnQp08fHn/88fhPGm+++SZ9+vTRk7ciIiJywJTwSplx7rnnMnHixPjxSy+9xCmnnMKOHTsijEpERETKOyW8UqZccsklTJgwIX68ePFizjzzTHbu3BlhVCIiIlKeKeGVMmfUqFH85S9/iR/PmzeP8847j7y8vAijEhERkfJKCa+USTfccAPXXntt/HjGjBlccsklWj5SRERE9pkSXimzbrvtNkaMGBE/fuSRRxg1apRW0REREZF9ooRXyiwz45577uGCCy6Il919992MHTs2wqhERESkvFHCK2VaWloakydP5vTTT4+XjR8/nr/+9a8RRiUiIiLliRJeKfMyMjL417/+RZ8+feJlf/zjH7n77rsjjEpERETKCyW8Ui4cdNBBzJw5kxNPPDFe9vvf/57JkydHGJWIiIiUB0p4pdyoWrUqzz77LJmZmfGyYcOG8fjjj0cYlYiIiJR1SnilXKlRowbz58+nTZs2AOTn5zN06FCmTZsWcWQiIiJSVinhlXKndu3aLFq0iKOOOgqAvLw8zjvvPGbOnBlxZCIiIlIWKeGVcql+/fosXryYFi1aAEHSe8455zB37tyIIxMREZGyRgmvlFuHHHIIixcvpmnTpgDs2rWLM888k+zs7GgDExERkTJFCa+Ua4cddhhLliyhcePGAOzcuZPTTz+d559/PuLIREREpKxQwivlXuPGjVmyZAmHHXYYALm5uQwcOJDFixdHHJmIiIiUBUp4JSU0bdqUpUuXcsghhwCwY8cO+vfvz7JlyyKOTERERKKmhFdSRvPmzVm6dCn169cHYPv27fTt25dVq1ZFHJmIiIhESQmvpJSWLVuyZMkS6tatC8DWrVvp1asXq1evjjgyERERiYoSXkk5Rx99NDk5OdSuXRuALVu20KNHD1599dWIIxMREZEoKOGVlNS2bVsWLVpEzZo1Adi8eTNZWVm88sorEUcmIiIipS0j6gCSwcyuAO4Barv7pgLnhgFti2i6zN2nJdQ9Ezi5iLpr3f2BkohXkuOYY45h0aJFZGVlsWXLFr7//nuysrJYtGgRHTp0iDo8ERERKSUpl/CaWTpwSTFVLgeKynaqANMSjs8Hziyi7nOAEt4yrkOHDixatIju3buzZcsWNm/eTPfu3ZX0ioiIVCApMaTBAh3M7BIgh6ITWoDmwL3uboVsIwqp+2wRdfsl6eVICcvMzCQnJ+cnwxu6d++uB9lEREQqiJRIeIHqwGpgEnBiUZXMrA5wMPD+Xl63+T7UlTIs1tObmPT26NGDl19+OeLIREREJNlSJeHdDpyTsL1dRL1fhvs9JrFmVh+ouTd1pXzo0KEDOTk51KpVC9id9L700ksRRyYiIiLJlBIJr7vnufv02Ab8t4iqzcP912Z2sZn91cz+18wK6xWO1d1gZoPN7FYz+7OZ9TMzK/EXIaWiffv2P0l6t2zZQs+ePbU4hYiISApLuYfW9iDWwzsHqJt4wszmAEPc/YcCdScWrAu8aGZnuvuXe7rh2rVryczMLPScxpBG49hjj2Xx4sVkZWWxadOmeNK7YMECOnfuHHV4IiIiFVpRedPatWv3+5op0cO7D2K9tvOAo4BqQBdgGTAIeLiQui8DxwJVgV8Bs4GuwEwzq2jvX8o45phjWLx4cXxxih9++IFevXqxYsWKiCMTERGRkmbuHnUMJc7McoBuFJiH18w6A3Xd/dkC9WsSjPttBBzl7u+ZWTt2z9KwM6FuBrAKaA/0dff/FBPH6g4dOnRQT27Z9cYbb9CtWzc2btwIQPXq1Zk/fz5du3aNODIRERFJlJmZyZo1a9a4e+FdwMWoUD2U7r6yYLIblm8GssPDY8OyN9x9VmKyG5bvAp5KrCvlV7t27ViyZAl16tQBYOvWrfTu3Ztly5ZFHJmIiIiUlAqV8O7BlnC/q4TrShnXtm1blixZQt26wVDtWNKbk5MTbWAiIiJSIipMwmtmR5uZm9kzRVQ5Idy/ZmbVzCzfzN7aU92SjVKi0qZNG5YsWUK9evUA2L59O/369SM7O3sPLUVERKSsqzAJL/Au8BHQ18xOSjxhZhcCHYEcd3/f3bcBS4HWZnZegbpZwFkE8/PmJD9sKS2tW7fmhRde4JBDDgEgNzeXU089ldmzZ0ccmYiIiByICpPwevB03hVAPrDQzOaY2T1mtgB4BPgWuDyhyVXAZuAJM1sQ1p0NzAdygYvdPa90X4Uk21FHHcXy5ctp1KgRADt37uTMM89k2rRpEUcmIiIi+6vCJLwA7j6P4EGz6eF+GHAk8BDQ0d3fSai7BmgHPEgwW8Mwgl7gGUAXd19autFLaWnevDnLly+nSZMmAOTl5TF48GCmTp0acWQiIiKyP1Jy4Ql3zyrm3NvAeUWdL1D3E+DSEgpLypEmTZrw4osvcvLJJ/P++++Tn5/PhRdeyPbt2xk+fHjU4YmIiMg+qFA9vCL74rDDDmPZsmUcffTRALg7l156Kffcc0/EkYmIiMi+UMIrUoxDDjmEF154gXbt2sXLRo4cyZ133hlhVCIiIrIvlPCK7EHdunVZsmQJHTp0iJdde+21/OUvf4kwKhEREdlbSnhF9sLBBx9MTk4OXbp0iZfdeOONjBkzhlRcnltERCSVKOEV2Us1atRgwYIFdOvWLV42fvx4rrrqKiW9IiIiZZgSXpF9UL16dbKzs+ndu3e87B//+AeXXHIJeXmalllERKQsUsIrso+qVKnCnDlzGDhwYLxs8uTJDB48mB9//DHCyERERKQwSnhF9kPlypV5+umnOe+83VM6T58+nUGDBrF9+/YIIxMREZGClPCK7KeMjAymTp3KiBEj4mX/+c9/6NWrF5s3b44wMhEREUmUtITXAr3N7HYzW2Fmn5vZj2b2vZmtM7NpZna5mTVKVgwiyZaWlsa9997L6NGj42XLly+nW7dufPPNNxFGJiIiIjElnvCaWTUzGw18AGQD1wCdgIOBr4FdQHPgLOAe4EMzm2Fmx5d0LCKlwcwYP34848ePj5e9+uqrnHDCCXz++ecRRiYiIiJQwgmvmV0MrANuBbYDfwZ6Awe7ezV3P9zd6wIZQGvgt8AMoD+w1Mz+ZWZNSjImkdIyevRo7rvvPswMgPfee4+uXbvywQcfRByZiIhIxVbSPbwPASuBzu7e2t1vcvfn3f0nAxo98I67T3H384CGwJXAicBFJRyTSKkZMWIEU6dOJT09HYBPPvmErl278tZbb0UcmYiISMVV0gnvce5+pru/tC+N3H2zu98F/BL4dwnHJFKqhgwZwtNPP03lypUB+PrrrznxxBN5+eWXI45MRESkYirRhNfd1xxg+x3u/k5JxSMSlUGDBjFv3jyqVasGwKZNm8jKymLx4sURRyYiIlLxaFoykSTp3r07ixYtolatWgBs3bqVvn37Mnv27IgjExERqVhKJOE1s7ZmNtzM2oTHrc3sQTN71Mz6lMQ9RMqjTp06sWzZMurXrw9Abm4uZ555Jg8//HDEkYmIiFQcB5zwmtkAYA3wV2C1mfUHFgNNgUbAs2bW60DvI1JetWnThhUrVtC4cWMA8vLyuOSSS7jtttsijkxERKRiKIke3huA28Ppxi4CHgcmuntvd+8J3AFcVwL3ESm3mjdvzqpVq2jdunW8bPTo0Vx99dW4e4SRiYiIpL6SSHjbAFPCf/8bqEEwt27M48CvSuA+IuVaw4YNWb58OV26dImX/f3vf+fCCy9k165dEUYmIiKS2krqobV8AHfPB3YAmxLObQFqldB9RMq1WrVqsWjRIgYMGBAvmzp1Kqeeeirbtm2LMDIREZHUVRIJ70dAy4TjrsAnCceNgS9L4D4iKaFKlSrMmjWL3/zmN/GyefPm0bNnTzZu3BhhZCIiIqmpJBLeB4CDYgfu/qa7J/4+ewqQUwL3EUkZGRkZPPLII1xzzTXxshUrVnDCCSfw+eefRxiZiIhI6jnghNfd73X3OcWc/6O7X3yg9xFJNWbGHXfcwe233x4vW7t2LZ07d+a9996LMDIREZHUEvnCE2aWZmYDo45DJCrXXnstU6ZMIT09HYBPP/2Url27snr16ogjExERSQ2RJbxmdqSZ/RX4FJgVVRwiZcHQoUN5+umnqVy5MgDfffcdJ598Ms8//3zEkYmIiJR/pZrwmll1M/utmb0ArAWuBRoCL5RmHCJl0aBBg1i4cCE1a9YEYNu2bfTv358nn3wy4shERETKt1JJeM3sRDN7GPgCeJBgJoeNwN+Ao929W2nEIVLWHX/88SxfvpxDDjkEgJ07dzJkyBDuvPPOiCMTEREpv0piaeFKZna+mV1lZs0Tyhua2fVm9i7BUsMXAVWBZwEDnnL369xdT+eIJGjTpg0rV66kRYsW8bJrr72WUaNGkZ+fH2FkIiIi5dMBJbxmdhCwDJgEDAFWm9k1ZjaHYC7eWwnm6H2bYHnhw9190IGFLJL6jjjiCFauXEnHjh3jZf/3f//HueeeS25uboSRiYiIlD8H2sN7DpAJDHD3jsAZwO3AAGAzcB/Qyd3bufud7v7VAd5PpMKoU6cOixcvZuDA3ZOYzJgxg549e7Jp06ZiWoqIiEiiA01464T718L9W+HegenAQ+7+8gHeQ6TCqlq1KjNnzuTSSy+Nly1btoyuXbuyYcOGCCMTEREpPw404X0WyAVmmNnlwAyCpNeB4cDLZvaqmV1pZvUP8F4iFVJ6ejr3338/N998c7zsnXfeoVOnTrzxxhsRRiYiIlI+HFDC6+7vA/2AfOAKYCFwDNAYGAusB35FMBvDp2Y208zOOKCIRSogM2Ps2LFMmTKFjIwMAL788ktOOOEEcnJyog1ORESkjCuJpYWXuHvPcJzuOHfPd/cv3f2v7n4UcDLwKPAjcCrBUAcH2plZmwO9v0hFMnToUObOnUv16tUB2LJlC3369NFcvSIiIsVI+jy87v6Cu19MsMDEcGAFwbRknYHXzexFM7sk2XGIpIq+ffuydOlS6tcPRgnF5uq94447Io5MRESkbCq1ldbcfau7P+TuJwBHA3cAXxIkvhNLKw6RVNC+fXteeuklmjePT33Nddddxx/+8Afy8vIijExERKTsKdWlhWPc/T13v55grO9pwKwo4hApz4444gheeumln8zVe9ddd3HGGWewbdu2CCMTEREpWyJJeGPC8b5z3P3MKOMQKa8Km6t3zpw5nHTSSXz1laa9FhERgRJOeM2salm4hkhFEpurd+TIkfGyNWvW0LFjR9auXRthZCIiImVDSffwfmhmo8ys8r42NLNjzGwWcE0JxySS8tLT07nrrruYMGECaWnBf9YbNmygS5cuLFq0KOLoREREolXSCe9/gL8DX5jZfWbWvbgeWzNrbmaXm9mLwBqCOXz1/84i+2nUqFE89dRTVKlSBYDNmzfTp08fpkyZEm1gIiIiESrRhNfdLySYdeFl4FJgAfC9mb1mZtlm9qSZPW1mS8zsK2AdcA/QlGChiqPc/YWSjEmkojnttNN+Mm3Zrl27uPjii7nxxhtx94ijExERKX0l/tCau7/s7n3YPfXYa0BroA/wa4JZGU4Mqz8FDAGahAtV5JZ0PCIV0XHHHcfLL7/MkUceGS/7y1/+wgUXXMCPP/4YYWQiIiKlLyNZF3b3dcBoADOrBjQC6gLbga/d/Ytk3dvMriDoOa7t7psKnBsGtC2i6TJ3n1ag/kXAKOBIYBOQDdyQzPhFSkKTJk1YtWoVp556KkuWLAHgiSeeYMOGDcyaNYvatWtHHKGIiEjpSFrCm8jdtxEMX1iX7HuZWTpQ3MptlwMdijhXBYgnvGZ2A/AXYAPwOHAoMBTobWaZ7v7fEglaJElq1arFggULGDZsGI8++igAS5cupXPnzjz33HM0a9Ys4ghFRESSL9J5eEuKBTqESxTnUHRCC9AcuNfdrZBtRMI1fwmMA94C2rr7pe4+iGB55MbAbcl6PSIlqVKlSkyZMoVx48bFy9atW0fHjh1ZuXJldIGJiIiUkpRIeIHqwGpgErvHB/+MmdUBDgbe34trjgDSgT+7++aE8inA58CQcKiGSJlnZvzpT3/iscceo1KlSgB8++23dOvWjX/9618RRyciIpJcqZLwbgfOSdjeLqLeL8P93iS8WUA+wZjdOA8ec18KVCaYkUKk3LjgggtYsGABtWrVAiA3N5fBgwdz0003aQYHERFJWSmR8Lp7nrtPj21AUWNrm4f7r83sYjP7q5n9r5kV1it8DLDB3bcUci62fFXLAwxdpNSdfPLJrFq1iqZNm8bL/vSnPzFkyBB27NgRXWAiIiJJUioPrZUhsR7eOQQzRsSZ2RxgiLv/YGY1gUrAV0VcZ2O4r7OnG65du5bMzMxCz61evXpvYhYpcUceeSSrV69m0KBBLF++HIB//vOffPDBB8yZM4cGDRpEHKGIiFRUReVNa9euLbR8b5RaD6+ZNTCzk8xsQHj8i9K6d4JYD+884CigGtAFWAYMAh4Oz9cM90XNC7w13Fe0LwySQurUqUNOTg5Dhw6Nl61atYrjjjuON998M8LIRERESlbSEzYzawvcBxwfFnl434lm1gC42N03JDuO0IPAU+7+bELZyjAJfxs4x8yOBL4PzxW1LPJB4X7bnm7YqlUr9eRKmVWpUiUmT55M69atGT16NO7Ohg0b6NKlC//+978ZMGBA1CGKiEgFU1TelJmZyZo1a/brmknt4TWzxgQPeHUh6FVdA1h4+jWgG/CymTVKZhwx7r6yQLIbK9/M7ofTjgW+A/IIZnQoTGw4xOclHqRIKTMzrrvuOp5++mmqVQsmHtm6dSuDBg1iwoQJephNRETKvWQPabiRYNhAT3cfCLwQO+HutwFnAfXDelGLPZy2y913Eszk0MzMqhdSt0W41+++kjJOO+00li1bxqGHHgpAfn4+V111FZdddhk7d+6MODoREZH9l+yEdwCQ7e5LCjvp7rMJForok+Q4MLOjzczN7JkiqpwQ7l8L988TzMPbo8B10oGeBL27byUjVpGoHHvssbzyyiu0b98+Xvbggw/Sp08fNm7cWExLERGRsivZCW8d9vyz/3qgYZLjAHgX+Ajoa2YnJZ4wswuBjkCOu8fm6J1IMN74z2aWOJZ3NHA4cI/rt15JQYcccgjLly/nzDPPjJfl5OTQqVMn1q9fH2FkIiIi+yfZCe+HFL/ML8DRFD1vbokJk9MrCBaTWGhmc8zsHjNbADwCfAtcnlD/VeAOoD3wpplNNLNFwM3Ay8Dfkx2zSFSqVKnC9OnTGTt2bLxs/fr1dOzYkUWLFkUYmYiIyL5LdsL7OHCcmV1X2EkzG0kwlGBGkuMAwN3nETyUNj3cDwOOBB4COrr7OwXqX0+QJO8AfkMwj+8EgjHJmqFfUpqZcfPNNzN16lQqV64MwKZNm+jduzf33HNPxNGJiIjsPUvmr/JmVhmYT5DUrieYjqwpMJNgJbNmwDqgi7tvSlogETGz1R06dOigacmkvFuxYgWDBg3im2++iZcNHz6ce+65h0qVKkUYmYiIVBThtGRr3L3wlSmKkdQeXnfPBXoBtxCM521GMC3ZGUAj4FHghFRMdkVSSZcuXXjllVdo27ZtvOzBBx+kR48e/Pe/SR+RJCIickCSvtKau//o7jcCDYBWwIkE42IPdveL3f3bZMcgIgfu8MMPZ+XKlZxxxhnxshdeeIHMzExef/31CCMTEREpXqktLeyBd919ubu/Fvb+ikg5Uq1aNWbMmMGf//xnzII1ZDZs2EDXrl2ZMaNUhuKLiIjss1JJeM2slZn1MbNTi9pKIw4ROXBmxo033siMGTOoXj1Yl2Xbtm2cffbZjBs3jvz8/IgjFBER+amMZF7czJoRPKDWtrhqBPPdpiczFhEpWWeccQYrVqxgwIABbNiwAYA///nPvPbaa0ydOjWeDIuIiEQtqQkvcA/QjmCmhlXAriTfT0RKUdu2bXn11Vc59dRTWbZsGQAzZ86kS5cuzJ07lyOOOCLiCEVERJKf8J4ILHb3vkm+j4hEpE6dOuTk5DBy5EgeeOABAN58800yMzN5+umnOemkk/ZwBRERkeRK9hjePOD9PdYSkXItIyOD+++/n/vuu4+MjOB79LfffkuPHj145JFHIo5OREQqumT38M4HeppZFa1MJpL6RowYQevWrTnjjDP47rvv6NixI6ecckr8/Lp168jOzmbz5s3UrFmTfv360bJlSwDcPT7zg4iISElKdsJ7DfACsMTM/gZ8ABQ6HZm7ayJPkRRw8skns2bNGsaOHcuDDz5I1apVycnJ4dZbb2XBggUkru5oZvTq1YsxY8aQlZWlpFdERJIi2QlvOvA9cBzwxF7UFZEUcMQRR/Doo4+SlpbG5MmTGT58OHl5eT+r5+7Mnz+fhQsXMmnSJC666CIlvSIiUuKSnfDeBbQG3gFepYjeXRFJPWlpaeTk5BSZ7CbKy8tj2LBhNG3alKysrNIJUEREKoxkJ7wnAWuAru6uKclEKphbb711j8luTF5eHuPHj1fCKyIiJS7ZszRsB15TsitS8axbt44FCxbsU5v58+ezfv36JEUkIiIVVbIT3mlAlplVTvJ9RKSMyc7O/skDanvD3cnOzk5SRCIiUlElO+H9I/Bf4Ckz62hmVZN8PxEpIzZv3lyq7URERIqS7DG83yf8ux9Q1NPX7u7JjkVESlHNmjX3uU16ejpt27ZNQjQiIlKRJTvJXArs22+aIpIS+vXrh5nt1bCGevXqcemll3LZZZfRpEmTUohOREQqkqQmvO6elczri0jZ1bJlS3r16sX8+fOLrde1a1dmz55NvXr1AK3GJiIiJU/DCEQkacaMGcPChQuLnJqsa9euPP/881qNTUREkiqpCa+ZPbWXVd3dz0pmLCJSutydrKwsJk2axLBhw36W9NarV4/Zs2dTtWpVrcYmIiJJlewe3tP3cN4BQ+N8RVJObPzuRRddRNOmTRk/fjzz58+P995eeuml1KtXT6uxiYhI0iU74W1WxD3rAl2Bq4DVwIVJjkNEIhBLerOyssjKymL9+vVkZ2ezZcsWRo4cCWg1NhERST7b14nhS/TmZocAbwP3uvv/RhZIkpjZ6g4dOnRYvXp11KGIlEnr1q3jqKOO2qcFKsyM9957jxYtWiQxMhERKWsyMzNZs2bNGnfP3Ne2yV54olju/hWQDZwfZRwiEg2txiYiIqUh0oQ3dDDQMOogRKT0HehqbOvXry/JcEREJEUle5aG4pZaqgqcCfQG3klmHCJSNu3PamyJ7e6++27q1q3L2LFjSUsrC9/fRUSkLEr2Q2ub2PMMDAaMT3IcIlIG7ctqbDFmRr9+/QB49913yc7OZsWKFUydOpXatWsnK1QRESnHkp3wLqHohPdH4BNgqrsvTnIcIlIG7e1qbIl69+5NixYt+OKLL/jPf/4DwLPPPkv79u2ZOXMmxx57bLLCFRGRciqpvwG6e5a7dy9i6+vuw5XsilRsY8aMIT09fa/qpqenM2bMGAAaNGjAFVdcET/38ccf06VLFx544IF9fhBORERSmwa9iUhkEldj21PSm56ezqRJk+jWrRvuTnp6OnfddRdPPvkk1atXByA3N5cRI0Zw/vnn88MPP5TGSxARkXKgRBNeM/vV/m4lGYeIlA+Jq7EtWLCAPn36/GzJYDOjT58+PP/884UuKzx48GBeeuklWrZsGS978sn/z96dx8dZlf//f12Z7rTpF6iACiWUpCUgW4JA+SAZYTKM7IsCIkuApIVaCkg/iPOrsiegslmRgsHkQwEFWaQiHTpTGNkpNFBbGmjCLptUgQANpZ2e3x+TGbNvzcwkk/fz8ZjHNPd97plrGC1vTs59nT9SVFTEqlWr0vZZRERk8BrQjSfMbBP93CbYOde732kOIdp4QqR3WofYxG5sTU1N5OZCtYoAACAASURBVObmEggEkptMtA+7ra1bt46Kigruuuuu5LExY8Zw0003ceaZZ6b+Q4iISEptzsYTA33T2u30M/CKyPDVOsTm5+cntx3ublx748aN484778Tr9XLuueeyfv16vvzyS8466yyi0SgLFixg3LhxA167iIgMfgMaeJ1zZQP5eiIifVVRUcF+++3Hsccey+uvvw7AwoULef7557n//vspLCzMcIUiIpJuabtpzczyzexoMzvJzA42s4npem8RGV722GMPVqxYwfe///3ksVdeeYV99tmH22+/PYOViYhIJqQ88JrZVDN7GngVuB+4EwgD/zKzO8zs66muQUSGn/Hjx3PPPfdw0003MXr0aCC+zvf000/nrLPOorm5OcMViohIuqQ08JrZN4AngH2Jh9wrgSBwE/AWcDLwtJltk8o6RGR4MjNmzZrF008/zY477pg8/oc//IF9992XNWvWZLA6ERFJl1TP8F4CbAkc4ZwLOOcucc5d45yb45ybCpwGTAZ+nuI6RGQYKyoqYuXKlRx99NHJY6tWraKoqIg//vGPGaxMRETSIdWB93Ag4pwLdXbSOXcHsKRlnIhIykyYMIEHHniAG264gVGjRgHwxRdfcPLJJzNz5ky+/PLLDFcoIiKpkurAOwl4vYcxrwHfSHEdIiKYGeeddx5PPvkkO+ywQ/L4rbfeyj777MMrr7ySwepERCRVUh143wem9TBmZ+DTFNchIpL07W9/m5UrV3L44f/95dLLL79MUVERf/jDHxjIDXlERCTzUh14HwQONrPyzk6a2fcBPxBJcR0iIm1MnDiRv/71r1x//fXJJQ7Nzc3MmDGDa665RkscRESyyEDvtNbeFcCRwC1mdiHwCPAesBVQQrx7w6fALwbyTc1sFvFOEFs65z7pYezhwEPAjc6589udOw44qItL651ztwxEvSKSGWbG+eefT0lJCRUVFRx66KHMnDmTyZMnZ7o0EREZQCkNvM65f5vZAcCNwPF0XN7wLDDTOffaQL2nmXmAs3o5NhfoLrT+CDiui3OP9HCtiAwRe++9N8uWLSMnJ/5Lr4aGBkKhEE1NTWyzzTYce+yxTJo0KcNViohIf6V6hhfn3IfASWa2FbAP8RvZ1gErByrompkBe7c8yoCiXl56LfDNbs5PAR52zqmLhEgWc86Rk5NDNBqlsrKSSCSCc47p06ezaNGiZNhtHYRzc3MJBAIUFBQkXyP+V5GIiAw2KQ28ZnYzcIdz7inn3H+ItyBLhS2A5X25wMwOBsqBvwDHdDFsCvGNM0QkSyWCak1NDRUVFcRiMQCmT5/O0qVLGTt2bIcgnGBm+Hw+gsEgXq9XoVdEZJBK9U1rM4HHzewNM7vSzApT9D7NwA9aPVZ3N9jMtgCqgeeA+V2M+RqQS7xtmohkKTMjGo22CbuTJk1i0aJFjB07lpqaGnw+H+FwuEP3Bucc4XAYn89HbW0tZqYODyIig1CqA+8xwJ3AROJbCq8ys+VmdoGZfX2g3sQ5F3PO3Zt4AB/1cEklsD3xGd5NXYyZ0vL8jpmdZGaVZnaZmQVMUzgiWaWysjIZdgFmzJjBpEmTOgThrsRiMcrLy4lGo5rhFREZhCwdsxFmNgIoJX7j2lHE1/HGgMeAO4D7nXOfD+D7RYl3gejQpaHlJrongCucc5eambeljjZdGszsZOJh/d/A1u3e4hngOOfcBz3UsXzs2LFFhYWdT2wvX96nVRgikgINDQ1MmzYtOTPr8Xh4/fXXmTx5Mn6/n3A43OvX8vv9PPLII6kqVURkWCguLu70eH19Pc3NzXXOuc4HdCPVM7wAOOc2OucWO+fKge2Ih9/fA7sDNUC3wXGgmNlo4DbgFeKzvN1JzPC+AOwFjAX2ABYB04G/mFla/vmJSOqEQqE2yxBKS0uZPHkyDQ0NRCJ9axEeDodpbGwc6BJFRGQzpbxLQ3vOuU1m9j7wT+I9ebclHibT4VJgKnCgc+6rHsY+CKwk3qVhQ8uxlWZ2PLAM2A/w0cONeIWFhZrJFRnEmpqa2vw8bVq8e2L7INwbzjlCoRCzZ88esPpERIabrnJTcXExdXV1/XrNtATeljWv/wMc3fLYGTDiWw/fANyVhhr2AOYCNznnnulpvHNuJfHA2/74RjO7n3gLtL1IXecJEUmD3NzcNj+PHz8e6BiEe6u/14mISOqk9FfyZnakmd1GfMnC34ELia/frSE+O7q9c+4nzrkXUllHiyLiAf9cM3OJB/H1uwDntRyr7cVrfdbyvDEFdYpIGgUCgTY3mn3+efx2gvZBuLf6e52IiKROqmd4H2x5bgbuJT6T23qJQDqtJr7jW3vbE7+Z7iXioXyZmY0DPie+ffBunVzzPy3PK1JRqIikT0FBQbLtGMCrr74K/DcI92VZg5kRCAQAeO6559hvv/0GvmAREemzVN909QhwOrCtc+5E59yDGQq7OOeWOefOb/8Aftsy5O8tx+5yzq0j3slhVzP7YevXaenqcDzx/rzRNH4EEUmRYDCIx+MB4jeevf3228kg3BelpaXk5+fz1ltvccABBzBr1iyam5tTUbKIiPRBSgOvc+57zrmFA9lyLI0uAJqAu8wsYmY3mdkiIAysB85wznXfnFNEBj3nHF6vl+rqajweD7FYjFtuuQVoG4R74vF4CAaDANxyyy1s2rSJm2++meLiYlau7HA7gIiIpJHaanXBOVdHvG3a74m3KCsHvg3cB+zvnNOWwyJZILFsoaysjEgkgt/v5/e//z1r165tE4S74/F4qK6upqSkhE2bNtHQ0JA8V19fzz777MMNN9ygXdhERDIkKwOvc87rnLP2m050MTbaMvb8Ts697Zyb4Zyb4pwb7Zz7unPuJOfcP1JTuYhkQiL0er1eHnnkEZ5++mmi0SgbNmxoE4Tb76JmZvj9fpYuXUpZWRnOOXJycrjnnnu44YYbGD16NABfffUVF1xwAYFAgH/961+Z+IgiIsNaWnZaG67MbHlRUVGR+vCKDF3OuWTQbWxsJBQK0dTURG5uLoFAgPz8/A7jElauXMkJJ5zAK6+8kjw2adIk/u///o/DDjssfR9CRCQLtPTh7ddOawq8KaTAKyLr16/nggsu4Oabb25zfObMmVx33XWMGzcuQ5WJiAwtmxN4s3JJg4jIYDF69Gh+97vf8dBDD7H11lsnj99yyy3ssccePP/88xmsTkRkeBjQwGtme/T3MZB1iIgMNocffjirV6/m0EMPTR577bXXmD59OpdccgkbN2ofGxGRVBnojSdeAvq7RqJ3vX9ERIaobbbZhsWLF7NgwQLmzp3LunXriMViXH755Tz88MPcddddFBQUZLpMEZGsM9CB93b6H3hFRLKemXHOOedQWlrKiSeeSF1dHQAvvPACe+65J3fccQfHHnts8ga4hoaGDjfKJUJxZzfKiYhIRwMaeJ1zZQP5eiIi2So/P59ly5Zx2WWXUVVVxcaNG9lrr7343ve+h5kRjUaprKwkEom06d9rZvh8PoLBIF6vV6FXRKQXBrRLw+asxc3G3rbq0iAivbFs2TJ+/OMfs3jxYiZNmkRNTQ0VFRXEYl1v5pjY7CLR/1ehV0Sy3eZ0adAaXhGRDNt33315+umnGTlyJNFotMewCxCLxSgvLycvLw+v15ueQkVEhiit4RURGQRGjhwJQGVlZY9hNyEWi1FVVaXAKyLSA63hFREZJBoaGohEIn26JhwO09jYmNzxTUREOkrbxhNmto2ZfcfMDmv5eXy63ltEZCgIhUL09b4K5xyhUChFFYmIZIeBXtLQgZl9C7gZOKDlkGt531vNbBvgDOfcO6muQ0RksGtqakrrdSIiw0VKZ3jNbAfgCWB/YDFQByRuJV4BlAAvmNk3U1mHiMhQkJubm9brRESGi1QvafgFMA44xDl3BPBk4oRz7hrgeOBrLeNERIa1QCDQ5/ZiZkYgEEhRRSIi2SHVgfcwIOSce7yzk865RUAU8Ke4DhGRQe2zzz6joKAAn8/Xp+tKS0vJz8+nqamJ119/PUXViYgMbakOvFsB7/UwphHYLsV1iIgMaokNaoLBIB5P79qSezwegsEgAM899xy77747v/vd7/p845uISLZLdeB9AyjqYcwuwEcprkNEZFBraGhg7dq1eL1eqqurewy9iZ3WSkpKcM7x2GOPsW7dOn784x/j8/l45x3dCywikpDqwHsnsI+ZXdTZSTObDfwPcF+K6xARGdS8Xi9HHXUUzc3NlJWVEYlE8Pv9Hdb0mhl+v5+lS5dSVlbGpk2bMDPef//95JhHH32U3XbbjdraWs32iogAlsq/DM1sNBAmHmobibcjywP+AuwJ7AQ0APs75z5JWSEZYmbLi4qKihK/qhQR6Y7f7+fzzz9n0aJFTJo0CYDGxkZCoRBNTU3k5uYSCASSm0x8+umnTJw4EYDm5mYuvPBCFixY0CbkHn744dx2221su+226f9AIiIDqLi4mLq6ujrnXHFfr01p4AUws1HAPOAcYOtWp9YDfwLmOuf+ndIiMkSBV0T6IhqN4vP52HLLLamoqODss89m8uTJHca99dZb3HrrrRx++OEccMABbc4lZn7/+c9/Jo9tueWW3HzzzZx44okp/wwiIqkyqANv8o3iv5ebSjz0fgG84pxbn5Y3zxAFXhHpLeccZkZtbS3l5eXEYjFycnLw+/1MnTqVCRMm8Nlnn7FmzRqWLl3KrbfeSllZWfK61j7//HPOPfdcamtr2xw/9thjWbBgAdtss00aP5mIyMAY9IHXzA4AdnDO3d3yswEVwMPOuX92e/EQpsArIn2RCK/RaJSqqirC4XCb5QlmRmlpKcFgMHmzWnd9ex9++GHOPPNMPvzww+Sxrbbait/+9recdNJJfe75KyKSSYM28JrZOOLrdQ8BnnPOHdBy3ANsADYCv3TOzUtZERmkwCsifdU6xHa3frensJvwySefcPbZZ3P33Xe3OX7kkUdy6623st126gopIkPDYA6884DLgRBwlXPuqVbnTgWCxJc5nOicuzdlhWSIAq+IDBYPPvggM2fObDPbO3HiRH7zm99w6qmnarZXRAa9zQm8qW5LdjKwCji8ddgFcM4tBPYjvjHFBSmuQ0RkWDv66KN59dVXOeWUU5LHPv30U04//XQOO+ww3nuvpz2CRESGrlQH3h2BZ1wX08jOuSbgEaAwxXWIiAx7EydOZOHChTz00EN84xvfSB4PhULssssu3HbbberbKyJZaUSKX/8TYPsexowH9Ls0EZE0Ofzww3nllVc477zzeP/995k2bRrjx49n5cqVzJs3j7PPPpsddtgh02WKiAyYVAfevwJnmdnJzrm72p80s+nA0cCSFNchIiKtTJgwgT/84Q+dnnv77bdZtmwZ3/72t7W2V0SyQqoD7y+AALDQzH4CRIEPgInAtwEf8Z68wRTXISIiLVp3eGhoaOjQCaKgoIDJkyfzySef0NzczNe//vUMVywisnlSGnidc/8ys/2B3wDHAkXthjwHnOOcezmVdYiISFzrXr+VlZVEIpEOvX59Ph/BYBCv18vo0aO59957Oe6448jJSfVtHyIiqZHyv72ccx84504AtgUOBX4EfB+Y5pyb7px7KdU1iIjIf8NuTU0NPp+vw8YWiTHhcBifz0dtbS1jx47F6/Vy1FFH8dprr2WochGRzZPqJQ1Jzrn/AOF0vZ+IiLSVmNmtqKggFot1OzYWi1FeXk5eXh5er5cjjjiCiRMnJs93tRQCer8phohIuqQt8AKY2Y+B45xzh6TzfUVEJK6ysrLHsJsQi8WoqqrC6/UyY8YMcnJyer0UQqFXRAaTdC/Iyge8aX5PEREhPisbiUT6dM2LL77Ixo0bycnJ6dNSCDNTT18RGTR0B4KIyDARCoX6HEIrKioYMWJEn5dCRKNRzfCKyKCR1iUNIiKSOU1NTX0a7/F4mDlzJtC7pRAej4fS0lKmTZvGm2++2d8yRUQGXLoDr6Fd1UREMiI3N7dP40tLS5k8eXKPSyEmTZrEjBkzmDlzJpMnT97cMkVEBlxKA6+ZTQaanHOftBy6DLi+3Zj/B4x1zr2fylpERIa7QCDQp7W106ZNA7pfCjF9+nQWLVrEpEmTAHVvEJHBKdUzvG8ANwAXAjjnPgY+bjfmMuA0YMsU1yIiMqwVFBQkbzrrjfHjxwNdL4WYPn06S5cuZezYsereICKD2oAHXjOb0/pHoKjdsfbvfzTgGeg6RESko2AwyKOPPtqr1mTr1q0DOl8KMWnSJBYtWsTYsWOpqanp8oa2RPeGRx99lOrqasrKyhR6RSTtUjHDewPgiIddBxwElPRwzR9TUIeIiLTinMPr9VJdXU15eXm3odfj8fC9730P6HwpxIwZM5g0aVK/N7IQEUmnVLQlOwM4s+VhxHdXO6OLx+nAwcApKahDRERaSYTWsrIyIpEIfr+/w0yrmeH3+1m6dCmlpaU455JLIRL62r0hIbGRhYhIug34DK9z7v8Sfzaz04FFrY+JiEjmJEKv1+vF6/XS2NjY4Saz/Px8oO1NZq2XQvS2e0NnwuEwjY2NyfcQEUmHlG484Zz7rnPud6l8j86Y2Swzcy0dIHoae3jL2Bu6OF9mZi+a2Rdm9q6Z3WZmXx/4qkVE0qP1rG5+fj6zZ88mGAwye/bsNkE0Ma71UgiPx9Or7g1dcc4RCoUAqKur025sIpIWAxp4zSzXzEa3+7lXjwGswQOc1dt6gVu6OT8PqAG2Bu4E6ogvw3jOzL62+dWKiAx+7ZdC7LXXXkDfN7JISFx3//33EwgEtEmFiKTcQM/wfgxUtvr5k5ZjPT3+szlvanFFZnYWEAWKennptcA3u3jNnYFLgZeBbznnZjjnjgQqgB2AazanZhGRoaT1UoiysjKg7xtZJCSu++yzz1iyZAmFhYVcc801bNiwYaDKFRFpY6DX8D4BvNbq58eJd2pItS2A5X25wMwOBsqBvwDHdDLkbOLt0i5zzrWexqgFrgRONrPZzrl1/apYRGSIaX+DW183ski8RiAQAOKbVAB8+eWXXHzxxdxxxx1UV1ez3377DVzRIiIM8Ayvc87bes1uy8/f7c1jM9+6GfhBq8fq7gab2RZANfAcML+LYV5gExBqfdDF/2Z/AhgN6G9lERm22ndv6I3S0tLkOuGLLroouQsbwKpVq5g+fTrnnHNOv5dLiIh0xrLxhgEzixLv/btlq22NW5+/ETiH+NKHScBjwI3OufNbjfkKeM85l9fJ9ZcQX+4w0zl3azd1LB87dmxRYWFhp+eXL+/TpLSIyKATjUbx+Xy9ak3m8XhYunQpJSX/bc3+1VdfceWVV/LLX/6S9evXJ49vt912zJ8/n+OPP16bVIgMM8XFxZ0er6+vp7m5uc451/mAbqS0SwOAmQXMbKGZPW5my82srpNH2pKfmR0AzAYqnXOruhiTC4wEPuziZRLbI2818BWKiAwN7bs3dMfj8VBdXU1JSUmbJRCjRo3i8ssvZ8WKFXznO99JHv/ggw/4wQ9+wOGHH87bb7+dss8gIsNDKnZaSzKzcuJdEHr6z/O0TDO3dJC4DXiFtjfXtZe4E2N9F+e/aHnu8Z9fYWGhZnJFJCu17t6Ql5dHVVUV4XC4TaA1M0pLSwkGg8mw29mM7bRp0/j73/9OTU0NF110Ef/+978BWLx4MYWFhVx66aVccMEFeDye5PUNDQ0deggnlkho+2KRoaur3FRcXExdXV2/XjOlgRe4EPgSmEl8A4pML8q6FJgKHOic+6qbcYlbhcd2cX5Uy7NuWBORYa2/G1l09VpnnnkmxxxzDHPmzOHOO+8EYN26dTzwwAOce+65jBgxgmg0SmVlJZFIpEO49vl8BINBvF6vQq+IJKU68OYBf3bO3ZHi9+mRme0BzAVucs4908Pw/wAxoKuNK7ZueX5vgMoTERmyOtvIoqdx3dlqq6244447OPPMM5kxYwaffvopixYtYsyYMdTU1FBRUdHpmmHnHOFwmEcffZTq6mrKysoUekUESP0a3n8BG1P8Hr1VRDzgn9uys5ozM0f8hjWA81qO1TrnNhBvr7ZTS0eH9hJbEXW6BlhERDbfwQcfzOrVq/nTn/7EpEmTiEajXYbd1mKxGOXl5USjUYVdEQFSP8P7R+B0M5vonPs0xe/Vk9XAjZ0c3x44HngJ+DuwrOX4UuLLHw4G/poY3LKT2yHEZ3dfTmG9IiLD3qhRozjkkEMAqKys7FU3CIiH3qqqKrxebwqrE5GhItWB93LgQODvZjYX+AfxNb0dpHp9r3NuGf8Ns0lm5iUeeP/eui0ZcCvxzScuM7OIc6655fjFxEPy/+eysaebiMgg1NDQQCQS6dM14XCYxsbG5BpiERm+Ur2k4RPimzPsATwCvE8KthZOBefcS8CvgL2BVWZ2q5k9RnyXtReA6zJZn4jIcBIKhfq0oxvE1/SGQqGeB4pI1kv1DO/TpKnlWCo4535qZm8S79t7KvARcANwiXOu05lqEREZeP3deS1xXeub19TOTGT4SWngdc55U/n6A/G+zrko3fQJds7dDNy8+VWJiEh/5ebm9jyoE1OnTk2GWLUzExm+Ur7TmoiIyOYKBAJ9DqFf+9rXOOaYYzAzampq8Pl8HTbGgP+2M/P5fNTW1iZ7C4tI9hjQGV4ze72HIRuBz4G3gWeA251z7w9kDSIikn0KCgqSgbW3fvnLXyY3quhLO7O8vDx1dxDJMgM9w5vXwyMf2As4CqgC6s3suAGuQUREslAwGMTj8fRq7KhRozjppJOA/rUzE5HsMtCBd6dePHYh3sf2emAMcGfLLmgiIiKdSmxfXF1d3WPo9Xg8PPTQQ4wZM2az2pmJSPYY0MDrnHurF481zrnHnHMXAscAo4HzBrIOERHJLol1tWVlZUQiEfx+f4c1vWaG3+9n6dKllJaWAmpnJiJxqW5L1i3nXMjMngW+m8k6RERk8EuEXq/Xi9frpbGxsUN7scQmE4lOC5vbzkxEskNGA2+LlcTX9YqIiHSr9axufn4+s2fP7nZcf9uZ9fc6ERmcBkNbslHAF5kuQkREsk9/2pmZGYFAIEUViUgmZDTwmtkYoBRoyGQdIiKSnRLtzPqitLSU/Px8Nm3alKKqRCTdMhZ4zWw74B7g68CfMlWHiIhkt760M/N4PASDQQCuvvpqFi5cqE0oRLLAQG888YdeDBsL7ADs2/L+TwM3DWQdIiIi0LadWXl5ebf9eD0eD9XV1ZSUlLB27Vquv/561q5dy80338yCBQvYYw910BQZqgb6prWyPoz9HPg9MM8517uO4CIiIn3Qup1ZXl4eVVVVHbYXNjNKS0sJBoOUlJSwceNGTj31VNauXQvAM888w957781ZZ51FZWUlkyZNytTHEZF+soH8VY2Znd7DkBjxG9TeAlY65zYM2JsPQma2vKioqGj58uWZLkVEZFhLtCkDetXO7JNPPuHiiy+murq6zaxwbm4ul19+ObNmzWLkyJEZ+Swiw1VxcTF1dXV1zrnivl47oIFX2lLgFREZ2l566SVmz57NU0891eb4tGnTmD9/fnKDCxFJvc0JvIOhLZmIiMigtNdee/HEE09w5513sv322yePv/rqq/j9fo444ghee+21DFYoIr2hwCsiItINM+Pkk09mzZo1zJs3j3HjxiXP/e1vf6OwsJCLLrqIzz77LINVikh3FHhFRER6YezYsVxxxRXU19dz/PHHJ49v2LCBX/3qVxQUFFBbW6v+vSKDkAKviIhIH0yePJl7772Xxx57jN133z15/MMPP+SMM85g33335bnnnstghSLSngKviIhIP3i9Xl588UVuuummNq3Kli9fzv77788pp5zC+++/n8EKRSRBgVdERKSfPB4Ps2bNorGxkR//+MdtWpXdeeedFBQUcNVVV7F+/foMVikiCrwiIiKbaeLEifz2t79lxYoV+Hy+5PEvvviCefPmscsuu3D//fdrm2KRDFHgFRERGSCFhYWEw2H+8pe/MGXKlOTxN998k+OPP56DDjqIF198MYMVigxPCrwiIiID7Oijj+aVV16hsrKS3Nzc5PEnn3yS4uJiTjvtNK3vFUkjBV4REZEUGDlyJD/72c9Ys2YNZWVleDweIL598cKFC8nPz+fSSy9l3bp1Ga5UJPsp8IqIiKTQtttuS01NDS+88AIlJSXJ4+vWreOyyy6joKCAhQsXqn+vSAop8IqIiKTBXnvtRTQa5YEHHiA/Pz95/L333uO0007j29/+Nk899VQGKxTJXgq8IiIiaXTMMcdQX1/Pr371K7baaqvk8bq6Og488ECOP/543njjjQxWKJJ9FHhFRETSbMSIEcydO5eGhgZmzZrFqFGjkufuv/9+dtllF/73f/+XpqamDFYpkj0UeEVERDJkq6224qabbmLFihV873vfSx7/6quv+PWvf83OO+/MggULiMViGaxSZOhT4BUREcmwXXbZhYcffpglS5bwrW99K3l87dq1nHPOOey+++4sWbIkgxWKDG0KvCIiIoNEaWkpL730EjfffDPbbrtt8nh9fT2HHnoohx56KC+//HIGKxQZmhR4RUREBhGPx8PZZ59NQ0MDc+fOZcyYMclzS5YsYY899uCMM87QxhUifaDAKyIiMghNmDCBX/3qV9TX1/P9738fMwNg06ZN1NbWsvPOOzNv3jw+//zzDFcqMvgp8IqIiAxieXl5/PnPf+bpp59m+vTpyePNzc1cddVVTJkyhQULFrBx48YMVikyuCnwioiIDAH7778/Tz/9NPfddx8FBQXJ4x999BHnnHMOu+22G3/9619xzvXq9VqPa2hoYP78+Vx11VXMnz+fhoaGTseJDFUKvCIiIkPIcccdx+rVq7nxxhvZZpttksfXrFnDUUcdxUEHHcTy5cu7fQ3nHGZGNBrF7/czbdo05syZw7x585gzZw7Tpk3D7/cTjUYxM4VenvBuPwAAIABJREFUGfIUeEVERIaYESNGMGfOHBobG7nooovYYostkueefPJJ9tlnH0488UTefPPNDtcmwm5NTQ0+n49wONwh0DrnCIfD+Hw+amtrFXplyFPgFRERGaImTJjANddcwyuvvMIpp5yCx+NJnrvnnnuYOnUqP/nJT/j444+TxxMzuxUVFT1uaBGLxSgvL0/O9IoMVQq8IiIiQ9z222/PwoULeeGFF/D5fMnjGzZs4Prrr2ennXbi2muvTc7SVlZW9nr3tlgsRlVVVUrqFkkXBV4REZEssddeexEOh1m8eHGbHds+/fRTIpEIZkZDQwORSKRPrxsOh2lsbBzockXSRoFXREQkywQCAVasWEF1dTXf/OY3AZg2bRoAoVCoz+txnXOEQqEBr1MkXRR4RUREslBOTg5nnXUWjY2NXH755UyaNAmApqamfr1ef68TGQxGZLoAERERSZ0xY8bw85//PLkjW25ubr9ep7/XiQwGWRl4zWwWcBOwpXPuk3bnvgFUAocCWwNrgceAK5xzr7QbexxwUBdvU++cu2WgaxcREUmF8ePHA/HlDn1tM2ZmBAKBVJUmknJZF3jNzAOc1cW5rYFlwDeBJ4AHgd2Ak4EjzWw/51x9q0t+BBzXxVs9AijwiojIkFJQUJDsv9tbpaWl5Ofnp7AqkdTKijW8FldkZmcBUaCoi6H/SzzsBp1zBznnznbOfQe4GJgABNuNnwI87JyzTh76T10RERmSgsFgm5693fF4PASD7f/1KDK0ZEXgBbYAlgPVwIHdjDsG+AK4rt3xG4EYUNzu+BTgtQGqUUREJOOcc3i9Xqqrq3sMvR6Ph+rqakpKSrTTmgxp2RJ4m4EftHqs7mLcJuA559z6To47YF3igJl9DchFgVdERLJIYv1uWVkZkUgEv9/fYRc1M8Pv97N06VLKyspobm7m1FNPZfHixQq+MiRlxRpe51wMuDfxs5nN7mLcrl28xDnE/1m07sQ9peX5HTM7CdgD2AA8Azzi9P94EREZohKh1+v14vV6aWxsJBQK0dTURG5uLoFAILlmd+3atRx55JE8++yz3HnnnRx44IFce+217Lvvvhn+FCK9Z9mY28wsCpTQSZeGVmMOAw4Hdge+AzwAnO6c+6zl/MnAncC/iXdzaO0Z4Djn3Ac91LF87NixRYWFhZ2eX758eW8/koiISNp98cUXXHnllVx//fWsX9/2l6NHH300V199NbvsskuGqpNsVVzcfoVpXH19Pc3NzXXOuc4HdCNbljT0x77ALOJhF2DHlkdCYob3BWAvYCzxWd5FwHTgL2Y2nP/5iYhIlttiiy2oqqqivr6ek08+uc2a3wcffJDddtuN0047jbfeeiuDVYr0bNjO8LaMG0M82M4BZgJvA/nOuQ1mtjv/7dKwodU1I4i3NtsbONQ5t6Sb119eVFRUpJlcERHJBitWrODiiy/usM3wyJEjKS8v5xe/+AXbbbddhqqTbFdcXExdXZ1mePvKOfelc261c+5s4HFgMi0zvs65lc65B1uH3ZbjG4H7W37cK60Fi4iIZNCee+7J4sWLeeyxxzjggAOSxzds2MDNN9/MlClT+OlPf8rHH3+cwSpFOho2gdfMDjGzqJmd38WQ51uet+3Fy33W8rxx8ysTEREZWrxeL0899RSLFi1ijz32SB5vbm7ml7/8JXl5eVx55ZXJ7Yxb/za5oaGB+fPnc9VVVzF//nwaGhqS57Lxt84yOAybwAusJ77M4dguzk9ueX7HzMaZ2SYze7mLsf/T8rxiIAsUEREZSo488khefPFF7rrrLqZOnZo83tTUxM9//nNOOOEENmzYgJkRjUbx+/1MmzaNOXPmMG/ePObMmcO0adPw+/1Eo9E+b3ks0lvDKfA+B7wPHGhmbTanMDM/8S2E3wKedc6tI7718K5m9sN2Y73A8cT780ZTX7aIiMjglZOTww9/+ENWr17NLbfcwuTJ8fmjSZMmcfvttzNy5EhqamqS2xm3D7TOOcLhMD6fj9raWoVeSYms6MPbGy03os0B7gYeNbPFwHvAVOC7wJfE25IllilcADwG3NWyZfGrwA7A94jPFp/R0v9XRERk2PN4PMyYMYOysjLmz5/PV199xaRJk4hGo1RUVBCLdf+vzFgsRnl5OXl5eXi93vQULcPGsAm8AM65e80sAFwEHASMB/5FvN9ulXNudauxdS2dGuYBPuI3s/0HuA+odM79I931i4iIDHajRo3iwgsvZNOmTQBUVlb2GHYTYrEYVVVVCrwy4LIy8DrnvN2cCwPhXr7O28CMASpLRERk2MjJyaGhoYFIJNLz4FbC4TCNjY3Jnd5EBsJwWsMrIiIiaRQKhfq8Htc516HPr8jmUuAVERGRlGhqakrrdSJdUeAVERGRlMjNzU3rdSJdUeAVERGRlAgEAphZn64xMwKBAABXXnkl9fX1qShNhhkFXhEREUmJgoICfD5fn64pLS0lPz+ft956i0suuYTddtuNk046qc2ObCJ9pcArIiIiKRMMBvF4PL0a6/F4CAaDANxyyy1s2rQJ5xx33303hYWFnHrqqbz++uupLFeylAKviIiIpIRzDq/XS3V1dY+h1+PxUF1dTUlJCc45DjvsML7zne8kz8diMe644w6mTZvGmWeeydtvv53q8iWLKPCKiIhISiS2CS4rKyMSieD3+zus6TUz/H4/S5cupaysDOccZsaBBx7I448/TigUYv/990+O37hxIzU1NeTn53P22Wfz7rvvbnadrVunNTQ0MH/+fK666irmz5/fZimFtjweukxfXuqY2fKioqKi5cuXZ7oUERGRjEmEWIDGxkZCoRBNTU3k5uYSCASSm0y0Htf++r/97W9ceumltP936qhRo6ioqGDevHlst912/a4tGo1SWVlJJBJpE2zNDJ/PRzAYxOv1dlmjpF5xcTF1dXV1zrnivl6rwJtCCrwiIiIDZ9OmTTz44INcdtllrFixos25MWPGMGvWLC6++GK+9rWv9er1EuG1pqaGioqKbrdATiy5aD0LLem1OYFXSxpERERkSMjJyeHYY4+lrq6Ou+++m1133TV57ssvv+S6664jLy+PuXPn8tFHH/X4eomZ3Z7CLsTXEJeXlxONRhV2hyAFXhERERlScnJyOOGEE1i5cmXyRraEdevWce2117Ljjjty4YUX8q9//avb16qsrOwx7CbEYjGqqqo2q3bJDAVeERERGZJycnL40Y9+xOrVq5M3siU0NzcnZ3wvuOACPvzwww7XNzQ0EIlE+vSe4XCYxsbGza5d0kuBV0RERIa0nJwcysrKqK+v57bbbqOgoCB5rrm5mRtuuIG8vDzOP/98Pvjgg+S5UCjU584LzjlCodCA1S7pocArIiIiWWHEiBGceeaZyRnfqVOnJs99+eWX3HjjjeTl5fHQQw8B0NTU1K/36e91kjkKvCIiIpJVRowYQVlZGatXr6a2trbNGt/169cnlzHk5ub26/X7e51kjgKviIiIZCWPx8Ppp5/Oyy+/zO23384uu+wCwKuvvgpAIBDoc8cFMyMQCAx4rZJaCrwiIiKS1TweD6eeeiqrVq1i4cKFvPvuu7z99tsUFBTg8/n69FqlpaVtbo6ToUGBV0RERIYFj8fDKaecwosvvsjHH38MQDAYxOPx9Pr6YDCYyhIlRRR4RUREZFjxeDzsueeeOOfwer1UV1f3GHoTO62VlJT0ubODZJ4Cr4iIiAxLZoZzjrKyMiKRCH6/v8OaXjPD7/ezdOlSbSs8hI3IdAEiIiIimZIIvV6vF6/XS2NjI6FQiKamJnJzcwkEAsk1u01NTaxdu5YpU6ZkuGrpKwVeERERGdZaz9jm5+cze/bsNuffeustFixYQHV1NWvXruWoo47ikksuoaioKN2lSj9pSYOIiIhIJ5xzLFu2jFNOOYWrr76atWvXArBo0SKKi4s59NBDeeaZZzJcpfSGAq+IiIhIJ8yMfffdl8cff5xIJMJ3v/vdNueXLFnCAQccQElJCY899phuZhvEFHhFREREumFmHHLIITz66KM88cQTHTasePzxxzn44IPZb7/9+Nvf/qbgOwgp8IqIiIj00oEHHsjixYtZtmwZxxxzTJt2Zs8//zxHHHEEe+65J/fddx+bNm3KYKXSmgKviIiISB/ts88+PPDAA7z44ouceOKJjBw5Mnlu5cqVfP/732fXXXfljjvuYOPGjRmsVECBV0RERKTfdt99d/70pz+xatUqTj/9dEaPHp089+qrr3LqqacydepUqqur+eqrrzJY6fCmwCsiIiKymaZOnUptbS319fXMmDGDcePGJc+98cYbVFRUMGXKFH7zm9/Q3NycwUqHJwVeERERkQGy0047ccstt7BmzRrOO+88JkyYkDz37rvvct5557HDDjtwxRVX8Mknn2Sw0uHFdCdh6pjZ8qKioqLly5dnuhQRERHJgI8++ohrr72WW2+9lY8//rjNuQkTJjBz5kwuvPBCtttuu36/R+vtjhsaGjrsFFdQUNBh3FBUXFxMXV1dnXOuuK/XKvCmkAKviIiIAHz66afceOON/O53v+PDDz9sc27UqFGcdtpp/OxnP+vztsWJEBuNRqmsrCQSibRpi2Zm+Hw+gsEgXq93SIfezQm8WtIgIiIikmITJ07kF7/4Ba+//jrXXXcdeXl5yXNfffUV1dXVFBQUcOKJJ/KPf/yjV6+ZCK81NTX4fD7C4XCHHsDOOcLhMD6fj9raWsxsWPYJVuAVERERSZNx48ZxwQUXsGbNGm677TZ23XXX5LlNmzZxzz33sOeeexIIBHjqqae6fa3EzG5FRQWxWKzbsbFYjPLycqLR6JCd4d0cCrwiIiIiaTZy5EjOPPNM/vGPf3Dfffex3377tTn/yCOPcOCBBzJ9+vRud2+rrKzsMewmxGIxqqqqNrv2oUiBV0RERCRDPB4Pxx13HM888wxLlizB5/O1mYF99tlnOeKII9h9992566672mxi0dDQQCQS6dP7hcNhGhsbB6z+oUKBV0RERCTDzIzS0lLC4TDPPvssxx57bJtti19++WV+9KMfkZ+fn1zqEAqF+rwe1zlHKBQa0NqHAgVeERERkUFk33335f7772flypWcfvrpjBkzJnnurbfeYvHixQA0NTX16/X7e91QpsArIiIiMggVFhZSW1vLq6++yrnnnktubi4An3/+OUDy577q73VDmQKviIiIyCA2efJkfvOb3/D6668zb948PvjgAwACgUCfOy6YGYFAIBVlDmojMl2AiIiIiPRs66235oorruDzzz+nqamJgoKCZP/d3iotLSU/Pz+FVQ5OmuEVERERGULGjx+fXJYQDAbb3NzWHY/HQzAYTGVpg1ZWBl4zm2Vmzsz+XyfnvmFmtWb2vpl9ZWbvmdmdZrZLF69VZmYvmtkXZvaumd1mZl9P/acQERER6ZpzDq/XS3V1dY+h1+PxUF1dTUlJiXZaywZm5gHO6uLc1sAy4HSgAfgD8BpwMrDMzArbjZ8H1ABbA3cCdS3XPmdmX0vVZxARERHpSWKb4LKyMiKRCH6/v8OaXjPD7/ezdOlSysrK2LBhQ/Kmt+EkK9bwWvzb3bvlUQYUdTH0f4FvAkHnXHKrETP7KXA1EARObTm2M3Ap8DJwgHOuqeX4GcSD8jXAmQP/aURERER6JxF6vV4vXq+XxsZGQqEQTU1N5ObmEggEkmt2165dy5FHHsmqVasoLy/nwgsvZPvtt8/wJ0gPy4ZpbTMbD3zWyaktnXOftBr3CrA9sLVzbn2r42OAz4E1zrldW479CpgLnOCc+3OrsQb8k/is71bOuXXd1LW8qKioaPny5Zv1+URERET66+OPP+amm27ixhtvZO3atcnjI0aM4IQTTuBnP/sZ3/rWtzJYYe8UFxdTV1dX55wr7uu12bKkoRn4QavH6i7GbQKeax12Wx13QOvw6m053mY7Ehf/L4QngNFA242vRURERAaZLbfckvPOO4+LL76YnXbaKXl848aN3HXXXey+++4ceuihRKPRrF3fmxWB1zkXc87dm3gAH3Uxblfn3CGdnDqH+PKO1htS7wm845zrbOa4vuW5YHPqFhEREUmHCRMmcOGFF/Lqq69y++23s/fee7c5v2TJEr773e9SXFzM3XffzcaNGzu8Rusw3NDQwPz587nqqquYP38+DQ0NnY4bLLJiSUN7ZhYFSmi3pKHdmMOAw4Hdge8ADwCnO+c+M7Nc4FNgmXOuwyyumc0BbgR+5py7ups6lo8dO7aosLCw0/Na6iAiIiKZsGnTJpYsWcKvf/1rHn300Q4hdYcdduCCCy6goqKC8ePH45zDzIhGo1RWVhKJRNpcY2b4fD6CwSBerzc5vj+KiztfsVBfX09zc/OwXtLQH/sCs4iHXYAdWx4AiT332i99SPii5TkrbvoTERGR4SUnJ4dAIEAkEuH555/nxBNPZNSoUcnz77zzDj/5yU/YY489+OKLLzAzampqkhtdtA/IzjnC4TA+n4/a2trkzXSDxbANbM65S83samAKMAeYCfzNzPKBDS3DxnZxeeJ/EV3esJZQWFiomVwREREZtIqLi/nTn/7E66+/znXXXcfChQtpamoC4Ic//CFbbLEF0WiUiooKYrFYt68Vi8UoLy8nLy8Pr9fbr3q6yk0tN6316zWH8wwvzrkvnXOrnXNnA48Dk4nP+P4HiAEdNq5osXXL83upr1JEREQk9aZMmcJvf/tb3nzzTa666iry8vKYOXMmAJWVlT2G3YRYLEZVVVXPA9No2AReMzvEzKJmdn4XQ55ved7WObeB+IYUO5nZFp2MTWxCvWqg6xQRERHJpC233JJgMMiaNWuYPHkyDQ0NRCKRni9sJRwO09jYmKIK+27YBF7i63FLgGO7OD+55fmdluelgAc4uPWglp3cDiE+u/vywJcpIiIiknkjR44EIBQK9Xk9rnOOUCjU88A0GU6B9zngfeBAMzuw9Qkz8wPHAW8Bz7YcvpV4b97LzKz1Wt6LiW9ecZMbTKuxRURERFIgsZ43XdelwrC5ac05t6GlndjdwKNmtpj4LO1U4LvAl8Tbkm1sGf9Sy25rFwGrzGwp8b67XuAF4Lr0fwoRERGR9MrNze150ABelwrDaYaXlk0pAsDfgYOAcmAX4E5gH+fc39uN/ynx1mVfAqcCOwM3AIc4575MY+kiIiIiGREIBPrcU9fMCAQCKaqo77Jyhtc55+3mXBgI9+G1bgZuHoCyRERERIacgoKCZP/d3iotLSU/P7/ngWkyrGZ4RURERKTvgsEgHo+nV2M9Hg/BYDDFFfWNAq+IiIiIdMk5h9frpbq6usfQ6/F4qK6upqSkZFDttKbAKyIiIiJdSmwTXFZWRiQSwe/3d1jTa2b4/X6WLl1KWVkZzrk+r/tNpaxcwysiIiIiAycRer1eL16vl8bGRkKhEE1NTeTm5hIIBJJrdgdb2AUFXhERERHphdYhNj8/n9mzZ/c4brDQkgYRERERyWoKvCIiIiKS1RR4RURERCSrKfBmieLiYoqLizNdhqSIvt/spe82u+n7zW76focOBV4RERERyWoKvCIiIiKS1RR4RURERCSrKfCKiIiISFZT4BURERGRrGbOuUzXkLXM7N9jx47dqrCwMOXvVV9fD0A63kvST99v9tJ3m930/WY3fb/pVV9fT3Nz83+cc1v39VoF3hQyszeAXODNDJciIiIiMtTlAU3OuZ36eqECr4iIiIhkNa3hFREREZGspsArIiIiIllNgVdEREREspoCr4iIiIhkNQVeEREREclqCrwiIiIiktUUeEVEREQkqynwioiIiEhWU+AVERERkaymwCsiIiIiWU2BV0RERESymgKviIiIiGQ1BV4RERERyWoKvCIiIiKS1RR4RURERCSrKfCKiIiISFZT4BURERGRrKbAKyIiIiJZTYFXRERERLLaiEwXkM3M7A0gF3gzw6WIiIiIDHV5QJNzbqe+XqjAm1q5Y8eO3aqwsHCrVL9RfX09AIWFhal+K8kAfb/ZS99tdtP3O/SsX7+eTz/9lE2bNpGTk8PEiRMZPXp0p2P1/aZXfX09zc3N/bpWgTe13iwsLNxq+fLlKX+j4uJiANLxXpJ++n6zl77b7Kbvd/BzzmFmRKNRKisriUQiOOeS59999118Ph/BYBCv15scD/p+0624uJi6uro3+3OtAq+IiIgMS4nwWlNTQ0VFBbFYrNMx4XCYRx99lOrqasrKytqE3sGgdT0NDQ2EQiGamprIzc0lEAhQUFDQYdxwo8ArIiIiw1JiZrersNtaLBajvLycvLw8vF5vegrshZ5mqM2syxnq4URdGnpgZlub2Xwze8PM1pvZWjP7i5ntnenaREREZPNUVlb2GHYTYrEYVVVVyZ9zcjIbo1rPUPt8PsLhcJuwmxgTDofx+XzU1tZiZh3GDAcKvN0wsy2B54HZwPtALbAKOBp40syKM1ediIiIbI6GhgYikUifrgmHwzQ2NgKw//77p6KsXuvPDHU0GtUMr3RwMbATcJVz7gDn3EznnBe4ABgH3JTJ4kRERKT/QqFQn2c7nXOEQiEAdtxxx1SU1SebM0M9nGgNb/eOA5qBq9odvxH4KbCfmU12zr2d9sra0R2i2U3fb/bSd5vd9P0Obk1NTZt13dy5c5k7d+5AltQnmzNDnZ+fn6KqBifN8HbB4vP9ecArzrk2Td9c/D8H/9ny4/ZpLk1EREQGQG5ublqvG2ibO0M9nGiGt2s5wA+Ate1PmFkusEvLjx+msygREREZGIFAoM83cZkZgUAghVX13ubOUA8nCrxdcM7FgL+0P25mHmABMB5Y6Zx7rbvXqa+vTzambk+/6hIREcmcgoKCZHeD3iotLR00ywGG+gx1V7rKTYmd7fpDSxr6wMx2BqLAD4EvgZkZLUhEREQ2SzAYxOPx9Gqsx+MhGAymuKLeS8xQ98VgmqFOJxuOvdj6ysxGAnOBnwNjgbeBk5xzz/Rw3fKioqIizeSKiIgMPok+trW1tZSXl3fb7cDj8QzKndb8fn+fZqj9fj+PPPJICitKnZatheucc31uC6sZ3h6YWR7wLFAJjAJ+C3yrp7ArIiIig1ti/W5ZWRmRSAS/398hyJoZfr+fpUuXDrqwC0N7hjqdtIa3G2b2DeBJ4JvAS8AZzrmXMluViIiIDJRE6PV6vXi9XhobGwmFQjQ1NZGbm0sgEEiu2R1sYTdRd3V1da9nqEtKSgbd50gHBd7uXUs87D4MHOecW5/hekRERGSAtQ5/+fn5zJ49u8dxg0HrGeq8vDyqqqo6bC9sZpSWlhIMBodt2AUF3i6Z2TjgGOAj4ut1FXZFRERkUBnKM9TppMDbtWJgDPAWcEU3/wO53Dn3n7RVJSIiItLKUJ2hTicF3q5t1/I8reXRlRsABV4RERGRQUqBtwvOuT8Dw/c/hURERESyhNqSiYiIiEhWU+AVERERkaymwCsiIiIiWU2BV0RERESymgKviIiIiGQ1BV4RERERyWoKvCIiIiKS1RR4RURERCSraeMJERGRYcg5l9xqtqGhgVAoRFNTE7m5uQQCAQoKCjqMExmqBm3gNbOVwIHOuU8zXYuIiEg2SYTYaDRKZWUlkUgE51zyvJnh8/kIBoN4vV6FXhnyBvOSht2A0e0PmtlEM7spA/WIiIgMeYnwWlNTg8/nIxwOtwm7iTHhcBifz0dtbS1m1mGMyFAy6AKvmT1sZpcCDtihkyHjgJlpLUpERCRLJGZ2KyoqiMVi3Y6NxWKUl5cTjUY1wytD2mBc0vAy4AUMWGZmnwErgBeBfwC7AO9nrDoREZEhrrKyssewmxCLxaiqqsLr9aa2KJEUGnSB1zn3vwBmth6YDnwD2KvlcTjxmi/KWIEiIiJDWENDA5FIpE/XhMNhGhsbyc/PT1FVIqk16AJvK1sAMedcHfBQposRERHJBqFQqM/rcZ1zhEIhZs+enaKqRFJrUK3hNbN7zWxcy487Oa2QFxERGVBNTU1pvU5kMBhsM7wfACNb/vyqmX1BfN3uS60eK51zX2aoPhERkSEtNzc3rdeJDAaDLfB6gA0tf96B+LrdPVue5wI7A87M1jjndstMiSIiIkNXIBDoc5sxMyMQCKSwKpHUGlRLGoCzgAktf/4N8HfnXKVz7gTn3DQgFzio5ZyIiIj0UUFBAT6fr0/XlJaW6oY1GdIGW+B9B9iv5c/HEu+5m+ScW+ece8Y5d0vaKxMREckSwWAQj8fTq7Eej4f/v717j4+rrvM//vowbaG3QOkFEIECDQUXkaZsFbUkukkIiBdYQF1QCpaLu1gV1p8aEXBdUrwACvhb0Swt6uquXGRllWjSNijIRVtYQQpN5KrlVqCkF5pC8tk/zpkwmcwkc5JM5pzJ+/l4zON0zvl+Zz6T05nzme98L42NjUWOSKS44pbwXg7cbGbrCBaeOMvMjjEzdRwSEREZBe5OTU0Nzc3NQya9qVSK5uZmqqurtdKaJFqsEl53/z5wOPCfBAtPLAHagJfN7DEzu8XMLjazD5QwTBERkcRK999dsmQJbW1t1NfXD1hFzcyor69n1apVLFmypG85YpGkitugNdz9UeDrZvYJ4N3AFoIkOL34RB1wAbBHyYIUERFJsHTSW1NTQ01NDZ2dnbS0tNDV1UVFRQUNDQ19fXaV7Eo5iF3CmxYOUktbG95ERERkFGQmsfPmzcu7qISSXSkHJevSYGaXmtkzZrbTzDrM7CIzmzh0TRERERGRwpUk4TWzs4CLgb0IWpkPBr4C3FSKeERERESkfJWqhfc8YCdwGvBmoBZYB5xgZqeUKCYRERERKUOlSngPBm5y95+4+0Z3X00wGO1l4OMliklEREREylCpEt4ZQGfmDnffDPwCqCpJRCIiIiJSlko5D29vjn1PATPHOhARERERKV+xWngCeB3QTA0iIiIiMmpKOQ+6ZwkyAAAgAElEQVTvl8MBavcC94W32M4LLCIiIiLJVKoEcxWwAPib8HZm5kEz+ybwAHA/sN7dc3V/EBEREREZUkkSXnevAzCzg4CjMm4LgN0Jlg72sHi3mT0E3O/u55YgXBERERFJsJJ2IXD3x4DHgJ+m95nZIfRPgo8MtwsBJbwiIiIiEkns+sy6+wZgA/BjAAsW8T6MIOEVEREREYkkdglvNnd34OHwJiIiIiISSdymJRMRERERGVVKeEVERESkrCnhFREREZGyFvs+vCIiIiPl7gRjoKGjo4OWlha6urqoqKigoaGBysrKAeVEpHzELuE1sx8Af3X3L5Y6FhERSb50Etve3k5TUxNtbW0E46EDZkZtbS2NjY3U1NQo6RUpQ3Hs0nA68MFSByEiIsmXTl5XrFhBbW0tra2t/ZLddJnW1lZqa2tZuXIlZjagjIgkWxwTXhERkVGRbtk9++yz6enpGbRsT08PS5cupb29XS28ImVGCe8QzGyKmX3VzDrN7FUz22Bml5jZxFLHJiIiQ2tqahoy2U3r6elh+fLlRY5IRMZa7PrwxomZTQBuAY4F1gKrCJY6vhR4u5m9z/W7l4hIbHV0dNDW1hapTmtrK52dncybN69IUUm5SKcAmalA9r582yhlo9QpVgwTJ05k5syZpFIpIHmDP5XwDu50gmT3h8AZ6eTWzFYCZwAfA35QsuhERGRQLS0tkfvjujstLS2cf/75RYqq/3MNti2kTFLLFiuJG8u4o5QrVtlCXsNIy+6+++4sWrSIVCqV2MGfSngH909AD/D5rJbc5QQJ71KU8IpICZX64l+ssiN9/N13350ZM2bQ1dXFcKTrPf/882zatKngWIYb71DlCn2usXrcoV7LSB+3WDGMdlkgZ1JnZv32p/+dXXawMvket5Cyo/m4EydOZOHChaRSKVasWJG3P7x7MPhz9erVNDc3s2TJklglvUp48zCz6cAC4H53fybzmLs/ambPAe8ws13dvbskQYqMUDklOKWKpZTxRtlX6OMkoWx2uVzHDjzwQGbMmEFFRcWAsoVI19u4cSOdnZ3DiqGQspA/WYmSMBW7bK46hZYd6vmixhAlYSvWa4tLEjcW9t57byZNmhR58OfcuXOpqakZmyALoIQ3vyOAFPCnPMfXAzXAAcCGMYopdpKeVBQrhqTEXeixKM9VqscdT2Wz74/VBb1UZXPFv8suu7DnnnsydepUJkyYQE9PD9u3b+fFF18E4PXXXwegoaEh8jRjZkZDQwMQ9FvcZ599hvXaorayicTR7NmzgeEN/lTCmwwzw+1zeY6/HG73HOxB/vjHPxb8wXb22Wfzve99r9++c845h+9///sF1z/33HP77fvsZz/Lb3/724LqNzY2ctJJJ/Xbd/rpp/PII48UVP+KK65g8eLF/S7SJ5xwQr+fAwfT3NzMoYce2nff3Vm8eHFBdQFuvPFGZs6c2Vd306ZNfPjDHy64fktLS7/7HR0dfOpTnyqo7p577skNN9zQ99wA9957L5dddllB9Q866CC+8Y1v9Lsot7a2ct111xVUv6qqis997nN9992dm266iVtuuaWg+u95z3v4xCc+0XffzGhubmbNmjUF1T/55JM59dRT++oCXH755axdu7ag+p/85Cc59thj++278MIL+fOf/1xQ/Ysvvpi3v/3t/ZKJ0047jZdeeqmg+t/5zneYP39+v321tbUF1QW4+eabmTVrVt9zb9q0acB7aTB33313X12ARx55hCVLlpBKpairq2P+/PlMmzaNrVu38uijj/LrX/+a3t5eAGbNmjXg/+5vfvMbLrjggoKe+9BDD+VHP/pRv3233HILTU1NBdVfvHgxV111Vb991113XcGfWyeeeCJf+tKX+u277LLL+NnPfpaz/KxZszjnnHM499xz2X///Qcc7+7u5oYbbuC0007jvvvuo7Kysm/+3ULV1dUxb948nnzySY444oi+v3UuV155Jcccc0y/fQ0NDQV/7v3whz/ksMMO67fvqKOOKjjW22+/vS8hAXjhhRc47rjjCq7/hz/8od/99evX87GPfayguuPt/162Ul9zx+L/XkNDA7fffvuIB38ed9xxA/6vZH8JXbt2baT/+1Ep4c0v/TtYvu4K28JtrP6G7t7vNtgHdbbu7m62bt3ar36h3+Yg6PP2wgsv9MUBRHr+zZs3971Ro7TGpL344ot9cafvR5H9IfHyyy/nKTlQb28vmzdv7rdv+/btkeqny6eTnnQLVSHMjF133bXfz4eTJk0quP5uu+3WL2FL7yvUlClTmDVrVr+EM8rzT5s2re/50/UnTCj8rVVRUdF30c9sAYxSf4899ii4fLYpU6YwderUvvuTJ0+OVH/ixP6zHE6ZMoXGxsa8Sd2TTz4Z6cJeLo4++mh+/vOf9/1fyTdK/JxzzuGkk07ipptu4rzzzqOxsZHVq1cX9HmWSqVobGwEguQpymeYSLlJNwSMdPDnIYccMiDhHWuxStZi5rVwm+/Klb6aD5rVzJ49m2eeeWawIqNmxowZ7Lvvvn3/Kc0s0oV3r7324pBDDgGC/6hmFinpmTt3LkcccURffSBS0jJv3jwOP/zwQX/yHcz8+fOZM2dOX73nnsvXOJ/bW97yln7PHeX5J0yYQGVlZb/6Uc77pEmTOOCAA/rVj5KATZw4sa/P4XD+9ukvR5mvOeqXpczBQe7Oa6+9NkiN/l555RU2btzYVxdg586dBdffuHEjGzb071kU5QvDk08+2ff3Gs5PzU8//TTbtm3rq1toy3Ja+rWbGTNmzOCEE07glFNOAfIndU1NTVxwwQWcdtpp/b6smRlbtmwp+Ll7enr6nTszY8eOHZHqZ5eP8kW5t7eXnp6efl+Wcr33jj76aFatWsXkyZMLHiV+xhlnsHnzZmpqamhubmbp0qWDxpZKpWhubqa6uppNmzaNuy8UYyHzV4sDDzyQOXPmsGPHjmEPLpTimjZtGsCIB39Onz591GIaLhtOS1oxmdkTQIe715U4jvcAq4Fmdz87x/E24O+Afdz92TyPsbaqqqqq0J91JZ6S1Lc37rFMmTKFSZMmYWb09vbS3d3Ntm3bxiSGocpFLVvo80d53N13350FCxZEmvqnp6eH++67r+8XhqFiGCyW4fwdhvO4UWPYbbfdOOGEE9htt90GHSWelk5clyxZQnd3N6lUigkTJtDe3s7y5csHLC9sZtTV1dHY2Eh1dTWvv/46d911Fy+99NKYjIIf7uPGqexQg9AmTJjArFmzmD17ds5ff7q7u9m0aRObNm2K9GVVimvOnDnst99+XHPNNSxbtixy/WuuuWZUp/dbuHAh69atW+fuC6PWjV3CGxdmtg+wEbjX3d+R4/gTwFR3n519LKOMEl6REhvrLwXDTcwh+JVml112iZzU9fT08Pzzz/eVj9MXq5GWhWCU+L777kt7ezu1tbUFd01oa2ujpqaGZ599lpkzZ/Z1Hens7BzQap5eZGLnzp386U9/GtCiNdpfbqI+bhzKDve1zZ49m+rq6r5fDAdbsGDHjh3ceeed/bqkldsgzOGULdYXrKHKVlRUUFlZSUdHB/Pnz895nvMxMzZs2DCqC7go4S0SM3sYOBh4k7u/mLF/PvAI8BN3/4dB6ivhlXHP/Y15GJO2Mk8pDDepK1fpa5SZUV9fH2nwWX19Pb/61a9wd3bs2MGECRNIpVI5+3f39vby2muv8dprr/V15xnrhD9KnTjEUEjZqVOnUllZGflXi4cffpitW7f2PVaURD3736UoG8cvLMMpa2Z86EMfYurUqcN+/40mJbxFYmbLgG8D17n7eeG+icCtwPHAu939rkHqK+GVcS39gZnUlXlKIQ4XlTiKSwuTRJN+T0f91cLd2blzZ6wT/mI/brFfa6FlhvsLy6pVq6iurh6ybBQjSXj7/qi6DbwRDOq7C3DgbuC7BPPvOnBNAfXXVlVVuch41Nvb6+7u119/vadSKQ/fNzlvqVTKV6xY0a/eeLRhwwY3s0H/Vtk3M/OOjo5Sh150V199daS/S/p2zTXXlDr0cW/NmjVDfgZkfhasWbOm1CFLht7e3r7P5RUrVpT087yqqsqBtT6MnK7weXvGIXd/HagDrgTeTLCcsAPLwpuI5JFu2Y2yMk97e/u4buEdydQ/5W6ko8SldIazYIHER3q6S3dnyZIltLW1UV9fn7OfcX19PatWrYrdssKgacmG5O7bgQvDm4hEkPSVecaakrr8RrpEsJTGSBcskPhIJ701NTXU1NQMOvgzbskuKOEVkSIplwtd5gd3sQfdKanLb6RLBEtpjHTBAomXzM+4efPm5T1HcUt2gfh2aTCzG83snIz7883sFDPLOw2YiMRHOfw8n05i29vbqa+vZ/78+SxbtoyLLrqIZcuWMX/+fOrr6/u6YkR9vdnSSV0U4yWpSy8RHEV6iWApHf1qIXER24QXOAZ4AMDMZgL3As3An8zsraUMTESGlvQLXTrZXbFiBbW1tQMWK0iXaW1tpba2lpUrV4446VVSN7jGxkZSqVRBZTOXCJbS0a8WEhdxTninA+m1Wf8eeAKYCXwfuKxEMYlIgZJ+oSvVoDsldbml+w42NzcP+ffJXCJ4pK3uMjL61ULiIs4J71MEiz4AnAz8IJw1YSUwYOUzEYmXcrjQjfXociV1+ZXDKPHxSL9aSFzEduEJM/t/BNOA/RL4DDDf3R8zs8OA37v7tJIGWAAtPCHjXZIXUSjVQgeZ/YaXL18+oCuFmVFXV0djY2NfsjuekrrM15u0UeLjVRwWLJDyMJKFJ2I7S4O7fz38sDoW+Gd3fyw8tAh4smSBiUjBGhsbWb16dcEXujj9PF+q0eVJn/qn2JI8Snw8yvzVYunSpQWttDYev8hJ8cU24YUg6QW+nrV7L+A/SxCOiESQ9AtdKQfdxTWpG8sp2qQ8ZHZFmTt3rn61kJKJdcJrZrsQ9ONNAZ3u/nqYBItIzCX9Qpf0QXejLbOrRVNTE21tbQPOZW1tLY2NjdTU1MTqXEpp6VcLiYM49+H9W+CnwP6AAVuAHwIXu/tLpYytUOrDK5LcPpel6sMbR5lTtA01a0W6tV6DxkRktJVlH17ge8BDwPHAduBvgU8D68zs7e7+XCmDE5HCxPXn+aGkR5dHGXRXrqPLhzNF29y5c8f1MtEiEi9xnpasErjA3de7+5PufpO7LwbuBK4pcWwiMg5oTtw3jPUUbSIioynOCe89BAPUsl1K0OorIlI0mhP3DR0dHbS1tUWq09raSmdnZ5EiEhGJJlYJr5mtMrMrzOx04HrgW2a2X1axmcCLYx+diIwnWujgDSOZok1EJA7i1of3buBI4MPAm8J9HWZ2C7COYLaGjxP05RURKSqNLg+Ucoo2EZHREKuE190vSv/bzGYBC8LbkcBZBP16HWgCbi1FjCIyviR10N1o0hRtIpJ0sUp4M7n7JqA1vAFgZpOBt4U3EREZAw0NDX2t3YUyMxoaGooYlYhI4WLVhzeTme1qZl8zs/Vm9piZ/Tdwgrvf4+7XlTo+EZHxIj1FWxTlOkWbiCRTbBNe4JvAqYSD14BngOvN7GYzi23LtIhIOdIUbSKSZHFOHE8BTnL336V3mNklwC+BLwD/WqrARGR0ZQ746ujoGDAwrLKyckA5GTuZU7QtXbo073y8qVSKhoYGLr74YhYtWqTzJSKxEeeEdzfg+cwd7v6cmX0WWIESXpGykE6K2tvbaWpqoq2trV9fUTOjtraWxsZGampqipZExSXpjkscmTKnaJs7dy7Lly+ntbW17zzNmjWLc889l2XLljFnzpx+9URE4iDOCe8dwCeAL2bt/wu5F6QQkYRJJ20rVqzIu2ytu9Pa2srq1atpbm4uyny3cUu6Sx1HLvmmaJs+fTqnnnoqkydPBuKTpIuI9OPusbwBhwEvA98D3kLQ33g34NvAvaWOr8DXsLaqqspFJL81a9Z4KpVygikHB72lUilfs2bNqD5/b2+vu7tff/31Q8aRSqV8xYoV/eqVWxxRpZ9/zZo1XldX52bWL1Yz87q6ur7zVup4RSS5qqqqHFjrw8jJYjtozd3XA9XAEcBDwA5gK/ARtPCESNloamrK2yc0W09PD8uXLx/V50+3qOZrYc5+/qVLl9Le3j7qLZVxiSMKz2ihr62t7dfNIbNMa2srtbW1rFy5MvL0ZiIioyG2CS+Au//R3d8BHEqQ6B4HVLr7PaWNTERGQ0dHB21tbZHqtLa20tnZOapxlDrpjlschUpiki4i41PJEl4zu9TMnjGznWbWYWYXmdnEXGXdfYO73+Lure6utSpFykRLS0vk1j53p6WlZdRiiEvSHZc4okpaki4i41NJEl4zOwu4mGDw2QTgYOArwE2liEdESqOra3jfX4dbL5c4JN1xiiOKpCbpIjL+lKqF9zxgJ3Aa8GagFlgHnGBmp5QoJhEZYxUVFWNaL5c4JN1xiiOKJCbpIjI+lSrhPRi4yd1/4u4b3X01UEcwK8PHSxSTiIyxhoaGyP05zYyGhoZRiyEOSXec4ogiiUm6iIxPpUp4ZwD9ftNy983AL4CqkkQkImOusrKS2traSHXq6uqYN2/eqMUQh6Q7TnFEkcQkXUTGp1LO0tCbY99TwMyxDkRESqexsZFUKlVQ2VQqRWNj46g+fxyS7jjFEUUSk3QRGZ/iNi3Z60DOmRpEpPx4uHJXc3PzkElvKpWiubmZ6urqUZ/HtdRJd9ziKFQSk3QRGZ9KmfB+2cweNLNmMzvHzI4k3ksdi8goSy9CsGTJEtra2qivrx/QYmhm1NfXs2rVqqItKxyHpDsucUSVtCRdRMYnK8WHpZm1AguAPcNd2UFcBTwA3A+sd/dc3R9iz8zWVlVVVa1du7bUoYjEWmYS29nZSUtLC11dXVRUVNDQ0NDXIjjayW7287e3t7N8+fIBK4aZGXV1dTQ2NvYlmeUcR9R4V65cydKlSwedjzedpBfjS4uIjA8LFy5k3bp169x9YdS6JUl4+57c7CDgqIzbAmD38HA6sG6CpYXvd/dzxzzIEVDCK5IcpU664xZHoZKWpItIciU24c3FzA6hfxJ8JDANcHcv7HezmFDCKyLjQdKSdBFJppEkvLHrM+vuG4ANwI8BLPh0PAyI/OJERKT4MpPYefPmcf755w9ZTkRkLMUu4c3mQRP0w+FNRERERCSSuE1LJiIiIiIyqpTwioiIiEhZU8IrIiIiImVNCa+IiIiIlDUlvCIiIiJS1pTwioiIiEhZU8I7BDOrNrPbzWyTmXWbWaeZXW5m00odm4iIiIgMTQnvIMzsJGA1UB1uf0wwd/HngRYzi/08xiIiIiLjnRK2PMxsF+BaoBt4u7s/GO6fCvwaeBewFPhuyYIUERERkSGphTe/o4F9gP9KJ7sA7r4N+Gp499RSBCYiIiIihVPCm99B4fb+HMeeDrdvHqNYRERERGSY1KUhvzuBE4F1OY4tCrfPjV04IiKjw90xMwA6OjpoaWmhq6uLiooKGhoaqKysHFBORCTJlPDm4e6PA49n7zezg4Gm8O7Phnqc9evXs3DhwpzH1q5dO5IQRUQiSyex7e3tNDU10dbWhrv3HTczamtraWxspKamRkmviIy5fHnT+vXrh/2Y6tIQgZl9BLgP2Dvcfqe0EYmIFC6dvK5YsYLa2lpaW1v7JbvpMq2trdTW1rJy5UrMbEAZEZGkMX2QDc3M5hIkt8eHu24GznL3riHqra2qqqpSS66IxEV7ezu1tbX09PQMWTaVStHW1kZNTU3xAxMRGcLChQtZt27dOnfP3QQ8iHHVwmtmc83MC7ztEdb5B+BBgmR3I/ARdz95qGRXRCSOmpqaCkp2AXp6eli+fHmRIxIRKb7x1od3K/AfBZbdaWZnACvC+9cCje6+pSiRiYgUWUdHB21tbZHqtLa20tnZybx584oUlYhI8Y2rhNfdNwGnF1LWzGYSJLkGnOHuPyhmbCIixdbS0hK5P66709LSwvnnn1+kqEREim9cdWmI6IPANOC6JCS7CxcuzDuqUZJP57d8jeW57eoaXk+s4dYTvXfLnc5vcoyrFt6I3hVu55jZt/KU6XT3a8cqIBGRkaioqBjTeiIicaGEN7+9w+2Jg5S5g6Dbg4hI7DU0NESeZszMaGhoKGJUIiLFpy4Nebj7+9zdhrjVlDpOEZFCVVZWUltbG6lOXV2dBqyJSOIp4RURGUcaGxtJpVIFlU2lUjQ2NhY5IhGR4lPCKyIyTrg7NTU1NDc3D5n0plIpmpubqa6u1kprIpJ4WmmtiMzsxcmTJ+952GGHFf250utLj8VzydjT+S1fpTq3W7Zs4dlnn2XLloFTi0+fPp199tmHadOmjWlM5Ujv3fKm8zu21q9fz6uvvvqSu8+MWlcJbxGZ2eNABfBEiUMREQFg+vTpUw866KB5qbCJt7u7m66uLnp6ekilUlRUVLDrrrsC0NPT0/PYY491btmyZVtJgxYRCcwFutz9wKgVlfCKiIiISFlTH14RERERKWtKeEVERESkrCnhFREREZGypoRXRERERMqaEl4RERERKWtKeBPOzKaY2VfNrNPMXjWzDWZ2iZlNLHVsUjgzm2lm15jZ42bWbWabzOxWM1uQVW6CmV1gZg+b2XYze8LMrjKz6aWKXaIzs/PNzM3sM1n7dX4Tyszeb2Z3mdkrZtZlZneY2d9lldH5TSAz28fMms1so5ntNLOnzexaM5uRVU7X4xjTtGQJZmYTgP8BjgXWhrcjgUXA7cD7XCc49sIPzbXAgcDdwIPAfKAa2A4c4+5rw7LNwCeAR4DfAPOA9wIPAEe7+44xfwESiZkdADwETAM+6+7fyjim85tAZnYe8G/As8AvgL0IPpchOG96/yaUmc0k+Hw+AGgjmFf/KIJr7Z+Ad7j7Vl2PE8DddUvoDVgCOPADwi8v4f6V4f6PlzpG3Qo6j18Lz9e/Zu3/TLj/nvB+TXh/FTApo9xXwv0Xl/q16FbQ+f5VeL4c+EzGfp3fBN6A/YCdwDpgj4z9x4Xn7Tad3+TegG+E5+fCjH0GXB/u/0K4T9fjmN/UwptgZvZ7YAGwn7s/k7F/PkELwm/d/ZhSxSeFMbMOYF9gpru/mrHfgI3A3gStC1cAJwOL3P33GeWmAS8Bz7r7/mMZu0RjZmcB/w78N/BBMlp4zexGdH4Tx8y+DnyOoKXv3qxjdwD7u/uBOr/JZGYPAG8FpnpGC7yZVQIbgNvd/Xhdj+NPfXgTKuzztQC4P/PNBeDujwLPAe8ws11LEZ8UJkxq5wKPZCa7AB58G/1LePfNwDHAC5kXy7DcVoLWpf3MLPJyizI2zOxNBF9abgRuzVFE5zeZ6oEnspNdAHev9jeWQNX5TSYjaKHNlu6Xu03X42RQwptcRwApgj5EuawneEMeMGYRyXDsApwCLMs+YGYVwKHpu8Ac4OE8j7M+3FaOdoAyav4/wYXzU9kHzGxfdH4Tx8ymErT+PWBmu4QD174a3hrCL7Q6v8l2B8G1tu8z2sx2Ab4Q3l2NrseJMKHUAciwzQy3z+U5/nK43XMMYpFhcvcecrT2mVkK+C7BwKYHgS3hIZ3vBDKzjxB0YTjT3Z8L86BMej8n0z4EX1p3AO3A4qzjd5nZh9D5TbJLgaOBr5nZ+wmS178lGJB2O0EXpYawrM5vjKmFN7kqwm13nuPbwq2+1CSMmR1McPH8KMGF9Fx0vhPLzGYBVwNt7r4yTzGd32RKT0v1YYKZGeoJvqQeANwAvItg0JLOb3KlByQCvBs4myDZBXic4Nc3nd8EUMKbXK+F28l5jk8Kt9vHIBYZBWY20cy+SNCi+27gKeC97n43Ot9Jdg0wFThnkDI6v8mUPi+9wInu3uru29z9KYLE6C/A+wh+7gad3yS6ieC9+x8E00VOJWjhXQ38I8EsDnr/JoAS3uR6Ptzuked4+ie0jWMQi4yQmc0F7gGaCD4crwUOD5Nd0PlOJDM7HvgI8GV3f3yQojq/ybQ13P7Z3fv1z3X31wjm5AU4JNzq/CaImR3JG/PqftzdN7j7dnf/A0EXpWeATwKbwyo6vzGmhDe5Hgm3b81zfB6wyd2fHaN4ZJjC0ft3AlUEE9Af5e6fcvctGcWeImgdGOx895J/UIyUxqJwe0W4spqbmQMrwv1XhffPQuc3iZ4It1vzHE//lK33bzLND7d3uHtv5oFwdo17CboppKcr0/U4xtSfJKHc/RkzWw8sMLOZ7v5i+lg4798BwE9KFqBEcQXBPLy/BE5y9wH9wNy9J5zT8zgze6u7P5g+ZmZ7ECRW97p711gFLQW5B/h2jv1vAeoIVtu6H7gLWIjOb6K4+ytm9jBwqJlV5Dg/R4XbPxKM9tf5TZZ0o8M+eY6n+3A/TDCYTdfjGFMLb7J9l+Dn78vSO8I1u68M736nFEFJ4cxsCvAh4AXgI7mS3QzfDbeXh9PipOfx/RpB37FrixmrROfuLe7+mewb8OOwyM/CfS3o/CbV94EpwJXh7CoAmNmJBHPv3hbOzarzmzx3EiS9J5vZOzMPmFkdwViL37r7C+h6HHtaaS3BwrW77wDeSdCS9L9ANcHcrde6+4D5PiVezGwxQSvfo0DLIEX/xd1fMrP/Ak4lGNj2O4JWwaMILqofKHa8MjrMbAlBt4a+ldbC/Tq/CRMmub8g6Ou5nuCz+E0ELfjPAUe7+5NhWZ3fhDGzfyBYLhigDXgSOBCoBV4BFrv7Q7oex58S3oQLWwi/SvAhOotgmpR/I3iD6eTGnJmdAvy0gKIHuvsT4Yfq54EzCVZf+yvB9EeXu/vO4kUqo2mQhFfnN4HMbBJwAUFf7LnAiwRztF7i7k9nlNP5TSAzO4rgvFUTdGPYRJD8fsXdOzPK6XocY0p4RURERKSsqQ+viIiIiJQ1JbwiIiIiUtaU8IqIiIhIWVPCKyIiIiJlTQmviIiIiJQ1JbwiIiIiUtaU8E3Xp6kAAAjiSURBVIqIiIhIWVPCKyIiIiJlTQmviIiIiJQ1JbwiIiIiUtaU8IqIiIhIWVPCKyLjkpnVmJnnuXWb2cNmdoGZ6XNyGMzsb8zsVTM7OMexg8zsWjN71My2m9lmM3vIzK4ws0PzPN6lg5yvbWb2WzM7YZixTjOzF8ysbjj1RST+JpQ6ABGREvsrcFPGfQP2Ao4FrgCOAJaMfViJdy1wo7v/OXOnmZ0BfA+YBNwH3AFMAd4KXAB8ysw+5e7X5Xnce4F7Mu5PAA4C6oDbzOxMd18ZJVB332pm3wSuNbO3uvvOKPVFJP7M3Usdg4jImDOzGmANcIe71+Q4vi9wPzAbWODuD4xpgAlmZh8EbgXe6u4PZex/H3AbsAn4sLuvyapXT/DlYypwjLvflXHsUuAS4CvufmmO56wHfgW8COzt7q9HjHk68Begyd2/FqWuiMSffqoTEcnB3f8K3BzePSZfOTObPDYRFU8RXsNXgTVZye6uwPeBHuB92ckugLv/GjiP4Nr0hShPGNZ9BJgJvCVqwO6+BfgB8EUz2z1qfRGJNyW8IiL5bQu3U6Ffv9+LzOxUM3sUyGyF3M3MvmRm681sh5ltNLNmM9s/+4HN7EQzu8vMXjazrWZ2n5mdPpxyZrYyjOvIHPXdzB7IuL8k3He6mZ1vZk8BP844voeZfd3MHgv7Mj9hZleZ2cxC/mBmdgxB94QfZx06BdiHoJvD7wd5iJuAfweeLOT5svQ7X2E8leE5eCI8J0+b2c1mtjhH/Z8CuwMfHcZzi0iMKeEVEcnvqHDbkbX/gwQJ3WPAjdDXgvlr4F+BrcANwFrgDOCPZnZEurKZfRq4BZgT1r8NOAD4oZl9Mmq5YVoGfANYB9wePt9M4HfA54CngRVAJ/AZ4H4ze3MBj3t8uP1t1v76cHvbYJXdfae7L3X38wt5EWlhl4T5gBPEjJntB/weOAtYD6wEHgI+ALSHyXmm+4AdwKlRnltE4k+D1kREMoSzMuxPkBBWA88Cv8gqdhRwsrvfnLHvS8BioNHdl2c8Xg3QBvwb8K5w9+eAF4C3ufv2sNwewIPAF8OyUcoNx5EE/WQzB4BdBRwGnObuma2+pwM/BL4GnDbE49YDLwMbsvanZ19YP4KYBzCzScDfAJcD04Cfu/sL4eGPErTY9hvIZmZnAtcTJLa/Se93924z+wPwTjOb6O6vjWasIlI6SnhFZLyrNrN8o3e3AB9191ez9t+VmeyGSfI/ErQsXp5Z0N3bzexXwPFmtre7PwvMAHqBPYHtYbnNZvZ+ghki0gotNxw/zUx2zWxPggRxVWayGz7nj8zsi8AHzWzCEAPCDgE6feCI6BnhdkvmTjOblr0vw3vcvT1r3yVmdkme8o8CmS3fawm+GPxXVrkHw+20HI/xFPBuYC4DW/ZFJKGU8IrIeJc9LRnATuBx4GZ3fz5HnT9m3T+EYLDUFuAqM8sun05O5xO0GH+PoJvAQ2b2c6CdYJBX9kwQhZYbjuzXsIjgmjDbzL6Vo/xEgr6x+5Knf204+G0qsDnH4e3hdkrW/p3At7P21QBvyxN39rRk6cd+ELjF3bvTO919FbDKzGaFfXYPJJjC7P15HhvgpXC7F0p4RcqGEl4RGe863f0zEetkz9OaHtA1F/j0IPWmA7j7Z83sbuATBIO5PgZgZg8CX3L326KUy8fMBvuMz/cajghvg76GPPYMt1tzHPtr+LiVvNHCSjjnbb+/v5ndQP6EtyXXtGS5mNmbCL40HE8wv/IrBF0qfkfQdSOXV8LtYK9TRBJGg9ZEREYuPTvAre5ug9z+J13B3X/q7scCexC0aH6doPXxZjM7KGq5PAqaWSHrNXxriNfw0CCPkW7Frchx7M5w21BALFWFBj2E/yBIdi8C3uTue7j70cB3BqmTTnRfHqUYRCQGlPCKiIzcI0A38DbLsRSxmX3ezH5iZhUWLKt7q5mdAsFAKXe/w90/D/wLQdeBRYWWC58i3VqbPZ/uOyK8hv8NtwtyHTSzb4bTnw3or5FhM/Aqb7T0ZvoR8BpwWq5p2jKe5z3A4YWFnF/YN7gauMfdm9z9mYzDg31R2CPcvjBIGRFJGCW8IiIj5O47gP8k6CP6T5nHzOwDwGUEq391EawydgLwBTOryChnwMLw7lMRymVuT8ooNw24OMJreJxgmd/qMObM1/BJ4MKgWP7lOcNjDwL7ZSf+7v4UwVLNUwiWAD44u76Z/R3B37G30LgH0RM+zl7hlHHp5ziAYOq4fPYnSNr/MgoxiEhMqA+viMjo+GfgncDVZnYqQavvPIJuCC8CZwK4e5eZXQ18FnjEzH4DdAFHE7Rs/tLdfwdQaDmCBRO+DPyzmR1FkKy9l2BwWa5Bd/mcTdD14GdmtiqsfwRBS/Kfw1iG0h6WP5yBA+MuAvYGlgDrzexOgoFhuxAk8QsI5sL9MnBdhLgHcPdXzewnwOnAA2Z2B/BmoI5g1oZDgGPN7EJ3vwLAzFIEU87dkTn4TUSSTy28IiKjwN03EXQhuJqgpffjBD+d/zuwwN2fyCj+eYKkbjPwIYLFKSYSdFX4+6jl3H0DwcwDfwhjeC/w3wQtxAXPJevuHQQJ3w8I5un9GEE/4G8Bi9w91+wL2X4Wbgcsx+zuPe5+JnAs8D8ESwCfSbAQxBaCqd3ezcB5j4frPILYpxO8llnAWe7+cYK+0LsTfIFIW0AwVdnto/T8IhITNsivUyIiIpGFSxl3ufuApDfOzOxKglbuA9z9paHKi0hyqIVXRERG25eBxWaWb2qx2An7PJ8JXKVkV6T8qIVXRERGnZndBmx194+WOpZCmNmFwAXAIe6+bajyIpIsSnhFRGTUmdlcghkbFrn7+tJGM7hwhbjHgE+7+09LHY+IjD4lvCIiIiJS1tSHV0RERETKmhJeERERESlrSnhFREREpKwp4RURERGRsqaEV0RERETKmhJeERERESlrSnhFREREpKwp4RURERGRsqaEV0RERETKmhJeERERESlrSnhFREREpKwp4RURERGRsqaEV0RERETK2v8B8kXWqzAzrjUAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 360x360 with 2 Axes>" ] }, "metadata": { "image/png": { "height": 351, "width": 350 } }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 360x360 with 2 Axes>" ] }, "metadata": { "image/png": { "height": 351, "width": 350 } }, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 360x360 with 2 Axes>" ] }, "metadata": { "image/png": { "height": 351, "width": 350 } }, "output_type": "display_data" }, { "data": { "image/png": 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8IiIiEquU8Mpehg8fzt/+9rfI8WOPPcZdd90VYEQiIiIi+08JrxTorrvu4txzz40cjxo1iueffz7AiERERET2jxJeKVBCQgITJkyga9eukbYhQ4Ywa9asAKMSERER2XdKeKVQVatWZfLkyRx99NEA7Nq1izPPPFM1ekVERMqZp59+GjPba6tUqRJHHHEEF154IUuWLAk6zMBUCjoAKd/q1q1LRkYGHTp04KeffmLz5s306tWLRYsWRer2ioiIxCN3x8wAWL16Nenp6WRlZZGUlERaWhotWrTYq1/QWrZsSa9evSLHW7Zs4aOPPuKFF17gpZde4qGHHmLYsGH7de8jjzySX3/9lV9//bW0wi0zSnilWE2aNGHatGl06dKFLVu28MMPP9CzZ0/ee+89kpKSgg5PRESk1OUlsZmZmYwZM4bp06fvVrHIzEhNTWXUqFGkpKSUm6S3Y8eOjBs3bq/2WbNmcdZZZ3HFFVfQsmXL3aYsVgRxOaXBzIabmZtZ3ULOdzWzDDP7xcyyzewDMzuvkL5DzOzDcL+1ZvaEmR0a3e+g/GnTpg2TJk0iMTERgJUrV3LWWWexc+fOgCMTEREpXXnJ61NPPUVqaioZGRl7led0dzIyMkhNTY1MJyjPJTxPOeUUxo0bR25uLjfddFPQ4ZS5uEt4zSwRuLSI82cAmUB7YArwNnAs8JKZnbVH31uAp4AGwPPAEmAwsMDMDopG/OVZ7969efTRRyPHmZmZ/PnPfy7Xf8FFRET2Vd7I7rBhw8jJySmyb05ODkOHDiUzM7NcjPAW5eKLL6ZevXrMmzePL7/8EoDt27fz73//m7Zt21KrVi0aNGhAx44dGT9+fGThqbzv7euvv+a3337DzBgyZEjkvqtXr2bo0KEceeSRVKtWjcaNGzNw4EDmzJkTxLdZoLhIeC2knZldSiiZbVdIv5rA08D3wAnuPtjdzwa6AduBO/L1PQq4DfgYON7dL3P3vsAwoDFwd9S+oXLsz3/+M7feemvk+LnnnmP06NEBRiQiIlL6xowZU2yymycnJ4exY8dGOaIDV6lSJbp16wbAwoULARg0aBB//etf2b59O+effz59+vTh66+/5pprruGKK64A4IgjjmDEiBHUrl2bKlWqMGLEiMg84W+//ZYOHTrw5JNP0rJlS4YMGcLxxx/Pm2++SUpKCu+++24w3+ye3D3mN6AW4AVsdffoNzzcfl4B93gW2AgkhY//Fe57zh79DFgLbANqFBPX4nbt2nm8yc3N9cGDB+/2Z/3AAw8EHZaIiFQwK1eu9JUrV5b6fT/77DM3s4LyikI3M/PVq1eXeiwl9dRTTznggwcPLrLfyJEjHfD/+7//83Xr1jngqampvnPnzkif9evXe7169bxmzZq7XfuHP/zB69Sps1vb3Xff7YA/9dRTu7U/+eSTDvhVV11VbOwl/RzbtWvnwGLfj1wxLkZ4ga3AOfm2lYX060UoUX1jzxPufpG713P3rHBTCpALpO/Rz4E5QFXgxNIIPtaYGY899hipqamRtr/85S9MnDgxwKhERERKR3p6+j5P13N30tPTi+8YsJo1awKwc+dOzIyxY8dy5513UqnS73UMGjRowGGHHUZ2dnax90tOTmbs2LGcd97ur0KdcMIJAGzevLkUo99/cVGlwd1zgFfzjs3s6kK6dgY+cfdtZnYK0B2oAiwG3nD3Xfn6tga+dfdNBdxnVXjfAqiQKzFUrlyZ1157jW7durF06VJyc3MZNGgQ77zzDt27dw86PBERkf2WlZVVfKdSvK4s5ZUUO/jggzn44IO56aab2LlzJwsXLmT16tV8+eWXfPjhh3z88cclul+PHj3o0aMH69evZ86cOXz55Zd88cUXTJ48OZrfxj6Li4S3JMysEnAwsNLMXgHO3qPLSjPr6+5fmFkSUBn4sZDbbQzv6xf33FWrVpGcnFzgucWLF5co9vKqVq1aTJs2jU6dOvHll1+yY8cOzjjjDObNm0erVq2CDk9ERGS/7G/JzVgo1Zn3slqTJk2A0FzlsWPHsnnzZipXrkyzZs046aSTOOKII/juu++Kvd/333/PZZddxtSpU3F36tSpQ8uWLTnppJNYtWpVsdcXpLC8aX/vB3Hy0loJ1QvvuwNdCU19SAIaEXoB7TjgdTNLCLdD6EW2guSN8VeYHxgKc/DBBzNjxgwOOihUtGLz5s307NmTr776KtjARERE9lNaWto+V1wwM9LS0qIUUenYvn07c+fOpVatWnTt2pVnnnmGm2++mY4dO7JkyRK2bt3KJ598wpNPPkn9+sWO6QFwwQUXMHXqVO644w6+//57fv31V9577z2uuuqqKH83+6YiJWxVwnsDBrv7O+HjTcBNZpZCaE7uScDq8LnqxdxrS3EPbdmyZcyP5BanadOmZGRk0LVrVzZv3sxPP/1Ejx49eP/99yOJsIiISKxo0aJFpP5uSfXs2ZPmzZtHMaoD9+ijj5KVlcWgQYOoVq0aU6ZMAeChhx7i6KOPjvTbuXMn3377bbH327x5M7Nnz6ZTp06MGjVqt3NffPHFfsdZWN6UnJy838sjV6QR3rxZ09uA6QWcfz28bwlsAHKAAheuIFSXF0LlzQRo3bo1kydPpnLlykDoP/RevXqVm8nqIiIi+2LUqFGRxZaKk5iYuFfCV96kp6dzww03UK1aNe64I1SFtUqV0PjdN998E+m3c+dOrr/+ejZu3LjXPRISEiK1eSH0fSckJPDjjz+yffvvvxT/+uuvueWWW6L1reyXCpPwuvtvhObebnX33AK65E1TMHffCXwONA3X7t1T3o9wK0o/0tiVkpLCCy+8EPk10NKlS+nXrx87duwIODIREZGSc3dSUlJ4/PHHi016ExMTefzxx+nevXu5WIhp4cKFjBw5MrINHz6c7t27c9ppp+HuvPDCCzRt2hSASy65BDOjX79+XHzxxQwePJgjjzySyZMnc/LJJwOhOr3r168HoHHjxmRnZ3POOefw/PPPU716df70pz/xxRdf0KZNG6644grOOOMMjj76aDp06EBCQgLTpk3j3nvvDezPI2J/apmV943Q4hMF1eF9K9zevIBrngmf6xQ+fjB83HePfonAt4Rq8VoxccRlHd7iPPDAA7vVJjz//PM9Jycn6LBERCTORKsOr3uo5ry7+6xZs7xXr1571eU1M+/Vq5dnZmbu1j8oeXV499wqV67sTZs29cGDB/vy5cv3uu7VV1/1Vq1aebVq1fwPf/iD//Wvf/WNGzf6/Pnz/dBDD/WkpCRfu3atu7vPnDnTmzZt6pUqVfKRI0e6u/vmzZt95MiRfvjhh3uNGjX8xBNP9Oeee87d3W+66SavXr26Dxw4sMjYy6IOr3k5+GmktJlZJqGX0+q5+6/52vsBrxFaTnigu28Nt58IvAt8CrR2dzezNoSWEl4KdMnX92ZCK7Ld7O5jioljcbt27drF+xzegtx6663885//jByPHDmS//znPwFGJCIi8Sbvrf2WLVtG5f7uHvmt5Zo1a0hPTycrK4ukpCTS0tIic3bz95N9V9LPMTyHd4m7F1zGoQgV6aU13P11M3sCuJRQGbJ3Cc3T7UWoIsMQD/8E4O5LzexfwA3ACjObQajubgqwCPh3AN9CzLj99tv54YcfePzxxwEYN24chx56KDfccEPAkYmIiJRM/iS2efPmXH11wWX+leyWfxVmDm8+w4ArCFVnOB/oAkwhNJVht1f/3P1GQssRbwMuAo4CxgE93H1bWQYda8yMhx9+mL59+0babrzxxkgCLCIiIlJW4nKE191TijjnwCPhrST3egh4qHQiq1gSExP53//+R48ePZg/fz4Al19+OfXq1WPgwIEBRyciIiIVRUUc4ZUyVK1aNaZOncrxxx8PQG5uLn/605+YPr2gynAiIiIipU8Jr0RdnTp1mDFjBs2aNQNCNf7OPPNM3n///YAjExERkYpACa+UiYMPPphZs2Zx6KGHArB161Z69+7N8uXLA45MRERE4p0SXikzTZo0YebMmdSrVw+ArKwsevTocUDLD4qIiEjsKqvyuEp4pUwde+yxTJ8+nZo1QwvY/fzzz6SkpPD991qlWURE9k1eObDc3IIWUJVYkJfwRru0mxJeKXPt2rVj6tSpVK1aFYBvv/2WU089lQ0bNgQcmYiIxJK8f0eys7MDjkT2V95nl/dZRosSXgnEySefzMSJEyNrlH/66af06tWLzZs3BxyZiIjEitq1awOwbt06Nm3aRG5ubpn9ilz2n7uTm5vLpk2bWLduHfD7ZxktcVmHV2LD6aefzjPPPMNFF12Eu7N48WLOOOMMpk2bFvWf9EREJPbVr1+f7OxstmzZwnfffRd0OLKfatSoQf369aP6DI3wSqAuuOACxo8fHzmePXs2Z599Nrt27QowKhERiQUJCQk0btyYgw46iGrVqmmJ3xhiZlSrVo2DDjqIxo0bk5AQ3ZRUI7wSuKuuuooNGzZw6623AjBlyhQuvvhinnvuuaj/BRARkdiWkJBAw4YNadiwYdChSDmmbELKhVtuuYVrr702cvziiy9y+eWXay6WiIiIHDAlvFIumBn33nsvf/7znyNtjz/+OCNHjlTSKyIiIgdECa+UG2bGo48+yvnnnx9pu//++7n55psDjEpERERinRJeKVcSExN59tlnOeussyJtY8eO5c477wwwKhEREYllSnil3KlUqRL/+9//6NWrV6TtlltuYdy4cQFGJSIiIrFKCa+US1WqVOGNN97g5JNPjrRde+21PPLIIwFGJSIiIrFICa+UW9WqVeOtt96iY8eOkbYrr7ySZ599NsCoREREJNYo4ZVyrVatWrzzzju0bt0aCC1HeMkll/Dqq68GHJmIiIjECiW8Uu7VqVOHGTNm0LJlSwBycnL405/+xNSpUwOOTERERGKBEl6JCQ0aNGDWrFkcddRRAOzatYv+/fszc+bMgCMTERGR8k4Jr8SMQw45hMzMTBo3bgzAjh07OP3005kzZ07AkYmIiEh5poRXYsoRRxzB7NmzadSoEQDbtm2jd+/ezJs3L+DIREREpLxSwisxp2nTpsyePZuDDjoIgK1bt9K7d2/ef//9gCMTERGR8kgJr8Sko48+mnfffZeGDRsCkJ2dTc+ePVm4cGHAkYmIiEh5o4RXYtaxxx7L7NmzqV+/PgCbN28mNTWVRYsWBRyZiIiIlCdKeCWmHXfcccyePZt69eoBsGnTJnr06MGSJUsCjkxERETKCyW8EvOOP/54MjMzqVOnDgBZWVmceuqpLF26NODIREREpDxQwitxoVWrVmRmZpKUlATAb7/9ximnnMJHH30UcGQiIiISNCW8EjfatGnDrFmzqF27NgC//vorKSkprFixIuDIREREJEhKeCWutGvXjpkzZ1KrVi0ANm7cSPfu3Vm5cmXAkYmIiEhQlPBK3Gnfvj3Tp0+PJL0bNmyge/fufPLJJwFHJiIiIkFQwitx6cQTT+Sdd96hRo0aAKxfv56TTz5ZSa+IiEgFpIRX4lbnzp2ZNm0a1atXB+Dnn3+mW7dumt4gIiJSwSjhlbjWtWvX3ZLe9evX061bN73IJiIiUoEo4ZW4161bNzIyMiLTGzZs2MDJJ5/MsmXLAo5MREREyoISXqkQunTpstuLbHnVG7Qim4iISPxTwisVRufOnZkxY0Yk6c1bnGLRokUBRyYiIiLRpIRXKpSOHTvutiJb3jLECxYsCDgyERERiRYlvFLhJCcnk5mZSZ06dQDYtGkTPXr0YP78+QFHJiIiItGghFcqpLZt2zJ79mzq1asHQHZ2Nj179mTu3LkBRyYiIiKlTQmvVFitW7fm3XffpX79+gBs2bKFXr16MXv27IAjExERkdKkhFcqtOOPP545c+bQoEEDALZu3UpaWhozZ84MODIREREpLUp4pcI77rjjmDt3LgcddBAA27Zto0+fPqSnpwccmYiIiJQGJbwiwLHHHsu8efM45JBDANi+fTt9+/bltddeCzgyEREROVCVgg4gGsxsOPAAUM/df93j3FDg+EIunefur+TrOwA4uZC+q9z9kdKIV8qHFi1aMG/ePLp3787atWvZtWsX55xzDhMmTGDQoEFBhyciIiL7Ke4SXjOJZ8SkAAAgAElEQVRLBC4tosuVQLtCzlUDXsl3fAEwoJC+0wAlvHHmqKOOYv78+XTv3p2vvvqKnJwcLrzwQrZu3cqllxb1n5WIiIiUV3ExpcFC2pnZpUAmhSe0AM2AB93dCtiuKKDv1EL6pkXp25GANWnShPnz59OiRQsA3J2hQ4dy//33BxyZiIiI7I+4SHiBmsBi4HGga2GdzKw+UBf4vIT3bbYPfSWOHHroocyfP5/jj/999suIESO46667AoxKRERE9ke8JLxbgXPybSsL6XdUeF9sEmtmBwFJJekr8alhw4bMmTOH9u3bR9r+/ve/M3r0aNw9wMhERERkX8TFHF53zwFezTs2s6sL6dosvP/JzC4BjgGygVnuvucSW3l9vzWz84FWwE7gPWCaK+OpEOrWrcusWbNIS0tj3rx5ANxxxx1kZ2dz7733YmYBRygiIiLFiYuEdx/kjfBOBhrkP2Fmk4FB7r55j76P7tkXeM/MBrj7uuIeuGrVKpKTkws8t3jx4pLGLQGqVasWGRkZnHnmmUyfPh2A//znP2zevJmHH36YhIR4+UWJiIhI8ArLm1atWrXf96xo/1Lnjdq+TWh0twbQCZgH9AWeLKDvIqANUJ3QKO+bQGfgdTOraH9+FVb16tWZMmUKZ5xxRqTtscceY/DgwezatSvAyERERKQ4Fo+/mTezTKA7e9ThNbMTgQbuPnWP/kmE5v0eDhzj7p+Z2Qn8XqVhZ76+lYCFQFugt7u/U0Qci9u1a9dOI7nxY9euXQwaNIhXXvm9et2AAQN48cUXqVKlSoCRiYiIxLfk5GSWLFmyxN0LHgIuQoUaoXT3BXsmu+H2LCBvHdk24bbl7v5G/mQ33L4LmJS/r1QclSpV4sUXX2TIkCGRtkmTJnHGGWewZcuW4AITERGRQlWohLcYm8L7kvx+el/6SpxJTEzkiSeeYPjw4ZG2jIwMevTowa+//lrElSIiIhKECpPwmtmxZuZm9lYhXbqE98vMrIaZ5ZrZx8X1Ld0oJVYkJCTw3//+l7///e+Rtvfff59u3brx008/BRiZiIiI7ClqCW949bOeZnaPmb1vZt+b2Q4z+83MVpvZK2Z2pZkdHq0Y9vAp8BXQ28y67RHrxUAHINPdP3f3LcAc4Dgz+9MefVOAgYTq82ZGP2wpr8yMMWPGcM8990TaVqxYwUknncQ333wTYGQiIiKSX6knvOHR0ZuALwjNi70e6EhohbOfCE0DaEYoaXwA+NLMJprZSaUdS37hurnDgVxgpplNNrMHzGw68AzwC3BlvkuuBbKAF8xserjvm0AGsB24JFz/Vyq4v/3tbzz22GOR8mSff/45nTp14pNPPgk4MhEREYFSTnjDizmsBsYQWv3sdqAnUNfda7j7Ee7egFD93+OAPwMTgdOAOWb2spk1Kc2Y8nP3twm9aPZqeD8UOBp4Aujg7p/k67sEOAF4jFCCPpTQKPBEoJO7z4lWnBJ7hg4dyssvv0zlypUB+OGHH+jSpYtqLYuIiJQDpVqWzMxygdeBse7+wT5clwQMBm4CHnH3f5RaUAFSWbKKJyMjg7POOoutW7cCULNmTd566y26d+8ecGQiIiKxrTyVJWvv7gP2JdmFUFkwdx9PaHWz/5VyTCJlpmfPnsycOZOkpCQAsrOz6d27N1OmTAk4MhERkYqrVBPe8DSAA7l+W/5pBSKxqFOnTsydO5eGDRsCsH37dvr168dzzz0XcGQiIiIVU4UpSyZSlk444QQWLFjAEUccAUBOTg4XXXQR//3vfwOOTEREpOIplYTXzI43s2Fm9sfw8XFm9piZTTCzXqXxDJFY06xZMxYsWMAxxxwTafvLX/7C7bffTjwu6S0iIlJeHXDCa2Z9gCXAXcBiMzsNmA0cCRwOTDWz1AN9jkgsOuyww5g/fz7t2rWLtN12220MHz6cnBxVtRMRESkLpTHCewtwT7jc2BDgeeBRd+/p7j2AfwE3lMJzRGJS/fr1mT179m6VGh5++GHOPfdctm/fHmBkIiIiFUNpJLx/BJ4Of/0/oDahWrV5ngdalcJzRGJWrVq1mDZtGv3794+0TZo0id69e5OVlRVgZCIiIvGvtF5aywVw91xgG/BrvnObgDql9ByRmFW1alVeffVVrrzy9wX9Zs+eTdeuXVm3bl2AkYmIiMS30kh4vwJa5DvuDHyT77gxoH/NRYCEhAQeeOAB/vGP39dWWb58OZ06dWLNmjUBRiYiIhK/SiPhfQSoknfg7ivcfVe+86cDmaXwHJG4YGaMHj2aRx99lISE0F/Br7/+mk6dOrFkyQGVshYREZECHHDC6+4PuvvkIs7/3d0vOdDniMSbYcOGMXHiRKpWrQrAL7/8Qrdu3Zg+fXrAkYmIiMSXwBeeMLMEMzsj6DhEgtCvXz+mT58eWYp4y5YtnHbaabz00ksBRyYiIhI/Akt4zexoM7sL+A54I6g4RILWtWtX5s+fT6NGjQDYtWsXgwYN4v777w84MhERkfhQpgmvmdU0sz+b2VxgFfA3oBEwtyzjEClv/vjHP7Jw4UKaN28OgLszYsQIbrrpJq3KJiIicoDKJOE1s65m9iTwA/AYoUoOG4F7gWPdvXtR14tUBI0bN2bBggUkJydH2u6++26GDBnCzp07A4xMREQktpXG0sKVzewCM7vWzJrla29kZjea2aeElhoeAlQHpgIGTHL3G9z9swONQSRe1K9fn3fffZdevXpF2iZMmECfPn3YtGlTgJGJiIjErgNKeM2sCjAPeBwYBCw2s+vNbDKhWrxjCNXoXUloeeEj3L3vgYUsEt9q1KjBlClTuPDCCyNt06dPp0uXLvzwww8BRiYiIhKbDnSE9xwgGejj7h2A/sA9QB8gC3gI6OjuJ7j7/7n7jwf4PJEKoXLlykyYMIGbb7450rZ8+XI6dOjAypUrA4xMREQk9hxowls/vF8W3n8c3jvwKvCEuy86wGeIVEhmxh133MEjjzxCYmIiAGvXrqVz587Mnj074OhERERix4EmvFOB7cBEM7sSmEgo6XVgGLDIzJaa2UgzO+gAnyVSIV122WW8+eab1KhRA4CsrCx69uzJiy++GHBkIiIiseGAEl53/xxIA3KB4cBMoDXQGLgZWAO0IlSN4Tsze93M+h9QxCIVUJ8+fZg7dy4NGzYEYOfOnQwaNIh77rlHZctERESKURpLC7/r7j3C83Rvc/dcd1/n7ne5+zHAycAEYAdwJqGpDg6cYGZ/PNDni1QUbdu2ZdGiRRx11FGRthtvvJGrrrqKnJycACMTEREp36Jeh9fd57r7JYQWmBgGvE+oLNmJwEdm9p6ZXRrtOETiwR/+8Ac++OADOnXqFGl76KGH6NevH1u2bAkwMhERkfKrzFZac/dsd3/C3bsAxwL/AtYRSnwfLas4RGJdvXr1mDVrFv369Yu0TZkyhZSUFH7++ecAIxMRESmfynRp4Tzu/pm730horu9ZwBtBxCESq6pVq8bEiRMZMWJEpO2DDz7gxBNPZM2aNQFGJiIiUv4EkvDmCc/3nezuA4KMQyQWJSQkMG7cOMaNG4eZAfDll1/SsWNH5s+fH3B0IiIi5UepJrxmVr083EOkIhkxYgSvvPIKVatWBWDjxo2ccsopKlsmIiISVtojvF+a2Qgzq7qvF5pZazN7A7i+lGMSiXsDBw5k1qxZ1KtXD4AdO3YwaNAg7rjjDpUtExGRCq+0E953gH8DP5jZQ2Z2SlEjtmbWzMyuNLP3gCWEavjOKuWYRCqEzp0771W2bPTo0QwePJgdO3YEGJmIiEiwSjXhdfeLCVVdWARcBkwHfjOzZWaWbmYvmtlrZvaumf0IrAYeAI4ktFDFMe4+tzRjEqlImjVrxgcffEDXrl0jbc8++yypqals2LAhwMhERESCU+ovrbn7Infvxe+lx5YBxwG9gPMIVWXI+9d4EjAIaBJeqGJ7accjUtHUq1ePGTNmcOGFF0ba5syZQ8eOHfn8888DjExERCQYUavS4O6r3f0md+8A1AGOAU4C2gKHu/sh7n6Ou7/k7jujFYdIRVSlShUmTJjAP/7xj0jb559/TocOHZg3b16AkYmIiJS9MilL5u5bwgnw++6+zN1/KIvnilRkZsbo0aN58cUXqVKlCvB7BYfnn38+4OhERETKTqB1eEUk+s4//3wyMzMjFRx27tzJhRdeyO23364KDiIiUiEo4RWpADp37szixYt3q+Bw2223ceGFF7J9u6bOi4hIfFPCK1JBNG3alEWLFtGtW7dI2wsvvECPHj345ZdfAoxMREQkupTwilQgdevWZcaMGVx00UWRtnnz5pGcnMzKlSsDjExERCR6lPCKVDCVK1fmmWee4Y477oi0ff3115x44om8/fbbAUYmIiISHWWW8JrZwWbWzcz6hI9rldWzRWR3ZsbNN9/Mq6++SrVq1QDYvHkzZ5xxBv/5z3/0MpuIiMSVqCe8Zna8mc0BfgAygTfDpx41s+lm1jjaMYhIwQYOHMj8+fNp1KgRALm5uVx33XUMGzZMyxGLiEjciGrCG05m5wCdgLeBJYCFTy8DugOLzOzwaMYhIoVr27YtH374IW3bto20PfHEE3qZTURE4ka0R3hvBWoAPdz9DGBu3gl3vxsYCBwU7iciAWnUqBHz58/nnHPOibTNnTtXL7OJiEhciHbC2wdId/d3Czrp7m8SmubQK8pxiEgxqlWrxssvv8ztt98eadPLbCIiEg+infDWB74vps8aoFGU4xCREjAzbr31ViZOnEj16tUBvcwmIiKxL9oJ75dAu2L6HAv8XJoPNbPhZuZmVreAc0PNbFwh2zkF9B9iZh+aWbaZrTWzJ8zs0NKMV6S8GTBggF5mExGRuBHthPd5oL2Z3VDQSTO7GugCTCytB5pZInBpEV2uBEYUsvXY4163AE8BDQh9L0uAwcACMzuotGIWKY/atGmjl9lERCQuRDvh/T9gHjDWzD4FzgQws4lmtga4D1gN3F74LYpnIe3M7FJCc4KLGlVuBjzo7lbAdkW+ex4F3AZ8DBzv7pe5e19gGNAYuPtAYhaJBXkvs5177rmRtrlz59KuXTuWL18eYGQiIiIlF9WE1923A6nAnYTm8zYlVJasP3A4MAHo4u6/HuCjagKLgceBroV1MrP6QF3g8xLc8wogEbjd3bPytT9NaF7yIDOrsb8Bi8SKatWq8dJLL/GPf/wDs1BVwW+++YZOnToxadKkgKMTEREpXtQXnnD3He5+K3Aw0JJQQtoWqOvul7h7afxudCtwTr6tsDpKR4X3JUl4U4BcID1/o4fe2pkDVAVO3I9YRWKOmTF69GgmTpxIjRqhn/O2bNnCwIEDueWWW8jNzQ04QhERkcKV2dLCHvKpu89392Xh0d/SuneOu7+at1H4S3DNwvufzOwSM7vLzEabWUGjwq2Bb919UwHnVoX3LQ4wdJGY0r9/fxYuXEiTJk0ibXfeeSdnnXUWWVlZRVwpIiISnEpl8RAza0lo3mu1wvqEa/JGW94I72RCL6JFmNlkYJC7bzazJKAy8GMh99kY3tcv7oGrVq0iOTm5wHOLFy8uScwi5cof//hHPvzwQwYMGMDs2bMBmDJlCh06dGDKlCm0aKGfA0VEZP8VljetWrWqwPaSiPbSwk3NbBmwgtDSwq8VsL0e3peFvBHet4FjCK0C14nQi3V9gSfD55PC+8JGobPD+zL5gUGkvKlfvz7Tp0/nmmuuibR99tlntG/fnmnTpgUYmYiIyN6inbA9AJwAZAALgV1Rfl5xHgMmufvUfG0LzKwPoXm/55jZ0cBv4XPVC7lPlfB+S3EPbNmypUZyJS5VqlSJ++67j3bt2nH55Zezfft2srKy6NOnD2PHjuVvf/tb5CU3ERGRkiosb0pOTmbJkiX7dc9oJ7xdgdnu3jvKzykRd19QSHuWmaUTqt/bhtCIcw6hig4FyZsOUdwqciJxb/DgwRx33HH07duXH3/8kdzcXG688UY+/PBDnnzyyciKbSIiIkGJ9ktrOZSsIkJ5kPdy2i5330ko7qZmVrOAvs3D+xVlEplIOdehQweWLl1K+/btI20vvfQSnTt35ptvvgkwMhERkegnvBlADzMr9GW1smJmx4aXG36rkC5dwvtl4f0MQnV4T93jPomEVmT7ntCiFCJCaJGKefPmMXjw4EjbsmXLSE5OZs6cOQFGJiIiFV20E97rCSWN75rZeWbWwcxaFbRFOQ6AT4GvgN5m1i3/CTO7GOgAZLp73oj0o4ADt5tZ/t/J3gQcATwQrskrImFVqlTh6aefZvz48SQmJgKwfv16Tj31VB544AH0V0ZERIIQ7Tm8iYReAGsPvFCCvlHj7m5mw4E3gJnhObvfEKrW0AP4BbgyX/+lZvYv4AZghZnNIFR3NwVYBPw7mvGKxLKrr76a448/ngEDBrBx40Z27drF1VdfzcKFC3n44Yc1r1dERMpUtBPe8cBxwCfAUgov81Um3P1tM2sDjCb0Ql0vQrV2nwDudPcv9+h/o5l9BVwNXERoQYtxwP9z921lGbtIrElJSWHp0qWcfvrprFgRmu4+YcIEli1bxuuvv86RRx4ZbIAiIlJhWDR/xWhmvwGfAZ3dPeiSZGXOzBa3a9euncqSSUW2detWLr30Ul588cVIW7169Xj55Zfp2bNngJGJiEgsCZclW+LuBa9MUYRoz+HdCiyriMmuiIRUr16d559/nvvuuy8yr3fjxo2kpaUxduxYzesVEZGoi3bC+wqQYmZVo/wcESnHzIxrrrmGzMxMGjZsCEBubi6jRo1iwIABbNq0qZg7iIiI7L9oJ7x/JzTvdVK4QoPeVBGpwLp27cry5cvp0KFDpO31118nOTmZTz75JMDIREQknkU74f0N6AikAe8Dm80sp4BNUx5EKoi8er2XX355pG316tW0b9+e1157LcDIREQkXkW7SsMcQrVsRUQiKleuzMMPP0ynTp244oor2L59O9nZ2QwYMIAbb7yRO++8MzLfV0RE5EBFNeF195Ro3l9EYtuQIUNo3bo1ffv2Ze3atQDcfffdfPDBB/zvf/+jQYMGAUcoIiLxINpTGkREitS2bVs++ugjTjnllEjbzJkzadu2LUuWLAkwMhERiRdRHeE1s0kl7OruPjCasYhI+VW/fn0yMjIYNWoU99xzDwDffvstnTt35r///S9Dhw7FzAKOUkREYlW05/D2K+a8A4bm+YpUeImJidx999106tSJiy66iOzsbHbs2MFll13GnDlzePjhh6lRo0bQYYqISAyK9pSGpgVsLYBOwLXAt8BrQFKU4xCRGNG/f3+WLFnCMcccE2l79tln6dChA5999lmAkYmISKyKasLr7l8XsH3u7gvd/T7gRCAFuCmacYhIbDn66KNZsmQJ5513XqRt5cqVJCcnM3HixAAjExGRWBToS2vu/iOQDlwQZBwiUv7UqFGDF198kQcffJAqVaoAsHnzZs4++2xGjhzJzp07A45QRERiRXmo0lAXaBR0ECJS/pgZV155JfPnz+fwww+PtN93331069YtUspMRESkKFFNeM0sqYjtEDO7EugJrIlmHCIS25KTk1m+fDm9evWKtC1YsIDWrVszY8aMACMTEZFYEO0R3l+BjYVs3wP/JVQpYmyU4xCRGFevXj3S09P55z//GVmF7ZdffqFnz57885//JDc3N+AIRUSkvIp2WbJ3Kbzk2A7gG+A5d58d5ThEJA6YGbfccgsnnXQS5557Lr/88gvuzq233srcuXN54YUXtDqbiIjsRUsLi0jMOfXUU1m+fDn9+/dnwYIFALzzzju0bt2aSZMm0bFjx4AjFBGR8qQ8vLQmIrLPDj30UObOncu1114baVu7di1dunTh/vvvx13r2YiISEipjvCaWav9vdbdPyrNWEQk/lWqVIl///vfdOvWjYsvvpjNmzeza9cuRowYwcyZM3n66aepW7du0GGKiEjASntKw1L2f5ngxNIMREQqjv79+7Ns2TLOPPNMPv74YwDeeOMNWrduzSuvvKIpDiIiFVxpJ7wT2P+EV0RkvzVr1ozFixdzzTXX8OijjwLwzTff0KVLF+666y6uu+46zCzgKEVEJAilmvC6+5DSvJ+IyL6oWrUqjzzyCD169ODSSy+NTHG4/vrrmTVrFhMmTKB+/fpBhykiImWszF5aM7PmZnaWmZ1vZqeaWZ2yeraIVCznnnsuy5Yto1Wr318reOutt2jVqhXvvfdegJGJiEgQop7wmtnRZjYf+BSYBDwPZAA/mdlzZnZotGMQkYqnWbNmfPDBB1x55ZWRtrVr13LyySdzzz33aKEKEZEKJNpLCx8GzAE6Ekpy7wBGAQ8AXwODgPlmdnA04xCRiqlKlSo8+OCDvPrqq9SuXRuAXbt2ceONN3L66aezfv36gCMUEZGyEO0R3v8H1APOcPc0d/9/7n63u1/j7kcDFwNNgNFRjkNEKrCBAwfy0Ucf0aZNm0hbeno6rVq1Yu7cuQFGJiIiZSHaCe/pwHR3Ty/opLs/B7wT7iciEjVHHnkkCxcu5Oqrr460/fDDD6SkpDBmzBhNcRARiWPRTngbAl8U0+dz4LAoxyEiQuXKlRk/fjxvvPEGdeqE3pvNycnh5ptvplevXvz4448BRygiItEQ7YT3B+CYYvocBfwW5ThERCLOPPNMli9fTnJycqRtxowZnHDCCbzzzjsBRiYiItEQ7YT3DeBUMxta0EkzOxvoBUyPchwiIrtp3Lgx7733Htddd12k7eeff6Z3795cf/317NixI8DoRESkNEU74f0n8BXwiJmtMrNxZnaDmd1lZu8BLxMa3b01ynGIiOylcuXK3HvvvUydOpUGDRpE2u+99146derEmjVrAoxORERKS1QTXnf/BTgJeAVoDlwD3AXcAJwILAC6u/vn0YxDRKQop512Gh9//DEpKSmRtg8//JA2bdrw3HPPBReYiIiUiqgvPOHuP7r7+cAhQBpwITAAaOHuJ7n78mjHICJSnEMOOYQZM2YwduxYKlUKrbqenZ3NRRddxIUXXsimTZsCjlBERPZXtBeeeMjMugC4+wZ3f8fdX3D31zWqKyLlTUJCAjfddBPz58+nSZMmkfbnn3+eNm3asGjRogCjExGR/RXtEd7LgXfN7Eszu8PMWkb5eSIiB6xDhw6sWLGC8847L9L2xRdf0LlzZ1544QXcPdK+evVqXnnlFbZs2bJb2/jx47nzzjsZP348q1evjpzLf62IiJSNSlG+fz/gbOAMQksK/93MlgLPAS+5+w9Rfr6IyH6pXbs2L730EmlpaVx99dVkZ2fToUMH+vfvj5mRmZnJmDFjyM7OZvr06VSvXj3SNn369N0SWzMjNTWVUaNGkZKSgrtjZgF+dyIiFUu0X1p7090vBg4mtJraU0Bj4F7gGzN7x8wuNrNa0YxDRGR/DRkyhKVLl9K9e3fefPNNqlevzlNPPUVqaioffvghb7zxxm5tGRkZe43iujsZGRmkpqby9NNPY2Ya6RURKUNRf2kNwN13ufvb7j4UaAT0BB4DTiCUBK8rizhERPZH8+bNmTFjBg0bNiQzM5Nhw4aRk5PDZZddtldbUXJychg6dCiZmZka4RURKUNlkvDm5+65hFZg+w74HjCgelnHISKyLxITEwEYM2YMOTk5JCYmcvnll+/WVhI5OTmMHTs2anGKiMjeyiThtZCuZvYvM/sMWA7cQWi0dxyhmrwiIuXa6tWrmT49tDBkz549adKkyW5tJZWRkaFFLUREylC0y5L1NbMnCE1ZmA38FWhIaBpDKnCEu1/n7qr1IyLlXnp6emTu7THHHLNXW0m5O+np6aUen4iIFCzaVRreCO+3Aq8CLwBT3X1nlJ8rIlLqsrKyIl/XqlVrr7b9vZeIiERXtBPeaYSS3NfcfXOUnyUiElVJSUmRrzdv3rxX2744+uijdytPtnr1atLT08nKyiIpKYm0tDRatGgBoDJmIiIHKKoJr7ufFs37i4iUpbS0tEhJsU8//XSvtpI66aSTGDBgwG71fFW7V0Qkesq8SkNZMLPhZuZmVrcEfU8P9x1XwLkBZjaukO3y6EQvIuVVixYtSE1NBUIvnn3zzTe7tZVEw4YNmTp1KgkJCardKyJSRuIu4TWzRODSEvZNAh4possFwIhCtv4HFqmIxKJRo0aRmJhITk4OjzzyyG5tJXHFFf+fvTsPj7o6+z/+vhl2IajEXQEhEAOP2ibSglUzhWQaasWl6q9K1dgCakultrW2U/SpVUj1aauWtm55BCqt1ha14BJNgFAfxS2ptWiERApStxq3gCKQ5Pz+mMw42WeSWZKZz+u65prk+z1nvvdkwnDnzDn3uZRRo0apdq+ISAKlRMLbUvYs18y+CVQCuRF2/SVwRBfnxxNYZGcd3Ip6GbaI9DPOObxeL6WlpXg8Hu644w7q6+tbHevK4MGDueqqqwDV7hURSaSUSHiB/YAqoBQ4KZIOZjYDmAs82EWz8cCrvY5ORFJCcFpBcXExFRUV5ObmMnv2bHbv3h065vP52o3Emhk+n4/nn3+eESNGqHaviEiCxbtKQ6LsBs4J+/5aYHJnjc1sPwLJ8TPAUuCMDtocBGSghFdEwgSTXq/Xi9frpa6ujocffpgvf/nLrY61rbiQlZUVeoze1O5dsGBBrJ+SiEjKi2nCa2bH9bSvc+7FXvRtIlDnNxhHd/8jLAGOBGYT2AijI+Nb7neY2deA44B9wEbgMafVIyJpK3wENysrq1UyGzzWUWLa2NjIwIEDVbtXRCTBYj3C+wLQ00QwshUfvWRmJwILgOucc5vMzNtJ0wkt93cAo9uc22hmZznn3uruejU1NeTl5XV4rqqqKrKgRSQlDBwYeMvtae3envYTEelPOsubampqevyYsU54f0/PE964M7MhwP8CrxAY5e1KcIT3eeAqYDMwEbiewMjwg2Z2onOuOU7hipI9rtEAACAASURBVEiK6kntXjOjqEhrZUVEeiKmCa9zrjiWjxcHPwUmASc55/Z20/avwD9pvRXyP83sq8CzwOeBAuDxrh4kJydHI7ki0kqwdm95eXnEfQoLC9tNnRARSUWd5U15eXlUV1f36DFTYg5vJFpi+wHwW+fcxgji+SeBhLft8UYzux/4LPAZukl4RUQ64vf7WbduXUSlyTweD36/PwFRiYikpnSaw5tL4Pl+x8y+08H5hWa2EFgRwUj1zpb7xhjGJyJpIrye79y5c7tMej0eD6WlpeTn52t7YRGRHkqnObwvA7d0cPxI4KsEkvUNwLNmNhzYBdQ456Z00OcLLff/iEegIpLawuv5jhs3jpKSknbbC5sZhYWF+P1+8vPzaWxsjHg3NxERaS1t5vA6554lMPe2lZYqDV8FNjjnvht2/AngFDM7zzl3TwftXyWwq5uISNQ6qufbWe3e+vp6TjvtNA455BDuvPNODjrooCRHLyLSvyRs4wkzOxjIBkY65x4xsxHOuV2Jun4PXAGsB/7YsmXxZuAoYBawB7i4pf6viEiPtK3n27Z27yeffMItt9zCL37xC+rr6wF48sknKS0t5fTTT09orCIi/VnctxY2s/9qGS19k8CI6OqWU3eYWYWZHRXvGHrCOVcNHAvcSaBE2VxgKrAKmOaceyKJ4YlIGhg6dCgLFizgK1/5SuhYfX09Z5xxBnPmzOG9995LYnQiIv1HXBPelmT2CWAa8ChQDQSHNP4B5APPm9kRsbyuc87rnDPn3AcRtK1safvdDs695pyb75wb75wb4pw7zDn3tXhXlBARCdpvv/1YtmwZDz30UKupDH/84x+ZPHkyDz30UBKjExHpH+I9wnsNMByY6Zz7CvB/wRPOuRsIzIU9qKWdiIh04tRTT2Xz5s2cffbZoWNvv/02p512GhdeeCEffNDt3/ciImkr3gnvl4Ey59zfOjrpnFtNYJqDL85xiIj0ewcccAB//vOf+fOf/8zo0Z/ueH733XeTk5PDI488ksToRET6rngnvAcCb3TTpg44NM5xiIikjLPPPpvNmzdzxhlnhI699dZbnHrqqRQXF/Phhx8mMToRkb4n3gnvvwhs+NCVY4B34hyHiEhKGT16NA888AB/+tOfOPDAA0PHV6xYweTJk3nssceSGJ2ISN8S74T3D8AJZvbDjk6a2QICmzisinMcIiIp6dxzz2Xz5s2cdtppoWNvvPEGRUVFfPOb36ShoSGJ0YmI9A3xTnh/ATwJlJjZZmA2gJmtMrM6Ajuf1QLXxjkOEZGUlZmZyerVq1m5ciX7779/6Phdd93F5MmTKS8vT2J0IiLJF9eE1zm3BygAFhOYz3s0gbJkZwJHENiK+AuRlA8TEZGuzZkzh1deeYVZs2aFjr3++uv4fD7mz5/Pzp07kxidiEjyxH3jCefcXufcNcDBQA5wEvBZYH/n3MXOuXfjHYOISLo45JBDePjhh/n973/PqFGjQsfvvPNOpkyZwtq1a5MYnYhIcsQ94QUwsxOBc51zm51zTwEvAheZ2ZGJuL6ISDoxMy644AJeeeUVvvSlL4WO79ixg4KCAr75zW+qkoOIpJV477Q23MweJ7Db2sI2170N2Gpm18czBhGRdHXooYfy6KOPsmzZMjIyMkLH77rrLo455hjWrFmTxOhERBIn3iO83yMwh/cx4MrgQedcE3AR8CrwYzM7u+PuIiLSG2ZGcXExmzdvbjW396233mL27Nmcd9551NfXJzFCEZH4M+dc/B7c7GWgETjedXAhM8sAXgJec859IW6BJImZVeXm5uZWVVUlOxQREQDuueceFixYwHvvvRc6Nnr0aO69915mzpyJmQFQW1tLWVkZDQ0NZGRkUFRUxMSJEwFwzoXaiYgkSl5eHtXV1dXOubxo+w6MR0BhxgIrO0p2AZxzDWb2GHBWnOMQERHgvPPOC1VtuP/++wGYNGkSX/jCFzAzKisrWbJkCRUVFYS/dZsZBQUF+P1+vF6vkl4R6VfiPaXhA6C7hWkjCJQqExGRBBg9ejSrVq3iwQcfZPLkyaxevZphw4axbNkyCgoKKC8vp+04hXOO8vJyCgoKWL58OWbWro2ISF8V74R3DeAzs/M7Omlm04HTgb/FOQ4REWnj9NNPp6qqiszMTCorK5k3bx5NTU1d9mlqamLu3LlUVlZqhFdE+o14T2m4BigC7jaz7wGVwFvAKGAqgQVtHwH+OMchIiIdGDp0KABLlizpNtkNOuCAA9i2bVscoxIRia24JrzOuf+Y2TTg1wR2V8tt0+QZ4DLn3EvxjENERDpXW1tLRUVFRG2nT5/O6tWryczMDPXV4jYR6eviPcKLc+4t4FwzOxDIAzKBPcA/nXO18b6+iIh0raysLKL5uNOnT2ft2rUMGzZMi9tEpF+Je8Ib5Jx7DyhP1PVERCQyDQ0N3bbJzMxstbits/m+wcVt69ato7S0lOLiYiW9IpJ0CdlaOMjMvm1m2shdRKQPCd+FrTPz58/X4jYR6bcSmvACWYA3wdcUEZEuFBUVdZmUejweLrnkEiC6xW1NTU2UlJTEJEYRkd5IdMIrIiJ9zMSJEykoKOj0fGFhIWPGjIlqcVtQeXk5dXV1vQ1RRKRXlPCKiAh+vx+Px9PhuezsbCDyxW3hnHOUlZX1Oj4Rkd5IdMJraFc1EZE+xTmH1+ultLS0w6R3xIgRQGSL2zrS034iIrES14TXzMaY2f5hh64Fjm7TZn8zOyyecYiISOeC2wQXFxdTUVGBz+drNad3165dQGSL2zrS034iIrES77Jk/wJuBr4P4Jx7H3i/TZtrgQuBA+Ici4iIdCKY9Hq9XrxeL3V1daENJf7rv/4L+HRxWzTTGsyMoqKieIUtIhKRmCe8ZnZ5+LdAbptjba9/OtDxxDEREUmY8FHdrKwsFixY0Op8cHFbeXnkJdULCwvJysqiubmZAQM+/VAxvDavdmsTkXiLxwjvzYAjkOw64BQgv5s+98QhDhERiTG/38+6desiKk3m8Xjw+/0A3HjjjUyZMoXTTjstlMRqtzYRSZR4JLwXt9wbcBfwOJ0ntM3ADmBDHOIQEZEYCl/cNnfu3C6TXo/HQ2lpKfn5+dTX1/PLX/4SgFdffZWMjAzt1iYiCRXzhNc5tyL4tZldBKwOPyYiIv1T+OK2cePGUVJSQnl5ebvR2cLCQvx+P/n5+TQ2NnL++edTX1+P3+8nIyMj6t3axo0bh9frjfOzE5FUFtdFa865L8bz8UVEJLG6WtwWnH+blZUFBEZqBw4cyD333MPChQt7tVubEl4R6Q2Ltoh4lw9mlgHscc7tCfs+Is65lCvUaGZVubm5uVVVVckORUSkT6itrSU7OzvqSg9btmwJJdIikp7y8vKorq6uds7lRds31nV43weWhH3/Qcux7m7vxTgOERHpg7Rbm4gkQ6ynNDwBvBr2/d8IVGoQERHRbm0ikhQxTXidc96uvhcRkfSm3dpEJBniurWwiIhIuOBubdHQbm0i0lvx3loYMysC5gBjgf0I1Odty/VkArKIiPQvvdmtDbRDm4j0TFwTXjObC9xOx0luOM3zFRFJEz3dra2pqQmPx6Md2kQkavGe0vB94BPgQmB/59yATm6eOMchIiJ9QPhubR5P12/94bu1NTc34/F4WLZsWWiEuG21h+AObQUFBSxfvjxUM1hEJN4J7zhglXNuZSrW2RURkeiE79ZWUVGBz+drNwprZvh8PtauXUtxcTH79u1jwIABUe/QVllZqRFeEQHiP4f3P0BjnK8hIiL9SE92awPt0CYiPRfvhPce4CIzG+Wc+zDO1xIRkX4ifOQ1KyuLBQsWdNmutraWioqKqK5RXl5OXV2ddmgTkbhPafgZgY0oNphZgZkdbGYZHd3iHIeIiPRj2qFNRHoj3iO8HxCo0OABHuuinYtlLGb2LeC3wAHOuQ+6aXsq8BBwi3Puux2cLwYWApMIPJ8yYJFz7s1YxSsiIl2L5Q5tKm0mkn7infA+RYJLjpmZB/hmhG0zCJRN6+z8IuA6YAfwB+Aw4CKg0MzynHPv9D5iERHpTqx2aAsmsW1Lm2VmZjJ//nyGDBkSaqtkVyR1xDXhTdTWwhZ4V/psy60YyI2w6y+BIzp5zAnAT4GXgBODVSbM7GLgLuAG4Bu9iVtERCIT3KEtmmkNbXdoCya7y5Yta1XtYfr06axevZrMzExAo74iqSjuO60lyH5AVTQdzGwGMBd4EDijgyaXEpiKcW2bkmrLgeuB881sgXPu4x5FLCIiEevNDm07duygvr6ez372s+1Km02fPp21a9cybNgwbWghksJiumjNzLZ2c9tiZtVm9qCZXWVmh8Xo0ruBc8JuL3cT535AKfAMsLSTZl6gmcCc3RAXeBd8AhgCfL43QYuISOT8fn+3m1UEhe/QdvvttzN69GigdWmzzMxMVq9ezbBhw7ShhUiKi3WVhnHd3LKAzwCzgRKgxszO6u1FnXNNzrm/BG9Ad3NrlwBHEhjhbe6kzfHADufczg7O1bTcT+xRwCIiEpWe7tD27rvvUldXx5gxY9qVNps/fz6ZmZna0EIkDcR6SsPREbQZQmDe7FeAbwN/MLPPO+dejHEsHTKzE4EFwHXOuU1m5u2gTQYwCHi7k4d5v+X+wO6uV1NTQ15eXofnqqqimoUhIpK2wndoGzduHCUlJe1GY82MwsJC/H4/+fn5OOcYNmwY5557LtC6tJnH4+GSSy4BtKGFSF/TWd5UU1PT4fFIxDThdc5tj7DpFmC9mZUDjxAo+xVRZYXeMLMhwP8CrxAY5e1McFnvnk7Of9RynypzoEVE+rxod2gzM4YPH85ZZwU+SAwvUVZYWNjhqG9HPB4PX/rSl5g9ezZHHnkkZsaHH37IqFGj4vdkRSSmkpqwOefKzOxp4IsJuuRPCdTTPck5t7eLdvta7od1cn5wy323C9ZycnI0kisiEiPR7tAWLrxEWXZ2NtD1hhaZmZl897vf5dvf/jb7779/b8IWkSh0ljfl5eVRXV3do8fsCyOU/yQwrzeuzOw44AfAb51zG7tp/h7QBHT2Dje65f6NGIUnIiJxFl7abMSIEUDnG1pMnz6dRx55hFGjRmmTCpEU0BcS3sF8OkUgnnIJPN/vmNl3Oji/0MwWAiucc8Vm9iowwcz2c861jS+4MfumOMYrIiIxFF7abNeuXUDHG1pMnz6d9evXM3jw4A43qQhSuTKR/iPWVRqiYmZDgUKgNgGXexm4pYPbqpbzL7R8/3jL92sJ1OGdEf4gLTu5zSQwuvtS3KMWEZGYCZY227x5M/DpqG9QZmYma9asCSW7KlcmkhqSlvCa2aHAfQS267033tdzzj3rnPtu2xvwm5YmG1qO/bHl+zsIbIt8rZmFz+X9EYGSZr91emcTEek3wkubrVu3jtdeey006hs0f/58Ro8eHRrZVbkykdQQ0ykNZnZXBM2GAUcBn2u5/lPAb2MZRyw4514ws/8BfghsMrO1BOrueoHngV8lMTwREYlS29Jm69ato7i4GL/fz7p16wBCpcpA5cpEUkms5/AWR9F2F3AnsMg5F9k7SoI5564ys20E6vZeQGBDi5uB/3bOfZLM2EREJHrhpc0AGhsbQ6O+f/nLXxgzZgxAROXK2iovL6euri5UGk1E+o5YJ7wXd3O+icACte3AP51z+7pp3yPOOW8UbSuBTj+Dcs7dCtza+6hERKQvCJ92MHDgwNCo70knnRQ63lW5ss445ygrK+u0VJqIJE+sN55YEcvHExERibfgqG/4yGxn5cq609N+IhJfSa3SICIi0he0XWzWUbmySPS0n4jElxJeERGRNtqWK4uEmVFUVBSniESkN5TwioiItNG2XFkkCgsLycrK4p133mH79u1xikxEekIJr4iISAeCm1REwuPx4Pf7AbjpppvIzs7mJz/5SWhHNxFJLiW8IiIiYZxzrTap6C7p9Xg8lJaWkp+fz7vvvsudd97Jnj17WLJkCVlZWaxYsYLm5uYERS8iHVHCKyIiEiY4dzdYrqyiogKfz9duTq+Z4fP5WLt2LcXFxTjn+Ne//sVBBx0UavP2229TXFzM1KlTefLJJxP6PETkU6bdcePHzKpyc3Nzq6qqkh2KiIhEKfj/YzDRrauro6ysjIaGBjIyMigqKgqVMnPOhdo1Nzdzxx13cPXVV1NfX9/qMb/61a9y4403Mn78+AQ+E5HUkJeXR3V1dbVzLi/avkp440gJr4hI+tq1axeLFi3i1ltvZe/evaHjgwcPZsGCBSxatIgDDjggiRGK9C+9SXg1pUFERCQORowYwc0330xNTQ2nnnpq6PjevXv51a9+xYQJE7j55pvZt29fq13damtrWbp0KYsXL2bp0qXU1taGzmmQSqRnlPCKiIjE0fjx43nooYfYsGEDxx13XOj4+++/z3333UdTUxNmRmVlJT6fj+zsbC6//HIWLVrE5ZdfTnZ2Nj6fj8rKytCucCISHSW8IiIiCXDKKafwwgsvsGLFCo444ggyMzNZvXo1Q4cOZdmyZRQUFFBeXt4uoXXOUV5eTkFBAcuXL1fSK9IDSnhFREQSxMy48MILefXVV/nTn/5EZmYmlZWVzJs3j6ampi77NjU1MXfu3NBIr4hETgmviIhIgg0ZMoQZM2YAsGTJkm6T3aCmpiZKSkriGZpISlLCKyIikiS1tbVUVFRE1ae8vJy6uro4RSSSmpTwioiIJElZWVnU83Gdc5SVlcUpIpHUpIRXREQkSRoaGhLaTyRdKeEVERFJkoyMjIT2E0lXSnhFRESSpKioKOqKC2ZGUVERALfddlu77YtFpD0lvCIiIkkyceJECgoKoupTWFhIVlYW27dv59vf/jbjx49n8eLFfPzxx3GKUqT/U8IrIiKSRH6/H4/HE1Fbj8eD3+8H4Pbbb6e5uZmdO3eyaNEiJkyYQGlpKY2NjZ32D18gt2/fvojaiaQCJbwiIiJJ4pzD6/VSWlrabdLr8XgoLS0lPz8f5xxTp05l7NixofNvvfUW8+bN49hjj2X16tUd7thmZuzduxeAQYMGUVtby9KlS1m8eDFLly6ltrYWQLu5ScpRwisiIpIkwcSyuLiYiooKfD5fuzm9ZobP52Pt2rUUFxeHEtczzzyTuro6brrpJkaPHh1q/8orr3D66adz0kkn8fTTTwOfJruNjY0MHjyYyspKfD4f2dnZXH755SxatIjLL7+c7OxsfD5faDc3Jb2SKky/zPFjZlW5ubm5VVVVyQ5FRET6sGBCClBXV0dZWRkNDQ1kZGRQVFREVlZWu3bhdu3axfXXX89vfvMbPvroo1bnVq5cyZw5c9i7dy+DBw9m2bJl3W5lHBxNDk+wRZItLy+P6urqaudcXrR9lfDGkRJeERFJpLfffpsf/ehHrFy5ksbGRjweD1u3bmXMmDEAVFZWUlBQENFWxh6Ph4qKCrxeb5yjFolMbxJeTWkQERFJEYcccgjLli1j06ZNnHrqqRQWFjJmzJjQArUlS5ZElOwCNDU1UVJSEs9wRRJmYLIDEBERkdjKzs7moYceYuvWrcCnC9QqKiqiepzy8nLq6upCUypE+iuN8IqIiKSo8ePHh74uKyuLehGac46ysrJYhyWScEp4RURE0kBDQ0NC+4n0JUp4RURE0kBGRkZC+4n0JUp4RURE0kBRUVHU5cXMjKKiojhFJJI4SnhFRETSwMSJEykoKIiqT2FhoRasSUpQwisiIpLigovV/H5/t1sYB3k8Hvx+PwCrVq1ix44dcYtPJN6U8IqIiKS44DbBXq+X0tLSbpPe4E5r+fn51NfXc+mllzJx4kS+973vUV9fn6CoRWJHCa+IiEgaCCa9xcXFVFRU4PP52s3pNTN8Ph9r166luLiYTz75hNNOO436+nr27NnDTTfdxNFHH821117Lzp07k/RMRKKnrYXjSFsLi4hIX+OcCyW6dXV1lJWV0dDQQEZGBkVFRaE5u8F2jz76KFdeeSUvvfRSq8fJzMzkJz/5CZdddhlDhgxJ+POQ9NObrYWV8MaREl4REUkFzjnuuecerr766tDubUFHHHEE//3f/01xcTGDBg1KUoSSDnqT8GpKg4iIiHTJzDj//PPZsmULv/vd7zjssMNC515//XXmz5/PpEmTuPvuu2lqakpipCIdU8IrIiIiEfF4PFx22WX861//YsmSJRx44IGhc9u2bePCCy9k8uTJ3HfffTQ3NycxUpHWlPCKiIhIVIYMGcKPf/xjtm/fzqJFixg1alTo3JYtW/h//+//cfzxx7N69Wo0dVL6AiW8IiIi0iMjRozguuuuY/v27Vx55ZXst99+oXObNm3i9NNPZ+rUqTz22GNKfCWplPCKiIhIr4waNYobb7yRbdu28Z3vfIdhw4aFzlVVVVFUVMRJJ53Ehg0bkhilpDMlvCIiIhITmZmZ/PrXv+bVV19l3rx5DB48OHTuqaeewuv1MmPGDJ5++ukkRinpSAmviIiIxNRhhx3GHXfcQW1tLV//+tcZOHBg6Nz69euZPn06X/7yl/n73/+exCglnaRkwmtm3zIzZ2b7d3DucDNbbmZvmtleM3vDzP5gZsd00PYsM7u5k9sliXk2IiIi/dOYMWO4++67qamp4Zxzzmm1pfGjjz5Kbm4uZ511VrtNLURiLeUSXjPzAN/s5Nxo4FngIqAWuAt4FTgfeNbMctp0mQMs7OR2ZjziFxERSTVZWVncd999vPjii8yePbvVlsYPPPAAxx57LOeddx6bN29OYpSSylIi4bWAXDP7JlAJ5HbS9ErgCMDvnDvFOXepc+5k4EfASMDfpv144BHnnHVwK4rT0xEREUlJkydP5q9//SvV1dX4fL7Qcecc9957L5MnT2bOnDls2bIliVFKKkqJhBfYD6gCSoGTumh3BvAR8Ks2x28BmoC2W9WNJzACLCIiIjHymc98hscee4yNGzeSn58fOt7c3Mwf//hHcnJy+PrXv67EV2ImVRLe3cA5YbeXO2nXDDzjnNvTwXEHfBw8YGYHARko4RUREYmLadOmUVlZyYYNG/jCF74QOt7c3Mwf/vAHcnJyuOCCC6itrU1ilJIKUiLhdc41Oef+ErwB73TSbrJzbmYHpy4DBgIVYcfGt9zvMLOvmdkSM7vWzIosfPKRiIiI9Mopp5zC//3f/7F+/XpOPPHE0PHm5mZWrlxJTk4OF154IXV1dUmMUvozS8WdT8ysEsgHDnDOfdBJmy8DpwLHAicDDwAXOed2tpw/H/gD8C4wuk33jcBZzrm3uomjatiwYbk5OW3XwgVUVVVF+pRERETSxvr161m0aBFPPfVUq+Mej4c5c+Zw9dVXk5WVlaToJN7y8trOMA2oqalh9+7d1c65jht0ISVGeHvoc8C3CCS7AGNbbkHBEd7ngc8Aw4DjgNXAdOBBM0vnn5+IiEhcfPGLX+TJJ59k7dq1TJs2LXS8qamJ3//+9xxzzDEUFxfz6quadSiRSdsR3pZ2QwkktpcDlwCvAVnOuX1mdiyfVmnYF9ZnIIHSZp8FvuSce7yLx6/Kzc3N1UiuiIhIz61bt46f/OQn7XZo83g8XHDBBSxatIgJEyYkKTpJlLy8PKqrqzXCGy3n3CfOuZedc5cCfwPG0DLi65z7p3Pur+HJbsvxRuD+lm8/k9CARURE0tCMGTPYuHEjFRUVfP7znw8db2pqYvny5WRnZ1NcXMzWrVuTGKX0ZWmT8JrZTDOrNLPvdtLkuZb7QyJ4uJ0t9429j0xEREQiMXPmTJ5++mnKy8vbJb4rVqxg0qRJXHzxxUp8pZ20SXiBPQSmOXS2Q9qYlvsdZjbczJrNrLO9DoO1U/4RywBFRESkewUFBZ0mvuEjvqrqIEHplPA+A7wJnGRmrTanMDMfcBawHXjaOfcx8AQw2czOa9PWC3yVQH3eyviHLSIiIh0JJr6PP/44n/vc50LHGxsbWbFiBdnZ2Zx//vnU1NQkMUrpC9Im4W2Zi3t5y7frzOyvZnarma0FyoC9BMqSBacpXAE0AH80swoz+62ZrQbKCYwWX+yca0rw0xAREZE2CgsLeeaZZ3j88ceZOnVq6HhzczP33HMPU6ZM4eyzz+bFF19MYpSSTGmT8AK0bEpRBGwATgHmAscQqLd7gnNuQ1jbagI1eu8kUK1hLjAVWAVMc849kdjoRUREpCuFhYU8++yzVFRUtNrAwjnHqlWrOP7445k9e7bq4KehlEx4nXNe55x1VJLMOVfunCt0zh3gnBvknDvCOXeBc67ddsTOudecc/Odc+Odc0Occ4c5577mnNOfiCIiIn3UzJkzefLJJ9mwYQP5+fmtzq1Zs4YTTjiBoqIiNm7cmKQIJdFSMuEVEREROeWUU6isrGTjxo0UFha2OvfYY49x4oknMmPGDDZs2NDJI0iqUMIrIiIiKW3atGk8/vjjPP/888yaNQszC51bv349Xq+Xk046ifLyclJxQy5RwisiIiJpIi8vj0ceeYQXXniB008/HY/HEzr35JNP4vP5+PznP8/DDz+sxDfFKOEVERGRtHLcccfx4IMPsmnTJs4555xWie9zzz3HV77yFXJzc3nggQdobm5OYqQSK0p4RUREJC0dc8wx3HfffbzyyivMmTOHQYMGhc698MILnHXWWRx33HHce++9NDWpEml/poRXRERE0lpWVhYrV66ktraWiy++mCFDhoTOvfTSS5x33nlMmjSJ0tJS9uzZk8RIpaeU8IqIiIgAY8eO5a677mLr1q1ceumlDBs2LHRu69atzJs3j/Hjx3PzzTfz0UcfJTFSiZYSXhEREZEwhx9+OLfeeivbtm3jiiuuYOTIkaFzb7zxBldccQVjxozhuuuu4/33309ipBIpJbwiIiIiHTj44IP51a9+xWuvvcbVV1/N6NGjQ+fee+89rrnmGsaMGcMPf/hD3nrrrSRGKt1RwisiIiLS/HueoAAAIABJREFUhf3335+f/exn7NixgxtvvJHDDz88dG7Xrl38z//8D+PGjeNb3/oW27ZtS16g0iklvCIiIiIRGDZsGFdeeSXbtm3j1ltv5eijjw6d27NnD7feeitZWVlccMEF1NTUJDFSaUsJr4iIiEgUBg0axKWXXkpdXR0rV65k8uTJoXNNTU2sXLmSKVOmcOaZZ/L8888nMVIJUsIrIiIi0gMDBgxgzpw5bNq0idWrV3PCCSeEzjnnePDBB5k6dSqFhYVs2LBBu7clkRJeERERkV4wM0477TSee+451q1bh9frbXW+oqICr9fL9OnTWbNmDc3Nza2S39raWpYuXcrixYtZunQptbW1oXNKkmNDCa+IiIhIjHzxi19k/fr1PPfcc5x66qkMGPBpqvXMM89QUlLC3r17MTMqKyvx+XxkZ2dz+eWXs2jRIi6//HKys7Px+XxUVlZiZkp6Y0AJr4iIiEiMnXDCCTz00ENs2rSJr33tawwaNIjMzExWr17N0KFDWbZsGQUFBZSXl7dLaJ1zlJeXU1BQwPLly5X0xsDAZAcgIiIikqpycnK455572L59O3//+9/JzMyksrKSefPm0dTU1GXfpqYm5s6dy7hx49pNk5DoaIRXREREJM7Gjh3LGWecAcCSJUu6TXaDmpqaKCkpiWdoaUEJr4iIiEiC1NbWUlFREVWf8vJy6urq4hRRelDCKyIiIpIgZWVlUc/Hdc5RVlYWp4jSgxJeERERkQRpaGjoVb9Ip0JIa0p4RURERBIkIyOjV/2uv/56fve737F79+5YhpXylPCKiIiIJEhRURFmFlUfM6OoqAiAp59+mm9/+9uMGTOG6667jvfeey8eYaYcJbwiIiIiCTJx4kQKCgqi6lNYWEhWVhY7duzg8ccfB6C+vp5rrrmGI488ku985zts3bo1HuGmDCW8IiIiIgnk9/vxeDwRtfV4PPj9fgAOOuggrr32Wg499NDQ+d27d/Ob3/yGiRMncvbZZ/PMM8/EJeb+TgmviIiISII45/B6vZSWlnab9Ho8HkpLS8nPz8c5x9ChQ1m0aBHbt2/ntttuY+LEiaG2zc3NrFq1imnTpvGFL3yBv/71rzQ3N8f76fQbSnhFREREEiS4TXBxcTEVFRX4fL52c3rNDJ/Px9q1aykuLsY516rN4MGDueSSS9i8eTN//etfmT59eqv+Tz31FGeccQaTJk3itttu4+OPP07Ic+vLTHszx4+ZVeXm5uZWVVUlOxQRERHpQ8KT2Lq6OsrKymhoaCAjI4OioiKysrLatevKc889R0lJCWvWrKGxsbHVudGjR/Otb32LBQsWcPDBB8f+ySRIXl4e1dXV1c65vGj7KuGNIyW8IiIikkjbtm3jhhtuYOXKlezatavVuSFDhnDhhRfy/e9/n+zs7CRF2HO9SXg1pUFEREQkRYwbN45bb72Vf//73/zsZz/jsMMOC53bs2cPd955J8cccwxf+cpX+Nvf/hb1rm/9lRJeERERkRQzatQorr76arZv387//u//Mnny5FbnH374YfLz85k6dSp/+tOf2k2DSDVKeEVERERS1KBBg/jGN77Bpk2bePTRR8nPz291fvv27bz44ou8/fbbSYowMQYmOwARERERia/gbm1FRUX84x//oKSkhDfffJNVq1aRmZkJQG1tbbvFc8HSZ5EunuurlPCKiIiIpJHjjz+ee++9l+bmZgYMGEBlZSVLliyhoqKi1ZxeM6OgoAC/34/X6+3XSa+mNIiIiIikGeccAwYMYNmyZRQUFFBeXt5uAZtzjvLycgoKCli+fHmohnB/pIRXREREJM2YGZWVlcybN4+mpqYu2zY1NTF37lwqKysxMz755JMERRk7SnhFRERE0tCSJUu6TXaDmpqaKCkpAeCdd97h+9//Ptu3b49neDGlhFdEREQkzdTW1lJRURFVn/Lycurq6jjqqKN4+eWXGT9+PGeffTYbN26MU5Sxo4RXREREJM2UlZVFPR/XOUdZWRkAkyZNorm5mVWrVnHiiSfy6quvxiPMmFHCKyIiIpJmGhoaetUvJycndOzEE0/koIMOiklc8aKyZCIiIiJpJiMjo1f9Lr30UvLy8vjlL3/J+eefz8iRI2MZXswp4RURERFJM0VFRVGXGQtuXhE0depU7r333niEF3Oa0iAiIiKSZiZOnEhBQUFUfQoLC8nKyopTRPGlhFdEREQkDfn9fjweT0RtPR4Pfr8/zhHFT0omvGb2LTNzZrZ/B+cON7PlZvamme01szfM7A9mdkwnj1VsZn83s4/M7HUz+18zOyz+z0JEREQkPpxzeL1eSktLu016PR4PpaWl5Ofna6e1vsLMPMA3Ozk3GngWuAioBe4CXgXOB541s5w27RcBy4DRwB+A6pa+z5hZ316OKCIiItKJ4Pzd4uJiKioq8Pl8mFm7Nj6fj7Vr11JcXIxzrl2b/iIlFq1Z4Kf/2ZZbMZDbSdMrgSMAv3OuJKz/VcDPAT9wQcuxCcBPgZeAE51zDS3HLyaQKN8AfCP2z0ZEREQk/oJJr9frxev1UldXR1lZGQ0NDWRkZFBUVBSas9ufk10A669D0+HMbASws4NTBzjnPghr9wpwJDDaObcn7PhQYBewxTk3ueXY/wA/AM51zv05rK0B/yYw6nugc+7jLuKqys3Nza2qqurV8xMRERFJd3l5eVRXV1c75/Ki7ZsqUxp2A+eE3V7upF0z8Ex4sht23AHhyau35XhZeEMX+AvhCWAI8PneBi4iIiIi8ZUSUxqcc03AX4Lfm9mCTtpN7uQhLiPwswjfVPp4YIdzrqOR45qW+4nA+qgDFhEREZGESYmEtyfM7MvAqcCxwMnAA8DilnMZwCDg7U66v99yf2B316mpqSEvr+ORd011EBEREWmts7yppqamw+ORSNuEF/gc8K2w78e23DYBwf322k59CPqo5b7P/PyCvxxKotOLXvf0pNc9Pel1T0963WOjzyRsieac+6mZ/RwYD1wOXAI8bGZZwL6WZsM66T645b7TBWtBOTk5+iUVERERiVBneVPLorUePWaqLFrrEefcJ865l51zlwJ/A8YQmN7wHtAEtNu4osXolvs34h+liIiIiPRG2iS8ZjbTzCrN7LudNHmu5f4Q59w+AhtSHG1m+3XQNriR9KZYxykiIiIisZU2CS+B+bj5wJmdnB/Tcr+j5X4t4AFmhDdq2cltJoHR3ZdiH6aIiIiIxFI6JbzPAG8CJ5nZSeEnzMwHnAVsB55uOXwHgdq815pZ+FzeHxHYvOK3LhV27RARERFJcSmx01pbZlZJYDS37U5rZwN/IjA/91ECo7STgC8CnwCznHMbwtrfAPwQ2EpgxHcigQ0pngdOds590k0c7w4bNuzAnJycmD23zgRLdSTiWtJ36HVPT3rd05Ne9/Sk1/1TNTU17N69+z3n3OjuW7eWVglvy7lCAknsCcAI4D/AOqDEOdduhzYzuwxYQKCawzvAKuC/nXMNEcTxLwIlzrb14umIiIiICIwDGpxzR0fbMSUTXhERERGRoHSawysiIiIiaUgJr4iIiIikNCW8IiIiIpLSlPCKiIiISEpTwisiIiIiKU0Jr4iIiIikNCW8IiIiIpLSlPCKiIiISEpTwisiIiIiKU0Jr4iIiIikNCW8IiIiIpLSlPCKiIiISEpTwisiIiIiKU0Jr4iIiIikNCW8IiIiIpLSlPCKiIiISEpTwisiIiIiKU0Jr4iIiIikNCW8IiIiIpLSlPCKiIiISEpTwisiIiIiKU0Jr4iIiIikNCW8IiIiIpLSlPCKiIiISEobmOwAUpmZ/QvIALYlORQRERGR/m4c0OCcOzrajkp44ytj2LBhB+bk5BwY7wvV1NQAkJOTE+9LSR+i1z096XWPrz179vDhhx/S3NzMgAEDGDVqFEOGDEl2WHrd05Re90/V1NSwe/fuHvVVwhtf23Jycg6sqqqK+4Xy8vIASMS1pO/Q656e9LrHlnMOM6OyspIlS5ZQUVGBcy50/vXXX6egoAC/34/X6w21TzS97ulJr/un8vLyqK6u3taTvprDKyIiaSuYvC5btoyCggLKy8tbJbvBNuXl5RQUFLB8+XLMrF0bEenblPCKiEjaCo7szps3j6ampi7bNjU1MXfuXCorK5MywisiPaeEV0RE0tqSJUu6TXaDmpqaKCkpiXNEIhJrSnhFRCRt1dbWUlFREVWf8vJy6urq4hSRiMSDEl4REUlbZWVlUc/Hdc5RVlYWp4hEJB5UpSFFaPVmetLr3r+Fr/avra2lrKyMhoYGMjIyKCoqYuLEie3agV73WGpoaEhov97Q656e9LrHhhJeEZEk6K4Ulpn1iVJYqS4jIyOh/UQkOZTwiogkWHgprM6qAwRLYa1bt47S0lKKi4uV9MZBUVFR1GXGzIyioqI4RhU/Pf1UQaS/U8IrIpJgPSmFNW7cOLxeb2ICTHHhydzEiRND9XcjVVhYSFZWVrzCixt9qiDpTIvWRESSQKWwkiM86Vu2bBkAfr8fj8cTUX+Px4Pf749niHGhDTYk3SnhFRFJMJXCSo62Sd8Pf/hD6uvr8Xq9lJaWdpv0ejweSktLyc/P73eJoDbYkHSnhFdEJMFUCis52iZ99fX1nHbaaezevZvi4mIqKirw+Xztkjwzw+fzsXbt2n49l1qfKkg60xxeEZEE60+lsFJN26Tv6aefZubMmaxevRqv14vX66Wurq7dYq7gnN3+muz25lOF/jhfWaQtJbwiIjEQ6ep3UCmsZOks6du4cSM5OTnMmzePSy+9lKysLBYsWNDhY/THZBd696lCZz8Lkf5ECa+ISC9Fuvr9F7/4Bccdd1zalcLqK7pK+urr6ykpKeGGG27A5/MxadIkRo4cyc6dO5k5cyazZ89OcLSxpU8VJN0p4RUR6YVoaupOnTqVd999N61KYfUlkSRvzc3NlJWVtZovfcghh/T7hFefKki606I1EZFeiGb1+969e7nhhhuA9CiF1dekc9IX/FQhGvpUQVKJEl4RkV6KZvX7bbfdxocffpgWpbD6mnRO+oKfKkRDnypIKlHCKyLSC9Gufq+vr2fWrFk0NzenRSmsviTdkz59qiDpTHN4RUR6oSer3zdu3Mj999/PV7/61ZQuhdUX+f1+1q1bF9GIfColfc650KcKc+fO7fL5t/1UQb97kgqU8IqI9EJPV7Fv2bKlVSKRiqWw+pp0TvqCVUGKi4sZN24cJSUl7bYXNjMKCwvx+/0p87xFgpTwioj0QjovhOpv0j3pCz5/faog6UgJr4hIL6imbv+S7kmfPlWQdKWEV0SkF1RTt/9R0ieSflSlQUSkl7T6XUSkb1PCKyLSC+ELoVRTV0Skb1LCKyLSC+ELoVRTV0Skb+qzc3jN7J/ASc65D5Mdi4hIV9J9IZT0L+G/g7W1te1+VydOnNiunUh/12cTXmAKMKTtQTMbBSxxzn078SGJiHRMC6GkPwgmsZWVlSxZsoSKiop2ZdkKCgrw+/14vV4lvZIy+tyUBjN7xMx+CjjgqA6aDAcuSWhQIiIi/VwweV22bFmoskjbueTOOcrLyykoKGD58uVRl9wT6av64gjvS4AXMOBZM9sJ/AP4O/AicAzwZtKiExER6YeCI7vz5s3rdmvlpqYm5s6dy7hx4/B6vYkJUCSO+lzC65y7EsDM9gDTgcOBz7TcTiUQ8w+TFqCIiEg/tWTJkm6T3aCmpiZKSkqU8EpK6HMJb5j9gCbnXDXwULKDERGR9JNKC7xqa2upqKiIqk95eTl1dXXaKEX6vT6V8JrZX4ALnXMfA0c752qTHZOIiKSnVFvgVVZWFvV8XOccZWVlnS7CFOkv+tqitbeAQS1fbzaznWb2pJn91szmmdlUMxuazABFRCT1peICr4aGhoT2E+lL+tQIL+AB9rV8fRSBebvHt9z/AJgAODPb4pybkpwQRUSkt/r6VIFUXOCVkZGR0H4ifUlfS3i/CfwU+Bj4NXCRc+7h4EkzG04gAT4uKdGJiEiv9ZepAqm2wKuoqCjqUWgzo6ioKI5RiSRGX5vSsAP4fMvXZxKouRvinPvYObfROXd7wiMTEZFe6y9TBXqzwKuvmjhxIgUFBVH1KSws1II1SQl9LeH9ObDKzKoJbDzxDTM7xcz0eYqISAroyVSBysrKhI/w9maBV1/m9/vxeDwRtfV4PPj9/jhHJJIYfSrhdc7dCfwXcC+BjSeKgQrgfTPbamb3m9k1ZjY7iWGKiEgv9GSqQKKl4gIv5xxer5fS0tJuk16Px0NpaSn5+fl9eiGeSKT6VMIL4Jzb7Jy7EagFTgJGAp8DFgOvA4XA75MXoYiI9FR/mSqQigu8glNDiouLqaiowOfztRs5NzN8Ph9r166luLi4z5daE4lUX1u0FuKcyw77tqrlJiIi/Vh/qQWbqgu8gs/J6/Xi9Xqpq6trVyEjOGdXya6kkqQlvGb2U+ASYDSwHVgB3OCc29dVv0RrqQzxY+A84AgCC+v+ACxJZqx9vaSPiKSu3rz/9JepAsEFXuXl5RH36S8LvMJfk6ysrE7/kND/HZJKkpLwmtk3gGvCDk0ArgWmAqcnI6aOmNlA4H7gSwRGmNcSqAn8U+DzZnaqS8Lkpv5S0kdEUk9v33/601QBv9/PunXrIppvrAVeIn2bJWMyupk9S6CW7sXABuAY4AYgF/iac+7PCQ+qA2ZWDCwD7iZQE9i1HF8OXNRyrNP5xGZWlZubm1tVFbvZGOElfbpb5RxcdKB5WCKxla6fsMTi/ae2tpbs7Oyopwps2bIloaOnwZiXL1/O3Llz9V4rCRH+7yL4dUfHetq2t+c7+3rkyJEMHBj/MdS8vDyqq6urnXN50fZNVsL7LvCoc+7rYcf2B+qAjc650xIeVAfM7Dngs8BRzrk3w45nA68ATzjnTumif8wTXoDKykoKCgoiHnWoqKjo08XQRfqTdP+EJRbvPz6fL6qpAj6fj8ceeyzaUHst/LUuKSlpVzPYzCgsLMTv94eqGXT0Wic7Memrj9VXY+wLP49Iv+5JnB316821BgwYwGGHHcakSZM6vEYs9SbhTdYc3gMIJLchzrkPzOxhILqq2HFiZiMJJLt/D092IVBJwszeBqaZ2RDn3J5ExpZqu/+I9BeRjHC6lk0T1q1b12rUr7GxMaH/0UbTNpLzmZmZeDyeHr//NDY2smvXLvbff/8eTxV49913QyPpGRkZDBo0qF37ffv28cEHH/Dhhx/S1NTUq5/B8OHDGTt2bEQLvPbt28fWrVv56KOP2iUJsUxiuusTSdu+eK1o+iXyWpFcN5bXgo7nTocfi+TrSNt29sd4R+c7+nrPnkD6k5mZ2eHj9CXJrNLQ3MGx1wgsYusLjgM8wEudnK8BvMBYYEuCYupVSZ/+sJgi2fr6CEBffaxYPm5fjeuQQw7hiCOOiHrThHHjxuH1ennrrbd44403evSfdndxxjIZCTrwwAMZPnw4Ho+HoUOH4vF4ev3+s3XrVqZMmRKqBRvpVIH8/Hz27NnDli1byM3NZciQIUDn00kOOuggMjIyeOqpp3jvvfe6fO7d/Tz++c9/Mn78eCZMmNDhAq+PPvqI2tpa6urqQv/5d/RY8UhGOmsb74Qpltfqadt4xxjLa0XStr9+CrRnzx7+/e9/JzuMiPS1smSNQPs/2ZMjmHi/3cn591vuD+zqQV588cWIf5HnzZvHHXfc0erY/PnzufPOO1m4cCE333xzr0v6LFy4kF//+tcdtrvmmms455xzWh0799xzqampieg6S5cubTeSPGPGDN55552I+t97771MmTKl1bFjjz02or4Q+I/14IMPBgLP+T//+Q8+ny/i/s8//3yrn+3LL7/MRRddFFHfzMxM1qxZ06r/E088wVVXXRVR/0mTJlFaWhqKHWD16tX88pe/jKj/tGnTWLx4cav+K1as4O67746o/6xZs1i4cGGrY7fccguPPvpoRP3PO+885syZE/reOcfPfvYznnvuuYj6X3bZZa1eK+ccP/jBD9i6dWtE/a+66iry8lp/wnXJJZfw/vvvd9Kjteuvv57x48e3ev3Cn4/H4wnF0tMRTuccJ554Is3N7f/Wv/fee1v9h7h161Z+9KMfRXSNAw44gNLS0lbvM88//zxLliyJqP+ECRO46aabMDMGDx7MmDFjOPjggznwwPZvbb19/1mxYgXPPvss69ato7i4mHHjxkU0VWD37t0sXLiQW265hSFDhkQ8nWTatGnMmDGDp59+utP4rrzySs4444xWj3HxxRezefPm0DGPx8OXvvQlZs+ezZFHHgnAjh07WLNmDTNnzuTkk09m7Nixof6zZs2ivr4+op/P3XffTU5OTqtjJ5xwQkR9AR599FEOOuig0PfvvPMOs2bNirj/888/3+r7mpoaLrjggoj6ZmZmtttZ7m9/+xvf+973Iup/zDHHsHLlylbH7r///oh/d08++WRuuummVsduv/127rzzzoj6n3nmmfzkJz9pdWzx4sU88MADEfWfN28el1xySatjV1xxBU888URE/f1+P2eddVarY1//+td55ZVXIur/q1/9ilNOaT2rsqioKKG/e+GLSd944w2OOOKIiPu3fS+pqqqK6vrRSmbCe7WZnQM8AzzbcutLCXjwVexsusJHLfcJiXnEiBFA70v6jBw5stM2H3/8Me+//z7OudAvYqT/sQO89957vP766wA96v/vf/+bIUOGdDtq1ZktW7aE/qE75yJOdoLaJvbbt2+PuG9jY2O7wvhvvfVWxP337t3b7nrhI1Pd+eSTT3jjjTdaHdu1a1dU/d99913MrN1HVZFoampi3759PR6lGDRoEMOGDQMIxRDp9qcAo0aN4tBDD23Vf8CAyPfVOfLIIxk/fnyn8RcWFjJmzJhej3D6fL4Ot55t++lLND97j8cTSraCtm3bFnH/QYMGcfjhh7PffvsxYcKE0DSB8NHTGTNmMH369Ji8/zz99NPMnDmT1atXRzRVoL6+ngsuuIC7776bYcOGRT2dZM2aNeTk5HSaBAwaNCg0YhwU/D3IzMxk/vz5XHLJJYwZM6Zd31mzZrFjxw4GDx5MY2Njj342IpIYyUow1xKYHzul5XZx+Ekz+wXwAvB3oMY519H0h3gL1tgd1sn5wS33H3f1IAcddBBvvvlmV00iEkxeelvSZ+fOnZ22GTx4MMOHD291LJqkYeTIke3m8UTT/5BDDuGoo46KuH1bRx99dKvr/+c//4mq/4QJE1ol2dEmHWPGjGnVf8eOHRH3HzRoEIcddlirY6NGjYq4/5AhQ0KjPMEY2r6WXRk8eHCr3y3nXFQrboPF7MP/WOloJLMzn3zyCR9++GGr60eTQLz77rvs2LGj1c8/2j+2goleR0lvdnZgH5zejnBOmjSpw4T3tddea3Xdt9/u7IOl9pqbm3nzzTdb9Y/mj73GxkYaGxuZOHEiHo+nw9HThQsXMn369Ji9/2zcuJGcnBzmzZvHFVdc0eFUge3bt3PbbbdRWlrK/PnzyczM7PF0knnz5nW6PXH47y18+vpPnz6d1atXh95TOps+MXbsWPbt28err77KRx991OE1RCT5klKlIXRxs/HACWG3zwLB/+WDge0BNhFYPHZJuweJX2xfBNYBpc65eR2crwBmAoc55zocyotHlYb+UtInkZI9f7UvzaWN5fzWVJvD25Ovgw4//HCOOuooFi9ezKJFi9qd787ixYvx+/1s27aN1157rdPrRBJXLOfrOucYNGgQJ598MoMHD+509LSoqIhHH3201+8/Tz31FP/5z3/axXXIIYcwYsQIBg4cSGNjIzt37myV9M+aNYvhw4f3uLrDRx99xJo1a1r9EdbZzwMCI7sFBQUMHDgw4ukTjY2NrF+/vtNPZiKd+2lmHHzwwaEyT8HFfuF/wPen+al9YS6s5t3GT3AO75QpU9pNSYyH/lilAQDn3FZgK3Bf8JiZTaJ1EvyZlvs8AjuzJUpwEk1nk0izgPrOkt14SeXdf3qquzdWkaCe/hER/KSityOchx56KKNHj+7yWtHEFYu2I0aMYPDgwV2OnpaXl/Paa6/16v1n3759HHzwwRx88MFdxjVw4ECGDBkSGlkdPnw4w4cP7/V0ks997nOhqRVd/TwGDhzI5MmTGThwYNTTJ7xeL//4xz9Cn0509/Nve91DDz2UQw89lKFDh7Zr88knn/Dmm2/y5ptvsm/fvm6fR2fXiqRtZzH29I+qnlwrmq8jbRvrPxbDxTtB7+yx4n2t7vrt3bu3w1j6or40ZxYA59wWAlUP/ghggZ9qDoGEN5FxvGlmNcBnzWy0c+7d4DkL1OEdC9yTyJiCtPuPSM/09o+joqKi0PSNaK5ZVFQEwNChQztMZvqCrhbjNTU1cfvtt4dGqnvy/jNo0KBe/eHd2+kk48ePj6pvT6dPfOYzn6G5uTmq5MvMGDJkSOgPq86mTxx99NGMHTuWjz/+uNcl12LRL1F/lCU6rra/Z739OXb0mLHuF+9rddbO4/EwcuTIqNZcJEufS3jbcoGf7Mstt0S7DbgFWAxcCmBmg4BftZz/baIDcs71qKSPc6lV/F4kGVL1E5ZIRk/vuOMOrrjiiqS9//R2wVy0elqNoye7TQV/PpFOn9hvv/30ft6H9KdkP16PFc2ak6QJfkyhW/sbgT8IngQcsJFAAlzT8v3SCPpX5ebmulhrbm52zjm3fv165/P5nJm5lpgc4MzM+Xw+V1lZ2aq9iPTe+vXrncfjafVvrrObx+MJ/Tvsy379619H9HymTZvmPv74Y+dc4t9/Io2x7W3p0qVRX2vLli3tnld3NzNztbW1UV8r+PO56667uv298ng8btmyZa36iaST3NxcB1S5HuR0fX6EN5mcc41mVghcB5xLYD7xv4DLgd8kK67gR6qRlPRxGtkViZngv7tU+4Ql0lHQaEuKxfJ593YU9p+OAAAaX0lEQVQ6STR6O30iGsGR3Z5MnxCRyCnh7YZz7mPg+y23PiP8P5GOSvp01E5EeieYcEWzaUJfT3YhusV44SXFfvzjHyfs/SeR00n6y/QJEYmcEl4RkSik4ics0Y6e1tfX8/Of/5xvfOMbXW5mE2uJWrDb22oc0dB28SKJEfmuAAliZr83s44rhIuI9AEdfcLi9/tZsGBBqySkPyS78OnoaTQSvRgvfDpJdyvC204niVbwD4BoJGP6hIhErs8lvMDXgdOTHYSISDrx+/0RlxZKRrnD8OkkFRUV+Hy+dkmpmeHz+Vi7di3FxcU9HmH//+3deZhcVZnH8e+PZhOSGAnKIktYAqJsIRkGVOioCWRwQRxAGB0kCDM4owOCPmAEQdEIjAijcdyiiYziyKLMoBJNookCAhpgTCRAIoSwy56EJZHmnT/OKSgqXZ2uXupW3f59nqeemzr33Kq3cqq73jp9lmZ+AWj28AmzocpDGszMhrh2mYzXzOEkZRw+YTaUOeE1Mxvi2mkyXjMm7DbzC0AzV58wG8qc8JqZWSkn4/VVM78AlHUzE1u/6vdMvd31autZ36kvA/oHk6QXgTsi4o1Fx9Jfkhbut99++y1cuLDoUMzMrEHVicZgfgGYP38+EydO7PXwiXnz5tHZ2dnn57PiVd4zvd1dz0lvMm7cOG655ZZbImJco9c64R1ETnjNzKwnlURm1qxZvR4+0Z8JeVa8StvNnDlzvRuOuM1fqT8Jbyuu0mBmZjYkNHP1CWsNfdldb/78+W7zfvIYXjMzswJ5/PTQ4931ms8Jr5mZWcG8XfzQ4d31iuEhDWZmZmZN4t31iuGE18zMzKxJvLteMVox4V0BPFB0EGZmZmYDzbvrFaPlxvBGxOiiYzAzMzMbDN5drxit2MNrZmZmVkqV3fUa4d31+s8Jr5mZmVkTTZ06lY6Ojl7V7ejoYOrUqYMcUfk54TUzMzNrksqayzNmzFhv0lvZaa2zs7PhlR3slZzwmpmZmTWJd9crRstNWjMzMzMrM++u13xOeM3MzMyazLvrNVfLDmmQdIWkf6q6v7ukoyS9tsi4zMzMzKy9tGzCCxwM3AYgaRRwEzAD+JOkvYoMzMzMzMzaRysnvMOBh/K//x5YDowCvg18oaCYzMzMzKzNtHLCuwLYJf/7SODSiHgBmAUcUFRQZmZmZtZeWnnS2neBr0n6OfA24ORcviGwWWFRmZmZmVlbadmENyIuzDMTDwU+ERF351P7A/cWFpiZmZmZtZWWTXghJb3AhTXFWwH/XUA4ZmZmZtaGWjrhlbQBaRxvB7AsIl7ISbCZmZmZWa+07KQ1SX8D/Bm4A7gdeFzSdElbFBuZmZmZmbWTlk14gW8Bi4E9gZ2ADwP7ALdI2qrIwMzMzMysfbRywjsGOC0ilkTEvRFxZUQcBFwHfLXg2MzMzMysTbRywnsjaYJarXOBw5obipmZmZm1q5aatCZpHmk74VtJ6/BeIumIiLivqtoo4PEi4jMzMzOz9tNSCS/wO2Bf4P3AtrlsqaQfA7eQVms4DjilmPDMzMzMrN20VMIbEWdV/i1pS2Bsvu0LnEAa1xvANODqImI0MzMzs/bSUglvtYh4DJiTbwBIehVppYZ9iorLzMzMzNpLyya8kjYBPge8B9gEWAR8PyKuIE1oMzMzMzNbr1ZepeFLwNHkyWvAQ8B3JV0lqWUTdTMzMzNrLa2cOB4FvC8ibqgUSDoH+DlwJvD5ogIzMzMzs/bRyj28mwJ/qS6IiEeAjwNTConIzMzMzNpOKye8C0jbCde6n+43pDAzMzMzW0crD2k4E7hB0ijSGN47gI1Ja/D+qcjAzMzMzKx9tGzCGxFLJHUC3wIWAy+QeqQfBw4vMjYzMzMzax8tm/ACRMQfgQMk7QbsCawCboqIlcVGZmZmZmbtorAxvJLOlfSQpLWSlko6S9JG3dWNiLsi4scRMcfJrpmZmZk1opCEV9IJwGdIk882BHYBPgtcWUQ8ZmZmZlZeRfXwngysBT4AbAdMBG4B3iXpqIJiMjMzM7MSKirh3QW4MiJ+GBEPRsSvgEnAk8BxBcVkZmZmZiVUVML7GmBZdUFEPAX8DNivkIjMzMzMrJSK3HjixW7KVgCjmh2ImZmZmZVXq+209gLQ7UoNZmZmZmZ9UeQ6vGfnCWo3ATfnW0uvC2xmZmZm7aeoBHMeMBZ4U75NqT4p6UvAbcCtwJKI6G74g5mZmZnZehWS8EbEJABJOwPjq25jgVcDpwGRq6+RtBi4NSL+uYBwzczMzKyNFTqEICLuBu4GLq+U5W2Eq5PgffNxHOCE18zMzMwa0nJjZiPiLuAu4DIASQL2ICW8ZmZmZmYNabmEt1ZEBHB7vpmZmZmZNaTVliUzMzMzMxtQTnjNzMzMrNSc8JqZmZlZqTnhNTMzM7NSc8JrZmZmZqXmhNfMzMzMSs0Jr5mZmZmVmhNeMzMzMys1J7xmZmZmVmpOeM3MzMys1Fp+a2EzMzMzGxwRgSQAli5dyuzZs1m5ciUjRoxg8uTJjBkzZp167cgJr5mZmdkQVEli58+fz7Rp05g7dy4R8dJ5SUycOJGpU6cyYcKEtk56PaTBzMzMbIipJK8zZ85k4sSJzJkz5xXJbqXOnDlzmDhxIrNmzULSOnXahRNeMzMzsyGm0rN70kkn0dXV1WPdrq4uTjzxRObPn+8eXjMzMzNrH9OmTVtvslvR1dXFF7/4xUGOaPA44TUzMzMbYpYuXcrcuXMbumbOnDksW7ZskCIaXE54zczMzIaY2bNnNzweNyKYPXv2IEU0uJzwroekTknXSnpM0hpJyySdL2lY0bFVGzduHOPGjSs6DGsyt/vQ5HYfmtzuQ9NgtfvKlSubel3RnPD2QNL7gF8Bnfl4GWkptzOA2ZK8rJuZmZm1nREjRjT1uqI5YatD0gbAdGAN8LcRsSiXbw78EngLcCLwjcKCNDMzM+uDyZMnN7zMmCQmT548iFENHvfw1ncgsA3wo0qyCxARzwDn5btHFxGYmZmZWX+MGTOGiRMnNnTNpEmT2HXXXQcposHlhLe+nfPx1m7O3ZeP2zUpFjMzM7MBNXXqVDo6OnpVt6Ojg6lTpw5yRIPHQxrquw44Arilm3P75+Mj63uQJUuW1B1svnDhwj4HZ2ZmZtZXEcGECROYMWMGJ554Yo/r8XZ0dDBjxgw6Ozubsr1wvbxpyZIlfX5MtesWcUWRtAspGd4aOD0ivtxD3cclbbHpppt2e36PPfYYsLgqb4KBfExrfW73ocntPjS53YemZrT7qlWrePjhh1m1atU654YPH84222zDsGHNW5yqXmL7/PPPExFPRMSoRh/TCW8DJB0DfA3YArgZODgi1vRQ/x5gBLC8KQGamZmZ9dLw4cM333nnnXftyOMa1qxZw8qVK+nq6qKjo4MRI0awySabANDV1dV19913L1u1atUzBYY8GlgZETs1eqET3l6QNJqU6B6Wi64CToiI9lyMzszMzGwIGVKT1iSNlhS9vI3M1/wDsIiU7D4IHBMRRzrZNTMzM2sPQ23S2mrgB72su1bSh4CZ+f50YGpErDvAxczMzMxaloc01CFpFGns7TDgQxFxabERmZmZmVlfDKkhDQ06nJTsftPJrpmZmVn7GmpDGhrxlnx8naRL6tRZFhHTmxWQmZmZmTXOPbz1bZ2PRwCn1LkdWUxoL5O0maTzJC2T9JykuySdI2mjomOz/pM0StJXJd0jaY2kxyRdLWlsTb0NJZ0m6XZJz0paLuliScOLit0GjqSP5sm0p9aUu91LSNK7JV0v6WlJKyUtkPSOmjpu+xKRtI2kGZIelLRW0n2Spkt6TU09f+b3kcfwtjFJGwI/BQ4FFubbvqSd4K4F3hlu4LaVf9EtBHYCfkdaLWR3oBN4lrQO9MJcdwbwYeAO4DfArsDbgduAAyPi+aa/ABsQknYEFpOGWH08Ii6pOud2LxlJJwNfBx4GfgZsRfodD6lN/TNfMnnO0EJgR2Auaf7QeNLn+Z+AAyJitT/z+ykifGvTG3A8EMCl5C8vuXxWLj+u6Bh961f7XpDb8fM15afm8hvz/Qn5/jxg46p6n83lnyn6tfjWr/fBL3I7BnBqVbnbvWQ3YHtgLWlL+5FV5X+X2/Qat335bsC/53Y7vapMwHdz+Zm5zJ/5/bi5h7eNSfo9MBbYPiIeqirfnfSt/7cRcXBR8Vn/SFoKvB4YFRHPVZWLtCb01qQegYtIw2v2j4jfV9UbBjwBPBwROzQzdhsYkk4AvgP8D2ki7Us9vJKuwO1eKpIuBD5J6tG7qebcAmCHiNjJbV8ukm4D9gI2j6qeeUljgLuAayPiMH/m94/H8LapPE5rLHBr9RsfICLuBB4BDpC0SRHxWf/kpHY0cEd1sgsQ6Vvq/fnudsDBwKPVH3y53mpST9H2khrehtGKJWlb0peZK4Cru6nidi+fQ4DltckuQER0xsvbqbrty0WkHtpalXG5z/gzv/+c8LavvYEO0vie7iwh/bDs2LSIbCBtABwF/FvtCUkjgDdU7gKvA26v8zhL8nHMQAdog+4/SR+CH6s9Ien1uN1LRdLmpF6+2yRtkCeunZdvk/OXYLd9OS0gfZ6/9Pte0gbAmfnur/Bnfr95WbL2NSofH6lz/sl83KIJsdgAi4guuunVk9QBfIM0gWkRUNn5z++DEpF0DGkIw5SIeCTnOtX8818+25C+6D4PzAcOqjl/vaT34rYvo3OBA4ELJL2blLz+DWlC2rWkYU2Tc123ex+5h7d9jcjHNXXOP5OP/lJTEpJ2IX0QHkv6UPxn/D4oHUlbAl8B5kbErDrV3O7lU1l+6v2klRkOIX2x3RH4Hmlt+Fm47cuoMlER4K3ASaRkF+Ae0l/y3O795IS3ff01H19V5/zG+fhsE2KxQSRpI0mfIvXovhVYAbw9In6H3wdl9FVgc+Cfeqjjdi+fSpu9CBwREXMi4pmIWEFKgO4H3kn6sza47cvkStLP+w9IS09uTurh/RXwL6RVHPwz309OeNvXX/JxZJ3zlT97PdiEWGyQSBoN3AhMI/1Cmw7smZNd8PugVCQdBhwDnB0R9/RQ1e1ePqvz8c8R8YrxuRHxV9KavAC75aPbvgQk7cvL6+oeFxF3RcSzEfEH0rCmh4CPAE/lS9zufeSEt33dkY971Tm/K/BYRDzcpHhsgOVZ+tcB+5EWkx8fER+LiFVV1VaQvtH39D54kfoTXKy17J+PF+Wd1UJSADNz+cX5/gm43ctmeT6urnO+8idr/8yXy+75uCAiXqw+kVfduIk0TKGyXJk/8/vIYz3aVEQ8JGkJMFbSqIh4vHIur8m3I/DDwgK0gXARaR3enwPvi4h1xm5FRFden/PvJO0VEYsq5ySNJCVQN0XEymYFbf1yI/Af3ZS/EZhE2lHrVuB6YBxu99KIiKcl3Q68QdKIbtpufD7+kTSr321fDpUOjG3qnK+M7b6dNJnNn/l95B7e9vYN0p+5v1ApyPtpfznf/VoRQVn/SdoMeC/wKHBMd8lulW/k4/l5KZvKOr4XkMZ7TR/MWG3gRMTsiDi19gZclqv8JJfNxu1eRt8GNgO+nFdkAUDSEaS1d6/Ja7C67cvjOlLSe6SkN1efkDSJNG/jtxHxKP7M7xfvtNbG8r7aC4A3k3qG/g/oJK3ROj0i1lm/09qDpINIvXl3ArN7qPq5iHhC0o+Ao0kT224g9f6NJ31Avmew47XBJel40rCGl3Zay+Vu9xLJSe7PSGM6l5B+r29L6t1/BDgwIu7Ndd32JSHpH0jbBQPMBe4FdgImAk8DB0XEYn/m948T3jaXewLPI/3i25K0hMnXSW9+N26bknQUcHkvqu4UEcvzL8IzgCmk3dceIC1ldH5ErB28SK0Zekh43e4lI2lj4DTSOO3RwOOktVjPiYj7quq57UtE0nhSe3aShjE8Rkp+PxsRy6rq+TO/j5zwmpmZmVmpeQyvmZmZmZWaE14zMzMzKzUnvGZmZmZWak54zczMzKzUnPCamZmZWak54TUzMzOzUnPCa2ZmZmal5oTXzMzMzErNCa+ZmZmZlZoTXjMzMzMrNSe8ZmZmZlZqTnjNbEiSNEFS1LmtkXS7pNMk+fdkH0h6k6TnJO3SzbmdJU2XdKekZyU9JWmxpIskvaHO453bQ3s9I+m3kt7Vx1iHSXpU0qS+XG9mrW/DogMwMyvYA8CVVfcFbAUcClwE7A0c3/yw2t504IqI+HN1oaQPAd8CNgZuBhYAmwF7AacBH5P0sYj4Zp3HvQm4ser+hsDOwCTgGklTImJWI4FGxGpJXwKmS9orItY2cr2ZtT5FRNExmJk1naQJwK+BBRExoZvzrwduBV4LjI2I25oaYBuTdDhwNbBXRCyuKn8ncA3wGPD+iPh1zXWHkL58bA4cHBHXV507FzgH+GxEnNvNcx4C/AJ4HNg6Il5oMObhwP3AtIi4oJFrzaz1+U91ZmbdiIgHgKvy3YPr1ZP0quZENHgG4TWcB/y6JtndBPg20AW8szbZBYiIXwInkz6bzmzkCfO1dwCjgDc2GnBErAIuBT4l6dWNXm9mrc0Jr5lZfc/k4+bwinG/Z0k6WtKdQHUv5KaSPi1piaTnJT0oaYakHWofWNIRkq6X9KSk1ZJulvTBvtSTNCvHtW8314ek26ruH5/LPijpo5JWAJdVnR8p6UJJd+exzMslXSxpVG/+wyQdTBqecFnNqaOAbUjDHH7fw0NcCXwHuLc3z1fjFe2V4xmT22B5bpP7JF0l6aBurr8ceDVwbB+e28xamBNeM7P6xufj0pryw0kJ3d3AFfBSD+Yvgc8Dq4HvAQuBDwF/lLR35WJJpwA/Bl6Xr78G2BH4L0kfabReH/0b8O/ALcC1+flGATcAnwTuA2YCy4BTgVslbdeLxz0sH39bU35IPl7T08URsTYiToyIj/bmRVTkIQm7A0GKGUnbA78HTgCWALOAxcB7gPk5Oa92M/A8cHQjz21mrc+T1szMquRVGXYgJYSdwMPAz2qqjQeOjIirqso+DRwETI2IL1Y93gRgLvB14C25+JPAo8A+EfFsrjcSWAR8KtdtpF5f7EsaJ1s9AexiYA/gAxFR3ev7QeC/gAuAD6zncQ8BngTuqimvrL6wpB8xr0PSxsCbgPOBYcD/RsSj+fSxpB7bV0xkkzQF+C4psf1NpTwi1kj6A/BmSRtFxF8HMlYzK44TXjMb6jol1Zu9uwo4NiKeqym/vjrZzUnyv5B6Fs+vrhgR8yX9AjhM0tYR8TDwGuBFYAvg2VzvKUnvJq0QUdHben1xeXWyK2kLUoI4rzrZzc/5fUmfAg6XtOF6JoTtBiyLdWdEvyYfV1UXShpWW1blbRExv6bsHEnn1Kl/J1Dd872Q9MXgRzX1FuXjsG4eYwXwVmA06/bsm1mbcsJrZkNd7bJkAGuBe4CrIuIv3Vzzx5r7u5EmS60CLpZUW7+SnO5O6jH+FmmYwGJJ/wvMJ03yql0Jorf1+qL2NexP+kx4raRLuqm/EWls7OupM742T37bHHiqm9PP5uNmNeVrgf+oKZsA7FMn7tplySqPvQj4cUSsqRRGxDxgnqQt85jdnUhLmL27zmMDPJGPW+GE16w0nPCa2VC3LCJObfCa2nVaKxO6RgOn9HDdcICI+Lik3wEfJk3m+kcASYuAT0fENY3Uq0dST7/j672GvfOtx9dQxxb5uLqbcw/kxx3Dyz2s5DVvX/H/L+l71E94Z3e3LFl3JG1L+tJwGGl95adJQypuIA3d6M7T+djT6zSzNuNJa2Zm/VdZHeDqiFAPt59WLoiIyyPiUGAkqUfzQlLv41WSdm60Xh29Wlmh5jVcsp7XsLiHx6j04o7o5tx1+Ti5F7Hs19ug1+MHpGT3LGDbiBgZEQcCX+vhmkqi++QAxWBmLcAJr5lZ/90BrAH2UTdbEUs6Q9IPJY1Q2lb3aklHQZooFRELIuIM4HOkoQP797ZefopKb23teroHNPAa/i8fx3Z3UtKX8vJn64zXqPIU8Bwv9/RW+z7wV+AD3S3TVvU8bwP27F3I9eWxwZ3AjRExLSIeqjrd0xeFkfn4aA91zKzNOOE1M+uniHge+G/SGNF/rT4n6T3AF0i7f60k7TL2LuBMSSOq6gkYl++uaKBe9fF9VfWGAZ9p4DXcQ9rmtzPHXP0aPgKcnqrV354zn1sEbF+b+EfECtJWzZuRtgDepfZ6Se8g/T++2Nu4e9CVH2ervGRc5Tl2JC0dV88OpKT9/gGIwcxahMfwmpkNjE8Abwa+IuloUq/vrqRhCI8DUwAiYqWkrwAfB+6Q9BtgJXAgqWfz5xFxA0Bv65E2TDgb+ISk8aRk7e2kyWXdTbqr5yTS0IOfSJqXr9+b1JP85xzL+szP9fdk3YlxZwFbA8cDSyRdR5oYtgEpiR9LWgv3bOCbDcS9joh4TtIPgQ8Ct0laAGwHTCKt2rAbcKik0yPiIgBJHaQl5xZUT34zs/bnHl4zswEQEY+RhhB8hdTTexzpT+ffAcZGxPKq6meQkrqngPeSNqfYiDRU4e8brRcRd5FWHvhDjuHtwP+Qeoh7vZZsRCwlJXyXktbp/UfSOOBLgP0jorvVF2r9JB/X2Y45IroiYgpwKPBT0hbAU0gbQawiLe32VtZd97ivTibFPpz0WrYEToiI40hjoV9N+gJRMZa0VNm1A/T8ZtYi1MNfp8zMzBqWtzJeGRHrJL2tTNKXSb3cO0bEE+urb2btwz28ZmY20M4GDpJUb2mxlpPHPE8BLnaya1Y+7uE1M7MBJ+kaYHVEHFt0LL0h6XTgNGC3iHhmffXNrL044TUzswEnaTRpxYb9I2JJsdH0LO8QdzdwSkRcXnQ8ZjbwnPCamZmZWal5DK+ZmZmZlZoTXjMzMzMrNSe8ZmZmZlZqTnjNzMzMrNSc8JqZmZlZqTnhNTMzM7NSc8JrZmZmZqXmhNfMzMzMSs0Jr5mZmZmVmhNeMzMzMys1J7xmZmZmVmpOeM3MzMys1JzwmpmZmVmp/T/GeSOp5tKK2gAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 360x360 with 2 Axes>" ] }, "metadata": { "image/png": { "height": 351, "width": 350 } }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 360x360 with 2 Axes>" ] }, "metadata": { "image/png": { "height": 351, "width": 350 } }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 360x360 with 2 Axes>" ] }, "metadata": { "image/png": { "height": 351, "width": 350 } }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 360x360 with 2 Axes>" ] }, "metadata": { "image/png": { "height": 351, "width": 350 } }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 360x360 with 2 Axes>" ] }, "metadata": { "image/png": { "height": 351, "width": 350 } }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 360x360 with 2 Axes>" ] }, "metadata": { "image/png": { "height": 351, "width": 350 } }, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 360x360 with 2 Axes>" ] }, "metadata": { "image/png": { "height": 351, "width": 350 } }, "output_type": "display_data" }, { "data": { "image/png": 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e9O/fnyFDhgScXERERGKpU6dOZGVlBR0j7qjgLaXy/qs1vx9RAEybNo2bb76ZLVu2sHr1atq2bUtqampJxhQREREJnB5aS2A9evTg+OOPB2Dv3r1cdNFFbN26NeBUIiIiJcPdS81LYksFbwKrUqUKM2bMoGrVqgBs3LiRfv36ceDAgYCTiYiIiJQcFbwJrlmzZkyZMiWy//rrrzNy5MjgAomIiEjcSUoKlYT79+8POElsqOAtAy699FJuu+22yP4f/vAH3njjjQATiYiISDxp0KABAFdddRXjxo0LOE30qeAtIx555BHat28PhMY09e3bly+++CLYUCIiIhIX7rrrLk455RTefPNNFixYEHScqDMNlI4dM1uVmpqaumrVqqCjALBp0yZatWrFt99+C8Cpp57KihUrqFy5csDJREREjtzatWsBaNmyZcBJpDiK+j22adOG1atXr3b3Nkd6D/XwliF169ZlxowZlCsXmo3ugw8+YODAgXo6VERERBKaCt4ypl27dgetwPL8888zfvz4ABOJiIiIxJYK3jJoyJAh9O/fP7J/xx13sGTJkgATiYiIiMSOCt4yyMx44oknOOOMM4DQFCQXX3wxmzZtCjiZiIiISPSp4C2jKleuzMyZM6levToAmzdv5uKLL2bfvn0BJxMRERGJLhW8ZVijRo148cUXI5NNL1++nGHDhgWcSkRERCS6VPCWcV27dmXEiBGR/ccee4xnnnkmwEQiIiIi0aWCV7jvvvvo3bt3ZH/QoEGsXr06wEQiIiIi0aOCVzAznnnmGZo2bQrAnj176NGjR2SBChEREZHSTAWvAJCSksIrr7zCMcccA4RWZevduzd79+4NOJmIiIhI8ajglYgWLVrwwgsvYGYArFixgiFDhgScSkRERKR4VPDKQbp3787o0aMj+5MmTeKxxx4LMJGIiIhI8ajglV/47W9/y+WXXx7Zv+2221i0aFGAiURERESOngpe+QUzY/LkyZGV2HJycrj44ovZsGFDwMlEREQkP1OmTMHMfvEqV64c9evX5+qrry7TMzCVCzpALJjZzcB4oIa7b83nfAfgD0AqUAn4CHjE3V/Ip+0A4HagObAVyATud/f/xuwDxIEqVaowa9YsUlNT+f777/nxxx/p0aMHy5cvp2rVqkHHExERiTl3jzzXsm7dOjIzM8nOziYlJYVu3brRrFmzX7QLWsuWLcnIyIjs79y5k/fee4/nn3+eqVOn8vjjjzNo0KCjunajRo3YunUrW7f+orSKewlX8JpZMnBDIed7AjOB7cAsoCpwATDVzHa7+8t52t4PjAC+Ap4DTgCuBbqaWRt33xyzDxIHGjZsyMyZMzn//PPZv38/H374Iddccw3Tp0+Pm/+wRUREYiG3iM3KymLUqFHMmzcPd4+cNzPS09MZPnw4aWlpcVP0nn322YwdO/YXxxcsWMBFF13E4MGDadmyJR06dAggXXASYkiDhaSa2Q1AFqGe2/zaVQWmAN8Ap7n7te5+KdAR2AM8mKftScAfgQ+BU939RnfvBQwCGgD/G7MPFEc6dOjA//3f/0X2Z8yYwciRIwNMJCIiElu5xevkyZNJT09n7ty5BxW7uW3mzp1Lenp6ZDjBoW3iyfnnn8/YsWM5cOAA9957b9BxSlxCFLyEemlXAROBwv7Jci1QC7jb3TfmHnT3NcA0oL6ZpYQPDwaSgT+5e3aea0whVDD3M7MqUfsEceymm27ipptuiuw/8MADzJ49O8BEIiIisZPbszto0CBycnIKbZuTk8PAgQPJysqKix7ewvTv358aNWrw9ttv8/nnnwOhxabGjBlD69atOeaYY6hVqxZnn30248aNY//+/QCRz7Zhwwa2bduGmTFgwIDIddetW8fAgQNp1KgRlSpVokGDBvTp04fFixcH8THzlSgF7y7gsjyvjwpolwHsBl4+9IS7X+PuNfIUt2nAAUJjdvO2c2AxUBFoG43wpcG4ceM499xzgdC/aq+88ko+/PDDgFOJiIjExqhRow5b7ObKyck5aErPeFWuXDk6duwIhObaB+jXrx933nkne/bs4YorrqB79+5s2LCB2267jcGDBwNQv359br/9dqpVq0aFChW4/fbbI+OEv/rqK8466ywmTZpEy5YtGTBgAKeeeiqzZs0iLS0tbmZ5SogxvO6eA/wrd9/MhhbQtB3wsbvvNrPzgU5ABUK9wy+7+/48bc8AvnL37flcZ2142wxYUNz8pUH58uWZMWMGrVu35uuvv2bHjh1069aN1atXU6dOnaDjiYiIRM26deuYN2/eEb1n7ty5rF+/nqZNm8YoVXQ0adIEgI0bN/Ltt9/y0ksvkZ6ezuuvv065cqGy8Pvvv6dZs2ZMnTqViRMn0rRpU8aOHcvMmTPZunXrQWOE//nPf7Jt2zYmT558UK/v5MmTuf7663nxxRc577zzSvQz5ichCt6iMLNywHHAR2Y2Dbj0kCYfmVkvd/8sPKyhPPBtAZf7Mbytebj7rl27ljZt2uR7btWqVUXKHi/q1KnDq6++Srt27di1axcbN26kV69eLFy4kIoVKwYdT0REJCoyMzOPeDyuu5OZmcnQoQX1ucWH3JmW9u3bh5kxevRoOnfuHCl2AWrVqsWvfvWrIv0kt02bNowePZq+ffsedPy0004D4KeffjrijAXVTWvXrs33eFGUmYIXqBHediJUyF4GvAFUAe4A7gFmmlkrIHcc754CrrUjvC1Lv38AnHHGGUydOpWLL74Yd2f58uVcf/31PPvss3E/dklERKQosrOzD98oiu8rSblTih133HEcd9xx3Hvvvezbt48VK1awbt06Pv/8c955550iD1vs0qULXbp0YcuWLSxevJjPP/+czz77LO6e9SlLBVuF8NaAa919Tnh/O3CvmaURGpPbHlgXPlf5MNfaebibtmzZstT15B5O7969eeihh7jnnnsAeP755znllFMYPnx4wMlERESKLyUl5fCNovi+kpT7sFrDhg2B0Fjl0aNH89NPP1G+fHmaNGlC+/btqV+/Phs3bizsUgB888033Hjjjbz22mu4O8ceeywtW7akffv2R90jW1Dd1KZNm6NePCNRHloritw+9d1AfgNzZoa3LYEfgBygegHXqhXefhO1dKXM3XffTf/+/SP7999/PzNmzAgwkYiISHR069btiH9qaWZ069YtRomiY8+ePbz11lscc8wxdOjQgX/84x/87ne/4+yzz2b16tXs2rWLjz/+mEmTJlGz5mFHbQJw1VVX8dprr/Hggw/yzTffsHXrVpYuXcott9wS409zZMpMwevu2wiNvd3l7gfyaZI7TMHcfR/wKdA4PHfvoXJHpH8Q/aSlg5nx5JNP0r59eyA0dqlfv3688847AScTEREpnmbNmpGenn5E7+natWvcP7A2YcIEsrOz6d27N5UqVeKVV14B4PHHH6d169YkJycDofG9X3311WGv99NPP7Fw4ULOOecchg8fzgknnBA599lnn8XmQxylMlPwhi0FaphZfn8izwxv3wtv5xOah7dz3kbhldy6EOrdLdPzclWoUIGXX36ZE088EYDdu3fTvXt3/vvfhF51WUREyoDhw4dHCsDDSU5OjvthfZmZmfz2t7+lUqVKPPhgaJ2tChVCIzS//PLLSLt9+/Zx11138eOPP/7iGklJSZG5eSH0uZOSkvj222/Zs+fnx542bNjA/fffH6uPclTKWsH7ZHj7NzOLjM81s7bAFcD7wPLw4QmAA3/K2xa4F6gPjPd4XlKlhNSuXZvXX3+datWqAbBp0yZ69OjBrl27Ak4mIiJydNydtLQ0Jk6ceNiiNzk5mYkTJ9KpU6e4WGltxYoVDBs2LPK6+eab6dSpExdeeCHuzvPPP0/jxo0BuO666zAzLr74Yvr378+1115Lo0aNmD17dmQqsX79+rFlyxYAGjRowI4dO7jssst47rnnqFy5MldeeSWfffYZrVq1YvDgwfTs2ZPmzZtz1llnkZSUxBtvvMFf/vKXwH4/Itw94V6Elhd2oHo+5yaGz30O/IPQIhS7gGwg9ZC2/xtu+ymhAnhBeP/fQKUi5FiVmprqZcEbb7zhycnJHv798UsuucQPHDgQdCwREUlgH330kX/00UcxuXbu32ELFizwjIwMN7PI33GAm5lnZGR4VlbWQe2DMnny5IPy5b7Kly/vjRs39muvvdbff//9X7zvX//6l59++uleqVIlP/HEE/3OO+/0H3/80ZcsWeInnHCCp6Sk+Ndff+3u7m+++aY3btzYy5Ur58OGDXN3959++smHDRvm9erV8ypVqnjbtm392WefdXf3e++91ytXrux9+vQpNHtRv8fU1FQHVvlR1IbmcfCvkWgzsyxC04/VcPeth5wz4EbgFqAFoVkaFgB/cPdfrNBmZkOAoUATYDMwPdz2sHOPmNmq1NTU1ESbpaEg48aN47bbbovs33///YwYMSLARCIikshyZwFo2bJlTK7v7pGH19avX09mZibZ2dmkpKTQrVu3yJjdvO3kyBX1ewzP0rDa3fOfqLcQCTktmbunFXLOgb+HX0W51uPA49FJlthuvfVWPvroI5544gkAHnzwQZo3b84111wTcDIREZEjl7eIbdq0aYGLSqjYjX9lbQyvxNi4cePo3Pnn5/xuuOGGuFlHW0RERMomFbwSVeXKlWP69On8+te/BkJPe/bq1YtPPvkk4GQiIiJSVqnglairXr06mZmZ1KlTBwgttZiRkcHmzZsDTiYiIiJlkQpeiYkTTzyR1157jUqVKgGhOf4uvPBCTVcmIiIiJU4Fr8TMmWeeyQsvvBAZzL9q1SquuOIKDhzIb6E7ERERkdhQwSsx1bt3b8aOHRvZnzVrFnfffXeAiURERCRelNT0uCp4JeZuu+22g6ZyGTNmDI899liAiUREJBHk/gRRPzksvXIL3lhP7aaCV0rE2LFj6dGjR2T/1ltv5dVXXw0wkYiIlHYVK1YEYMeOHQEnkaOV+93lfpexooJXSkRycjIvvPACrVq1AkL/Gr/88stZs2ZNwMlERKS0qlatGgCbNm1i+/btHDhwoMR+RC5Hz905cOAA27dvZ9OmTcDP32WsJORKaxKfqlatyuuvv85ZZ53Fxo0b2blzJxdccAErV66kQYMGQccTEZFSpmbNmuzYsYOdO3eycePGoOPIUapSpQo1a9aM6T3Uwyslqm7dumRmZkb+Jffdd9/RtWtXtm7dGnAyEREpbZKSkmjQoAF16tShUqVKWuK3FDEzKlWqRJ06dWjQoAFJSbEtSdXDKyXulFNOYebMmWRkZJCTk8Mnn3xCjx49mD9/fmTeXhERkaJISkqidu3a1K5dO+goEsfUwyuB6Ny5M5MmTYrsL1myhH79+ulJWxEREYk6FbwSmP79+zNq1KjI/owZM7jtttv0wIGIiIhElQpeCdS9997LzTffHNkfP348Dz/8cICJREREJNGo4JVAmRl/+9vfuOiiiyLH7rnnHp577rkAU4mIiEgiUcErgUtOTmbq1Km0a9cucmzAgAHMnz8/wFQiIiKSKFTwSlyoVKkSr776Ks2bNwdg//79XHTRRVqYQkRERIpNBa/EjRo1ajBv3jzq1q0LhJYbzMjI4Isvvgg2mIiIiJRqKnglrjRo0IA5c+ZEFqbYvHkz6enpfP/99wEnExERkdJKBa/EndNOO41Zs2ZRvnx5AD799FO6devGzp07A04mIiIipZEKXolLaWlpPPPMM5H9lStX8pvf/IZ9+/YFmEpERERKIxW8Erf69u3LmDFjIvtz5syhf//+Wo1NREREjogKXolrd9xxB/fcc09kf+rUqQwbNkyrsYmIiEiRqeCVuDd69Giuv/76yP64ceMYOXJkgIlERESkNFHBK3HPzJgwYQK9e/eOHPv973/P448/HmAqERERKS1U8EqpkJyczIsvvkinTp0ix2655RamTZsWYCoREREpDVTwSqlRsWJFZs+eTatWrQBwd/r168e8efMCTiYiIiLxTAWvlCrVqlVjzpw5nHTSSUBoCeLevXuzYsWKgJOJiIhIvFLBK6VOnTp1ePPNNyNLEO/atYsLLriAtWvXBpxMRERE4pEKXimVGjZsyJtvvkn16tUB2LrF3UWgAAAgAElEQVR1K507d+bLL78MOJmIiIjEGxW8Umq1bNmSN954g8qVKwOwadMmOnfuzObNmwNOJiIiIvFEBa+UameffTYvv/wy5cqVA+DTTz+lc+fObN26NeBkIiIiEi9U8Eqp17VrV5577jmSkkJ/nD/44AMyMjLYsWNHwMlEREQkHqjglYRw+eWX8/e//z2y/+9//5uePXuye/fuAFOJiIhIPFDBKwlj4MCBjBkzJrKflZVFnz592LdvX4CpREREJGgqeCWh3HHHHfzpT3+K7L/22mtcffXV5OTkBJhKREREgqSCVxLO73//e+68887I/osvvsiNN96IuweYSkRERIJSLugAsWBmNwPjgRruvvWQcwOBUwt469vuPi1P20uA8wpou9bd/17AOQmQmfHwww+zfft2JkyYAMCkSZOoVq0af/3rXzGzgBOKiIhISUq4gtfMkoEbCmkyBEgt4FwlYFqe/auASwpo+waggjdOmRmPP/4427dv55///CcAjz76KCkpKfzP//xPwOlERESkJCXEkAYLSTWzG4AsCi5oAZoAj7m75fManE/b1wpo2y1GH0eiJCkpiaeffppevXpFjo0YMYJHHnkkwFQiIiJS0hKi4AWqAquAiUCHghqZWU2gOvBpEa/b5AjaShwqV64c06ZNo0uXLpFjd999N0888USAqURERKQkJUrBuwu4LM/rowLanRTeHraINbM6QEpR2kp8q1ixIrNmzaJdu3aRYzfffDNPP/10gKlERESkpCTEGF53zwH+lbtvZkMLaNokvP3OzK4DWgA7gAXu/lYBbb8ysyuA04F9wFLgDdcj/6VKlSpVyMzMpFOnTqxZswZ357rrrqNChQpcccUVQccTERGRGEqIgvcI5PbwzgZq5T1hZrOBfu7+0yFtJxzaFlhqZpe4+6bD3XDt2rW0adMm33OrVq0qam6JgpSUFObNm0fHjh1Zu3YtBw4c4Oqrr6Z8+fL06dMn6HgiIiICBdZNa9euPeprJsqQhqLK7bV9nVDvbhXgHOBtoBcwKZ+2K4FWQGVCvbyzgHbATDMra79/pV6tWrXIysqiefPmAOTk5NC3b19mzZoVcDIRERGJFUvEn8ybWRbQiUPm4TWztkAtd3/tkPYphMb91gNauPt/zOw0fp6lYV+etuWAFUBr4AJ3n1NIjlWpqamp6smNP5s2beLcc8/ls88+A6B8+fLMnDmT7t27B5xMRERE8tOmTRtWr1692t3z7wIuRJnqoXT35YcWu+Hj2UBmeLdV+Nj77v5y3mI3fHw/8FLetlL61K1bl0WLFnHiiScCsG/fPn7zm98wb968gJOJiIhItJWpgvcwtoe3+6PcVuJUvXr1WLRoEfXr1wdg79699OzZk6ysrGCDiYiISFSVmYLXzH5tZm5mrxbQ5Nzw9l0zq2JmB8zsw8O1jW5KKWkNGzZk0aJFnHDCCQDs2bOH7t2789Zbh07aISIiIqVVmSl4gU+AL4ALzKxj3hNm1h84C8hy90/dfSewGDjZzK48pG0a0IfQ/LxZsY8tsda4cWMWLVrEcccdB8CuXbvo1q0by5YtCziZiIiIREOZKXjD8+beDBwA3jSz2WY23szmAf8AvgeG5HnLHUA28LyZzQu3nQXMBfYA14Xn/5UE0LRpUxYtWkTt2rUB2LFjBxkZGaxcuTLgZCIiIlJcZabgBXD31wk9aPav8HYg0Bx4CjjL3T/O03Y1cBrwJKHZGgYS6gWeDpzj7otLNr3EWosWLVi4cCE1a9YEYPv27XTp0oV33nkn4GQiIiJSHAm58IS7pxVy7iPgyoLOH9L2S+DGKMWSUuDkk09mwYIFnHfeeWzbto3s7GzS0tJYsGABqampQccTERGRo1CmenhFiuL000/nzTffpFq1agBkZ2dz/vnns3r16oCTiYiIyNFQwSuSj9TUVBYsWEBKSgrwc9GrRURERERKHxW8IgVo06bNL4rezp0760E2ERGRUkYFr0ghUlNTycrK4thjjwV+Lnr//e9/B5xMREREikoFr8hhtG7d+qCid/v27XTu3JkVK1YEnExERESKQgWvSBG0atWKhQsXUr16dQB++uknunTpwvLlywNOJiIiIocTs4LXQrqa2Z/NbJmZfWNme81sm5mtM7NpZjbEzOrFKoNINJ1xxhksXLiQGjVqAD8XvVqRTUREJL5FveA1sypmdi/wGZAJ3AWcDVQHvgP2E1rIoQ8wHvjczKabWftoZxGJttNPP/2gonfHjh2kp6ezdOnSgJOJiIhIQaJa8JrZdcA6YBSwC/gT0BWo7u5V3L2+u9citODFycD1hFYuuxBYbGYvmFnDaGYSibbTTjuNRYsWRVZkyy16lyxZEnAyERERyU+0e3ifApYDbd39ZHf/H3ef7+7ZeRt5yMfuPsXdrwTqAsOADsCAKGcSibpTTz2VRYsWUatWLQB27txJ165dWbRoUcDJRERE5FDRLnjPdPdL3P2I5mxy92x3HwecBLwY5UwiMXHKKaf8oujNyMhg7ty5AScTERGRvKJa8Lp7sdZedffd7v5xtPKIxNrJJ5/M4sWLqVOnDgB79uyhZ8+ezJ49O+BkIiIikkvTkokUU8uWLXn77bc54YQTANi7dy+XXHIJ06dPDziZiIiIQJQKXjM71cwGmdkp4f2TzexJM3vazDKicQ+ReNasWTOWLFlCgwYNANi/fz99+/blueeeCziZiIiIFLvgNbPuwGrgIWCVmV0ILAQaAfWA18wsvbj3EYl3jRo1YunSpTRu3BiAnJwcrrnmGiZOnBhwMhERkbItGj289wN/Dk83NgB4Dpjg7l3dvQvwMPDbKNxHJO7Vq1ePJUuW0Lx5cwDcnUGDBjF+/PiAk4mIiJRd0Sh4TwGmhH/9IlCN0Ny6uZ4DTo/CfURKhbp16/L2229z6qmnRo4NHTqURx55JMBUIiIiZVe0Hlo7AODuB4DdwNY857YDx0bpPiKlQu3atVm8eDGpqamRY3fffTcjRowIMJWIiEjZFI2C9wugWZ79dsCXefYbAJuicB+RUqV69epkZWVxzjnnRI498MAD3Hfffbh7gMlERETKlmgUvH8HKuTuuPsH7r4/z/keQFYU7iNS6lSrVo158+bRqVOnyLGHHnqIO+64Q0WviIhICSl2wevuj7l7gbPsu/t97n5dce8jUlpVrVqVzMxMMjJ+nqHv0Ucf5YYbbiAnJyfAZCIiImVD4AtPmFmSmfUMOodILFWqVInZs2fTu3fvyLHJkyfTt29f9u7dG2AyERGRxBdYwWtmzc3sIWAj8HJQOURKSoUKFZg+fTr9+vWLHJs+fTo9e/Zk586dASYTERFJbCVa8JpZVTO73szeAtYCdwN1gbdKModIUMqVK8czzzzDkCFDIsfmzp1Lly5d2LZtW4DJREREEleJFLxm1sHMJgH/BZ4kNJPDj8BfgF+7e6fC3i+SSJKSkhg/fjzDhw+PHFu2bBkdO3Zk8+bNASYTERFJTNFYWri8mV1lZneYWZM8x+ua2T1m9gmhpYYHAJWB1wADXnL337r7f4qbQaS0MTNGjhzJn//858ix999/n3PPPZeNGzcGmExERCTxFKvgNbMKwNvARKAfsMrM7jKz2YTm4h1FaI7ejwgtL1zf3XsVL7JI4rj77ruZMGECSUmh/xTXrVtHu3btWLduXcDJREREEkdxe3gvA9oA3d39LOA3wJ+B7kA28Dhwtruf5u6PuPu3xbyfSMIZNGgQ//znPylXrhwAGzdupH379rz33nsBJxMREUkMxS14a4a374a3H4a3DvwLeMrdVxbzHiIJ7/LLL2fWrFlUrFgRgC1bttCxY0eWLl0acDIREZHSr7gF72vAHmC6mQ0BphMqeh0YBKw0szVmNszM6hTzXiIJ7cILL2Tu3Lkcc8wxAGRnZ9O5c2fmzZsXcDIREZHSrVgFr7t/CnQDDgA3A28CZwANgN8B64HTCc3GsNHMZprZb4qVWCSBdezYkcWLF1OjRg0Adu/eTffu3Zk2bVrAyUREREqvaCwtvMjdu4TH6f7R3Q+4+yZ3f8jdWwDnAU8De4HehIY6OHCamZ1S3PuLJJpWrVqxdOlSTjjhBAD27dtH3759GT9+fMDJRERESqeYz8Pr7m+5+3WEFpgYBCwjNC1ZW+A9M1tqZjfEOodIadKiRQuWLVtGkyahmf7cnaFDh/LAAw/g7gGnExERKV1KbKU1d9/h7k+5+7nAr4GHgU2ECt8JJZVDpLRo2LAhy5cvp3Xr1pFjI0aM4KabbiInJyfAZCIiIqVLiS4tnMvd/+Pu9xAa63sR8HIQOUTiXe3atVm0aBHnn39+5NiTTz7JpZdeyu7duwNMJiIiUnoEUvDmCo/3ne3ulwSZQySeHXPMMWRmZnLZZZdFjs2cOZOMjAyys7MDTCYiIlI6RLXgNbPK8XANkURToUIFpk6dyi233BI5tnjxYjp06MCmTZsCTCYiIhL/ot3D+7mZ3W5mFY/0jWZ2hpm9DNwV5UwiCSEpKYlx48YxYsSIyLH333+fc845h08//TTAZCIiIvEt2gXvHGAM8F8ze9zMzi+sx9bMmpjZEDNbCqwmNIfvgihnEkkYZsb999/PhAkTSEoK/ee7YcMGzjnnHN55552A04mIiMSnctG8mLv3N7O/AaOAG8OvHDNbC/wX+BGoBNQCWgC1CU1R9i2hhSr+6u57ipvDzG4GxgM13H3rIecGAqcW8Na33X3aIe0HALcDzYGtQCZwv7v/t7g5RY7WoEGDqFOnDldccQV79uyJLEU8e/bsgx5wExERkSgXvADuvhLIMLNmwA1AF6AVcNohTTcDLxFajni6u++Lxv3NLDl834IMAVILOFcJiBS8ZnY/MAL4CngOOAG4FuhqZm3cfXM0MoscjYsvvpg5c+bQs2dPtm/fzo4dO7jgggt45pln6Nu3b9DxRERE4kbUC95c7r4OuBfAzKoA9Qj17O4CvotmD6mZGdA6/BpAwQUtQBPgMXe/pZA2mNlJwB+BD4H27p4dPn4dMAn4X+D64mYXKY7zzjuPt99+m/T0dL777jv27dvHFVdcwcaNG7nzzjuDjiciIhIXSmRaMnff6e7r3H2Zu78bg+EAVYFVwESgQ0GNzKwmUB0oyhM+g4Fk4E+5xW7YFOAboF+4kBcJ1Gmnncby5csjq7IB3HXXXdx+++0cOHAgwGQiIiLxIdB5eKNoF3BZntdHBbQ7KbwtSsGbBhwgNGY3wkPrui4GKhJaJU4kcI0aNWLFihWceeaZkWN/+9vftECFiIgICVLwunuOu/8r90VofHB+crvAvjOz68zsITP7vZnl1yt8BvCVu2/P59za8LZZMaOLRE2tWrVYtGgRPXv2jBybMWMGXbp04YcffggwmYiISLBiNoY3TuX28M4mNJ44wsxmA/3c/SczSwHKE5o9Ij8/hrc1D3fDtWvX0qZNm3zPrVq1qiiZRYqscuXKzJw5k1tvvZXHH38cgCVLltCuXTvmzJnDiSeeGHBCERGRwhVUN61duzbf40WRED28RyC3h/d1QtOiVQHOAd4GehF6GA0gJbwtaIq0HeFtWfsHg5QCycnJjB8/noceeihy7D//+Q9nnXUWa9asCTCZiIhIMMpawfYk8JK7v5bn2HIz605o3O9lZtYc2BY+V9CiGRXC252Hu2HLli3Vkyslzsy45557qF+/Ptdddx379u1j8+bNnHvuucyYMYOMjIygI4qIiOSroLqpTZs2rF69+qiuWWI9vGZ2nJl1DBeXmNkxJXXvXO6+/JBiN/d4Nj8/nNYK+AHIITSjQ35yh0N8E/WQIlF01VVXkZmZyTHHhP5z27lzJ927d2fKlCnBBhMRESlBMS94zexUM1tMaKW1LGBW+NQEM5tnZg1inaGIch9O2x9eBONToLGZVc2nbdPw9oMSSSZSDJ07d2bp0qUcf/zxAOTk5HDdddcxYsQIQpOOiIiIJLaYFrzhYnYxoXGyrwOrCS0lDPAu0AlYaWb1YpkjnOXXZuZm9moBTc7NkwtgPqF5eDsfcp1kQqvHfUNoUQqRuHfqqaeyatUqWrRoETn2wAMPMHDgQPbti8oihyIiInEr1j28DxB6MKyLu/cE3so94e7/C/QB6oTbxdonwBfABWbWMe8JM+sPnAVkuXvuHL0TAAf+ZGZ5x/LeC9QHxru6x6QUqVevHsuXL6dDh59n4Zs0aRIXXHAB27ZtK+SdIiIipVusC97uQKa7L8rvpLvPIjTMIeZP0ISL05sJLSbxppnNNrPxZjYP+AfwPTAkT/s1wMOEliv+wMwmmNkC4EFgJTAm1plFou3YY49l/vz5XH755ZFjCxYs4JxzzmHDhg0BJhMREYmdWBe8NTn8g13rgboxzgGAu79O6KG0f4W3A4HmwFPAWe7+8SHt7yFUJO8GriE0j+9YQj3WWr5KSqUKFSowdepUhg8fHjn28ccfc9ZZZ7Fy5coAk4mIiMRGrAvez4HUw7T5NQWvjHZU3D3N3c3dt+Zz7iN3v9LdG7h7RXdv6O4D3f3zAq71uLuf4u6Vw23vCM/qIFJqmRkjR45k8uTJlCsXmp1w8+bNdOzYkRkzZgScTkREJLpiXfA+B5xpZr/N76SZDSX0sNj0GOcQkXwMGDCAOXPmkJISWmtl9+7d9OnThzFjxkRmcMg7VH3dunWMGzeOkSNHMm7cONatWxc5pyHtIiISr2K98MQjwAXAaDO7Ifd+ZjYdOANoDKwD/hTjHCJSgPPPP58VK1bQtWtXvvrqK9ydO++8k5ycHO666y7MjKysLEaNGsW8efMOKmzNjPT0dIYPH05aWhrujpkVcjcREZGSF9MeXnffA6QDIwmN521MaFqy3wD1gKeBc/MbeiAiJadFixasWrUqsn557dq1ue666zAzJk+eTHp6OnPnzv1FL667M3fuXNLT05kyZQpmpp5eERGJOzFfWtjd9wIPmNkfCD0gVgvYAXwcLohFJA7UqVOHt956iyuvvJKTTz6Z2rVrk5WVxaBBg8jJySn0vTk5OQwcOJBGjRqRlpZWMoFFRESKKOYFb67wtGCflNT9ROTIVapUienTp0fm5R01atRhi91cOTk5jB49WgWviIjEnRIpeM2sJdAAqFRQm/CcvCISsKSkJGrUqMG6deuYN2/eEb137ty5rF+/nqZNmx6+sYiISAmJacFrZo2BmcCphTUjtKJZciyziMiRyczMPOLxuO5OZmYmQ4cOjVEqERGRIxfrHt7xwGnAXGAFsD/G9xORKMnOPrrppo/2fSIiIrES64K3A7DQ3S+I8X1EJMpy5+YtqfeJiIjESqwXnsgBPo3xPUQkBrp163bEc+qaGd26dYtRIhERkaMT64J3LtDFzAp8WE1E4lOzZs1IT08/ovd07dpVD6yJiEjciXXBexehh9EWmVlfMzvLzE7P7xXjHCJyFIYPH05yctGeJ01OTmb48OExTiQiInLkYj2GNxnYBpwJPF+EtiISJ9ydtLQ0Jk6cyMCBAwudjzc5OZmJEyfSqVMnLS8sIiJxJ9Y9vOOAk4GPgRcILSVc0EtE4kjuMsEDBgxg3rx5ZGRk/KKQNTMyMjKYP38+AwYMYNeuXQwePJgtW7YElFpEROSXYt3D2xFYDbRzd01JJlLK5Ba9aWlppKWlsX79ejIzM8nOziYlJYVu3bpFxuxu2bKFXr16sWzZMt544w1ee+01Tj755IA/gYiISOwL3l3Auyp2RUqvvL26TZs2zXdRCXfn6aefZvny5QBs2LCBtm3bMnXqVHr06FFiWUVERPIT6yEN04A0M6sY4/uISIDMjP/3//4f06ZNo3LlygD89NNP9OrVi9GjRx/xim0iIiLRFOuC9z5gM/BSeIaGyjG+n4gEqE+fPixZsoS6desCoZ7f4cOH07dvX3bu3BlwOhERKatiXfBuA84GugHLgJ/MLCefl4Y8iCSIVq1a8e6779K2bdvIsWnTptGuXTu+/PLLAJOJiEhZFesxvIsB/SxTpIw57rjjWLRoEUOGDGHSpEkAvPfee6SmpvLSSy9x3nnnBZxQRETKkpgWvO6eFsvri0j8qlChAk899RStW7dm2LBh5OTk8P3339OlSxfGjh3LLbfcEnREEREpI2I9pEFEyrihQ4cyf/58atSoAcD+/fsZOnQogwYNYu/evQGnExGRsiCmPbxm9lIRm7q794llFhEJTqdOnVizZg3du3fnww8/BGDixIl88MEHzJw5k+OPPz7ghCIikshiPYb34sOcd8DQOF+RhNewYUNWrFhB//79mT59OgDLli2jdevWzJo1izPPPDPghCIikqhiPaShcT6vZsA5wB3AV8AMICXGOUQkDlSpUoVp06bx4IMPRha0+O9//0uHDh149tlnA04nIiKJKqYFr7tvyOf1qbuvcPdHgbZAGnBvLHOISPwwM373u98xe/ZsjjnmGAD27NnDNddcw5133sn+/ZqlUEREoivQh9bc/VsgE7gqyBwiUvJ69OjBypUradKkSeTYmDFj6Nq1K5s3bw4wmYiIJJp4mKWhOlA36BAiUvJatGjBO++8Q0ZGRuRYVlYWrVq1YuXKlQEmExGRRBLTgtfMUgp5HW9mQ4CuwPpY5hCR+JWSksLrr7/O8OHDI8e++eYbzj33XJ566qkAk4mISKKIdQ/vVuDHAl7fAP9HaKaI0THOISJxLCkpiZEjRzJz5szIuN69e/cycOBAbrzxRvbs2RNwQhERKc1iPS3ZIgqecmwv8CXwrLsvjHEOESkFLrroIlavXk3Pnj35z3/+A8CTTz7JO++8w4wZM6hfv37ACUVEpDTS0sIiEleaNWvG6tWrufrqq5k5cyYAK1eupHXr1kybNo20tLRgA4qISKkTDw+tiYgcpGrVqrz00ks89NBDJCcnA7BlyxbS09MZM2YM7lqrRkREii6qPbxmdvrRvtfd34tmFhEp3cyMe+65hzPPPJPLL7+cH374gZycHO68806WLVvG5MmTqVq1atAxRUSkFIj2kIY1HP0ywcnRDCIiiaFLly68++679OrVizVr1gAwbdo0PvjgA2bNmkXTpk0DTigiIvEu2gXv0xx9wSsikq/69euzbNkyBg8ezJQpUwBYu3YtqampPPfcc/Tq1SvYgCIiEteiWvC6+4BoXk9EJFfFihWZPHkybdu25fbbb2fv3r1s376d3r17c++99/Lggw9GxvuKiIjkVWIPrZlZUzO7yMyuMLPOZnZsSd1bRBLH4MGDWbx4MXXr/rxA40MPPUSXLl349ttvA0wmIiLxKuYFr5k1N7MlwCfAS8BzwFzgOzN71sxOiME9bzYzN7PqRWjbI9x2bD7nLjGzsQW8bop2bhEpmrPPPpv333+fTp06RY4tXLiQM844g8WLFweYTERE4lGslxb+FbAYOJtQkfsgMBwYD2wA+gFLzOy4KN4zGbihiG1TgL8X0uQq4PYCXr8pXlIRKY7atWszf/587rvvPswMgG+//Zbzzz+fhx9+WFOXiYhIRKx7eP8A1AB6uns3d/+Du/+vu9/m7s2B/kBD4PfFuYmFpJrZDUAWkFrEt/4FqFfI+SbAa+5u+by6FSeziBRfcnIyo0aN4pVXXuHYY0OjpHJycvjtb3/LRRddxNatWwNOKCIi8SDWBW8PYJ67Z+Z30t2fBeaE2xVHVWAVMBHoUJQ3mFlnYCAws5BmTYBPi5lNRGKse/fuvPfee7Ru3TpybPbs2bRu3ToylZmIiJRdsS54awOfHabNp8CvinmfXcBleV4fFdbYzKoSKo6XA+MKaFMHSEEFr0ip0LBhQ5YtW8ZNN/08vP6LL76gffv2ZGbm+29uEREpI6I9D++h/gu0OEybk4BtxbmJu+cA/8rdN7Ohh3nLKKA+0JtQUZ6fJuHtV2Z2BXA6sA9YCrzhGiAoEncqVKjAE088wXnnncd9993H1VdfzU033UTDhg2DjiYiIgGKdcH7MnCrmQ1094mHnjSzS4EMYGqMc+S9Z3tgKDDC3T8ws7QCmp4U3k4Aah1ybqmZXeLu/5+9e4+PsyzzP/65OpTSFoJABdxCKZAYClol8QDiklEm44iHRRRWESUsCXXdUpaDAiMIuDYBFAQrIhhsRVB3V1BYV6fMpAy6u4ja0JVDpBNKwR8iyzlYainD9ftjMmOa8yR5Mod8369XXtM8z/3MXGFS+s2T+76vP431ej09PTQ2Ng57bv369eMrWkSKctJJJ3HiiSey0065/8VlMhkSiQR9fX3U1NQQi8Woq6sDwN0Li95ERKT0RspNPT09E37OoAPvvwAfAq43s3OAtcAfgT2BJnK7N7wIfDHgOgAwsznAjcDvyd3lHU3+Du9vgfPIbatWR26niQ8DPzGzd7n7awGVKyIT5O7stNNOpNNp2tvbSaVSO+zaYGZEIhHi8TjhcFihV0SkygUaeN392f47qtcAH2Xo9IZfAcvcfbrmyV4CvBF4t7u/MsbY24H7ye3SsL3/2P1m9lHg18A7gQi5RXcjWrJkie7kikyjfHhdvXo1bW1tZLPZYcckk0nWrVtHZ2cnLS0tCr0iImVipNzU2NhId3f3hJ4z6Du8uPtTwMfNbE/gbeTmzL4M3D+NQRczWwqcC1zr7veMNd7d7ycXeAcff9XMbgMOB97KGIFXRKaXmZFOp0cMuwNls1laW1tZvHgx4XB4egoUEZFpF3TjievM7CgAd3/O3e909++7+0+mM+z2ayAX8M/o76zmZubAXf3nz+w/tmYcz/VS/+OrAdQpIpPU3t4+ZtjNy2azdHR0BFyRiIiUUtB3eJcBp5vZ4+RaCt/i7hOfcTw5D5GbWjHYfuSmW2wA7gZ+bWbzgD8DPe5+2DDXHNX/+L9BFCoiE5fJZEilUkVdk0wm6e3tpba2doepDVrsJiJSHYIOvMcBHwM+SK6l8AVmtgG4Gfihuz8Z8OsXuPuvyc293UH/Lg0fBe52938ecGQ6MyoAACAASURBVPyXwNFm9gl3/8Ew4x8h19VNRMpIIpEouq2wu7NhwwYOPvjgwpQILXYTEakegU5pcPc73P3TwN7kuqmtBvYn19L3cTO708w+bWa7BlnHBJ0F9AHfN7OUmV1rZncASWAbcGr//r8iUkb6+vqKvmbBggUce+yxhcVukUiEZDI5JDjnF7tFIhHWrFmDmRUdrkVEZPoF3WkNyC30cvefu3srsC/QDHwbeDO5EDzmfrbTzd27ydX3bXJblLUCbwduBY5w91+WsDwRGUFNTU3R15x++unMmzev6MVu6XRad3hFRCpA4Ls0DObur5nZk8D/I7cn7z7A3Cl+jXARY9PAsP9iufvjwOlTU5WITIdYLFbUnddQKFRoRzyRxW7a3UFEpPxNyx1ey3m3mX3FzDaS2+7ry+Tu9l5Nbk9bEZFJq6urIxKJjHt8c3MzixYtmtRiNxERKW9Bb0v2ITO7kdyUhbuBc8jtw7uaXNOG/dz9bHf/bZB1iMjMEo/HCYVC4xq7ZMkSYOKL3RKJRNH1iYjI9Ar6Du/twKnArsCPgI8A+7h7q7uvc632EJEp5u6Ew2E6OzvHDL2hUIiTTz4ZmNhit8lcJyIi0yfowLsWOIVcyP17d799QJteEZEpl5+/29LSQiqVIhqNDllYZmZEo1G6urpoaGgAJrbYbTLXiYjI9Al00Zq7vz/I5xcRGU4+9IbDYcLhML29vUMaSNTW1gJ/bSBR7GK3/Oscd9xxalYhIlLmpn2XBhGR6TAwWNbW1rJ8+fJRx+UXuyWTyXG/xplnnsnChQvVrEJEpMxNyy4NIiKVoJjFbvvssw+XX365mlWIiFQABV4REYpf7Pazn/2MnXfeWc0qREQqgAKviAgTX+w2kWYVIiIyvTSHV0SkXzGL3YBJNasY+DwiIhKsKQ28ZrZ0ote6+++mshYRkYkY72I3mFyzitGeV0REptZU3+HdAEx0Ncb4VoqIiJQJNasQEakMUx14b2LigVdEpKKoWYWISGWY0sDr7i1T+XwiIuVsos0qYrFYgFWJiMhgmsMrIjJBE2lW0dzcrAVrIiLTTHN4RUQmIR6Ps27dunFtTRYKhYjH40Buh4d8y2EREQmW5vCKiEzQwGYVra2to4beUChEZ2cnTU1NPPPMM7zrXe/iE5/4BFdccQW77LLLNFYtIjLzaA6viMgEDWxWsXjxYjo6Ooa0FzYzmpubicfjNDU1sXXrVj70oQ/xzDPPsGrVKlKpFD/84Q9ZunTCM8JERGQM09Z4wsz2BuqB3dz9Z2a2q7v/ebpeX0QkCMU0q3B3Xn75ZebNm1e4vqenh7e97W10dHRw1llnMWuWGmCKiEw1K3bT9KJfwOxNwHXAu/oPubvvZGbfB/YGTnX3PwRaRImY2fqGhoaG9evXl7oUESkj7s7Xv/51zjvvPLZt21Y4Hg6Hufnmm1m4cGEJqxMRKU+NjY10d3d3u3tjsdcGeivBzPYHfgkcAfwc6AbybYz+F2gCfmtm+r+7iMwYZsaZZ55Jd3c3hx56aOH4Aw88wHe+8x22bNlSwupERKpP0FMavgjMA45x91+Y2deABgB3v9zMeoCf9I9bFnAtIiJl5dBDD+W+++7j/PPP59577+X2229nwYIFQG4Xh8FTI/K7Orj7Di2QRURkdEEH3mOBhLv/YriT7n6HmaWBaMB1iIiUpZ133pmrrrqKbDZLKBQinU7T3t5OKpUasvgtEokQj8cJh8MKvSIiRQh6dcSewB/HGNML7BtwHSIiZcvdCYVCrF69utDIYvD6CncnmUwSiURYs2ZN0R3eRERmsqAD76P0T2EYxSHA0wHXISJStsyMdDpNW1vbmA0sstksra2tpNNp3eEVERmnoAPvLcDbzOzzw500s+XAUcCtAdchIlLW2tvbx9WtDXKht6OjI+CKRESqR9CB96vAfwMdZvYw8GEAM7vVzHqBa4AMcGnAdYiIlK1MJkMqlSrqmmQySW9vb0AViYhUl0ADr7tvAyLASnLzeQ8kty3ZR4CF5FoRH+XuLwRZh4hIOUskEkXPx3V3EolEQBWJiFSXwDutufsrwBfN7GLgjcBewBbg9/2BWERkRuvr65vUdc8++yx77bXXVJYkIlJVpqWHpZm9CzjR3R929/8BfgecYmb7Tcfri4iUs5qamkld19HRwbXXXqtdG0RERhB0p7V5ZnYnuW5rZw563W8Bm8zsy0HWICJS7mKxWNE7LpgZsVgMgAcffJDly5fT3NzMH/5QlZ3aRUQmJeg7vGeTm8O7Fvhc/qC7Z4FTgEeAC8zsYwHXISJSturq6ohEIkVd09zcTG1tLU888QR33nknAF1dXRx22GF0dnYOe7d34LFMJsOqVatYuXIlq1atIpPJDDtORKQaBB14TwIeAD7g7v898IS7fw94J7nGFGcFXIeISFmLx+OEQqFxjQ2FQsTjcQD23ntvPvOZzxTuEL/00ku0tbXxvve9b4e7vfnObOl0mmg0Sn19PStWrODCCy9kxYoV1NfXE41GC/v7KvSKSDUJOvAeANzjI/yf0937yN39XRJwHSIiZcvdCYfDdHZ2jhl6Q6EQnZ2dNDU14e7Mnj2ba6+9lnXr1rFo0aLCuGQyyWGHHcbNN99cCLvq5CYiM1XQgfcFYKyFabuS26pMRGRGyofLlpYWUqkU0Wh0yJxeMyMajdLV1UVLS0shxOaFw2F6enpYtmzZDnd7e3p61MlNRGY8C/IneDP7FnAacIq7f3+Y80cC64A73f3vAiukRMxsfUNDQ8P69etLXYqIVICBIba3t5dEIkFfXx81NTXEYjFqa2uHjBtOOp3m05/+NH/84x/ZtGkTixYtIhqNkkwmx11LNBpl7dq1k/uCRESmUGNjI93d3d3u3ljstUEH3r2BXwP7A/cBaeBPwO7A28ktaNsCvMvdHwyskBJR4BWRUnn55Zfp7OxkxYoVZDIZ6uvri5qiYGZs3LixELJFREptMoE36E5r/wccAdwKvIXcrg1XAF8AosBvgKZqDLsiIqU0b948VqxYAaiTm4hI4I0n3P1P7n4isA/wPuCTwMeAenc/0t03TPVrmtlnzczN7HXjGPuB/rFXj3C+xczuM7MtZvaEmd1oZm+Y6ppFRIIy2U5uIiKVLvDWwnnu/hww/glkE2RmIXLzhscztga4fpTzFwL/AvwBuAV4A7n9g5vNrNHdn558xSIiwZpsJzcRkUo3La2F88zsn8ysK4DnNTNrMLPTyM0TbhjnpVcCC0d4zoOBS4AHgTe5++nu/iGgjdyc5MsnW7eIyHSYbCc3EZFKN62BF6gFwgE873xgPdAJvHs8F5jZe4FW4CcjDPkMEAIu7d8vOG8NuWYZJ5nZvIkWLCIyXSbTyU1EpBpMd+ANylbghAEfD4022MzmkwvH9wKrRhgWBl4Ddli10d9E45fAHHKd4kREyt5EO7mJiFSDaZvDGyR3zwI/yn9uZsvHuKSdXEOMDwMLRhjzFuAP7v7SMOd6+h/rgLuKq1ZEZHoN7OTW2to6avOJgZ3cXn75ZWbPns3s2bMLz5OfGpHJZIbsE1xXVzdknIhIOZjuwGuUuKuamb0LWA78i7s/YGbhYcbUALOBp0Z4muf7H/cc6/V6enpobBx+uzjtzysi02FgJ7fFixfT0dExpL2wmdHc3Ew8HqepqYmtW7dyzDHHsGXLFn70ox9RV1dX6NjW3t5OKpUacn0kEiEejxMOhxV6RWTCRspNPT09wx4fj0ADr5ktAvrc/YX+Q5cCXxs05nXAXHd/Msha+l9rDnAj8Htyd3lHkl+avG2E81v6H6viDrmIVL986A2Hw4TD4VE7uT3//PMce+yx/OpXv2LBggXsueeemBmrV68esT2xu5NMJlm3bh2dnZ3Dtj8WESmVoAPbo8DVwDkA7v48f707mncp8Glgj4BrgdyuC28E3u3ur4wybnv/49wRzu/c//jyWC+4ZMkS3ckVkbIwMHzW1tayfPnws7922203jj32WO677z5OP/10FixYQDqdHjHsDpTNZmltbeXAAw+kqalpSusXkZlhpNzU32ltQs855YHXzFYM/BRoGHRs8Ov/HbndEAJlZkuBc4Fr3f2eMYY/B2SBkRpX7NX/+McpKk9EpGzstNNOXHTRRZx44onsvvvuALS3t48ZdgEWLFjA6aefzpIlS4IuU0Rk3IK4w3s14OTCrgNHA2P9mP+DAOoYrIHc13uGmZ0xzPkzzexM4Lvu3mJmjwAHm9l8d98yaGx+r54HAqxXRKSk6uvrgdwCtVQqNeb4I488kjvuuIMFCxYUrtPCNhEpB0EE3lP7Hw34DnAnIwfa18h1Mbs7gDoGewi4Zpjj+wEfBTb01/Hr/uNd5KY/vBf4j/zg/k5ux5C7u/tggPWKiJSFRCKxwwK14Rx55JF0dXUxd+5cLWwTkbIz5YHX3b+b/7OZnQLcMfBYqbj7r/lrmC3o36Xho8Dd7v7PA07dQK75xKVmlnL3rf3HzycXkr/gY/0LICJSBfr6+kY9v2DBAu644w7mzp2rhW0iUpYCXbTm7u8J8vmD5O4bzOwrwOeBB/pbIteRa0jxW+CqEpYnIjJtampqRj0/0YVtixcvJhwOT2GlIiLDm9JOa2ZW07/118DPx/UxlXVMFXc/D/gs8BfgU8DB5OYoH+PufyllbSIi0yUWi414FzYUCrFs2TJg/AvbIBd6Ozo6pqxGEZHRTPUd3ucZsA0Z8AK5hWtj8amsxd3DRYxNM0ozDHe/Drhu8lWJiFSmuro6IpEIyWRyyLnm5mYWLVo07oVtAyWTSXp7ewv7/4qIBGWqA+8vgUcGfP4Lxhd4RUSkjMXjcdatWzfkDm5+J4fxLGwbzN1JJBIj7gcsIjJVpjTwDr6zWsydVhERKU/5Dm2dnZ20trbuEHp33XVXYOyFbXmhUIjm5mbq6+vZddddC4FZRCRIao0rIiKjyrclbmlpYfHixXR0dJBMJnF3/vznPwNjL2zLN6RYtmwZixYtmo6yRUQKAg+8ZhYDPgkcAMxn+Pmy7u6NQdciIiITkw+94XCYcDhMb28viUSi0GQiv7BtuGkNakghIqUWaOA1s1bgekZZFNZP83xFRMrcwCBaW1u7w9zbkRa2qSGFiJSDKd2WbBjnkNvS69PA69x91ggfoYDrEBGRgMXjcUKhv/7vfHBDinwgHnwXON+QIhKJsGbNmhHvFIuITFTQgXcxcKu73+zu41vRICIiFWfgwrZ86J1oQ4p0Oq07vCIypYIOvP8HvBrwa4iISIkNXNiWSqWIxWJqSCEiZSPowPsD4P1mtnvAryMiIiU2cGHbz3/+80k3pBARmSpBB94vkWtEcbeZRcxs70pqLSwiIsUZPBVhMg0pRESmStDbkr1AboeGELB2lHFT2lpYRETKw3gbUox03W233cbRRx9d2NJMRGQigg6Z/4O2HBMRmbHGakgx1nV33303ra2tXHHFFfzDP/wDs2YF/YtJEalGgQZetRYWEZnZRmtIMRIzIxaLAbBx40aef/552tra+M53vsP111/Pm9/85qDKFZEqpR+VRUQkMPmGFMVobm6mtraWLVu2cP/99xeO33PPPRx++OGcc845hZbGIiLjMaWB18w2jfGx0cy6zewnZnaemb1hKl9fRETKz+CGFKMJhULE43EA5s+fz8MPP8yKFSsK12ezWa666ioOOeQQfvzjH6tBhYiMy1Tf4V08xkct8Fbgw0AH0GNmx09xDSIiUiaGa0gxklAoRGdnJ01NTYUgO3/+fK655hq6u7t5+9vfXhj7xBNPcPzxx/O+972PTCYT6NcgIpVvqgPvgeP4OAQ4BvgasAtwi5ktneI6RESkDAxuSBGNRodsXWZmRKNRurq6aGlpwd2HjFm6dCn33nsv119/PXvssUfheDKZ5LDDDuOCCy5gy5Yt0/I1iUjlsVL+OsjMYsDPgNXuflrJCgmIma1vaGhoWL9+falLEREpqYEhtre3l0QiQV9fHzU1NcRiMWpra4eMG8mzzz7LWWedxc0337zDlIaFCxdyzTXXcPzxx6s1sUgVamxspLu7u9vdG4u9tqSBF8DM/gfY190PKmkhAVDgFREJzq9+9SuWLVvG7373u8KxI488kp///OfsvnuuwWcmkxkSruvq6oDxhWsRKR+TCbzl0OzhfnLzekVERMbtiCOO4L777uO6667jwgsvZMmSJXR1dTF37lzS6TTt7e2kUqkd7gKbGZFIhHg8TjgcVugVmSHKYVuynQFNvBIRkaLNmjWLf/qnf2LTpk2sXbuWuXPnsnr1aiKRCMlkcsguDu5OMpkkEomwZs2aovcIFpHKVNLAa2a7AM2AltiKiMiE7bHHHuy2226k02na2trIZrOjjs9ms7S2tpJOp3WHV2QGKFngNbN9gX8D3gD8sFR1iIhI9Whvbx8z7OZls1k6OjoCrkhEysGUzuE1s++MY9hcYH/gHf2v/z/AtVNZh4iIzDyZTIZUKlXUNclkkt7e3sIuESJSnaZ60VpLEWP/DHwbuNDdx/fjuIiIyAgSiUTR83HdnUQiwfLlywOqSkTKwVQH3lPHOJ8lt0DtMeB+d98+xa8vIiIzVF9f37ReJyKVY0oDr7t/dyqfT0REZLxqamomdd3dd9/NUUcdxU47lcOOnSIylcphWzIREZFJi8ViRe+4YGbEYjEALrvsMpYuXUpXV1cQ5YlICSnwiohIVairqyMSiRR1TXNzM7W1tTz22GPceeed9PT0EIlEOO6443j00UcDqlREppsCr4iIVI14PE4oFBrX2FAoRDweB2Djxo3MmTOncO7222/nkEMOIR6Ps2WLeiOJVDoFXhERqQruTjgcprOzc8zQGwqF6OzspKmpCXenubmZhx9+mI985COFMa+88godHR3U1dXxgx/8QB3ZRCqYAq+IiFSFfJvglpYWUqkU0Wh0yJxeMyMajdLV1UVLSwvuXhiz//77c9ttt5FOpznssMMK1zz55JOcdNJJHHXUUXR3d0/r1yQiU8P0E2twzGx9Q0NDw/r160tdiojIjDEwxPb29pJIJOjr66OmpoZYLFZoMjFw3GCvvfYa1113HRdffDHPPvts4biZceqpp9LR0cHee+8d/BcjIgWNjY10d3d3u3tjsdcq8AZIgVdEpLK9+OKLXHDBBXz729/m1VdfLRyvqanh4osvZvny5ey8884lrFBk5phM4NWUBhERkRHsvvvufPOb32TDhg00NTUVjvf19XHOOedw6KGHcvvtt2t+r0iZU+AVEREZw2GHHUY6nebWW29l0aJFheOPPPIIxx13HOFwmA0bNpSwQhEZjQKviIjIOB1//PFkMhm+/OUvs9tuuxWO/+IXv6ChoYGVK1eSzWYLxzOZDKtWrWLlypWsWrWKTCZTOKe7wiLTR4FXRESkCDvvvDNf+MIX6O3tpaWlpbAF2hFHHMHZZ59NKBQinU4TjUapr69nxYoVXHjhhaxYsYL6+nqi0SjpdLqwq4SIBE+BV0REZAL23ntvVq9eTXd3Nx/+8Ie54447mDt3LqtXryYSiZBMJocEWncnmUwSiURYs2aNQq/INNmp1AUEwcw+C1wL7OHuLww69zdAO/A+YC/gGeAu4F/c/feDxh4PHD3Cy/S4+/VTXbuIiFSWpUuXcvvttwOQTqdpa2vbYVrDcLLZLK2trSxevJhwODwNVYrMbFUXeM0sBJw2wrm9gF8DC4FfArcDhwEnAR8ys3e6e8+ASz4JHD/CS60FFHhFRKSgvb19zLCbl81m6ejoUOAVmQZVMaXBchrM7DQgDTSMMPRz5MJu3N2PdvfPuPvfAucDuwHxQeMPAn7m7jbMRyygL0dERCpQJpMhlUoVdU0ymaS3tzegikQkryoCLzAfWA90Au8eZdxxwBbgqkHHrwGywOCNjA8CHpmiGkVEpIolEomi5+O6O4lEIqCKRCSvWqY0bAVOGPD5pcChw4x7DbjX3bcNc9yBl/MHzOz1QA0KvCIiMg59fX3Tep2IjF9VBF53zwI/yn9uZstHGDdcCAb4R3L/LQb+Luqg/sc/mNnHgaXAduAeYK2P88f4np4eGhuH74CnlsMiItWjpqZmUtc98cQTLFy4cCpLEqlII+Wmnp6eYY+PR1UE3okws2OBDwBvBv4W+DGwcsCQg/sfbyC3m8NA95jZ8e7+p8ALFRGRihCLxYreZszMiMVyS0La2tpYsGAB7e3t7LfffkGVKTIjzdjAC7wD+OyAzw/o/3ig//P8Hd7fAucBDwN1wJeBDwM/MbN3uftro73IkiVLdCdXRGQGqKurK+y/O17Nzc3U1tby2GOPsXbtWl577TX+7d/+jbPOOosLLrhgwneNRSrZSLmpsbGR7u7uCT1ntSxaK5q7XwLMJbct2fXkdnb4TzOb3T/kdnKL3D7k7v/r7n9x9/uBjwL3Ae8EItNeuIiIlK14PF7ovDaWUChEPJ7bHOjOO+/ktddy90+2bdvGZZddxkEHHcQ3vvENtm/fHli9IjPFjA28AP0h9iF3/wzwC2ARuekNuPv97n67u28fdM2rwG39n751WgsWEZGy5e6Ew2E6OzvHDL2hUIjOzk6amppwd9ra2vjpT39KfX19Ycyzzz7LGWecwaGHHsqPf/xjdWQTmYQZE3jN7BgzS5vZP48w5Df9j/uM4+le6n98dfKViYhINcjP321paSGVShGNRjGzIWOi0ShdXV20tLTg7oUxH/jAB3jooYf41re+xb777lu4pre3l+OPP56jjjqKe++9d1q/JpFqMWMCL7ANaAI+MsL5Rf2PfzCzeWb2mpk9OMLYo/of/3cqCxQRkcqWD73hcJi1a9eyceNGVq1axcqVK1m1ahUbN25k7dq1hTu7gwPxrFmzWLZsGZs2beKiiy5i1113LZy75557OOKIIzjhhBN45BHtmClSDKvGX5GYWZpcuN3D3V/oPzYbeIzcHdwmd/+vAeOjwM+A/wfUuvurZnY3cDRwkrv/YMDYMNAFPArU92+JNlId6xsaGhq0aE1ERCbiqaeeIh6P893vfneHlsWzZ8/mH//xH/niF7/IXnsN3khIpDr1L1rrdvfh9y0bxYy5w9s/F3dF/6frzOx2M7vOzLqABPAKcEr/HF2As4A+4PtmljKza83sDiBJ7m7xqaOFXRERkcnaZ599uPHGG7n//vsL25cBbN++na9//escdNBBdHR08PLLL4/yLCIyYwIvgLv/CIgB+bu3rcAhwC3A29z97gFju8nt0fttcluUtQJvB24FjnD3X05v9SIiMlMtWbKEn//859x111289a1/XS/d19dHPB7n4IMP5oYbbuDVV7W0RGQ4VRl43T3s7pafzjDoXNLdm919D3ef7e4L3f1T7v7QMGMfd/fT3f0gd5/j7m9w94+7+++m5ysRERH5q3A4THd3NzfffDOLFi0qHP/Tn/7EsmXLOPTQQ7ntttu0o4PIIFUZeEVERKqVmfHJT36S3t5errzyyh3m8GYyGT760Y/yjne8g3Q6XboiRcqMAq+IiEgFmj17NmeffTabN2/mggsuYP78+YVzv/3tb3nPe95DLBbjf/9XGwqJKPCKiIhUsF133ZX29nY2bdpEW1sbs2fPLpxbu3Ythx9+OJ/85Cd59NFHS1ilSGkp8IqIiFSBvffemxtuuIGHHnqI448/vrDHr7vz/e9/n/r6es444wyefvrpElcqMv0UeEVERKpIbW0tt956K7/5zW94z3veUzi+fft2vvGNb3DggQdy6aWX8uc//7mEVYpMLwVeERGRKtTY2Mi6deu48847ectb3lI4vmXLFi655BLe9ra3cc8992hHB5kRdip1ASIiIhKc5uZm7rvvPv71X/+VL3zhC2zatIkjjzySO+64gwULFgC53R0SiQR9fX3U1NQQi8Woq6sDGLYFskilUeAVERGpcmbGxz/+cU444QRuu+02PvjBDzJ37lzS6TTt7e2kUqkd7vSaGZFIhHg8TjgcVuiViqcpDSIiIjNEKBTiYx/7GHPnzmX16tVEIhGSyeSQaQ3uTjKZJBKJsGbNGsxMUx+koinwioiIzCBmRjqdpq2tjWw2O+rYbDZLa2sr6XRad3iloinwioiIzDDt7e1jht28bDZLR0dHwBWJBEuBV0REZAbJZDKkUqmirkkmk/T29gZUkUjwFHhFRERmkEQiUfR8XHcnkUgA8P3vf5/HH388iNJEAqPAKyIiMoP09fVN6rqHHnqIuro6zjjjDJ566qmpLE0kMAq8IiIiM0hNTc2krnvppZd45ZVXCl3bLrjgAp5//vmpLFFkyinwioiIzCCxWKzoHRfMjFgsBuRaFOdt3bqVyy67jAMPPJCVK1eqXbGULQVeERGRGaSuro5IJFLUNc3NzdTW1gJw7bXX8sMf/rDwOcCLL77IhRdeyAEHHMBXvvIVXn755SmtWWSyFHhFRERmmHg8TigUGtfYUChEPB4vfG5m/P3f/z0PP/wwnZ2dLFq0qHDuueee4/Of/zyLFy/ma1/7Glu3bp3y2kUmQoFXRERkBnF3wuEwnZ2dY4beUChEZ2cnTU1NQ3Z2mDVrFqeddhq9vb1cffXV7LvvvoVzTz/9NGeffTYHHnggq1atYtu2bYF8LSLjpcArIiIyg+TbBLe0tJBKpYhGo0Pm9JoZ0WiUrq4uWlpacPcR5/3Onj2bM888k82bN/OVr3yFvffeu3Duqaee4gc/+AFbtmwpHMtkMqxatYqVK1eyatUqMplM4ZzaF0tQTN9cwTGz9Q0NDQ3r168vdSkiIiI7GBhie3t7SSQS9PX1UVNTQywWK8zRHS3sDmfr1q1cc801XHnlldTV1dHV1cXcuXNJp9O0t7eTSqV2CLZmRiQSIR6PEw6Hi349mTkaGxvp7u7udvfGYq9V4A2QAq+IiMxUW7ZswcyYN28eq1evpq2tbdR2xvnpE2PdUZaZazKBV1MaREREKxQduAAAIABJREFUZMrNnz+fefPmkU6nxwy7ANlsltbWVtLptMKuTDkFXhEREQlMe3v7mGE3L5vN0tHREXBFMhMp8IqIiEggMpkMqVSqqGuSySS9vb0BVSQzlQKviIiIBCKRSBS984K7k0gkAHjttdeCKEtmIAVeERERCURfX9+krvvGN77Bv//7vyv4yqQp8IqIiEggampqJnXdI488woknnshhhx3GD3/4w3HPBRYZTIFXREREAhGLxYreccHMiMViAGzcuBGA3//+93ziE5/g0EMP5ZZbblHwlaIp8IqIiEgg6urqiEQiRV3T3NxMbW0t2WyW2tpa5s2bVzi3ceNGTj75ZA455BBuuukmXn311akuWaqUAq+IiIgEJh6PEwqFxjU2FAoRj8cLf161ahWPPvooK1asYP78+YVxvb29nHLKKdTX17NmzRq2b98eSO1SPRR4RUREJBDuTjgcprOzc8zQm++01tTUtMPODnvvvTfXXHMNjz76KGeddRa77rpr4dymTZs49dRTeeMb38iNN96o4CsjUuAVERGRQJgZ7k5LSwupVIpoNDpkTq+ZEY1G6erqGrWt8Otf/3quuuoqNm/ezLnnnrvDgrjNmzfT2trKwQcfzPXXX88rr7wS+NcmlcWK3R9Pxs/M1jc0NDSsX7++1KWIiIiUzMAQ29vbSyKRoK+vj5qaGmKxGLW1tUPGjeX555/n8ssv51vf+hYvvvjiDucWLlxIPB7ntNNOY86cOUXXmMlkhtRYV1dXdI0ytRobG+nu7u5298Zir1XgDZACr4iISLBeeOEFrrjiCr75zW8OCb5veMMbuOCCC2htbWXu3LkjPkc+xKbTadrb20mlUjtMqzAzIpEI8XiccDis0Fsikwm8mtIgIiIiFet1r3sd7e3tPP7441x00UXssccehXNPPvkkK1as4IADDuDyyy/npZdeGnJ9PryuXr2aSCRCMpkc0h3O3Ukmk0QiEdasWVOYqiGVQ4FXREREKl5NTQ1f+tKXeOyxx7jkkkvYa6+9Cueefvppzj//fBYtWsTFF1/Mc889VziXv7Pb1tY25v6+2WyW1tZW0um07vBWGAVeERERqRq77bYbF198MZs3b2blypXss88+hXMvvPACX/rSl1i0aBGf//znCwG3vb193M0sstksHR0dgdQuwanKwGtmnzUzN7PXDXPub8xsjZk9aWavmNkfzewWMztkhOdqMbP7zGyLmT1hZjea2RuC/ypERERkonbddVfi8TiPPfYYV111Ffvtt1/h3JYtW7j//vsJhUJkMhlSqVRRz51MJunt7Z3qkiVAVRd4zSwEnDbCub2AXwOnABngO8AjwEnAr81syaDxFwKrgb2AW4Du/mvvNbPXB/U1iIiIyNSYM2cOZ511Fps2beJb3/oWBx10EAD19fUAJBKJoufjujuJRGLKa5XgVEXgtZwGMzsNSAMNIwz9HLAQiLv70e7+GXf/W+B8YDcgPuA5DwYuAR4E3uTup7v7h4A2YH/g8qC+HhEREZlas2fPZtmyZWzcuJGbbrqJAw88EIC+vr4JPd9Er5PS2KnUBUyR+cB49v46DtgCXDXo+DXASmDgNhefAULApe4+8Lt6DfBl4CQzW+7uL0+0aBEREZleoVCIT33qU4W7ugMbWBQjf532760M1RJ4twInDPj8UuDQYca9Btzr7tuGOe7AwPAa7j++w+8s3N3N7JfA3wPvBO6aVOUiIiIy7fLhMxaLFb3NmJlx3HHHaf/eClIVUxrcPevuP8p/AE+PMO5Qdz9mmFP/SC78D5y1/hbgD+4+dNM+6Ol/rJtM3SIiIlJadXV1RCKRoq752Mc+xsKFC7V/bwWplju8RTOzY4EPAG8G/hb4MblpDZhZDTAbeGqEy5/vf9xzrNfp6emhsXH4hiDqwCYiIlJ68XicdevWjWtrslAoxOWXXz6h/XsXL15MOByeoqqr10i5qaenZ9jj41EVd3gn6B3AZ8mFXYAD+j8A8hN6Bk99yNvS/zhjf2AQERGpBu5OOByms7OTUCg06thQKMSNN95YWPCm/Xsrx4wNbO5+iZldBhwErACWAf9pZrXA9v5hIzXe3rn/ccwFa0uWLNGdXBERkTKVn2bQ0tLC4sWL6ejoGDI9wcxobm4mHo/T1NQEMKn9e2tra6f0a6g2I+WmxsZGuru7J/ScMzbwArj7X4CHgM/078F7NLk7vr8EssCQxhX98v0K/xh4kSIiIhKofOgNh8OEw2F6e3uH7LaQD6nbtm1jzpw5k9q/d/ny5UF8GTKKGRN4zewY4CLgJ+5+9TBDfkMu8O7j7tvN7BHgYDOb7+5bBo3N/2j2QHAVi4iIyHQZuHtCbW3tiKF0zpw5gPbvrTQzaQ7vNqAJ+MgI5xf1P/6h/7GL3D687x04qL+T2zHk7u4+OPVlioiISLmb7P69Mr1mUuC9F3gSeLeZvXvgCTOLAscDjwG/6j98A7m9eS81s4Fzec8H9gOude0tIiIiMiPl9+8thpkRi8UCqkhGM2MCr7tvJ7c4DWCdmd1uZteZWRe55hKvAKe4+6v94zcAXwEOBx4wsxvM7C5yXdZ+y9BubSIiIjJDTGT/3ubmZmpra7UXbwnMmMAL0N+UIgbcTW6+bitwCHAL8DZ3v3vQ+PPIbV32F+BTwMHA1cAx/QveREREZIaKx+NjbmWWFwqFiMfjAHzta1/jxhtvZNu2kXY/lalWlYHX3cPubu7+wjDnku7e7O57uPtsd1/o7p9y94dGeK7r3P0wd5/r7ovc/Sx314xzERGRGazY/Xs7OztpamrimWeeoaOjg9bWVg444AA6Ojp44YUhcUWmWFUGXhEREZEgDdy/N5VKEY1Gh8zpNTOi0ShdXV20tLTwyiuvcOKJJ/LMM88A8NRTTxGPx9lvv/3453/+Zx5//PFSfCkzgmkeSXDMbH1DQ0ODGk+IiIhUJ3cvBN3R9u/Nj3vuuee48sorueGGGwrBNy8UCnHCCSdw3nnn8da3vnXav5Zy1994otvdh+89PAoF3gAp8IqIiMhwtm7dSmdnJ1dffTWbNm0acv69730v5513Hs3NzUXvBlGtJhN4NaVBREREZJrNnTuXM844g0wmw7/+67/S2Lhjhlu3bh3ve9/7WLp0Kd/73vfYvn17iSqtDgq8IiIiIiUya9YsTjzxRH77299y99138/73v59Zs/4azx544AE+/elPs3jxYr761a+qU9sEKfCKiIiIlIGjjz6an/3sZ4WQu8suuxTO/fGPf+Rzn/sc++23H+eeey5PPPFECSutPAq8IiIiImVkyZIlfPe73+XRRx/l3HPPZc899yyce+mll7jyyitZvHgxn/rUp7j//vtLWGnlUOAVERERKUP77rsvX/nKV3j88cf56le/yqJFiwrnXn31VW6++WaWLl1a2PpMGxGMTIFXREREpIzNnz+fc845h02bNvG9732Pt7zlLTucTyaTRCIR3vzmN/Pd735XHdyGocArIiIiUgFCoRAnn3wyGzZsKITcgVuWPfjgg7S0tLBo0SK+/OUvD9nndyZT4BURERGpMJFIhGQyye9+9ztOOukk5syZUzj3f//3f1x00UXsv//+nH766fz+978vYaXlQYFXREREpEK96U1v4pZbbmHz5s2cf/75LFiwoHDuL3/5C9/+9rdZsmQJxx57LOvWrZv0PN+B12cyGVatWsXKlStZtWoVmUxm2HHlQJ3WAqROayIiIjKd/vKXv/Cd73yHr3/96zz88MNDzr/pTW/i3HPP5eMf//gOd4XHI98eOZ1O097eTiqV2iHYmhmRSIR4PE44HN6h7fJUUKc1EREREWGXXXbhs5/9LD09PfzHf/wH4XB4h9D5wAMPTGiebz68rl69ujCdYvBNU3cvzC1es2YNZlY2d3oVeEVERESqjJnxwQ9+kLvuuov77rtv0vN883d229rayGazo47NZrO0traSTqen9A7vZCjwioiIiFSxt7zlLYV5vueddx6vf/3rC+cGzvN9//vfP+o83/b29jHDbl42m6Wjo2NK6p8KCrwiIiIiM8C+++7LZZddxuOPP861115LfX39DucTiQTHHHPMsPv5ZjIZUqlUUa+XTCbp7e2dktonS4FXREREZAYZOM/3pz/96ZB5vvn9fD/84Q8XjiUSiaLn47o7iURiyuqeDAVeERERkRnIzPjABz7AXXfdxYYNGzj55JPZZZddCuff8573FEJuX1/fhF5jotdNNQVeERERkRlu6dKlfO9732Pz5s3E43EOP/xwWltbC3d+a2pqJvS8E71uqinwioiIiAgA++yzDytXrqS7u3uHJhaxWKzoHRfMjFgsNtUlTogCr4iIiIiMqq6ujkgkUtQ1zc3N1NbWBlRRcRR4RURERGRM8XicUCg0rrGhUIh4PB5wReOnwCsiIiIio3J3wuEwnZ2dY4beUChEZ2cnTU1N6rQmIiIiIpUh3ya4paWFVCpFNBodMqfXzIhGo3R1ddHS0lJoR1wOdip1ASIiIiJS/vKhNxwOEw6H6e3tJZFI0NfXR01NDbFYrDBnt5zCLijwioiIiMg4DQyxtbW1LF++fMxx5UBTGkRERESkqinwioiIiEhVU+AVERERkaqmwFslGhsbaWxsLHUZMgK9P+VL70150/tT3vT+lC+9NztS4BURERGRqqbAKyIiIiJVTYFXRERERKqaAq+IiIiIVDUFXhERERGpaubupa6hapnZs3Pnzt1zyZIlgb9WT08PANPxWlI8vT/lS+9NedP7U970/pSvanxvenp62Lp163Puvlex1yrwBsjMHgVqgM0lLkVERESk0i0G+tz9wGIvVOAVERERkaqmObwiIiIiUtUUeEVERESkqinwioiIiEhVU+AVERERkaqmwCsiIiIiVU2BV0RERESqmgKviIiIiFQ1BV4RERERqWoKvCIiIiJS1RR4RURERKSqKfCKiIiISFVT4BURERGRqqbAKyIiIiJVTYFXRERERKqaAq+IiIiIVDUFXhERERGpagq8IiIiIlLVFHhFREREpKop8IqIiIhIVdup1AVUMzN7FKgBNpe4FBEREZFKtxjoc/cDi71QgTdYNXPnzt1zyZIlewb9Qj09PQAsWbIk6JeSCdD7U7703pQ3vT/lTe9P+ZqO92bbtm28+OKLvPbaa8yaNYvdd9+dOXPmBPZ6PT09bN26dULXKvAGa/OSJUv2XL9+feAv1NjYCMB0vJYUT+9P+dJ7U970/pQ3vT/lK4j3xt0xM9LpNO3t7aRSKdy9cP6JJ54gEokQj8cJh8OF8VOlsbGR7u7uzRO5VnN4RURERGRU+fC6evVqIpEIyWRyh7CbH5NMJolEIqxZswYzGzKmVBR4RURERGRU+Tu7bW1tZLPZUcdms1laW1tJp9NTeod3MhR4x2Bme5nZKjN71My2mdkzZvYTMzu81LWJiIiITJf29vYxw25eNpulo6Mj4IrGT4F3FGa2B/AbYDnwJLAGeAD4O+C/zKyxdNWJiIiITI9MJkMqlSrqmmQySW9vb0AVFUeBd3TnAwcCK939Xe6+zN3DwFnAPODaUhYnIiIiMh0SiUTR83HdnUQiEVBFxdEuDaM7HtgKrBx0/BrgPOCdZrbI3R+f9soG0QrZ8qb3p3zpvSlven/Km96f8jXV701fX9+0XjfVdId3BJabZb0Y+L2777Dpm+d+xPl//Z/uN82liYiIiEyrmpqaab1uqukO78hmAScAzww+YWY1wCH9nz41nUWJiIiITLdYLFb0NmNmRiwWC7Cq8VPgHYG7Z4GfDD5uZiHgW8CuwP3u/shoz9PT01PY/Hkw/SpIREREKkFdXV1h/93xam5upra2tujXGik35bvHTYSmNBTBzA4G0sAngL8Ay0pakIiIiMg0icfjhEKhcY0NhULE4/GAKxo/K5cOGOXMzGYD5wIXAXOBx4GPu/s9Y1y3vqGhoUF3ckVERKSS5TutrVmzhtbW1lH34w2FQnR2dtLS0jKl7YX7Wwt3u3vR28LqDu8YzGwx8CugHdgZ+AbwprHCroiIiEi1yM/fbWlpIZVKEY1GhwRZMyMajdLV1TXlYXeyNId3FGb2N8B/AQuBDcCp7r6htFWJiIiITL986A2Hw4TDYXp7e0kkEvT19VFTU0MsFivM2S2nsAsKvGO5klzY/RlwvLtvK3E9IiIiIiUzMMTW1tayfPnyMceVA01pGIGZzQOOA54mN19XYVdERESkAukO78gagV2Ax4B/GeUnlS+5+3PTVpWIiIiIFEWBd2T79j/W93+M5GpAgVdERESkTCnwjsDd/x0orwkoIiIiIlI0zeEVERERkaqmwCsiIiIiVU2BV0RERESqmgKviIiIiFQ1BV4RERERqWoKvCIiIiJS1bQtmYiIiMx47l5oh5vJZEgkEvT19VFTU0MsFqOurm7IOKkcCrwiIiIyo+VDbDqdpr29nVQqhbsXzpsZkUiEeDxOOBxW6K1AmtIgIiIiM1Y+vK5evZpIJEIymdwh7ObHJJNJIpEIa9aswcyGjJHyVjWB18zuN7PdS12HiIiIVI78nd22tjay2eyoY7PZLK2traTTad3hrTBVE3iBw4A5gw+a2e5mdm0J6hEREZEK0N7ePmbYzctms3R0dARckUy1ig+8ZvYzM7sEcGD/YYbMA5ZNa1EiIiJSETKZDKlUqqhrkskkvb29AVUkQaj4wAs8CIQBA35tZi+Y2d1mdrWZ/QNwFvBkKQsUERGR8pRIJIqej+vuJBKJgCqSIFT8Lg3u/jkAM9sGHAn8DfDW/o8PkPsaP1+yAkVERKRs9fX1Tet1UhoVH3gHmA9k3b0b+GmpixEREZHyV1NTM63XSWlU9JQGM/uRmc3r//RA1x4hIiIiUoRYLFb0jgtmRiwWC6giCUJFB17gT8Ds/j8/bGYvmdl/m9m1ZtZmZm83s11KWaCIiIiUr7q6OiKRSFHXNDc3U1tbG1BFEoRKD7whYHv/n/cHPg78J/B64FzgHuAlM3uwNOWJiIhIuYvH44RCoXGNDYVCxOPxgCuSqVbpgfc0YLf+P38duNvd2939RHevB2qAo/vPiYiIiOzA3QmHw3R2do4ZekOhEJ2dnTQ1NanTWoWp9MD7B+Cd/X/+CLk9dwvc/WV3v8fdr5/2ykRERKTs5dsEt7S0kEqliEajQ+b0mhnRaJSuri5aWloK7YilclT6Lg2XAbea2f3kGk/8g5n9D7DB3bVfiIiIiIwpH3rD4TDhcJje3l4SiQR9fX3U1NQQi8UKc3YVditTRQded/+2mf0C+Dty++62AF8CQmb2GLAh/+Hud5SsUBERESlrA0NsbW0ty5cvH3OcVI5Kn9KAuz/s7lcAGeDd5Ob0vgNYCTwBNAM3la5CERERESmlir7DO1D/IrW89f0fIiIiIjLDVcwdXjO7xMyeNLNXzCxjZhea2eyxrxQRERGRmawiAq+Z/QPwRWAfcnelDwYuBX5UyrpEREREpPxVROAFPgO8AnwS2A+IAN3AB83shFIWJiIiIiLlrVIC78HAj9z9B+7+R3dfR24x2vPAp0tbmoiIiIiUs0oJvHsAvQMPuPsL5NoIN5SkIhERERGpCJUSeAFeG+bY48Be012IiIiIiFSOSgq8w3kV0E4NIiIiIjKiStqH96L+BWr3Ar/u/6ik+kVESmpgS9RMJjOkdWpdXd2QcSIi1aBSAmMXcDhwWP/HqQNPmtlXybUQvg/ocffhpj+IiMxY+RCbTqdpb28nlUrh7oXzZkYkEiEejxMOhyccehWqZabR93xlqIjA6+7NAGZ2EPC2AR+HA7sDZwP5/3NvM7MHgPvcfVkJyhURKSv5f2hXr15NW1sb2Wx22DHJZJJ169bR2dlJS0tL0f9AT1eoFikX+p6vHDbwjalEZvZGdgzBbwV2BdzdQyWubX1DQ0PD+vXqciwipZVOp4lEIsOG3cFCoRCpVIpwODzu5x9PqB74/BMN1SLlQt/z06+xsZHu7u5ud28s9tpKX7SGu2909++7+9nufjS5O75vAlpKW5mISPlob28fV9gFyGazdHR0FPX8+btcY/3Dn3/+1tZW0um0/uGXiqXv+cpS8YF3MM95yN2/V+paRETKQSaTIZVKFXVNMpmkt7d37IEDBB2qRcqNvucrR9UFXhGZWgOnPWUyGVatWsXKlStZtWoVmUxm2HFSXhKJRNHvj7uTSCTGPX66QrVIudD3fGVR4BWREQ1ckBGNRqmvr2fFihVceOGFrFixgvr6eqLRaOHXdAq95amvry/w66YjVIuUE33PVxYFXhEZ1sAFGZFIhGQyOeR/7vmV/ZFIhDVr1ij0lqmamprAr5uOUC1STvQ9X1kUeEVkWFqQUT1isVjR74uZEYvFxj1+OkK1SDnR93xlqfjAa2Y3mZlmgYsEQAsyqkNdXR2RSKSoa5qbm6mtrR33+OkI1SLlRN/zlaXiAy9wMvB3pS5CpNpoQUZ1icfjhELj25o8FAoRj8eLev7pCNUi5UTf85WlGgKviARACzKqh7sTDofp7OwcM/TmN8hvamoq+v0POlSLlBt9z1cOBV4RGZYWZFSP/GLClpYWUqkU0Wh0yK9izYxoNEpXV9eE2wpPR6gWKRf6nq8sO5W6ABEpT1qQUV3yoTccDhMOh+nt7SWRSNDX10dNTQ2xWKzwq9aRwu7A45lMZofrjzvuOPbbbz9aWlpYvHgxHR0dQ3b2MDOam5uJx+OFf/i1yFEq1cAfJPU9X/6s0n/SMLPXgN+7+6GlrmUwM1vf0NDQsH79+lKXIlK0TCZDfX19UXcjzIyNGzdqjloVGrgnc3t7O6lUasg/7CtWrOCKK65g5513BphQqBapNAO/l/U9H6zGxka6u7u73b2x2GsVeAOkwCuVLhqNkkwmixq/du3aACuSUhi4J/NY29Tts88+/Od//ieNjUX/eyQiMqrJBF5NaRCREcXjcdatWzeurclm8oKM0X7VH4vFqKurGzKukhSzJ/NTTz3FO9/5TlKpFOFweHoKFBEZgwKviAxr4IKM1tbWUYPO4AUZlRjqJmo8v+qPRCLE43HC4XDF/veZyJ7MCrwiUi60S4OIDGs6VvZXupnSfll7MotIpdMdXhEZ0VSs7K9mE2m/vHjx4oq78zmZPZmXL18eUFUiIuOnwCsioxoYYmtra0cMMDMt7ObNhF/1a09mEal01RB4HweeCOrJzWwecAHwCWAh8AfgFqDd3bcH9bpjqfZFMiKlMt6/W/nzE/1VfyVt3aY9mUWk0lV84HX3xUE9t5ntBNwGvA9YD3QBbwUuAd5pZh/wEkzGmymLZESm23j/bn3zm9+ktrZ2xvyqPxaLFT332MyIxWIBViUy8wz8O5j/80iPEz2XfwyFQsydO3fqv4gSqfjAG7CTyYXd7wGn5MOtma0BTgE+Bdw0nQWNZz/M/CKZdevW0dnZOSMXE4kUq5i/WzfddBNf+tKXZsyv+uvq6gqL8sarubm5ou5iV7OpCkkTGTsdrzWTvq7xjh/u89GuG27szjvvzCGHHMLuu+9ONVDgHd0/AVngvEF3cjvIBd5WpjnwzpRFMiLTrZi/W/nAWs2/6h/4j+KsWbMmvCfz9u3befXVV0sSYMptzHS+5kSeq9jnHG9NxdQzVXVUa80w/HqJ/LGRHkcbO9yxrVu3su+++/LKK68Mub5SKfCOwMx2Aw4H7nP3Jweec/eHzewp4Agzm+Pu26aztpmwSEYqU7nfMRlpzJ577kkoFBr3362HH34YmPyv+p988sn/396dx8lVlfkf/zx0QiArZBlWSSAJYYeQgOIIaaHTBBUUHVxGZAImEP0hEXQUGlQUSAbHyCoKRPYRRxARVGA6hCSARCQJmySkY8CwJCGNCdkXOs/vj3Orqa6u6lR1V9etuv19v173dbvuPafqVJ2uqueeOgsbN27stOdVjDTdunXj0EMPbdeczNu2bWPBggVs29ZyuEN7v+g7EnDEFZyUqsxtpYXiB0nZgqVcadu677bStKfM+d5fOZS5o+XoTCtWrCjZY5WKAt7cjgCqgL/lOL8QqAYGA4tLVKZEDpIpZUtHXAFVOacp1nMvpCyF3me+ZcqVtq377t+/P4MGDSrovVVfX8+yZcs69FP/xo0befbZZ9tV5vbURz5pM4+lvmBXrlzJSSedxPjx4xkyZAhTp05tNeewmTF27Fjq6uoYM2YM77//PnPmzOHdd99t1xd9KYOTQtN0pBz5BjKdFYyKdFUKeHMbEO1X5ji/Otr3b+tOXnzxxbw/cCZOnMgtt9zS4ti5557LrbfeyuTJk7n22ms7PEhm8uTJXH/99VnT1dXV8dnPfrbFsTPPPJNFixbl9Tg//elPOeGEE1ocGzduHI2NjXnlv/POOzn44IObywzw4Q9/OK+8AA8++CADBw5svt3Y2MhnPvOZvPM/8cQTLR578eLFTJo0Ka+8/fv35957722Rf+7cuVx++eV55R86dCjXXXddi/yPPPIIN910U175R48eTV1dXYv/jV//+tfcd999eeU/8cQTOffcc5tvuzu33norM2fOzCv/6aef3vy/k/p/nzZtGgsWLMgr/4QJE6ipqWmR/+KLL+a1117LK/8ll1zCMccc0+K9ds4557B69eo2cgWTJ09m5MiRBb23mpqauPnmm7nqqqva/VP/lVdeydSpU4EP/vdS5X/11Vc577zz8irLgAED+P3vf98i2Hnqqaf4zne+k1f+gw46iLvuuqtF/gceeIApU6a0SHfcccfx0EMP5TUn87p166itrWXu3Lk7fPzTTz+dSy+9tMWxq666it/97nd5lX/ixImtXqsLL7yQJ598Mq/8cX/u3X333c2feymjR4/OKy+Ez4lBgwY13161ahWnnHJK3vmfe+65FrcXLlzIV77ylbzyDhw4kEcffbTFsTlz5nDRRRfllf+ggw7innvuaXEs2/9eLscffzzXXHNA+zm7AAAe20lEQVRNi2M333wzt956a1759b/Xsf+9t956i7333rv59ttvv80+++yTd/7Mz9t58+YV9PiFUsCbW6qTXa7uChuifUlew969ewMdnw+zT58+OdNs2bKF9evX4+7NW75dJwDWrFnT/DNIe/IvX76cnj17ttn61JY333yT9evXN9/OJ9jJzJ/unXfeyTvv9u3bW6V/77338s7f1NTUqrybN2/OO7+7s337diAELGbGTjvlv5DizjvvTL9+/VoEPT169Mg7f79+/Rg8eHCL/IWM7h04cCBDhgxpvm1m7LzzznnnHzRoEPvtt1+LY1VVVXnlbe9765ZbbuHCCy9s10/9jY2NLb6UM9+XPXv2zLscZsYuu+zS4li3boV9LOXzv/LMM89w8MEHM3HiRCZNmpR1TuYtW7bQ2NjItGnT8gp2RURKJTEBr5ndB9S7+y3R7RGEbgmz3H1VO+4y1eks17d26tt4Y1t3MmjQIJYvX95WkrykArmODpJZt25dm2n22GOPFse6d++e92PsueeeDB06FPigpaqQL96hQ4dyyCGHNN8uNOAdPnx4c0uHu7NqVWHVnj6/KsD777+fd96qqiqGDBnSosxvvPFG3vm7d+/O3nvv3SJ/IXXdrVs3evfu3eJiId+AD0LAvm3bthb5C3n+GzZsaA74U/exZUv+XdsbGxtZunRpu/O/+eabLFq0qEXAnW/52/veamxs5NRTT2XOnDkF/dS/adMmTj311BatMCtXrmzROl3Ixdr27dtZvXp1i/wbNmxoI0fr/OvXr2/x2uUaqNLY2MjUqVO5+uqrqa2t5cADD+Swww5j7NixbN68mTVr1mBmFTcLhYgknxUaVJSraBDZqe7+rJkNAP4OGKGF9iR3f6nA+/s4MBOY7u4Ts5yfAZwE7OXuWXt3m9m8o48++uh58+YV+Gxya2hoYMSIEQUFg2bG4sWLy7YPb6l0Rp/VUvX7LdZjlFv/4fY+93zT55O/X79+HHrooe1+by1btox99tmnOWBs66f+rVu38sILL7BmzZoOlbmQPs7FTttW+vb2Q4bi9NONu39snIO1SlnmuAdUSedbsWIFPXr04Mgjj2zRZSZuo0aNYv78+fPdfVSheRPTwgv0AVJNqZ8DXgdGAz8ErgJOK/D+Up1oDs9xfhjQmCvY7SyaD7P9NJAjWYoZ3G/fvr3d7619992XpqYmtm7dSo8ePbL+1N/U1MTmzZvZtGlTi18C4rxAKpcLm0KOFfL8CylTPvfX0eC+VBckxSxzPo+RLX1HgvtSXgiUSzniuHgptBxJkKSAdxkwlLD0778Bd7n7+xYWiXi60Dtz9+VmthAYaWYD3P3d1Lmou8Rg4N6ilLxA7R0kI5IknfHh3N731o5WJKqqqqJXr1706tWraGVNknIIyvPJX4n3E9druKN0hTxeoY+bb7njLHM+aYtV5vY+vz333LPVuUqWpID3NuBnZvYn4ONAanh9NyD/ESAt/QK4jtBCPAnAzLoDP43O/6zdpW0nd2/XIBl3rbQm0ha9t+KT9JalriQJgbvSBKkL9aRITMDr7j+OPixPBr7t7kujU8cC/2jn3d4EfAE4z8yOBF4AxgAHATe6e8Etxx1lFia5L2SQjL6QRXZM7y2RjtPFi5SrxAxay8XMvgP0cPcr2pm/J3AF8HlgIPAa8HNCwNvmi9cZg9ZS0r9o2xokoy9kkcLovSUiUp46MmgtUQGvme1E6MdbBSxx9/znVeqc8nRawCsiIiLSlXQk4M1/ZvoyZ2bHEKYiWwS8ArxrZjeaWZsroYmIiIhIsiUm4AVuAV4GDgP2B74KHAnMN7M92sooIiIiIsmVpIB3OHCRuy9093+4+/3ufjzwFHBDzGUTERERkZgkKeCdC2Rryb0c+ERpiyIiIiIi5aKipyUzs8eB54EFhHl4rzWz0939jbRkA4B3s+UXERERkeSr6IAXeAY4ijBX7t7RsQYzewCYT5it4SxgcjzFExEREZG4VXTA6+6Xpf42s4HAyGg7CjiH0K/XgSnAg3GUUURERETiVdEBbzp3bwTqow0AM9uVMFPDkXGVS0RERETilZiA18x6AD8CTgN6AC8B97j7fYQBbSIiIiLSBSUm4AV+AnwKuAnYAhwC3GZmXwS+EPeqayIiUhrpyz43NDS0Wh56+PDhrdKJSLIlKeA9A/isu/85dcDMfgD8CbgYuDKugomISGmkgthZs2YxZcoUZsyYgbs3nzczampqqKuro7q6ulOCXgXcIuUnSQHvLsA76QfcfaWZXQjcjgJeEZFESwWQt99+OxMnTqSpqSlrmvr6embOnMn06dMZP358UQPPcgi4RaS1JC08MZuwnHCmN8m+IIWIiCRIKtDMFeyma2pqYsKECcyaNavowe7tt99OTU0N9fX1LYLdVJr6+npqamq44447MLNWaUSk+JIU8F4MTDKzW8zsEDPbycx2IczB+7eYyyYiIiUwZcqUHQa7KU1NTUydOrVojx13wC0iuSUm4HX3hcAY4AjgZWAzsB74Ilp4QkQk8RoaGpgxY0ZBeerr61myZEnRyhBnwC0iuSUm4AVw9xfd/SPAQYRA9xRguLtrWjIRkYR79NFHC+4e4O48+uijRXn8cgi4RSS7igl4zexyM1tuZlvNrMHMLjOz7tnSuvtid3/A3evdfW2pyyoiIqW3dm37Pu7bmy9T3AG3iORWEQGvmZ0DfJ8w+KwbMBT4IXB/nOUSEZHy0bdv35LmyxR3wC0iuVVEwAtMArYCXwb2BWqA+cCnzOyMOAsmIiLlYdy4cQUPADMzxo0bV5THjzvgFpHcKiXgHQrc7+73uvvb7j4TGAusBs6Kt2giIlIOhg8fTk1NTUF5xo4dy7Bhw4ry+HEH3CKSW6UsPLE70KJXv7uvMbM/Elp7RUQqnlbo6ri6ujpmzpyZ10wJVVVV1NXVFe2xUwF3fX193nmKGXCLSG6VEvACbM9ybBkwoNQFEREpNq3Q1XHuTnV1NdOnT2fChAltBr1VVVVMnz6dMWPGFPW1jDPgLme6mJO4VUqXhlzeB7LO1CAiUim0QldxpF6T8ePHM2PGDGpra1sFT2ZGbW0tjz/+eKcsK5wKuKuqqtpMmxlwJ1n6xVxtbS0jRozgggsu4LLLLuOCCy5gxIgR1NbWNi/CkfTXQ+JhlfCPZWbbgSZgEfAX4Nlo+xxQ5+5tf7LExMzmHX300UfPmzcv7qKISJmbNWsWNTU1ebcMzpgxg+rq6s4vWAVKD2KXLFnSqjUx1YWgM1oT04O7qVOntrp4MTPGjh1LXV1d0VuXy1H6xdyOVqBLXQQU+0JEkmPUqFHMnz9/vruPKjRvpQS89cBIoH90KLPQ1wDPAwuAhe6erftDySngFZF81dbWFtT3s7a2lscee6wTSyTtFWfAXY50MSfFkviAN8XMDgBGp20jgX7R6dQT2UJYWniBu59X8kKmUcArIvloaGhgxIgRBf2Ua2YsXry4YgY8qQ9n16WLOSmWjgS8lTRoDXdfCiwFfpM6ZmYH0jIIPirajwJiDXhFRPLRkRW6zj///E4qVfFoQF7X1ZHllivlYk4qQ6UPWkstI/wrd7/I3U8gtPgeBoyPt2QiIvlJ8gpdGpDXtWm5ZSkXFR/wZvLgFXe/O+6yiIjkI8krdKVadnc0YAmgqamJCRMmNI/Wl8qX5Is5qSyJC3hFRCpN0lfomjJlSl4DliAEvVOnTu3kEkmpJPliTiqLAl4RkZjFvSRuZ+pIH06pfEm/mJPKoYBXRKQM1NXV7XCxgpRKWqFLfTi7tiRfzEllUcArIomUHmQ1NDRwww03cNVVV3HDDTfQ0NCQNV1ckrxCl/pwSlIv5qSyKOAVkcSptKVM414StzOpD2fXluSLOaksFbXwRKXRwhMipVfJS5kmcYWurrCohrRNyy1LsXSZldYqjQJekXhoKdPyopW2JIkXc1J6XWalNRGRfLRnGiwFvJ2nrq6OmTNn5n0Boj6cyZMexA4bNiznCoEKdqWzqA+viCSKpsEqL+rDKSLlQAGviCSKpsEqL0kekCcilUNdGkQkUTQNVvlJBb3V1dVUV1erD6eIlJwCXhFJFE2DVZ7Uh7P8pF9cNDQ0tLoIGT58eKt0IpVKAa+IJEpqKdNCp8HSUqbSlaRPFTZlyhRmzJjRaqqwmpoa6urqqK6uVtArFU99eEUkUbSUqUjb0ueqrqmpaTUvbipNfX09NTU13HHHHWWxQItIRyjgFZHE0VKmIrmlWnZ3tDALhGn7JkyY0LwqoUilUsArIomiabBEdqw9c1WLVDIFvCKSKJoGS6RtmqtauiINWhORxNE0WCK5dWSu6lyza4iUOwW8CTFqVFhWet68eTGXRLJR/ZRevtNg1dTUsGbNGtVNmdJ7p/iKOVe16qd8qW5aUpcGEenS1qxZE3cRREpKc1VLV6SAV0REpAtJzVVdCM1VLZVOAa+IiEgXormqpStSwCsiItLFaK5q6WoU8IqIiHQhmqtauiLTP3DnMbN3d9111/4HH3xwpz/WwoULASjFY0nhVD/lS3VT3lQ/nWvdunWsWLGCdevWtTrXp08f9tprL3r37p0zv+qnfCWxbhYuXMimTZv+6e4DCs2rgLcTmdlrQF/g9ZiLIiIi0qxPnz69DjjggGFVURPvli1bWLt2LU1NTVRVVdG3b1969OgBQFNTU9PSpUuXrFu3bkOshRaBIcBad9+/0IwKeEVEREQk0dSHV0REREQSTQGviIiIiCSaAl4RERERSTQFvCIiIiKSaAp4RURERCTRFPBWODPraWZXmNkSM9tkZovN7Adm1j3usnUlZjbAzG4ws9fMbIuZNZrZg2Y2MiNdNzO7yMxeMbONZva6mV1jZn3iKntXZGbnm5mb2Tczjqt+YmJmp5rZ02b2npmtNbPZZnZSRhrVT4mZ2V5mNt3M3jazrWb2hpndaGa7Z6TTd1EJmdnXo8+w3bKcK+h9YmbjzWyBmW0ws7fM7JdmtlfnP4vS0rRkFczMugF/AE4G5kXbUcCxwCPAJ10V3OmiD/55wP7AM8BLwAhgDLAROMHd50VppwNfBRYBc4BhwInA88Bx7r655E+gizGzwcDLQG/gQne/Nu2c6icGZjYJ+DmwAvgjsAfhcw3C6673TwzMbADhs20wMIMwp/xowvfM34CPuPt6fReVlplVAc8CRwO7u/uajPN5v0/M7DLgCuAN4FFgL+AU4G1glLuv6vQnVCrurq1CN2A84MBdRBcv0fE7ouNnxV3GrrABV0ev95UZx78ZHZ8b3a6Obj8O7JyW7ofR8e/H/Vy6wgY8Fr3eDnwz7bjqJ576+BCwFZgP7JZ2/JTodX9Y9RNb3fx39Np+K+2YAbdFxy+Ojum7qPPrwggB7leBJ9M+w3bLSJf3+wQYCrxPaADom3b87CjtbXE/76K+hnEXQFsHKg/+Gv2z7pVxfET0zzon7jJ2hQ1oILTk7ppx3IDlUV3sB9wX/X1MRrre0Rf+srifS9I34JyoDh7MEvCqfuKpkx9Hr/uHs5ybDbym+omtbp4HmoBdMo4Pj+riT9FtfRd1fl30Tgty07fMgDfv9wkfXNCckZHWgLeAzUDPuJ97sTb14a1QUV+ckcACd1+efs7dXwVWAh8xsx5xlK+rMDMjLHW4yN03pZ/z8MnxZnRzX+AEYJW7/zUj3XpC69aHzKzg5RIlP2a2NzCN8IXwYJYkqp941AKvu/tfMk+4+xj/YAlR1U/pGSEgypTql7tB30Ulswk4I217JUe6Qt4n1cB2QleG9LROaEXuAXy4SOWPnQLeynUEUEXoR5XNQsKH0uCSlahr2onw4XNB5gkz6wsclLoJ/Au5P6QWRvvhxS6gNLuJ8OX9jcwTZrYPqp+SM7NewOHA82a2UzRw7YpoGxddUKp+4jOb8D3T/PlmZjsBF0c3Z6LvopJw9yZ3vz+1Aa361rbjfXIk8Ia7r8sjbcXrFncBpN0GRPuVOc6vjvb9S1CWLsvdm8jSWhgNKvgF4Wekl4DUB4rqKwZm9kXg08DZ7r4yiqPS6f0Uj70IF42bgVnA8Rnnnzazz6D6icvlwHHA1WZ2KiEIOoYwIO0R4JfAuCit6iZ+eb9PogaZ7vmkLVLZYqcW3srVN9pvyXF+Q7TXRU2JmdlQwpf3lwhf5Oeh+oqNmQ0ErgdmuPsdOZKpfuKRmtrqC4SZGWoJF4mDgTuBfyUMfFL9xCM1mBDgY8BEQrAL8BrhlyvVTfkopC66XL0p4K1c26L9rjnO7xztN5agLAKYWXczu4TQovsxYBlwors/g+orTjcAvYBz20ij+olH6nXdDpzu7vXuvsHdlxGCqzeBTxJ+MgfVT6ndT3jf/A9hAFovQgvvTODrhEFPeu+Uj0LqosvVmwLeyvVOtG816XQk9dPG2yUoS5dnZkOAucAUwgfFjcBhUbALqq9YmNkngC8C33P319pIqvqJx/po/3d3b9Hv0N23EebkBTgw2qt+SsTMjuKDeXXPcvfF7r7R3Z8jdA9aDnwNSM0Bq7qJXyGfY/8kzMDRZepNAW/lWhTtD89xfhjQ6O4rSlSeLisa/f8UYY7E54HR7v6NjIEAywhXym3V13ZyDzaQ9jk22k+LViVyM3Pg9uj4NdHtc1D9xOH1aL8+x/nUz6p6/5TeiGg/2923p5+IRvz/hfBzd2oRA30XxS/v90l0Qfl3YP9o8Gi2tBDm6E0EBbwVKpr+ZSEwMloNp5mZjSD0gauPo2xd0DRgH+BPhJWHns9MEA1umw3sYWYtPoyipSGPBf7i7mtLUN6uZC5wXZYt9d6YE91+GtVPybn7e4Qg9aBoEE2m0dH+RVQ/pZa6YM+1xGyq//Ur6LuoLLTje+ZxQnehEzPSVgEnEVp3c82+UXninghYW/s3wlQxDvwi7Vh3ws+ADvxr3GVM+gb0JMyP+A7QZwdpT4vq5Y/ATtExA26Ojv973M+nq2x8sDJU+sITqp946iK1IuF0oCrt+OnR8YdUP7HUS19gLWHg2kczzo0lLDQxJ7qt76LS188ssi88kff7hDAAcTthYOKuaccvjdLWxf08i7lZ9OSkAkXrl88GPkpoyXoBGEOY+/VGd28136gUl5kdT2glfJWMybsz/Mjd/2lm/wt8njCw7c/AKEIr1sPuflpnl1cCMxtP6NZwobtfm3Zc9VNiUWvSHwn9RRcSPsv2JgRVK4Hj3P0fUVrVTwmZ2b8TlgsGmAH8A9gfqAHeA45395f1XVR6ZjaL8Brv7u5rMs7l/T4xs6uB7wBLCS2+wwkLUjxHqN/NJEXcEbe2jm2EFsZpwBuElsZXCBPrW9xl6wobYdGJbMs9Zm5DovTdCFfPSwh93/4OfJ+0Nc+1laTexpPRwqv6ibU+diYsZrCY0KK4HLgN+JDqJ/a6GU1YnfAdwsj+5cDdwLCMdPouKm29zCJLC290rqD3CWHw4d+ielsGXAP0jfs5FntTC6+IiIiIJJoGrYmIiIhIoingFREREZFEU8ArIiIiIommgFdEREREEk0Br4iIiIgkmgJeEREREUk0BbwiIiIikmgKeEVEREQk0RTwioiIiEiiKeAVERERkURTwCsiIiIiiaaAV0S6JDOrNjPPsW0xs1fM7CIz0+dkO5jZoWa2ycyGZjl3gJndaGavmtlGM1tjZi+b2TQzOyjH/V3eRn1tMLMnzexT7SxrbzNbZWZj25NfRMpft7gLICISs7eA+9NuG7AHcDIwDTgCGF/6YlW8G4H73P3v6QfN7D+AW4CdgWeB2UBP4HDgIuAbZvYNd785x/3+BZibdrsbcAAwFnjYzM529zsKKai7rzeznwA3mtnh7r61kPwiUv7M3eMug4hIyZlZNfAEMNvdq7Oc3wdYAAwCRrr78yUtYAUzs08DDwKHu/vLacc/CTwMNAJfcPcnMvLVEi4+egEnuPvTaecuB34A/NDdL8/ymLXAY8C7wJ7u/n6BZe4DvAlMcferC8krIuVPP9WJiGTh7m8Bv41unpArnZntWpoSdZ5OeA5XAE9kBLs9gFuBJuCTmcEugLv/HzCJ8N10cSEPGOVdBAwADim0wO6+DrgLuMTM+hWaX0TKmwJeEZHcNkT7XtCi3+9lZvZ5M3sVSG+F3MXMLjWzhWa22czeNrPpZrZf5h2b2elm9rSZrTaz9Wb2rJmd2Z50ZnZHVK6jsuR3M3s+7fb46NiZZna+mS0DfpV2fjcz+7GZLY36Mr9uZteY2YB8XjAzO4HQPeFXGafOAPYidHP4axt3cT/wS+Af+Txehhb1FZVneFQHr0d18oaZ/dbMjs+S/zdAP+BL7XhsESljCnhFRHIbHe0bMo5/mhDQLQXug+YWzP8DrgTWA3cC84D/AF40syNSmc1sMvAA8C9R/oeBwcDdZva1QtO10wXAfwPzgUeixxsA/Bn4T+AN4HZgCfBNYIGZ7ZvH/X4i2j+Zcbw22j/cVmZ33+ruE9z9/HyeRErUJWEE4IQyY2YfAv4KnAMsBO4AXgZOA2ZFwXm6Z4HNwOcLeWwRKX8atCYikiaalWE/QkA4BlgB/DEj2Wjg39z9t2nHLgWOB+rcfWra/VUDM4CfA/8aHf5PYBVwpLtvjNLtBrwEXBKlLSRdexxF6CebPgDsGuBg4Mvunt7qeyZwN3A18OUd3G8tsBpYnHE8NfvCwg6UuRUz2xk4FPgvoDfwkLuvik5/idBi22Igm5mdDdxGCGznpI67+xYzew74qJl1d/dtxSyriMRHAa+IdHVjzCzX6N11wJfcfVPG8afTg90oSP46oWXxv9ITuvssM3sM+ISZ7enuK4Ddge1Af2BjlG6NmZ1KmCEiJd907fGb9GDXzPoTAsTH04Pd6DHvMbNLgE+bWbcdDAg7EFjirUdE7x7t16UfNLPemcfSfNzdZ2Uc+4GZ/SBH+leB9JbveYQLg//NSPdStO+d5T6WAR8DhtC6ZV9EKpQCXhHp6jKnJQPYCrwG/Nbd38mS58WM2wcSBkutA64xs8z0qeB0BKHF+BZCN4GXzewhYBZhkFfmTBD5pmuPzOdwLOE7YZCZXZslfXdC39h9yNG/Nhr81gtYk+X0xmjfM+P4VuC6jGPVwJE5yp05LVnqvl8CHnD3LamD7v448LiZDYz67O5PmMLs1Bz3DfDPaL8HCnhFEkMBr4h0dUvc/ZsF5smcpzU1oGsIMLmNfH0A3P1CM3sG+CphMNdXAMzsJeBSd3+4kHS5mFlbn/G5nsMR0dbmc8ihf7Rfn+XcW9H9DueDFlaiOW9bvP5mdie5A95Hs01Llo2Z7U24aPgEYX7l9whdKv5M6LqRzXvRvq3nKSIVRoPWREQ6LjU7wIPubm1sf0hlcPffuPvJwG6EFs0fE1off2tmBxSaLoe8ZlbIeA7X7uA5vNzGfaRacftmOfdUtB+XR1mOzrfQO/A/hGD3MmBvd9/N3Y8DftZGnlSgu7pIZRCRMqCAV0Sk4xYBW4AjLctSxGb2XTO718z6WlhW90EzOwPCQCl3n+3u3wV+ROg6cGy+6aKHSLXWZs6n+5ECnsML0X5ktpNm9pNo+rNW/TXSrAE28UFLb7p7gG3Al7NN05b2OB8HDsuvyLlFfYPHAHPdfYq7L0873daFwm7RflUbaUSkwijgFRHpIHffDPya0Ef0/6WfM7PTgKsIq3+tJawy9ingYjPrm5bOgFHRzWUFpEvffzYtXW/g+wU8h9cIy/yOicqc/hy+BnwrJMu9PGd07iXgQ5mBv7svIyzV3JOwBPDQzPxmdhLhddyeb7nb0BTdzx7RlHGpxxhMmDoul/0IQfubRSiDiJQJ9eEVESmObwMfBa43s88TWn2HEbohvAucDeDua83seuBCYJGZzQHWAscRWjb/5O5/Bsg3HWHBhO8B3zaz0YRg7UTC4LJsg+5ymUjoevA7M3s8yn8EoSX571FZdmRWlP4wWg+MuwzYExgPLDSzpwgDw3YiBPEjCXPhfg+4uYByt+Lum8zsXuBM4Hkzmw3sC4wlzNpwIHCymX3L3acBmFkVYcq52emD30Sk8qmFV0SkCNy9kdCF4HpCS+9ZhJ/OfwmMdPfX05J/lxDUrQE+Q1icojuhq8LnCk3n7osJMw88F5XhROD3hBbivOeSdfcGQsB3F2Ge3q8Q+gFfCxzr7tlmX8j0u2jfajlmd29y97OBk4E/EJYAPpuwEMQ6wtRuH6P1vMftNYlQ9j6E5zIQOMfdzyL0he5HuIBIGUmYquyRIj2+iJQJa+PXKRERkYJFSxmvdfdWQW85M7OfElq5B7v7P3eUXkQqh1p4RUSk2L4HHG9muaYWKztRn+ezgWsU7Iokj1p4RUSk6MzsYWC9u38p7rLkw8y+BVwEHOjuG3aUXkQqiwJeEREpOjMbQpix4Vh3XxhvadoWrRC3FJjs7r+JuzwiUnwKeEVEREQk0dSHV0REREQSTQGviIiIiCSaAl4RERERSTQFvCIiIiKSaAp4RURERCTRFPCKiIiISKIp4BURERGRRFPAKyIiIiKJpoBXRERERBJNAa+IiIiIJJoCXhERERFJNAW8IiIiIpJoCnhFREREJNH+PxSARnLrVnm7AAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 360x360 with 2 Axes>" ] }, "metadata": { "image/png": { "height": 351, "width": 350 } }, "output_type": "display_data" } ], "source": [ "for comp, value in v.items(): # iteration for different compositions\n", " for key, value in au_eos.items(): # iteration for different gold scales\n", " # set pressure scale to use\n", " p = au_eos[key].cal_pst(v_std[comp])\n", " # set equation to use\n", " model = fit_model[key]\n", " params = model.make_params(v0=v0[comp], k0=k0[comp], k0p=k0p[comp])\n", " params['v0'].vary = False\n", " params['k0p'].vary = False\n", " # perform fitting\n", " fitresult = model.fit(unp.nominal_values(p), params, v=unp.nominal_values(v[comp]))\n", " print('***'+key+' '+comp)\n", " print(fitresult.fit_report())\n", " eos.plot.static_fit_result(fitresult, title=key)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 0, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "nav_menu": {}, "toc": { "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 6, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
Dima806/udacity-mlnd-capstone
capstone-step3-final.ipynb
1
165139
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Udacity MLND Capstone Project\n", "## \"Determination of students’ interaction patterns with an intelligent tutoring system and study of their correlation with successful learning\"\n", "### Step 3 (comparison of learning rates between clusters)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import time\n", "from scipy import stats\n", "from scipy.optimize import minimize" ] }, { "cell_type": "code", "execution_count": 2, "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>learning_parameter</th>\n", " <th>difficulty_parameter</th>\n", " <th>number of attempts</th>\n", " <th>number of incorrect attempts</th>\n", " <th>cluster_index</th>\n", " <th>frac_incorrect_atts</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.172964</td>\n", " <td>0.577597</td>\n", " <td>303</td>\n", " <td>146.0</td>\n", " <td>1</td>\n", " <td>0.481848</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-0.011161</td>\n", " <td>0.623980</td>\n", " <td>295</td>\n", " <td>187.0</td>\n", " <td>5</td>\n", " <td>0.633898</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-0.084896</td>\n", " <td>0.459276</td>\n", " <td>529</td>\n", " <td>269.0</td>\n", " <td>6</td>\n", " <td>0.508507</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.044947</td>\n", " <td>0.459728</td>\n", " <td>1286</td>\n", " <td>556.0</td>\n", " <td>2</td>\n", " <td>0.432348</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.066242</td>\n", " <td>0.486793</td>\n", " <td>821</td>\n", " <td>369.0</td>\n", " <td>1</td>\n", " <td>0.449452</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " learning_parameter difficulty_parameter number of attempts \\\n", "0 0.172964 0.577597 303 \n", "1 -0.011161 0.623980 295 \n", "2 -0.084896 0.459276 529 \n", "3 0.044947 0.459728 1286 \n", "4 0.066242 0.486793 821 \n", "\n", " number of incorrect attempts cluster_index frac_incorrect_atts \n", "0 146.0 1 0.481848 \n", "1 187.0 5 0.633898 \n", "2 269.0 6 0.508507 \n", "3 556.0 2 0.432348 \n", "4 369.0 1 0.449452 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stud_learning = pd.read_csv('student_learning_final.csv')\n", "stud_learning.drop(['Unnamed: 0'], axis=1, inplace=True)\n", "cluster_index = pd.read_csv(\"cluster_index.csv\", header=None)\n", "stud_learning['cluster_index'] = cluster_index[1]\n", "stud_learning['frac_incorrect_atts'] = stud_learning['number of incorrect attempts'] / stud_learning['number of attempts']\n", "stud_learning.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['learning_parameter', 'difficulty_parameter', 'number of attempts',\n", " 'number of incorrect attempts', 'cluster_index', 'frac_incorrect_atts'],\n", " dtype='object')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stud_learning.columns" ] }, { "cell_type": "code", "execution_count": 4, "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>num_sess</th>\n", " <th>num_days</th>\n", " <th>num_probs</th>\n", " <th>num_atts</th>\n", " <th>num_hints</th>\n", " <th>frac_corr_atts</th>\n", " <th>frac_3s_atts</th>\n", " <th>frac_1s_hints</th>\n", " <th>time_atts</th>\n", " <th>time_hints</th>\n", " <th>max_probl_views</th>\n", " <th>max_atts</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>89</td>\n", " <td>9</td>\n", " <td>79</td>\n", " <td>303</td>\n", " <td>213</td>\n", " <td>0.518152</td>\n", " <td>0.184818</td>\n", " <td>0.286385</td>\n", " <td>9577.000</td>\n", " <td>3660.999</td>\n", " <td>1.101266</td>\n", " <td>3.835443</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>86</td>\n", " <td>7</td>\n", " <td>59</td>\n", " <td>295</td>\n", " <td>111</td>\n", " <td>0.366102</td>\n", " <td>0.071186</td>\n", " <td>0.063063</td>\n", " <td>10409.000</td>\n", " <td>2570.000</td>\n", " <td>1.610169</td>\n", " <td>5.000000</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>181</td>\n", " <td>10</td>\n", " <td>150</td>\n", " <td>529</td>\n", " <td>180</td>\n", " <td>0.491493</td>\n", " <td>0.102079</td>\n", " <td>0.077778</td>\n", " <td>14850.000</td>\n", " <td>2295.000</td>\n", " <td>1.240000</td>\n", " <td>3.526667</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>457</td>\n", " <td>14</td>\n", " <td>215</td>\n", " <td>1288</td>\n", " <td>687</td>\n", " <td>0.566770</td>\n", " <td>0.148292</td>\n", " <td>0.066958</td>\n", " <td>25290.001</td>\n", " <td>7743.000</td>\n", " <td>2.148837</td>\n", " <td>5.990698</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>267</td>\n", " <td>13</td>\n", " <td>166</td>\n", " <td>821</td>\n", " <td>602</td>\n", " <td>0.550548</td>\n", " <td>0.068210</td>\n", " <td>0.267442</td>\n", " <td>20504.667</td>\n", " <td>5347.334</td>\n", " <td>1.660606</td>\n", " <td>4.945783</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " num_sess num_days num_probs num_atts num_hints frac_corr_atts \\\n", "0 89 9 79 303 213 0.518152 \n", "1 86 7 59 295 111 0.366102 \n", "2 181 10 150 529 180 0.491493 \n", "3 457 14 215 1288 687 0.566770 \n", "4 267 13 166 821 602 0.550548 \n", "\n", " frac_3s_atts frac_1s_hints time_atts time_hints max_probl_views \\\n", "0 0.184818 0.286385 9577.000 3660.999 1.101266 \n", "1 0.071186 0.063063 10409.000 2570.000 1.610169 \n", "2 0.102079 0.077778 14850.000 2295.000 1.240000 \n", "3 0.148292 0.066958 25290.001 7743.000 2.148837 \n", "4 0.068210 0.267442 20504.667 5347.334 1.660606 \n", "\n", " max_atts \n", "0 3.835443 \n", "1 5.000000 \n", "2 3.526667 \n", "3 5.990698 \n", "4 4.945783 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stud_data = pd.read_hdf('stud_data.hdf','test')\n", "stud_data.head()" ] }, { "cell_type": "code", "execution_count": 5, "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>num_sess</th>\n", " <th>num_days</th>\n", " <th>num_probs</th>\n", " <th>num_atts</th>\n", " <th>num_hints</th>\n", " <th>frac_corr_atts</th>\n", " <th>frac_3s_atts</th>\n", " <th>frac_1s_hints</th>\n", " <th>time_atts</th>\n", " <th>time_hints</th>\n", " <th>max_probl_views</th>\n", " <th>max_atts</th>\n", " <th>learning_parameter</th>\n", " <th>difficulty_parameter</th>\n", " <th>number of attempts</th>\n", " <th>number of incorrect attempts</th>\n", " <th>cluster_index</th>\n", " <th>frac_incorrect_atts</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>89</td>\n", " <td>9</td>\n", " <td>79</td>\n", " <td>303</td>\n", " <td>213</td>\n", " <td>0.518152</td>\n", " <td>0.184818</td>\n", " <td>0.286385</td>\n", " <td>9577.000</td>\n", " <td>3660.999</td>\n", " <td>1.101266</td>\n", " <td>3.835443</td>\n", " <td>0.172964</td>\n", " <td>0.577597</td>\n", " <td>303</td>\n", " <td>146.0</td>\n", " <td>1</td>\n", " <td>0.481848</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>86</td>\n", " <td>7</td>\n", " <td>59</td>\n", " <td>295</td>\n", " <td>111</td>\n", " <td>0.366102</td>\n", " <td>0.071186</td>\n", " <td>0.063063</td>\n", " <td>10409.000</td>\n", " <td>2570.000</td>\n", " <td>1.610169</td>\n", " <td>5.000000</td>\n", " <td>-0.011161</td>\n", " <td>0.623980</td>\n", " <td>295</td>\n", " <td>187.0</td>\n", " <td>5</td>\n", " <td>0.633898</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>181</td>\n", " <td>10</td>\n", " <td>150</td>\n", " <td>529</td>\n", " <td>180</td>\n", " <td>0.491493</td>\n", " <td>0.102079</td>\n", " <td>0.077778</td>\n", " <td>14850.000</td>\n", " <td>2295.000</td>\n", " <td>1.240000</td>\n", " <td>3.526667</td>\n", " <td>-0.084896</td>\n", " <td>0.459276</td>\n", " <td>529</td>\n", " <td>269.0</td>\n", " <td>6</td>\n", " <td>0.508507</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>457</td>\n", " <td>14</td>\n", " <td>215</td>\n", " <td>1288</td>\n", " <td>687</td>\n", " <td>0.566770</td>\n", " <td>0.148292</td>\n", " <td>0.066958</td>\n", " <td>25290.001</td>\n", " <td>7743.000</td>\n", " <td>2.148837</td>\n", " <td>5.990698</td>\n", " <td>0.044947</td>\n", " <td>0.459728</td>\n", " <td>1286</td>\n", " <td>556.0</td>\n", " <td>2</td>\n", " <td>0.432348</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>267</td>\n", " <td>13</td>\n", " <td>166</td>\n", " <td>821</td>\n", " <td>602</td>\n", " <td>0.550548</td>\n", " <td>0.068210</td>\n", " <td>0.267442</td>\n", " <td>20504.667</td>\n", " <td>5347.334</td>\n", " <td>1.660606</td>\n", " <td>4.945783</td>\n", " <td>0.066242</td>\n", " <td>0.486793</td>\n", " <td>821</td>\n", " <td>369.0</td>\n", " <td>1</td>\n", " <td>0.449452</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " num_sess num_days num_probs num_atts num_hints frac_corr_atts \\\n", "0 89 9 79 303 213 0.518152 \n", "1 86 7 59 295 111 0.366102 \n", "2 181 10 150 529 180 0.491493 \n", "3 457 14 215 1288 687 0.566770 \n", "4 267 13 166 821 602 0.550548 \n", "\n", " frac_3s_atts frac_1s_hints time_atts time_hints max_probl_views \\\n", "0 0.184818 0.286385 9577.000 3660.999 1.101266 \n", "1 0.071186 0.063063 10409.000 2570.000 1.610169 \n", "2 0.102079 0.077778 14850.000 2295.000 1.240000 \n", "3 0.148292 0.066958 25290.001 7743.000 2.148837 \n", "4 0.068210 0.267442 20504.667 5347.334 1.660606 \n", "\n", " max_atts learning_parameter difficulty_parameter number of attempts \\\n", "0 3.835443 0.172964 0.577597 303 \n", "1 5.000000 -0.011161 0.623980 295 \n", "2 3.526667 -0.084896 0.459276 529 \n", "3 5.990698 0.044947 0.459728 1286 \n", "4 4.945783 0.066242 0.486793 821 \n", "\n", " number of incorrect attempts cluster_index frac_incorrect_atts \n", "0 146.0 1 0.481848 \n", "1 187.0 5 0.633898 \n", "2 269.0 6 0.508507 \n", "3 556.0 2 0.432348 \n", "4 369.0 1 0.449452 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stud_data = stud_data.join(stud_learning)\n", "stud_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Determine what clusters more successful in learning in terms of fraction of correct attempts:" ] }, { "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>num_sess</th>\n", " <th>num_days</th>\n", " <th>num_probs</th>\n", " <th>num_atts</th>\n", " <th>num_hints</th>\n", " <th>frac_corr_atts</th>\n", " <th>frac_3s_atts</th>\n", " <th>frac_1s_hints</th>\n", " <th>time_atts</th>\n", " <th>time_hints</th>\n", " <th>max_probl_views</th>\n", " <th>max_atts</th>\n", " <th>learning_parameter</th>\n", " <th>difficulty_parameter</th>\n", " <th>number of attempts</th>\n", " <th>number of incorrect attempts</th>\n", " <th>frac_incorrect_atts</th>\n", " </tr>\n", " <tr>\n", " <th>cluster_index</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>157381</td>\n", " <td>9790</td>\n", " <td>119787</td>\n", " <td>463356</td>\n", " <td>319334</td>\n", " <td>746.328247</td>\n", " <td>143.134751</td>\n", " <td>320.395430</td>\n", " <td>1.209619e+07</td>\n", " <td>3310766.636</td>\n", " <td>1700.710013</td>\n", " <td>5073.330503</td>\n", " <td>533.615744</td>\n", " <td>590.950677</td>\n", " <td>463202</td>\n", " <td>197852.0</td>\n", " <td>0.427140</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>22030</td>\n", " <td>1535</td>\n", " <td>17766</td>\n", " <td>72336</td>\n", " <td>33803</td>\n", " <td>203.188547</td>\n", " <td>91.172770</td>\n", " <td>13.541033</td>\n", " <td>1.645484e+06</td>\n", " <td>459433.500</td>\n", " <td>466.191313</td>\n", " <td>1403.399037</td>\n", " <td>426.118912</td>\n", " <td>218.872902</td>\n", " <td>71890</td>\n", " <td>36290.0</td>\n", " <td>0.504799</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>16010</td>\n", " <td>1876</td>\n", " <td>14923</td>\n", " <td>33757</td>\n", " <td>2842</td>\n", " <td>646.793540</td>\n", " <td>12.846483</td>\n", " <td>2.963157</td>\n", " <td>1.822630e+06</td>\n", " <td>45694.000</td>\n", " <td>1209.648207</td>\n", " <td>3165.984896</td>\n", " <td>2763.923125</td>\n", " <td>546.419214</td>\n", " <td>33752</td>\n", " <td>12311.0</td>\n", " <td>0.364749</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>36928</td>\n", " <td>5409</td>\n", " <td>32754</td>\n", " <td>125483</td>\n", " <td>52632</td>\n", " <td>1256.070663</td>\n", " <td>41.842531</td>\n", " <td>26.817449</td>\n", " <td>5.753900e+06</td>\n", " <td>1572946.500</td>\n", " <td>2775.185284</td>\n", " <td>9999.657523</td>\n", " <td>1684.809382</td>\n", " <td>1333.332278</td>\n", " <td>125470</td>\n", " <td>63394.0</td>\n", " <td>0.505252</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>121977</td>\n", " <td>12125</td>\n", " <td>108264</td>\n", " <td>372790</td>\n", " <td>134056</td>\n", " <td>1088.841940</td>\n", " <td>61.644813</td>\n", " <td>46.097252</td>\n", " <td>1.566643e+07</td>\n", " <td>3317271.000</td>\n", " <td>2253.456633</td>\n", " <td>6800.545131</td>\n", " <td>138.585166</td>\n", " <td>891.014094</td>\n", " <td>372649</td>\n", " <td>171703.0</td>\n", " <td>0.460763</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>329574</td>\n", " <td>21053</td>\n", " <td>265370</td>\n", " <td>848789</td>\n", " <td>258225</td>\n", " <td>1112.744922</td>\n", " <td>68.669698</td>\n", " <td>62.199569</td>\n", " <td>3.145390e+07</td>\n", " <td>4868593.500</td>\n", " <td>2292.982438</td>\n", " <td>5939.409007</td>\n", " <td>-11.449252</td>\n", " <td>711.539726</td>\n", " <td>847416</td>\n", " <td>334224.0</td>\n", " <td>0.394404</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " num_sess num_days num_probs num_atts num_hints \\\n", "cluster_index \n", "1 157381 9790 119787 463356 319334 \n", "2 22030 1535 17766 72336 33803 \n", "3 16010 1876 14923 33757 2842 \n", "4 36928 5409 32754 125483 52632 \n", "5 121977 12125 108264 372790 134056 \n", "6 329574 21053 265370 848789 258225 \n", "\n", " frac_corr_atts frac_3s_atts frac_1s_hints time_atts \\\n", "cluster_index \n", "1 746.328247 143.134751 320.395430 1.209619e+07 \n", "2 203.188547 91.172770 13.541033 1.645484e+06 \n", "3 646.793540 12.846483 2.963157 1.822630e+06 \n", "4 1256.070663 41.842531 26.817449 5.753900e+06 \n", "5 1088.841940 61.644813 46.097252 1.566643e+07 \n", "6 1112.744922 68.669698 62.199569 3.145390e+07 \n", "\n", " time_hints max_probl_views max_atts learning_parameter \\\n", "cluster_index \n", "1 3310766.636 1700.710013 5073.330503 533.615744 \n", "2 459433.500 466.191313 1403.399037 426.118912 \n", "3 45694.000 1209.648207 3165.984896 2763.923125 \n", "4 1572946.500 2775.185284 9999.657523 1684.809382 \n", "5 3317271.000 2253.456633 6800.545131 138.585166 \n", "6 4868593.500 2292.982438 5939.409007 -11.449252 \n", "\n", " difficulty_parameter number of attempts \\\n", "cluster_index \n", "1 590.950677 463202 \n", "2 218.872902 71890 \n", "3 546.419214 33752 \n", "4 1333.332278 125470 \n", "5 891.014094 372649 \n", "6 711.539726 847416 \n", "\n", " number of incorrect attempts frac_incorrect_atts \n", "cluster_index \n", "1 197852.0 0.427140 \n", "2 36290.0 0.504799 \n", "3 12311.0 0.364749 \n", "4 63394.0 0.505252 \n", "5 171703.0 0.460763 \n", "6 334224.0 0.394404 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stud_data_sum = stud_data.groupby('cluster_index').agg(np.sum).copy()\n", "stud_data_sum['frac_incorrect_atts'] = stud_data_sum['number of incorrect attempts'] / stud_data_sum['number of attempts']\n", "stud_data_sum" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interestingly, group 3 has the smallest fraction of incorrect attempts (~36.5%). Also, not surprisingly, `'frac_incorrect_atts'` in group 1 (with large `'frac_1s_hints'`) is significantly (`p-value = 1.75e-8`) smaller than in group 2 (with small `'frac_1s_hints'` and large `'frac_3s_atts'`):" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Ttest_indResult(statistic=-5.729328888101044, pvalue=1.7528523291077249e-08)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr1 = np.array(stud_data[stud_data['cluster_index'] == 1]['frac_incorrect_atts'])\n", "arr2 = np.array(stud_data[stud_data['cluster_index'] == 2]['frac_incorrect_atts'])\n", "arr1 = arr1[~np.isnan(arr1)]\n", "arr2 = arr2[~np.isnan(arr2)]\n", "stats.ttest_ind(arr1,arr2, equal_var = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, the difference of `'frac_incorrect_atts'` between students with \"gaming\" and non-gaming behaviour is **not significant** (`p-value = 0.83`):" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Ttest_indResult(statistic=0.20874210756084033, pvalue=0.83466558756318543)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr_gam = np.array(stud_data[stud_data['cluster_index'] <= 2]['frac_incorrect_atts'])\n", "arr_nongam = np.array(stud_data[stud_data['cluster_index'] > 2]['frac_incorrect_atts'])\n", "arr_gam = arr_gam[~np.isnan(arr_gam)]\n", "arr_nongam = arr_nongam[~np.isnan(arr_nongam)]\n", "stats.ttest_ind(arr_gam,arr_nongam, equal_var = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notably, group 5 (students with medium `'num_sess'` and `'num_probs'`) has **significantly smaller** `'frac_incorrect_atts'` than group 4 (students with small `'num_sess'` and `'num_probs'`) but **significantly smaller** `'frac_incorrect_atts'` than in group 6 (students with large `'num_sess'` and `'num_probs'`):" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ttest_indResult(statistic=-10.153852159747615, pvalue=5.9895165350814053e-24)\n", "Ttest_indResult(statistic=-16.552377685289887, pvalue=1.8740538669177719e-59)\n" ] } ], "source": [ "arr4 = np.array(stud_data[stud_data['cluster_index'] == 4]['frac_incorrect_atts'])\n", "arr5 = np.array(stud_data[stud_data['cluster_index'] == 5]['frac_incorrect_atts'])\n", "arr6 = np.array(stud_data[stud_data['cluster_index'] == 6]['frac_incorrect_atts'])\n", "arr4 = arr4[~np.isnan(arr4)]\n", "arr5 = arr5[~np.isnan(arr5)]\n", "arr6 = arr6[~np.isnan(arr6)]\n", "print(stats.ttest_ind(arr5,arr4, equal_var = False))\n", "print(stats.ttest_ind(arr6,arr5, equal_var = False))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In other words, for students with non-gaming behaviour `'frac_incorrect_atts'` **steadily decreases with learning experience**.\n", "\n", "Other differences between groups:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "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>num_sess</th>\n", " <th>num_days</th>\n", " <th>num_probs</th>\n", " <th>num_atts</th>\n", " <th>num_hints</th>\n", " <th>frac_corr_atts</th>\n", " <th>frac_3s_atts</th>\n", " <th>frac_1s_hints</th>\n", " <th>time_atts</th>\n", " <th>time_hints</th>\n", " <th>max_probl_views</th>\n", " <th>max_atts</th>\n", " <th>learning_parameter</th>\n", " <th>difficulty_parameter</th>\n", " <th>number of attempts</th>\n", " <th>number of incorrect attempts</th>\n", " <th>frac_incorrect_atts</th>\n", " </tr>\n", " <tr>\n", " <th>cluster_index</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>121.623648</td>\n", " <td>7.565688</td>\n", " <td>92.571097</td>\n", " <td>358.080371</td>\n", " <td>246.780526</td>\n", " <td>0.576761</td>\n", " <td>0.110614</td>\n", " <td>0.247601</td>\n", " <td>9347.902114</td>\n", " <td>2558.552269</td>\n", " <td>1.314304</td>\n", " <td>3.920657</td>\n", " <td>0.412377</td>\n", " <td>0.456685</td>\n", " <td>357.961360</td>\n", " <td>152.899536</td>\n", " <td>0.421592</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>56.342711</td>\n", " <td>3.925831</td>\n", " <td>45.437340</td>\n", " <td>185.002558</td>\n", " <td>86.452685</td>\n", " <td>0.519664</td>\n", " <td>0.233178</td>\n", " <td>0.034632</td>\n", " <td>4208.397711</td>\n", " <td>1175.021739</td>\n", " <td>1.192305</td>\n", " <td>3.589256</td>\n", " <td>1.089818</td>\n", " <td>0.559777</td>\n", " <td>183.861893</td>\n", " <td>92.813299</td>\n", " <td>0.479562</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>14.436429</td>\n", " <td>1.691614</td>\n", " <td>13.456267</td>\n", " <td>30.439134</td>\n", " <td>2.562669</td>\n", " <td>0.583222</td>\n", " <td>0.011584</td>\n", " <td>0.002672</td>\n", " <td>1643.489630</td>\n", " <td>41.202885</td>\n", " <td>1.090756</td>\n", " <td>2.854811</td>\n", " <td>2.492266</td>\n", " <td>0.492713</td>\n", " <td>30.434626</td>\n", " <td>11.100992</td>\n", " <td>0.402156</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>15.246903</td>\n", " <td>2.233278</td>\n", " <td>13.523534</td>\n", " <td>51.809661</td>\n", " <td>21.730801</td>\n", " <td>0.518609</td>\n", " <td>0.017276</td>\n", " <td>0.011072</td>\n", " <td>2375.681462</td>\n", " <td>649.441164</td>\n", " <td>1.145824</td>\n", " <td>4.128678</td>\n", " <td>0.695627</td>\n", " <td>0.550509</td>\n", " <td>51.804294</td>\n", " <td>26.174236</td>\n", " <td>0.479627</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>62.648690</td>\n", " <td>6.227530</td>\n", " <td>55.605547</td>\n", " <td>191.468927</td>\n", " <td>68.852594</td>\n", " <td>0.559241</td>\n", " <td>0.031661</td>\n", " <td>0.023676</td>\n", " <td>8046.446327</td>\n", " <td>1703.785824</td>\n", " <td>1.157399</td>\n", " <td>3.492833</td>\n", " <td>0.071179</td>\n", " <td>0.457634</td>\n", " <td>191.396507</td>\n", " <td>88.188495</td>\n", " <td>0.440491</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>181.383599</td>\n", " <td>11.586681</td>\n", " <td>146.048431</td>\n", " <td>467.137589</td>\n", " <td>142.116125</td>\n", " <td>0.612408</td>\n", " <td>0.037793</td>\n", " <td>0.034232</td>\n", " <td>17310.899564</td>\n", " <td>2679.468079</td>\n", " <td>1.261961</td>\n", " <td>3.268800</td>\n", " <td>-0.006301</td>\n", " <td>0.391601</td>\n", " <td>466.381948</td>\n", " <td>183.942763</td>\n", " <td>0.386384</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " num_sess num_days num_probs num_atts num_hints \\\n", "cluster_index \n", "1 121.623648 7.565688 92.571097 358.080371 246.780526 \n", "2 56.342711 3.925831 45.437340 185.002558 86.452685 \n", "3 14.436429 1.691614 13.456267 30.439134 2.562669 \n", "4 15.246903 2.233278 13.523534 51.809661 21.730801 \n", "5 62.648690 6.227530 55.605547 191.468927 68.852594 \n", "6 181.383599 11.586681 146.048431 467.137589 142.116125 \n", "\n", " frac_corr_atts frac_3s_atts frac_1s_hints time_atts \\\n", "cluster_index \n", "1 0.576761 0.110614 0.247601 9347.902114 \n", "2 0.519664 0.233178 0.034632 4208.397711 \n", "3 0.583222 0.011584 0.002672 1643.489630 \n", "4 0.518609 0.017276 0.011072 2375.681462 \n", "5 0.559241 0.031661 0.023676 8046.446327 \n", "6 0.612408 0.037793 0.034232 17310.899564 \n", "\n", " time_hints max_probl_views max_atts learning_parameter \\\n", "cluster_index \n", "1 2558.552269 1.314304 3.920657 0.412377 \n", "2 1175.021739 1.192305 3.589256 1.089818 \n", "3 41.202885 1.090756 2.854811 2.492266 \n", "4 649.441164 1.145824 4.128678 0.695627 \n", "5 1703.785824 1.157399 3.492833 0.071179 \n", "6 2679.468079 1.261961 3.268800 -0.006301 \n", "\n", " difficulty_parameter number of attempts \\\n", "cluster_index \n", "1 0.456685 357.961360 \n", "2 0.559777 183.861893 \n", "3 0.492713 30.434626 \n", "4 0.550509 51.804294 \n", "5 0.457634 191.396507 \n", "6 0.391601 466.381948 \n", "\n", " number of incorrect attempts frac_incorrect_atts \n", "cluster_index \n", "1 152.899536 0.421592 \n", "2 92.813299 0.479562 \n", "3 11.100992 0.402156 \n", "4 26.174236 0.479627 \n", "5 88.188495 0.440491 \n", "6 183.942763 0.386384 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stud_data_mean = stud_data.groupby('cluster_index').agg(np.mean).copy()\n", "stud_data_mean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a significant increase of `'frac_3s_atts'` for groups 4-5-6:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ttest_indResult(statistic=16.462911716329312, pvalue=5.0996180356250809e-59)\n", "Ttest_indResult(statistic=6.2931050213512316, pvalue=3.4739480257167589e-10)\n" ] } ], "source": [ "arr4 = np.array(stud_data[stud_data['cluster_index'] == 4]['frac_3s_atts'])\n", "arr5 = np.array(stud_data[stud_data['cluster_index'] == 5]['frac_3s_atts'])\n", "arr6 = np.array(stud_data[stud_data['cluster_index'] == 6]['frac_3s_atts'])\n", "arr4 = arr4[~np.isnan(arr4)]\n", "arr5 = arr5[~np.isnan(arr5)]\n", "arr6 = arr6[~np.isnan(arr6)]\n", "print(stats.ttest_ind(arr5,arr4, equal_var = False))\n", "print(stats.ttest_ind(arr6,arr5, equal_var = False))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ", a significant increase of 'frac_1s_hints' for groups 4-5-6:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ttest_indResult(statistic=13.252648797032892, pvalue=3.4429894092371465e-39)\n", "Ttest_indResult(statistic=9.0112499269936261, pvalue=3.2062949164427214e-19)\n" ] } ], "source": [ "arr4 = np.array(stud_data[stud_data['cluster_index'] == 4]['frac_1s_hints'])\n", "arr5 = np.array(stud_data[stud_data['cluster_index'] == 5]['frac_1s_hints'])\n", "arr6 = np.array(stud_data[stud_data['cluster_index'] == 6]['frac_1s_hints'])\n", "arr4 = arr4[~np.isnan(arr4)]\n", "arr5 = arr5[~np.isnan(arr5)]\n", "arr6 = arr6[~np.isnan(arr6)]\n", "print(stats.ttest_ind(arr5,arr4, equal_var = False))\n", "print(stats.ttest_ind(arr6,arr5, equal_var = False))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ", and a significant decrease of 'max_atts' for groups 4-5-6:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ttest_indResult(statistic=-11.66474597325503, pvalue=6.3441131333456629e-31)\n", "Ttest_indResult(statistic=-5.813163173033753, pvalue=6.640246772802727e-09)\n" ] } ], "source": [ "arr4 = np.array(stud_data[stud_data['cluster_index'] == 4]['max_atts'])\n", "arr5 = np.array(stud_data[stud_data['cluster_index'] == 5]['max_atts'])\n", "arr6 = np.array(stud_data[stud_data['cluster_index'] == 6]['max_atts'])\n", "arr4 = arr4[~np.isnan(arr4)]\n", "arr5 = arr5[~np.isnan(arr5)]\n", "arr6 = arr6[~np.isnan(arr6)]\n", "print(stats.ttest_ind(arr5,arr4, equal_var = False))\n", "print(stats.ttest_ind(arr6,arr5, equal_var = False))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Increase of `max_probl_views` is significant between groups 5 and 6:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ttest_indResult(statistic=1.625027093046111, pvalue=0.1042377703597938)\n", "Ttest_indResult(statistic=15.833103986243177, pvalue=1.9284145705097942e-54)\n" ] } ], "source": [ "arr4 = np.array(stud_data[stud_data['cluster_index'] == 4]['max_probl_views'])\n", "arr5 = np.array(stud_data[stud_data['cluster_index'] == 5]['max_probl_views'])\n", "arr6 = np.array(stud_data[stud_data['cluster_index'] == 6]['max_probl_views'])\n", "arr4 = arr4[~np.isnan(arr4)]\n", "arr5 = arr5[~np.isnan(arr5)]\n", "arr6 = arr6[~np.isnan(arr6)]\n", "print(stats.ttest_ind(arr5,arr4, equal_var = False))\n", "print(stats.ttest_ind(arr6,arr5, equal_var = False))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we see, increasing \"experience\" (in group sequence 4-5-6) also leads to:\n", "- increase of \"gaming\" fractions `'frac_3s_atts'` and `'frac_1s_hints'`;\n", "- increase of `'max_probl_views'` (so the problems are viewed in more details);\n", "- decrease of `'max_atts'` (so there are smaller attempts per problem)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculate mean leaning parameter and its dispersion for each group:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because learning parameter is determined from the fit of the learning curve, it is essential to analyse learning curves starting with some reasonable minimum number of attempts." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we see, there is a very large spread in learning parameters: between -9.97 and 24.9:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 8980.000000\n", "mean 0.616437\n", "std 3.188777\n", "min -9.965780\n", "25% -0.054653\n", "50% 0.063602\n", "75% 0.218565\n", "max 24.908305\n", "Name: learning_parameter, dtype: float64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stud_data['learning_parameter'].describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Moreover, for ~1/3 of students learning parameter is **negative**:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3179" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stud_data[stud_data['learning_parameter'] < 0].shape[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What best describes \"negative learners\"? First, look on extreme examples. Take \"extreme negative learners\" (students with learning rate < -0.5, and compare them with \"extreme positive learnens\" (students with learning rate > 0.5):" ] }, { "cell_type": "code", "execution_count": 17, "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>num_sess</th>\n", " <th>num_days</th>\n", " <th>num_probs</th>\n", " <th>num_atts</th>\n", " <th>num_hints</th>\n", " <th>frac_corr_atts</th>\n", " <th>frac_3s_atts</th>\n", " <th>frac_1s_hints</th>\n", " <th>time_atts</th>\n", " <th>time_hints</th>\n", " <th>max_probl_views</th>\n", " <th>max_atts</th>\n", " <th>learning_parameter</th>\n", " <th>difficulty_parameter</th>\n", " <th>number of attempts</th>\n", " <th>number of incorrect attempts</th>\n", " <th>cluster_index</th>\n", " <th>frac_incorrect_atts</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>161.000000</td>\n", " <td>161.000000</td>\n", " <td>161.000000</td>\n", " <td>161.000000</td>\n", " <td>161.000000</td>\n", " <td>161.000000</td>\n", " <td>161.000000</td>\n", " <td>161.000000</td>\n", " <td>161.000000</td>\n", " <td>161.000000</td>\n", " <td>161.000000</td>\n", " <td>161.000000</td>\n", " <td>161.000000</td>\n", " <td>161.000000</td>\n", " <td>161.000000</td>\n", " <td>161.000000</td>\n", " <td>161.000000</td>\n", " <td>161.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>34.708075</td>\n", " <td>2.720497</td>\n", " <td>32.254658</td>\n", " <td>51.360248</td>\n", " <td>14.006211</td>\n", " <td>0.688847</td>\n", " <td>0.025639</td>\n", " <td>0.050475</td>\n", " <td>3113.850932</td>\n", " <td>371.161491</td>\n", " <td>1.092419</td>\n", " <td>2.245839</td>\n", " <td>-1.227948</td>\n", " <td>0.161278</td>\n", " <td>51.291925</td>\n", " <td>11.478261</td>\n", " <td>3.509317</td>\n", " <td>0.310729</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>56.546180</td>\n", " <td>2.928761</td>\n", " <td>51.954220</td>\n", " <td>78.542310</td>\n", " <td>26.625058</td>\n", " <td>0.171281</td>\n", " <td>0.055839</td>\n", " <td>0.151386</td>\n", " <td>5421.411714</td>\n", " <td>472.810955</td>\n", " <td>0.224871</td>\n", " <td>1.444326</td>\n", " <td>1.702650</td>\n", " <td>0.099337</td>\n", " <td>78.469551</td>\n", " <td>16.197256</td>\n", " <td>1.275333</td>\n", " <td>0.171545</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>2.000000</td>\n", " <td>0.000000</td>\n", " <td>0.200000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>26.000000</td>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>-9.965780</td>\n", " <td>0.001000</td>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>0.024793</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>5.000000</td>\n", " <td>1.000000</td>\n", " <td>5.000000</td>\n", " <td>10.000000</td>\n", " <td>2.000000</td>\n", " <td>0.588235</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>546.000000</td>\n", " <td>46.000000</td>\n", " <td>1.000000</td>\n", " <td>1.441860</td>\n", " <td>-1.112757</td>\n", " <td>0.102926</td>\n", " <td>10.000000</td>\n", " <td>3.000000</td>\n", " <td>3.000000</td>\n", " <td>0.181818</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>11.000000</td>\n", " <td>1.000000</td>\n", " <td>11.000000</td>\n", " <td>21.000000</td>\n", " <td>5.000000</td>\n", " <td>0.727273</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>961.000000</td>\n", " <td>172.000000</td>\n", " <td>1.000000</td>\n", " <td>1.705882</td>\n", " <td>-0.759733</td>\n", " <td>0.154174</td>\n", " <td>21.000000</td>\n", " <td>5.000000</td>\n", " <td>4.000000</td>\n", " <td>0.272727</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>35.000000</td>\n", " <td>3.000000</td>\n", " <td>35.000000</td>\n", " <td>53.000000</td>\n", " <td>13.000000</td>\n", " <td>0.814286</td>\n", " <td>0.022222</td>\n", " <td>0.000000</td>\n", " <td>2215.000000</td>\n", " <td>487.000000</td>\n", " <td>1.093023</td>\n", " <td>2.411765</td>\n", " <td>-0.594690</td>\n", " <td>0.211333</td>\n", " <td>53.000000</td>\n", " <td>13.000000</td>\n", " <td>4.000000</td>\n", " <td>0.411765</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>397.000000</td>\n", " <td>13.000000</td>\n", " <td>362.000000</td>\n", " <td>588.000000</td>\n", " <td>184.000000</td>\n", " <td>0.975207</td>\n", " <td>0.333333</td>\n", " <td>1.000000</td>\n", " <td>41457.000000</td>\n", " <td>2319.000000</td>\n", " <td>2.600000</td>\n", " <td>11.000000</td>\n", " <td>-0.503115</td>\n", " <td>0.529482</td>\n", " <td>588.000000</td>\n", " <td>136.000000</td>\n", " <td>6.000000</td>\n", " <td>0.800000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " num_sess num_days num_probs num_atts num_hints \\\n", "count 161.000000 161.000000 161.000000 161.000000 161.000000 \n", "mean 34.708075 2.720497 32.254658 51.360248 14.006211 \n", "std 56.546180 2.928761 51.954220 78.542310 26.625058 \n", "min 1.000000 1.000000 1.000000 2.000000 0.000000 \n", "25% 5.000000 1.000000 5.000000 10.000000 2.000000 \n", "50% 11.000000 1.000000 11.000000 21.000000 5.000000 \n", "75% 35.000000 3.000000 35.000000 53.000000 13.000000 \n", "max 397.000000 13.000000 362.000000 588.000000 184.000000 \n", "\n", " frac_corr_atts frac_3s_atts frac_1s_hints time_atts time_hints \\\n", "count 161.000000 161.000000 161.000000 161.000000 161.000000 \n", "mean 0.688847 0.025639 0.050475 3113.850932 371.161491 \n", "std 0.171281 0.055839 0.151386 5421.411714 472.810955 \n", "min 0.200000 0.000000 0.000000 26.000000 0.000000 \n", "25% 0.588235 0.000000 0.000000 546.000000 46.000000 \n", "50% 0.727273 0.000000 0.000000 961.000000 172.000000 \n", "75% 0.814286 0.022222 0.000000 2215.000000 487.000000 \n", "max 0.975207 0.333333 1.000000 41457.000000 2319.000000 \n", "\n", " max_probl_views max_atts learning_parameter difficulty_parameter \\\n", "count 161.000000 161.000000 161.000000 161.000000 \n", "mean 1.092419 2.245839 -1.227948 0.161278 \n", "std 0.224871 1.444326 1.702650 0.099337 \n", "min 1.000000 1.000000 -9.965780 0.001000 \n", "25% 1.000000 1.441860 -1.112757 0.102926 \n", "50% 1.000000 1.705882 -0.759733 0.154174 \n", "75% 1.093023 2.411765 -0.594690 0.211333 \n", "max 2.600000 11.000000 -0.503115 0.529482 \n", "\n", " number of attempts number of incorrect attempts cluster_index \\\n", "count 161.000000 161.000000 161.000000 \n", "mean 51.291925 11.478261 3.509317 \n", "std 78.469551 16.197256 1.275333 \n", "min 2.000000 1.000000 1.000000 \n", "25% 10.000000 3.000000 3.000000 \n", "50% 21.000000 5.000000 4.000000 \n", "75% 53.000000 13.000000 4.000000 \n", "max 588.000000 136.000000 6.000000 \n", "\n", " frac_incorrect_atts \n", "count 161.000000 \n", "mean 0.310729 \n", "std 0.171545 \n", "min 0.024793 \n", "25% 0.181818 \n", "50% 0.272727 \n", "75% 0.411765 \n", "max 0.800000 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stud_data[stud_data['learning_parameter'] < -0.5].describe()" ] }, { "cell_type": "code", "execution_count": 18, "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>num_sess</th>\n", " <th>num_days</th>\n", " <th>num_probs</th>\n", " <th>num_atts</th>\n", " <th>num_hints</th>\n", " <th>frac_corr_atts</th>\n", " <th>frac_3s_atts</th>\n", " <th>frac_1s_hints</th>\n", " <th>time_atts</th>\n", " <th>time_hints</th>\n", " <th>max_probl_views</th>\n", " <th>max_atts</th>\n", " <th>learning_parameter</th>\n", " <th>difficulty_parameter</th>\n", " <th>number of attempts</th>\n", " <th>number of incorrect attempts</th>\n", " <th>cluster_index</th>\n", " <th>frac_incorrect_atts</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>1118.000000</td>\n", " <td>1118.000000</td>\n", " <td>1118.000000</td>\n", " <td>1118.000000</td>\n", " <td>1118.000000</td>\n", " <td>1118.000000</td>\n", " <td>1118.000000</td>\n", " <td>1118.000000</td>\n", " <td>1118.000000</td>\n", " <td>1118.000000</td>\n", " <td>1118.000000</td>\n", " <td>1118.000000</td>\n", " <td>1118.000000</td>\n", " <td>1118.000000</td>\n", " <td>1118.000000</td>\n", " <td>1118.000000</td>\n", " <td>1118.000000</td>\n", " <td>1080.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>9.767442</td>\n", " <td>1.384615</td>\n", " <td>9.294275</td>\n", " <td>20.773703</td>\n", " <td>9.701252</td>\n", " <td>0.658382</td>\n", " <td>0.040541</td>\n", " <td>0.031332</td>\n", " <td>975.757603</td>\n", " <td>238.600179</td>\n", " <td>1.078327</td>\n", " <td>2.370444</td>\n", " <td>4.841834</td>\n", " <td>0.523349</td>\n", " <td>20.770125</td>\n", " <td>6.887299</td>\n", " <td>3.181574</td>\n", " <td>0.318386</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>13.127075</td>\n", " <td>0.965203</td>\n", " <td>12.326656</td>\n", " <td>28.717120</td>\n", " <td>28.702815</td>\n", " <td>0.254652</td>\n", " <td>0.118064</td>\n", " <td>0.109242</td>\n", " <td>1235.419496</td>\n", " <td>402.316758</td>\n", " <td>0.271104</td>\n", " <td>1.544594</td>\n", " <td>7.776401</td>\n", " <td>0.346560</td>\n", " <td>28.710326</td>\n", " <td>10.527272</td>\n", " <td>0.927130</td>\n", " <td>0.226612</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1.000000</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>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>0.500000</td>\n", " <td>0.001000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>2.000000</td>\n", " <td>5.000000</td>\n", " <td>1.000000</td>\n", " <td>0.541964</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>185.750000</td>\n", " <td>9.000000</td>\n", " <td>1.000000</td>\n", " <td>1.200000</td>\n", " <td>0.533341</td>\n", " <td>0.252888</td>\n", " <td>5.000000</td>\n", " <td>1.000000</td>\n", " <td>3.000000</td>\n", " <td>0.166667</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>6.000000</td>\n", " <td>1.000000</td>\n", " <td>5.000000</td>\n", " <td>12.000000</td>\n", " <td>3.000000</td>\n", " <td>0.666667</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>644.500000</td>\n", " <td>94.500000</td>\n", " <td>1.000000</td>\n", " <td>2.000000</td>\n", " <td>0.793611</td>\n", " <td>0.520733</td>\n", " <td>12.000000</td>\n", " <td>3.000000</td>\n", " <td>3.000000</td>\n", " <td>0.315789</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>11.000000</td>\n", " <td>1.000000</td>\n", " <td>11.000000</td>\n", " <td>25.000000</td>\n", " <td>9.000000</td>\n", " <td>0.833333</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1199.000000</td>\n", " <td>311.000000</td>\n", " <td>1.026931</td>\n", " <td>3.000000</td>\n", " <td>1.853239</td>\n", " <td>0.749445</td>\n", " <td>25.000000</td>\n", " <td>9.000000</td>\n", " <td>4.000000</td>\n", " <td>0.444444</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>121.000000</td>\n", " <td>12.000000</td>\n", " <td>116.000000</td>\n", " <td>332.000000</td>\n", " <td>661.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>10044.000000</td>\n", " <td>6247.000000</td>\n", " <td>5.000000</td>\n", " <td>12.000000</td>\n", " <td>24.908305</td>\n", " <td>1.669793</td>\n", " <td>332.000000</td>\n", " <td>121.000000</td>\n", " <td>6.000000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " num_sess num_days num_probs num_atts num_hints \\\n", "count 1118.000000 1118.000000 1118.000000 1118.000000 1118.000000 \n", "mean 9.767442 1.384615 9.294275 20.773703 9.701252 \n", "std 13.127075 0.965203 12.326656 28.717120 28.702815 \n", "min 1.000000 1.000000 1.000000 0.000000 0.000000 \n", "25% 2.000000 1.000000 2.000000 5.000000 1.000000 \n", "50% 6.000000 1.000000 5.000000 12.000000 3.000000 \n", "75% 11.000000 1.000000 11.000000 25.000000 9.000000 \n", "max 121.000000 12.000000 116.000000 332.000000 661.000000 \n", "\n", " frac_corr_atts frac_3s_atts frac_1s_hints time_atts time_hints \\\n", "count 1118.000000 1118.000000 1118.000000 1118.000000 1118.000000 \n", "mean 0.658382 0.040541 0.031332 975.757603 238.600179 \n", "std 0.254652 0.118064 0.109242 1235.419496 402.316758 \n", "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "25% 0.541964 0.000000 0.000000 185.750000 9.000000 \n", "50% 0.666667 0.000000 0.000000 644.500000 94.500000 \n", "75% 0.833333 0.000000 0.000000 1199.000000 311.000000 \n", "max 1.000000 1.000000 1.000000 10044.000000 6247.000000 \n", "\n", " max_probl_views max_atts learning_parameter difficulty_parameter \\\n", "count 1118.000000 1118.000000 1118.000000 1118.000000 \n", "mean 1.078327 2.370444 4.841834 0.523349 \n", "std 0.271104 1.544594 7.776401 0.346560 \n", "min 1.000000 0.000000 0.500000 0.001000 \n", "25% 1.000000 1.200000 0.533341 0.252888 \n", "50% 1.000000 2.000000 0.793611 0.520733 \n", "75% 1.026931 3.000000 1.853239 0.749445 \n", "max 5.000000 12.000000 24.908305 1.669793 \n", "\n", " number of attempts number of incorrect attempts cluster_index \\\n", "count 1118.000000 1118.000000 1118.000000 \n", "mean 20.770125 6.887299 3.181574 \n", "std 28.710326 10.527272 0.927130 \n", "min 0.000000 0.000000 1.000000 \n", "25% 5.000000 1.000000 3.000000 \n", "50% 12.000000 3.000000 3.000000 \n", "75% 25.000000 9.000000 4.000000 \n", "max 332.000000 121.000000 6.000000 \n", "\n", " frac_incorrect_atts \n", "count 1080.000000 \n", "mean 0.318386 \n", "std 0.226612 \n", "min 0.000000 \n", "25% 0.166667 \n", "50% 0.315789 \n", "75% 0.444444 \n", "max 1.000000 " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stud_data[stud_data['learning_parameter'] >= 0.5].describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we see, \"extreme negative learners\" are very similar to \"extreme positive learners\" in terms of `'frac_corr_atts'`. However, they opened much more sessions (`'num_sess'`), tried to solve more problems (`'num_probs'`), made more attempts to solve the problems (`'num_atts'`) and spent more time (`time_atts`) for solving them." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "cluster_index\n", "1 20\n", "3 57\n", "4 58\n", "5 14\n", "6 12\n", "Name: num_sess, dtype: int64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stud_data[stud_data['learning_parameter'] < -0.5].groupby('cluster_index').agg(len)['num_sess']" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "cluster_index\n", "1 88\n", "2 93\n", "3 501\n", "4 404\n", "5 28\n", "6 4\n", "Name: num_sess, dtype: int64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stud_data[stud_data['learning_parameter'] >= 0.5].groupby('cluster_index').agg(len)['num_sess']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notably, most of \"extreme learners\" belong to groups 3 and 4 that have the smallest `'num_atts'`:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>num_sess</th>\n", " <th>num_days</th>\n", " <th>num_probs</th>\n", " <th>num_atts</th>\n", " <th>num_hints</th>\n", " <th>frac_corr_atts</th>\n", " <th>frac_3s_atts</th>\n", " <th>frac_1s_hints</th>\n", " <th>time_atts</th>\n", " <th>time_hints</th>\n", " <th>max_probl_views</th>\n", " <th>max_atts</th>\n", " <th>learning_parameter</th>\n", " <th>difficulty_parameter</th>\n", " <th>number of attempts</th>\n", " <th>number of incorrect attempts</th>\n", " <th>frac_incorrect_atts</th>\n", " </tr>\n", " <tr>\n", " <th>cluster_index</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>121.623648</td>\n", " <td>7.565688</td>\n", " <td>92.571097</td>\n", " <td>358.080371</td>\n", " <td>246.780526</td>\n", " <td>0.576761</td>\n", " <td>0.110614</td>\n", " <td>0.247601</td>\n", " <td>9347.902114</td>\n", " <td>2558.552269</td>\n", " <td>1.314304</td>\n", " <td>3.920657</td>\n", " <td>0.412377</td>\n", " <td>0.456685</td>\n", " <td>357.961360</td>\n", " <td>152.899536</td>\n", " <td>0.421592</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>56.342711</td>\n", " <td>3.925831</td>\n", " <td>45.437340</td>\n", " <td>185.002558</td>\n", " <td>86.452685</td>\n", " <td>0.519664</td>\n", " <td>0.233178</td>\n", " <td>0.034632</td>\n", " <td>4208.397711</td>\n", " <td>1175.021739</td>\n", " <td>1.192305</td>\n", " <td>3.589256</td>\n", " <td>1.089818</td>\n", " <td>0.559777</td>\n", " <td>183.861893</td>\n", " <td>92.813299</td>\n", " <td>0.479562</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>14.436429</td>\n", " <td>1.691614</td>\n", " <td>13.456267</td>\n", " <td>30.439134</td>\n", " <td>2.562669</td>\n", " <td>0.583222</td>\n", " <td>0.011584</td>\n", " <td>0.002672</td>\n", " <td>1643.489630</td>\n", " <td>41.202885</td>\n", " <td>1.090756</td>\n", " <td>2.854811</td>\n", " <td>2.492266</td>\n", " <td>0.492713</td>\n", " <td>30.434626</td>\n", " <td>11.100992</td>\n", " <td>0.402156</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>15.246903</td>\n", " <td>2.233278</td>\n", " <td>13.523534</td>\n", " <td>51.809661</td>\n", " <td>21.730801</td>\n", " <td>0.518609</td>\n", " <td>0.017276</td>\n", " <td>0.011072</td>\n", " <td>2375.681462</td>\n", " <td>649.441164</td>\n", " <td>1.145824</td>\n", " <td>4.128678</td>\n", " <td>0.695627</td>\n", " <td>0.550509</td>\n", " <td>51.804294</td>\n", " <td>26.174236</td>\n", " <td>0.479627</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>62.648690</td>\n", " <td>6.227530</td>\n", " <td>55.605547</td>\n", " <td>191.468927</td>\n", " <td>68.852594</td>\n", " <td>0.559241</td>\n", " <td>0.031661</td>\n", " <td>0.023676</td>\n", " <td>8046.446327</td>\n", " <td>1703.785824</td>\n", " <td>1.157399</td>\n", " <td>3.492833</td>\n", " <td>0.071179</td>\n", " <td>0.457634</td>\n", " <td>191.396507</td>\n", " <td>88.188495</td>\n", " <td>0.440491</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>181.383599</td>\n", " <td>11.586681</td>\n", " <td>146.048431</td>\n", " <td>467.137589</td>\n", " <td>142.116125</td>\n", " <td>0.612408</td>\n", " <td>0.037793</td>\n", " <td>0.034232</td>\n", " <td>17310.899564</td>\n", " <td>2679.468079</td>\n", " <td>1.261961</td>\n", " <td>3.268800</td>\n", " <td>-0.006301</td>\n", " <td>0.391601</td>\n", " <td>466.381948</td>\n", " <td>183.942763</td>\n", " <td>0.386384</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " num_sess num_days num_probs num_atts num_hints \\\n", "cluster_index \n", "1 121.623648 7.565688 92.571097 358.080371 246.780526 \n", "2 56.342711 3.925831 45.437340 185.002558 86.452685 \n", "3 14.436429 1.691614 13.456267 30.439134 2.562669 \n", "4 15.246903 2.233278 13.523534 51.809661 21.730801 \n", "5 62.648690 6.227530 55.605547 191.468927 68.852594 \n", "6 181.383599 11.586681 146.048431 467.137589 142.116125 \n", "\n", " frac_corr_atts frac_3s_atts frac_1s_hints time_atts \\\n", "cluster_index \n", "1 0.576761 0.110614 0.247601 9347.902114 \n", "2 0.519664 0.233178 0.034632 4208.397711 \n", "3 0.583222 0.011584 0.002672 1643.489630 \n", "4 0.518609 0.017276 0.011072 2375.681462 \n", "5 0.559241 0.031661 0.023676 8046.446327 \n", "6 0.612408 0.037793 0.034232 17310.899564 \n", "\n", " time_hints max_probl_views max_atts learning_parameter \\\n", "cluster_index \n", "1 2558.552269 1.314304 3.920657 0.412377 \n", "2 1175.021739 1.192305 3.589256 1.089818 \n", "3 41.202885 1.090756 2.854811 2.492266 \n", "4 649.441164 1.145824 4.128678 0.695627 \n", "5 1703.785824 1.157399 3.492833 0.071179 \n", "6 2679.468079 1.261961 3.268800 -0.006301 \n", "\n", " difficulty_parameter number of attempts \\\n", "cluster_index \n", "1 0.456685 357.961360 \n", "2 0.559777 183.861893 \n", "3 0.492713 30.434626 \n", "4 0.550509 51.804294 \n", "5 0.457634 191.396507 \n", "6 0.391601 466.381948 \n", "\n", " number of incorrect attempts frac_incorrect_atts \n", "cluster_index \n", "1 152.899536 0.421592 \n", "2 92.813299 0.479562 \n", "3 11.100992 0.402156 \n", "4 26.174236 0.479627 \n", "5 88.188495 0.440491 \n", "6 183.942763 0.386384 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stud_data.groupby('cluster_index').agg(np.mean)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Together with the smallest group 2, groups 3 and 4 also correspond to the largest `'learning_parameter'` variation across their members:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "cluster_index\n", "1 2.575010\n", "2 4.092726\n", "3 6.263133\n", "4 3.261689\n", "5 0.904354\n", "6 0.151328\n", "Name: learning_parameter, dtype: float64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stud_data.groupby('cluster_index').agg(np.std)['learning_parameter']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that **both** absolute average value and standard deviation come to zero with increasing `'num_atts'`: a possible manifestation of the [Plateau effect](https://en.wikipedia.org/wiki/Plateau_effect)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, I try to explain the observed **increase** of `'frac_incorrect_atts'` for 5 and more attempts.\n", "\n", "The group with the largest `'frac_incorrect_atts'` is **group 4** that also has the largest `'max_atts'` (number of maximal attempts averaged for all assessed problems). Consequently, students from two groups with the smallest `'frac_incorrect_atts'` (**groups 3 and 6**) also have the smallest `'max_atts'`:" ] }, { "cell_type": "code", "execution_count": 23, "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>frac_incorrect_atts</th>\n", " <th>max_atts</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>frac_incorrect_atts</th>\n", " <td>1.000000</td>\n", " <td>0.660316</td>\n", " </tr>\n", " <tr>\n", " <th>max_atts</th>\n", " <td>0.660316</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " frac_incorrect_atts max_atts\n", "frac_incorrect_atts 1.000000 0.660316\n", "max_atts 0.660316 1.000000" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stud_data.groupby('cluster_index').agg(np.mean)[['frac_incorrect_atts', 'max_atts']].corr()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.51717864000939062" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stud_data.corr()['frac_incorrect_atts']['max_atts']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a result, students from groups 3 and 6 contribute less (and students from group 4 contribute more) to problems with large number of attempts and **distort** the averaged learning curve towards larger `'frac_incorrect_atts'`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparison with \"benchmark\" model (student with \"gaming\" vs \"non-gaming\" behaviour):" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "stud_data['gaming_index'] = stud_data['cluster_index'].replace({1: 0, 2: 0, 3: 1, 4: 1, 5: 1, 6:1})" ] }, { "cell_type": "code", "execution_count": 26, "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>num_sess</th>\n", " <th>num_days</th>\n", " <th>num_probs</th>\n", " <th>num_atts</th>\n", " <th>num_hints</th>\n", " <th>frac_corr_atts</th>\n", " <th>frac_3s_atts</th>\n", " <th>frac_1s_hints</th>\n", " <th>time_atts</th>\n", " <th>time_hints</th>\n", " <th>max_probl_views</th>\n", " <th>max_atts</th>\n", " <th>learning_parameter</th>\n", " <th>difficulty_parameter</th>\n", " <th>number of attempts</th>\n", " <th>number of incorrect attempts</th>\n", " <th>cluster_index</th>\n", " <th>frac_incorrect_atts</th>\n", " </tr>\n", " <tr>\n", " <th>gaming_index</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>106.475371</td>\n", " <td>6.721068</td>\n", " <td>81.633828</td>\n", " <td>317.918101</td>\n", " <td>209.576855</td>\n", " <td>0.563511</td>\n", " <td>0.139055</td>\n", " <td>0.198182</td>\n", " <td>8155.293081</td>\n", " <td>2237.507499</td>\n", " <td>1.285995</td>\n", " <td>3.843756</td>\n", " <td>0.569575</td>\n", " <td>0.480607</td>\n", " <td>317.562018</td>\n", " <td>138.956677</td>\n", " <td>1.232047</td>\n", " <td>0.435068</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>69.155449</td>\n", " <td>5.546676</td>\n", " <td>57.753393</td>\n", " <td>189.282934</td>\n", " <td>61.378341</td>\n", " <td>0.562639</td>\n", " <td>0.025360</td>\n", " <td>0.018928</td>\n", " <td>7497.856889</td>\n", " <td>1344.003427</td>\n", " <td>1.169468</td>\n", " <td>3.551144</td>\n", " <td>0.627261</td>\n", " <td>0.477355</td>\n", " <td>189.072927</td>\n", " <td>79.730226</td>\n", " <td>4.613023</td>\n", " <td>0.434249</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " num_sess num_days num_probs num_atts num_hints \\\n", "gaming_index \n", "0 106.475371 6.721068 81.633828 317.918101 209.576855 \n", "1 69.155449 5.546676 57.753393 189.282934 61.378341 \n", "\n", " frac_corr_atts frac_3s_atts frac_1s_hints time_atts \\\n", "gaming_index \n", "0 0.563511 0.139055 0.198182 8155.293081 \n", "1 0.562639 0.025360 0.018928 7497.856889 \n", "\n", " time_hints max_probl_views max_atts learning_parameter \\\n", "gaming_index \n", "0 2237.507499 1.285995 3.843756 0.569575 \n", "1 1344.003427 1.169468 3.551144 0.627261 \n", "\n", " difficulty_parameter number of attempts \\\n", "gaming_index \n", "0 0.480607 317.562018 \n", "1 0.477355 189.072927 \n", "\n", " number of incorrect attempts cluster_index frac_incorrect_atts \n", "gaming_index \n", "0 138.956677 1.232047 0.435068 \n", "1 79.730226 4.613023 0.434249 " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stud_data.groupby('gaming_index').agg(np.mean)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Creating visualisation:" ] }, { "cell_type": "code", "execution_count": 27, "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>Anon Student Id</th>\n", " <th>Session Id</th>\n", " <th>Duration (sec)</th>\n", " <th>Student Response Type</th>\n", " <th>Problem Name</th>\n", " <th>Problem View</th>\n", " <th>Attempt At Step</th>\n", " <th>Outcome</th>\n", " <th>Day</th>\n", " <th>x</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Stu_001d187b1b375fe98b88696b250177f0</td>\n", " <td>647501</td>\n", " <td>102.0</td>\n", " <td>1</td>\n", " <td>2218</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>2004-11-10</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Stu_001d187b1b375fe98b88696b250177f0</td>\n", " <td>647501</td>\n", " <td>46.0</td>\n", " <td>0</td>\n", " <td>2218</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>0.0</td>\n", " <td>2004-11-10</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Stu_001d187b1b375fe98b88696b250177f0</td>\n", " <td>647792</td>\n", " <td>70.0</td>\n", " <td>1</td>\n", " <td>3093</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>2004-11-10</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Stu_001d187b1b375fe98b88696b250177f0</td>\n", " <td>647792</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>3093</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>2004-11-10</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Stu_001d187b1b375fe98b88696b250177f0</td>\n", " <td>647792</td>\n", " <td>2.0</td>\n", " <td>1</td>\n", " <td>3093</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>2.0</td>\n", " <td>2004-11-10</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Anon Student Id Session Id Duration (sec) \\\n", "0 Stu_001d187b1b375fe98b88696b250177f0 647501 102.0 \n", "1 Stu_001d187b1b375fe98b88696b250177f0 647501 46.0 \n", "2 Stu_001d187b1b375fe98b88696b250177f0 647792 70.0 \n", "3 Stu_001d187b1b375fe98b88696b250177f0 647792 22.0 \n", "4 Stu_001d187b1b375fe98b88696b250177f0 647792 2.0 \n", "\n", " Student Response Type Problem Name Problem View Attempt At Step \\\n", "0 1 2218 1.0 1.0 \n", "1 0 2218 1.0 2.0 \n", "2 1 3093 1.0 1.0 \n", "3 1 3093 1.0 1.0 \n", "4 1 3093 1.0 2.0 \n", "\n", " Outcome Day x \n", "0 2.0 2004-11-10 0 \n", "1 0.0 2004-11-10 1 \n", "2 2.0 2004-11-10 0 \n", "3 2.0 2004-11-10 0 \n", "4 2.0 2004-11-10 0 " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_hdf('data.hdf','test')\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "stud_list = data['Anon Student Id'].unique()\n", "#print(stud_list[:5])\n", "stud_dict = {stud: cluster_index.loc[i, 1] for i, stud in enumerate(stud_list)}" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Stu_001d187b1b375fe98b88696b250177f0'" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stud_list[0]" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cluster_index.loc[0, 1]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stud_dict['Stu_001d187b1b375fe98b88696b250177f0']" ] }, { "cell_type": "code", "execution_count": 32, "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>cluster_index</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Stu_001d187b1b375fe98b88696b250177f0</th>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>Stu_00b8a37b3ab49bfe7d7a77014d1e4cf8</th>\n", " <td>5.0</td>\n", " </tr>\n", " <tr>\n", " <th>Stu_00c6652f296f103913139157c79a856f</th>\n", " <td>6.0</td>\n", " </tr>\n", " <tr>\n", " <th>Stu_01080cce4b1a14b3fd81d684421daed4</th>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>Stu_0153c9b08d68e42d9a2bb5f70086df00</th>\n", " <td>1.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " cluster_index\n", "Stu_001d187b1b375fe98b88696b250177f0 1.0\n", "Stu_00b8a37b3ab49bfe7d7a77014d1e4cf8 5.0\n", "Stu_00c6652f296f103913139157c79a856f 6.0\n", "Stu_01080cce4b1a14b3fd81d684421daed4 2.0\n", "Stu_0153c9b08d68e42d9a2bb5f70086df00 1.0" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stud_df = pd.DataFrame()\n", "for item in stud_dict:\n", " #print(item, stud_dict[item])\n", " stud_df.loc[item, 'cluster_index'] = int(stud_dict[item])\n", "stud_df.head()" ] }, { "cell_type": "code", "execution_count": 33, "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>Anon Student Id</th>\n", " <th>Session Id</th>\n", " <th>Duration (sec)</th>\n", " <th>Student Response Type</th>\n", " <th>Problem Name</th>\n", " <th>Problem View</th>\n", " <th>Attempt At Step</th>\n", " <th>Outcome</th>\n", " <th>Day</th>\n", " <th>x</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Stu_001d187b1b375fe98b88696b250177f0</td>\n", " <td>647501</td>\n", " <td>102.0</td>\n", " <td>1</td>\n", " <td>2218</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>2004-11-10</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Stu_001d187b1b375fe98b88696b250177f0</td>\n", " <td>647501</td>\n", " <td>46.0</td>\n", " <td>0</td>\n", " <td>2218</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>0.0</td>\n", " <td>2004-11-10</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Stu_001d187b1b375fe98b88696b250177f0</td>\n", " <td>647792</td>\n", " <td>70.0</td>\n", " <td>1</td>\n", " <td>3093</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>2004-11-10</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Stu_001d187b1b375fe98b88696b250177f0</td>\n", " <td>647792</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>3093</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>2004-11-10</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Stu_001d187b1b375fe98b88696b250177f0</td>\n", " <td>647792</td>\n", " <td>2.0</td>\n", " <td>1</td>\n", " <td>3093</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>2.0</td>\n", " <td>2004-11-10</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Anon Student Id Session Id Duration (sec) \\\n", "0 Stu_001d187b1b375fe98b88696b250177f0 647501 102.0 \n", "1 Stu_001d187b1b375fe98b88696b250177f0 647501 46.0 \n", "2 Stu_001d187b1b375fe98b88696b250177f0 647792 70.0 \n", "3 Stu_001d187b1b375fe98b88696b250177f0 647792 22.0 \n", "4 Stu_001d187b1b375fe98b88696b250177f0 647792 2.0 \n", "\n", " Student Response Type Problem Name Problem View Attempt At Step \\\n", "0 1 2218 1.0 1.0 \n", "1 0 2218 1.0 2.0 \n", "2 1 3093 1.0 1.0 \n", "3 1 3093 1.0 1.0 \n", "4 1 3093 1.0 2.0 \n", "\n", " Outcome Day x \n", "0 2.0 2004-11-10 0 \n", "1 0.0 2004-11-10 1 \n", "2 2.0 2004-11-10 0 \n", "3 2.0 2004-11-10 0 \n", "4 2.0 2004-11-10 0 " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_125 = data[(data['Anon Student Id'].isin(stud_df[stud_df['cluster_index'] == 1].index)) | \\\n", " (data['Anon Student Id'].isin(stud_df[stud_df['cluster_index'] == 2].index)) | \\\n", " (data['Anon Student Id'].isin(stud_df[stud_df['cluster_index'] == 5].index))]\n", "data_125.head()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_2 = data[(data['Anon Student Id'].isin(stud_df[stud_df['cluster_index'] == 2].index))]" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "s1 = data[data['Outcome'] <= 1].groupby(['x']).agg(len)['Problem Name']\n", "\n", "s2 = data[data['Outcome'] == 1].groupby(['x']).agg(len)['Problem Name']\n", "\n", "s1[8] = s1.loc[8:].sum()\n", "for i in range(9, int(s1.index.max()+1)):\n", " try:\n", " s1.drop(i, inplace=True)\n", " except ValueError:\n", " pass\n", "\n", "s2[8] = s2.loc[8:].sum()\n", "for i in range(9, int(s2.index.max()+1)):\n", " try:\n", " s2.drop(i, inplace=True)\n", " except ValueError:\n", " pass" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_1 = data[(data['Anon Student Id'].isin(stud_df[stud_df['cluster_index'] == 1].index))]\n", "data_2 = data[(data['Anon Student Id'].isin(stud_df[stud_df['cluster_index'] == 2].index))]\n", "data_3 = data[(data['Anon Student Id'].isin(stud_df[stud_df['cluster_index'] == 3].index))]\n", "data_4 = data[(data['Anon Student Id'].isin(stud_df[stud_df['cluster_index'] == 4].index))]\n", "data_5 = data[(data['Anon Student Id'].isin(stud_df[stud_df['cluster_index'] == 5].index))]\n", "data_6 = data[(data['Anon Student Id'].isin(stud_df[stud_df['cluster_index'] == 6].index))]\n", "\n", "s1_1 = data_1[data_1['Outcome'] <= 1].groupby(['x']).agg(len)['Problem Name']\n", "\n", "s2_1 = data_1[data_1['Outcome'] == 1].groupby(['x']).agg(len)['Problem Name']\n", "\n", "s1_1[8] = s1_1.loc[8:].sum()\n", "for i in range(9, int(s1_1.index.max()+1)):\n", " try:\n", " s1_1.drop(i, inplace=True)\n", " except ValueError:\n", " pass\n", "\n", "s2_1[8] = s2_1.loc[8:].sum()\n", "for i in range(9, int(s2_1.index.max()+1)):\n", " try:\n", " s2_1.drop(i, inplace=True)\n", " except ValueError:\n", " pass\n", " \n", "s1_2 = data_2[data_2['Outcome'] <= 1].groupby(['x']).agg(len)['Problem Name']\n", "\n", "s2_2 = data_2[data_2['Outcome'] == 1].groupby(['x']).agg(len)['Problem Name']\n", "\n", "s1_2[8] = s1_2.loc[8:].sum()\n", "for i in range(9, int(s1_2.index.max()+1)):\n", " try:\n", " s1_2.drop(i, inplace=True)\n", " except ValueError:\n", " pass\n", "\n", "s2_2[8] = s2_2.loc[8:].sum()\n", "for i in range(9, int(s2_2.index.max()+1)):\n", " try:\n", " s2_2.drop(i, inplace=True)\n", " except ValueError:\n", " pass\n", " \n", "s1_3 = data_3[data_3['Outcome'] <= 1].groupby(['x']).agg(len)['Problem Name']\n", "\n", "s2_3 = data_3[data_3['Outcome'] == 1].groupby(['x']).agg(len)['Problem Name']\n", "\n", "s1_3[8] = s1_3.loc[8:].sum()\n", "for i in range(9, int(s1_3.index.max()+1)):\n", " try:\n", " s1_3.drop(i, inplace=True)\n", " except ValueError:\n", " pass\n", "\n", "s2_3[8] = s2_3.loc[8:].sum()\n", "for i in range(9, int(s2_3.index.max()+1)):\n", " try:\n", " s2_3.drop(i, inplace=True)\n", " except ValueError:\n", " pass\n", "\n", "s1_4 = data_4[data_4['Outcome'] <= 1].groupby(['x']).agg(len)['Problem Name']\n", "\n", "s2_4 = data_4[data_4['Outcome'] == 1].groupby(['x']).agg(len)['Problem Name']\n", "\n", "s1_4[8] = s1_4.loc[8:].sum()\n", "for i in range(9, int(s1_4.index.max()+1)):\n", " try:\n", " s1_4.drop(i, inplace=True)\n", " except ValueError:\n", " pass\n", "\n", "s2_4[8] = s2_4.loc[8:].sum()\n", "for i in range(9, int(s2_4.index.max()+1)):\n", " try:\n", " s2_4.drop(i, inplace=True)\n", " except ValueError:\n", " pass\n", "\n", "s1_5 = data_5[data_5['Outcome'] <= 1].groupby(['x']).agg(len)['Problem Name']\n", "\n", "s2_5 = data_5[data_5['Outcome'] == 1].groupby(['x']).agg(len)['Problem Name']\n", "\n", "s1_5[8] = s1_5.loc[8:].sum()\n", "for i in range(9, int(s1_5.index.max()+1)):\n", " try:\n", " s1_5.drop(i, inplace=True)\n", " except ValueError:\n", " pass\n", "\n", "s2_5[8] = s2_5.loc[8:].sum()\n", "for i in range(9, int(s2_5.index.max()+1)):\n", " try:\n", " s2_5.drop(i, inplace=True)\n", " except ValueError:\n", " pass\n", " \n", "s1_6 = data_6[data_6['Outcome'] <= 1].groupby(['x']).agg(len)['Problem Name']\n", "\n", "s2_6 = data_6[data_6['Outcome'] == 1].groupby(['x']).agg(len)['Problem Name']\n", "\n", "s1_6[8] = s1_6.loc[8:].sum()\n", "for i in range(9, int(s1_6.index.max()+1)):\n", " try:\n", " s1_6.drop(i, inplace=True)\n", " except ValueError:\n", " pass\n", "\n", "s2_6[8] = s2_6.loc[8:].sum()\n", "for i in range(9, int(s2_6.index.max()+1)):\n", " try:\n", " s2_6.drop(i, inplace=True)\n", " except ValueError:\n", " pass" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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KebPGes5Ik6+ba1CD2blTlIAWLBBloNIyb15Bz7ZdO/l5zz2GtF9lYS3P5+Hh\nUaI83zffyLPx+PHjmTVrVon9W+T5gDx5vsIM7pIlSzCZTEyfPp2VK1cyadKk8rwtu5k/H2bNEg+v\nuE3r/NcbNsD+/TB5suyPGyeBfyC6tO3aSTBg27YyJdyypRjdTZvy+4iOFqNc1H1M5uTJhQvtM5gl\nLec4O4tAvZ+f/PT1lZxe22N+fuDil8NSv4Osc0qgp5svHwQF02moB24vSF/5SlX+5q3a8DCwFK0/\nNO9PN3ur9wGzi7nuHFrHFXHuXiAWrS3faFEodRUiwlPYHITlCVQDn9icMwEnECU9u7HL4GqtDdmP\nOkZoqCToz5wpXzTDS1eAqoC3oRTMng3vvgsffije7uOP5+t/1lZK8kiLax/o5lbq60uiquT5AJyd\nnRkzZgwLFiyodIObkQF33SWv7XiLhbJgweXHzp6V7a+/yj42ax58UH66ukpQosU4Nmokht3WWBZm\nQH195T3a83/zV1ISd0ZGciozk+datWJ2UBDONeEfTio49QBetTnzK1CSIOg3KOUBHALeQOtVVuf6\ncLnYzhpgAkq5onW2zTlXJAf3GBCOtTJQGWtQ2OvhGtQxLDJ8AweKosmmTdC1a9n7+89/pO7yhx9K\n0v+wYdLfrFlw++3iNRiUj379+jF16lRmz55NTk4OP/74I1Om2E5ICRZ5vvHjxxcqz3f77bcXKc8X\nFBTEypUrL+tba82RI0do27YtWmu+//57OnbsWHlvGHmQe+mly483by5/w2fO5HuMSonX2ry5eLIN\nGkhUvru7/P05OUkbJ6eybcVd27Wr6N3Wr1/5f+s5ubm8cOIEz8bEEOThwaZu3ehdhGpURSlVVTCB\nyExpvM3xeKAoTaVUxFPdDOQAI4GVKDWB/LTVJkh6j22fLuZ7FqyTl29UW5T+LRSO3b96pVQ3YDAS\nlVzA49ValzwfZVDjqFdP1qh69YIbb5Tk/WbNSt+P5Z+6fn145BGZol62TPJ1x42Dp56SNbG77y67\nh2JQUJ6vcePGJcrzTZo0iVdeeYWGDRuyZMkSQOT5brrpJrp27cqwYcMKlec7fPgwgwcPvkyeT2vN\nhAkTSE5ORmtN165dee+99yrt/Z47B7//LgZt0SJZrli6VAKX0tJklmbkSPkZGgqdOjn276tBg8q/\nR0x6OndFRbE5OZnxjRvzTrt21C/GwteaDAKtEwHrgIEIlAoAZlERaauiGDQUaInI0lrf+z+FXVJo\nN3bK883LhyIeAAAgAElEQVRCtP+OI08E1hdprXVJbn6tp6aJFxRFYeIFu3ZB//7QsaOseVXUl1Zu\nLvz4o0znbd4s0c0PPCBbYGDF3KMqqQ6F+OuKPF9srAT4HT4sUcSzZ8MLL8haaGqqPBhWp9nTigjo\nKokv4uOZevAgGnivfXvGVZek3xK4TLxAppTTgLFo/ZXV8f8CndH6ajs7ngC8j9ae5v0/gb1ofb9V\nm9uA5YBXIVPKljbhSOBwLtAA8YSbIML0J9E6xL53an9a0EPAfVrr1lrr3lrrPlZbnTe2tZ2wMEmJ\n2LlT1soKyQYpE05O4oFs2iRbv34S+NKypXjBMTEVc5+6xJQpUwgLC6N79+6MHj26VsrzRUVB9+5w\n4gSsWSPi7Y88IjMpvr5wxRXVy9hC5RrblJwcJkRFMTYqik716rG7Z88aY2wLRVR5tiNyeNb8CyjN\ninoYBaeJtxTRZ0SRxlZ4DfgCaIwo5F2NeLrbgWdLMR67Da4TpawZWRJKqdlKKa2UesfqmFJKzVNK\nxSql0pVS65VSnWyuc1dKva2USlRKXVJKfa+Uam7Txt9c6znJvH2mlPKzadNSKfWDuY9EpdRCJU9W\n1m26KKU2mMdyWik1V9WGxMIycOONkn+4erWsxVY0/frBd99JxOiYMTJF2LatpBXt2lXx96utLF++\nnF27dhEdHc3s2cUFc5aOQYMGVbh3W1q0hldflQpm8fHw6acwaBCMHQv+/rVoerQUbE1OpltEBJ/H\nxzM3KIg/w8Jo7enp6GFVBK8DE1FqMkoFo9RbQDPgfQCUehGl8m2SUhNQ6k5z2w4o9ShwP/kZNpiv\nvQKl3jS3mwxM5PLgLFu6Am+btW9NgDtan0GmqyvF4L4HVFiooVKqN5JetMfm1CzgEWA6EhWWAPym\nlLKuqv0mMBoYCwwA6gM/KqWs05GWI0LBw8xbd6xklMxtf0LklQaY+7oVqzUApVR94DdkCj0ceBB4\nDAlXr5PMmCGeRGWWawwJERGEo0elAMePP0K3bhIx/ccftvmFBnWFrVtlpuWxx8R7XbwYbJaQ6xQm\nrfnP8eP027mTbK3ZEBbG/NatcXGqJQklWq8EZgJzgF1IzuwNaH3c3KIpIh1rzRwgAtgGjAHuRus3\nrPo8BtwADDT3+RQwo9Ac3IJkkb+MGo94tyBKQc0LvaII7F3DVUjCcBNgH1DA/dZa3233DWXxeQcw\nGXgG2Ke1fsB8j1jgHa31C+a2nojRfVRrvch87VlgktZ6mblNC2Rt+Xqt9RqlVDAQCfTXWm82t+mP\nKDt01FofUEpdjxjcIK31SXObu4CPgEZa62Sl1H3Ay0BjrXW6uc0cJA+subb54GrzGm5RpKRAZQuM\nXLwogt1vvileTc+eEtk8apTj5M2Kojqs4dY0SvrMTp2S9dnPPxdDGxAgVZzqWhEVa05mZDA+KooN\nSUnc3rAhi9q3x8/V1dHDKjM1QID+VyQneDlKfYB4vAuB8YAfWve2tyt7H4deAIYg4db+SG1l6600\nfACs0lqvszneGjHoeXlSZkP3J/m5Vz2Q3CjrNieBKKs2fZAQceu5/s2I0oN1myiLsTWzBinV1cOq\nzUaLsbVq0wxoZd9brb18843k50ZHV+59/PxkCjsmRgpmJCVJGlGHDmKI09NL7MKgBnLpkqzJtm8P\nK1fKw1XHjlL5qS4b21UJCXSNiCAiJYUlHTrwRUhIjTa2NYQ55KcozUE82w8RezW1NB3Za3CnAXdq\nrcO11jdqrUdYb/beTCl1D1KXck4hp5uYfxaWe9XEqo0JSCyhzVlrD9T8OsGmje19Es19F9cm3upc\nnaZHD5nmbVJFn4SHh6R9REXBqlWSYnHffVJ68oUXROHIoPbw3HPw7LOSzmMyyczGpk1Sr7gucslk\nYnJ0NLdFRnKlpyc7e/ZkYtOmtaJWdbVH661o/bv5dQJa/wutvdA6DK13l3B1Aew1uOlAyYVhi0Ep\n1QH4D2K4i4sIM6gBBAXJNJ+fn3iZGRlVc19nZxg9WnKC160Twz9nDrRoIbm8J0+W3IdB5TNjxoxS\ny/Jt3JhfVvHRR2UN/59/YPBgWLu2avJYqyM7UlLoHhHBx3FxPNGyJZu7daOdl5ejh2VQBuw1uG8A\nM8sZodsHqeaxXymVo5TKQcKrp5lfnzO3s41nbwxYamPGIRVIbLM0bds0tB6r+XUjmza297FUNymu\nTWOrcwZIpZ5rr5ViAxWVLmQPSkmE6s8/w+7dEkCzcKFMc0+YAPv2Vd1YahqVKc8HEBERwYVSTjlk\nZUlE+nPPSWDc++9LRbKbb5bAuTokqZtHrta8euIEvXfs4JLJxO9du/Jimza41ZbAqDqIvb+5AcA4\nIEYp9Ys5FSdvs7OP1UAXJDfKskUg+U1hwEHEkOXlSSmpiTmA/PXY7UjAlnWb5kCwVZstgDdi4C30\nQfQMrdsE26QT/QvINN/D0maAeQzWbWIppUJEbcbVVbQ/V650XFpGaCh89plIod1/v0w5d+kiqUwb\nN9atyGZHy/OZTCYee+wxFhRWnNiGnBxZk8/MFDnIH3+UCmSPPgpPPw3/93/w1VeynFDXiM3MZOie\nPTx29CgjAgLYHR5uiMTXAuwt7ZgIlEt2T2t9EbhofUwpdQk4r7XeZ95/E3hSKRWNGOA5SADUcnMf\nSUqpxcACpVQC4hW/jqQXrTW3iVJK/Q9YpJSyFHtdBPyotT5g3v8V2A98qpR6BAgAXgE+1Fonm9ss\nR6KolyqlngfaA08A820jlOs6s2bBwYPinbRtK1+UjiAoSKKZn35ahBIWLpRa0L17i1jCyJFSbKOq\nGDSo5DY33igGxtJ+4kTZEhPBVohn/fri+6oO8nzvvPMOI0eOpGkxi61aiyhAbKxEof/xB1x/PXTu\nDFOnSsrP9Onyu6yLztz3iYncHR1Nem4uH7Rvz2RjrbbWYNefs9Z6UnFbBY5nATJ9/V/E+20KDNFa\np1i1mQl8C6xEoo9TgRG6oHrDnYgm4hrzthsJ4ba8HxMwHCkfttnc19dI8WtLmyTEo21mHst/kTxd\nK/lpA5Dp3ffek7W2yZNF8NqRBASI0T1+HN55R9KJbrlFcnwXLxaPykJtKpZgLc/n4+NTojzfnWax\n4vHjx7Np06YS+7fI8zk7O+fJ81kTGxvLV199xfRi9ByTk0XK7sQJ8VybNhVjm5kpBU8WL4a5c0VX\ntq4Z2zSTiWkHD3LTvn209PBge48e3NOsmWFsHY1SfSlY58Fy3BmlSlVpsVS6FWYB+isRb/GSUqoe\nkKm1LtOikNZ6kM2+BuaZt6KuyUQKYxT5X621voAIDBd37xPAjSW02YskSRuUgJsbfP019Okjxu3v\nv/M1cB2Fl5dMMU+dKtPMCxbIA8HTT4vs4NSp9guDl4WSPNLi2gcGlv76kqhseb6dO3dy+PBh2prz\ndtLS0mjbti2HDx8mPV1yapOSRJ3nyisl4C46WlKARo2CX3+VamYPlUphtHawJzWVsZGRRKal8Ujz\n5rzQpg3ude2Jo/qyEXH+EmyO+5nP2V0RwK7fqFKqsVLqb2ArMtVqCR56nYIKDQZ1GH9/+Okn8XiH\nD4fz5x09IsHFRbyn7dvlS71TJ5libtmy5GtrEv369eOHH34gIyOD1NTUYksxWuT5gELl+YAi5fly\nc3NZuXIl/fv3L9Dn8OHDiYuLIyYmhpiYGLy8vIiOPsyJE+LVpqSINF6nTvK3opQE2g0ZIlHIixfX\nPWOrteatU6cI376d8zk5rAkN5dW2bQ1jW71QFBTssdAAqe9gN/Z6uG8gOagBiMq9ha8oWKvSoI5z\n5ZVSb/naa/O9Fje3kq+rCpQShZnNm+ULPjk5/zhIoYWaPMXsaHm+wkhJgYQE0aFt1kyC7CxkZ8t0\n/7Zt8OWXku5Vl4jPymJidDT/O3+eGwMC+LhDBxpWl38WA1DKErekgaUoZbUYhTMQCvxdqi7tLO0Y\nD1yrtd6nlEoBumqtjyqlWiOlGatvWa4qoi6WdiyOZctkGnfZMpnarY5kZcn0JoiXtWJF+XI9q0Np\nx+ogz3fxohjThg0lQCoz8/JI48xMCbSLjY0Cghk6tNxDrFH8fO4ck6KjSTaZePXKK5lWh9dqq21p\nR6Us9ffHITE+1tUGspBslUVobTvVXCT2erie5hvY0tBmEAYGgAjL33lnfqH52FhZOwUpmBEXJ8bO\nw0M2y2vLT19fKVYP4gW5uEgwFMg0ZEXMuFmciQ8/hGnTIDxcFIs6dy5/345iypQpREZGkpGRwYQJ\nExwiz3funBjUwED5/dsa2/R0OHRIKkg1bpz/e64LZJhMPH70KAtPn6ZzvXr8HhxM57qYZFwT0FoC\nbZWKAV5E67Tydmmvh/sjsEdr/aTZww1Fppa/BExa69vLO5CajuHhFs3kyVKScfNm2e/bVwrQF0dw\nsKz7AfTvL4b4d7MYV5s2EoBjbbCtjbWHh6QCmVNQeegh6W+KOUls7lwx2PXqiRzg+++L1u+oUTIF\n+umn8rq0VAcP1xFkZ8sDVaNG4Okp+bVOToU/FKWliWcLUif5+PHa+5ktOHGCcB+fvPzZ/ZcuMWLv\nXo5lZDDjiit4uU0bPByowJGak4O3S6niZiuFauvhWlCqI+CCOX3V6nhnIAet7a4ob++nPQvYoET5\n3h0JlOoE+AL97L2ZQd3ko48K7m/YIB5QRoZshb22Xsp6/PGCykDTp0seZ1HXZmQUFCCPiBAP2cKr\nrxYUPVi5UvR4J0yAH36QtcSnn5b1XCN2pWhyc2X24cwZmTr28hKDW9R3eEoKHD4sv8v27Wt/QYtw\nHx9uj4xkZXAwB9LTefDwYXK05j+tWzM7KMihY9ueksL1e/awPDiY6+pqzUz7+QhJC7WtX9cF0RkY\nYG9HdhlcrXWkUqoLIk2XCXggAVP/1SLEa2BgN66ustk7k2aTTlrqSNaNGwvup6WJsUhKkoL469ZJ\n8YWXX5bzLi5SxGPXLvjkE6hfv/pJAToSrUUs4tQpWQf39ZVa1sUZ0KQkqQTm5ibGtrbHBmmtaeHu\nzqQmTRi2dy/ZWuOqFF+FhDC6USNHD4/2np5c4+dHm9ohVl/ZdAX+KeT4VkTU3m7sMrhKqZbASa31\nM4WdM+e0GhjUGJycJDVlxIh8g56YKLmvv/8uxuPtt2V9MSkJ1qyBq67KL0NYF+NbtJac2VOnIDVV\nvNn27eWBpDjOn4djx6R9u3YFI5VrC7las//SJf5MSuLPixfZmJTEGXMOs6eTE9la83iLFg41tmkm\nEy8cP86TQUH4uLjwRadODhtLDSMXybm1xQ9JGbIbe6eUj1FI4q9SKsB8znj+N6jxBAZKOUVLtcKb\nbpIiHmlposd71VVSmOGtt6Sq1jXXyM8rr6x9Bthkyjeqbm75HmpurswABAXlB0UVx9mzUvHL21vK\nflaDJcMKITs3lx2pqXnGdVNSEhfMohDN3d0Z7OfHQD8/3JXisaNHebRFC96LjeUaf3+H1EQ+npHB\nzfv2sTs1lV7163NToK3+i0ExbABmo9QYLBUNpfLUk0DJJdqssPfPv6jEX2+MKGWHktTaFfckEx7n\n8wvJZzRwItPXGd9jhgpieRg0SKaVb7kFxo4Vg9O9O1x3nUxBm+tG0KKFGN/775cp1poyXaq1BDil\np+dv9etLalROjkQSBwVJeo+Hh/z09JSZgaKm2CdOnMiGDRvw9fUlJwdmz15Kr15htGlTs6fl000m\n/klOzvNgtyQnk2auyNXe05PRDRsywNeXgb6+BHl4oJRi3YUL3B4ZyZchIQz292ewn1+B/arCMo7s\n3Fx+6tKF6y3h/gb28jhiWA+glGWBagDgTykrERZrcJVSC80vNfCiUso6LNoZ6AXsKs0NDSoW9yQT\nZ8M8aLgrA4/zuWQ0cMrbNyg/QUGyzjt5Mjz1lHi/S5ZIhPPBg2J4//hDlG7GjIE9e+ScJfDWZCrc\n0NhGsIJ8MW5LSWFWJZTA0lo81vR0SEnJISfHhfR0MawWXFzy85Ld3KBDh/wcand3ebCwhwULXqFP\nn1uJixPj3Lp1zQs+S8rJYbPV9PC2lBSytUYBXb29+XfTpgz09WWAnx+Ni3jC2paSUsC4Dvb358uQ\nELalpFSJwdVas/D0aR45fJj2Xl5817nz5Tq6NekJ0VFoHYVSocAMRNkOYBXwDlqfKk1XJXm4Xcw/\nFSKBZ52LmwXsAF4tzQ0NKhaP87k02JtBfA8PvE/lkNbUJc/4GlQMXl5SwKN7d4mYPnBAqml16CDb\nfffJVOvevdCkSUFd4MhI8RotQakWA2yJYLV8IVt7Q+Xh2WefY9myz2nYsCGBgS3o2rUH8+c/yuDB\ng2jWLIxduzYxdOhYhg8fzdy5d3PxYiINGzbk44+X0KZNSyZNmsiNN97Irbfeio+PyPOlpqayfv16\n5s6di4+PT16lqXfffRenQixpYqLkWQcGyvuuCdPt8VlZbDQb1z+TktidmooGXJWip48PDzdvzgA/\nP/rVr4+fnYvQhT04Da6iKeV0k4l7Dx7k0/h4bg4M5NOOHfGxns9ftkyiD8+elXyu11+X5HmDwtH6\nNOLplotiDa7WejCAUmoJ8KCVdJ1BNcLZBChIDXLF42wO7oaxrXCUEhm9Ll3Ekw0Pl3Si666T805O\n4ig0NlcZn3noELtSU8nKAqcMcDmf72U6O8vW2NmNoXv20NTNjTNZWQR7eTE/Job5MTGFjiHM25s3\nzYoQubmS/mQ9Hbxt2zaWL8+X5+vSpTshIfnyfPXqZbFrVwSurjBy5AimTs2X53vkkfLL8+XmSurP\niy/Opl69Zxk69Fpefvkl3C1uczXieEYGf168yJ9JSWy8eJED5jwxLycn+tSvzzOtWjHQ15er6tfH\nq4bNhZ/MyGDU/v1EpKQwv1Ur5gQF4WT91LNgAcyenf9kmJCQn6RuGN3CUSoEmAK0AaagdRxKjQSO\no/Vue7uxd6JHU8garlKqnlLqY3tvZlB5OOWA86VcMhq6cKavB9meNcCtqIEMHSq1f5s2lddvvFG8\nwL2bW8FAIXd3QEFWNrhnuRCg3TiRmUkjZzfqq+InnNIzJI913z7YsUO852PHJBc2KwuiojYzfHi+\nPN+oUSMKlKq866478iKsK1qeLzdX1rjvuedF/v77ILt2bePChfO8bMm1qiQWnDjBugsXChxbd+EC\nC07kJ05orYm6dIlFsbHcFRlJyy1baPX33/xfdDSrzp6lnZcXC9q04e/u3bnYvz9rw8J4plUrBvv7\n1zhjC/DyiRMcSEvju86dmduqVb6xvXhRvNrHHy84DQMSGfjUU1U/2JqAUtcC2xGlvKGAZV6+A8Uo\n2xWGvUFTExDx9RSb457A/wF3l+amBhWH9Zqt+/lcLrZ3Jbm1K7H9PfE/mIXP8ZzSxa0blEjbtlIp\na8IEePhhqVK1aFHBNm+WoE1oMsEvcReYcDSS+zyCWHEplnE5rQhX/rRvDz4+Mi0bGyupSU5OcPKk\nRAt7eMjaqKenbO7ucj4goPi10sqS5zOZ5EEgJQW6d2+KZL64M2nSJF59tXJXnIqamn++dWvePHlS\nPNikJBLNqkdN3NwY6OvL435+DPD1pXO9egW9vxqK1ppkkwlfFxcWXHkl05s3p4NlvdZkksCDJ5+U\nKeSiOGFkdxbBC8AstH4bqbRoYR2iz243xXq4SqkG5tQfBfib9y1bQ0RPNr6UgzeoQDJ9nfPWbBXg\nfzCbwF2ZuKTnciHYnfirPMj2qvlfKNUNHx8RZ3j2WfjsMxgwoGAAUkn8mXyBSTGRrOocwrvhrfmq\nSwhzciI57HMBSy2CevVkec3iQTdvLnWe27aFK66QaGJPz3wj6wh5vuxsWdNOSZHgKJNJ6uBorVm9\nejWdK7kwtSUQafT+/VyzaxdD9+zhknn98qEjR9idmsrwBg1Y3KEDh3r1IrZPH1Z26sT9V1xBqLd3\nrTC2AE8cPUqfHTtIycnBy9k539hu2SL5bPfcU7yxhdqnV1lxdAZ+KOR4IqKgZzclebiJ5E8nRxZy\nXgOXFcMwqDqsU38utnXF73A29eJNeMWbuNTMhQvBbpzp54nfoSx8YgxvtyJxcpISkF27wl13SYnD\nVq3EGJeEbQTrdQH+fNVJIlhdXOSYj0/BvkqyDVUtzzd8+C0cOCDFQNq2FUH5a64Zx9mzZ9FaExYW\nxvvvl6oQT6nQWrPu4kVeO3mSCzk5rLt4kYYuLtzaqFFeBPEV1XD9uDK4vkEDXJXKnwI/cwaeeEIK\ngxeGUqA18yZMYN4nn0hk4AsvVN2AaxYXgWaIOpA13YFSRSkXK16glLoa8W7/AEYD1pLiWcBxrXVs\naW5YW3G0eIF2ghND6hH0v4JjyHFXnA9xI72xC24XTQTuzcT1UtG/88oQL6gLREXB6dNR+PsH06KF\n5Kw6wnmqKnm+jAxJi8rJkepR9jxkFEZZBB+yc3NZmZDAa6dOsSs1FV9nZ7K1ZmqzZnwWH1/lea6O\nYktSEltTUniwefP8g1lZUpnl2WclQs8WDw8JmGrRAubPRy1dip44UYytgwKmaoB4wStAH+BW4CBi\naBsDnwKfofU8e7sqKUp5g9xPtUZKOxrhr9WQDD8nEsMKf5J3ydQ03JlJWtMczge7E9vXE7/D2dSP\nyUaVLBRlYCfBwRKH4uYmS2FpaTJDV9X5p1Uhz3f8uMTfaC1pUXYsDVcIF7Oz+eDMGRaeOsVpc1T3\nI82b80lcHN927sxgf39GBAQ4pLhEVfNRbCzTDh2ipbs79zRtKp7tL7/AzJn5cky23HYbvPIKBAWR\nlJPDI336QFwcGUeOOFS1qAbwFPAZcBpxQCOROhRfAs+XpiO75PnyGivVDGgJFMiU1lr/WZqb1kYc\n5eFebOtKUtvLE9d9D2fhd7hgpSmTG5wPcSetiXi7AfsycUst+Ps3PNyyExUVRceOwZw+LQFP3t4i\nJVib6gqkpkJ0tNRDbt8eylv73h4PNyY9nTdPnWJxXBypJhOD/fx4pEULrm/QgFdPnqzSAiKOJis3\nl4cOH+bd2Fj+5e/PFyEhNDh+XKKPi1qz79wZFi6UOqTAuMhIlidcrpn+TFAQ81q3rszhF0q193At\nKNUO6IHEPu0ojSyfBXvFC5oBy5EyVprLSz0aj0cOwu9wNn6Hs0lv4ERCL0+8T2YTsD+r0LbOWRC4\nK5O0JjmcD3HnjMXbPWZ4uxWFUhLc5OUl9ZejomR9s6q8wMogM1Oio5OSINmcid+xY35Vqsrin+Rk\nXjt5kq/PnsVJKcY0asTDzZvTzWr+2pHFJaqa+Kwsbtu/n41JSTzWogX/adQIl7lzpWhFViH/835+\nInt1773g4sL57GweOnyY5QkJdPLyYknHjvTasQM9aFCVv5cag1KuyNrtv9A6EjhUnu7sTQt6EzAB\nIcA2YBgyh/0sUEqxNIPKwNNc7CK1hSse50zUizMV2k4B9eJMeJxL43yIOxfbu5HW2JmAvZd7uwZl\np0EDWS47fFg8Qkux/5pAbq6oAiUlydRxRiFVQvfulZ/NmslWUZi05vvERF47eZLNycn4OjvzaIsW\nTL/iCprXdgHdYtiWnMyo/fs5l53N8uBgxq5dC7NmSd6YLUpJIYvnn8/7o1t99iz3HTrE2aws5gQF\nMScoCPeaVm/TEWidjVKF1qEoC/Ya3KuB4VrraCU3P6u13qyUygSeA36riMEYlI/6h7PICHTmXCd3\n3JPScUkv+m/EORsa7s7kUly+t+t7JJus3FzcjH/ECsHLC0JCpCBETIys6zZvXj3rCmdlifdq8WJN\nJvne9vaWADBfX/FolYKICOjZs2Lvf8lkYmlcHG+eOsXh9HSC3N15s21b7m7SpGBJwjrIp3FxTDlw\ngCZubmz29KTbqFHw11+FN+7XT3Qlu3UDIDEri+mHD/NFQgJd69Xj5y5dCswQPGOpOWpQHP8FHkep\nyWhdiuS/y7H3L9kTSRECiVRuhERrRQKh5RmAQcXhfzib7NM5nOnrSWKoO423ZpQ4VVwv3oTH+TTO\nB7uT1M6NXtu3s6RjxwL/lAZlx8VF1jpPnZKKUOnpsq7raE1Yi7atZao4zSxL4uoqRTV8faUGdGXH\n0pi05qmjR3k/NpbzOTn08vFhZUgIowIDcamOTyZVTHJODo8fPUpfLy++/PRTAv/738JLmzVrJgFR\nY8fmhcd/lZDA/YcOcTEnh/mtWvFEy5aXPUw7Ys3WbpSaBjyGSMPuB2ai9cbiL8Ky1roDUGjtbXV8\nEFKswpbgEtZjewHXAkNQai9QMFhH61EljsmMvQY3GuiIzGXvAu5VSp0E7kcitwyqCa7pmoD9mSSG\neZB0petlgVOF4ZwNDfdkkhaXQ3xvN3rt2MHsli2ZExRkeLsVgFKSheHpKRG+lnVdW+GWyiYnp+Ba\nrKVQh7e3FNLw9ZUxlpTOVNwUstaaOXPm8NVXX+Hs7Mx9993HjBkzLmuXbjIRn5XFqcxMXkxI4KbA\nQB5p3px+vr6XVbSqi5zPzsbXxYX6wMa9ewl66ilcz527vKGbGzzyiFSR8hbbEp+Vxf0HD/J1YiI9\nvL35vWtXunh7X35tdUapO4C3gGmINN404BeUCkHroktiKeUGfAH8iczMFkYnCqa4llARhFTgO/sG\nXjz2Gty3gCbm188C/wPGAplI2UeDakS9OBPpp7JJutIVj/Mmu5WDvBJM7AgP56HDh3nu+HG+TUxk\nSYcO9Kxfv5JHXDcIDBSDduSIpEJec42I3FtYt07qNM+aVTH301o8aouRzU/LzCEgwCXPiy3tjG1x\nBnfp0qWcPHmS6OhonJycSLCKhtVak2IyEZeVRbLJhBPg4+zMgV69LpeNq8Ocy86mR0QEd2Vk8PyM\nGbTdt6/Qdr+17cXz10zmeG4zeH4DGrjU1JkLwe7kuoDfoWzOxsQzQld+McBKyG54GFiK1h+a96ej\n1DDgPmB2Mde9DOxBROOLMrgJaJ1YxLmCKOWELJueRuuypaFYYZf7orVeprVean69A2gFhAMttdZf\nlQ9fmEwAACAASURBVHcQBhVPg6gsXNI0iaHumEoxfdnA1ZVPgoP5sUsXzmdn03vHDp48epQMU+FB\nWAalw6KV2707TJwIX34phnHdOrj9dlEhKg/z5z9Hu3YdCA/vzw03jGX27Fc5fRr+7/8G8f77M5ky\npSfr17+FUjHceus1dO8eyrXXXssJcx3diRMnsmrVqrz+vM2e0fr16xk4cCDDhw+nQ4cO3HvvvXk1\nl6157733mDt3bp5sX6NGjcjVmsTsbCLT0jiYnk56bi5XuLkRWq8eDVxdDWNrQ0BsLOM3beLme+8V\npQpb2reHn3/mntFzOe4vTz857oqz3d0519UDl7Rcmm1Ox7emZh+Il9oD+NXmzK9A32KuG46UG55e\nwh0iUOoMSv2OUoNLaKuBfci0drkpUzSC1joNmSO3G6XU/cBUxFiDzMk/r7X+yXxeIWUipwD+wD/A\n/Vrr/VZ9uCP6u2ORdeXfgWnaSgRYKeUPLARGmg99D0zXWl+0atMSWQi/BkhHUp4e1VpnWbXpAryD\nzN+fBxYBz+nSJC47ECcTNNyVyZk+Hpzr7E7DnZmlKus4PCCA/eHhPHzkCC+eOMHqxESWdOzIVYa3\naxczZ8KuXcW3adJEivtYZEmDg2H+fNkKIywM3nyz4DGtJYo4KQk2bRJ5vqVLd6N1Nnfd1Z3evXvQ\ntatMX7u5ZbFjRwQAI0aMYMKEfHm+GTPKL88HcOTIEVauXMm3335LYGAgT7/2Gt5BQWRrjaeTE63c\n3Wng6lprahhXFCatmXvoEHd8/z2hTz/Nc4WFhnt7wzPPwIwZMpW84Sfxaq9w4UJHN7QT+Edn1oYS\nroFIqqmtax4PXFfoFZK6+iFwC1qnFrEucgbxkLchtSTGA7+j1NVFrg1rrVHqoHlMh0v9TmyoygW6\nU4iAb3egJ1IucrVSyhJ0NQt4BHk6CQcSgN+UUtbRO28iJSbHAgOA+sCPSinr0I7l5nsMM2/dkSoh\nAJjb/gT4mPsYi5Tses2qTX0k8jrePJYHkcX7h8v5GVQpbim5+B/IIr2xC6ktSv9s5efqyscdO/JL\nly6kmEz03bGDWUeOkG54uxVC48ZidGNjZbq5iLLHl5GbKwb2xAlxgPbvl6CsiIjN3HDDTYSGetC3\nr8jz1auXH6B1xx135PVR0fJ8FjIzM3F2d+ebTZu4bvx4HrjnHjydnGjn6UmIlxeBbm6GsbXhfFYW\nN/z2G/+JjeW7PXsKz8OaMEEqSD36aF4llRwPRUIPd851ccc1JZemm9OpX/ONbVn5DHgPrf8psoXW\nB9D6fbTejtZb0Hoasjz6WAl9PwG8glLlVuKosnh7rbXtovNTSqn7gD5KIr9mAi9prb8GUEpNQIzu\nncAipZQv8G9gktb6N3Ob8cBx5KlnjVIqGDGy/bXWW8xtpgIblVIdtNYHgCHIonmQ1vqkuc0s4COl\n1FNa62RgHKJ5OEFrnQ7sU0p1BB5WSr1eU7xcAJ/jOWQEOHO+oxvuF0xlyrUdFhDAvvBwHjtyhFdO\nnuQ7s7fb114LUQex9UQLwzKN/Pjj8MEH4u3efrtECcfGFlwrtRSfOHRIAp60lvQiHx8x3L6+sGkT\nXLhQdF3jypLns5Cak0PjK64geOhQzmZnM+qWW3hu2jTaG1PGRbJ31y5uPnqUkz4+fPjKK0z++eeC\nDXr2lDSf3r3zDmmt+ejMGWL7S5kv/8hMfE7UKkObiNR9aGxzvDEQV8Q11wBXo5RFTEcBTiiVA0xD\n6w+KuO4fYEwJ4/kMsQe7USoLmRXNR+sGhV1UGA4JQVVKOSulxgDewF9AayQoK2/O3mzo/iR/zr4H\n4GrT5iQQZdWmDxJRZp2kthkJ47ZuE2UxtmbWAO7me1jabDSPwbpNM/KnxGsECgjYl4lztiaxqwe5\nZfyN+7q48EGHDvwaGkpGbi79d+7k4cOHSTO83TJhMbZffgkvvQRffCGBVCtXirGNjRXDevKkeLF7\n94pHm5EhebHt2skUc7t2IuHn7u4YeT6tNeezs4m6dIno9HQGDh/Oob/+oku9epzYupX27dtX4qdY\ng7lw4f/ZO+/wqKr8cb9n+kx6T0gg9F5FdFEsqCiWFbv7tbAW1u5PXdvacRUL7uLa11VUdNW1rrpW\n7B0pgnQUSEICpPfJzGTK+f1x7iSTySSZkEIg932e+8zce88998xA5nM/nbcefJDpu3fjCgT4+tpr\nWwrbtDRYvBh++qmFsM13uTh27Vou+fVXLDWaVrt/CVtQrr1VwKywM7No+dseygRgcsh2J0owTgba\nizOajDI1t8cNqCjpS4CrUBpx6BY1bWq4QojngGuklHVCiMOBH2QXk341v+iPgA0lGE+VUq4TQgSF\nYSSbfbb2PhP11BMeXVZCcwR1JqooR5MaJ6WUQojSsDHh9wk+UYWOCW+7VBJyLq+dj9nnMDZCyjoP\npdPsVI22kLIxcunHaJiVnMz6adO4eft2Hi4q4n8VFSweNYrDExO7ccX7PytWKGGrlbfl2GPhjTfg\n00+biwf9+qtK0YmLa118IhI92Z6vsrGRAw48sKk935FHHsnU449nTX09fsAqBIOsVv52xx3MPe88\nljz+OLGxsTz77LPd+bXt+/j9BJ57jjvXrmXB6adz8MaNvH3nnQwIpvyYTHD11XDnnao0o0ZASp7a\ntYubt21DCMFTI0Zw/8dr9i9B25JFwEsIsRylNF2GUnhUv0ch7gcOQsqjAZCyZXSZEAcCgRbHhbgW\nldq6AeXDPQ84BeWmbBspF3f502i0Z1I+D7gVqEMlC2ehTLxdYQvqiSIB5TddIlQysk4PY68IEL+9\nkdqhFlX6sSQ6zbS9xgoZyQbyxwc4Ys0a4gq8JP7aiKGHFN79ralCpNSfMWNa+3GlVLEyGeHGtTa4\n4YYbmD9/flN7vqlTldHmq6++ajEuNzeXL774otX1GRkZLFu2rGn/wQcfBMBqNGKMieGld96hwe+n\n1Oul3O/HbjAwwGIh0WRSJmaLhQ8+6Hq7yf2S77+n+qabOO+kk/jg9NO56MMPefIf/8AatCIcc4xq\nrTd2bIvLtrlcXLx5M1/X1HBsUhL/GjWKXJuNB+ggKm9fRsrXECIFuB0le9YDJyBlgTYiCxjWyVkt\nwENADkr73QCciJQftnsVBCOn/4Aqbyy1a18nJNA2GtoTuPnA1UKIpSjL5HQhRFWkgdF2C9KigIOR\nXquEENNQtZiDnY8zgNCk5lCbfTEqci2VlonKGcC3IWPShBAiqOVq0c/pYfMcGra0YFRc6JhI/gNo\n24fQ50n8zYs72UjleK30o7trrmhbpTJpVY+0UJdrxpVmJGW9J+q8X52WhNYl3tPyid3Rnk9KiVdK\n3IFA01ahCYXtWkCPAAZZraTvT62QeoKXX1ZO+p2qPtBnhx/OJ9Om8cQ//sHl776rNNTBg+Hhh1VS\ndoj5wi8ljxUVcWteHhYhWDxqFBdmZvafwiBSPgk82ca5Czq49gXghbBjC4GFnV6Hit/5GEhGCVpQ\nRZ/uQYjZqNigqGhP4N4IPItKMpbAf9sYJ9nzbkEGlO80DyXIZqFCthFC2FBRxEEb+SrAq415RRuT\nA4yh2a7/I8ovPD3k2HQgJmzM7UKInJB0olmoIh6rQsY8KISwSSndIWN2oR5E9kmEhNRfPOw+VCv9\nuKLj0o8dYfCrnF9HsY+K8VZKDrITu8NL0pae03a7ysIdO/bblm6vvPJK1GMDUuIJEaqukPehj0xG\nYNphh3HYEUfQqBWvyLRYdGEbidpa2LJFdax480344APw+9mdnExWZSVnfPMNB8ydy9Ddu1UVlFtv\nVZWiwvocbmlo4KLNm/mhtpYTk5N5etQosnu6PZNOWzyC0rDPI5heKkQi8LJ2bna0E7UpcLWo4neF\nmrgSFdm7xyZlIcQDqHScQlRKzjnAkaimCFII8Q/gViHEZlSd5ttRft5XtPXUCCEWAws1n2wFys6/\nFvhMG7NJCPExKqr5Eu3WTwPvy+ankKWop5QXhRDXAykoM8MzWoQy2j3vAl4QQtwLjESFht+9L0Uo\nR8LskiRv8FAxyUbNUDOJ2zou/RgNtqpQbdeEM8tIwjYvCfnNbn93sgFPgpGEvO65554yLS6uRZPy\nL6uqmvb7Ct3ZgccXIkhDBasn7L+yRQhsBgOpZjM2g6FpMwuBEIJan4/tbjdZFgtlXi9xRiPx/bGx\nQCCgotk2b1ZbUMBu3gy7W8ff/PuYY7jk+uv58aqrmLRtmxK2Z58NCxdC2AOeX0oWFRZyZ34+doOB\nF0eP5ryMjP6j1fZNZgAHE1LLASmrEeIW2g7iikiHfy1SymqhqnH81sWgqUzg39prDUpQHi+l/EQ7\nvxBVzOIJmgtfHCulrAuZ41rAB7xGc+GLuVLKUF3qHOAxVFQxqMIXV4V8Hr9QFUmeRDnjXagnlRtD\nxtQIIWZpa1kJVKHydBd14fP3GWJ3+3GneqkZrpV+rOoeE7AhAMmblbZbNslK9Wgr7mQjKZsa8cQb\nqBxnJXmjB59dNPe6Ei1fZegxaDKxraqrQ0qJBAIos2dAGx/Qjoe+D4SNDb/umuxsTlm/npNTU3m/\nooLHhg9nfExMl7slqTz5rv84dlbgSilpDDMDBwWrL0SwCsBmMOAwGkkOEao2gwFjO+sOCtuhNhvx\nJhNxRmOL/T2hzz+7Op0qci1UoG7erI65XB1fr3HcihVc8e67jCzUEiOEUGHpYWxwOrlo82aW19Vx\nSmoqT44YQZau1fYFPKiaD+HEAZ3y4Ypo/9NrVZ7OpdlpvBF4RUrp6cwN91diYmKk07lnpTbbC0zq\nKQJG2H2IHWmArB9cGDWlMzw4aU/XFjBAxQQLDZmmjqvh9zHsBgMJJhMJRiOJJpN6bzI1v2/juKm4\nmNSEBNJSUppKG3aGYo8HR5jWWOvz0eD3k6n98AbaEKqeMDOwCVoIU5vRiM1gwKppqz2xts4gpaSi\nooK6ujqG7M2ONVKqsPBwobpli8rD2kO2Z2Wx8A9/4LFHH8UcnjqXm6v6NWp4AwEWFhby1/x84oxG\nHh8xgrPT0zv8d9obvxtt0ZWgRiFEg5Sy4yTxvYUQLwFTUHUglmtHD0ZVtvoZKaPuJxDVo6kQYizw\nESq6WGs9zZ+A+UKI2VLKTdHeUKdvYPArf27x72xUjrOSuqZzpR87nD8Aab80UuGD+oFmbKU+HKV+\nkCEKbPBZT8oIx9RL0Mf87NwDVSa7EAho8d6AKsJgCD/ezthVdXXcvH07J6ek8E55OVcMGMAAq5Ua\nn48av59qn0+917YCt5sav58anw9XhBrCAElCML+6muFFRRi1exrCXkXoftg5r5RUeb2kms1YDAac\n2jrsBgNbAK+ULbRVAJMQmLXNpJl/zUJgFMqK4CI8S3/PqSJya7CIkZRRYLPZyMnJ6cKKOoHbDVu3\nRjYDN3d16DomE5+feCJnXXopErjq/fcZ/9tvzecdDliwoGn3l/p6Lty8mdX19ZyZlsbjI0b0uG+8\n5qehWDNrsOU2dx9yF6TgKU4g4eDtPXrvfZT/h7LO/oiKIwIlOz9EVSGMms50C1oDnB/0c2rlD/+N\nKrd4XGduqtM3sNYGSPy1kerRVuoH+okr7FKadSvcyQYaMkwkbG2kbpCZhHzvHkcwn5ya2m3r+rKq\nitvy8nh73LhWPtzQQKq2aAwEmgRxtSagm977fCxv43jo+HDB2R52g4FRDgejHQ7GaK+jHQ5G2O3Y\ne7ph7b7Ayy/DbbcpjTQ7W3WFGDSopcaan698r91FUhKMHt28jRqFHDWKx+x2/pyXxyiHg3fHj2f4\nXXc1r23QICVszz2XxkCA+woKWLBjB8kmE2+OG8fpaWndt752sGbWUPbuFNLmrMaWW4G7IKVpXycC\nUlYBJ2rRymO0o5s66KEbkWgF7qHAtJCgIqSUtUKI24BlbV+m0xv8adlbzFv5DmnOanbFp7Lw8Lm8\nN66jJhiK+Hwf7lQjVVrpx+7CnWygbLKNtDVubJUBbJX+Fvt7kxV1dS2E68ykJF4fO5YVdXVRCVyL\nwUCaxULaHmoiUkpcgUBEQVzj8/F6aSmfV1dzTno69w0dykCrVa8/3BYvvgh/+hMES1AWFcG993bP\n3AYDDBnSJFBbCNjU1BauEk8gwOW//srzeXnMSUnhpTFjiDOZVL3Oc89tMe2qujou2ryZtU4n56an\n88iIEaSYO9HSq4vYcitIOeEXSt86EGOci4DL0iR8dSIghAnVzH4zqjd88LgZkHQitilagesGIpUS\nStDO6ewlTln/BX/5+gWMmg02p7aMBz5+HCAqoSuA1LWN7DrUTvkkGy6/v1u0Jk+CsYVwtVUGSFvj\nxpNg3OsCN1Lqz8ykpKiEbXcghMBhNOIwGhkQ5v/8sqqKX5xO7sjN5aldu5jncpFrs/XKuvYpdu9W\nBajvuQe6Wl40Lq61QB01CoYPhyi++90eD6dt2MCy2lruyM1l/uDBrR6Q5uflcUtuLnfn57Nwxw4y\nLBbeGz+e33ej5SYa/C4zdSsHU/fzYKTXhK8yjviDt+rCtn3eRJUZDg+avRoVwXxatBNFK3D/Bzwj\nhPgTzRrtdFTKzXvR3kyn+5n/+b+ahG0Qh8/DTd+8GLWWa2yUpK7zUHqgjRu3bePxbqh/Gyn1R2m6\nelGMtgg3bc9MTOyUqXu/R0r49lt44gl4+23wtVQs5v/xj8xfsqTt6wcNailQg++zsvY4sG9FbS2n\nrF9Ptc/HG2PHckZ6esRxdxcU8HpZGZsaGrgwM5NFw4aR2Itara/eSt3yodStGYT0mrBkV+ItjyN+\naj51qwdhH1KuC922mYGquhjOUlS6aNREK3CvAZagKjoFHycNKGF7bWduqNN9JLpqSXBHDvjIri0j\nuaGGSkd0HX3s5X7i87w8wS5mJSczp5efvHW6bureb6mvh5degiefjNyQXePuCy5oFrgOB9x4Y7Nw\nHTkS6XDg1wLPfFo1LZ+U+BobW+6HvfdJiVdLrwo/9+COHTRKyU0DB1Lp8/HEzp2trtvhUYkcdX4/\nH02YwOyUlN741gDwVtup/WkY9etyIGDAMWYXttxyqr8aTfqpq7DlVmAbVNHCp6vTihggkqbgQ6UG\nRU1UAldr3j5HCDGcEKexlLLLDXl19pxqezwV9gRSXTWtzgngs2cv596jLubtcUdF9QSf+Gsjwyck\ncdHmzfxy4IHk6KbMXmVvm7r7HJs2KSG7ZAnU1bU5rCQpiTsvvBCAQf/5Dz6TCV9iIj6rVQnHujp8\nK1d2KlCts8wvKOhwTJHHw/Hr1nFXbi7zezgVylseS82yYTg3DgABsROKiD94G+akBmp+GtpCuNpy\nK0ibsxpPcYIucCOzFjgbuDvs+P/RXOoxKjqVsa4JWF3I9iH+evQ8Hvz4Mey+1vnXya5aFn3wMKds\n+IrbjruSwsTMCDM0IyS8OnYsB6xcyXmbNvH55Mk9tWwdncj4fPDee8psHKG5Qiju8eM56b77+Dyk\nAXCh1uXhwNhYpickNKVNmbQt9L1JCMwGQ8v99sZqr7fm5VHh9fLimDHYtOvbGhvcDF9/jTzyyJ78\n5gDwFMdT++NwGn7NRJgCxE3NJ35aHqb45lCbSKk/ttwKXdi2zb3A2wgxFAj+pzwaJXDP6MxE/bAu\n2/5F0E970zcvMqC2DJ/BiCXQMojk8PzVLF18Jf+Y8X88O+1U/Ia2g6JGOhw8MXIkF2zezP1RPLXr\n6HQLJSXwzDPw9NMq0rgtjEbknDn898orucHhIM/t5uSUFP42bBgjly/vFaF2VXY2lV4vk2Jje/xe\n0eIuTKLmx+G489IRVi/x07cSf2A+Rseet+LU0ZDyfwhxKqrccLBZ/WrgNKT8X2em0gXufsB742Y2\nCV6T38cly9/mmu9fxepvDlyy+zzc8tULnLzxG/4y+2rWZY1oc765GRksraxkfn4+qYkGbNV6oJNO\nDyAl/PCD0mbffBO87dTYzsiAP/2J1RdcwHW1tXxdU8N4o5FPJ07kmOTkHl/q/8rLKfN6uSgri9P2\nIF/2rtzcbl+TlODOS6Pmx+F4ipIxODwkHr6ZuAMKMFi7N6e+3yPl+8D7XZ1mz4vG6vRJfEYTT04/\ni+Muepzvcye2Oj+udDvvvHQ9t3/+DI7GyDWIhBA8NXIkg2w2yidZCeiPZTrdidOptNkpU2DGDHj1\n1baF7aGHwiuvULx1K/POPZephYVsaGjgqREjWD11agth2zNCTbKgoIA569fz7O7d+PfQD9ydPttA\nAN56C4qXzKD0jYPw1dhJOnoD2Zd9QcL0bbqw7UmEiEWI+BZbJ+hQ4AohTEKIK4QQ3di/RKenyU/O\n5tyzF3DDCddSbWtp+jLKAPNWvsvSxVdy5LaVEa+PN5l4dexY/FZBxTgrfbzMvM6+wK+/wnXXqWpQ\nl1wCv/wSeZzDoYpZrF6N++uvefDQQxm5ejUvlpTw55wcfjvoIC7LzsYUVq+6uwORnH4/Z2/cyO15\neZyTns7nkya12+Chp/F6VZ2PcePgjDMg0GgiefZasi/9kvgD8zGYdUtUjyDEQIT4H0I4UY13qrSt\nmk5WNo2mW5BPCPEQqrWezr6EELw54Ri+HHogd37+DHM2fd3idE5tKS+8OZ93xxzBX4/+ExUxLWub\nHBwfT+JvXqpHWagv9xO3U39y1ukkfj+8/74yG3/6aftjR4yAK66ACy5AJiTwdnk5N65Y0cJPO8Lh\n6JVl57tczFm/nvVOJw8NHcr1AwfutRZ5bjc8/7zq5pefDxMmKKPAzT9/hdBtlL3BC6g2rpejeqLv\nsf4RrbFwGXAAoEfR7INUxCRyzck38t9xM7l36ZPk1LZsazxn09cckbeKBTMvBnlCixSi+Dwv7hQj\nVWMs2Kr9mJ26rqsTBWVl8Oyz8M9/tt91x2CAk06CK6+EY44Bg4HVdXVct2aN8tPGxPSanzbIV1VV\nnLFhAz4p+aCX82ZDqatTMWR//zsUF8PBB8Ojj6qvSwj4y5q9sqz+yMHAdKRc1+HIDohW4D4D/F0I\nkQusAlr0oZNS/tzVhej0PF8NO5BjL36C6757mYtWvodRNpugEt31PPTRI3DMOvVXPnw4oPJ5U9Z5\n2H2onbJJVrKWuRG65UonElLCTz8pbfb115vrG0ciNVWZjS+9VLWrQ7UAvD0vj+eKi0kxm3lqxAjm\nZWW1Mh333PIlT+3axTVbtzLcbufd8eMZ2UsadSiVlfDYY/DII1BVBUcdpfozzJy5z3W63F8oALql\nLFi0AvcV7TVSA3YJ6C1L9hEaLHYWHDWP98YcwQMfP8a40rCcvC++UDarO++EG24AwOSRpKzzUDbV\nRtVIC8mb9VQDnRBcLmXjfOIJ+LmDZ+/f/U5ps2eeCVodabffzz+KiliwYweeQIA/5+Rwe25ur5Y+\nBChpbOSW7duZnZzMy2PGtOj92xsUF8OiRfDUU6q41u9/D7feqr4ynb3KNcD9CHEpUuZ3ZaJo/0ft\nxQ7ROj3BuqwRzJm7iItWvst1372C3edpPul2q7/0V19l8uQLWDNgFI4yP3H5XuoGm7FV+HGUdV9n\nIZ19lG3blHR47jmlirWFzQbnnKME7QEHNB2WUio/7bZt5LndzElJ4aFe9NMGqfZ6STCZyLRaWXbA\nAYxyOHq1O1NBgfLPLl6sAqPOOgtuuQUmtk4y0Nk7vAE4gG0I0UBzT1yFlFH7O6It7aj7bvdDfEYT\n/zr4dD4eeQgLPnmCwwrCnELr1vH2uhtYMvUk/nbY+cgtqu1exQQrlu9dmDy6P7df8fLL6kGssFBp\np+4OGoUNGwaXXw4XXghhPtjVdXVcu3Ur32h+2s8mTeLovVDCssjtZvrq1Vyfk8O1AwcyJiam1+69\neTM88ID6WoWAuXPh5ptV7JhOn+KG7pooapuJEOJ44EpgKHCclLJQCDEPyJNSft5dC9LpfXYkZXH+\n2fdw6oYveXjZEqhoLvFmQHLhqv9x7K/LuOPYy/nEcTC7p9upmGglfYUb3aXUD/B6VRu8++9v7tDT\nlrAVAk44QWmzxx2ngqJCKPZ4uC0vj+c1P+0/R47k4szMXvPThjPAauXU1FSOSIzUfbRrLFwI06Yp\n32uQL7+Ed96BXbtULq3NpgKzb7gBBg7s9iXodAdSLu6uqaL6Xy6EOBd4HfgNZV4OOleMwE3dtRid\nvYgQ/Hf8Uapg/PnntzqdXVfGc2/9ladfvp/cNVW4U4zUDuldH5tOL1JQoPrNnnaaCnC6555W7fBa\nkJysuvNs3arSgI4/voWwdfv9PFBQwIjly3kpJJ/20gEDel3Y+qVkfl4e+S4XBiF4dMQIpsR1qulL\nVEybpszDX36p9h97TD2DPPooLF0Kf/mLSvN55BFd2PY5QgtahBe66ELhi2g13JuAP0kp/6NptUGW\nAX/tzA11+jhpaSq7/rzz4LLLIC+vxemTtnzHjNtXM/3hp9g8KgdbpR9rTe+HLQ/+S99JC89/4MS9\nvYSu43LBN9/Axx+rbfPm6K99/nk4+2yw21udklLyVlkZN27fTv5e9NMGqfZ6OWfTJj6qrMRuNHJz\nhA5N3cXMmaqg1imnQGKiyo6Kj4e77lIGgB5QqnW6jyqEyELKUlSBi0j+M0Eng4ajFbgjgB8jHK8H\nOiXhdfYRjj0W1q3jn0fNZd6KdzCFphB5nPx44+WMeX4J1ePjSPspgEGvibFvISVs2dIsYL/+umOf\nbCRyc+GCCyKe+rmujuv6gJ82yGankznr17Pd7eafI0dy6YDuK57n9aqvc+3altvOnep8ba36k3r7\nbehFN7HOnnMsUKm9n9Vdk0YrcHcBI2ld+OJwYFt3LUanjxETwwMzL+J/Y4/ggY8eZUJJ8z91otPJ\nW3ffxeGPPEJKeiF1hSn4jLqJuU9TWwuff64E7CefKLNxtMTFQUODqhwVxOGABQtaDe1rflqADyoq\nOGfjRqwGA19MmsRhXVAvi4tbC9aNG5vLQZvNMGaM0nBjYuA//1F+2meegeXLW/p0dfoooXFJIzdE\n+QAAIABJREFU3RijFK3A/RfwaIg5eaAQ4jBgITC/uxaj0zfZkDGMU+Yu4oKV73H9d//G4VUpRIds\n2MDdzz/P7fPmcd+3z7DCNoWfs8fs5dVGx8kbvtRaGpazKz6VhYfPbeq4tN8QCMCaNc0C9ocf2vfD\nhmIywSGHwOzZyvE4ebLKtb3tNmUbHTRICdtzz226xO3383BREfft5XzaUKSUPLhjB7fm5TE5NpZ3\nxo9nkM0W1bVutwppCBeupSGF2gYMUOk7xx2nXidOhFGjwGJRvtuzzoL//lcJ2Vmz1P7rr+tCt78S\nbVrQQiFEAvApYAO+BDzA36SUT/Tg+nT6CH6DkcUHnconI6ezYOmTHJGnChz85dVX+WzqVBZcdB4r\nL7mU5WkTWXjEH6m37h0fXSg2r5tUZzVpzmpSGmpIdVaR2lDNgYUbmFHwS5OZPKe2jL99+Aijy/J5\nY+Kx7IpPw2Oy7OXV7yFlZSoi55NP1FZa2vE1QXJzlYCdPVuVN4oP8xade24LARukr/lpgzT4/Vy8\nZQv/KS3lD+npLB41CoextbtNStWCN1ywbtnSrNDbbDB+vCqrGBSsEyaoeLK2WLGipXCdOVPtr1ih\nC9z+ipCdaDclhHAAY1HRzRullPU9tbB9jZiYGOl0OjseGIG+HAAUcW1ScvKmr7nz82dIbahhZ2oq\nE599ltySEn686ioqrfHcNesylo6c3r3rkhKqq6GkhLPueZdUZzWpzipSGmpIc1aT2lBNivaa6qwm\nxrsHPkmNkthkiuLT2ZmQTlFCOkUJGRTFq9ed8Wl4zNaWa9tb+HyqnGLQF7tqlfqeosFmgyOPbNZi\nR43qdO3AUD/thJgYHh4+vFf9tG2l3qxYAWnn7+biLVu4f+hQbtKaDzidsGFDa+EaWrcjN7dZqE6c\nCJMmqUqnEWT1XqUv/250BiFEg5SyX3i2O1u7TALBX7FOlRoSQtwCnAaMQmnHy4BbpJTrQ8YI4C7g\nEiAJ+Am4Ukq5IWSMFfgb8H+AHfgcuEJKWRQyJgl4FDhZO/QecLWUsjpkzCDgCeAowIUqX3mDlLIx\nZMwE4HHgIJQD/WngHtmZp5T9ESF4b+yRfDPkAG774jnOXP8Zzy9cyJwFC7h13jz+/tRT/Ou/C/ho\n5CHcdcyllMa1XfzdEPCT0lBLiiYkg0IzTdsPHufflyptTXOUvd7DHzGjvpKM+kqm7oocrVsWk0hR\nfAZFCenAtzB4sNpyc9XWk9pdYaHSXj/+GD77DGpqor92zJhmAXv44REjiztifl4elw4YwO1hftp5\nWVm93r4umHoT1CS//BLOPEvy2n8EgxsyeWh3Is5v7JypCdatW5ufR2JjlZZ61lkttdaEhF79CDp9\nESEGIOWubp82GtmhCbkHgUsBCyoc2oPy7d4spexQlRBCfAL8B1ihXf9XYDowVkpZqY25GbgduADY\nAtwJzABGSSnrtDFPAXOAPwIVqPrOicBUKaVfG/MRMAgI+pyfBbZLKX+vnTcCa7Tr/4xqvbQEeEtK\nebU2Jh74FfhGW+to4HlgvpTy7+Gfr19puGEckr+G+z55gr/NPZMnTj2VD2++meOXLwcggEAgqbXG\n8POAUdTa4khtqCLFWUNqQzXJDbUY9sduu+npzUI4KIhD33cmVNXthm+/bdZiN26M/tr4eNWF57jj\n1NbFJu1uvx/7t98SazTiCQS4RvPTJvRy3eFQPv1UCc3p0+GzLyT+AQ3Yyx0465TwF0JpqKFa68SJ\n6p9iL8ZxdZm+/LvRGfqkhiuEH1BpQUIsBc5Eyk482bYxbZQC9zlUmPTNNKcHTQfuBz6TUl7U6RsL\nEYtq5nuKlPJ/mna7C3hcSrlAG2MHSlGa59OaH7kMuFBK+bI2ZiAqevp4KeUnQogxwEZghpTye23M\nDOBbYLSUcotWNesDIFdKWaiNOQ8lmNOllLVCiMtRDxkZUkqXNuZ2VE/EnHAttz8LXACr18Nly9/g\nuQtnUZKUxNqLLyazvfq6vUCjwUR5TCIVjgTKYxIpdyRRHpNIRm0ZJ/36PRZ/cwCRTxjIT8zC7msk\ns76iRSelHiEtLbIwXr9eNQEoKlKJmgMHwm+/qTzZaJk6VQnX2bNV5ftuCFra6fGwpLiYp3ftYofH\nwxytP+3wveCnramBZcvgu+/g+++VRb2hQZ2zWCWJExqYc5CNaZONTJyoGrbHxvb6Mnucvvy70Rna\nFLhCXAHcCGQBG4BrkfLbKCYcAfwMCKSMDTt3BEpJG4eSNwuR8p8R5qhGteTbhBABIAMpyzr3yVoT\n7WPpmcBpUsrQDtLbhRClwFtApwUuEIfyBQd/lYcAmcDS4AAppUsI8Q1wCMqcOxVV5Sp0TKEQYpM2\n5hPUg0A98EPIvb5HtRQ8BKU5Twc2BYWtxieAVbvHl9qYb4PCNmTMPcBgoGVFiH6Ox2zlkUPPI2fd\nLrbOjmHuLbfw8c03Y+hu63tMDGRksMptoSImkXJHImVNQjVJE6yJlMckUmuNadMn+dWGA9uMUjb7\nvWTWVZBTU0pOTYl6rVWv2TUlZNV1g0AuK1PbihVtj6mqar8pQJC0NJXkOXu2ek1P79raNLyBAO9X\nVLB4924+qKxsce7digrerajgrtxc5g/p2d4mO3YowRoUsGvXKrOwwQCTJ0smH9XIii9NXH6xgVde\nEfxnYYwelLSvI8TZwCPAFcB32utHCDEWKdtusCyEBWVJ/QY4IuzcEOBD4DngPJT19EmEKEPKt8Jm\n+gL4DCGC5qQ3ECJymzQpj432Y0UrcJ3AzgjHd6L8n3vCIyizblBjztReS8LGlQDZIWP8QHmEMZkh\nY8pCNVAppdQeDkLHhN+nXJs7dExR2JiSkHO6wI1AkXUAMb/6+XTaNP5+1lnc+NprHV5TZYtr1kJj\nkihv0kgTm4Tqf/96mhIkmin29C4+3b83bmabaUBeo5nCxEwKEzMjnjf5fWTWVzQJ479NS1A5rfn5\naissbJmv2t0YjSplJ6jFTpnSrbbRzU4nzxUXs6S4mFKvlyyLhVsGDeKizEyGOxyIr75CHnlkt90v\nFL8f1q1rFq7ffaeUfVBa6u9+pyo1HXoo2MfW8f8+LOGHGwYx4MEt/GXeME45xaqn3uwf/Bl4ASmf\n0favRojZKAvjLe1c9yCwFviacIELlwG70NyGwCaEOBjVnCBc4J6PckkOB44G8tlzWddEtAL3MeAu\nIcQFIeZVO3CHdq5TCCEWoZ4uZgT9rjr7DzE7Axxct4xb583jyDVr+OB3v2P+kiUAVNriuO73NzQJ\n1UpHAt5oCmb0sBbVGXxGk4paTsgA4G/zw8xpPp8qMRQqhEO3wsLo82GDDBzYnLJz9NHdHtlT7/Px\nRlkZi3fv5vvaWkxCcFJKChdnZjI7ObnHilY4ncokHBSwP/4IdXXqXHY2zJihhOuMGSqgyWRSHX5u\nz8vjxV9LsK3K5cp/1bDojNFYDAay9NSbfR+lpU5FBceGshRlpWzruhOBk4ApwBkRRkwnxDqq8Qnw\nR4QwI2Vz2z0pnSilEISYjDJnV9NF2hS4Qoj3wg4dCewUQqzV9ido13fK2S2EeBj4AzBTShna/bxY\ne80AQk0GGSHnilF1K1NRvtzQMd+GjEkTQoiglqv5h9PD5jk0bGmp2tyhYzLCxmSEnNNpAwFk/1xD\n5vBK/u+OO9iWnc38JUtoMFmZf8wlfD106t5eYs9iMjVHKx9+eOvzfr9qFxMqhAsKVJ+2SOUVs7LU\n+W6OAJZS8lNtLYuLi/lPaSn1fj+j7HYWDh3K3MxMMiyRc5Hv6kLg1a5dSrAGtdc1a9TXIYTKcz3v\nvGYBO2hQy49c4/Px4PYdPFxUhJSSGwYO5NZHc1oV1pg5Uxe2+zjB3+JI1s5jIl4hxADgGeBUpKxv\n428lE/gswpwm7Z67I84t5WEh97Fpx/Yo57A9DbcibD9c5e60SVUI8QhwNkrYhudb5KEE2SxUJDNC\nfbjDUI5zgFWo5r+zUGk8CCFygDE0+2x/BGJRTzPBY9NRDwahY24XQuSEpBPNQkVerwoZ86AQwhYS\nhT0L5WjP7+xn728sHT6Dg79Yw3/PVLm45990CwW2bLbEDcPkDGByyf7b2s9oVBrrwIFwWPPfMjNn\nwiWXNEcAgUoveuihbhW2ZY2NvFRSwuLdu9nY0IDDYOCs9HQuzszk0IQERAf3itZnGwiogOpQARvs\nhWG3w8EHq445M2YoU3F71RZf2L2bG7dvp9zr5dz0dBYMHUpulBWjdPoFLwFPIeVPPTK7EJeigoZz\nAYkQBcCDSPmvzkzTpsCVUl7YtRW2RAjxBMoufgpQJYQIOsjqpZT1mp/1H8CtQojNqJSc21EBUK9o\na6oRQiwGFmo+2WBa0Fq0Jxcp5SYhxMfA00KIS7R7PA28L6Xcou0vRUW9vSiEuB6VFvQQ8IyUslYb\n8woqJ/gFIcS9qFrSfwHu7vd5uFFQPdzMW8ObjQj/Pj4srsAvMTdIzM4AJmcAs1O9Nzv7cSOEYBWn\ndson7il+Kfm0spLFxcW8W16OV0oOjovjXyNHcnZ6OvEdpPW0V2DiJq1Bp8ul9oPC9YcfVJ0SUO73\nGTPgqqvU65QpHQdPSymRgEEIKn0+xsfE8NDQoRwYXgFLZ38jGE8TycLYlnXxKOAIhLhL2xeAASF8\nwBWaYGzLaumjdVxQMypd9Q7gYVQAFyhFcBFCJCLlwmg+FHS+8EVXuEJ7DS8EfTfN9ZgXoopZPEFz\n4Ytjgzm4GteivqDXaC58MTfMF3wOyrf8ibb/HnBV8KSU0i+Uvf9JVASzC3iZZk06KNxnaWtZiYqm\n/jtKwOt0QOJWL4lblUukYHYMgz52EjCDN8aAN9aAL0bgjTHQGGegId0IhmatyuBpKYBNTsnWhgYG\n22x7tQB+r9BG+cQ9Jc/l4vniYl4oLqbQ4yHVbOaq7GwuzspiXCdygSMWmDgTrrlGtcH97jtV5CpY\nwH/MGDjjjGYf7LBhnVPSyxsbOXXDBv6UlcXczEyuycnhupycDrVvnf0AKRsRYhXKovhGyJlZtLa0\nBpkQtj8HuA1VtCgY8PsjcGrYuFnAyhb+29ZcAVyKloqq8QlCbEFlrXSvwNUqN80HZqJ8oS1+9aSU\nHeYhSCk7/EvRNMf5tNMQQUrpAa7WtrbGVKHCvtu71w6Ug729MetQHZF0uogAjF4wVgewVbdMqZEC\nfA4lgNUm8MUYaMgwEbCo/zYjli/HLATD7XZGORxUjTQ3CWWTM4CxvT+Xfobb7+e/5eUs3r2bz6ur\nEcCxSUksGjaMk1NTsezBQ8vMmfDkk3DyyUp4rl+vfK933qkK9U+bBtddpwTsIYdAStvFxdqlwe/H\nYTSSbDaTZDJh0QRsb1ew0tnrLAJeQojlKKXoMmAAoHJmhbgfOAgpjwYgpGKhdv5AIBB2/J/AVShL\n6tOoOJ4LUFUL2yMDpfyFs4zmrJaoiFbDfRGVKLwE5WTWTao6UZOwNXL6WhAh0YSnn/CKoX5NK77/\nj5PZ0tDAFpeLLQ0N1A42t9SKG8PM0/UBzA0BTA0S0c7/1urh5iZNvLvYWwUJGuMM1OeYcGapB5XB\nNht3Dx7MBZmZUXfICcXvVxHEH34IH3ygApwAfvkFRoyAefOU9jp1qirL3BUqvV7uLSjg3yUlbJw2\njVSLhfcmhCstOv0GKV9DiBSUWzELWA+cgJTBnpJZwLBOzpmHECegTMOXo+Jx/l+EHNxwfkPFHoX3\nojwb5fqMmmgF7pHAEVLKnzszuU7PU/PTUKyZNdhym2Pc3AUpeIoTSDh4eztX9h5dEWhBrfjCrKwW\nx3Nv+QCfXWnFQfO0N8aAK82EMydEGwpITK4Q83R9iK/YCzXDLd0ucHuTgAmcWSbqc0w0JhghIHGU\n+Ikt8rLthiMwdFIzLC9XZZo//FBVkaysbE77nTcP3noLrrwS/vlPpdUeGh7r30ncfj+P79zJgh07\nqPX5uDAzU3+a11FI+STK7Rfp3AUdXPsC8EKE418DB3RyJXcDr6Fa0n6vHTsUFTF9VmcmilbgbiPM\njKzTN7Bm1lD27hTS5qzGlluBuyClaX9/RkhU0FWDv2WCGEoIhZunvTEGXKnGVloxQMlUK0aPxOSW\nGD0SY/BV2/qaMVMCnmQD9dlmGjKNSKPAXOsnaaOHmN2+JvN6NMJWSli9WgnYDz9UJROlVMWrTjoJ\nTjxR9XFds0b5cN96S5mXjzqqa71dA1LyWmkpt+blke92Mzs5mYVDhzJhf6zBqLNvI+WbCDEdVYwj\nmN+7CVX6sZ1Sca2JVuBeA9wvhLgBWK8Xq+g72HIrSP39akrfnkrclALq1w5sEr79FYMPrDUBrDVh\nvmLAZxdUj7DQMKDZP+xO0/4MpGwd2ROQGBtDhLBbYgoKY3cAo0dS7fWSYDL1eECPzypwZpuozzbh\nizEgvJKYnT5ii3xYagNRPxjU1qqC/x9+CB99BLu17MNp05RP9sQTlZk41NXbnb1dv66u5oZt21hZ\nV8fk2Fg+nTiRY5KTOzeJjk5vIuVyVP2ILhGtwN2Kigj+GWj1wyKl7GOdIvsX0m9ENpqp/Wk45vQa\nEDKi7OjvCMDskqSt9RDY4MHgVxHUuR+rphNSgN8i8FsFfpt69dma930OA54k0SSogyR9/z12g4EB\nFgvZVitlk6yathxoIaiNHokhyhLMQd+yFOBKM1KfY8KVZgQhsFb6SdjqxlHij2o+KWHTpmYt9ttv\nVaGrhARVHfKEE1QBq4zwhIkQgqk/oexJgYm78/OZn59PjtXKktGjOS8jo9Nmbx2dfZVoBe6rQALw\n/9CDpvocwuRHWL2YEhrwlsZT8up0jPENxI7bScz4nZiT96yL0f5KxS/DcM50kR5QKX31WUYadqdi\n+CWWlEnbMHkk1LZ9fcBAC6F8y+nj2OnxsKuxkV0eD43xBlzpAmlsLUgMjUFTdbMwbmXKbpTUDLcg\nDVCfbSZgFRjdAeK3e4nd6cPc0PGfX8Br4IMPmoVsfr46PmECXH+9ErLTp3dLI6EOKWlUQXMZFgu/\nT0nBIgTX5uRg72sd3XV0ephoBe6BwEEyPPRaZ6/jLkih/L0ppJ+6CltuBQ3bUil/7wAMDg81y4ZT\n8+MILFlVxI7fiWPMLoz2fTdAqCtIwG8TmNwSe3I19fcegO90Dwmptbh3pOBaPBHu2ohrnAN7qQ9H\niR97hR8RQYM0BMDgkphdSvD9eeDAFucHv/YBEuVL9jdpyAalMYcIam+qAb9FtPArq8WqeWsHm7GX\n+Ykt8mEv97cbbQ3grbbj2paOa3s6nh0pnLRIFao65hhV0en441Utjd6kwe9n/IoV/D4lhedGj+aA\nuDgOiIvr3UXo6PQRohW4GwG9vEsfxFOc0MJn6xhWTvppq/AUJxBz+ioaNg6gfn0OlZ+Op/LzsdiH\nlRI7vgj70DKEqYd7vvYRXCkGqkdaCJgEA75zYUurIWFSHpX/nobls2oaSxKI/902DNvdeKQF51Aj\nzuwAwi+xl/lxlPiwl/kxdCJyQQBGHxjrJdRLIPJ3LdG0ZaugdrCZhgGmZl+AQeDKMGGpC+Aoa31z\n6Re4i5KVkN2Wjq9SBRyZkpzETtrBm/cN4fDDu56y01n8UrK0spLjU1JwGI08PGwYB+nVoXR0oha4\ntwOLtAbs61D1jJuQUlZGvEqnx4mU+mPLrWgSwPEH5RF/UB6NpXHUr8+hYeMAyn7LxGBrxDFmF7Hj\ndmIZUL1f+nsbYwVVoyy4k8wYVsdi/SyB4nUpNBYnKoetCOApUhUaar8d1dz+AsDkh/hGGlK8NCR7\nMdo8xLhdGB0eRFwjJpsHY4wHg6MRn0/1K9gTBGDyqECstLUeWOsBWvqWQ/HVWXFtV1qsOz8V2WgC\nox/bwEriphRgH1qKOVnVYj722N7vsLS0spIbt21jrdPJd1OmcGhCAudldqo2gI7Ofku0PxMfaq9L\naem/Fdq+7ozp41jS60g+ahNJR27GnZ9K/fpsnOsGUr96MKakemLG7yR2XKSWx/seXougKiYRV1Eq\nvJ8MaxIJeEy4hMSSVU3C9K0Y7B5qfhhB7KQd1K/JJfHITZji3fgbrAScFvwNVvzaq6/ISqDeQW19\nBvhb/1c3P6EqK2VkqJrBZcVTmoSx0eHBGKNeDTEejI5GDJbIqnKknGpXfgoNWzIx2L24tqXjLVVt\n+YxxLmLG7MQ+rBRbbkWbc/YWa+vruXHbNpZWVTHUZuO1sWM5RNdqdfYXhDgd1Re3VaVFpDwt2mmi\nFbh6s6v9BGGQ2IeWYR9aRsBjomFLJvUbcqj5dhQ1347iiI0wd66qg9vNLVd7FL/TQkNhKnVl6Xh/\nTYFyZUc1JTmxjd2JfXAZtkEVGGy+FrnKttwK7IMrmvbbe+iQEnzSRG18DNY8I6LSgttoIz45iYGu\nOBLr7TjLTTSWxuN3WpGeyBFJwuyLIIwbCbiN1PwwnIRDf8Ne7qP4s3F4ClIBpY1bs6tIPGIz9mGl\nmFPr+oRVosjt5o78fJYUF5NoMrFo2DCuyM7Gur/XvNbpPwjxAKpJ/Teo6lR7HDQclcCVqjqHzn6G\nweojdmIRsROL8NXYcW7Iprh4FPPmqa4up5wCLl8atiHlCEPfCkwPeA14ipJx56fiyk9t0vyI92Ic\nU0nc9N+IyS7DlOBqdW2439uWW0HanNV4ihPazV8WAszCR0p9DaQBaSDSjGQf6eanOlXhbazDQcza\nahwlfkxVEGiw4G+wEHBaldbcYMHvtKrjTiu+WhuNxQn4nRaQSkhVfzkWvgSQ2AaXEzuxENuQMoy2\nvtNGqdbnY+GOHSwqKsIvJdcPHMitgwaR1Bthzzo6vcsFwLlI+VpXJ4q2eUG7pbD0ko/7PqYEFwmH\nbGXz/aNYsQJefBFefRUqKw/CEOMmZswuYsfvxJxeu1c0q0BAVTv69FO1FX51rDLvGgNYcypJPHwz\nhkkVmNNrsdW3HwzWkd+7MzjK/CyfOpVCt5t3yst5u7ycjcPM1Ay3YGwI4Cj14yiux17dTp4RSnsO\nuM34nVZqlw3DuSGH+OlbSTq8U6Vae42ndu1iwY4d/F96OguGDGGI3b63l6Sj01OYaO6T3uWJomEl\nSo0O/akNVXl0H+5+ghBw0EFqW7QIcv6wkvoNOdT9PJi6lUMxp9USM24nMWN3Yorz9OhafLU2XHlp\nuPNTyVis6vwCjB8PcVMKMI2toOoPLmK2u4gr1LS/+h5dUpsMtNm4OieHq3NyGHjHBzSkm3BlGKkb\nZMKTZCDrRzegGgyY6wOtUnyEAKPdi7c0Htf2NBIO+Y261YOw7+GDwJ4gpaTe76e4sZHdjY0UNzaS\nbDI1VYE6Zd06jk5K4uqcHK7KzuaoxESm6X5anf2fZ1Edhe7p6kTRCtzwcEczMAXVb/CWri5Cp29i\nsYBjZAmOkSX4XWYaNmdRvz6H6q/GUP31aGy55cSML8IxoqRbgnYCHhPuHcm489Nw5ac2pbkYY9yc\nfpqq6Zt7SAO/OWq455FNAJjXG7BV9K30JqMX4nb6iNvpI2AEn009pwaMUPw7G7GFPpI3NyIBaaQp\n3Sjct2wbVNFiv6sMuPdDpEFgrVXfV9VIMz6HoSktyW8RSFNL84W91Ef6z+rBqvQAK99U7OLvBb90\neS35D5zY5Tl0dHoJO3ApQhwDrCUsSwcp/xztRNH6cAsiHN4qhKgB7gI+ivaGOvsmRruXuCk7iJuy\nA29lDM4N2dRvyKbi/SlUWnw4Ru4mZvxObIMqojY5y4DAsysRd34q7vxUPLsSQRoQZh/WgZXETd6B\nbXAZ5tR6Fv71GObn53Nh4W4STCZiNEFl72PCNhyDHyxOpc6KAKSu8WByqTV7Eg2UTrNhK/fjKPHj\nW9E533LApOorBwVmoElwKiE6YcUKkkwmvpkyBYDKsVaQkLlCadvuZNX4wNgosVQHQho2BFT9aG0/\nSFDw6uj0Mw5AtQc0AJPDznUquGUPswebyIuwAJ39HHOyk8TDfiVhxq94ipJxrs/GuTkL5/qBKl1l\n3E6k34BDS1kJ4spPwZWXhjnBhSs/FXdBCrLRDEgsWTXE/2479sFlWAdUNxXlCBihZoiZ4T/9hEdK\nrsjO5o7cXKa9/+le+vR7jpC0KGBhbJTEFvloyDDiyjDB+GI8lQG8JSbMdQECFoF/YA0Jheo7rB1s\nwp1kJH21Enxlk6zNjReC9/A3C8oRdjsjQnyrSZsbEYHm34esZe6e/Lg6OvsHUh7WXVNFGzQV3spD\noBoAzwe2dNdidPYthADbwEpsAytJOmYDrq0ZONfnUPvTUJAG6lYOJnZyAbaBVdSvz8adl64KTgDG\nhAZixuzCNrgcW25Fq5KTUkB9jonq4WYCVgNnpqRw35AhDHc49sZH7RHMDZLkTY0kbYLGBAMNGUYa\nMkxUjrO2GBezy6fMzmHP0vH5PmJ3+prqLxs9EuFrDrR4+9TxLcYHTck6Ojp7gBAWYCjqL3E7Una6\nTm60Gm45rVVnARSiut7r9HMM5gAxY3YTM2Y3/norzk0DqFs9iPrVQ6hfPQSQWHKqiB27E9vgckyJ\nDW2anhvSjFSNsuCLNWCt9JP0s4vXbxnXq5+nNxE0txNM/NWLN1bVXm7qx6spxfEFPuILmlOD7BV6\nl0wdnR5HCDPwV1SbWivqT9aFEI8AdyJl1Pl6e1r4IoBq+71VduJmOv0DY6yH+Gl5xE/Lo+KT8dSv\nye1UiktDpgp6T1vlxl7m73MN4HsSAVjqJdTrwlRHp49wHzAXuBr4Tjt2GLAAJUMjNK+MjF74QqfH\ncBeosoQdpbj4rIKq0RYStnux1AVI3tSI8NNhdxwdHR2dXuA84GKkfD/k2BaEKAH+RXcJ3Ai+24jo\nzQt0wokmxSWY2G3wSzyJBhpjBZY6MOg2Ex0dnb5DIvBbhOO/aueipiMNN5LvNhwZxTw6/Yz2yida\nhlRQN9iMK81Ixk9uDD7I/sala7Q6Ojp9kbXAVSiTcihXA51KSu9IULbXtGA2yoms6yPsHcI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"text/plain": [ "<matplotlib.figure.Figure at 0x7f28b629c080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax1 = plt.subplots()\n", "fig_size = plt.rcParams[\"figure.figsize\"]\n", "fig_size[0] = 8.3\n", "fig_size[1] = 4.7\n", "plt.rcParams[\"figure.figsize\"] = fig_size\n", "plt.xlim(0.5,8.5)\n", "plt.bar(s1.index, s1, width=0.9)\n", "#plt.bar(s2.index, s2, width=0.9)\n", "#plt.legend(['CORRECT', 'INCORRECT'])\n", "\n", "plt.xlabel(\"Attempt number\", size=14)\n", "plt.ylabel(\"Number of attempts\", size=14)\n", "ax1.tick_params(axis ='both', which='major', length=0, labelsize =14, color='black')\n", "ax1.tick_params(axis ='both', which='minor', length=0)\n", "labels = [item.get_text() for item in ax1.get_xticklabels()]\n", "labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8+']\n", "#labels = ['2', '4', '6', '8+']\n", "#print(labels)\n", "\n", "ax2 = ax1.twinx()\n", "ax2.plot(s1.index, s2/s1, 'r-o', linewidth=4, label='Average')\n", "ax2.plot(s1_1.index, s2_1/s1_1, 'c-+', label='group 1')\n", "ax2.plot(s1_2.index, s2_2/s1_2, 'b-+', label='group 2')\n", "ax2.plot(s1_3.index, s2_3/s1_3, 'c-.', label='group 3')\n", "ax2.plot(s1_4.index, s2_4/s1_4, 'b-.', label='group 4')\n", "ax2.plot(s1_5.index, s2_5/s1_5, 'c-x', label='group 5')\n", "ax2.plot(s1_6.index, s2_6/s1_6, 'b-x', label='group 6')\n", "ax2.legend()\n", "\n", "ax2.set_ylabel('Fraction of incorrect attempts', size=14, color='r')\n", "ax2.tick_params('y', colors='r')\n", "ax2.tick_params(axis ='both', which='minor', length=0)\n", "ax2.tick_params(axis ='both', which='major', length=0, labelsize =14, color='red')\n", "\n", "ax1.set_xticklabels(labels)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
rishizsinha/project-beta
code/.ipynb_checkpoints/OpenFMRI modeling-checkpoint.ipynb
4
3262874
null
bsd-3-clause
gibiansky/blog
posts/fully-connected-neural-networks/post.ipynb
1
22070
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In the [previous post](http://andrew.gibiansky.com/blog/machine-learning/hessian-free-optimization/), we looked at Hessian-free optimization, a powerful optimization technique for training deep neural networks. In the next several, I'm going to look into implementation details of deep convolutional networks.\n", "\n", "I'm going to begin by reviewing simple fully connected neural networks, re-deriving the backpropagation algorithm for computing the error gradient, and using a clever method ([Pearlmutter, 1993](http://bcl.hamilton.ie/~barak/papers/nc-hessian.pdf)) to find an algorithm for computing the Hessian of neural network error times some vector." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Gradient Computation in Fully Connected Networks\n", "---\n", "Recall that a standard fully-connected neural network of $L$ layers has three types of layers:\n", "\n", "- An input layer (with units $u_i^0$) whose values are fixed by the input data.\n", "- Hidden layers (with units $u_i^\\ell$) whose values are derived from previous layers.\n", "- An output layer (with units $u_i^L$) whose values are derived from the last hidden layer.\n", "\n", "The neural network learns by adjusting a set of weights, $w_{ij}^\\ell$, where $w_{ij}^\\ell$ is the weight from some unit $u_i^\\ell$'s output to some other unit $u_j^{\\ell+1}$. \n", "\n", "### Forward Propagation ###\n", "\n", "The output of a neural network unit is the output of the last layer $u^L$. We use the following terminology:\n", "\n", "- $x_i^\\ell$: The total *input* to unit $u_i^\\ell$ ($i$th unit in layer $\\ell$)\n", "- $y_i^\\ell$: The *output* of unit $u_i^\\ell$.\n", "\n", "In order to compute the output from an input, we simply apply some nonlinearity $\\sigma(x)$ to the input. The input to the first layer, as we will see, is simply set, while the inputs to subsequent layers are computed as weighted sums of the outputs of the previous layer.\n", "\n", "This gives us the following **forward propagation** algorithm to compute the output of a neural network:\n", "\n", "> **Forward Propagation**:\n", ">\n", "> 1. Compute activations for layers with known inputs:\n", "> $$y_i^\\ell = \\sigma(x_i^\\ell) + I_i^\\ell$$\n", ">\n", "> 2. Compute inputs for the next layer from these activations:\n", "> $$x_i^\\ell = \\sum_j w_{ji}^{\\ell - 1} y_j^{\\ell - 1}$$\n", ">\n", "> 3. Repeat steps 1 and 2 until you reach the output layer, and know values of $y^L$.\n", "\n", "As we defined earlier, $x_i^\\ell$ is the input to a given unit, whereas $y_i^\\ell$ is the activation, or output, of that unit.\n", "In these equations, when a unit has no inputs, the $\\sigma(x)$ term in $y_i^\\ell$ becomes a constant, allowing the unit to be set completely externally via the value $I_i^\\ell$. This corresponds to the input layer in the neural network. Thus, we start with some set activations at the input layer, compute inputs to the neurons at the next layer, use the nonlinearity to get their activations, and continue propagating values until we reach the output layer. At that point, we can compute the final activations $y^L$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to tell how well the neural network is doing, we compute the error $E(y^L)$. This error function can be a number of different things, such as binary cross-entropy or sum of squared residuals. However, we require that the derivative $\\frac{dE(y^L)}{dy_i^L}$ depends only on $y_i^L$. This is the case with the functions listed previously, and effectively means that our error must be computed per-output and summed. This rules out a lot of more complex error functions that would be harder to learn with.\n", "\n", "### Backpropagation ###\n", "\n", "The purpose of being able to compute the error, of course, is to be able to optimize the weights to minimize the error; that is, the process of *learning*. We learn via an algorithm known as **backpropagation**, which we can derive in a similar manner to forward propagation. In order to use gradient descent (or another algorithm) to train our network, we need to compute the derivative of the error with respect to each weight. Using the chain rule, we get that\n", "$$\\frac{\\partial E}{\\partial w_{ij}^\\ell} = \\frac{\\partial E}{\\partial x_j^{\\ell + 1}}\\frac{\\partial x_j^{\\ell + 1}}{\\partial w_{ij}^\\ell}$$\n", "Note that we only get a contribution from $x_j^{\\ell +1}$ since that weight appears nowhere else. Looking at the equation for forward propagation $\\left(x_i^\\ell = \\sum_j w_{ji}^{\\ell - 1} y_j^{\\ell - 1}\\right)$, we see that the partial with respect to any given weight is just the activation from its origin neuron. Thus, the chain rule above becomes\n", "$$\\frac{\\partial E}{\\partial w_{ij}^\\ell} = y_i^\\ell \\frac{\\partial E}{\\partial x_j^{\\ell+1}}$$\n", "We already know all the values of $y$, so we just need to compute the partial with respect to the input $x_j$. However, we know that $y_i^\\ell = \\sigma(x_i^\\ell) + I_i^\\ell$, so we can once more use the chain rule to write\n", "$$\\frac{\\partial E}{\\partial x_j^\\ell} = \\frac{\\partial E}{\\partial y_j^\\ell}\\frac{\\partial y_j^\\ell}{\\partial x_j^\\ell}= \\frac{\\partial E}{\\partial y_j^\\ell}\\frac{\\partial}{\\partial x_j^\\ell}\\left(\\sigma(x_j^\\ell) + I_j^\\ell\\right) = \\frac{\\partial E}{\\partial y_j^\\ell} \\sigma'(x_j^\\ell)$$\n", "\n", "Again, $y_j^\\ell$ is the only expression in which we ever see an $x_j^\\ell$ term, so it is the only contribution to the chain rule. The only bit we have yet to derive is the derivative with respect to the activation $y_i^\\ell$. If $\\ell = L$ (that is, we're looking at the output layer), then we know that the partial is just the derivative of the error function, which is directly a function of those activations:\n", "$$\\frac{\\partial E}{\\partial y_i^L} = \\frac{d}{d y_i^L} E(y^L).$$\n", "As we discussed earlier, we require that this derivative is just a function of $y_i^L$ and none of the other activations in the output layer.\n", "\n", "Finally, if we are not looking in the output layer, then we simply use chain rule once more:\n", "$$\\frac{\\partial E}{\\partial y_i^\\ell} = \\sum \\frac{\\partial E}{\\partial x_j^{\\ell + 1}} \\frac{\\partial x_j^{\\ell + 1}}{\\partial y_i^\\ell} = \\sum \\frac{\\partial E}{\\partial x_j^{\\ell + 1}} w_{ij}.$$\n", "Unlike the previous two applications, we see that $y_i^\\ell$ is in many expressions throughout the neural network (the entire next layer). Applying the chain rule, we sum over all these contributions, and find that what we get is the derivatives of the inputs to the next layer *weighted by how much $y_i^\\ell$ matters* to each input. Intuitively speaking, this means that the error at a particular node in layer $\\ell$ is the combination of errors at the next nodes (layer $\\ell + 1$), weighted by the size of the contribution of the node in layer $\\ell$ to each of those nodes in layer $\\ell +1$.\n", "\n", "These equations are complete, and allow us to compute the gradient of the error (the partial with respect to all of the weights). The full algorithm follows.\n", "\n", "> **Backpropagation:**\n", ">\n", "> 1. Compute errors at the output layer $L$:\n", ">$$\\frac{\\partial E}{\\partial y_i^L} = \\frac{d}{d y_i^L} E(y^L)$$\n", ">\n", "> 2. Compute partial derivative of error with respect to neuron input (sometimes known as \"deltas\") at first layer $\\ell$ that has known errors:\n", "> $$\\frac{\\partial E}{\\partial x_j^\\ell} = \\sigma'(x_j^\\ell)\\frac{\\partial E}{\\partial y_j^\\ell}$$\n", ">\n", "> 3. Compute errors at the previous layer (backpropagate errors):\n", "> $$\\frac{\\partial E}{\\partial y_i^\\ell} =\\sum w_{ij}^\\ell \\frac{\\partial E}{\\partial x_j^{\\ell + 1}}$$\n", ">\n", "> 4. Repeat steps 2 and 3 until deltas are known at all but the input layer.\n", ">\n", "> 5. Compute the gradient of the error (derivative with respect to weights):\n", "> $$\\frac{\\partial E}{\\partial w_{ij}^\\ell} = y_i^\\ell \\frac{\\partial E}{\\partial x_j^{\\ell+1}}$$\n", "> Note that in order to compute derivatives with respect to weights in a given layer, we use the activations in that layer and the deltas for the *next* layer. Thus, we never need to compute deltas for the input layer." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hessian-Vector Product\n", "---\n", "In the [previous post](http://andrew.gibiansky.com/blog/machine-learning/hessian-free-optimization/), we saw an optimization method that needed to be able to compute the Hessian (matrix of second derivatives) times some vector. Computing the Hessian is incredibly computationally intense, and using finite differences (as discussed there) can be numerically unstable. Thus, in order to learn in neural networks, we want to be able to compute the Hessian-vector product $Hv$ in a better way.\n", "\n", "It turns out that given a forward and backward propagation algorithm for computing a gradient, we can use these algorithms to derive a Hessian-vector product algorithm. The method for deriving this algorithm is known as the $\\mathcal{R}\\{\\cdot\\}$ method.\n", "\n", "### The $\\mathcal{R}\\{\\cdot\\}$ Method\n", "Let $H$ be the Hessian of our error function. As we showed earlier, we know that the Hessian-vector product is actually just a directional derivative:\n", "$$Hv = \\lim_{\\varepsilon\\to 0} \\frac{\\nabla E(x + \\varepsilon v) - \\nabla E(x)}{\\varepsilon}.$$\n", "By the definition of the derivative, we find that another way to write this without limits is\n", "$$Hv = \\frac{\\partial}{\\partial \\varepsilon} \\nabla E(x + \\varepsilon v){\\huge\\mid}_{\\varepsilon = 0}$$\n", "\n", "Let us then define an operator $\\mathcal{R}_v$ which converts a gradient computation into a Hessian-vector product:\n", "$$\\newcommand\\Rv[1]{\\mathcal{R}_v\\left\\{#1\\right\\}}$$\n", "$$\\Rv{f(x)} = \\frac{\\partial}{\\partial \\varepsilon} f(x+\\epsilon v){\\huge\\mid}_{\\varepsilon = 0}$$\n", "Using a trivial substitution, we can see that now we can write $Hv = \\Rv{\\nabla E(x)}$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Properties of $\\Rv{\\cdot}$:\n", "\n", "Before using this operator to derive Hessian-vector product algorithms, let's take a look at some of its properties. As we might expect, it is a linear operator:\n", "\n", "$$\\begin{align*}\n", "\\Rv{a f(x) + b g(x)} = a \\Rv{f(x)} + b \\Rv{g(x)}\n", "\\end{align*}$$\n", "\n", "This can be easily verified by simply plugging in the definition of $\\Rv{\\cdot}$ and doing some algebra.\n", "\n", "More interestingly, since it is an evaluation of a derivative, it obeys the laws of differential operators. For instance, the product rule is obeyed:\n", "\n", "$$\\begin{align*}\n", "\\Rv{f(x)g(x)} &= \\frac{\\partial}{\\partial \\varepsilon} f(x+\\epsilon v)g(x+\\epsilon v){\\huge\\mid}_{\\varepsilon = 0} \\\\\n", "\n", "&= \\left(g(x+\\epsilon v)\\frac{\\partial}{\\partial \\varepsilon} f(x+\\epsilon v)+f(x+\\epsilon v)\\frac{\\partial}{\\partial \\varepsilon} g(x+\\epsilon v)\\right){\\huge\\mid}_{\\varepsilon = 0}\\\\\n", "\n", "&= f(x)\\Rv{g(x)} + g(x)\\Rv{f(x)}\n", "\\end{align*}$$\n", "\n", "In the same way, we get a chain rule:\n", "\n", "$$\\Rv{f(g(x))} = f'(g(x))\\Rv{g(x)},$$\n", "\n", "and find that it is not affected by other derivatives\n", "\n", "$$\\Rv{\\frac{d}{dt}f(x)} = \\frac{d}{dt}\\Rv{f(x)}.$$\n", "\n", "Finally, applying $\\Rv{\\cdot}$ to $f(x) = x$ just gives us $v$:\n", "\n", "$$\\Rv{x} = \\frac{\\partial}{\\partial \\varepsilon} (x+\\epsilon v){\\huge\\mid}_{\\varepsilon = 0} = v{\\huge\\mid}_{\\varepsilon = 0} = v.$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Using the $\\Rv{\\cdot}$ method\n", "Now that we've taken a look at this operator, how do we use this? Well, we've *defined* the operator to be the operator that converts a gradient into the Hessian-vector product. So, in order to compute the Hessian-vector product for a neural network, we simply apply this operator to the equations for computing the gradient; the resulting set of equations will dictate the algorithm we must use in order to compute the Hessian-vector product.\n", "\n", "Our gradient computation is comprised of two steps. The first step, forward propagation, is dictated by the following set of equations:\n", "\n", "$$\\begin{align*}\n", "y_i^\\ell &= \\sigma(x_i^\\ell) + I_i^\\ell\\\\\n", "x_i^\\ell &= \\sum_j w_{ji}^{\\ell - 1} y_j^{\\ell - 1}\n", "\\end{align*}$$\n", "\n", "Let's go step-by-step (equation-by-equation) and apply $\\Rv{\\cdot}$ to these equations. By applying the chain rule to $\\sigma(x)$, the first one becomes\n", "\n", "$$\\begin{align*}\n", "\\Rv{y_i^\\ell} &= \\Rv{\\sigma(x_i^\\ell) + I_i^\\ell}\\\\\n", "&= \\sigma'(x_i^\\ell)\\Rv{x_i^\\ell}\n", "\\end{align*}$$\n", "\n", "After moving the operator inside the sum and applying the product rule, the second forward propagation equation becomes\n", "\n", "$$\\begin{align*}\n", "\\Rv{x_i^\\ell} &= \\Rv{\\sum_j w_{ji}^{\\ell - 1} y_j^{\\ell - 1}} \\\\\n", "&= \\sum_j \\Rv{w_{ji}^{\\ell - 1} y_j^{\\ell - 1}} \\\\\n", "&= \\sum_j \\left(w_{ji}^{\\ell - 1} \\Rv{y_j^{\\ell - 1}} + \\Rv{w_{ji}^{\\ell - 1}}y_j^{\\ell - 1}\\right)\n", "\\end{align*}$$\n", "\n", "Note that since $v$ is a vector in the weight space, $w_{ji}^{\\ell - 1}$ as a function just picks out a particular component, and thus\n", "\n", "$$\\Rv{w_{ji}^{\\ell - 1}} = v_{ji}.$$\n", "\n", "Thus, the final set of forward propagation equations is\n", "\n", "$$\\begin{align*}\n", "\\Rv{y_i^\\ell} &= \\sigma'(x_i^\\ell)\\Rv{x_i^\\ell}\\\\\\\n", "\\Rv{x_i^\\ell} &= \\sum_j \\left(w_{ji}^{\\ell - 1} \\Rv{y_j^{\\ell - 1}} + v_{ji}^{\\ell - 1}y_j^{\\ell - 1}\\right)\n", "\\end{align*}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we can derive the backward propagation equations for Hessian-vector product. If you understood the previous derivation, it may make sense to skip to the end of this section and just take a look at the results. For thoroughness, however, we will derive the new backpropagation equations. The original backpropagation equations are:\n", "\n", "$$\\begin{align*}\n", "\\frac{\\partial E}{\\partial y_i^L} &= \\frac{d}{d y_i^L} E(y^L) = e_i(y_i^L)\\\\\n", "\\frac{\\partial E}{\\partial y_i^\\ell} &=\\sum w_{ij}^\\ell \\frac{\\partial E}{\\partial x_j^{\\ell + 1}}\\\\\n", "\\frac{\\partial E}{\\partial x_j^\\ell} &= \\sigma'(x_j^\\ell)\\frac{\\partial E}{\\partial y_j^\\ell}\\\\\n", "\\frac{\\partial E}{\\partial w_{ij}^\\ell} &= y_i^\\ell \\frac{\\partial E}{\\partial x_j^{\\ell+1}}\n", "\\end{align*}$$\n", "\n", "Note that in the first one, we wrote the error explicitly as a function of $y_i^L$ only, since that comes from the requirement we originally imposed on the error function $E$.\n", "\n", "Once more, we can apply the $\\Rv{\\cdot}$ operator to each of these equations. Applying the chain rule for our operator, the first one becomes\n", "\n", "$$\\begin{align*}\n", "\\Rv{\\frac{\\partial E}{\\partial y_i^L}} &= \\Rv{e_i(y_i^L)}\\\\\n", "&= e_i'(y_i^L) \\Rv{y_i^L}\\\\\n", "\\end{align*}$$\n", "\n", "The second equation, after applying linearity and the product rule (as we did before to $w_{ij}^\\ell$) becomes\n", "\n", "$$\\begin{align*}\n", "\\Rv{\\frac{\\partial E}{\\partial y_i^\\ell}} &=\\Rv{\\sum w_{ij}^\\ell \\frac{\\partial E}{\\partial x_j^{\\ell + 1}}}\\\\\n", " &=\\sum \\Rv{w_{ij}^\\ell \\frac{\\partial E}{\\partial x_j^{\\ell + 1}}}\\\\\n", " &=\\sum \\left(\\Rv{w_{ij}^\\ell} \\frac{\\partial E}{\\partial x_j^{\\ell + 1}} + w_{ij}^\\ell \\Rv{\\frac{\\partial E}{\\partial x_j^{\\ell + 1}}}\\right)\\\\\n", " &=\\sum \\left(v_{ij}^\\ell \\frac{\\partial E}{\\partial x_j^{\\ell + 1}} + w_{ij}^\\ell \\Rv{\\frac{\\partial E}{\\partial x_j^{\\ell + 1}}}\\right)\\\\\n", "\\end{align*}$$\n", "\n", "The equation for backpropagating deltas (the third equation) becomes\n", "\n", "$$\\begin{align*}\n", "\\Rv{\\frac{\\partial E}{\\partial x_j^\\ell}} &= \\Rv{\\sigma'(x_j^\\ell)\\frac{\\partial E}{\\partial y_j^\\ell}}\\\\\n", " &= \\Rv{\\sigma'(x_j^\\ell)}\\frac{\\partial E}{\\partial y_j^\\ell}+\\sigma'(x_j^\\ell)\\Rv{\\frac{\\partial E}{\\partial y_j^\\ell}}\\\\\n", " &= \\sigma''(x_j^\\ell)\\Rv{x_j^\\ell}\\frac{\\partial E}{\\partial y_j^\\ell}+\\sigma'(x_j^\\ell)\\Rv{\\frac{\\partial E}{\\partial y_j^\\ell}}\\\\\n", "\\end{align*}$$\n", "\n", "Finally, the equation that used to be the gradient computation (fourth equation) becomes (after a product rule)\n", "\n", "$$\\begin{align*}\n", "\\Rv{\\frac{\\partial E}{\\partial w_{ij}^\\ell}} &= \\Rv{y_i^\\ell \\frac{\\partial E}{\\partial x_j^{\\ell+1}}} \\\\\n", " &= \\Rv{y_i^\\ell} \\frac{\\partial E}{\\partial x_j^{\\ell+1}}+y_i^\\ell \\Rv{\\frac{\\partial E}{\\partial x_j^{\\ell+1}}} \\\\\n", "\\end{align*}$$\n", "\n", "This gives us the full set of Hessian-vector backpropagation equations:\n", "\n", "$$\\begin{align*}\n", "\\Rv{\\frac{\\partial E}{\\partial y_i^L}} &= e_i'(y_i^L) \\Rv{y_i^L}\\\\\n", "\\Rv{\\frac{\\partial E}{\\partial y_i^\\ell}} &=\\sum \\left(v_{ij}^\\ell \\frac{\\partial E}{\\partial x_j^{\\ell + 1}} + w_{ij}^\\ell \\Rv{\\frac{\\partial E}{\\partial x_j^{\\ell + 1}}}\\right)\\\\\n", "\\Rv{\\frac{\\partial E}{\\partial x_j^\\ell}} &= \\sigma''(x_j^\\ell)\\Rv{x_j^\\ell}\\frac{\\partial E}{\\partial y_j^\\ell}+\\sigma'(x_j^\\ell)\\Rv{\\frac{\\partial E}{\\partial y_j^\\ell}}\\\\\n", "\\Rv{\\frac{\\partial E}{\\partial w_{ij}^\\ell}} \n", " &= \\Rv{y_i^\\ell} \\frac{\\partial E}{\\partial x_j^{\\ell+1}}+y_i^\\ell \\Rv{\\frac{\\partial E}{\\partial x_j^{\\ell+1}}}\n", "\\end{align*}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These two sets of equations (for forward and backward propagation) give us, via their structure, a forward and backward propagation algorithm to compute the Hessian-vector product. The **Hessian-vector product forward propagation** algorithm is as follows:\n", "\n", "> 1. Initialize $\\Rv{x_i^0}$. Since these are constants (your input layer), this will be zero.\n", ">\n", "> 2. Compute $\\Rv{y_i^\\ell}$ for the current layer via\n", "> $$\\Rv{y_i^\\ell} = \\sigma'(x_i^\\ell)\\Rv{x_i^\\ell}$$\n", "> \n", "> 3. Compute the $\\Rv{x_i^\\ell}$ at the next layer via\n", "> $$\\Rv{x_i^\\ell} = \\sum_j \\left(w_{ji}^{\\ell - 1} \\Rv{y_j^{\\ell - 1}} + v_{ji}^{\\ell - 1}y_j^{\\ell - 1}\\right).$$\n", ">\n", "> 4. Repeat steps 2 and 3 until the last (output) layer is reached.\n", "\n", "After running forward propagation, we must run **Hessian-vector product backward propagation**:\n", "\n", "> 1. Initialize $\\Rv{\\frac{\\partial E}{\\partial y_i^L}}$ at the output layer via\n", "> $$\\Rv{\\frac{\\partial E}{\\partial y_i^L}} = e_i'(y_i^L) \\Rv{y_i^L}.$$\n", ">\n", "> 2. Compute $\\Rv{\\frac{\\partial E}{\\partial x_j^\\ell}}$ at the current layer via\n", "> $$\\Rv{\\frac{\\partial E}{\\partial x_j^\\ell}} = \\sigma''(x_j^\\ell)\\Rv{x_j^\\ell}\\frac{\\partial E}{\\partial y_j^\\ell}+\\sigma'(x_j^\\ell)\\Rv{\\frac{\\partial E}{\\partial y_j^\\ell}}.$$\n", ">\n", "> 3. Compute $\\Rv{\\frac{\\partial E}{\\partial y_i^\\ell}}$ at the previous layer via\n", "> $$\\Rv{\\frac{\\partial E}{\\partial y_i^\\ell}} = \\sum \\left(v_{ij}^\\ell \\frac{\\partial E}{\\partial x_j^{\\ell + 1}} + w_{ij}^\\ell \\Rv{\\frac{\\partial E}{\\partial x_j^{\\ell + 1}}}\\right)$$\n", ">\n", "> 4. Iterate steps 2 and 3 until we reach the last hidden (non-input) layer.\n", ">\n", "> 5. Compute the components of the Hessian-vector product via\n", "> $$\\Rv{\\frac{\\partial E}{\\partial w_{ij}^\\ell}} \n", " = \\Rv{y_i^\\ell} \\frac{\\partial E}{\\partial x_j^{\\ell+1}}+y_i^\\ell \\Rv{\\frac{\\partial E}{\\partial x_j^{\\ell+1}}}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Conclusion\n", "---\n", "We started with a basic description of fully connected feed-forward neural networks, and used it to derive the forward propagation algorithm and the backward propagation algorithm for computing gradients. We then used the method developed by Pearlmutter to develop an adjoint algorithm pair that, in a forward and a backward pass, computes the Hessian-vector product $Hv$ for any vector $v$ in the weight space. This allows us to create second order optimization algorithms that use the Hessian *without* ever computing the Hessian itself, permitting more performant neural network training algorithms." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
longyangking/ML
Notes/preprocess/Iris Demo.ipynb
1
241734
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Iris Demo\n", "+ Check any null and invalid values\n", "+ Ensure the properties of features and labels\n", "+ Convert the string value into computational forms\n", "+ PCA -> Cluster Verification (optional)\n", "+ Logistic Regreesion/SVM (optional)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import Iris DataSet from sklearn" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.datasets import load_iris\n", "irisdata = load_iris()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Convert data form into Pandas format" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "features = pd.DataFrame(irisdata['data'])\n", "features.columns = irisdata['feature_names']\n", "targets = pd.DataFrame(irisdata['target'])\n", "targets = targets.replace([0,1,2],irisdata['target_names'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Check if there is any null values" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "sepal length (cm) 0\n", "sepal width (cm) 0\n", "petal length (cm) 0\n", "petal width (cm) 0\n", "dtype: int64" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 0\n", "dtype: int64" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "targets.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3 group to classify" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['setosa', 'versicolor', 'virginica'], dtype=object)" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "targets[0].unique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 150 instances and 4 features" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(150, 4)" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Convet all the unique string values into integers. Perform label encoding on the data" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.preprocessing import LabelEncoder\n", "labelencoder=LabelEncoder()\n", "for col in targets.columns:\n", " targets[col] = labelencoder.fit_transform(targets[col])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Check the encoded values" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 2], dtype=int64)" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "targets[0].unique()" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "0 50\n", "1 50\n", "2 50\n", "dtype: int64\n" ] } ], "source": [ "print(targets.groupby(0).size())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot boxplot to visualize the distribution of the data" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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tOHSoeMv0oUNlVyItrNmXGUmSekBK6cL59xFxCfAoxXsv7lpm+GMppcfbVJq0\nJmo1GBuDyUkYHS27Gul3uZIuSVqJ51Gskv9qmX4B3BcRD0fEbRFxRvtLk6TuY0iXJC0pIgK4Abgr\npbTUV6tngMuBdwLvAH4O3BERp7a/SknqLm53kSQt53PACHDmUp1SSnuAPfOafhQRLwXGgYvbV54k\ndR9DuiRpURHxGeBC4KyU0kwLv+Ielgn3AOPj4wwODh7RVqlUqFQqLUwpSeWrVqtUq9Uj2mZnZ1c8\n3pAu1Q0NwTXXFFdJTwf0twFnp5T2tfhrTqXYBrOkiYkJRv32nqQustBCw9TUFGNjYysab0iX6oaG\nYNu2squQ8hARnwMqwBbg1xFxXP2j2ZTSXL3PtcCLUkoX1++vAh4CpoE+4APAm4DzOly+JK17hnRJ\n0kKuoDjN5Y6G9kuBr9X/8xBw4rzPng1cD5wAHAR+ApyTUrqzrZVKUhcypEuSfkdKadnTv1JKlzbc\nXwdc17aipDU0PAw7d8LGjWVXIi3MkC5JknpOfz9s3lx2FdLiPCddkiRJyowhXZIkScqMIV2SJEnK\njCFdqjt0CKani6skSVKZDOlSXa0Gp5xSXCVJkspkSJckSZIyY0iXJEk9Z2ameMv0zEzZlUgLM6RL\nkqSeMzMD27cb0pUvQ7okSZKUGUO6JEmSlBlDuiRJkpSZVYX0iPhoRDwVEZ9cos/Z9T7zf34bES9c\nzdySJElStzqq1YER8Rrgg8D9K+iegFcA//50Q0qPtjq31A7Dw7BzJ2zcWHYlkiSp17W0kh4RzwV2\nAJcB/7bCYY+llB49/NPKvFI79ffD5s3FVZIkqUytbnf5LHBzSukHK+wfwH0R8XBE3BYRZ7Q4ryRJ\n0qr19cHISHGVctT0dpeIeA9wKvDqFQ6ZAS4HfgwcDXwAuCMiXptSuq/Z+SVJklZrZASmp8uuQlpc\nUyE9Il4M3ACcm1J6ciVjUkp7gD3zmn4UES8FxoGLm5lfkiRJ6gXNrqSPAS8ApiIi6m3PBN4QER8G\njk4ppRX8nnuAM5frND4+zuDg4BFtlUqFSqXSXNWSlIlqtUq1Wj2ibXZ2tqRqJEm5ajak3w68sqHt\nq0AN+PgKAzoU22WWfRHvxMQEo6OjTRUoSTlbaKFhamqKsbGxkiqSJOWoqZCeUvo1sGt+W0T8Gvhl\nSqlWv78WeFFK6eL6/VXAQ8A00EexJ/1NwHmrrl6SJEnqQmvxxtHG1fMh4MR5988Grgd+AtxBsRJ/\nTkrpjjVdxd2WAAAgAElEQVSYW1ozMzOwbVtxlSRJKlPLLzM6LKX0+w33lzbcXwdct9p5pHabmYHt\n22HLFhgaKrsaSZLUy9ZiJV2SJEnSGjKkS5KknrNrV/GW6V27lu8rlcGQLkmSes7cXBHQ5+bKrkRa\nmCFdkiRJyowhXZIkScqMIV2SJEnKjCFdquvrg5GR4ipJklSmVZ+TLnWLkRGYni67CkmSJFfSJUmS\npOy4ki5Jkta9gwcPsnv37hX3f+wx+OAHi+vU1Mrn2bRpEwMDAy1UKDXHkK6u1ewDu1U+sCWpfLt3\n72ZsbKzpcf/jfzTXf3JyktHR0abnkZplSFfXavWB3Swf2JJUvk2bNjE5OdmReaROMKSra/nAlqTe\nMTAw4IKJuoohXV3LB7YkSVqvPN1FkiRJyowhXZIkScqMIV2SJEnKjCFdkiRJyowhXZIkScqMIV2S\nJEnKjCFdkiRJyowhXZIkScqMIV2SJEnKjCFdkiRJyowhXZIkScqMIV2SJEnKjCFdkiRJyowhXZIk\nScqMIV2SJEnKzKpCekR8NCKeiohPLtPvjRExGRFzEbEnIi5ezbxSu1Sr1bJLkLIQER+LiHsi4vGI\n2B8R346IV6xgnM97rRs+85WzlkN6RLwG+CBw/zL9NgDfAb4PvAr4FPCliDiv1bmldvGBLT3tLODT\nwO8B5wLPAm6LiP7FBvi813rjM185O6qVQRHxXGAHcBlw9TLdrwQeTCl9pH7/QES8HhgHvtfK/JKk\n9kopXTj/PiIuAR4FxoC7Fhnm817ryi9+8YuyS5AW1epK+meBm1NKP1hB39OB2xvabgVe1+LckqTO\nex6QgF8t0cfnvdYVQ7py1vRKekS8BzgVePUKhxwP7G9o2w8cExFHp5SeaLYGSVLnREQANwB3pZR2\nLdHV570krZGmQnpEvJjiQX1uSunJ9pQEQB9ArVZr4xTS75qdnWVqaqrsMtRj5j3r+sqsYwmfA0aA\nM9vwu33eqzRPPvmkz3x1VDPP+2ZX0seAFwBT9ZUVgGcCb4iIDwNHp5RSw5hHgOMa2o4DHl9iVWUD\nwNatW5ssT1q9sbGxsktQ79oA/FPZRcwXEZ8BLgTOSinNLNPd573WHZ/5KskGlnneNxvSbwde2dD2\nVaAGfHyBgA5wN/Dmhrbz6+2LuRW4CNgLzDVZoyStN30UD+xbS67jCPWA/jbg7JTSvhUM8XkvSUtb\n8fM+Fs7VKxcR/wDcm1L6k/r9tcCLUkoX1+83AP9M8c+lfw2cQ7Fl5sKUUuMXjCRJGYiIzwEVYAuw\nZ95HsymluXofn/eS1CZr8cbRxpQ/BJz49Icp7QXeQnHO7n0UR3G93we2JGXtCuAY4A7g4Xk/757X\nx+e9JLXJqlfSJUmSJK2ttVhJlyRJkrSGDOmSJElSZgzp6nkRcVZE3BQRv4iIpyJiS9k1SZLaw2e+\n1gtDugTPofiS24f43S9CS5K6i898rQvNnpMudZ2U0neB78LTrz+XJHUpn/laL1xJlyRJkjJjSJck\nSZIyY0iXJEmSMmNIlyRJkjJjSJckSZIy4+ku6nkR8RzgZcDhb/lvjIhXAb9KKf28vMokSWvNZ77W\ni0jJI0LV2yLibOAf+N3zcm9MKb2vhJIkSW3iM1/rhSFdkiRJyox70iVJkqTMGNIlSZKkzBjSJUmS\npMwY0iVJkqTMGNIlSZKkzBjSJUmSpMwY0iVJkqTMGNIlSZKkzBjSJUmSpMwY0iVJkqTMGNIlSZKk\nzBjSJUmSpMwY0iVJkqTMGNIlSZKkzBjSJUmSpMwY0iVJkqTMGNIlSZKkzBjSJUmSpMwY0iVJkqTM\nGNIlSZKkzBjSJUmSpMwY0iVJkqTMGNIlSZKkzBjSJUmSpMwY0iVJkqTMGNIlSZKkzBjSJUmSpMwY\n0iVJkqTMGNIlSZKkzBjSJUmSpMwY0iVJkqTMGNIlSZKkzBjSJUmSpMwY0iVJkqTMGNIlSZKkzBjS\nJUmSpMwY0iVJkqTMGNIlSZKkzBjSJUmSpMy0PaRHxEMR8dQCP59u99ySpNZExMci4p6IeDwi9kfE\ntyPiFcuMOXuBZ/1vI+KFnapbkrpFJ1bSXw0cP+/nPCAB3+rA3JKk1pwFfBr4PeBc4FnAbRHRv8y4\nBLyc//PMH0opPdrOQiWpGx3V7glSSr+cfx8RbwV+mlL6x3bPLUlqTUrpwvn3EXEJ8CgwBty1zPDH\nUkqPt6k0SeoJHd2THhHPAi4CvtzJeSVJq/Y8ilXyXy3TL4D7IuLhiLgtIs5of2mS1H06/cXRtwOD\nwI0dnleS1KKICOAG4K6U0q4lus4AlwPvBN4B/By4IyJObX+VktRdIqXUuckivgs8kVJ62zL9ng9c\nAOwF5jpQmiSVqQ/YANzauEUwBxHxeYpn8pkppZkmx94B/CyldPEin/u8l9RLVvy8b/ue9MMi4iSK\nLx/9wQq6XwB8o70VSVJ2LgK+WXYR80XEZ4ALgbOaDeh19wBnLvG5z3tJvWjZ533HQjrwPmA/cMsK\n+u4F2LFjB8PDw+2sSTrC+Pg4ExMTZZehHlOr1di6dSvUn325qAf0twFnp5T2tfhrTqXYBrOYveDz\nXqt36NAh9u7du+L+Dz0EV199PX/+5/+Fl7xk5fNs2LCB/v7lDjmSFtbM874jIb2+n/ES4KsppadW\nMGQOYHh4mNHR0XaWJh1hcHDQ/82pTNls94iIzwEVYAvw64g4rv7RbEpprt7nWuBFh7eyRMRVwEPA\nNMU/6X4AeBPF0buL8XmvNXPmmUv9o82Rpqbg6qv/JxdeeBH+T08lWPZ536mV9HOBE4GvdGg+SdLq\nXEFxmssdDe2XAl+r/+chimf7Yc8GrgdOAA4CPwHOSSnd2dZKJakLdSSkp5S+BzyzE3NJklYvpbTs\n6V8ppUsb7q8DrmtbUZLUQzq5J13qqIMHD7J79+6mxszOzjI1NdXUmE2bNjEwMNDUGEmSpKUY0tW1\ndu/ezdjYWNPjmh0zOTnpXlpJWmeGh+Ev/7KC31dWrgzp6lqbNm1icnKyI/NIktaX/n74r/+1UnYZ\n0qIM6epaAwMDrnBLkqR1adkvBkm94tAhmJ4urpIkSWUypEt1tRqcckpxlSRJKpMhXZIkScqMIV2S\nJEnKjCFdkiRJyowhXZIk9ZyZGdi2rbhKOTKkS5KknjMzA9u3G9KVL0O6JEmSlBlDuiRJkpQZ3zgq\n1Q0Pw86dsHFj2ZVIkqReZ0iX6vr7YfPmsquQJElyu4skSZKUHUO6JEmSlBlDuiRJ6jl9fTAyUlyl\nHLknXZIk9ZyREZieLrsKaXGupEuSJEmZMaRLkiRJmTGkS3UzM7Btm6+IliRJ5TOkS3UzM7B9uyFd\nkiSVz5AuSZIkZcaQLkmSJGXGkC5JkiRlxpAuSZJ6zq5dsHlzcZVyZEiXJEk9Z26uCOhzc2VXIi3M\nkC5JkiRlxpAu1fX1Fa+J7usruxJJktTrjiq7ACkXIyMwPV12FZIkSa6kS5IkSdkxpEuSJEmZMaRL\nkiRJmTGkS5KknjM0BNdcU1ylHPnFUUmS1HOGhmDbtrKrkBbnSrokSZKUGUO6JEmSlBlDulS3axds\n3lxcJUmSymRIl+rm5oqAPjdXdiWSJKnXGdIlSZKkzBjSJUmSpMx0JKRHxAkR8fWIOBARByPi/ogY\n7cTckiRJjQ4dgunp4irlqO0hPSKeB/wQeAK4ABgG/gvwr+2eW5IkaSG1GpxySnGVctSJlxl9FNiX\nUrpsXtvPOjCvJEmStC51YrvLW4EfR8S3ImJ/RExFxGXLjpIkSZJ6VCdC+kbgSuAB4Hzg88BfRcQf\ndmBuacWGhuCaa4qrJElSmTqx3eUZwD0ppavr9/dHxCnAFcDXOzC/tCJDQ7BtW9lVSJIkdSakzwCN\nX8uoAe9YbuD4+DiDg4NHtFUqFSqVytpVJ0kdVK1WqVarR7TNzs6WVI0kKVedCOk/BE5uaDuZFXx5\ndGJigtFRT2qU1D0WWmiYmppibGyspIokSTnqxJ70CeD0iPhYRLw0It4LXAZ8pgNzS5IkSetO20N6\nSunHwNuBCvDPwJ8CV6WU/qbdc0uSJC1keBh27iyuUo46sd2FlNItwC2dmEuSJGk5/f2weXPZVUiL\n68R2F0nSOlPfonhPRDxef8fFtyPiFSsY98aImIyIuYjYExEXd6JeSeo2hnSp7tAhmJ4urpI4C/g0\n8HvAucCzgNsion+xARGxAfgO8H3gVcCngC9FxHntLlaSuk1HtrtI60GtBmNjMDkJHiqkXpdSunD+\nfURcAjwKjAF3LTLsSuDBlNJH6vcPRMTrgXHge20qVZK6kivpkqSVeB6QgF8t0ed04PaGtluB17Wr\nKEnqVoZ0SdKSIiKAG4C7Ukq7luh6PLC/oW0/cExEHN2u+iSpG7ndRZK0nM8BI8CZZRciSb3CkC5J\nWlREfAa4EDgrpTSzTPdHgOMa2o4DHk8pPbHUwPHxcQYHB49oW+jtrNJamZmBL34RLr8chobKrkbd\nqFqtUq1Wj2ibnZ1d8XhDuiRpQfWA/jbg7JTSvhUMuRt4c0Pb+fX2JU1MTDDqN7bVQTMzsH07bNli\nSFd7LLTQMDU1xdjY2IrGuyddkvQ7IuJzwEXAe4FfR8Rx9Z++eX2ujYgb5w37ArAxIj4RESdHxIeA\ndwGf7GjxktQFDOmSpIVcARwD3AE8PO/n3fP6DAEnHr5JKe0F3kJxrvp9FEcvvj+l1HjiiyRpGW53\nkeqGh2HnTti4sexKpPKllJZdxEkpXbpA250UZ6lLklbBkC7V9ffD5s1lVyFJkuR2F0mSJCk7hnRJ\nkiQpM4Z0SZLUc/r6YGSkuEo5ck+6JEnqOSMjMD1ddhXS4lxJlyRJkjJjSJckSZIyY0iX6mZmYNu2\n4ipJklQmQ7pUNzMD27cb0iVJUvkM6ZIkSVJmDOmSJElSZgzpkiRJUmYM6ZIkqefs2gWbNxdXKUeG\ndEmS1HPm5oqAPjdXdiXSwgzpkiRJUmYM6VJdX1/xmui+vrIrkSRJve6osguQcjEyAtPTZVchSZLk\nSrokSZKUHUO6JEmSlBm3u0iSpCzt2wcHDrTnd9dqR17X2rHHwkknted3qzcY0iVJUnb27YPhYTh4\nsL3zbN3ant87MFD8AWBQV6sM6ZIkKTsHDhQBfceOIqyvJ7VaEf4PHDCkq3WGdEmSlK3hYRgdLbsK\nqfP84qgkSZKUGUO6VLdrF2zeXFwlSZLKZEiX6ubmioA+N1d2JZIkqdcZ0iVJkqTMGNIlSZKkzBjS\nJUmSpMwY0iVJkqTMGNIlSZKkzLT9ZUYRcQ1wTUPz7pTSSLvnVvfZt694g1s71GpHXtvh2GN9+5wk\nSVpep944uhM4B4j6/W86NK+6yL59xZvnDh5s7zxbt7bvdw8MFH8EGNQlSdJSOhXSf5NSeqxDc6lL\nHThQBPQdO4qwvt7UasUfAAcOGNIlSdLSOhXSXx4RvwDmgLuBj6WUft6hudVlhodhdLTsKiRJktqn\nE18c/RFwCXABcAXwEuDOiHhOB+aWJEmS1p22r6SnlG6dd7szIu4Bfga8G/hKu+eXJEmS1ptObXd5\nWkppNiL2AC9bru/4+DiDg4NHtFUqFSqVSrvKk6S2qlarVKvVI9pmZ2dLqkaSlKuOh/SIeC5FQP/a\ncn0nJiYYdfOxpC6y0ELD1NQUY2NjJVUkScpR2/ekR8R1EfGGiPiPEXEG8G3gSaC6zFBJkiSpJ3Vi\nJf3FwDeB5wOPAXcBp6eUftmBuSVJkqR1pxNfHHUDuSRJktSEThzBKEmSJKkJhnRJkiQpM4Z0SZIk\nKTOGdEmSJCkzhnRJkiQpM4Z0SZIkKTOGdEmSJCkzhnRJkiQpM4Z0SZIkKTOGdEmSJCkzhnRJ0oIi\n4qyIuCkifhERT0XElmX6n13vN//ntxHxwk7VLEndwpAuSVrMc4D7gA8BaYVjEvBy4Pj6z1BK6dH2\nlCdJ3euosguQJOUppfRd4LsAERFNDH0spfR4e6qSpN7gSrokaS0FcF9EPBwRt0XEGWUXJEnrkSFd\nkrRWZoDLgXcC7wB+DtwREaeWWpUkrUNud5EkrYmU0h5gz7ymH0XES4Fx4OJyqpKk9cmQLklqp3uA\nM5frND4+zuDg4BFtlUqFSqXSrrokqa2q1SrVavWIttnZ2RWPN6RLktrpVIptMEuamJhgdHS0A+VI\nUmcstNAwNTXF2NjYisYb0iVJC4qI5wAvo/gyKMDGiHgV8KuU0s8j4i+AE1JKF9f7XwU8BEwDfcAH\ngDcB53W8eEla5wzpkqTFvBr4B4qzzxNwfb39RuB9FOegnziv/7PrfU4ADgI/Ac5JKd3ZqYIlqVsY\n0iVJC0op/b8scQpYSunShvvrgOvaXZck9QKPYJQkSZIyY0iXJEmSMmNIlyRJkjJjSJckSZIyY0iX\nJEmSMmNIlyRJkjJjSJckSZIy4znpWjfi0EFOYzf9tbIraU1/DU4D4tAmYKDsciRJUsYM6Vo3+vbu\nZoox2Fp2Ja0ZBqaA2t5JOHO07HIkSVLGDOlaN+Y2bGKUSb6xA4aHy66mebUaXLQVvrxhU9mlSJKk\nzBnStW6k/gHuZZRDw8A6XIg+BNwLpP6yK5EkSbnzi6OSJElSZgzpkiRJUmYM6ZIkSVJmDOmSJElS\nZgzpkiRJUmYM6ZIkSVJmDOmSJElSZgzpkiRJUmYM6ZIkSVJmfOOoJEnKThw6yGnspr9WdiXN66/B\naUAc2gQMlF2O1qmOh/SI+ChwLXBDSulPOj2/JEnKX9/e3UwxBlvLrqR5w8AUUNs7CWeOll2O1qmO\nhvSIeA3wQeD+Ts4rSZLWl7kNmxhlkm/sgOHhsqtpTq0GF22FL2/YVHYpWsc6FtIj4rnADuAy4OpO\nzStJktaf1D/AvYxyaBhYZ4vRh4B7gdRfdiVazzr5xdHPAjenlH7QwTklSZKkdacjK+kR8R7gVODV\nnZhPkiRJWs/aHtIj4sXADcC5KaUn2z2fJEmStN51YiV9DHgBMBURUW97JvCGiPgwcHRKKS00cHx8\nnMHBwSPaKpUKlUqlnfVKUttUq1Wq1eoRbbOzsyVVI0nKVSdC+u3AKxvavgrUgI8vFtABJiYmGB1d\nZ98WkaQlLLTQMDU1xdjYWEkVSZJy1PaQnlL6/9u73xBLr/oO4N+fmmazodm+WNxdIWGxtt3EQuKM\nWm0NsY2JNYGUphIZDKTG1iYRKtMXlkJB6otKK3HVlrSKFCOGAd+UBksT/1fEhMCOiSXZNS80mGiz\nJhXGYrKSmtMX90Ymk5ndmc3e55478/nAZbjnPuee38Duw3fPPs/v+WmSh1aPVdVPk/xPa20GH1EA\nAACTNWR3l9U23D0HAICdbvAnjiZJa+33prEuAADMgmntpAMAABsQ0gEAoDNCOgAAdEZIBwCAzgjp\nAADQGSEdAAA6I6QDAEBnhHQAAOiMkA4AAJ0R0gEAoDNCOgAAdEZIBwCAzgjpAADQGSEdAAA6I6QD\nAEBnhHQAAOiMkA4AAJ0R0gEAoDNCOgAAdEZIBwCAzgjpAADQGSEdgHVV1aVVdWdV/aCqnq2qazYx\n581VdaSqTlTVw1V1wxC1Amw3QjoAGzk3yf1JbknSTnVwVR1M8vkkX05ycZKPJflUVV0xuRIBtqeX\nTbsAAPrUWrsryV1JUlW1iSk3J/lua+394/ffqao3JVlM8sXJVAmwPdlJB+BMeUOSL60ZuzvJG6dQ\nC8BME9IBOFP2Jzm+Zux4kvOq6uwp1AMws4R0AADojGvSAThTHk+yb83YviQ/aa397GQTFxcXs2fP\nnueNLSwsZGFh4cxWCDCQpaWlLC0tPW9sZWVl0/OFdADOlHuSvG3N2JXj8ZM6fPhw5ubmJlIUwDSs\nt9GwvLyc+fn5Tc13uQsA66qqc6vq4qq6ZDz0yvH788eff6iqbl815Z/Hx/xdVf1GVd2S5O1JPjJw\n6QAzT0gHYCOvTfKtJEcy6pN+a5LlJH8z/nx/kvOfO7i19kiSq5O8JaP+6otJ3t1aW9vxBYBTcLkL\nAOtqrf1nTrKZ01p71zpjX0+yuf/LBWBDdtIBAKAzQjoAAHRGSAcAgM4I6QAA0BkhHQAAOiOkAwBA\nZ4R0AADojJAOAACdEdIBAKAzQjoAAHRm4iG9qm6qqgeqamX8+mZV/f6k1wUAgFk1xE76o0n+Mslc\nkvkkX0nyb1V14QBrAwDAzHnZpBdorf37mqG/rqqbk7whydFJrw8AALNm4iF9tap6SZLrkuxOcs+Q\nawMAwKwYJKRX1W9mFMp3JfnfJH/YWjs2xNoAADBrhurucizJxUlen+Sfknymqg4NtDYAAMyUQXbS\nW2v/l+S747ffqqrXJ3lfkptPNm9xcTF79ux53tjCwkIWFhYmUifApC0tLWVpael5YysrK1OqBoBe\nDXpN+iovSXL2qQ46fPhw5ubmBigHYBjrbTQsLy9nfn5+ShUB0KOJh/Sq+tsk/5Hk+0l+Ock7k1yW\n5MpJrw0AALNoiJ30lye5PcmBJCtJvp3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"text/plain": [ "<matplotlib.figure.Figure at 0xb937d68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "fig,axes = plt.subplots(nrows=2,ncols=2,figsize=(9,9))\n", "\n", "fig1 = axes[0,0].boxplot(features['sepal length (cm)'],patch_artist=True)\n", "fig2 = axes[0,1].boxplot(features['sepal width (cm)'],patch_artist=True)\n", "fig3 = axes[1,0].boxplot(features['petal length (cm)'],patch_artist=True)\n", "fig4 = axes[1,1].boxplot(features['petal width (cm)'],patch_artist=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Info of features" ] }, { "cell_type": "code", "execution_count": 69, "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 (cm)</th>\n", " <th>sepal width (cm)</th>\n", " <th>petal length (cm)</th>\n", " <th>petal width (cm)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>150.000000</td>\n", " <td>150.000000</td>\n", " <td>150.000000</td>\n", " <td>150.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>5.843333</td>\n", " <td>3.054000</td>\n", " <td>3.758667</td>\n", " <td>1.198667</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>0.828066</td>\n", " <td>0.433594</td>\n", " <td>1.764420</td>\n", " <td>0.763161</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>4.300000</td>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>0.100000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>5.100000</td>\n", " <td>2.800000</td>\n", " <td>1.600000</td>\n", " <td>0.300000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>5.800000</td>\n", " <td>3.000000</td>\n", " <td>4.350000</td>\n", " <td>1.300000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>6.400000</td>\n", " <td>3.300000</td>\n", " <td>5.100000</td>\n", " <td>1.800000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>7.900000</td>\n", " <td>4.400000</td>\n", " <td>6.900000</td>\n", " <td>2.500000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sepal length (cm) sepal width (cm) petal length (cm) \\\n", "count 150.000000 150.000000 150.000000 \n", "mean 5.843333 3.054000 3.758667 \n", "std 0.828066 0.433594 1.764420 \n", "min 4.300000 2.000000 1.000000 \n", "25% 5.100000 2.800000 1.600000 \n", "50% 5.800000 3.000000 4.350000 \n", "75% 6.400000 3.300000 5.100000 \n", "max 7.900000 4.400000 6.900000 \n", "\n", " petal width (cm) \n", "count 150.000000 \n", "mean 1.198667 \n", "std 0.763161 \n", "min 0.100000 \n", "25% 0.300000 \n", "50% 1.300000 \n", "75% 1.800000 \n", "max 2.500000 " ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features.describe()" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>sepal length (cm)</th>\n", " <th>sepal width (cm)</th>\n", " <th>petal length (cm)</th>\n", " <th>petal width (cm)</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>sepal length (cm)</th>\n", " <td>1.000000</td>\n", " <td>-0.109369</td>\n", " <td>0.871754</td>\n", " <td>0.817954</td>\n", " </tr>\n", " <tr>\n", " <th>sepal width (cm)</th>\n", " <td>-0.109369</td>\n", " <td>1.000000</td>\n", " <td>-0.420516</td>\n", " <td>-0.356544</td>\n", " </tr>\n", " <tr>\n", " <th>petal length (cm)</th>\n", " <td>0.871754</td>\n", " <td>-0.420516</td>\n", " <td>1.000000</td>\n", " <td>0.962757</td>\n", " </tr>\n", " <tr>\n", " <th>petal width (cm)</th>\n", " <td>0.817954</td>\n", " <td>-0.356544</td>\n", " <td>0.962757</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sepal length (cm) sepal width (cm) petal length (cm) \\\n", "sepal length (cm) 1.000000 -0.109369 0.871754 \n", "sepal width (cm) -0.109369 1.000000 -0.420516 \n", "petal length (cm) 0.871754 -0.420516 1.000000 \n", "petal width (cm) 0.817954 -0.356544 0.962757 \n", "\n", " petal width (cm) \n", "sepal length (cm) 0.817954 \n", "sepal width (cm) -0.356544 \n", "petal length (cm) 0.962757 \n", "petal width (cm) 1.000000 " ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features.corr()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Standardising the features" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ -9.00681170e-01, 1.03205722e+00, -1.34127240e+00,\n", " -1.31297673e+00],\n", " [ -1.14301691e+00, -1.24957601e-01, -1.34127240e+00,\n", " -1.31297673e+00],\n", " [ -1.38535265e+00, 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1.06445364e-01, 9.90221459e-01,\n", " 7.90590793e-01],\n", " [ 1.89829664e-01, -1.24957601e-01, 5.92161531e-01,\n", " 7.90590793e-01],\n", " [ 1.28034050e+00, 1.06445364e-01, 9.33355755e-01,\n", " 1.18500970e+00],\n", " [ 1.03800476e+00, 1.06445364e-01, 1.04708716e+00,\n", " 1.57942861e+00],\n", " [ 1.28034050e+00, 1.06445364e-01, 7.62758643e-01,\n", " 1.44795564e+00],\n", " [ -5.25060772e-02, -8.19166497e-01, 7.62758643e-01,\n", " 9.22063763e-01],\n", " [ 1.15917263e+00, 3.37848329e-01, 1.21768427e+00,\n", " 1.44795564e+00],\n", " [ 1.03800476e+00, 5.69251294e-01, 1.10395287e+00,\n", " 1.71090158e+00],\n", " [ 1.03800476e+00, -1.24957601e-01, 8.19624347e-01,\n", " 1.44795564e+00],\n", " [ 5.53333275e-01, -1.28197243e+00, 7.05892939e-01,\n", " 9.22063763e-01],\n", " [ 7.95669016e-01, -1.24957601e-01, 8.19624347e-01,\n", " 1.05353673e+00],\n", " [ 4.32165405e-01, 8.00654259e-01, 9.33355755e-01,\n", " 1.44795564e+00],\n", " [ 6.86617933e-02, -1.24957601e-01, 7.62758643e-01,\n", " 7.90590793e-01]])" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.preprocessing import StandardScaler\n", "scaler = StandardScaler()\n", "X = scaler.fit_transform(features)\n", "X" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PCA(optional)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ -2.26454173e+00, 5.05703903e-01, -1.21943348e-01,\n", " -2.30733235e-02],\n", " [ -2.08642550e+00, -6.55404729e-01, -2.27250832e-01,\n", " -1.03208244e-01],\n", " [ -2.36795045e+00, -3.18477311e-01, 5.14796236e-02,\n", " -2.78252250e-02],\n", " [ -2.30419716e+00, -5.75367713e-01, 9.88604444e-02,\n", " 6.63114622e-02],\n", " [ -2.38877749e+00, 6.74767397e-01, 2.14278490e-02,\n", " 3.73972870e-02],\n", " [ -2.07053681e+00, 1.51854856e+00, 3.06842583e-02,\n", " -4.39877494e-03],\n", " [ -2.44571134e+00, 7.45626750e-02, 3.42197636e-01,\n", 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-7.17510683e-01,\n", " 2.07556768e-01],\n", " [ 2.43549739e+00, 2.46654468e-01, -7.30234006e-01,\n", " 1.67936017e-02],\n", " [ 2.31608241e+00, 2.62618387e+00, -4.99619543e-01,\n", " 2.13160418e-01],\n", " [ 1.86037143e+00, -1.84672394e-01, 3.53330279e-01,\n", " -1.00039482e-01],\n", " [ 1.11127173e+00, -2.95986102e-01, -1.82659608e-01,\n", " 1.85740240e-01],\n", " [ 1.19746916e+00, -8.17167742e-01, -1.63213782e-01,\n", " 4.88404000e-01],\n", " [ 2.80094940e+00, 8.44748194e-01, -5.47000957e-01,\n", " -2.96321147e-01],\n", " [ 1.58015525e+00, 1.07247450e+00, 9.43392608e-01,\n", " -3.36074229e-02],\n", " [ 1.34704442e+00, 4.22255966e-01, 1.80028706e-01,\n", " 2.15906539e-01],\n", " [ 9.23432978e-01, 1.92303705e-02, 4.17394303e-01,\n", " -4.74424586e-03],\n", " [ 1.85355198e+00, 6.72422729e-01, -1.48203294e-02,\n", " -1.94875449e-01],\n", " [ 2.01615720e+00, 6.10397038e-01, 4.25914947e-01,\n", " -2.46764702e-01],\n", " [ 1.90311686e+00, 6.86024832e-01, 1.27799364e-01,\n", " -4.69214421e-01],\n", " [ 1.15318981e+00, -7.01326114e-01, 5.31464635e-01,\n", " 4.04135807e-02],\n", " [ 2.04330844e+00, 8.64684880e-01, 3.35266061e-01,\n", " -4.42781979e-02],\n", " [ 2.00169097e+00, 1.04855005e+00, 6.29268888e-01,\n", " -2.12588357e-01],\n", " [ 1.87052207e+00, 3.82821838e-01, 2.54532319e-01,\n", " -3.88890487e-01],\n", " [ 1.55849189e+00, -9.05313601e-01, -2.53819099e-02,\n", " -2.21322184e-01],\n", " [ 1.52084506e+00, 2.66794575e-01, 1.79277203e-01,\n", " -1.18903043e-01],\n", " [ 1.37639119e+00, 1.01636193e+00, 9.31405052e-01,\n", " -2.41461953e-02],\n", " [ 9.59298576e-01, -2.22839447e-02, 5.28794187e-01,\n", " 1.63675806e-01]])" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.decomposition import PCA\n", "pca = PCA()\n", "pca.fit_transform(X)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 2.91081808, 0.92122093, 0.14735328, 0.02060771])" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "covariance = pca.get_covariance()\n", "explained_variance = pca.explained_variance_\n", "explained_variance" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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pifKdImLH8oQlSZJUWUoZCP4zYIMmyjfMr5MkSepwSkmatgKebqL8qfw6SZKk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\nRePHj2fw4MGlhCpJkipQU50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"text/plain": [ "<matplotlib.figure.Figure at 0xdf16860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "plt.figure(figsize=(6, 4))\n", " \n", "plt.bar(range(4), explained_variance, alpha=0.5, align='center',\n", " label='individual explained variance')\n", "plt.ylabel('Explained variance ratio')\n", "plt.xlabel('Principal components')\n", "plt.legend(loc='best')\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The last 1 componen has less amount of variance of the data. The first 3 components retains more than 90% of the data.(Here, compared with only 4 features, there're enough instances to support the final results. We shall take all features into consideration)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Consider first 3 components and visualise it using K-means clustering" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.cluster import KMeans\n", "from sklearn.decomposition import PCA\n", "\n", "pca = PCA(n_components=3)\n", "x_pca = pca.fit_transform(X)\n", "\n", "kmeans = KMeans(n_clusters=3, random_state=5)\n", "x_clustered = kmeans.fit_predict(x_pca)\n", "\n", "y = targets.values\n", "y = y.reshape(y.size)" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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97U8B5ObmsnLlSlwuF61atSI4uIA49BcoIsLq1as5fvw4ycnJ+WJhiQjr16/n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\n0bJlS6pXr16k61i2bBnTp08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YsEFERD7++GOpWjU2Ly8kxJH3XSmkfv368tdff51W54oV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\nH8/gwYN5/PHHOXToEF27dmXQoEEsWLCA4OBgevbsyapVq9i9ezf169e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HkQogt4PEmaaE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"text/plain": [ "<matplotlib.figure.Figure at 0xc97f0b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "LABEL_COLOR_MAP = {0 : 'g',\n", " 1 : 'y',\n", " 2 : 'r'\n", " }\n", "\n", "label_color = [LABEL_COLOR_MAP[i] for i in x_clustered]\n", "y_color = [LABEL_COLOR_MAP[i] for i in y]\n", "\n", "fig,axes = plt.subplots(nrows=1,ncols=2,figsize=(5,3))\n", "\n", "axes[0].scatter(X[:,0],X[:,1], c= label_color)\n", "axes[0].set_title('PCA')\n", "axes[1].scatter(X[:,0],X[:,1], c= y_color)\n", "axes[1].set_title('True Cluster');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using K-means, we are able to segregate 3 classes well using the first 3 components with maximum variance. (Don't mind the color type, which is meaningless in clustering).\n", "\n", "You can apply PCA firstly before using machine learning in the next steps" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Splitting the data into training and testing dataset" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Default Logistic Regression(optional)" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.linear_model import LogisticRegression\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn import metrics\n", "\n", "modelLR = LogisticRegression(n_jobs=-1)\n", "\n", "modelLR.fit(X_train,y_train);" ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred = modelLR.predict(X_test)\n", "modelLR.score(X_test,y_pred)" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[15, 1, 0],\n", " [ 0, 5, 0],\n", " [ 0, 1, 8]])" ] }, "execution_count": 131, "metadata": {}, "output_type": "execute_result" } ], "source": [ "confusion_matrix=metrics.confusion_matrix(y_test,y_pred)\n", "confusion_matrix" ] }, { "cell_type": "code", "execution_count": 147, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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NIjPRVpby6VDKIbTKqff3BhICHEw5GIWIjHF5eahVB/fOtz151u/h5N04pUK7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\nlD3r16/XOjVrarzPp4NFtLH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"text/plain": [ "<matplotlib.figure.Figure at 0xc5e7710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "LABEL_COLOR_MAP = {0 : 'g',\n", " 1 : 'y',\n", " 2 : 'r'\n", " }\n", "\n", "pred_color = [LABEL_COLOR_MAP[i] for i in y_pred]\n", "test_color = [LABEL_COLOR_MAP[i] for i in y_test]\n", "\n", "fig,axes = plt.subplots(nrows=1,ncols=2,figsize=(5,2))\n", "\n", "axes[0].scatter(X_test[:,0],X_test[:,1], c= pred_color)\n", "axes[0].set_title('Predicted')\n", "axes[1].scatter(X_test[:,0],X_test[:,1], c= test_color)\n", "axes[1].set_title('True');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tuned Logistic Regression(optional)" ] }, { "cell_type": "code", "execution_count": 137, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.linear_model import LogisticRegression\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn import metrics\n", "from sklearn.model_selection import GridSearchCV\n", "\n", "LRs= LogisticRegression()\n", "\n", "tuned_parameters = {'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000] ,\n", " 'penalty':['l1','l2']\n", " }\n", "\n", "modelLR=GridSearchCV(LRs, tuned_parameters,cv=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Search best combinations of parameter values based on the dataset. \n", "+ \"C\": Inverse of regularization strength\n", "+ \"Penalty\": The norm used in the penalization" ] }, { "cell_type": "code", "execution_count": 140, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "GridSearchCV(cv=10, error_score='raise',\n", " estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False),\n", " fit_params={}, iid=True, n_jobs=1,\n", " param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n", " scoring=None, verbose=0)" ] }, "execution_count": 140, "metadata": {}, "output_type": "execute_result" } ], "source": [ "modelLR.fit(X_train,y_train)" ] }, { "cell_type": "code", "execution_count": 141, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'penalty': 'l1', 'C': 1000}\n" ] } ], "source": [ "print(modelLR.best_params_)" ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 142, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred = modelLR.predict(X_test)\n", "modelLR.score(X_test,y_pred)" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[15, 1, 0],\n", " [ 0, 4, 1],\n", " [ 0, 1, 8]])" ] }, "execution_count": 143, "metadata": {}, "output_type": "execute_result" } ], "source": [ "confusion_matrix=metrics.confusion_matrix(y_test,y_pred)\n", "confusion_matrix" ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "' precision recall f1-score support\\n\\n 0 1.00 0.94 0.97 16\\n 1 0.67 0.80 0.73 5\\n 2 0.89 0.89 0.89 9\\n\\navg / total 0.91 0.90 0.90 30\\n'" ] }, "execution_count": 144, "metadata": {}, "output_type": "execute_result" } ], "source": [ "auc_roc=metrics.classification_report(y_test,y_pred)\n", "auc_roc" ] }, { "cell_type": "code", "execution_count": 148, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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NIjPRVpby6VDKIbTKqff3BhICHEw5GIWIjHF5eahVB/fOtz151u/h5N04pUK7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\nlD3r16/XOjVrarzPp4NFtLH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"text/plain": [ "<matplotlib.figure.Figure at 0xcc53860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "LABEL_COLOR_MAP = {0 : 'g',\n", " 1 : 'y',\n", " 2 : 'r'\n", " }\n", "\n", "pred_color = [LABEL_COLOR_MAP[i] for i in y_pred]\n", "test_color = [LABEL_COLOR_MAP[i] for i in y_test]\n", "\n", "fig,axes = plt.subplots(nrows=1,ncols=2,figsize=(5,2))\n", "\n", "axes[0].scatter(X_test[:,0],X_test[:,1], c= pred_color)\n", "axes[0].set_title('Predicted')\n", "axes[1].scatter(X_test[:,0],X_test[:,1], c= test_color)\n", "axes[1].set_title('True');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### SVM(optional)" ] }, { "cell_type": "code", "execution_count": 149, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.svm import SVC\n", "\n", "svm= SVC()\n", "tuned_parameters = {\n", " 'C': [1, 10, 100,500, 1000], 'kernel': ['linear','rbf'],\n", " 'C': [1, 10, 100,500, 1000], 'gamma': [1,0.1,0.01,0.001, 0.0001], 'kernel': ['rbf'],\n", " #'degree': [2,3,4,5,6] , 'C':[1,10,100,500,1000] , 'kernel':['poly']\n", " }" ] }, { "cell_type": "code", "execution_count": 165, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.model_selection import RandomizedSearchCV\n", "modelsvm = RandomizedSearchCV(svm, tuned_parameters,cv=10,scoring='accuracy',n_iter=20)" ] }, { "cell_type": "code", "execution_count": 166, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.975\n" ] } ], "source": [ "modelsvm.fit(X_train, y_train)\n", "print(modelsvm.best_score_)" ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'mean_fit_time': array([ 0.0003 , 0.0006 , 0.0006 , 0.0006 , 0.00039999,\n", " 0.00050001, 0.00019999, 0.00070002, 0.00080004, 0.00059998,\n", " 0.00030003, 0.0003 , 0.00039999, 0.00050001, 0.00050001,\n", " 0.00049999, 0.00080006, 0.0003 , 0.00039997, 0.00029998]),\n", " 'mean_score_time': array([ 3.99994850e-04, 1.99985504e-04, 0.00000000e+00,\n", " 1.99985504e-04, 0.00000000e+00, 2.00033188e-04,\n", " 1.00016594e-04, 0.00000000e+00, 1.99985504e-04,\n", " 1.00016594e-04, 9.99927521e-05, 9.99927521e-05,\n", " 0.00000000e+00, 1.99985504e-04, 2.00009346e-04,\n", " 9.99927521e-05, 9.99927521e-05, 3.99994850e-04,\n", " 0.00000000e+00, 9.99927521e-05]),\n", " 'mean_test_score': array([ 0.95833333, 0.975 , 0.9 , 0.375 , 0.975 ,\n", " 0.95833333, 0.975 , 0.96666667, 0.9 , 0.375 ,\n", " 0.96666667, 0.95833333, 0.95833333, 0.96666667, 0.9 ,\n", " 0.95833333, 0.95833333, 0.975 , 0.975 , 0.95833333]),\n", " 'mean_train_score': array([ 1. , 0.97874474, 0.90183299, 0.37499581, 0.97501444,\n", " 1. , 0.97873561, 0.98427465, 0.90460243, 0.37499581,\n", " 0.97966185, 0.99813917, 1. , 0.98057928, 0.90460243,\n", " 0.99258314, 1. , 0.97778388, 0.98243129, 0.96572858]),\n", " 'param_C': masked_array(data = [1000 1 1 1 1000 10 10 10 10 10 1000 100 1000 100 100 1000 500 500 500 500],\n", " mask = [False False False False False False False False False False False False\n", " False False False False False False False False],\n", " fill_value = ?),\n", " 'param_gamma': masked_array(data = [1 0.1 0.01 0.001 0.001 1 0.1 0.01 0.001 0.0001 0.0001 0.1 0.1 0.001 0.0001\n", " 0.01 0.1 0.01 0.001 0.0001],\n", " mask = [False False False False False False False False False False False False\n", " False False False False False False False False],\n", " fill_value = ?),\n", " 'param_kernel': masked_array(data = ['rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf'\n", " 'rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf' 'rbf'],\n", " mask = [False False False False False False False False False False False False\n", " False False False False False False False False],\n", " fill_value = ?),\n", " 'params': ({'C': 1000, 'gamma': 1, 'kernel': 'rbf'},\n", " {'C': 1, 'gamma': 0.1, 'kernel': 'rbf'},\n", " {'C': 1, 'gamma': 0.01, 'kernel': 'rbf'},\n", " {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'},\n", " {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'},\n", " {'C': 10, 'gamma': 1, 'kernel': 'rbf'},\n", " {'C': 10, 'gamma': 0.1, 'kernel': 'rbf'},\n", " {'C': 10, 'gamma': 0.01, 'kernel': 'rbf'},\n", " {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'},\n", " {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'},\n", " {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'},\n", " {'C': 100, 'gamma': 0.1, 'kernel': 'rbf'},\n", " {'C': 1000, 'gamma': 0.1, 'kernel': 'rbf'},\n", " {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'},\n", " {'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'},\n", " {'C': 1000, 'gamma': 0.01, 'kernel': 'rbf'},\n", " {'C': 500, 'gamma': 0.1, 'kernel': 'rbf'},\n", " {'C': 500, 'gamma': 0.01, 'kernel': 'rbf'},\n", " {'C': 500, 'gamma': 0.001, 'kernel': 'rbf'},\n", " {'C': 500, 'gamma': 0.0001, 'kernel': 'rbf'}),\n", " 'rank_test_score': array([ 9, 1, 16, 19, 1, 9, 1, 6, 16, 19, 6, 9, 9, 6, 16, 9, 9,\n", " 1, 1, 9]),\n", " 'split0_test_score': array([ 1. , 1. , 1. , 0.35714286, 1. ,\n", " 1. , 1. , 1. , 1. , 0.35714286,\n", " 1. , 1. , 1. , 1. , 1. ,\n", " 1. , 1. , 1. , 1. , 1. ]),\n", " 'split0_train_score': array([ 1. , 0.99056604, 0.89622642, 0.37735849, 0.97169811,\n", " 1. , 0.97169811, 0.98113208, 0.89622642, 0.37735849,\n", " 0.98113208, 0.99056604, 1. , 0.98113208, 0.89622642,\n", " 0.99056604, 1. , 0.97169811, 0.98113208, 0.96226415]),\n", " 'split1_test_score': array([ 0.92307692, 1. , 0.92307692, 0.38461538, 1. ,\n", " 0.92307692, 1. , 1. , 0.92307692, 0.38461538,\n", " 1. , 0.92307692, 0.92307692, 1. , 0.92307692,\n", " 1. , 0.92307692, 1. , 1. , 0.92307692]),\n", " 'split1_train_score': array([ 1. , 0.98130841, 0.88785047, 0.37383178, 0.97196262,\n", " 1. , 0.99065421, 0.99065421, 0.88785047, 0.37383178,\n", " 0.99065421, 1. , 1. , 0.99065421, 0.88785047,\n", " 0.99065421, 1. , 0.97196262, 0.98130841, 0.97196262]),\n", " 'split2_test_score': array([ 0.84615385, 0.84615385, 0.84615385, 0.38461538, 0.92307692,\n", " 0.84615385, 0.92307692, 0.84615385, 0.84615385, 0.38461538,\n", " 0.84615385, 0.92307692, 0.92307692, 0.84615385, 0.84615385,\n", " 0.92307692, 0.92307692, 0.92307692, 0.92307692, 0.84615385]),\n", " 'split2_train_score': array([ 1. , 0.99065421, 0.90654206, 0.37383178, 0.98130841,\n", " 1. , 0.98130841, 0.98130841, 0.90654206, 0.37383178,\n", " 0.98130841, 1. , 1. , 0.98130841, 0.90654206,\n", " 0.98130841, 1. , 0.98130841, 0.99065421, 0.96261682]),\n", " 'split3_test_score': array([ 1. , 1. , 0.76923077, 0.38461538, 0.92307692,\n", " 1. , 0.92307692, 0.92307692, 0.76923077, 0.38461538,\n", " 0.92307692, 0.92307692, 0.92307692, 0.92307692, 0.76923077,\n", " 0.92307692, 0.92307692, 0.92307692, 0.92307692, 0.92307692]),\n", " 'split3_train_score': array([ 1. , 0.97196262, 0.90654206, 0.37383178, 0.98130841,\n", " 1. , 0.99065421, 0.99065421, 0.91588785, 0.37383178,\n", " 0.98130841, 1. , 1. , 0.98130841, 0.91588785,\n", " 1. , 1. , 0.99065421, 0.99065421, 0.96261682]),\n", " 'split4_test_score': array([ 0.91666667, 1. , 0.91666667, 0.41666667, 0.91666667,\n", " 0.91666667, 0.91666667, 1. , 0.91666667, 0.41666667,\n", " 1. , 0.91666667, 0.91666667, 1. , 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 1. ]),\n", " 'split4_train_score': array([ 1. , 0.97222222, 0.90740741, 0.37037037, 0.98148148,\n", " 1. , 0.98148148, 0.99074074, 0.90740741, 0.37037037,\n", " 0.98148148, 1. , 1. , 0.98148148, 0.90740741,\n", " 1. , 1. , 0.98148148, 0.98148148, 0.96296296]),\n", " 'split5_test_score': array([ 1. , 1. , 1. , 0.36363636, 1. ,\n", " 1. , 1. , 1. , 1. , 0.36363636,\n", " 1. , 1. , 1. , 1. , 1. ,\n", " 1. , 1. , 1. , 1. , 1. ]),\n", " 'split5_train_score': array([ 1. , 0.97247706, 0.89908257, 0.37614679, 0.97247706,\n", " 1. , 0.97247706, 0.98165138, 0.89908257, 0.37614679,\n", " 0.98165138, 1. , 1. , 0.98165138, 0.89908257,\n", " 0.99082569, 1. , 0.97247706, 0.97247706, 0.97247706]),\n", " 'split6_test_score': array([ 1. , 0.90909091, 0.81818182, 0.36363636, 1. ,\n", " 1. , 1. , 0.90909091, 0.81818182, 0.36363636,\n", " 0.90909091, 1. , 1. , 0.90909091, 0.81818182,\n", " 0.90909091, 1. , 1. , 1. , 0.90909091]),\n", " 'split6_train_score': array([ 1. , 0.98165138, 0.91743119, 0.37614679, 0.97247706,\n", " 1. , 0.98165138, 0.98165138, 0.9266055 , 0.37614679,\n", " 0.97247706, 1. , 1. , 0.97247706, 0.9266055 ,\n", " 1. , 1. , 0.98165138, 0.98165138, 0.96330275]),\n", " 'split7_test_score': array([ 1. , 1. , 0.90909091, 0.36363636, 1. ,\n", " 1. , 1. , 1. , 0.90909091, 0.36363636,\n", " 1. , 1. , 1. , 1. , 0.90909091,\n", " 1. , 1. , 1. , 1. , 1. ]),\n", " 'split7_train_score': array([ 1. , 0.98165138, 0.89908257, 0.37614679, 0.97247706,\n", " 1. , 0.97247706, 0.98165138, 0.89908257, 0.37614679,\n", " 0.98165138, 1. , 1. , 0.98165138, 0.89908257,\n", " 0.99082569, 1. , 0.97247706, 0.98165138, 0.97247706]),\n", " 'split8_test_score': array([ 1. , 1. , 0.81818182, 0.36363636, 1. ,\n", " 1. , 1. , 1. , 0.81818182, 0.36363636,\n", " 1. , 1. , 1. , 1. , 0.81818182,\n", " 1. , 1. , 1. , 1. , 1. ]),\n", " 'split8_train_score': array([ 1. , 0.97247706, 0.89908257, 0.37614679, 0.97247706,\n", " 1. , 0.97247706, 0.98165138, 0.90825688, 0.37614679,\n", " 0.97247706, 1. , 1. , 0.98165138, 0.90825688,\n", " 0.99082569, 1. , 0.97247706, 0.98165138, 0.95412844]),\n", " 'split9_test_score': array([ 0.90909091, 1. , 1. , 0.36363636, 1. ,\n", " 0.90909091, 1. , 1. , 1. , 0.36363636,\n", " 1. , 0.90909091, 0.90909091, 1. , 1. ,\n", " 0.90909091, 0.90909091, 1. , 1. , 1. ]),\n", " 'split9_train_score': array([ 1. , 0.97247706, 0.89908257, 0.37614679, 0.97247706,\n", " 1. , 0.97247706, 0.98165138, 0.89908257, 0.37614679,\n", " 0.97247706, 0.99082569, 1. , 0.97247706, 0.89908257,\n", " 0.99082569, 1. , 0.98165138, 0.98165138, 0.97247706]),\n", " 'std_fit_time': array([ 0.00045826, 0.0004899 , 0.0004899 , 0.0004899 , 0.00048989,\n", " 0.00050001, 0.00039997, 0.00045827, 0.00040002, 0.00048988,\n", " 0.0004583 , 0.00045826, 0.00048989, 0.00050001, 0.00050001,\n", " 0.00049999, 0.00040003, 0.00045826, 0.00048986, 0.00045822]),\n", " 'std_score_time': array([ 0.00048989, 0.00039997, 0. , 0.00039997, 0. ,\n", " 0.00040007, 0.00030005, 0. , 0.00039997, 0.00030005,\n", " 0.00029998, 0.00029998, 0. , 0.00039997, 0.00040002,\n", " 0.00029998, 0.00029998, 0.00048989, 0. , 0.00029998]),\n", " 'std_test_score': array([ 0.05404662, 0.05192955, 0.08054182, 0.01732187, 0.03676269,\n", " 0.05404662, 0.03676269, 0.05340031, 0.08054182, 0.01732187,\n", " 0.05340031, 0.04048439, 0.04048439, 0.05340031, 0.08054182,\n", " 0.04189912, 0.04048439, 0.03676269, 0.03676269, 0.05355015]),\n", " 'std_train_score': array([ 0. , 0.0071515 , 0.00758902, 0.00194076, 0.00416598,\n", " 0. , 0.0071622 , 0.00419869, 0.01038592, 0.00194076,\n", " 0.00542685, 0.00372211, 0. , 0.0048829 , 0.01038592,\n", " 0.00558832, 0. , 0.00614057, 0.00490513, 0.0059578 ])}" ] }, "execution_count": 170, "metadata": {}, "output_type": "execute_result" } ], "source": [ "modelsvm.cv_results_" ] }, { "cell_type": "code", "execution_count": 155, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'kernel': 'rbf', 'C': 1, 'gamma': 0.1}\n" ] } ], "source": [ "print(modelsvm.best_params_)" ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.933333333333\n" ] } ], "source": [ "y_pred= modelsvm.predict(X_test)\n", "print(metrics.accuracy_score(y_pred,y_test))" ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[16, 0, 0],\n", " [ 0, 4, 1],\n", " [ 0, 1, 8]])" ] }, "execution_count": 158, "metadata": {}, "output_type": "execute_result" } ], "source": [ "confusion_matrix=metrics.confusion_matrix(y_test,y_pred)\n", "confusion_matrix" ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "' precision recall f1-score support\\n\\n 0 1.00 1.00 1.00 16\\n 1 0.80 0.80 0.80 5\\n 2 0.89 0.89 0.89 9\\n\\navg / total 0.93 0.93 0.93 30\\n'" ] }, "execution_count": 159, "metadata": {}, "output_type": "execute_result" } ], "source": [ "auc_roc=metrics.classification_report(y_test,y_pred)\n", "auc_roc" ] }, { "cell_type": "code", "execution_count": 160, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10de9048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "LABEL_COLOR_MAP = {0 : 'g',\n", " 1 : 'y',\n", " 2 : 'r'\n", " }\n", "\n", "pred_color = [LABEL_COLOR_MAP[i] for i in y_pred]\n", "test_color = [LABEL_COLOR_MAP[i] for i in y_test]\n", "\n", "fig,axes = plt.subplots(nrows=1,ncols=2,figsize=(5,2))\n", "\n", "axes[0].scatter(X_test[:,0],X_test[:,1], c= pred_color)\n", "axes[0].set_title('Predicted')\n", "axes[1].scatter(X_test[:,0],X_test[:,1], c= test_color)\n", "axes[1].set_title('True');" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 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 }
lgpl-3.0
CenterForOpenScience/cas-overlay
OAuth2.ipynb
1
64895
{ "cells": [ { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json\n", "import requests" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[400] b'{\"error\":\"invalid_request\",\"error_description\":\"Invalid Code\"}'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/connectionpool.py:769: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.org/en/latest/security.html\n", " InsecureRequestWarning)\n" ] } ], "source": [ "##### Authorization Code #####\n", "\n", "code = 'AC-1-DtiwcIXEG7HH5qvphIMzcXXJqnVXiGDa9wTutGu4bRtPKRzCew'\n", "\n", "url = 'https://localhost:8443/oauth2/token'\n", "\n", "data = {\n", " 'code': code,\n", " 'client_id': 'gJgfkHAtz_FSyUOBgWiki_hyaBsa',\n", " 'client_secret': 'esiLUJc2idUh5HaikEK7EAjMDwIa',\n", " 'redirect_uri': 'http://localhost:5000/oauth/callback/googledrive/',\n", " 'grant_type': 'authorization_code',\n", "}\n", "\n", "resp = requests.post(url, data=data, verify=False)\n", "print('[{}] {}'.format(\n", " resp.status_code,\n", " json.dumps(json.loads(resp.content.decode('utf-8')), indent=2)\n", " )\n", ")\n", "\n", "# {\n", "# \"token_type\":\"Bearer\",\n", "# \"expires_in\":3600,\n", "# \"refresh_token\":\"RT-1-TWFngAfiHbS6OCC3STiO2eugtcMFVS64IJZfGgNltbzu0yOKLe\",\n", "# \"access_token\":\"AT-1-gmEZ30hDhj5a6ZcAiPT4CSMG0ezke5YfmuKNKHknIecfhwOKHbUl59AnkxkksaSv7YPxzQ\"\n", "# }\n", "#\n", "# * access_type = offline" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[200] b'{\"token_type\":\"Bearer\",\"expires_in\":3600,\"access_token\":\"AT-11-9CCh3wkCgYcgte7RhL7OGrxlEcwTBICfZc7UmuqRlPIbxJwYwftkYIZgPiKBNAPbmqVNWK\"}'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/connectionpool.py:769: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.org/en/latest/security.html\n", " InsecureRequestWarning)\n" ] } ], "source": [ "##### Grant Access Token #####\n", "\n", "refresh_token = 'RT-2-LzDUmjYaSXKIbBtId3megopvJ1EOrnSwXbybunHbiK4Aq6NHNQ'\n", "\n", "url = 'https://localhost:8443/oauth2/token'\n", "\n", "data = {\n", " 'client_id': 'gJgfkHAtz_FSyUOBgWiki_hyaBsa',\n", " 'client_secret': 'esiLUJc2idUh5HaikEK7EAjMDwIa',\n", " 'refresh_token': refresh_token,\n", " 'grant_type': 'refresh_token',\n", "}\n", "\n", "resp = requests.post(url, data=data, verify=False)\n", "print('[{}] {}'.format(\n", " resp.status_code,\n", " json.dumps(json.loads(resp.content.decode('utf-8')), indent=2)\n", " )\n", ")\n", "\n", "# {\n", "# \"token_type\":\"Bearer\",\n", "# \"expires_in\":3600,\n", "# \"access_token\":\"AT-1-gmEZ30hDhj5a6ZcAiPT4CSMG0ezke5YfmuKNKHknIecfhwOKHbUl59AnkxkksaSv7YPxzQ\"\n", "# }" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[200] b'{\"scope\":[\"osf.full+write\"],\"id\":\"b87aw\"}'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/connectionpool.py:769: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.org/en/latest/security.html\n", " InsecureRequestWarning)\n" ] } ], "source": [ "##### Profile & Scope Information #####\n", "\n", "access_token = 'a2e1267dd3704eaa86aba3dbc2b81ba7wMxNrSMktoUocOO6pMT4lJGCzpypn2xWVitaqpsT '\n", "# access_token = 'personal-access-token-id'\n", "\n", "# url = 'https://localhost:8443/oauth2/profile'\n", "url = 'https://staging2-accounts.osf.io/oauth2/profile'\n", "\n", "headers = {\n", " 'Authorization': 'Bearer {}'.format(access_token),\n", "}\n", "\n", "resp = requests.get(url, headers=headers, verify=False)\n", "print('[{}] {}'.format(resp.status_code, resp.content))\n", "# print('[{}] {}'.format(\n", "# resp.status_code,\n", "# json.dumps(json.loads(resp.content.decode('utf-8')), indent=2)\n", "# )\n", "# )\n", "\n", "# {\n", "# \"id\":\"abc123\",\n", "# \"scope\": [\n", "# \"user.email\",\n", "# \"profile.basic\"\n", "# ]\n", "# \"attributes\": [\n", "# {\"email\": \"test.user@domain.com\"},\n", "# {\"familyName\":\"User\"},\n", "# {\"givenName\":\"Test\"}\n", "# ]\n", "# }\n", "#\n", "# * attributes must be released by the service" ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[401] b'error=unauthorized'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/connectionpool.py:769: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.org/en/latest/security.html\n", " InsecureRequestWarning)\n" ] } ], "source": [ "##### User Application Authorizations Metadata #####\n", "\n", "access_token = 'AT-14-5LE9XUKbRNPXkEYjeZCHXc7BR2Nttz5HnjSOGu7fBiPRgGjrdDeXfYMe3QBcvPHOCA3NE1'\n", "\n", "url = 'https://localhost:8443/oauth2/metadata'\n", "\n", "headers = {\n", " 'Authorization': 'Bearer {}'.format(access_token),\n", "}\n", "\n", "resp = requests.post(url, headers=headers, verify=False)\n", "print('[{}] {}'.format(\n", " resp.status_code,\n", " json.dumps(json.loads(resp.content.decode('utf-8')), indent=2)\n", " )\n", ")\n", "\n", "# {\n", "# \"data\": [\n", "# {\n", "# \"client_id\":\"abc123\",\n", "# \"name\": \"OAuth Application Name\",\n", "# \"description\": \"OAuth Application Description\",\n", "# \"scope\": [\n", "# \"user.email\",\n", "# \"profile.basic\"\n", "# ]\n", "# },\n", "# {\n", "# \"client_id\":\"defxyz\",\n", "# \"name\": \"Another Application Name\",\n", "# \"description\": \"Another Application Description\",\n", "# \"scope\": [\n", "# \"user.email\",\n", "# \"profile.basic\"\n", "# ]\n", "# },\n", "# ]\n", "# }" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[200] b'{\"users\":1,\"description\":\"blah\",\"name\":\"abc\",\"client_id\":\"9c6e0a220b264077b387e8ae4e7c808b\"}'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/connectionpool.py:769: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.org/en/latest/security.html\n", " InsecureRequestWarning)\n" ] } ], "source": [ "##### Application Owner Metadata #####\n", "\n", "url = 'https://localhost:8443/oauth2/metadata'\n", "# url = 'https://staging2-accounts.osf.io/oauth2/metadata'\n", "\n", "params = {\n", " 'client_id': '9c6e0a220b264077b387e8ae4e7c808b',\n", " 'client_secret': 'hn8FoFJgFYB8yuG6katNwRPpadbSLKFW1uYUTHj0',\n", "}\n", "\n", "resp = requests.post(url, params=params, verify=False)\n", "print('[{}] {}'.format(\n", " resp.status_code,\n", " json.dumps(json.loads(resp.content.decode('utf-8')), indent=2)\n", " )\n", ")\n", "\n", "# {\n", "# \"client_id\":\"abc123\",\n", "# \"name\": \"OAuth Application Name\",\n", "# \"description\": \"OAuth Application Description\",\n", "# \"users\": 10\n", "# }" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[204] b''\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/connectionpool.py:769: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.org/en/latest/security.html\n", " InsecureRequestWarning)\n" ] } ], "source": [ "##### Revoke Token #####\n", "\n", "token = 'AT-14-5LE9XUKbRNPXkEYjeZCHXc7BR2Nttz5HnjSOGu7fBiPRgGjrdDeXfYMe3QBcvPHOCA3NE1'\n", "# token = 'RT-1-jHUxqfYcfoWkQzJ1R5iHO6PFz5m5hswBEnizoigOcmnflbXDSm'\n", "\n", "url = 'https://localhost:8443/oauth2/revoke'\n", "\n", "params = {\n", " 'token': token,\n", "}\n", "\n", "resp = requests.post(url, params=params, verify=False)\n", "print('[{}] {}'.format(resp.status_code, resp.content))\n", "\n", "# 204 = success" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ConnectionError", "evalue": "('Connection aborted.', ConnectionRefusedError(61, 'Connection refused'))", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, **response_kw)\u001b[0m\n\u001b[1;32m 543\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout_obj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 544\u001b[0;31m body=body, headers=headers)\n\u001b[0m\u001b[1;32m 545\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 340\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 342\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\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;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_validate_conn\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m 761\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sock'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# AppEngine might not have `.sock`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 762\u001b[0;31m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\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 763\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/connection.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[0;31m# Add certificate verification\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m \u001b[0mconn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new_conn\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 205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 133\u001b[0m conn = connection.create_connection(\n\u001b[0;32m--> 134\u001b[0;31m (self.host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[1;32m 135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\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[0;32m---> 88\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 89\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 79\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 61] Connection refused", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mProtocolError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 369\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 370\u001b[0;31m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 371\u001b[0m )\n", "\u001b[0;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, **response_kw)\u001b[0m\n\u001b[1;32m 596\u001b[0m retries = retries.increment(method, url, error=e, _pool=self,\n\u001b[0;32m--> 597\u001b[0;31m _stacktrace=sys.exc_info()[2])\n\u001b[0m\u001b[1;32m 598\u001b[0m \u001b[0mretries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/util/retry.py\u001b[0m in \u001b[0;36mincrement\u001b[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mread\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_stacktrace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mread\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[0;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/packages/six.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m 308\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtb\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[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 310\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, **response_kw)\u001b[0m\n\u001b[1;32m 543\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout_obj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 544\u001b[0;31m body=body, headers=headers)\n\u001b[0m\u001b[1;32m 545\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 340\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_conn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 342\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\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;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_validate_conn\u001b[0;34m(self, conn)\u001b[0m\n\u001b[1;32m 761\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sock'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# AppEngine might not have `.sock`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 762\u001b[0;31m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\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 763\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/connection.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[0;31m# Add certificate verification\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m \u001b[0mconn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new_conn\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 205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/connection.py\u001b[0m in \u001b[0;36m_new_conn\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 133\u001b[0m conn = connection.create_connection(\n\u001b[0;32m--> 134\u001b[0;31m (self.host, self.port), self.timeout, **extra_kw)\n\u001b[0m\u001b[1;32m 135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merr\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[0;32m---> 88\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 89\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/util/connection.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 79\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mProtocolError\u001b[0m: ('Connection aborted.', ConnectionRefusedError(61, 'Connection refused'))", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mConnectionError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-19-31c112e5abe6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 13\u001b[0m }\n\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 462\u001b[0m }\n\u001b[1;32m 463\u001b[0m \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 464\u001b[0;31m \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 465\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 466\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 574\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 575\u001b[0m \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 576\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\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 577\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 578\u001b[0m \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 414\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mProtocolError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 415\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 416\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 417\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mMaxRetryError\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;31mConnectionError\u001b[0m: ('Connection aborted.', ConnectionRefusedError(61, 'Connection refused'))" ] } ], "source": [ "##### User Revokes Application Access #####\n", "\n", "access_token = 'AT-14-5LE9XUKbRNPXkEYjeZCHXc7BR2Nttz5HnjSOGu7fBiPRgGjrdDeXfYMe3QBcvPHOCA3NE1'\n", "\n", "url = 'https://localhost:8443/oauth2/revoke'\n", "\n", "headers = {\n", " 'Authorization': 'Bearer {}'.format(access_token),\n", "}\n", "\n", "params = {\n", " 'client_id': 'gJgfkHAtz_FSyUOBgWiki_hyaBsa',\n", "}\n", "\n", "resp = requests.post(url, headers=headers, params=params, verify=False)\n", "print('[{}] {}'.format(resp.status_code, resp.content))\n", "\n", "# 204 = success" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[204] b''\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/connectionpool.py:769: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.org/en/latest/security.html\n", " InsecureRequestWarning)\n" ] } ], "source": [ "##### Application Administrators Revokes All Tokens #####\n", "\n", "url = 'https://localhost:8443/oauth2/revoke'\n", "\n", "params = {\n", " 'client_id': 'gJgfkHAtz_FSyUOBgWiki_hyaBsa',\n", " 'client_secret': 'esiLUJc2idUh5HaikEK7EAjMDwIa',\n", "}\n", "\n", "resp = requests.post(url, params=params, verify=False)\n", "print('[{}] {}'.format(resp.status_code, resp.content))\n", "\n", "# 204 = success" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[200] {\n", " \"links\": {\n", " \"first\": null,\n", " \"meta\": {\n", " \"per_page\": 10,\n", " \"total\": 72\n", " },\n", " \"next\": \"https://staging2-api.osf.io/v2/nodes/?page=2\",\n", " \"last\": \"https://staging2-api.osf.io/v2/nodes/?page=8\",\n", " \"prev\": null\n", " },\n", " \"data\": [\n", " {\n", " 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\"attributes\": {\n", " \"date_modified\": \"2015-07-30T15:19:31.901000\",\n", " \"collection\": false,\n", " \"description\": \"\",\n", " \"dashboard\": false,\n", " \"title\": \"Blue Monday\",\n", " \"public\": true,\n", " \"tags\": [],\n", " \"category\": \"project\",\n", " \"date_created\": \"2015-07-30T14:45:39.454000\",\n", " \"registration\": true\n", " },\n", " \"id\": \"2bwm8\"\n", " },\n", " {\n", " \"relationships\": {\n", " \"registrations\": {\n", " \"links\": {\n", " \"related\": {\n", " \"href\": \"https://staging2-api.osf.io/v2/nodes/3c4as/registrations/\",\n", " \"meta\": {\n", " \"count\": 0\n", " }\n", " }\n", " }\n", " },\n", " \"parent\": {\n", " \"links\": {\n", " \"self\": null\n", " }\n", " },\n", " \"files\": {\n", " \"links\": {\n", " \"related\": \"https://staging2-api.osf.io/v2/nodes/3c4as/files/\"\n", " }\n", " },\n", " \"node_links\": {\n", " \"links\": {\n", " \"related\": {\n", " \"href\": \"https://staging2-api.osf.io/v2/nodes/3c4as/node_links/\",\n", " \"meta\": {\n", " \"count\": 0\n", " }\n", " }\n", " }\n", " },\n", " \"contributors\": {\n", " \"links\": {\n", " \"related\": {\n", " \"href\": \"https://staging2-api.osf.io/v2/nodes/3c4as/contributors/\",\n", " \"meta\": {\n", " \"count\": 1\n", " }\n", " }\n", " }\n", " },\n", " \"children\": {\n", " \"links\": {\n", " \"related\": {\n", " \"href\": \"https://staging2-api.osf.io/v2/nodes/3c4as/children/\",\n", " \"meta\": {\n", " \"count\": 0\n", " }\n", " }\n", " }\n", " }\n", " },\n", " \"links\": {\n", " \"self\": \"https://staging2-api.osf.io/v2/nodes/3c4as/\",\n", " \"html\": \"https://staging2.osf.io/3c4as/\"\n", " },\n", " \"type\": \"nodes\",\n", " \"attributes\": {\n", " \"date_modified\": \"2015-06-29T17:20:50.849000\",\n", " \"collection\": false,\n", " \"description\": \"\",\n", " \"dashboard\": false,\n", " \"title\": \"Private API Project\",\n", " \"public\": true,\n", " \"tags\": [\n", " \"taggity\",\n", " \"toggity\"\n", " ],\n", " \"category\": \"project\",\n", " 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},\n", " \"children\": {\n", " \"links\": {\n", " \"related\": {\n", " \"href\": \"https://staging2-api.osf.io/v2/nodes/3dg5n/children/\",\n", " \"meta\": {\n", " \"count\": 0\n", " }\n", " }\n", " }\n", " }\n", " },\n", " \"links\": {\n", " \"self\": \"https://staging2-api.osf.io/v2/nodes/3dg5n/\",\n", " \"html\": \"https://staging2.osf.io/3dg5n/\"\n", " },\n", " \"type\": \"nodes\",\n", " \"attributes\": {\n", " \"date_modified\": \"2015-08-03T15:26:37.912000\",\n", " \"collection\": false,\n", " \"description\": null,\n", " \"dashboard\": false,\n", " \"title\": \"prereg sub project\",\n", " \"public\": true,\n", " \"tags\": [],\n", " \"category\": \"\",\n", " \"date_created\": \"2015-08-03T15:26:37.912000\",\n", " \"registration\": true\n", " },\n", " \"id\": \"3dg5n\"\n", " },\n", " {\n", " \"relationships\": {\n", " \"registrations\": {\n", " \"links\": {\n", " \"related\": {\n", " \"href\": \"https://staging2-api.osf.io/v2/nodes/4dfka/registrations/\",\n", " \"meta\": {\n", " 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Could ye leave up as built match. Dejection agreeable attention set suspected led offending. Admitting an performed supposing by. Garden agreed matter are should formed temper had. Full held gay now roof whom such next was. Ham pretty our people moment put excuse narrow. Spite mirth money six above get going great own. Started now shortly had for assured hearing expense. 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Career Statistics of Bengt-\\u00c5ke Gustafsson.\\n\\n\",\n", " \"dashboard\": false,\n", " \"title\": \"Bio &amp; Career Statistics\",\n", " \"public\": true,\n", " \"tags\": [\n", " \"Bio\",\n", " \"Career\",\n", " \"Statistics\",\n", " \"Sport\",\n", " \"Hockey\",\n", " \"Test\",\n", " \"Sweden\",\n", " \"Washington\",\n", " \"Capitals\",\n", " \"API\"\n", " ],\n", " \"category\": \"project\",\n", " \"date_created\": \"2015-07-01T12:52:50.747000\",\n", " \"registration\": true\n", " },\n", " \"id\": \"5h4te\"\n", " },\n", " {\n", " \"relationships\": {\n", " \"registrations\": {\n", " \"links\": {\n", " \"related\": {\n", " \"href\": \"https://staging2-api.osf.io/v2/nodes/5qkxg/registrations/\",\n", " \"meta\": {\n", " \"count\": 0\n", " }\n", " }\n", " }\n", " },\n", " \"parent\": {\n", " \"links\": {\n", " \"self\": null\n", " }\n", " },\n", " \"files\": {\n", " \"links\": {\n", " \"related\": \"https://staging2-api.osf.io/v2/nodes/5qkxg/files/\"\n", " }\n", " },\n", " \"node_links\": {\n", " \"links\": {\n", " \"related\": {\n", " \"href\": \"https://staging2-api.osf.io/v2/nodes/5qkxg/node_links/\",\n", " \"meta\": {\n", " \"count\": 0\n", " }\n", " }\n", " }\n", " },\n", " \"contributors\": {\n", " \"links\": {\n", " \"related\": {\n", " \"href\": \"https://staging2-api.osf.io/v2/nodes/5qkxg/contributors/\",\n", " \"meta\": {\n", " \"count\": 1\n", " }\n", " }\n", " }\n", " },\n", " \"children\": {\n", " \"links\": {\n", " \"related\": {\n", " \"href\": \"https://staging2-api.osf.io/v2/nodes/5qkxg/children/\",\n", " \"meta\": {\n", " \"count\": 0\n", " }\n", " }\n", " }\n", " }\n", " },\n", " \"links\": {\n", " \"self\": \"https://staging2-api.osf.io/v2/nodes/5qkxg/\",\n", " \"html\": \"https://staging2.osf.io/5qkxg/\"\n", " },\n", " \"type\": \"nodes\",\n", " \"attributes\": {\n", " \"date_modified\": \"2015-07-30T14:46:39.592000\",\n", " \"collection\": false,\n", " \"description\": \"\",\n", " \"dashboard\": false,\n", " \"title\": \"Registration test\",\n", " \"public\": true,\n", " \"tags\": [],\n", " \"category\": \"project\",\n", " \"date_created\": \"2015-07-30T14:45:39.386000\",\n", " \"registration\": true\n", " },\n", " \"id\": \"5qkxg\"\n", " },\n", " {\n", " \"relationships\": {\n", " \"registrations\": {\n", " \"links\": {\n", " \"related\": {\n", " \"href\": \"https://staging2-api.osf.io/v2/nodes/5v4uc/registrations/\",\n", " \"meta\": {\n", " \"count\": 0\n", " }\n", " }\n", " }\n", " },\n", " \"parent\": {\n", " \"links\": {\n", " \"self\": null\n", " }\n", " },\n", " \"files\": {\n", " \"links\": {\n", " \"related\": \"https://staging2-api.osf.io/v2/nodes/5v4uc/files/\"\n", " }\n", " },\n", " \"node_links\": {\n", " \"links\": {\n", " \"related\": {\n", " \"href\": \"https://staging2-api.osf.io/v2/nodes/5v4uc/node_links/\",\n", " \"meta\": {\n", " \"count\": 0\n", " }\n", " }\n", " }\n", " },\n", " \"contributors\": {\n", " \"links\": {\n", " \"related\": {\n", " \"href\": \"https://staging2-api.osf.io/v2/nodes/5v4uc/contributors/\",\n", " \"meta\": {\n", " \"count\": 1\n", " }\n", " }\n", " }\n", " },\n", " \"children\": {\n", " \"links\": {\n", " \"related\": {\n", " \"href\": \"https://staging2-api.osf.io/v2/nodes/5v4uc/children/\",\n", " \"meta\": {\n", " \"count\": 0\n", " }\n", " }\n", " }\n", " }\n", " },\n", " \"links\": {\n", " \"self\": \"https://staging2-api.osf.io/v2/nodes/5v4uc/\",\n", " \"html\": \"https://staging2.osf.io/5v4uc/\"\n", " },\n", " \"type\": \"nodes\",\n", " \"attributes\": {\n", " \"date_modified\": \"2015-07-14T14:13:52.415000\",\n", " \"collection\": false,\n", " \"description\": \"Bob\",\n", " \"dashboard\": false,\n", " \"title\": \"Bob\",\n", " \"public\": false,\n", " \"tags\": [],\n", " \"category\": \"data\",\n", " \"date_created\": \"2015-07-14T14:13:52.415000\",\n", " \"registration\": false\n", " },\n", " \"id\": \"5v4uc\"\n", " }\n", " ]\n", "}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/michael/.virtualenvs/ipython/lib/python3.4/site-packages/requests/packages/urllib3/connectionpool.py:769: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.org/en/latest/security.html\n", " InsecureRequestWarning)\n" ] } ], "source": [ "##### Profile & Scope Information #####\n", "\n", "access_token = 'a2e1267dd3704eaa86aba3dbc2b81ba7wMxNrSMktoUocOO6pMT4lJGCzpypn2xWVitaqpsT'\n", "# access_token = 'personal-access-token-id'\n", "\n", "# url = 'https://localhost:8443/oauth2/profile'\n", "url = 'https://staging2-api.osf.io:443/v2/nodes/'\n", "\n", "headers = {\n", " 'Authorization': 'Bearer {}'.format(access_token),\n", "}\n", "\n", "resp = requests.get(url, headers=headers, verify=False)\n", "print('[{}] {}'.format(\n", " resp.status_code,\n", " json.dumps(json.loads(resp.content.decode('utf-8')), indent=2)\n", " )\n", ")\n", "\n", "# {\n", "# \"id\":\"abc123\",\n", "# \"scope\": [\n", "# \"user.email\",\n", "# \"profile.basic\"\n", "# ]\n", "# \"attributes\": [\n", "# {\"email\": \"test.user@domain.com\"},\n", "# {\"familyName\":\"User\"},\n", "# {\"givenName\":\"Test\"}\n", "# ]\n", "# }\n", "#\n", "# * attributes must be released by the service" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
nagamanicg/ml_lab_ecsc_306
labwork/lab1/TF_helloworld.ipynb
2
1417
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Simple hello world using TensorFlow\n", "\n", "# Create a Constant op\n", "# The op is added as a node to the default graph.\n", "#\n", "# The value returned by the constructor represents the output\n", "# of the Constant op.\n", "\n", "hello = tf.constant('Hello, TensorFlow!')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Start tf session\n", "sess = tf.Session()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Run graph\n", "print sess.run(hello)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }
apache-2.0